diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md new file mode 100644 index 000000000..dff11e6aa --- /dev/null +++ b/.github/PULL_REQUEST_TEMPLATE.md @@ -0,0 +1 @@ +Fixes #ISSUE_NUMBER diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml deleted file mode 100644 index f0b91d8d5..000000000 --- a/.github/workflows/ci.yml +++ /dev/null @@ -1,35 +0,0 @@ -name: "Coverage Deploy to Codacy" - -on: - push: - branches: - - master - -jobs: - test_deploy: - runs-on: ubuntu-latest - - steps: - - uses: actions/checkout@v2 - - - - name: Set up Python - uses: actions/setup-python@v2 - with: - python-version: 3.8 - - - name: Install Python dependencies - run: | - python3 -m pip install --upgrade pip - python3 -m pip install .[test] - - - name: Test with pytest - env: - CODACY_API_TOKEN: ${{ secrets.CODACY_API_TOKEN }} - shell: bash - run: | - python3 -m pytest --cov-report term --cov-report xml:cobertura.xml --cov=pina - curl -s https://coverage.codacy.com/get.sh -o CodacyCoverageReporter.sh - chmod +x CodacyCoverageReporter.sh - ./CodacyCoverageReporter.sh report -r cobertura.xml -t $CODACY_API_TOKEN - diff --git a/.github/workflows/create-release.yml b/.github/workflows/create-release.yml deleted file mode 100644 index 946ce9d60..000000000 --- a/.github/workflows/create-release.yml +++ /dev/null @@ -1,18 +0,0 @@ -name: Releases - -on: - push: - tags: - - '*' - -jobs: - - build: - runs-on: ubuntu-latest - permissions: - contents: write - steps: - - uses: actions/checkout@v2 - - uses: ncipollo/release-action@v1 - with: - token: ${{ secrets.GITHUB_TOKEN }} diff --git a/.github/workflows/deployer.yml b/.github/workflows/deployer.yml new file mode 100644 index 000000000..a72bc6787 --- /dev/null +++ b/.github/workflows/deployer.yml @@ -0,0 +1,58 @@ +name: "Deployer" + +on: + push: + tags: + - "*" + +jobs: + + docs: ####################################################################### + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + + - name: Install Python dependencies + run: python3 -m pip install .[doc] + + - name: Build Documentation + run: | + make html + working-directory: docs/ + + - name: Deploy + uses: peaceiris/actions-gh-pages@v3 + with: + github_token: ${{ secrets.GITHUB_TOKEN }} + #deploy_key: ${{ secrets.DEPLOY_PRIVATE_KEY }} + publish_dir: ./docs/build/html + allow_empty_commit: true + + release_github: ############################################################# + runs-on: ubuntu-latest + permissions: + contents: write + steps: + - uses: actions/checkout@v4 + - uses: ncipollo/release-action@v1 + with: + token: ${{ secrets.GITHUB_TOKEN }} + + pypi: ####################################################################### + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + + - name: Install build + run: >- + python -m pip install build --user + + - name: Build a binary wheel and a source tarball + run: >- + python -m build --sdist --wheel --outdir dist/ . + + - name: Publish distribution to PyPI + if: startsWith(github.ref, 'refs/tags') + uses: pypa/gh-action-pypi-publish@release/v1 + with: + password: ${{ secrets.PYPI_API_TOKEN }} \ No newline at end of file diff --git a/.github/workflows/draft-pdf.yml b/.github/workflows/draft-pdf.yml deleted file mode 100644 index ab920ec47..000000000 --- a/.github/workflows/draft-pdf.yml +++ /dev/null @@ -1,23 +0,0 @@ -on: [push] - -jobs: - paper: - runs-on: ubuntu-latest - name: Paper Draft - steps: - - name: Checkout - uses: actions/checkout@v3 - - name: Build draft PDF - uses: openjournals/openjournals-draft-action@master - with: - journal: joss - # This should be the path to the paper within your repo. - paper-path: joss/paper.md - - name: Upload - uses: actions/upload-artifact@v1 - with: - name: paper - # This is the output path where Pandoc will write the compiled - # PDF. Note, this should be the same directory as the input - # paper.md - path: joss/paper.pdf diff --git a/.github/workflows/black-formatter.yml b/.github/workflows/master_cleaner.yml similarity index 86% rename from .github/workflows/black-formatter.yml rename to .github/workflows/master_cleaner.yml index ed0933c2b..43208544a 100644 --- a/.github/workflows/black-formatter.yml +++ b/.github/workflows/master_cleaner.yml @@ -1,4 +1,4 @@ -name: Black Formatter +name: Master Cleaner on: push: @@ -6,15 +6,14 @@ on: - master jobs: - linter: + formatter: name: runner / black runs-on: ubuntu-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - uses: psf/black@stable with: - options: "-l 80" src: "./pina" - name: Create Pull Request @@ -27,4 +26,4 @@ jobs: There appear to be some python formatting errors in ${{ github.sha }}. This pull request uses the [psf/black](https://github.com/psf/black) formatter to fix these issues. base: ${{ github.head_ref }} # Creates pull request onto pull request or commit branch - branch: actions/black + branch: actions/black \ No newline at end of file diff --git a/.github/workflows/monthly-tag.yml b/.github/workflows/monthly-tagger.yml similarity index 95% rename from .github/workflows/monthly-tag.yml rename to .github/workflows/monthly-tagger.yml index b77e42220..81bf4d265 100644 --- a/.github/workflows/monthly-tag.yml +++ b/.github/workflows/monthly-tagger.yml @@ -1,4 +1,4 @@ -name: Monthly Automated Tag +name: "Monthly Tagger" on: schedule: @@ -30,7 +30,7 @@ jobs: runs-on: ubuntu-latest needs: test steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v4 with: token: ${{ secrets.NDEMO_PAT_TOKEN }} diff --git a/.github/workflows/pypi-publish.yml b/.github/workflows/pypi-publish.yml deleted file mode 100644 index 8d2265e06..000000000 --- a/.github/workflows/pypi-publish.yml +++ /dev/null @@ -1,31 +0,0 @@ -name: "PYPI Publish" - -on: - push: - tags: - - "*" - -jobs: - docs: - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v2 - - - name: Set up Python 3.7 - uses: actions/setup-python@v1 - with: - python-version: 3.7 - - - name: Install build - run: >- - python -m pip install build --user - - - name: Build a binary wheel and a source tarball - run: >- - python -m build --sdist --wheel --outdir dist/ . - - - name: Publish distribution to PyPI - if: startsWith(github.ref, 'refs/tags') - uses: pypa/gh-action-pypi-publish@release/v1 - with: - password: ${{ secrets.PYPI_API_TOKEN }} diff --git a/.github/workflows/sphinx-build.yml b/.github/workflows/sphinx-build.yml deleted file mode 100644 index 1bed54c6a..000000000 --- a/.github/workflows/sphinx-build.yml +++ /dev/null @@ -1,26 +0,0 @@ -name: "Documentation Deploy" - -on: - push: - tags: - - "*" - -jobs: - docs: - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v4 - - - name: Create the new documentation - uses: ammaraskar/sphinx-action@7.4.7 - with: - pre-build-command: "python3 -m pip install .[docs]" - docs-folder: "docs/" - - - name: Deploy - uses: peaceiris/actions-gh-pages@v3 - with: - github_token: ${{ secrets.GITHUB_TOKEN }} - #deploy_key: ${{ secrets.DEPLOY_PRIVATE_KEY }} - publish_dir: ./docs/build/html - allow_empty_commit: true diff --git a/.github/workflows/tester.yml b/.github/workflows/tester.yml new file mode 100644 index 000000000..d21b750f5 --- /dev/null +++ b/.github/workflows/tester.yml @@ -0,0 +1,78 @@ +name: "Testing Pull Request" + +on: + pull_request: + branches: + - "master" + - "dev" + +jobs: + unittests: ################################################################# + runs-on: ${{ matrix.os }} + strategy: + fail-fast: false + matrix: + os: [windows-latest, macos-latest, ubuntu-latest] + python-version: [3.8, 3.9, '3.10', '3.11', '3.12'] + + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: ${{ matrix.python-version }} + + - name: Install Python dependencies + run: | + python3 -m pip install --upgrade pip + python3 -m pip install .[test] + + - name: Test with pytest + run: | + python3 -m pytest + + linter: #################################################################### + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + + - name: Run Black formatter (check mode) + uses: psf/black@stable + with: + src: "./pina" + + testdocs: ################################################################## + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + + - name: Install Python dependencies + run: python3 -m pip install .[doc] + + - name: Build Documentation + run: | + make html SPHINXOPTS+='-W' + working-directory: docs/ + + coverage: ################################################################## + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v4 + + - name: Install Python dependencies + run: | + python3 -m pip install --upgrade pip + python3 -m pip install .[test] + + - name: Generate coverage report + run: | + python3 -m pytest --cov-report term --cov-report xml:cobertura.xml --cov=pina + + - name: Produce the coverage report + uses: insightsengineering/coverage-action@v2 + with: + path: ./cobertura.xml + threshold: 80.123 + fail: true + publish: true + coverage-summary-title: "Code Coverage Summary" diff --git a/.github/workflows/testing_doc.yml b/.github/workflows/testing_doc.yml deleted file mode 100644 index e8b716dfa..000000000 --- a/.github/workflows/testing_doc.yml +++ /dev/null @@ -1,33 +0,0 @@ -name: Test Sphinx Documentation Build - -on: - push: - branches: - - "master" - paths: - - 'docs/**' - pull_request: - branches: - - "master" - paths: - - 'docs/**' - -jobs: - docs: - runs-on: ubuntu-latest - - steps: - - name: Checkout code - uses: actions/checkout@v2 - - - name: Set up Python - uses: ammaraskar/sphinx-action@7.4.7 - with: - pre-build-command: "python3 -m pip install .[docs]" - docs-folder: "docs/" - - - name: Build Sphinx documentation - run: | - cd docs - make html - diff --git a/.github/workflows/testing_pr.yml b/.github/workflows/testing_pr.yml deleted file mode 100644 index 7519e6b7c..000000000 --- a/.github/workflows/testing_pr.yml +++ /dev/null @@ -1,33 +0,0 @@ -name: "Testing Pull Request" - -on: - pull_request: - branches: - - "master" - -jobs: - build: - runs-on: ${{ matrix.os }} - strategy: - fail-fast: false - matrix: - os: [windows-latest, macos-latest, ubuntu-latest] - python-version: [3.8, 3.9, '3.10', '3.11', '3.12'] - - steps: - - uses: actions/checkout@v2 - - - - name: Set up Python - uses: actions/setup-python@v2 - with: - python-version: ${{ matrix.python-version }} - - - name: Install Python dependencies - run: | - python3 -m pip install --upgrade pip - python3 -m pip install .[test] - - - name: Test with pytest - run: | - python3 -m pytest diff --git a/.github/workflows/tutorial_exporter.yml b/.github/workflows/tutorial_exporter.yml new file mode 100644 index 000000000..30de93db9 --- /dev/null +++ b/.github/workflows/tutorial_exporter.yml @@ -0,0 +1,76 @@ +name: "Export Tutorials" + +on: + push: + branches: + - "dev" + - "master" + paths: + - 'tutorials/**/*.ipynb' + +jobs: + export_tutorials: + permissions: write-all + runs-on: ubuntu-latest + env: + TUTORIAL_TIMEOUT: 1200s + steps: + - uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: 3.8 + + - name: Install dependencies + run: | + # Dependencies for tutorials + python3 -m pip install --upgrade pip .[tutorial] black[jupyter] + - name: Setup FFmpeg + uses: FedericoCarboni/setup-ffmpeg@v2 + + - id: files + uses: jitterbit/get-changed-files@v1 + with: + token: ${{ secrets.GITHUB_TOKEN }} + format: space-delimited + + - name: Configure git + run: | + git config user.name "github-actions[bot]" + git config user.email 41898282+github-actions[bot]@users.noreply.github.com + + - name: Export tutorials to .py and .html + run: | + set -x + for file in ${{ steps.files.outputs.all }}; do + if [[ $file == *.ipynb ]]; then + filename=$(basename $file) + pyfilename=$(echo ${filename%?????})py + timeout --signal=SIGKILL $TUTORIAL_TIMEOUT python -Xfrozen_modules=off -m jupyter nbconvert $file --to python --output $pyfilename --output-dir=$(dirname $file) + htmlfilename=$(echo ${filename%?????} | sed -e 's/-//g')html + htmldir="docs/source"/$(echo ${file%??????????????} | sed -e 's/-//g') + timeout --signal=SIGKILL $TUTORIAL_TIMEOUT python -Xfrozen_modules=off -m jupyter nbconvert --execute $file --to html --output $htmlfilename --output-dir=$htmldir + fi + done + set +x + + - name: Run formatter + run: black tutorials/ + + - uses: benjlevesque/short-sha@v2.1 + id: short-sha + + - name: Remove unwanted files + run: | + rm -rf build/ tutorials/tutorial4/data/ + + - name: Create Pull Request + uses: peter-evans/create-pull-request@v5.0.2 + with: + labels: maintenance + title: Export tutorial changed in ${{ steps.short-sha.outputs.sha }} + branch: export-tutorial-${{ steps.short-sha.outputs.sha }} + base: ${{ github.head_ref }} + commit-message: export tutorials changed in ${{ steps.short-sha.outputs.sha }} + delete-branch: true diff --git a/.gitignore b/.gitignore index fd0e93ae8..11be017f7 100644 --- a/.gitignore +++ b/.gitignore @@ -1,6 +1,6 @@ # Byte-compiled / optimized / DLL files -__pycache__/ -*.py[cod] +**__pycache__/ +**.py[cod] *$py.class # C extensions @@ -138,5 +138,10 @@ dmypy.json cython_debug/ # Lightning logs dir -*/lightning_logs/* -lightning_logs/* +**lightning_logs + +# Tutorial logs dir +**tutorial_logs + +# tmp dir +**tmp* diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md new file mode 100644 index 000000000..1df8fa17d --- /dev/null +++ b/CODE_OF_CONDUCT.md @@ -0,0 +1,128 @@ +# Contributor Covenant Code of Conduct + +## Our Pledge + +We as members, contributors, and leaders pledge to make participation in our +community a harassment-free experience for everyone, regardless of age, body +size, visible or invisible disability, ethnicity, sex characteristics, gender +identity and expression, level of experience, education, socio-economic status, +nationality, personal appearance, race, religion, or sexual identity +and orientation. + +We pledge to act and interact in ways that contribute to an open, welcoming, +diverse, inclusive, and healthy community. + +## Our Standards + +Examples of behavior that contributes to a positive environment for our +community include: + +* Demonstrating empathy and kindness toward other people +* Being respectful of differing opinions, viewpoints, and experiences +* Giving and gracefully accepting constructive feedback +* Accepting responsibility and apologizing to those affected by our mistakes, + and learning from the experience +* Focusing on what is best not just for us as individuals, but for the + overall community + +Examples of unacceptable behavior include: + +* The use of sexualized language or imagery, and sexual attention or + advances of any kind +* Trolling, insulting or derogatory comments, and personal or political attacks +* Public or private harassment +* Publishing others' private information, such as a physical or email + address, without their explicit permission +* Other conduct which could reasonably be considered inappropriate in a + professional setting + +## Enforcement Responsibilities + +Community leaders are responsible for clarifying and enforcing our standards of +acceptable behavior and will take appropriate and fair corrective action in +response to any behavior that they deem inappropriate, threatening, offensive, +or harmful. + +Community leaders have the right and responsibility to remove, edit, or reject +comments, commits, code, wiki edits, issues, and other contributions that are +not aligned to this Code of Conduct, and will communicate reasons for moderation +decisions when appropriate. + +## Scope + +This Code of Conduct applies within all community spaces, and also applies when +an individual is officially representing the community in public spaces. +Examples of representing our community include using an official e-mail address, +posting via an official social media account, or acting as an appointed +representative at an online or offline event. + +## Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported to the community leaders responsible for enforcement at +pina.mathlab@gmail.com. +All complaints will be reviewed and investigated promptly and fairly. + +All community leaders are obligated to respect the privacy and security of the +reporter of any incident. + +## Enforcement Guidelines + +Community leaders will follow these Community Impact Guidelines in determining +the consequences for any action they deem in violation of this Code of Conduct: + +### 1. Correction + +**Community Impact**: Use of inappropriate language or other behavior deemed +unprofessional or unwelcome in the community. + +**Consequence**: A private, written warning from community leaders, providing +clarity around the nature of the violation and an explanation of why the +behavior was inappropriate. A public apology may be requested. + +### 2. Warning + +**Community Impact**: A violation through a single incident or series +of actions. + +**Consequence**: A warning with consequences for continued behavior. No +interaction with the people involved, including unsolicited interaction with +those enforcing the Code of Conduct, for a specified period of time. This +includes avoiding interactions in community spaces as well as external channels +like social media. Violating these terms may lead to a temporary or +permanent ban. + +### 3. Temporary Ban + +**Community Impact**: A serious violation of community standards, including +sustained inappropriate behavior. + +**Consequence**: A temporary ban from any sort of interaction or public +communication with the community for a specified period of time. No public or +private interaction with the people involved, including unsolicited interaction +with those enforcing the Code of Conduct, is allowed during this period. +Violating these terms may lead to a permanent ban. + +### 4. Permanent Ban + +**Community Impact**: Demonstrating a pattern of violation of community +standards, including sustained inappropriate behavior, harassment of an +individual, or aggression toward or disparagement of classes of individuals. + +**Consequence**: A permanent ban from any sort of public interaction within +the community. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant][homepage], +version 2.0, available at +https://www.contributor-covenant.org/version/2/0/code_of_conduct.html. + +Community Impact Guidelines were inspired by [Mozilla's code of conduct +enforcement ladder](https://github.com/mozilla/diversity). + +[homepage]: https://www.contributor-covenant.org + +For answers to common questions about this code of conduct, see the FAQ at +https://www.contributor-covenant.org/faq. Translations are available at +https://www.contributor-covenant.org/translations. diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 84ad81db4..3bde485db 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -1,40 +1,94 @@ -## How to contribute -We'd love to accept your patches and contributions to this project. There are just a few small guidelines you need to follow. - -### Submitting a patch - - 1. It's generally best to start by opening a new issue describing the bug or - feature you're intending to fix. Even if you think it's relatively minor, - it's helpful to know what people are working on. Mention in the initial - issue that you are planning to work on that bug or feature so that it can - be assigned to you. - - 2. Follow the normal process of [forking][] the project, and setup a new - branch to work in. It's important that each group of changes be done in - separate branches in order to ensure that a pull request only includes the - commits related to that bug or feature. - - 3. To ensure properly formatted code, please make sure to use 4 - spaces to indent the code. The easy way is to run on your bash the provided - script: ./code_formatter.sh. You should also run [pylint][] over your code. - It's not strictly necessary that your code be completely "lint-free", - but this will help you find common style issues. - - 4. Any significant changes should almost always be accompanied by tests. The - project already has good test coverage, so look at some of the existing - tests if you're unsure how to go about it. We're using [coveralls][] that - is an invaluable tools for seeing which parts of your code aren't being - exercised by your tests. - - 5. Do your best to have [well-formed commit messages][] for each change. - This provides consistency throughout the project, and ensures that commit - messages are able to be formatted properly by various git tools. - - 6. Finally, push the commits to your fork and submit a [pull request][]. Please, - remember to rebase properly in order to maintain a clean, linear git history. - -[forking]: https://help.github.com/articles/fork-a-repo -[pylint]: https://www.pylint.org/ -[coveralls]: https://coveralls.io -[well-formed commit messages]: http://tbaggery.com/2008/04/19/a-note-about-git-commit-messages.html -[pull request]: https://help.github.com/articles/creating-a-pull-request +# Contributing to PINA + +First off, thanks for taking the time to contribute to **PINA**! 🎉 Your help makes the project better for everyone. This document outlines the process for contributing, reporting issues, suggesting features, and submitting pull requests. + +--- + +## Table of Contents + +1. [How to Contribute](#how-to-contribute) +2. [Reporting Bugs](#reporting-bugs) +3. [Suggesting Enhancements](#suggesting-enhancements) +4. [Pull Request Process](#pull-request-process) +5. [Code Style & Guidelines](#code-style--guidelines) +6. [Community Standards](#community-standards) + +--- + +## How to Contribute + +You can contribute in several ways: +- Reporting bugs +- Suggesting features/enhancements +- Submitting fixes or improvements via Pull Requests (PRs) +- Improving documentation + +We encourage all contributions, big or small! + +--- + +## Reporting Bugs + +If you find a bug, please open an [issue](https://github.com/mathLab/PINA/issues) and include: +- A clear and descriptive title +- Steps to reproduce the problem +- What you expected to happen +- What actually happened +- Any relevant logs, screenshots, or error messages +- Environment info (OS, Python version, dependencies, etc.) + +--- + +## Suggesting Enhancements + +We welcome new ideas! If you have an idea to improve PINA: +1. Check the [issue tracker](https://github.com/mathLab/PINA/issues) or the [discussions](https://github.com/mathLab/PINA/discussions) to see if someone has already suggested it. +2. If not, open a new issue describing: + - The enhancement you'd like + - Why it would be useful + - Any ideas on how to implement it (optional but helpful) +3. If you are not sure about (something of) the enhancement, we suggest to open a discussion to collaborate on it with the PINA community + +--- + +## Pull Request Process + +Before submitting a PR: + +1. Ensure there’s an open issue related to your contribution (or create one). +2. [Fork](https://help.github.com/articles/fork-a-repo) the repository and create a new branch from `master`: + ```bash + git checkout -b feature/my-feature + ``` +3. Make your changes: + - Write clear, concise, and well-documented code + - Add or update tests where appropriate + - Update documentation if necessary +4. Verify your changes by running tests: + ```bash + pytest + ``` +5. Properly format your code. If you want save time, simply run: + ```bash + bash code_formatter.sh + ``` +7. Submit a [pull request](https://help.github.com/articles/creating-a-pull-request) with a clear explanation of your changes and reference the related issue if applicable. + +### Pull Request Checklist + - [ ] Code follows the project’s style guidelines + - [ ] Tests have been added or updated + - [ ] Documentation has been updated if necessary + - [ ] Pull request is linked to an open issue (if applicable) + +--- + +## Code Style & Guidelines +- Follow PEP8 for Python code. +- Use descriptive commit messages (e.g. `Fix parser crash on empty input`). +- Write clear docstrings for public classes, methods, and functions. +- Keep functions small and focused; do one thing and do it well. + +--- + +## Community Standards +By participating in this project, you agree to abide by our Code of Conduct. We are committed to maintaining a welcoming and inclusive community. diff --git a/README.md b/README.md index 0c32ffbdd..fad14ce7f 100644 --- a/README.md +++ b/README.md @@ -195,12 +195,20 @@ class Poisson(SpatialProblem): laplacian_u = laplacian(output_, input_, components=['u'], d=['x', 'y']) return laplacian_u - force_term + domains = { + 'g1': CartesianDomain({'x': [0, 1], 'y': 1}), + 'g2': CartesianDomain({'x': [0, 1], 'y': 0}), + 'g3': CartesianDomain({'x': 1, 'y': [0, 1]}), + 'g4': CartesianDomain({'x': 0, 'y': [0, 1]}), + 'D': CartesianDomain({'x': [0, 1], 'y': [0, 1]}) + } + conditions = { - 'gamma1': Condition(location=CartesianDomain({'x': [0, 1], 'y': 1}), equation=FixedValue(0.)), - 'gamma2': Condition(location=CartesianDomain({'x': [0, 1], 'y': 0}), equation=FixedValue(0.)), - 'gamma3': Condition(location=CartesianDomain({'x': 1, 'y': [0, 1]}), equation=FixedValue(0.)), - 'gamma4': Condition(location=CartesianDomain({'x': 0, 'y': [0, 1]}), equation=FixedValue(0.)), - 'D': Condition(location=CartesianDomain({'x': [0, 1], 'y': [0, 1]}), equation=Equation(laplace_equation)), + 'gamma1': Condition(domain='g1', equation=FixedValue(0.)), + 'gamma2': Condition(domain='g2', equation=FixedValue(0.)), + 'gamma3': Condition(domain='g3', equation=FixedValue(0.)), + 'gamma4': Condition(domain='g4', equation=FixedValue(0.)), + 'D': Condition(domain='D', equation=Equation(laplace_equation)), } ``` @@ -215,7 +223,8 @@ model = FeedForward( output_dimensions=len(problem.output_variables), input_dimensions=len(problem.input_variables) ) -pinn = PINN(problem, model, optimizer_kwargs={'lr':0.006, 'weight_decay':1e-8}) +optimizer = TorchOptimizer(torch.optim.Adam, lr=0.006, weight_decay=1e-8) +pinn = PINN(problem, model, optimizer=optimizer) trainer = Trainer(pinn, max_epochs=1000, accelerator='gpu', enable_model_summary=False, batch_size=8) # train diff --git a/SECURITY.md b/SECURITY.md new file mode 100644 index 000000000..b1dfe91f8 --- /dev/null +++ b/SECURITY.md @@ -0,0 +1,18 @@ +# Security Policy + +Security and bug fixes are generally provided only for the last minor version. Fixes are released either as part of the next minor version or as an on-demand patch version. + +Security fixes are given priority and might be enough to cause a new version to be released. + + +## Supported Versions + + +| Version | Supported | +| ------- | ------------------ | +| 0.2 | ✅ | +| 0.1 | ✅ | + +## Reporting a Vulnerability + +To ensure vulnerability reports reach the maintainers as quickly as possible, the preferred way is to use the ["Report a vulnerability"](https://github.com/mathLab/PINA/security/advisories/new) button under the "Security" tab of the associated GitHub project. This creates a private communication channel between the reporter and the maintainers. diff --git a/code_formatter.sh b/code_formatter.sh index 6dacf152d..d638d3552 100644 --- a/code_formatter.sh +++ b/code_formatter.sh @@ -2,51 +2,19 @@ ####################################### -required_command="yapf unexpand" -code_directories="pina tests" +required_command="black" +code_directories=("pina" "tests") ####################################### -usage() { - echo - echo -e "\tUsage: $0 [files]" - echo - echo -e "\tIf not files are specified, script formats all ".py" files" - echo -e "\tin code directories ($code_directories); otherwise, formats" - echo -e "\tall given files" - echo - echo -e "\tRequired command: $required_command" - echo - exit 0 -} - - -[[ $1 == "-h" ]] && usage - # Test for required program -for comm in $required_command; do - command -v $comm >/dev/null 2>&1 || { - echo "I require $comm but it's not installed. Aborting." >&2; - exit 1 - } -done - -# Find all python files in code directories -python_files="" -for dir in $code_directories; do - python_files="$python_files $(find $dir -name '*.py')" -done -[[ $# != 0 ]] && python_files=$@ - - -# Here the important part: yapf format the files. -for file in $python_files; do - echo "Making beatiful $file..." - [[ ! -f $file ]] && echo "$file does not exist; $0 -h for more info" && exit - - yapf --style='{ - based_on_style: pep8, - indent_width: 4, - column_limit: 80 - }' -i $file -done +if ! command -v $required_command >/dev/null 2>&1; then + echo "I require $required_command but it's not installed. Install dev dependencies." + echo "Aborting." >&2 + exit 1 +fi + +# Run black formatter +for dir in "${code_directories[@]}"; do + python -m black --line-length 80 "$dir" +done \ No newline at end of file diff --git a/docs/source/_cite.rst b/docs/source/_cite.rst index 71d537931..786134b5b 100644 --- a/docs/source/_cite.rst +++ b/docs/source/_cite.rst @@ -1,7 +1,7 @@ Cite PINA ============== -If PINA has been significant in your research, and you would like to acknowledge the project in your academic publication, +If **PINA** has been significant in your research, and you would like to acknowledge the project in your academic publication, we suggest citing the following paper: *Coscia, D., Ivagnes, A., Demo, N., & Rozza, G. (2023). Physics-Informed Neural networks for Advanced modeling. Journal of Open Source Software, 8(87), 5352.* diff --git a/docs/source/_contributing.rst b/docs/source/_contributing.rst new file mode 100644 index 000000000..dbc06912b --- /dev/null +++ b/docs/source/_contributing.rst @@ -0,0 +1,100 @@ +Contributing to PINA +===================== + +First off, thanks for taking the time to contribute to **PINA**! 🎉 Your help makes the project better for everyone. This document outlines the process for contributing, reporting issues, suggesting features, and submitting pull requests. + +Table of Contents +------------------------ + +1. `How to Contribute`_ +2. `Reporting Bugs`_ +3. `Suggesting Enhancements`_ +4. `Pull Request Process`_ +5. `Code Style & Guidelines`_ +6. `Community Standards`_ + +How to Contribute +------------------------ + +You can contribute in several ways: + +- Reporting bugs +- Suggesting features/enhancements +- Submitting fixes or improvements via Pull Requests (PRs) +- Improving documentation + +We encourage all contributions, big or small! + +Reporting Bugs +------------------------ + +If you find a bug, please open an `issue `_ and include: + +- A clear and descriptive title +- Steps to reproduce the problem +- What you expected to happen +- What actually happened +- Any relevant logs, screenshots, or error messages +- Environment info (OS, Python version, dependencies, etc.) + +Suggesting Enhancements +------------------------ + +We welcome new ideas! If you have an idea to improve PINA: + +1. Check the `issue tracker `_ or the `discussions `_ to see if someone has already suggested it. +2. If not, open a new issue describing: + - The enhancement you'd like + - Why it would be useful + - Any ideas on how to implement it (optional but helpful) +3. If you are not sure about (something of) the enhancement, we suggest opening a discussion to collaborate on it with the PINA community. + +Pull Request Process +------------------------ + +Before submitting a PR: + +1. Ensure there’s an open issue related to your contribution (or create one). +2. `Fork `_ the repository and create a new branch from ``master``: + + .. code-block:: bash + + git checkout -b feature/my-feature + +3. Make your changes: + - Write clear, concise, and well-documented code + - Add or update tests where appropriate + - Update documentation if necessary +4. Verify your changes by running tests: + + .. code-block:: bash + + pytest + +5. Properly format your code. If you want to save time, simply run: + + .. code-block:: bash + + bash code_formatter.sh + +7. Submit a `pull request `_ with a clear explanation of your changes and reference the related issue if applicable. + +Pull Request Checklist + +1. Code follows the project’s style guidelines +2. Tests have been added or updated +3. Documentation has been updated if necessary +4. Pull request is linked to an open issue (if applicable) + +Code Style & Guidelines +------------------------ + +- Follow PEP8 for Python code. +- Use descriptive commit messages (e.g. ``Fix parser crash on empty input``). +- Write clear docstrings for public classes, methods, and functions. +- Keep functions small and focused; do one thing and do it well. + +Community Standards +------------------------ + +By participating in this project, you agree to abide by our Code of Conduct. We are committed to maintaining a welcoming and inclusive community. diff --git a/docs/source/_rst/_installation.rst b/docs/source/_installation.rst similarity index 100% rename from docs/source/_rst/_installation.rst rename to docs/source/_installation.rst diff --git a/docs/source/_rst/_code.rst b/docs/source/_rst/_code.rst index 16a42986f..290629b31 100644 --- a/docs/source/_rst/_code.rst +++ b/docs/source/_rst/_code.rst @@ -11,22 +11,53 @@ The high-level structure of the package is depicted in our API. The pipeline to solve differential equations with PINA follows just five steps: - 1. Define the `Problem`_ the user aim to solve - 2. Generate data using built in `Geometries`_, or load high level simulation results as :doc:`LabelTensor ` + 1. Define the `Problems`_ the user aim to solve + 2. Generate data using built in `Geometrical Domains`_, or load high level simulation results as :doc:`LabelTensor ` 3. Choose or build one or more `Models`_ to solve the problem - 4. Choose a solver across PINA available `Solvers`_, or build one using the :doc:`SolverInterface ` - 5. Train the model with the PINA :doc:`Trainer `, enhance the train with `Callbacks`_ + 4. Choose a solver across PINA available `Solvers`_, or build one using the :doc:`SolverInterface ` + 5. Train the model with the PINA :doc:`Trainer `, enhance the train with `Callbacks`_ -PINA Features --------------- + +Trainer, Dataset and Datamodule +-------------------------------- .. toctree:: :titlesonly: - LabelTensor - Condition Trainer - Plotter + Dataset + DataModule + +Data Types +------------ +.. toctree:: + :titlesonly: + + LabelTensor + Graph + LabelBatch + +Graphs Structures +------------------ +.. toctree:: + :titlesonly: + + GraphBuilder + RadiusGraph + KNNGraph + + +Conditions +------------- +.. toctree:: + :titlesonly: + + ConditionInterface + Condition + DataCondition + DomainEquationCondition + InputEquationCondition + InputTargetCondition Solvers -------------- @@ -34,17 +65,19 @@ Solvers .. toctree:: :titlesonly: - SolverInterface - PINNInterface - PINN - GPINN - CausalPINN - CompetitivePINN - SAPINN - RBAPINN - Supervised solver - ReducedOrderModelSolver - GAROM + SolverInterface + SingleSolverInterface + MultiSolverInterface + PINNInterface + PINN + GradientPINN + CausalPINN + CompetitivePINN + SelfAdaptivePINN + RBAPINN + SupervisedSolver + ReducedOrderModelSolver + GAROM Models @@ -54,36 +87,60 @@ Models :titlesonly: :maxdepth: 5 - Network - KernelNeuralOperator - FeedForward - MultiFeedForward - ResidualFeedForward - Spline - DeepONet - MIONet - FourierIntegralKernel - FNO - AveragingNeuralOperator - LowRankNeuralOperator - -Layers + FeedForward + MultiFeedForward + ResidualFeedForward + Spline + DeepONet + MIONet + KernelNeuralOperator + FourierIntegralKernel + FNO + AveragingNeuralOperator + LowRankNeuralOperator + GraphNeuralOperator + GraphNeuralKernel + +Blocks ------------- .. toctree:: :titlesonly: - Residual layer - EnhancedLinear layer - Spectral convolution - Fourier layers - Averaging layer - Low Rank layer - Continuous convolution - Proper Orthogonal Decomposition - Periodic Boundary Condition Embedding - Fourier Feature Embedding - Radial Basis Function Interpolation + Residual Block + EnhancedLinear Block + Spectral Convolution Block + Fourier Block + Averaging Block + Low Rank Block + Graph Neural Operator Block + Continuous Convolution Interface + Continuous Convolution Block + Orthogonal Block + + +Reduction and Embeddings +-------------------------- + +.. toctree:: + :titlesonly: + + Proper Orthogonal Decomposition + Periodic Boundary Condition Embedding + Fourier Feature Embedding + Radial Basis Function Interpolation + +Optimizers and Schedulers +-------------------------- + +.. toctree:: + :titlesonly: + + Optimizer + Scheduler + TorchOptimizer + TorchScheduler + Adaptive Activation Functions ------------------------------- @@ -91,77 +148,97 @@ Adaptive Activation Functions .. toctree:: :titlesonly: - Adaptive Function Interface - Adaptive ReLU - Adaptive Sigmoid - Adaptive Tanh - Adaptive SiLU - Adaptive Mish - Adaptive ELU - Adaptive CELU - Adaptive GELU - Adaptive Softmin - Adaptive Softmax - Adaptive SIREN - Adaptive Exp + Adaptive Function Interface + Adaptive ReLU + Adaptive Sigmoid + Adaptive Tanh + Adaptive SiLU + Adaptive Mish + Adaptive ELU + Adaptive CELU + Adaptive GELU + Adaptive Softmin + Adaptive Softmax + Adaptive SIREN + Adaptive Exp -Equations and Operators -------------------------- +Equations and Differential Operators +--------------------------------------- .. toctree:: :titlesonly: - Equations - Differential Operators + EquationInterface + Equation + SystemEquation + Equation Factory + Differential Operators -Problem +Problems -------------- .. toctree:: :titlesonly: - AbstractProblem - SpatialProblem - TimeDependentProblem - ParametricProblem + AbstractProblem + InverseProblem + ParametricProblem + SpatialProblem + TimeDependentProblem -Geometries ------------------ +Problems Zoo +-------------- + +.. toctree:: + :titlesonly: + + AdvectionProblem + AllenCahnProblem + DiffusionReactionProblem + HelmholtzProblem + InversePoisson2DSquareProblem + Poisson2DSquareProblem + SupervisedProblem + + +Geometrical Domains +-------------------- .. toctree:: :titlesonly: - Location - CartesianDomain - EllipsoidDomain - SimplexDomain + Domain + CartesianDomain + EllipsoidDomain + SimplexDomain -Geometry set operations ------------------------- +Domain Operations +------------------ .. toctree:: :titlesonly: - OperationInterface - Union - Intersection - Difference - Exclusion + OperationInterface + Union + Intersection + Difference + Exclusion Callbacks --------------------- +----------- .. toctree:: :titlesonly: - Processing Callbacks - Optimizer Callbacks - Adaptive Refinment Callback + Processing callback + Optimizer callback + Refinment callback + Weighting callback -Metrics and Losses --------------------- +Losses and Weightings +--------------------- .. toctree:: :titlesonly: @@ -169,3 +246,6 @@ Metrics and Losses LossInterface LpLoss PowerLoss + WeightingInterface + ScalarWeighting + NeuralTangentKernelWeighting diff --git a/docs/source/_rst/_contributing.rst b/docs/source/_rst/_contributing.rst deleted file mode 100644 index d527a0ebe..000000000 --- a/docs/source/_rst/_contributing.rst +++ /dev/null @@ -1,37 +0,0 @@ -How to contribute -================= - -We'd love to accept your patches and contributions to this project. There are just a few small guidelines you need to follow. - -Submitting a patch ------------------- - - 1. It's generally best to start by opening a new issue describing the bug or - feature you're intending to fix. Even if you think it's relatively minor, - it's helpful to know what people are working on. Mention in the initial - issue that you are planning to work on that bug or feature so that it can - be assigned to you. - - 2. Follow the normal process of forking the project, and setup a new - branch to work in. It's important that each group of changes be done in - separate branches in order to ensure that a pull request only includes the - commits related to that bug or feature. - - 3. To ensure properly formatted code, please make sure to use 4 - spaces to indent the code. The easy way is to run on your bash the provided - script: ./code_formatter.sh. You should also run pylint over your code. - It's not strictly necessary that your code be completely "lint-free", - but this will help you find common style issues. - - 4. Any significant changes should almost always be accompanied by tests. The - project already has good test coverage, so look at some of the existing - tests if you're unsure how to go about it. We're using coveralls that - is an invaluable tools for seeing which parts of your code aren't being - exercised by your tests. - - 5. Do your best to have well-formed commit messages for each change. - This provides consistency throughout the project, and ensures that commit - messages are able to be formatted properly by various git tools. - - 6. Finally, push the commits to your fork and submit a pull request. Please, - remember to rebase properly in order to maintain a clean, linear git history. diff --git a/docs/source/_rst/_tutorial.rst b/docs/source/_rst/_tutorial.rst deleted file mode 100644 index 4e2d20504..000000000 --- a/docs/source/_rst/_tutorial.rst +++ /dev/null @@ -1,46 +0,0 @@ -PINA Tutorials -============== - -In this folder we collect useful tutorials in order to understand the principles and the potential of **PINA**. - -Getting started with PINA -------------------------- -.. toctree:: - :maxdepth: 3 - :titlesonly: - - Introduction to PINA for Physics Informed Neural Networks training - Introduction to PINA Equation class - PINA and PyTorch Lightning, training tips and visualizations - Building custom geometries with PINA Location class - - -Physics Informed Neural Networks --------------------------------- -.. toctree:: - :maxdepth: 3 - :titlesonly: - - Two dimensional Poisson problem using Extra Features Learning - Two dimensional Wave problem with hard constraint - Resolution of a 2D Poisson inverse problem - Periodic Boundary Conditions for Helmotz Equation - Multiscale PDE learning with Fourier Feature Network - -Neural Operator Learning ------------------------- -.. toctree:: - :maxdepth: 3 - :titlesonly: - - Two dimensional Darcy flow using the Fourier Neural Operator - Time dependent Kuramoto Sivashinsky equation using the Averaging Neural Operator - -Supervised Learning -------------------- -.. toctree:: - :maxdepth: 3 - :titlesonly: - - Unstructured convolutional autoencoder via continuous convolution - POD-RBF and POD-NN for reduced order modeling diff --git a/docs/source/_rst/adaptive_function/AdaptiveActivationFunctionInterface.rst b/docs/source/_rst/adaptive_function/AdaptiveActivationFunctionInterface.rst new file mode 100644 index 000000000..cf8b6551d --- /dev/null +++ b/docs/source/_rst/adaptive_function/AdaptiveActivationFunctionInterface.rst @@ -0,0 +1,8 @@ +AdaptiveActivationFunctionInterface +======================================= + +.. currentmodule:: pina.adaptive_function.adaptive_function_interface + +.. automodule:: pina.adaptive_function.adaptive_function_interface + :members: + :show-inheritance: diff --git a/docs/source/_rst/adaptive_functions/AdaptiveCELU.rst b/docs/source/_rst/adaptive_function/AdaptiveCELU.rst similarity index 71% rename from docs/source/_rst/adaptive_functions/AdaptiveCELU.rst rename to docs/source/_rst/adaptive_function/AdaptiveCELU.rst index 9736ee631..c4d6d5429 100644 --- a/docs/source/_rst/adaptive_functions/AdaptiveCELU.rst +++ b/docs/source/_rst/adaptive_function/AdaptiveCELU.rst @@ -1,7 +1,7 @@ AdaptiveCELU ============ -.. currentmodule:: pina.adaptive_functions.adaptive_func +.. currentmodule:: pina.adaptive_function.adaptive_function .. autoclass:: AdaptiveCELU :members: diff --git a/docs/source/_rst/adaptive_functions/AdaptiveELU.rst b/docs/source/_rst/adaptive_function/AdaptiveELU.rst similarity index 71% rename from docs/source/_rst/adaptive_functions/AdaptiveELU.rst rename to docs/source/_rst/adaptive_function/AdaptiveELU.rst index ad04717f1..aab273b08 100644 --- a/docs/source/_rst/adaptive_functions/AdaptiveELU.rst +++ b/docs/source/_rst/adaptive_function/AdaptiveELU.rst @@ -1,7 +1,7 @@ AdaptiveELU =========== -.. currentmodule:: pina.adaptive_functions.adaptive_func +.. currentmodule:: pina.adaptive_function.adaptive_function .. autoclass:: AdaptiveELU :members: diff --git a/docs/source/_rst/adaptive_functions/AdaptiveExp.rst b/docs/source/_rst/adaptive_function/AdaptiveExp.rst similarity index 71% rename from docs/source/_rst/adaptive_functions/AdaptiveExp.rst rename to docs/source/_rst/adaptive_function/AdaptiveExp.rst index 7d07cd52d..a7ee52b20 100644 --- a/docs/source/_rst/adaptive_functions/AdaptiveExp.rst +++ b/docs/source/_rst/adaptive_function/AdaptiveExp.rst @@ -1,7 +1,7 @@ AdaptiveExp =========== -.. currentmodule:: pina.adaptive_functions.adaptive_func +.. currentmodule:: pina.adaptive_function.adaptive_function .. autoclass:: AdaptiveExp :members: diff --git a/docs/source/_rst/adaptive_functions/AdaptiveGELU.rst b/docs/source/_rst/adaptive_function/AdaptiveGELU.rst similarity index 71% rename from docs/source/_rst/adaptive_functions/AdaptiveGELU.rst rename to docs/source/_rst/adaptive_function/AdaptiveGELU.rst index 86e587584..b4aef14dc 100644 --- a/docs/source/_rst/adaptive_functions/AdaptiveGELU.rst +++ b/docs/source/_rst/adaptive_function/AdaptiveGELU.rst @@ -1,7 +1,7 @@ AdaptiveGELU ============ -.. currentmodule:: pina.adaptive_functions.adaptive_func +.. currentmodule:: pina.adaptive_function.adaptive_function .. autoclass:: AdaptiveGELU :members: diff --git a/docs/source/_rst/adaptive_functions/AdaptiveMish.rst b/docs/source/_rst/adaptive_function/AdaptiveMish.rst similarity index 71% rename from docs/source/_rst/adaptive_functions/AdaptiveMish.rst rename to docs/source/_rst/adaptive_function/AdaptiveMish.rst index 4e1e3b435..d006df054 100644 --- a/docs/source/_rst/adaptive_functions/AdaptiveMish.rst +++ b/docs/source/_rst/adaptive_function/AdaptiveMish.rst @@ -1,7 +1,7 @@ AdaptiveMish ============ -.. currentmodule:: pina.adaptive_functions.adaptive_func +.. currentmodule:: pina.adaptive_function.adaptive_function .. autoclass:: AdaptiveMish :members: diff --git a/docs/source/_rst/adaptive_functions/AdaptiveReLU.rst b/docs/source/_rst/adaptive_function/AdaptiveReLU.rst similarity index 71% rename from docs/source/_rst/adaptive_functions/AdaptiveReLU.rst rename to docs/source/_rst/adaptive_function/AdaptiveReLU.rst index ea08c29a9..d0fe4de68 100644 --- a/docs/source/_rst/adaptive_functions/AdaptiveReLU.rst +++ b/docs/source/_rst/adaptive_function/AdaptiveReLU.rst @@ -1,7 +1,7 @@ AdaptiveReLU ============ -.. currentmodule:: pina.adaptive_functions.adaptive_func +.. currentmodule:: pina.adaptive_function.adaptive_function .. autoclass:: AdaptiveReLU :members: diff --git a/docs/source/_rst/adaptive_functions/AdaptiveSIREN.rst b/docs/source/_rst/adaptive_function/AdaptiveSIREN.rst similarity index 72% rename from docs/source/_rst/adaptive_functions/AdaptiveSIREN.rst rename to docs/source/_rst/adaptive_function/AdaptiveSIREN.rst index 96133bdd8..9f132547b 100644 --- a/docs/source/_rst/adaptive_functions/AdaptiveSIREN.rst +++ b/docs/source/_rst/adaptive_function/AdaptiveSIREN.rst @@ -1,7 +1,7 @@ AdaptiveSIREN ============= -.. currentmodule:: pina.adaptive_functions.adaptive_func +.. currentmodule:: pina.adaptive_function.adaptive_function .. autoclass:: AdaptiveSIREN :members: diff --git a/docs/source/_rst/adaptive_functions/AdaptiveSiLU.rst b/docs/source/_rst/adaptive_function/AdaptiveSiLU.rst similarity index 71% rename from docs/source/_rst/adaptive_functions/AdaptiveSiLU.rst rename to docs/source/_rst/adaptive_function/AdaptiveSiLU.rst index 2f359fded..722678611 100644 --- a/docs/source/_rst/adaptive_functions/AdaptiveSiLU.rst +++ b/docs/source/_rst/adaptive_function/AdaptiveSiLU.rst @@ -1,7 +1,7 @@ AdaptiveSiLU ============ -.. currentmodule:: pina.adaptive_functions.adaptive_func +.. currentmodule:: pina.adaptive_function.adaptive_function .. autoclass:: AdaptiveSiLU :members: diff --git a/docs/source/_rst/adaptive_functions/AdaptiveSigmoid.rst b/docs/source/_rst/adaptive_function/AdaptiveSigmoid.rst similarity index 72% rename from docs/source/_rst/adaptive_functions/AdaptiveSigmoid.rst rename to docs/source/_rst/adaptive_function/AdaptiveSigmoid.rst index 6f495a8ed..6002ffb31 100644 --- a/docs/source/_rst/adaptive_functions/AdaptiveSigmoid.rst +++ b/docs/source/_rst/adaptive_function/AdaptiveSigmoid.rst @@ -1,7 +1,7 @@ AdaptiveSigmoid =============== -.. currentmodule:: pina.adaptive_functions.adaptive_func +.. currentmodule:: pina.adaptive_function.adaptive_function .. autoclass:: AdaptiveSigmoid :members: diff --git a/docs/source/_rst/adaptive_functions/AdaptiveSoftmax.rst b/docs/source/_rst/adaptive_function/AdaptiveSoftmax.rst similarity index 72% rename from docs/source/_rst/adaptive_functions/AdaptiveSoftmax.rst rename to docs/source/_rst/adaptive_function/AdaptiveSoftmax.rst index 5cab9c65c..c2b4c9f09 100644 --- a/docs/source/_rst/adaptive_functions/AdaptiveSoftmax.rst +++ b/docs/source/_rst/adaptive_function/AdaptiveSoftmax.rst @@ -1,7 +1,7 @@ AdaptiveSoftmax =============== -.. currentmodule:: pina.adaptive_functions.adaptive_func +.. currentmodule:: pina.adaptive_function.adaptive_function .. autoclass:: AdaptiveSoftmax :members: diff --git a/docs/source/_rst/adaptive_functions/AdaptiveSoftmin.rst b/docs/source/_rst/adaptive_function/AdaptiveSoftmin.rst similarity index 72% rename from docs/source/_rst/adaptive_functions/AdaptiveSoftmin.rst rename to docs/source/_rst/adaptive_function/AdaptiveSoftmin.rst index a0e6c94ae..5189cb391 100644 --- a/docs/source/_rst/adaptive_functions/AdaptiveSoftmin.rst +++ b/docs/source/_rst/adaptive_function/AdaptiveSoftmin.rst @@ -1,7 +1,7 @@ AdaptiveSoftmin =============== -.. currentmodule:: pina.adaptive_functions.adaptive_func +.. currentmodule:: pina.adaptive_function.adaptive_function .. autoclass:: AdaptiveSoftmin :members: diff --git a/docs/source/_rst/adaptive_functions/AdaptiveTanh.rst b/docs/source/_rst/adaptive_function/AdaptiveTanh.rst similarity index 71% rename from docs/source/_rst/adaptive_functions/AdaptiveTanh.rst rename to docs/source/_rst/adaptive_function/AdaptiveTanh.rst index 3e486512f..9a9b380a3 100644 --- a/docs/source/_rst/adaptive_functions/AdaptiveTanh.rst +++ b/docs/source/_rst/adaptive_function/AdaptiveTanh.rst @@ -1,7 +1,7 @@ AdaptiveTanh ============ -.. currentmodule:: pina.adaptive_functions.adaptive_func +.. currentmodule:: pina.adaptive_function.adaptive_function .. autoclass:: AdaptiveTanh :members: diff --git a/docs/source/_rst/adaptive_functions/AdaptiveFunctionInterface.rst b/docs/source/_rst/adaptive_functions/AdaptiveFunctionInterface.rst deleted file mode 100644 index 7cdf754b7..000000000 --- a/docs/source/_rst/adaptive_functions/AdaptiveFunctionInterface.rst +++ /dev/null @@ -1,8 +0,0 @@ -AdaptiveActivationFunctionInterface -======================================= - -.. currentmodule:: pina.adaptive_functions.adaptive_func_interface - -.. automodule:: pina.adaptive_functions.adaptive_func_interface - :members: - :show-inheritance: diff --git a/docs/source/_rst/callback/adaptive_refinment_callback.rst b/docs/source/_rst/callback/adaptive_refinment_callback.rst new file mode 100644 index 000000000..8afad6571 --- /dev/null +++ b/docs/source/_rst/callback/adaptive_refinment_callback.rst @@ -0,0 +1,7 @@ +Refinments callbacks +======================= + +.. currentmodule:: pina.callback.adaptive_refinement_callback +.. autoclass:: R3Refinement + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/callback/linear_weight_update_callback.rst b/docs/source/_rst/callback/linear_weight_update_callback.rst new file mode 100644 index 000000000..fe45b56e2 --- /dev/null +++ b/docs/source/_rst/callback/linear_weight_update_callback.rst @@ -0,0 +1,7 @@ +Weighting callbacks +======================== + +.. currentmodule:: pina.callback.linear_weight_update_callback +.. autoclass:: LinearWeightUpdate + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/callbacks/optimizer_callbacks.rst b/docs/source/_rst/callback/optimizer_callback.rst similarity index 66% rename from docs/source/_rst/callbacks/optimizer_callbacks.rst rename to docs/source/_rst/callback/optimizer_callback.rst index 7ee418fac..0afdc2669 100644 --- a/docs/source/_rst/callbacks/optimizer_callbacks.rst +++ b/docs/source/_rst/callback/optimizer_callback.rst @@ -1,7 +1,7 @@ Optimizer callbacks ===================== -.. currentmodule:: pina.callbacks.optimizer_callbacks +.. currentmodule:: pina.callback.optimizer_callback .. autoclass:: SwitchOptimizer :members: :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/callbacks/processing_callbacks.rst b/docs/source/_rst/callback/processing_callback.rst similarity index 76% rename from docs/source/_rst/callbacks/processing_callbacks.rst rename to docs/source/_rst/callback/processing_callback.rst index bd3bbc840..a06bb8b17 100644 --- a/docs/source/_rst/callbacks/processing_callbacks.rst +++ b/docs/source/_rst/callback/processing_callback.rst @@ -1,7 +1,7 @@ Processing callbacks ======================= -.. currentmodule:: pina.callbacks.processing_callbacks +.. currentmodule:: pina.callback.processing_callback .. autoclass:: MetricTracker :members: :show-inheritance: diff --git a/docs/source/_rst/callbacks/adaptive_refinment_callbacks.rst b/docs/source/_rst/callbacks/adaptive_refinment_callbacks.rst deleted file mode 100644 index 11b313ee0..000000000 --- a/docs/source/_rst/callbacks/adaptive_refinment_callbacks.rst +++ /dev/null @@ -1,7 +0,0 @@ -Adaptive Refinments callbacks -=============================== - -.. currentmodule:: pina.callbacks.adaptive_refinment_callbacks -.. autoclass:: R3Refinement - :members: - :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/condition.rst b/docs/source/_rst/condition.rst deleted file mode 100644 index 088b966d6..000000000 --- a/docs/source/_rst/condition.rst +++ /dev/null @@ -1,7 +0,0 @@ -Condition -========= -.. currentmodule:: pina.condition - -.. autoclass:: Condition - :members: - :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/condition/condition.rst b/docs/source/_rst/condition/condition.rst new file mode 100644 index 000000000..51edfafff --- /dev/null +++ b/docs/source/_rst/condition/condition.rst @@ -0,0 +1,7 @@ +Conditions +============= +.. currentmodule:: pina.condition.condition + +.. autoclass:: Condition + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/condition/condition_interface.rst b/docs/source/_rst/condition/condition_interface.rst new file mode 100644 index 000000000..88459629b --- /dev/null +++ b/docs/source/_rst/condition/condition_interface.rst @@ -0,0 +1,7 @@ +ConditionInterface +====================== +.. currentmodule:: pina.condition.condition_interface + +.. autoclass:: ConditionInterface + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/condition/data_condition.rst b/docs/source/_rst/condition/data_condition.rst new file mode 100644 index 000000000..b7c322ea1 --- /dev/null +++ b/docs/source/_rst/condition/data_condition.rst @@ -0,0 +1,15 @@ +Data Conditions +================== +.. currentmodule:: pina.condition.data_condition + +.. autoclass:: DataCondition + :members: + :show-inheritance: + +.. autoclass:: GraphDataCondition + :members: + :show-inheritance: + +.. autoclass:: TensorDataCondition + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/condition/domain_equation_condition.rst b/docs/source/_rst/condition/domain_equation_condition.rst new file mode 100644 index 000000000..505c8b839 --- /dev/null +++ b/docs/source/_rst/condition/domain_equation_condition.rst @@ -0,0 +1,7 @@ +Domain Equation Condition +=========================== +.. currentmodule:: pina.condition.domain_equation_condition + +.. autoclass:: DomainEquationCondition + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/condition/input_equation_condition.rst b/docs/source/_rst/condition/input_equation_condition.rst new file mode 100644 index 000000000..4f5450e93 --- /dev/null +++ b/docs/source/_rst/condition/input_equation_condition.rst @@ -0,0 +1,15 @@ +Input Equation Condition +=========================== +.. currentmodule:: pina.condition.input_equation_condition + +.. autoclass:: InputEquationCondition + :members: + :show-inheritance: + +.. autoclass:: InputTensorEquationCondition + :members: + :show-inheritance: + +.. autoclass:: InputGraphEquationCondition + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/condition/input_target_condition.rst b/docs/source/_rst/condition/input_target_condition.rst new file mode 100644 index 000000000..960b7d6f4 --- /dev/null +++ b/docs/source/_rst/condition/input_target_condition.rst @@ -0,0 +1,23 @@ +Input Target Condition +=========================== +.. currentmodule:: pina.condition.input_target_condition + +.. autoclass:: InputTargetCondition + :members: + :show-inheritance: + +.. autoclass:: TensorInputTensorTargetCondition + :members: + :show-inheritance: + +.. autoclass:: TensorInputGraphTargetCondition + :members: + :show-inheritance: + +.. autoclass:: GraphInputTensorTargetCondition + :members: + :show-inheritance: + +.. autoclass:: GraphInputGraphTargetCondition + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/data/data_module.rst b/docs/source/_rst/data/data_module.rst new file mode 100644 index 000000000..b7ffb14e0 --- /dev/null +++ b/docs/source/_rst/data/data_module.rst @@ -0,0 +1,15 @@ +DataModule +====================== +.. currentmodule:: pina.data.data_module + +.. autoclass:: Collator + :members: + :show-inheritance: + +.. autoclass:: PinaDataModule + :members: + :show-inheritance: + +.. autoclass:: PinaSampler + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/data/dataset.rst b/docs/source/_rst/data/dataset.rst new file mode 100644 index 000000000..b49b41db1 --- /dev/null +++ b/docs/source/_rst/data/dataset.rst @@ -0,0 +1,19 @@ +Dataset +====================== +.. currentmodule:: pina.data.dataset + +.. autoclass:: PinaDataset + :members: + :show-inheritance: + +.. autoclass:: PinaDatasetFactory + :members: + :show-inheritance: + +.. autoclass:: PinaGraphDataset + :members: + :show-inheritance: + +.. autoclass:: PinaTensorDataset + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/geometry/cartesian.rst b/docs/source/_rst/domain/cartesian.rst similarity index 59% rename from docs/source/_rst/geometry/cartesian.rst rename to docs/source/_rst/domain/cartesian.rst index b57c02bb4..97f5e8974 100644 --- a/docs/source/_rst/geometry/cartesian.rst +++ b/docs/source/_rst/domain/cartesian.rst @@ -1,8 +1,8 @@ CartesianDomain ====================== -.. currentmodule:: pina.geometry.cartesian +.. currentmodule:: pina.domain.cartesian -.. automodule:: pina.geometry.cartesian +.. automodule:: pina.domain.cartesian .. autoclass:: CartesianDomain :members: diff --git a/docs/source/_rst/geometry/difference_domain.rst b/docs/source/_rst/domain/difference_domain.rst similarity index 50% rename from docs/source/_rst/geometry/difference_domain.rst rename to docs/source/_rst/domain/difference_domain.rst index fc0b29377..f25daa522 100644 --- a/docs/source/_rst/geometry/difference_domain.rst +++ b/docs/source/_rst/domain/difference_domain.rst @@ -1,8 +1,8 @@ Difference ====================== -.. currentmodule:: pina.geometry.difference_domain +.. currentmodule:: pina.domain.difference_domain -.. automodule:: pina.geometry.difference_domain +.. automodule:: pina.domain.difference_domain .. autoclass:: Difference :members: diff --git a/docs/source/_rst/domain/domain.rst b/docs/source/_rst/domain/domain.rst new file mode 100644 index 000000000..27adcf0bc --- /dev/null +++ b/docs/source/_rst/domain/domain.rst @@ -0,0 +1,9 @@ +Domain +=========== +.. currentmodule:: pina.domain.domain_interface + +.. automodule:: pina.domain.domain_interface + +.. autoclass:: DomainInterface + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/geometry/ellipsoid.rst b/docs/source/_rst/domain/ellipsoid.rst similarity index 59% rename from docs/source/_rst/geometry/ellipsoid.rst rename to docs/source/_rst/domain/ellipsoid.rst index 09af427ba..ee0d2b7a4 100644 --- a/docs/source/_rst/geometry/ellipsoid.rst +++ b/docs/source/_rst/domain/ellipsoid.rst @@ -1,8 +1,8 @@ EllipsoidDomain ====================== -.. currentmodule:: pina.geometry.ellipsoid +.. currentmodule:: pina.domain.ellipsoid -.. automodule:: pina.geometry.ellipsoid +.. automodule:: pina.domain.ellipsoid .. autoclass:: EllipsoidDomain :members: diff --git a/docs/source/_rst/geometry/exclusion_domain.rst b/docs/source/_rst/domain/exclusion_domain.rst similarity index 50% rename from docs/source/_rst/geometry/exclusion_domain.rst rename to docs/source/_rst/domain/exclusion_domain.rst index a07aafca1..8d18be199 100644 --- a/docs/source/_rst/geometry/exclusion_domain.rst +++ b/docs/source/_rst/domain/exclusion_domain.rst @@ -1,8 +1,8 @@ Exclusion ====================== -.. currentmodule:: pina.geometry.exclusion_domain +.. currentmodule:: pina.domain.exclusion_domain -.. automodule:: pina.geometry.exclusion_domain +.. automodule:: pina.domain.exclusion_domain .. autoclass:: Exclusion :members: diff --git a/docs/source/_rst/geometry/intersection_domain.rst b/docs/source/_rst/domain/intersection_domain.rst similarity index 50% rename from docs/source/_rst/geometry/intersection_domain.rst rename to docs/source/_rst/domain/intersection_domain.rst index a3c1356aa..8b2498661 100644 --- a/docs/source/_rst/geometry/intersection_domain.rst +++ b/docs/source/_rst/domain/intersection_domain.rst @@ -1,8 +1,8 @@ Intersection ====================== -.. currentmodule:: pina.geometry.intersection_domain +.. currentmodule:: pina.domain.intersection_domain -.. automodule:: pina.geometry.intersection_domain +.. automodule:: pina.domain.intersection_domain .. autoclass:: Intersection :members: diff --git a/docs/source/_rst/geometry/operation_interface.rst b/docs/source/_rst/domain/operation_interface.rst similarity index 52% rename from docs/source/_rst/geometry/operation_interface.rst rename to docs/source/_rst/domain/operation_interface.rst index 00a2d8467..0acd393dc 100644 --- a/docs/source/_rst/geometry/operation_interface.rst +++ b/docs/source/_rst/domain/operation_interface.rst @@ -1,8 +1,8 @@ OperationInterface ====================== -.. currentmodule:: pina.geometry.operation_interface +.. currentmodule:: pina.domain.operation_interface -.. automodule:: pina.geometry.operation_interface +.. automodule:: pina.domain.operation_interface .. autoclass:: OperationInterface :members: diff --git a/docs/source/_rst/geometry/simplex.rst b/docs/source/_rst/domain/simplex.rst similarity index 60% rename from docs/source/_rst/geometry/simplex.rst rename to docs/source/_rst/domain/simplex.rst index b5a83e44e..7accd7f84 100644 --- a/docs/source/_rst/geometry/simplex.rst +++ b/docs/source/_rst/domain/simplex.rst @@ -1,8 +1,8 @@ SimplexDomain ====================== -.. currentmodule:: pina.geometry.simplex +.. currentmodule:: pina.domain.simplex -.. automodule:: pina.geometry.simplex +.. automodule:: pina.domain.simplex .. autoclass:: SimplexDomain :members: diff --git a/docs/source/_rst/geometry/union_domain.rst b/docs/source/_rst/domain/union_domain.rst similarity index 50% rename from docs/source/_rst/geometry/union_domain.rst rename to docs/source/_rst/domain/union_domain.rst index ad172d792..921e430cf 100644 --- a/docs/source/_rst/geometry/union_domain.rst +++ b/docs/source/_rst/domain/union_domain.rst @@ -1,8 +1,8 @@ Union ====================== -.. currentmodule:: pina.geometry.union_domain +.. currentmodule:: pina.domain.union_domain -.. automodule:: pina.geometry.union_domain +.. automodule:: pina.domain.union_domain .. autoclass:: Union :members: diff --git a/docs/source/_rst/equation/equation.rst b/docs/source/_rst/equation/equation.rst new file mode 100644 index 000000000..33e19c957 --- /dev/null +++ b/docs/source/_rst/equation/equation.rst @@ -0,0 +1,7 @@ +Equation +========== + +.. currentmodule:: pina.equation.equation +.. autoclass:: Equation + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/equation/equation_factory.rst b/docs/source/_rst/equation/equation_factory.rst new file mode 100644 index 000000000..cf5d430d3 --- /dev/null +++ b/docs/source/_rst/equation/equation_factory.rst @@ -0,0 +1,19 @@ +Equation Factory +================== + +.. currentmodule:: pina.equation.equation_factory +.. autoclass:: FixedValue + :members: + :show-inheritance: + +.. autoclass:: FixedGradient + :members: + :show-inheritance: + +.. autoclass:: FixedFlux + :members: + :show-inheritance: + +.. autoclass:: Laplace + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/equation/equation_interface.rst b/docs/source/_rst/equation/equation_interface.rst new file mode 100644 index 000000000..cde7b0012 --- /dev/null +++ b/docs/source/_rst/equation/equation_interface.rst @@ -0,0 +1,7 @@ +Equation Interface +==================== + +.. currentmodule:: pina.equation.equation_interface +.. autoclass:: EquationInterface + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/equation/system_equation.rst b/docs/source/_rst/equation/system_equation.rst new file mode 100644 index 000000000..33c931cd9 --- /dev/null +++ b/docs/source/_rst/equation/system_equation.rst @@ -0,0 +1,7 @@ +System Equation +================= + +.. currentmodule:: pina.equation.system_equation +.. autoclass:: SystemEquation + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/equations.rst b/docs/source/_rst/equations.rst deleted file mode 100644 index 6826dde21..000000000 --- a/docs/source/_rst/equations.rst +++ /dev/null @@ -1,42 +0,0 @@ -Equations -========== -Equations are used in PINA to make easy the training. During problem definition -each `equation` passed to a `Condition` object must be an `Equation` or `SystemEquation`. -An `Equation` is simply a wrapper over callable python functions, while `SystemEquation` is -a wrapper arounf a list of callable python functions. We provide a wide rage of already implemented -equations to ease the code writing, such as `FixedValue`, `Laplace`, and many more. - - -.. currentmodule:: pina.equation.equation_interface -.. autoclass:: EquationInterface - :members: - :show-inheritance: - -.. currentmodule:: pina.equation.equation -.. autoclass:: Equation - :members: - :show-inheritance: - - -.. currentmodule:: pina.equation.system_equation -.. autoclass:: SystemEquation - :members: - :show-inheritance: - - -.. currentmodule:: pina.equation.equation_factory -.. autoclass:: FixedValue - :members: - :show-inheritance: - -.. autoclass:: FixedGradient - :members: - :show-inheritance: - -.. autoclass:: FixedFlux - :members: - :show-inheritance: - -.. autoclass:: Laplace - :members: - :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/geometry/location.rst b/docs/source/_rst/geometry/location.rst deleted file mode 100644 index 5d680a1e4..000000000 --- a/docs/source/_rst/geometry/location.rst +++ /dev/null @@ -1,9 +0,0 @@ -Location -==================== -.. currentmodule:: pina.geometry.location - -.. automodule:: pina.geometry.location - -.. autoclass:: Location - :members: - :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/graph/graph.rst b/docs/source/_rst/graph/graph.rst new file mode 100644 index 000000000..1921f83e0 --- /dev/null +++ b/docs/source/_rst/graph/graph.rst @@ -0,0 +1,9 @@ +Graph +=========== +.. currentmodule:: pina.graph + + +.. autoclass:: Graph + :members: + :private-members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/graph/graph_builder.rst b/docs/source/_rst/graph/graph_builder.rst new file mode 100644 index 000000000..2508aecb7 --- /dev/null +++ b/docs/source/_rst/graph/graph_builder.rst @@ -0,0 +1,9 @@ +GraphBuilder +============== +.. currentmodule:: pina.graph + + +.. autoclass:: GraphBuilder + :members: + :private-members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/graph/knn_graph.rst b/docs/source/_rst/graph/knn_graph.rst new file mode 100644 index 000000000..8ef0b190b --- /dev/null +++ b/docs/source/_rst/graph/knn_graph.rst @@ -0,0 +1,9 @@ +KNNGraph +=========== +.. currentmodule:: pina.graph + + +.. autoclass:: KNNGraph + :members: + :private-members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/graph/label_batch.rst b/docs/source/_rst/graph/label_batch.rst new file mode 100644 index 000000000..7cd4d2684 --- /dev/null +++ b/docs/source/_rst/graph/label_batch.rst @@ -0,0 +1,9 @@ +LabelBatch +=========== +.. currentmodule:: pina.graph + + +.. autoclass:: LabelBatch + :members: + :private-members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/graph/radius_graph.rst b/docs/source/_rst/graph/radius_graph.rst new file mode 100644 index 000000000..7414d2dc1 --- /dev/null +++ b/docs/source/_rst/graph/radius_graph.rst @@ -0,0 +1,9 @@ +RadiusGraph +============= +.. currentmodule:: pina.graph + + +.. autoclass:: RadiusGraph + :members: + :private-members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/layers/avno_layer.rst b/docs/source/_rst/layers/avno_layer.rst deleted file mode 100644 index 38d7ccbe2..000000000 --- a/docs/source/_rst/layers/avno_layer.rst +++ /dev/null @@ -1,8 +0,0 @@ -Averaging layers -==================== -.. currentmodule:: pina.model.layers.avno_layer - -.. autoclass:: AVNOBlock - :members: - :show-inheritance: - :noindex: diff --git a/docs/source/_rst/layers/convolution.rst b/docs/source/_rst/layers/convolution.rst deleted file mode 100644 index 3089fea47..000000000 --- a/docs/source/_rst/layers/convolution.rst +++ /dev/null @@ -1,8 +0,0 @@ -Continuous convolution -========================= -.. currentmodule:: pina.model.layers.convolution_2d - -.. autoclass:: ContinuousConvBlock - :members: - :show-inheritance: - :noindex: diff --git a/docs/source/_rst/layers/enhanced_linear.rst b/docs/source/_rst/layers/enhanced_linear.rst deleted file mode 100644 index ba30960e6..000000000 --- a/docs/source/_rst/layers/enhanced_linear.rst +++ /dev/null @@ -1,8 +0,0 @@ -EnhancedLinear -================= -.. currentmodule:: pina.model.layers.residual - -.. autoclass:: EnhancedLinear - :members: - :show-inheritance: - :noindex: \ No newline at end of file diff --git a/docs/source/_rst/layers/lowrank_layer.rst b/docs/source/_rst/layers/lowrank_layer.rst deleted file mode 100644 index 6e72feb68..000000000 --- a/docs/source/_rst/layers/lowrank_layer.rst +++ /dev/null @@ -1,8 +0,0 @@ -Low Rank layer -==================== -.. currentmodule:: pina.model.layers.lowrank_layer - -.. autoclass:: LowRankBlock - :members: - :show-inheritance: - :noindex: diff --git a/docs/source/_rst/layers/pod.rst b/docs/source/_rst/layers/pod.rst deleted file mode 100644 index 041be9973..000000000 --- a/docs/source/_rst/layers/pod.rst +++ /dev/null @@ -1,7 +0,0 @@ -PODBlock -====================== -.. currentmodule:: pina.model.layers.pod - -.. autoclass:: PODBlock - :members: - :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/layers/rbf_layer.rst b/docs/source/_rst/layers/rbf_layer.rst deleted file mode 100644 index 8736d1a2b..000000000 --- a/docs/source/_rst/layers/rbf_layer.rst +++ /dev/null @@ -1,7 +0,0 @@ -RBFBlock -====================== -.. currentmodule:: pina.model.layers.rbf_layer - -.. autoclass:: RBFBlock - :members: - :show-inheritance: diff --git a/docs/source/_rst/loss/loss_interface.rst b/docs/source/_rst/loss/loss_interface.rst index 6d4827d15..8ff78c01e 100644 --- a/docs/source/_rst/loss/loss_interface.rst +++ b/docs/source/_rst/loss/loss_interface.rst @@ -1,8 +1,8 @@ -LpLoss +LossInterface =============== -.. currentmodule:: pina.loss +.. currentmodule:: pina.loss.loss_interface -.. automodule:: pina.loss +.. automodule:: pina.loss.loss_interface .. autoclass:: LossInterface :members: diff --git a/docs/source/_rst/loss/lploss.rst b/docs/source/_rst/loss/lploss.rst index f95d1877c..37dfdfe3c 100644 --- a/docs/source/_rst/loss/lploss.rst +++ b/docs/source/_rst/loss/lploss.rst @@ -1,9 +1,6 @@ LpLoss =============== -.. currentmodule:: pina.loss - -.. automodule:: pina.loss - :no-index: +.. currentmodule:: pina.loss.lp_loss .. autoclass:: LpLoss :members: diff --git a/docs/source/_rst/loss/ntk_weighting.rst b/docs/source/_rst/loss/ntk_weighting.rst new file mode 100644 index 000000000..6d9d8816d --- /dev/null +++ b/docs/source/_rst/loss/ntk_weighting.rst @@ -0,0 +1,9 @@ +NeuralTangentKernelWeighting +============================= +.. currentmodule:: pina.loss.ntk_weighting + +.. automodule:: pina.loss.ntk_weighting + +.. autoclass:: NeuralTangentKernelWeighting + :members: + :show-inheritance: diff --git a/docs/source/_rst/loss/powerloss.rst b/docs/source/_rst/loss/powerloss.rst index 0b1a7d91b..e4dee43b8 100644 --- a/docs/source/_rst/loss/powerloss.rst +++ b/docs/source/_rst/loss/powerloss.rst @@ -1,9 +1,6 @@ PowerLoss ==================== -.. currentmodule:: pina.loss - -.. automodule:: pina.loss - :no-index: +.. currentmodule:: pina.loss.power_loss .. autoclass:: PowerLoss :members: diff --git a/docs/source/_rst/loss/scalar_weighting.rst b/docs/source/_rst/loss/scalar_weighting.rst new file mode 100644 index 000000000..5ee82a785 --- /dev/null +++ b/docs/source/_rst/loss/scalar_weighting.rst @@ -0,0 +1,9 @@ +ScalarWeighting +=================== +.. currentmodule:: pina.loss.scalar_weighting + +.. automodule:: pina.loss.scalar_weighting + +.. autoclass:: ScalarWeighting + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/loss/weighting_interface.rst b/docs/source/_rst/loss/weighting_interface.rst new file mode 100644 index 000000000..2b0fa1bdc --- /dev/null +++ b/docs/source/_rst/loss/weighting_interface.rst @@ -0,0 +1,9 @@ +WeightingInterface +=================== +.. currentmodule:: pina.loss.weighting_interface + +.. automodule:: pina.loss.weighting_interface + +.. autoclass:: WeightingInterface + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/models/avno.rst b/docs/source/_rst/model/average_neural_operator.rst similarity index 70% rename from docs/source/_rst/models/avno.rst rename to docs/source/_rst/model/average_neural_operator.rst index a083f6fdc..02211e9a8 100644 --- a/docs/source/_rst/models/avno.rst +++ b/docs/source/_rst/model/average_neural_operator.rst @@ -1,6 +1,6 @@ Averaging Neural Operator ============================== -.. currentmodule:: pina.model.avno +.. currentmodule:: pina.model.average_neural_operator .. autoclass:: AveragingNeuralOperator :members: diff --git a/docs/source/_rst/model/block/average_neural_operator_block.rst b/docs/source/_rst/model/block/average_neural_operator_block.rst new file mode 100644 index 000000000..0072ec9d0 --- /dev/null +++ b/docs/source/_rst/model/block/average_neural_operator_block.rst @@ -0,0 +1,8 @@ +Averaging Neural Operator Block +================================== +.. currentmodule:: pina.model.block.average_neural_operator_block + +.. autoclass:: AVNOBlock + :members: + :show-inheritance: + :noindex: diff --git a/docs/source/_rst/model/block/convolution.rst b/docs/source/_rst/model/block/convolution.rst new file mode 100644 index 000000000..4033d5d56 --- /dev/null +++ b/docs/source/_rst/model/block/convolution.rst @@ -0,0 +1,8 @@ +Continuous Convolution Block +=============================== +.. currentmodule:: pina.model.block.convolution_2d + +.. autoclass:: ContinuousConvBlock + :members: + :show-inheritance: + :noindex: diff --git a/docs/source/_rst/model/block/convolution_interface.rst b/docs/source/_rst/model/block/convolution_interface.rst new file mode 100644 index 000000000..f8e61c16c --- /dev/null +++ b/docs/source/_rst/model/block/convolution_interface.rst @@ -0,0 +1,8 @@ +Continuous Convolution Interface +================================== +.. currentmodule:: pina.model.block.convolution + +.. autoclass:: BaseContinuousConv + :members: + :show-inheritance: + :noindex: diff --git a/docs/source/_rst/model/block/enhanced_linear.rst b/docs/source/_rst/model/block/enhanced_linear.rst new file mode 100644 index 000000000..d08cf79bf --- /dev/null +++ b/docs/source/_rst/model/block/enhanced_linear.rst @@ -0,0 +1,8 @@ +EnhancedLinear Block +===================== +.. currentmodule:: pina.model.block.residual + +.. autoclass:: EnhancedLinear + :members: + :show-inheritance: + :noindex: \ No newline at end of file diff --git a/docs/source/_rst/layers/fourier.rst b/docs/source/_rst/model/block/fourier_block.rst similarity index 63% rename from docs/source/_rst/layers/fourier.rst rename to docs/source/_rst/model/block/fourier_block.rst index 132170069..c0fff4deb 100644 --- a/docs/source/_rst/layers/fourier.rst +++ b/docs/source/_rst/model/block/fourier_block.rst @@ -1,6 +1,6 @@ -Fourier Layers -=================== -.. currentmodule:: pina.model.layers.fourier +Fourier Neural Operator Block +====================================== +.. currentmodule:: pina.model.block.fourier_block .. autoclass:: FourierBlock1D diff --git a/docs/source/_rst/layers/fourier_embedding.rst b/docs/source/_rst/model/block/fourier_embedding.rst similarity index 75% rename from docs/source/_rst/layers/fourier_embedding.rst rename to docs/source/_rst/model/block/fourier_embedding.rst index f48cef150..77eb3960c 100644 --- a/docs/source/_rst/layers/fourier_embedding.rst +++ b/docs/source/_rst/model/block/fourier_embedding.rst @@ -1,6 +1,6 @@ Fourier Feature Embedding ======================================= -.. currentmodule:: pina.model.layers.embedding +.. currentmodule:: pina.model.block.embedding .. autoclass:: FourierFeatureEmbedding :members: diff --git a/docs/source/_rst/model/block/gno_block.rst b/docs/source/_rst/model/block/gno_block.rst new file mode 100644 index 000000000..19a532bab --- /dev/null +++ b/docs/source/_rst/model/block/gno_block.rst @@ -0,0 +1,8 @@ +Graph Neural Operator Block +=============================== +.. currentmodule:: pina.model.block.gno_block + +.. autoclass:: GNOBlock + :members: + :show-inheritance: + :noindex: diff --git a/docs/source/_rst/model/block/low_rank_block.rst b/docs/source/_rst/model/block/low_rank_block.rst new file mode 100644 index 000000000..366068f79 --- /dev/null +++ b/docs/source/_rst/model/block/low_rank_block.rst @@ -0,0 +1,8 @@ +Low Rank Neural Operator Block +================================= +.. currentmodule:: pina.model.block.low_rank_block + +.. autoclass:: LowRankBlock + :members: + :show-inheritance: + :noindex: diff --git a/docs/source/_rst/layers/orthogonal.rst b/docs/source/_rst/model/block/orthogonal.rst similarity index 59% rename from docs/source/_rst/layers/orthogonal.rst rename to docs/source/_rst/model/block/orthogonal.rst index 6dfc4009b..21d12998a 100644 --- a/docs/source/_rst/layers/orthogonal.rst +++ b/docs/source/_rst/model/block/orthogonal.rst @@ -1,6 +1,6 @@ -OrthogonalBlock +Orthogonal Block ====================== -.. currentmodule:: pina.model.layers.orthogonal +.. currentmodule:: pina.model.block.orthogonal .. autoclass:: OrthogonalBlock :members: diff --git a/docs/source/_rst/layers/pbc_embedding.rst b/docs/source/_rst/model/block/pbc_embedding.rst similarity index 77% rename from docs/source/_rst/layers/pbc_embedding.rst rename to docs/source/_rst/model/block/pbc_embedding.rst index d4d202314..f469644af 100644 --- a/docs/source/_rst/layers/pbc_embedding.rst +++ b/docs/source/_rst/model/block/pbc_embedding.rst @@ -1,6 +1,6 @@ Periodic Boundary Condition Embedding ======================================= -.. currentmodule:: pina.model.layers.embedding +.. currentmodule:: pina.model.block.embedding .. autoclass:: PeriodicBoundaryEmbedding :members: diff --git a/docs/source/_rst/model/block/pod_block.rst b/docs/source/_rst/model/block/pod_block.rst new file mode 100644 index 000000000..4b66e2c97 --- /dev/null +++ b/docs/source/_rst/model/block/pod_block.rst @@ -0,0 +1,7 @@ +Proper Orthogonal Decomposition Block +============================================ +.. currentmodule:: pina.model.block.pod_block + +.. autoclass:: PODBlock + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/model/block/rbf_block.rst b/docs/source/_rst/model/block/rbf_block.rst new file mode 100644 index 000000000..545f14d08 --- /dev/null +++ b/docs/source/_rst/model/block/rbf_block.rst @@ -0,0 +1,7 @@ +Radias Basis Function Block +============================= +.. currentmodule:: pina.model.block.rbf_block + +.. autoclass:: RBFBlock + :members: + :show-inheritance: diff --git a/docs/source/_rst/layers/residual.rst b/docs/source/_rst/model/block/residual.rst similarity index 58% rename from docs/source/_rst/layers/residual.rst rename to docs/source/_rst/model/block/residual.rst index 1af11e5b8..69741c74c 100644 --- a/docs/source/_rst/layers/residual.rst +++ b/docs/source/_rst/model/block/residual.rst @@ -1,6 +1,6 @@ -Residual layer +Residual Block =================== -.. currentmodule:: pina.model.layers.residual +.. currentmodule:: pina.model.block.residual .. autoclass:: ResidualBlock :members: diff --git a/docs/source/_rst/layers/spectral.rst b/docs/source/_rst/model/block/spectral.rst similarity index 68% rename from docs/source/_rst/layers/spectral.rst rename to docs/source/_rst/model/block/spectral.rst index 5635ba27c..3c80f3dd8 100644 --- a/docs/source/_rst/layers/spectral.rst +++ b/docs/source/_rst/model/block/spectral.rst @@ -1,6 +1,6 @@ -Spectral Convolution -====================== -.. currentmodule:: pina.model.layers.spectral +Spectral Convolution Block +============================ +.. currentmodule:: pina.model.block.spectral .. autoclass:: SpectralConvBlock1D :members: diff --git a/docs/source/_rst/models/deeponet.rst b/docs/source/_rst/model/deeponet.rst similarity index 100% rename from docs/source/_rst/models/deeponet.rst rename to docs/source/_rst/model/deeponet.rst diff --git a/docs/source/_rst/models/fnn.rst b/docs/source/_rst/model/feed_forward.rst similarity index 100% rename from docs/source/_rst/models/fnn.rst rename to docs/source/_rst/model/feed_forward.rst diff --git a/docs/source/_rst/models/fourier_kernel.rst b/docs/source/_rst/model/fourier_integral_kernel.rst similarity index 68% rename from docs/source/_rst/models/fourier_kernel.rst rename to docs/source/_rst/model/fourier_integral_kernel.rst index e45ba174d..b1fb484fe 100644 --- a/docs/source/_rst/models/fourier_kernel.rst +++ b/docs/source/_rst/model/fourier_integral_kernel.rst @@ -1,6 +1,6 @@ FourierIntegralKernel ========================= -.. currentmodule:: pina.model.fno +.. currentmodule:: pina.model.fourier_neural_operator .. autoclass:: FourierIntegralKernel :members: diff --git a/docs/source/_rst/models/fno.rst b/docs/source/_rst/model/fourier_neural_operator.rst similarity index 56% rename from docs/source/_rst/models/fno.rst rename to docs/source/_rst/model/fourier_neural_operator.rst index 3d102b3ad..e77494fd0 100644 --- a/docs/source/_rst/models/fno.rst +++ b/docs/source/_rst/model/fourier_neural_operator.rst @@ -1,6 +1,6 @@ FNO =========== -.. currentmodule:: pina.model.fno +.. currentmodule:: pina.model.fourier_neural_operator .. autoclass:: FNO :members: diff --git a/docs/source/_rst/model/graph_neural_operator.rst b/docs/source/_rst/model/graph_neural_operator.rst new file mode 100644 index 000000000..fbb8600e5 --- /dev/null +++ b/docs/source/_rst/model/graph_neural_operator.rst @@ -0,0 +1,7 @@ +GraphNeuralOperator +======================= +.. currentmodule:: pina.model.graph_neural_operator + +.. autoclass:: GraphNeuralOperator + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/model/graph_neural_operator_integral_kernel.rst b/docs/source/_rst/model/graph_neural_operator_integral_kernel.rst new file mode 100644 index 000000000..cf15a31a5 --- /dev/null +++ b/docs/source/_rst/model/graph_neural_operator_integral_kernel.rst @@ -0,0 +1,7 @@ +GraphNeuralKernel +======================= +.. currentmodule:: pina.model.graph_neural_operator + +.. autoclass:: GraphNeuralKernel + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/models/base_no.rst b/docs/source/_rst/model/kernel_neural_operator.rst similarity index 68% rename from docs/source/_rst/models/base_no.rst rename to docs/source/_rst/model/kernel_neural_operator.rst index 772261c5c..d693afac5 100644 --- a/docs/source/_rst/models/base_no.rst +++ b/docs/source/_rst/model/kernel_neural_operator.rst @@ -1,6 +1,6 @@ KernelNeuralOperator ======================= -.. currentmodule:: pina.model.base_no +.. currentmodule:: pina.model.kernel_neural_operator .. autoclass:: KernelNeuralOperator :members: diff --git a/docs/source/_rst/models/lno.rst b/docs/source/_rst/model/low_rank_neural_operator.rst similarity index 69% rename from docs/source/_rst/models/lno.rst rename to docs/source/_rst/model/low_rank_neural_operator.rst index f3f8277dd..22fe7cc93 100644 --- a/docs/source/_rst/models/lno.rst +++ b/docs/source/_rst/model/low_rank_neural_operator.rst @@ -1,6 +1,6 @@ Low Rank Neural Operator ============================== -.. currentmodule:: pina.model.lno +.. currentmodule:: pina.model.low_rank_neural_operator .. autoclass:: LowRankNeuralOperator :members: diff --git a/docs/source/_rst/models/mionet.rst b/docs/source/_rst/model/mionet.rst similarity index 100% rename from docs/source/_rst/models/mionet.rst rename to docs/source/_rst/model/mionet.rst diff --git a/docs/source/_rst/models/multifeedforward.rst b/docs/source/_rst/model/multi_feed_forward.rst similarity index 100% rename from docs/source/_rst/models/multifeedforward.rst rename to docs/source/_rst/model/multi_feed_forward.rst diff --git a/docs/source/_rst/models/fnn_residual.rst b/docs/source/_rst/model/residual_feed_forward.rst similarity index 100% rename from docs/source/_rst/models/fnn_residual.rst rename to docs/source/_rst/model/residual_feed_forward.rst diff --git a/docs/source/_rst/models/spline.rst b/docs/source/_rst/model/spline.rst similarity index 100% rename from docs/source/_rst/models/spline.rst rename to docs/source/_rst/model/spline.rst diff --git a/docs/source/_rst/models/network.rst b/docs/source/_rst/models/network.rst deleted file mode 100644 index 4df9e194b..000000000 --- a/docs/source/_rst/models/network.rst +++ /dev/null @@ -1,8 +0,0 @@ -Network -================ - -.. automodule:: pina.model.network - -.. autoclass:: Network - :members: - :show-inheritance: diff --git a/docs/source/_rst/operator.rst b/docs/source/_rst/operator.rst new file mode 100644 index 000000000..42746a6f8 --- /dev/null +++ b/docs/source/_rst/operator.rst @@ -0,0 +1,8 @@ +Operators +=========== + +.. currentmodule:: pina.operator + +.. automodule:: pina.operator + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/operators.rst b/docs/source/_rst/operators.rst deleted file mode 100644 index 59f7c7a79..000000000 --- a/docs/source/_rst/operators.rst +++ /dev/null @@ -1,8 +0,0 @@ -Operators -=========== - -.. currentmodule:: pina.operators - -.. automodule:: pina.operators - :members: - :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/optim/optimizer_interface.rst b/docs/source/_rst/optim/optimizer_interface.rst new file mode 100644 index 000000000..88c18e8f5 --- /dev/null +++ b/docs/source/_rst/optim/optimizer_interface.rst @@ -0,0 +1,7 @@ +Optimizer +============ +.. currentmodule:: pina.optim.optimizer_interface + +.. autoclass:: Optimizer + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/optim/scheduler_interface.rst b/docs/source/_rst/optim/scheduler_interface.rst new file mode 100644 index 000000000..ab8ee292e --- /dev/null +++ b/docs/source/_rst/optim/scheduler_interface.rst @@ -0,0 +1,7 @@ +Scheduler +============= +.. currentmodule:: pina.optim.scheduler_interface + +.. autoclass:: Scheduler + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/optim/torch_optimizer.rst b/docs/source/_rst/optim/torch_optimizer.rst new file mode 100644 index 000000000..3e6c9d912 --- /dev/null +++ b/docs/source/_rst/optim/torch_optimizer.rst @@ -0,0 +1,7 @@ +TorchOptimizer +=============== +.. currentmodule:: pina.optim.torch_optimizer + +.. autoclass:: TorchOptimizer + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/optim/torch_scheduler.rst b/docs/source/_rst/optim/torch_scheduler.rst new file mode 100644 index 000000000..5c3e4df36 --- /dev/null +++ b/docs/source/_rst/optim/torch_scheduler.rst @@ -0,0 +1,7 @@ +TorchScheduler +=============== +.. currentmodule:: pina.optim.torch_scheduler + +.. autoclass:: TorchScheduler + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/plotter.rst b/docs/source/_rst/plotter.rst deleted file mode 100644 index b6e94a717..000000000 --- a/docs/source/_rst/plotter.rst +++ /dev/null @@ -1,8 +0,0 @@ -Plotter -=========== -.. currentmodule:: pina.plotter - -.. automodule:: pina.plotter - :members: - :show-inheritance: - :noindex: diff --git a/docs/source/_rst/problem/abstractproblem.rst b/docs/source/_rst/problem/abstract_problem.rst similarity index 100% rename from docs/source/_rst/problem/abstractproblem.rst rename to docs/source/_rst/problem/abstract_problem.rst diff --git a/docs/source/_rst/problem/inverse_problem.rst b/docs/source/_rst/problem/inverse_problem.rst new file mode 100644 index 000000000..5ce306ffc --- /dev/null +++ b/docs/source/_rst/problem/inverse_problem.rst @@ -0,0 +1,9 @@ +InverseProblem +============== +.. currentmodule:: pina.problem.inverse_problem + +.. automodule:: pina.problem.inverse_problem + +.. autoclass:: InverseProblem + :members: + :show-inheritance: diff --git a/docs/source/_rst/problem/parametricproblem.rst b/docs/source/_rst/problem/parametric_problem.rst similarity index 100% rename from docs/source/_rst/problem/parametricproblem.rst rename to docs/source/_rst/problem/parametric_problem.rst diff --git a/docs/source/_rst/problem/spatialproblem.rst b/docs/source/_rst/problem/spatial_problem.rst similarity index 100% rename from docs/source/_rst/problem/spatialproblem.rst rename to docs/source/_rst/problem/spatial_problem.rst diff --git a/docs/source/_rst/problem/timedepproblem.rst b/docs/source/_rst/problem/time_dependent_problem.rst similarity index 52% rename from docs/source/_rst/problem/timedepproblem.rst rename to docs/source/_rst/problem/time_dependent_problem.rst index 93b8cb50b..db94121c2 100644 --- a/docs/source/_rst/problem/timedepproblem.rst +++ b/docs/source/_rst/problem/time_dependent_problem.rst @@ -1,8 +1,8 @@ TimeDependentProblem ==================== -.. currentmodule:: pina.problem.timedep_problem +.. currentmodule:: pina.problem.time_dependent_problem -.. automodule:: pina.problem.timedep_problem +.. automodule:: pina.problem.time_dependent_problem .. autoclass:: TimeDependentProblem :members: diff --git a/docs/source/_rst/problem/zoo/advection.rst b/docs/source/_rst/problem/zoo/advection.rst new file mode 100644 index 000000000..b83cc9d99 --- /dev/null +++ b/docs/source/_rst/problem/zoo/advection.rst @@ -0,0 +1,9 @@ +AdvectionProblem +================== +.. currentmodule:: pina.problem.zoo.advection + +.. automodule:: pina.problem.zoo.advection + +.. autoclass:: AdvectionProblem + :members: + :show-inheritance: diff --git a/docs/source/_rst/problem/zoo/allen_cahn.rst b/docs/source/_rst/problem/zoo/allen_cahn.rst new file mode 100644 index 000000000..ada3465d1 --- /dev/null +++ b/docs/source/_rst/problem/zoo/allen_cahn.rst @@ -0,0 +1,9 @@ +AllenCahnProblem +================== +.. currentmodule:: pina.problem.zoo.allen_cahn + +.. automodule:: pina.problem.zoo.allen_cahn + +.. autoclass:: AllenCahnProblem + :members: + :show-inheritance: diff --git a/docs/source/_rst/problem/zoo/diffusion_reaction.rst b/docs/source/_rst/problem/zoo/diffusion_reaction.rst new file mode 100644 index 000000000..0cad0fd67 --- /dev/null +++ b/docs/source/_rst/problem/zoo/diffusion_reaction.rst @@ -0,0 +1,9 @@ +DiffusionReactionProblem +========================= +.. currentmodule:: pina.problem.zoo.diffusion_reaction + +.. automodule:: pina.problem.zoo.diffusion_reaction + +.. autoclass:: DiffusionReactionProblem + :members: + :show-inheritance: diff --git a/docs/source/_rst/problem/zoo/helmholtz.rst b/docs/source/_rst/problem/zoo/helmholtz.rst new file mode 100644 index 000000000..af4ec7dbc --- /dev/null +++ b/docs/source/_rst/problem/zoo/helmholtz.rst @@ -0,0 +1,9 @@ +HelmholtzProblem +================== +.. currentmodule:: pina.problem.zoo.helmholtz + +.. automodule:: pina.problem.zoo.helmholtz + +.. autoclass:: HelmholtzProblem + :members: + :show-inheritance: diff --git a/docs/source/_rst/problem/zoo/inverse_poisson_2d_square.rst b/docs/source/_rst/problem/zoo/inverse_poisson_2d_square.rst new file mode 100644 index 000000000..727c17b47 --- /dev/null +++ b/docs/source/_rst/problem/zoo/inverse_poisson_2d_square.rst @@ -0,0 +1,9 @@ +InversePoisson2DSquareProblem +============================== +.. currentmodule:: pina.problem.zoo.inverse_poisson_2d_square + +.. automodule:: pina.problem.zoo.inverse_poisson_2d_square + +.. autoclass:: InversePoisson2DSquareProblem + :members: + :show-inheritance: diff --git a/docs/source/_rst/problem/zoo/poisson_2d_square.rst b/docs/source/_rst/problem/zoo/poisson_2d_square.rst new file mode 100644 index 000000000..718c33ccc --- /dev/null +++ b/docs/source/_rst/problem/zoo/poisson_2d_square.rst @@ -0,0 +1,9 @@ +Poisson2DSquareProblem +======================== +.. currentmodule:: pina.problem.zoo.poisson_2d_square + +.. automodule:: pina.problem.zoo.poisson_2d_square + +.. autoclass:: Poisson2DSquareProblem + :members: + :show-inheritance: diff --git a/docs/source/_rst/problem/zoo/supervised_problem.rst b/docs/source/_rst/problem/zoo/supervised_problem.rst new file mode 100644 index 000000000..aad7d5aa5 --- /dev/null +++ b/docs/source/_rst/problem/zoo/supervised_problem.rst @@ -0,0 +1,9 @@ +SupervisedProblem +================== +.. currentmodule:: pina.problem.zoo.supervised_problem + +.. automodule:: pina.problem.zoo.supervised_problem + +.. autoclass:: SupervisedProblem + :members: + :show-inheritance: diff --git a/docs/source/_rst/solvers/garom.rst b/docs/source/_rst/solver/garom.rst similarity index 64% rename from docs/source/_rst/solvers/garom.rst rename to docs/source/_rst/solver/garom.rst index 5fcd97f5c..0e5820f6f 100644 --- a/docs/source/_rst/solvers/garom.rst +++ b/docs/source/_rst/solver/garom.rst @@ -1,6 +1,6 @@ GAROM ====== -.. currentmodule:: pina.solvers.garom +.. currentmodule:: pina.solver.garom .. autoclass:: GAROM :members: diff --git a/docs/source/_rst/solver/multi_solver_interface.rst b/docs/source/_rst/solver/multi_solver_interface.rst new file mode 100644 index 000000000..7f68c83a4 --- /dev/null +++ b/docs/source/_rst/solver/multi_solver_interface.rst @@ -0,0 +1,8 @@ +MultiSolverInterface +====================== +.. currentmodule:: pina.solver.solver + +.. autoclass:: MultiSolverInterface + :show-inheritance: + :members: + diff --git a/docs/source/_rst/solvers/causalpinn.rst b/docs/source/_rst/solver/physics_informed_solver/causal_pinn.rst similarity index 56% rename from docs/source/_rst/solvers/causalpinn.rst rename to docs/source/_rst/solver/physics_informed_solver/causal_pinn.rst index 28f7f15ea..6fab9ef0e 100644 --- a/docs/source/_rst/solvers/causalpinn.rst +++ b/docs/source/_rst/solver/physics_informed_solver/causal_pinn.rst @@ -1,6 +1,6 @@ CausalPINN ============== -.. currentmodule:: pina.solvers.pinns.causalpinn +.. currentmodule:: pina.solver.physics_informed_solver.causal_pinn .. autoclass:: CausalPINN :members: diff --git a/docs/source/_rst/solvers/competitivepinn.rst b/docs/source/_rst/solver/physics_informed_solver/competitive_pinn.rst similarity index 58% rename from docs/source/_rst/solvers/competitivepinn.rst rename to docs/source/_rst/solver/physics_informed_solver/competitive_pinn.rst index 2bbe242b7..372cb0f3d 100644 --- a/docs/source/_rst/solvers/competitivepinn.rst +++ b/docs/source/_rst/solver/physics_informed_solver/competitive_pinn.rst @@ -1,6 +1,6 @@ CompetitivePINN ================= -.. currentmodule:: pina.solvers.pinns.competitive_pinn +.. currentmodule:: pina.solver.physics_informed_solver.competitive_pinn .. autoclass:: CompetitivePINN :members: diff --git a/docs/source/_rst/solver/physics_informed_solver/gradient_pinn.rst b/docs/source/_rst/solver/physics_informed_solver/gradient_pinn.rst new file mode 100644 index 000000000..66a490013 --- /dev/null +++ b/docs/source/_rst/solver/physics_informed_solver/gradient_pinn.rst @@ -0,0 +1,7 @@ +GradientPINN +============== +.. currentmodule:: pina.solver.physics_informed_solver.gradient_pinn + +.. autoclass:: GradientPINN + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/solvers/pinn.rst b/docs/source/_rst/solver/physics_informed_solver/pinn.rst similarity index 52% rename from docs/source/_rst/solvers/pinn.rst rename to docs/source/_rst/solver/physics_informed_solver/pinn.rst index e1c2b59cd..fdc31253b 100644 --- a/docs/source/_rst/solvers/pinn.rst +++ b/docs/source/_rst/solver/physics_informed_solver/pinn.rst @@ -1,6 +1,6 @@ PINN ====== -.. currentmodule:: pina.solvers.pinns.pinn +.. currentmodule:: pina.solver.physics_informed_solver.pinn .. autoclass:: PINN :members: diff --git a/docs/source/_rst/solvers/basepinn.rst b/docs/source/_rst/solver/physics_informed_solver/pinn_interface.rst similarity index 57% rename from docs/source/_rst/solvers/basepinn.rst rename to docs/source/_rst/solver/physics_informed_solver/pinn_interface.rst index c6507953d..2242cf8b4 100644 --- a/docs/source/_rst/solvers/basepinn.rst +++ b/docs/source/_rst/solver/physics_informed_solver/pinn_interface.rst @@ -1,6 +1,6 @@ PINNInterface ================= -.. currentmodule:: pina.solvers.pinns.basepinn +.. currentmodule:: pina.solver.physics_informed_solver.pinn_interface .. autoclass:: PINNInterface :members: diff --git a/docs/source/_rst/solvers/rba_pinn.rst b/docs/source/_rst/solver/physics_informed_solver/rba_pinn.rst similarity index 53% rename from docs/source/_rst/solvers/rba_pinn.rst rename to docs/source/_rst/solver/physics_informed_solver/rba_pinn.rst index b964ccef6..cf94b6df0 100644 --- a/docs/source/_rst/solvers/rba_pinn.rst +++ b/docs/source/_rst/solver/physics_informed_solver/rba_pinn.rst @@ -1,6 +1,6 @@ RBAPINN ======== -.. currentmodule:: pina.solvers.pinns.rbapinn +.. currentmodule:: pina.solver.physics_informed_solver.rba_pinn .. autoclass:: RBAPINN :members: diff --git a/docs/source/_rst/solver/physics_informed_solver/self_adaptive_pinn.rst b/docs/source/_rst/solver/physics_informed_solver/self_adaptive_pinn.rst new file mode 100644 index 000000000..2290059bd --- /dev/null +++ b/docs/source/_rst/solver/physics_informed_solver/self_adaptive_pinn.rst @@ -0,0 +1,7 @@ +SelfAdaptivePINN +================== +.. currentmodule:: pina.solver.physics_informed_solver.self_adaptive_pinn + +.. autoclass:: SelfAdaptivePINN + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/solvers/rom.rst b/docs/source/_rst/solver/reduced_order_model.rst similarity index 71% rename from docs/source/_rst/solvers/rom.rst rename to docs/source/_rst/solver/reduced_order_model.rst index 3ee534bb5..33a909515 100644 --- a/docs/source/_rst/solvers/rom.rst +++ b/docs/source/_rst/solver/reduced_order_model.rst @@ -1,6 +1,6 @@ ReducedOrderModelSolver ========================== -.. currentmodule:: pina.solvers.rom +.. currentmodule:: pina.solver.reduced_order_model .. autoclass:: ReducedOrderModelSolver :members: diff --git a/docs/source/_rst/solver/single_solver_interface.rst b/docs/source/_rst/solver/single_solver_interface.rst new file mode 100644 index 000000000..5b85f11b5 --- /dev/null +++ b/docs/source/_rst/solver/single_solver_interface.rst @@ -0,0 +1,8 @@ +SingleSolverInterface +====================== +.. currentmodule:: pina.solver.solver + +.. autoclass:: SingleSolverInterface + :show-inheritance: + :members: + diff --git a/docs/source/_rst/solvers/solver_interface.rst b/docs/source/_rst/solver/solver_interface.rst similarity index 70% rename from docs/source/_rst/solvers/solver_interface.rst rename to docs/source/_rst/solver/solver_interface.rst index 363e1dbb2..9bb11783e 100644 --- a/docs/source/_rst/solvers/solver_interface.rst +++ b/docs/source/_rst/solver/solver_interface.rst @@ -1,7 +1,8 @@ SolverInterface ================= -.. currentmodule:: pina.solvers.solver +.. currentmodule:: pina.solver.solver .. autoclass:: SolverInterface :show-inheritance: :members: + diff --git a/docs/source/_rst/solvers/supervised.rst b/docs/source/_rst/solver/supervised.rst similarity index 70% rename from docs/source/_rst/solvers/supervised.rst rename to docs/source/_rst/solver/supervised.rst index 895759e9e..19978f9a0 100644 --- a/docs/source/_rst/solvers/supervised.rst +++ b/docs/source/_rst/solver/supervised.rst @@ -1,6 +1,6 @@ SupervisedSolver =================== -.. currentmodule:: pina.solvers.supervised +.. currentmodule:: pina.solver.supervised .. autoclass:: SupervisedSolver :members: diff --git a/docs/source/_rst/solvers/gpinn.rst b/docs/source/_rst/solvers/gpinn.rst deleted file mode 100644 index ee076a5d7..000000000 --- a/docs/source/_rst/solvers/gpinn.rst +++ /dev/null @@ -1,7 +0,0 @@ -GPINN -====== -.. currentmodule:: pina.solvers.pinns.gpinn - -.. autoclass:: GPINN - :members: - :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/solvers/sapinn.rst b/docs/source/_rst/solvers/sapinn.rst deleted file mode 100644 index b20891fff..000000000 --- a/docs/source/_rst/solvers/sapinn.rst +++ /dev/null @@ -1,7 +0,0 @@ -SAPINN -====== -.. currentmodule:: pina.solvers.pinns.sapinn - -.. autoclass:: SAPINN - :members: - :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/tutorials/tutorial1/tutorial.rst b/docs/source/_rst/tutorials/tutorial1/tutorial.rst deleted file mode 100644 index d15cb6360..000000000 --- a/docs/source/_rst/tutorials/tutorial1/tutorial.rst +++ /dev/null @@ -1,385 +0,0 @@ -Tutorial: Physics Informed Neural Networks on PINA -================================================== - -|Open In Colab| - -.. |Open In Colab| image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial1/tutorial.ipynb - -In this tutorial, we will demonstrate a typical use case of **PINA** on -a toy problem, following the standard API procedure. - -.. raw:: html - -

- -.. raw:: html - -

- -Specifically, the tutorial aims to introduce the following topics: - -- Explaining how to build **PINA** Problems, -- Showing how to generate data for ``PINN`` training - -These are the two main steps needed **before** starting the modelling -optimization (choose model and solver, and train). We will show each -step in detail, and at the end, we will solve a simple Ordinary -Differential Equation (ODE) problem using the ``PINN`` solver. - -Build a PINA problem --------------------- - -Problem definition in the **PINA** framework is done by building a -python ``class``, which inherits from one or more problem classes -(``SpatialProblem``, ``TimeDependentProblem``, ``ParametricProblem``, …) -depending on the nature of the problem. Below is an example: - -Simple Ordinary Differential Equation -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Consider the following: - -.. math:: - - - \begin{equation} - \begin{cases} - \frac{d}{dx}u(x) &= u(x) \quad x\in(0,1)\\ - u(x=0) &= 1 \\ - \end{cases} - \end{equation} - -with the analytical solution :math:`u(x) = e^x`. In this case, our ODE -depends only on the spatial variable :math:`x\in(0,1)` , meaning that -our ``Problem`` class is going to be inherited from the -``SpatialProblem`` class: - -.. code:: python - - from pina.problem import SpatialProblem - from pina.geometry import CartesianProblem - - class SimpleODE(SpatialProblem): - - output_variables = ['u'] - spatial_domain = CartesianProblem({'x': [0, 1]}) - - # other stuff ... - -Notice that we define ``output_variables`` as a list of symbols, -indicating the output variables of our equation (in this case only -:math:`u`), this is done because in **PINA** the ``torch.Tensor``\ s are -labelled, allowing the user maximal flexibility for the manipulation of -the tensor. The ``spatial_domain`` variable indicates where the sample -points are going to be sampled in the domain, in this case -:math:`x\in[0,1]`. - -What if our equation is also time-dependent? In this case, our ``class`` -will inherit from both ``SpatialProblem`` and ``TimeDependentProblem``: - -.. code:: ipython3 - - ## routine needed to run the notebook on Google Colab - try: - import google.colab - IN_COLAB = True - except: - IN_COLAB = False - if IN_COLAB: - !pip install "pina-mathlab" - - from pina.problem import SpatialProblem, TimeDependentProblem - from pina.geometry import CartesianDomain - - class TimeSpaceODE(SpatialProblem, TimeDependentProblem): - - output_variables = ['u'] - spatial_domain = CartesianDomain({'x': [0, 1]}) - temporal_domain = CartesianDomain({'t': [0, 1]}) - - # other stuff ... - - -where we have included the ``temporal_domain`` variable, indicating the -time domain wanted for the solution. - -In summary, using **PINA**, we can initialize a problem with a class -which inherits from different base classes: ``SpatialProblem``, -``TimeDependentProblem``, ``ParametricProblem``, and so on depending on -the type of problem we are considering. Here are some examples (more on -the official documentation): - -* ``SpatialProblem`` :math:`\rightarrow` a differential equation with spatial variable(s) ``spatial_domain`` -* ``TimeDependentProblem`` :math:`\rightarrow` a time-dependent differential equation with temporal variable(s) ``temporal_domain`` -* ``ParametricProblem`` :math:`\rightarrow` a parametrized differential equation with parametric variable(s) ``parameter_domain`` -* ``AbstractProblem`` :math:`\rightarrow` any **PINA** problem inherits from here - -Write the problem class -~~~~~~~~~~~~~~~~~~~~~~~ - -Once the ``Problem`` class is initialized, we need to represent the -differential equation in **PINA**. In order to do this, we need to load -the **PINA** operators from ``pina.operators`` module. Again, we’ll -consider Equation (1) and represent it in **PINA**: - -.. code:: ipython3 - - from pina.problem import SpatialProblem - from pina.operators import grad - from pina import Condition - from pina.geometry import CartesianDomain - from pina.equation import Equation, FixedValue - - import torch - - - class SimpleODE(SpatialProblem): - - output_variables = ['u'] - spatial_domain = CartesianDomain({'x': [0, 1]}) - - # defining the ode equation - def ode_equation(input_, output_): - - # computing the derivative - u_x = grad(output_, input_, components=['u'], d=['x']) - - # extracting the u input variable - u = output_.extract(['u']) - - # calculate the residual and return it - return u_x - u - - # conditions to hold - conditions = { - 'x0': Condition(location=CartesianDomain({'x': 0.}), equation=FixedValue(1)), # We fix initial condition to value 1 - 'D': Condition(location=CartesianDomain({'x': [0, 1]}), equation=Equation(ode_equation)), # We wrap the python equation using Equation - } - - # sampled points (see below) - input_pts = None - - # defining the true solution - def truth_solution(self, pts): - return torch.exp(pts.extract(['x'])) - - problem = SimpleODE() - -After we define the ``Problem`` class, we need to write different class -methods, where each method is a function returning a residual. These -functions are the ones minimized during PINN optimization, given the -initial conditions. For example, in the domain :math:`[0,1]`, the ODE -equation (``ode_equation``) must be satisfied. We represent this by -returning the difference between subtracting the variable ``u`` from its -gradient (the residual), which we hope to minimize to 0. This is done -for all conditions. Notice that we do not pass directly a ``python`` -function, but an ``Equation`` object, which is initialized with the -``python`` function. This is done so that all the computations and -internal checks are done inside **PINA**. - -Once we have defined the function, we need to tell the neural network -where these methods are to be applied. To do so, we use the -``Condition`` class. In the ``Condition`` class, we pass the location -points and the equation we want minimized on those points (other -possibilities are allowed, see the documentation for reference). - -Finally, it’s possible to define a ``truth_solution`` function, which -can be useful if we want to plot the results and see how the real -solution compares to the expected (true) solution. Notice that the -``truth_solution`` function is a method of the ``PINN`` class, but it is -not mandatory for problem definition. - -Generate data -------------- - -Data for training can come in form of direct numerical simulation -results, or points in the domains. In case we perform unsupervised -learning, we just need the collocation points for training, i.e. points -where we want to evaluate the neural network. Sampling point in **PINA** -is very easy, here we show three examples using the -``.discretise_domain`` method of the ``AbstractProblem`` class. - -.. code:: ipython3 - - # sampling 20 points in [0, 1] through discretization in all locations - problem.discretise_domain(n=20, mode='grid', variables=['x'], locations='all') - - # sampling 20 points in (0, 1) through latin hypercube sampling in D, and 1 point in x0 - problem.discretise_domain(n=20, mode='latin', variables=['x'], locations=['D']) - problem.discretise_domain(n=1, mode='random', variables=['x'], locations=['x0']) - - # sampling 20 points in (0, 1) randomly - problem.discretise_domain(n=20, mode='random', variables=['x']) - -We are going to use latin hypercube points for sampling. We need to -sample in all the conditions domains. In our case we sample in ``D`` and -``x0``. - -.. code:: ipython3 - - # sampling for training - problem.discretise_domain(1, 'random', locations=['x0']) - problem.discretise_domain(20, 'lh', locations=['D']) - -The points are saved in a python ``dict``, and can be accessed by -calling the attribute ``input_pts`` of the problem - -.. code:: ipython3 - - print('Input points:', problem.input_pts) - print('Input points labels:', problem.input_pts['D'].labels) - - -.. parsed-literal:: - - Input points: {'x0': LabelTensor([[[0.]]]), 'D': LabelTensor([[[0.7644]], - [[0.2028]], - [[0.1789]], - [[0.4294]], - [[0.3239]], - [[0.6531]], - [[0.1406]], - [[0.6062]], - [[0.4969]], - [[0.7429]], - [[0.8681]], - [[0.3800]], - [[0.5357]], - [[0.0152]], - [[0.9679]], - [[0.8101]], - [[0.0662]], - [[0.9095]], - [[0.2503]], - [[0.5580]]])} - Input points labels: ['x'] - - -To visualize the sampled points we can use the ``.plot_samples`` method -of the ``Plotter`` class - -.. code:: ipython3 - - from pina import Plotter - - pl = Plotter() - pl.plot_samples(problem=problem) - - - -.. image:: tutorial_files/tutorial_16_0.png - - -Perform a small training ------------------------- - -Once we have defined the problem and generated the data we can start the -modelling. Here we will choose a ``FeedForward`` neural network -available in ``pina.model``, and we will train using the ``PINN`` solver -from ``pina.solvers``. We highlight that this training is fairly simple, -for more advanced stuff consider the tutorials in the **Physics Informed -Neural Networks** section of **Tutorials**. For training we use the -``Trainer`` class from ``pina.trainer``. Here we show a very short -training and some method for plotting the results. Notice that by -default all relevant metrics (e.g. MSE error during training) are going -to be tracked using a ``lightining`` logger, by default ``CSVLogger``. -If you want to track the metric by yourself without a logger, use -``pina.callbacks.MetricTracker``. - -.. code:: ipython3 - - from pina import Trainer - from pina.solvers import PINN - from pina.model import FeedForward - from pina.callbacks import MetricTracker - - - # build the model - model = FeedForward( - layers=[10, 10], - func=torch.nn.Tanh, - output_dimensions=len(problem.output_variables), - input_dimensions=len(problem.input_variables) - ) - - # create the PINN object - pinn = PINN(problem, model) - - # create the trainer - trainer = Trainer(solver=pinn, max_epochs=1500, callbacks=[MetricTracker()], accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional) - - # train - trainer.train() - -After the training we can inspect trainer logged metrics (by default -**PINA** logs mean square error residual loss). The logged metrics can -be accessed online using one of the ``Lightinig`` loggers. The final -loss can be accessed by ``trainer.logged_metrics`` - -.. code:: ipython3 - - # inspecting final loss - trainer.logged_metrics - - - - -.. parsed-literal:: - - {'x0_loss': tensor(1.0674e-05), - 'D_loss': tensor(0.0008), - 'mean_loss': tensor(0.0004)} - - - -By using the ``Plotter`` class from **PINA** we can also do some -quatitative plots of the solution. - -.. code:: ipython3 - - # plotting the solution - pl.plot(solver=pinn) - - - -.. image:: tutorial_files/tutorial_23_1.png - - - -.. parsed-literal:: - -
- - -The solution is overlapped with the actual one, and they are barely -indistinguishable. We can also plot easily the loss: - -.. code:: ipython3 - - pl.plot_loss(trainer=trainer, label = 'mean_loss', logy=True) - - - -.. image:: tutorial_files/tutorial_25_0.png - - -As we can see the loss has not reached a minimum, suggesting that we -could train for longer - -What’s next? ------------- - -Congratulations on completing the introductory tutorial of **PINA**! -There are several directions you can go now: - -1. Train the network for longer or with different layer sizes and assert - the finaly accuracy - -2. Train the network using other types of models (see ``pina.model``) - -3. GPU training and speed benchmarking - -4. Many more… - - diff --git a/docs/source/_rst/tutorials/tutorial1/tutorial_files/tutorial_16_0.png b/docs/source/_rst/tutorials/tutorial1/tutorial_files/tutorial_16_0.png deleted file mode 100644 index 3c906354f..000000000 Binary files a/docs/source/_rst/tutorials/tutorial1/tutorial_files/tutorial_16_0.png and /dev/null differ diff --git a/docs/source/_rst/tutorials/tutorial1/tutorial_files/tutorial_23_1.png b/docs/source/_rst/tutorials/tutorial1/tutorial_files/tutorial_23_1.png deleted file mode 100644 index e4d92c2ea..000000000 Binary files a/docs/source/_rst/tutorials/tutorial1/tutorial_files/tutorial_23_1.png and /dev/null differ diff --git a/docs/source/_rst/tutorials/tutorial1/tutorial_files/tutorial_25_0.png b/docs/source/_rst/tutorials/tutorial1/tutorial_files/tutorial_25_0.png deleted file mode 100644 index 64bd43af4..000000000 Binary files a/docs/source/_rst/tutorials/tutorial1/tutorial_files/tutorial_25_0.png and /dev/null differ diff --git a/docs/source/_rst/tutorials/tutorial10/tutorial.rst b/docs/source/_rst/tutorials/tutorial10/tutorial.rst deleted file mode 100644 index 469235447..000000000 --- a/docs/source/_rst/tutorials/tutorial10/tutorial.rst +++ /dev/null @@ -1,366 +0,0 @@ -Tutorial: Averaging Neural Operator for solving Kuramoto Sivashinsky equation -============================================================================= - -|Open In Colab| - -.. |Open In Colab| image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial10/tutorial.ipynb - -In this tutorial we will build a Neural Operator using the -``AveragingNeuralOperator`` model and the ``SupervisedSolver``. At the -end of the tutorial you will be able to train a Neural Operator for -learning the operator of time dependent PDEs. - -First of all, some useful imports. Note we use ``scipy`` for i/o -operations. - -.. code:: ipython3 - - ## routine needed to run the notebook on Google Colab - try: - import google.colab - IN_COLAB = True - except: - IN_COLAB = False - if IN_COLAB: - !pip install "pina-mathlab" - !mkdir "data" - !wget "https://github.com/mathLab/PINA/raw/refs/heads/master/tutorials/tutorial10/data/Data_KS.mat" -O "data/Data_KS.mat" - !wget "https://github.com/mathLab/PINA/raw/refs/heads/master/tutorials/tutorial10/data/Data_KS2.mat" -O "data/Data_KS2.mat" - - - import torch - import matplotlib.pyplot as plt - plt.style.use('tableau-colorblind10') - from scipy import io - from pina import Condition, LabelTensor - from pina.problem import AbstractProblem - from pina.model import AveragingNeuralOperator - from pina.solvers import SupervisedSolver - from pina.trainer import Trainer - -Data Generation ---------------- - -We will focus on solving a specific PDE, the **Kuramoto Sivashinsky** -(KS) equation. The KS PDE is a fourth-order nonlinear PDE with the -following form: - -.. math:: - - - \frac{\partial u}{\partial t}(x,t) = -u(x,t)\frac{\partial u}{\partial x}(x,t)- \frac{\partial^{4}u}{\partial x^{4}}(x,t) - \frac{\partial^{2}u}{\partial x^{2}}(x,t). - -In the above :math:`x\in \Omega=[0, 64]` represents a spatial location, -:math:`t\in\mathbb{T}=[0,50]` the time and :math:`u(x, t)` is the value -of the function :math:`u:\Omega \times\mathbb{T}\in\mathbb{R}`. We -indicate with :math:`\mathbb{U}` a suitable space for :math:`u`, i.e. we -have that the solution :math:`u\in\mathbb{U}`. - -We impose Dirichlet boundary conditions on the derivative of :math:`u` -on the border of the domain :math:`\partial \Omega` - -.. math:: - - - \frac{\partial u}{\partial x}(x,t)=0 \quad \forall (x,t)\in \partial \Omega\times\mathbb{T}. - - -Initial conditions are sampled from a distribution over truncated -Fourier series with random coefficients -:math:`\{A_k, \ell_k, \phi_k\}_k` as - -.. math:: - - - u(x,0) = \sum_{k=1}^N A_k \sin(2 \pi \ell_k x / L + \phi_k) \ , - -where :math:`A_k \in [-0.4, -0.3]`, :math:`\ell_k = 2`, -:math:`\phi_k = 2\pi \quad \forall k=1,\dots,N`. - -We have already generated some data for differenti initial conditions, -and our objective will be to build a Neural Operator that, given -:math:`u(x, t)` will output :math:`u(x, t+\delta)`, where :math:`\delta` -is a fixed time step. We will come back on the Neural Operator -architecture, for now we first need to import the data. - -**Note:** *The numerical integration is obtained by using pseudospectral -method for spatial derivative discratization and implicit Runge Kutta 5 -for temporal dynamics.* - -.. code:: ipython3 - - # load data - data=io.loadmat("dat/Data_KS.mat") - - # converting to label tensor - initial_cond_train = LabelTensor(torch.tensor(data['initial_cond_train'], dtype=torch.float), ['t','x','u0']) - initial_cond_test = LabelTensor(torch.tensor(data['initial_cond_test'], dtype=torch.float), ['t','x','u0']) - sol_train = LabelTensor(torch.tensor(data['sol_train'], dtype=torch.float), ['u']) - sol_test = LabelTensor(torch.tensor(data['sol_test'], dtype=torch.float), ['u']) - - print('Data Loaded') - print(f' shape initial condition: {initial_cond_train.shape}') - print(f' shape solution: {sol_train.shape}') - - -.. parsed-literal:: - - Data Loaded - shape initial condition: torch.Size([100, 12800, 3]) - shape solution: torch.Size([100, 12800, 1]) - - -The data are saved in the form ``B \times N \times D``, where ``B`` is -the batch_size (basically how many initial conditions we sample), ``N`` -the number of points in the mesh (which is the product of the -discretization in ``x`` timese the one in ``t``), and ``D`` the -dimension of the problem (in this case we have three variables -``[u, t, x]``). - -We are now going to plot some trajectories! - -.. code:: ipython3 - - # helper function - def plot_trajectory(coords, real, no_sol=None): - # find the x-t shapes - dim_x = len(torch.unique(coords.extract('x'))) - dim_t = len(torch.unique(coords.extract('t'))) - # if we don't have the Neural Operator solution we simply plot the real one - if no_sol is None: - fig, axs = plt.subplots(1, 1, figsize=(15, 5), sharex=True, sharey=True) - c = axs.imshow(real.reshape(dim_t, dim_x).T.detach(),extent=[0, 50, 0, 64], cmap='PuOr_r', aspect='auto') - axs.set_title('Real solution') - fig.colorbar(c, ax=axs) - axs.set_xlabel('t') - axs.set_ylabel('x') - # otherwise we plot the real one, the Neural Operator one, and their difference - else: - fig, axs = plt.subplots(1, 3, figsize=(15, 5), sharex=True, sharey=True) - axs[0].imshow(real.reshape(dim_t, dim_x).T.detach(),extent=[0, 50, 0, 64], cmap='PuOr_r', aspect='auto') - axs[0].set_title('Real solution') - axs[1].imshow(no_sol.reshape(dim_t, dim_x).T.detach(),extent=[0, 50, 0, 64], cmap='PuOr_r', aspect='auto') - axs[1].set_title('NO solution') - c = axs[2].imshow((real - no_sol).abs().reshape(dim_t, dim_x).T.detach(),extent=[0, 50, 0, 64], cmap='PuOr_r', aspect='auto') - axs[2].set_title('Absolute difference') - fig.colorbar(c, ax=axs.ravel().tolist()) - for ax in axs: - ax.set_xlabel('t') - ax.set_ylabel('x') - plt.show() - - # a sample trajectory (we use the sample 5, feel free to change) - sample_number = 20 - plot_trajectory(coords=initial_cond_train[sample_number].extract(['x', 't']), - real=sol_train[sample_number].extract('u')) - - - - -.. image:: tutorial_files/tutorial_5_0.png - - -As we can see, as the time progresses the solution becomes chaotic, -which makes it really hard to learn! We will now focus on building a -Neural Operator using the ``SupervisedSolver`` class to tackle the -problem. - -Averaging Neural Operator -------------------------- - -We will build a neural operator :math:`\texttt{NO}` which takes the -solution at time :math:`t=0` for any :math:`x\in\Omega`, the time -:math:`(t)` at which we want to compute the solution, and gives back the -solution to the KS equation :math:`u(x, t)`, mathematically: - -.. math:: - - - \texttt{NO}_\theta : \mathbb{U} \rightarrow \mathbb{U}, - -such that - -.. math:: - - - \texttt{NO}_\theta[u(t=0)](x, t) \rightarrow u(x, t). - -There are many ways on approximating the following operator, e.g. by 2D -`FNO `__ (for -regular meshes), a -`DeepOnet `__, -`Continuous Convolutional Neural -Operator `__, -`MIONet `__. In -this tutorial we will use the *Averaging Neural Operator* presented in -`The Nonlocal Neural Operator: Universal -Approximation `__ which is a `Kernel -Neural -Operator `__ -with integral kernel: - -.. math:: - - - K(v) = \sigma\left(Wv(x) + b + \frac{1}{|\Omega|}\int_\Omega v(y)dy\right) - -where: - -- :math:`v(x)\in\mathbb{R}^{\rm{emb}}` is the update for a function - :math:`v` with :math:`\mathbb{R}^{\rm{emb}}` the embedding (hidden) - size -- :math:`\sigma` is a non-linear activation -- :math:`W\in\mathbb{R}^{\rm{emb}\times\rm{emb}}` is a tunable matrix. -- :math:`b\in\mathbb{R}^{\rm{emb}}` is a tunable bias. - -If PINA many Kernel Neural Operators are already implemented, and the -modular componets of the `Kernel Neural -Operator `__ -class permits to create new ones by composing base kernel layers. - -**Note:**\ \* We will use the already built class\* -``AveragingNeuralOperator``, *as constructive excercise try to use the* -`KernelNeuralOperator `__ -*class for building a kernel neural operator from scratch. You might -employ the different layers that we have in pina, e.g.* -`FeedForward `__, -*and* -`AveragingNeuralOperator `__ -*layers*. - -.. code:: ipython3 - - class SIREN(torch.nn.Module): - def forward(self, x): - return torch.sin(x) - - embedding_dimesion = 40 # hyperparameter embedding dimension - input_dimension = 3 # ['u', 'x', 't'] - number_of_coordinates = 2 # ['x', 't'] - lifting_net = torch.nn.Linear(input_dimension, embedding_dimesion) # simple linear layers for lifting and projecting nets - projecting_net = torch.nn.Linear(embedding_dimesion + number_of_coordinates, 1) - model = AveragingNeuralOperator(lifting_net=lifting_net, - projecting_net=projecting_net, - coordinates_indices=['x', 't'], - field_indices=['u0'], - n_layers=4, - func=SIREN - ) - -Super easy! Notice that we use the ``SIREN`` activation function, more -on `Implicit Neural Representations with Periodic Activation -Functions `__. - -Solving the KS problem ----------------------- - -We will now focus on solving the KS equation using the -``SupervisedSolver`` class and the ``AveragingNeuralOperator`` model. As -done in the `FNO -tutorial `__ -we now create the ``NeuralOperatorProblem`` class with -``AbstractProblem``. - -.. code:: ipython3 - - # expected running time ~ 1 minute - - class NeuralOperatorProblem(AbstractProblem): - input_variables = initial_cond_train.labels - output_variables = sol_train.labels - conditions = {'data' : Condition(input_points=initial_cond_train, - output_points=sol_train)} - - - # initialize problem - problem = NeuralOperatorProblem() - # initialize solver - solver = SupervisedSolver(problem=problem, model=model,optimizer_kwargs={"lr":0.001}) - # train, only CPU and avoid model summary at beginning of training (optional) - trainer = Trainer(solver=solver, max_epochs=40, accelerator='cpu', enable_model_summary=False, log_every_n_steps=-1, batch_size=5) # we train on CPU and avoid model summary at beginning of training (optional) - trainer.train() - - - -.. parsed-literal:: - - GPU available: True (mps), used: False - TPU available: False, using: 0 TPU cores - IPU available: False, using: 0 IPUs - HPU available: False, using: 0 HPUs - - -.. parsed-literal:: - - Epoch 39: 100%|██████████| 20/20 [00:01<00:00, 13.59it/s, v_num=3, mean_loss=0.118] - -.. parsed-literal:: - - `Trainer.fit` stopped: `max_epochs=40` reached. - - -.. parsed-literal:: - - Epoch 39: 100%|██████████| 20/20 [00:01<00:00, 13.56it/s, v_num=3, mean_loss=0.118] - - -We can now see some plots for the solutions - -.. code:: ipython3 - - sample_number = 2 - no_sol = solver(initial_cond_test) - plot_trajectory(coords=initial_cond_test[sample_number].extract(['x', 't']), - real=sol_test[sample_number].extract('u'), - no_sol=no_sol[5]) - - - -.. image:: tutorial_files/tutorial_11_0.png - - -As we can see we can obtain nice result considering the small trainint -time and the difficulty of the problem! Let’s see how the training and -testing error: - -.. code:: ipython3 - - from pina.loss import PowerLoss - - error_metric = PowerLoss(p=2) # we use the MSE loss - - with torch.no_grad(): - no_sol_train = solver(initial_cond_train) - err_train = error_metric(sol_train.extract('u'), no_sol_train).mean() # we average the error over trajectories - no_sol_test = solver(initial_cond_test) - err_test = error_metric(sol_test.extract('u'),no_sol_test).mean() # we average the error over trajectories - print(f'Training error: {float(err_train):.3f}') - print(f'Testing error: {float(err_test):.3f}') - - -.. parsed-literal:: - - Training error: 0.128 - Testing error: 0.119 - - -as we can see the error is pretty small, which agrees with what we can -see from the previous plots. - -What’s next? ------------- - -Now you know how to solve a time dependent neural operator problem in -**PINA**! There are multiple directions you can go now: - -1. Train the network for longer or with different layer sizes and assert - the finaly accuracy - -2. We left a more challenging dataset - `Data_KS2.mat `__ where - :math:`A_k \in [-0.5, 0.5]`, :math:`\ell_k \in [1, 2, 3]`, - :math:`\phi_k \in [0, 2\pi]` for loger training - -3. Compare the performance between the different neural operators (you - can even try to implement your favourite one!) diff --git a/docs/source/_rst/tutorials/tutorial10/tutorial_files/tutorial_11_0.png b/docs/source/_rst/tutorials/tutorial10/tutorial_files/tutorial_11_0.png deleted file mode 100644 index 2f7e8cc0a..000000000 Binary files a/docs/source/_rst/tutorials/tutorial10/tutorial_files/tutorial_11_0.png and /dev/null differ diff --git a/docs/source/_rst/tutorials/tutorial10/tutorial_files/tutorial_5_0.png b/docs/source/_rst/tutorials/tutorial10/tutorial_files/tutorial_5_0.png deleted file mode 100644 index 0b355c37a..000000000 Binary files a/docs/source/_rst/tutorials/tutorial10/tutorial_files/tutorial_5_0.png and /dev/null differ diff --git a/docs/source/_rst/tutorials/tutorial11/logging.png b/docs/source/_rst/tutorials/tutorial11/logging.png deleted file mode 100644 index c4b421e19..000000000 Binary files a/docs/source/_rst/tutorials/tutorial11/logging.png and /dev/null differ diff --git a/docs/source/_rst/tutorials/tutorial11/tutorial.rst b/docs/source/_rst/tutorials/tutorial11/tutorial.rst deleted file mode 100644 index daed289c4..000000000 --- a/docs/source/_rst/tutorials/tutorial11/tutorial.rst +++ /dev/null @@ -1,550 +0,0 @@ -Tutorial: PINA and PyTorch Lightning, training tips and visualizations -====================================================================== - -|Open In Colab| - -.. |Open In Colab| image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial11/tutorial.ipynb - -In this tutorial, we will delve deeper into the functionality of the -``Trainer`` class, which serves as the cornerstone for training **PINA** -`Solvers `__. -The ``Trainer`` class offers a plethora of features aimed at improving -model accuracy, reducing training time and memory usage, facilitating -logging visualization, and more thanks to the amazing job done by the PyTorch Lightning team! -Our leading example will revolve around solving the ``SimpleODE`` -problem, as outlined in the `Introduction to PINA for Physics Informed -Neural Networks -training `__. -If you haven’t already explored it, we highly recommend doing so before -diving into this tutorial. -Let’s start by importing useful modules, define the ``SimpleODE`` -problem and the ``PINN`` solver. - -.. code:: ipython3 - - ## routine needed to run the notebook on Google Colab - try: - import google.colab - IN_COLAB = True - except: - IN_COLAB = False - if IN_COLAB: - !pip install "pina-mathlab" - - import torch - - from pina import Condition, Trainer - from pina.solvers import PINN - from pina.model import FeedForward - from pina.problem import SpatialProblem - from pina.operators import grad - from pina.geometry import CartesianDomain - from pina.equation import Equation, FixedValue - - class SimpleODE(SpatialProblem): - - output_variables = ['u'] - spatial_domain = CartesianDomain({'x': [0, 1]}) - - # defining the ode equation - def ode_equation(input_, output_): - u_x = grad(output_, input_, components=['u'], d=['x']) - u = output_.extract(['u']) - return u_x - u - - # conditions to hold - conditions = { - 'x0': Condition(location=CartesianDomain({'x': 0.}), equation=FixedValue(1)), # We fix initial condition to value 1 - 'D': Condition(location=CartesianDomain({'x': [0, 1]}), equation=Equation(ode_equation)), # We wrap the python equation using Equation - } - - # defining the true solution - def truth_solution(self, pts): - return torch.exp(pts.extract(['x'])) - - - # sampling for training - problem = SimpleODE() - problem.discretise_domain(1, 'random', locations=['x0']) - problem.discretise_domain(20, 'lh', locations=['D']) - - # build the model - model = FeedForward( - layers=[10, 10], - func=torch.nn.Tanh, - output_dimensions=len(problem.output_variables), - input_dimensions=len(problem.input_variables) - ) - - # create the PINN object - pinn = PINN(problem, model) - -Till now we just followed the extact step of the previous tutorials. The -``Trainer`` object can be initialized by simiply passing the ``PINN`` -solver - -.. code:: ipython3 - - trainer = Trainer(solver=pinn) - - -.. parsed-literal:: - - GPU available: True (mps), used: True - TPU available: False, using: 0 TPU cores - IPU available: False, using: 0 IPUs - HPU available: False, using: 0 HPUs - - -Trainer Accelerator -------------------- - -When creating the trainer, **by defualt** the ``Trainer`` will choose -the most performing ``accelerator`` for training which is available in -your system, ranked as follow: - -1. `TPU `__ -2. `IPU `__ -3. `HPU `__ -4. `GPU `__ or `MPS `__ -5. CPU - -For setting manually the ``accelerator`` run: - -- ``accelerator = {'gpu', 'cpu', 'hpu', 'mps', 'cpu', 'ipu'}`` sets the - accelerator to a specific one - -.. code:: ipython3 - - trainer = Trainer(solver=pinn, - accelerator='cpu') - - -.. parsed-literal:: - - GPU available: True (mps), used: False - TPU available: False, using: 0 TPU cores - IPU available: False, using: 0 IPUs - HPU available: False, using: 0 HPUs - - -as you can see, even if in the used system ``GPU`` is available, it is -not used since we set ``accelerator='cpu'``. - -Trainer Logging ---------------- - -In **PINA** you can log metrics in different ways. The simplest approach -is to use the ``MetricTraker`` class from ``pina.callbacks`` as seen in -the `Introduction to PINA for Physics Informed Neural Networks -training `__ -tutorial. - -However, expecially when we need to train multiple times to get an -average of the loss across multiple runs, ``pytorch_lightning.loggers`` -might be useful. Here we will use ``TensorBoardLogger`` (more on -`logging `__ -here), but you can choose the one you prefer (or make your own one). - -We will now import ``TensorBoardLogger``, do three runs of training and -then visualize the results. Notice we set ``enable_model_summary=False`` -to avoid model summary specifications (e.g. number of parameters), set -it to true if needed. - -.. code:: ipython3 - - from pytorch_lightning.loggers import TensorBoardLogger - - # three run of training, by default it trains for 1000 epochs - # we reinitialize the model each time otherwise the same parameters will be optimized - for _ in range(3): - model = FeedForward( - layers=[10, 10], - func=torch.nn.Tanh, - output_dimensions=len(problem.output_variables), - input_dimensions=len(problem.input_variables) - ) - pinn = PINN(problem, model) - trainer = Trainer(solver=pinn, - accelerator='cpu', - logger=TensorBoardLogger(save_dir='simpleode'), - enable_model_summary=False) - trainer.train() - - -.. parsed-literal:: - - GPU available: True (mps), used: False - TPU available: False, using: 0 TPU cores - IPU available: False, using: 0 IPUs - HPU available: False, using: 0 HPUs - - `Trainer.fit` stopped: `max_epochs=1000` reached. - Epoch 999: 100%|██████████| 1/1 [00:00<00:00, 133.46it/s, v_num=6, x0_loss=1.48e-5, D_loss=0.000655, mean_loss=0.000335] - - -.. parsed-literal:: - - GPU available: True (mps), used: False - TPU available: False, using: 0 TPU cores - IPU available: False, using: 0 IPUs - HPU available: False, using: 0 HPUs - - `Trainer.fit` stopped: `max_epochs=1000` reached. - Epoch 999: 100%|██████████| 1/1 [00:00<00:00, 154.49it/s, v_num=7, x0_loss=6.21e-6, D_loss=0.000221, mean_loss=0.000114] - - -.. parsed-literal:: - - GPU available: True (mps), used: False - TPU available: False, using: 0 TPU cores - IPU available: False, using: 0 IPUs - HPU available: False, using: 0 HPUs - - `Trainer.fit` stopped: `max_epochs=1000` reached. - Epoch 999: 100%|██████████| 1/1 [00:00<00:00, 62.60it/s, v_num=8, x0_loss=1.44e-5, D_loss=0.000572, mean_loss=0.000293] - - -We can now visualize the logs by simply running -``tensorboard --logdir=simpleode/`` on terminal, you should obtain a -webpage as the one shown below: - -.. image:: logging.png - -as you can see, by default, **PINA** logs the losses which are shown in -the progress bar, as well as the number of epochs. You can always insert -more loggings by either defining a **callback** (`more on -callbacks `__), -or inheriting the solver and modify the programs with different -**hooks** (`more on -hooks `__). - -Trainer Callbacks ------------------ - -Whenever we need to access certain steps of the training for logging, do -static modifications (i.e. not changing the ``Solver``) or updating -``Problem`` hyperparameters (static variables), we can use -``Callabacks``. Notice that ``Callbacks`` allow you to add arbitrary -self-contained programs to your training. At specific points during the -flow of execution (hooks), the Callback interface allows you to design -programs that encapsulate a full set of functionality. It de-couples -functionality that does not need to be in **PINA** ``Solver``\ s. -Lightning has a callback system to execute them when needed. Callbacks -should capture NON-ESSENTIAL logic that is NOT required for your -lightning module to run. - -The following are best practices when using/designing callbacks. - -- Callbacks should be isolated in their functionality. -- Your callback should not rely on the behavior of other callbacks in - order to work properly. -- Do not manually call methods from the callback. -- Directly calling methods (eg. on_validation_end) is strongly - discouraged. -- Whenever possible, your callbacks should not depend on the order in - which they are executed. - -We will try now to implement a naive version of ``MetricTraker`` to show -how callbacks work. Notice that this is a very easy application of -callbacks, fortunately in **PINA** we already provide more advanced -callbacks in ``pina.callbacks``. - -.. raw:: html - - - -.. code:: ipython3 - - from pytorch_lightning.callbacks import Callback - import torch - - # define a simple callback - class NaiveMetricTracker(Callback): - def __init__(self): - self.saved_metrics = [] - - def on_train_epoch_end(self, trainer, __): # function called at the end of each epoch - self.saved_metrics.append( - {key: value for key, value in trainer.logged_metrics.items()} - ) - -Let’s see the results when applyed to the ``SimpleODE`` problem. You can -define callbacks when initializing the ``Trainer`` by the ``callbacks`` -argument, which expects a list of callbacks. - -.. code:: ipython3 - - model = FeedForward( - layers=[10, 10], - func=torch.nn.Tanh, - output_dimensions=len(problem.output_variables), - input_dimensions=len(problem.input_variables) - ) - pinn = PINN(problem, model) - trainer = Trainer(solver=pinn, - accelerator='cpu', - enable_model_summary=False, - callbacks=[NaiveMetricTracker()]) # adding a callbacks - trainer.train() - - -.. parsed-literal:: - - GPU available: True (mps), used: False - TPU available: False, using: 0 TPU cores - IPU available: False, using: 0 IPUs - HPU available: False, using: 0 HPUs - - `Trainer.fit` stopped: `max_epochs=1000` reached. - Epoch 999: 100%|██████████| 1/1 [00:00<00:00, 149.27it/s, v_num=1, x0_loss=7.27e-5, D_loss=0.0016, mean_loss=0.000838] - - -We can easily access the data by calling -``trainer.callbacks[0].saved_metrics`` (notice the zero representing the -first callback in the list given at initialization). - -.. code:: ipython3 - - trainer.callbacks[0].saved_metrics[:3] # only the first three epochs - - - - -.. parsed-literal:: - - [{'x0_loss': tensor(0.9141), - 'D_loss': tensor(0.0304), - 'mean_loss': tensor(0.4722)}, - {'x0_loss': tensor(0.8906), - 'D_loss': tensor(0.0287), - 'mean_loss': tensor(0.4596)}, - {'x0_loss': tensor(0.8674), - 'D_loss': tensor(0.0274), - 'mean_loss': tensor(0.4474)}] - - - -PyTorch Lightning also has some built in ``Callbacks`` which can be used -in **PINA**, `here an extensive -list `__. - -We can for example try the ``EarlyStopping`` routine, which -automatically stops the training when a specific metric converged (here -the ``mean_loss``). In order to let the training keep going forever set -``max_epochs=-1``. - -.. code:: ipython3 - - # ~2 mins - from pytorch_lightning.callbacks import EarlyStopping - - model = FeedForward( - layers=[10, 10], - func=torch.nn.Tanh, - output_dimensions=len(problem.output_variables), - input_dimensions=len(problem.input_variables) - ) - pinn = PINN(problem, model) - trainer = Trainer(solver=pinn, - accelerator='cpu', - max_epochs = -1, - enable_model_summary=False, - callbacks=[EarlyStopping('mean_loss')]) # adding a callbacks - trainer.train() - - -.. parsed-literal:: - - GPU available: True (mps), used: False - TPU available: False, using: 0 TPU cores - IPU available: False, using: 0 IPUs - HPU available: False, using: 0 HPUs - - Epoch 6157: 100%|██████████| 1/1 [00:00<00:00, 139.84it/s, v_num=9, x0_loss=4.21e-9, D_loss=9.93e-6, mean_loss=4.97e-6] - - -As we can see the model automatically stop when the logging metric -stopped improving! - -Trainer Tips to Boost Accuracy, Save Memory and Speed Up Training ------------------------------------------------------------------ - -Untill now we have seen how to choose the right ``accelerator``, how to -log and visualize the results, and how to interface with the program in -order to add specific parts of code at specific points by ``callbacks``. -Now, we well focus on how boost your training by saving memory and -speeding it up, while mantaining the same or even better degree of -accuracy! - -There are several built in methods developed in PyTorch Lightning which -can be applied straight forward in **PINA**, here we report some: - -- `Stochastic Weight - Averaging `__ - to boost accuracy -- `Gradient - Clippling `__ to - reduce computational time (and improve accuracy) -- `Gradient - Accumulation `__ - to save memory consumption -- `Mixed Precision - Training `__ - to save memory consumption - -We will just demonstrate how to use the first two, and see the results -compared to a standard training. We use the -`Timer `__ -callback from ``pytorch_lightning.callbacks`` to take the times. Let’s -start by training a simple model without any optimization (train for -2000 epochs). - -.. code:: ipython3 - - from pytorch_lightning.callbacks import Timer - from pytorch_lightning import seed_everything - - # setting the seed for reproducibility - seed_everything(42, workers=True) - - model = FeedForward( - layers=[10, 10], - func=torch.nn.Tanh, - output_dimensions=len(problem.output_variables), - input_dimensions=len(problem.input_variables) - ) - - pinn = PINN(problem, model) - trainer = Trainer(solver=pinn, - accelerator='cpu', - deterministic=True, # setting deterministic=True ensure reproducibility when a seed is imposed - max_epochs = 2000, - enable_model_summary=False, - callbacks=[Timer()]) # adding a callbacks - trainer.train() - print(f'Total training time {trainer.callbacks[0].time_elapsed("train"):.5f} s') - - -.. parsed-literal:: - - Seed set to 42 - GPU available: True (mps), used: False - TPU available: False, using: 0 TPU cores - IPU available: False, using: 0 IPUs - HPU available: False, using: 0 HPUs - - - `Trainer.fit` stopped: `max_epochs=2000` reached. - Epoch 1999: 100%|██████████| 1/1 [00:00<00:00, 163.58it/s, v_num=31, x0_loss=1.12e-6, D_loss=0.000127, mean_loss=6.4e-5] - Total training time 17.36381 s - - -Now we do the same but with StochasticWeightAveraging - -.. code:: ipython3 - - from pytorch_lightning.callbacks import StochasticWeightAveraging - - # setting the seed for reproducibility - seed_everything(42, workers=True) - - model = FeedForward( - layers=[10, 10], - func=torch.nn.Tanh, - output_dimensions=len(problem.output_variables), - input_dimensions=len(problem.input_variables) - ) - pinn = PINN(problem, model) - trainer = Trainer(solver=pinn, - accelerator='cpu', - deterministic=True, - max_epochs = 2000, - enable_model_summary=False, - callbacks=[Timer(), - StochasticWeightAveraging(swa_lrs=0.005)]) # adding StochasticWeightAveraging callbacks - trainer.train() - print(f'Total training time {trainer.callbacks[0].time_elapsed("train"):.5f} s') - - -.. parsed-literal:: - - Seed set to 42 - GPU available: True (mps), used: False - TPU available: False, using: 0 TPU cores - IPU available: False, using: 0 IPUs - HPU available: False, using: 0 HPUs - - - Epoch 1598: 100%|██████████| 1/1 [00:00<00:00, 210.04it/s, v_num=47, x0_loss=4.17e-6, D_loss=0.000204, mean_loss=0.000104] - Swapping scheduler `ConstantLR` for `SWALR` - `Trainer.fit` stopped: `max_epochs=2000` reached. - Epoch 1999: 100%|██████████| 1/1 [00:00<00:00, 120.85it/s, v_num=47, x0_loss=1.56e-7, D_loss=7.49e-5, mean_loss=3.75e-5] - Total training time 17.10627 s - - -As you can see, the training time does not change at all! Notice that -around epoch ``1600`` the scheduler is switched from the defalut one -``ConstantLR`` to the Stochastic Weight Average Learning Rate -(``SWALR``). This is because by default ``StochasticWeightAveraging`` -will be activated after ``int(swa_epoch_start * max_epochs)`` with -``swa_epoch_start=0.7`` by default. Finally, the final ``mean_loss`` is -lower when ``StochasticWeightAveraging`` is used. - -We will now now do the same but clippling the gradient to be relatively -small. - -.. code:: ipython3 - - # setting the seed for reproducibility - seed_everything(42, workers=True) - - model = FeedForward( - layers=[10, 10], - func=torch.nn.Tanh, - output_dimensions=len(problem.output_variables), - input_dimensions=len(problem.input_variables) - ) - pinn = PINN(problem, model) - trainer = Trainer(solver=pinn, - accelerator='cpu', - max_epochs = 2000, - enable_model_summary=False, - gradient_clip_val=0.1, # clipping the gradient - callbacks=[Timer(), - StochasticWeightAveraging(swa_lrs=0.005)]) - trainer.train() - print(f'Total training time {trainer.callbacks[0].time_elapsed("train"):.5f} s') - - -.. parsed-literal:: - - Seed set to 42 - GPU available: True (mps), used: False - TPU available: False, using: 0 TPU cores - IPU available: False, using: 0 IPUs - HPU available: False, using: 0 HPUs - - Epoch 1598: 100%|██████████| 1/1 [00:00<00:00, 261.80it/s, v_num=46, x0_loss=9e-8, D_loss=2.39e-5, mean_loss=1.2e-5] - Swapping scheduler `ConstantLR` for `SWALR` - `Trainer.fit` stopped: `max_epochs=2000` reached. - Epoch 1999: 100%|██████████| 1/1 [00:00<00:00, 148.99it/s, v_num=46, x0_loss=7.08e-7, D_loss=1.77e-5, mean_loss=9.19e-6] - Total training time 17.01149 s - - -As we can see we by applying gradient clipping we were able to even -obtain lower error! - -What’s next? ------------- - -Now you know how to use efficiently the ``Trainer`` class **PINA**! -There are multiple directions you can go now: - -1. Explore training times on different devices (e.g.) ``TPU`` - -2. Try to reduce memory cost by mixed precision training and gradient - accumulation (especially useful when training Neural Operators) - -3. Benchmark ``Trainer`` speed for different precisions. diff --git a/docs/source/_rst/tutorials/tutorial12/tutorial.rst b/docs/source/_rst/tutorials/tutorial12/tutorial.rst deleted file mode 100644 index 054213259..000000000 --- a/docs/source/_rst/tutorials/tutorial12/tutorial.rst +++ /dev/null @@ -1,176 +0,0 @@ -Tutorial: The ``Equation`` Class -================================ - -|Open In Colab| - -.. |Open In Colab| image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial12/tutorial.ipynb - -In this tutorial, we will show how to use the ``Equation`` Class in -PINA. Specifically, we will see how use the Class and its inherited -classes to enforce residuals minimization in PINNs. - -Example: The Burgers 1D equation --------------------------------- - -We will start implementing the viscous Burgers 1D problem Class, -described as follows: - -.. math:: - - - \begin{equation} - \begin{cases} - \frac{\partial u}{\partial t} + u \frac{\partial u}{\partial x} &= \nu \frac{\partial^2 u}{ \partial x^2}, \quad x\in(0,1), \quad t>0\\ - u(x,0) &= -\sin (\pi x)\\ - u(x,t) &= 0 \quad x = \pm 1\\ - \end{cases} - \end{equation} - -where we set :math:`\nu = \frac{0.01}{\pi}` . - -In the class that models this problem we will see in action the -``Equation`` class and one of its inherited classes, the ``FixedValue`` -class. - -.. code:: ipython3 - - ## routine needed to run the notebook on Google Colab - try: - import google.colab - IN_COLAB = True - except: - IN_COLAB = False - if IN_COLAB: - !pip install "pina-mathlab" - - #useful imports - from pina.problem import SpatialProblem, TimeDependentProblem - from pina.equation import Equation, FixedValue, FixedGradient, FixedFlux - from pina.geometry import CartesianDomain - import torch - from pina.operators import grad, laplacian - from pina import Condition - - - -.. code:: ipython3 - - class Burgers1D(TimeDependentProblem, SpatialProblem): - - # define the burger equation - def burger_equation(input_, output_): - du = grad(output_, input_) - ddu = grad(du, input_, components=['dudx']) - return ( - du.extract(['dudt']) + - output_.extract(['u'])*du.extract(['dudx']) - - (0.01/torch.pi)*ddu.extract(['ddudxdx']) - ) - - # define initial condition - def initial_condition(input_, output_): - u_expected = -torch.sin(torch.pi*input_.extract(['x'])) - return output_.extract(['u']) - u_expected - - # assign output/ spatial and temporal variables - output_variables = ['u'] - spatial_domain = CartesianDomain({'x': [-1, 1]}) - temporal_domain = CartesianDomain({'t': [0, 1]}) - - # problem condition statement - conditions = { - 'gamma1': Condition(location=CartesianDomain({'x': -1, 't': [0, 1]}), equation=FixedValue(0.)), - 'gamma2': Condition(location=CartesianDomain({'x': 1, 't': [0, 1]}), equation=FixedValue(0.)), - 't0': Condition(location=CartesianDomain({'x': [-1, 1], 't': 0}), equation=Equation(initial_condition)), - 'D': Condition(location=CartesianDomain({'x': [-1, 1], 't': [0, 1]}), equation=Equation(burger_equation)), - } - -The ``Equation`` class takes as input a function (in this case it -happens twice, with ``initial_condition`` and ``burger_equation``) which -computes a residual of an equation, such as a PDE. In a problem class -such as the one above, the ``Equation`` class with such a given input is -passed as a parameter in the specified ``Condition``. - -The ``FixedValue`` class takes as input a value of same dimensions of -the output functions; this class can be used to enforced a fixed value -for a specific condition, e.g. Dirichlet boundary conditions, as it -happens for instance in our example. - -Once the equations are set as above in the problem conditions, the PINN -solver will aim to minimize the residuals described in each equation in -the training phase. - -Available classes of equations include also: - ``FixedGradient`` and -``FixedFlux``: they work analogously to ``FixedValue`` class, where we -can require a constant value to be enforced, respectively, on the -gradient of the solution or the divergence of the solution; - -``Laplace``: it can be used to enforce the laplacian of the solution to -be zero; - ``SystemEquation``: we can enforce multiple conditions on the -same subdomain through this class, passing a list of residual equations -defined in the problem. - -Defining a new Equation class ------------------------------ - -``Equation`` classes can be also inherited to define a new class. As -example, we can see how to rewrite the above problem introducing a new -class ``Burgers1D``; during the class call, we can pass the viscosity -parameter :math:`\nu`: - -.. code:: ipython3 - - class Burgers1DEquation(Equation): - - def __init__(self, nu = 0.): - """ - Burgers1D class. This class can be - used to enforce the solution u to solve the viscous Burgers 1D Equation. - - :param torch.float32 nu: the viscosity coefficient. Default value is set to 0. - """ - self.nu = nu - - def equation(input_, output_): - return grad(output_, input_, d='t') +\ - output_*grad(output_, input_, d='x') -\ - self.nu*laplacian(output_, input_, d='x') - - - super().__init__(equation) - -Now we can just pass the above class as input for the last condition, -setting :math:`\nu= \frac{0.01}{\pi}`: - -.. code:: ipython3 - - class Burgers1D(TimeDependentProblem, SpatialProblem): - - # define initial condition - def initial_condition(input_, output_): - u_expected = -torch.sin(torch.pi*input_.extract(['x'])) - return output_.extract(['u']) - u_expected - - # assign output/ spatial and temporal variables - output_variables = ['u'] - spatial_domain = CartesianDomain({'x': [-1, 1]}) - temporal_domain = CartesianDomain({'t': [0, 1]}) - - # problem condition statement - conditions = { - 'gamma1': Condition(location=CartesianDomain({'x': -1, 't': [0, 1]}), equation=FixedValue(0.)), - 'gamma2': Condition(location=CartesianDomain({'x': 1, 't': [0, 1]}), equation=FixedValue(0.)), - 't0': Condition(location=CartesianDomain({'x': [-1, 1], 't': 0}), equation=Equation(initial_condition)), - 'D': Condition(location=CartesianDomain({'x': [-1, 1], 't': [0, 1]}), equation=Burgers1DEquation(0.01/torch.pi)), - } - -What’s next? ------------- - -Congratulations on completing the ``Equation`` class tutorial of -**PINA**! As we have seen, you can build new classes that inherits -``Equation`` to store more complex equations, as the Burgers 1D -equation, only requiring to pass the characteristic coefficients of the -problem. From now on, you can: - define additional complex equation -classes (e.g. ``SchrodingerEquation``, ``NavierStokeEquation``..) - -define more ``FixedOperator`` (e.g. ``FixedCurl``) diff --git a/docs/source/_rst/tutorials/tutorial13/tutorial.rst b/docs/source/_rst/tutorials/tutorial13/tutorial.rst deleted file mode 100644 index 1b932909f..000000000 --- a/docs/source/_rst/tutorials/tutorial13/tutorial.rst +++ /dev/null @@ -1,327 +0,0 @@ -Tutorial: Multiscale PDE learning with Fourier Feature Network -============================================================== - -|Open In Colab| - -.. |Open In Colab| image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial13/tutorial.ipynb - -This tutorial presents how to solve with Physics-Informed Neural -Networks (PINNs) a PDE characterized by multiscale behaviour, as -presented in `On the eigenvector bias of Fourier feature networks: From -regression to solving multi-scale PDEs with physics-informed neural -networks `__. - -First of all, some useful imports. - -.. code:: ipython3 - - ## routine needed to run the notebook on Google Colab - try: - import google.colab - IN_COLAB = True - except: - IN_COLAB = False - if IN_COLAB: - !pip install "pina-mathlab" - - import torch - - from pina import Condition, Plotter, Trainer, Plotter - from pina.problem import SpatialProblem - from pina.operators import laplacian - from pina.solvers import PINN, SAPINN - from pina.model.layers import FourierFeatureEmbedding - from pina.loss import LpLoss - from pina.geometry import CartesianDomain - from pina.equation import Equation, FixedValue - from pina.model import FeedForward - - -Multiscale Problem ------------------- - -We begin by presenting the problem which also can be found in Section 2 -of `On the eigenvector bias of Fourier feature networks: From regression -to solving multi-scale PDEs with physics-informed neural -networks `__. The -one-dimensional Poisson problem we aim to solve is mathematically -written as: - -.. math:: - - \begin{equation} - \begin{cases} - \Delta u (x) + f(x) = 0 \quad x \in [0,1], \\ - u(x) = 0 \quad x \in \partial[0,1], \\ - \end{cases} - \end{equation} - -We impose the solution as -:math:`u(x) = \sin(2\pi x) + 0.1 \sin(50\pi x)` and obtain the force -term -:math:`f(x) = (2\pi)^2 \sin(2\pi x) + 0.1 (50 \pi)^2 \sin(50\pi x)`. -Though this example is simple and pedagogical, it is worth noting that -the solution exhibits low frequency in the macro-scale and high -frequency in the micro-scale, which resembles many practical scenarios. - -In **PINA** this problem is written, as always, as a class `see here for -a tutorial on the Problem -class `__. -Below you can find the ``Poisson`` problem which is mathmatically -described above. - -.. code:: ipython3 - - class Poisson(SpatialProblem): - output_variables = ['u'] - spatial_domain = CartesianDomain({'x': [0, 1]}) - - def poisson_equation(input_, output_): - x = input_.extract('x') - u_xx = laplacian(output_, input_, components=['u'], d=['x']) - f = ((2*torch.pi)**2)*torch.sin(2*torch.pi*x) + 0.1*((50*torch.pi)**2)*torch.sin(50*torch.pi*x) - return u_xx + f - - # here we write the problem conditions - conditions = { - 'gamma0' : Condition(location=CartesianDomain({'x': 0}), - equation=FixedValue(0)), - 'gamma1' : Condition(location=CartesianDomain({'x': 1}), - equation=FixedValue(0)), - 'D': Condition(location=spatial_domain, - equation=Equation(poisson_equation)), - } - - def truth_solution(self, x): - return torch.sin(2*torch.pi*x) + 0.1*torch.sin(50*torch.pi*x) - - problem = Poisson() - - # let's discretise the domain - problem.discretise_domain(128, 'grid') - -A standard PINN approach would be to fit this model using a Feed Forward -(fully connected) Neural Network. For a conventional fully-connected -neural network is easy to approximate a function :math:`u`, given -sufficient data inside the computational domain. However solving -high-frequency or multi-scale problems presents great challenges to -PINNs especially when the number of data cannot capture the different -scales. - -Below we run a simulation using the ``PINN`` solver and the self -adaptive ``SAPINN`` solver, using a -``FeedForward`` model. We used a ``MultiStepLR`` scheduler to decrease the learning rate -slowly during training (it takes around 2 minutes to run on CPU). - -.. code:: ipython3 - - # training with PINN and visualize results - pinn = PINN(problem=problem, - model=FeedForward(input_dimensions=1, output_dimensions=1, layers=[100, 100, 100]), - scheduler=torch.optim.lr_scheduler.MultiStepLR, - scheduler_kwargs={'milestones' : [1000, 2000, 3000, 4000], 'gamma':0.9}) - trainer = Trainer(pinn, max_epochs=5000, accelerator='cpu', enable_model_summary=False) - trainer.train() - - # training with PINN and visualize results - sapinn = SAPINN(problem=problem, - model=FeedForward(input_dimensions=1, output_dimensions=1, layers=[100, 100, 100]), - scheduler_model=torch.optim.lr_scheduler.MultiStepLR, - scheduler_model_kwargs={'milestones' : [1000, 2000, 3000, 4000], 'gamma':0.9}) - trainer_sapinn = Trainer(sapinn, max_epochs=5000, accelerator='cpu', enable_model_summary=False) - trainer_sapinn.train() - - # plot results - pl = Plotter() - pl.plot(pinn, title='PINN Solution') - pl.plot(sapinn, title='Self Adaptive PINN Solution') - - - -.. parsed-literal:: - - GPU available: True (mps), used: False - TPU available: False, using: 0 TPU cores - IPU available: False, using: 0 IPUs - HPU available: False, using: 0 HPUs - Epoch 4999: 100%|██████████| 1/1 [00:00<00:00, 97.66it/s, v_num=69, gamma0_loss=2.61e+3, gamma1_loss=2.61e+3, D_loss=409.0, mean_loss=1.88e+3] - - -.. parsed-literal:: - - GPU available: True (mps), used: False - TPU available: False, using: 0 TPU cores - IPU available: False, using: 0 IPUs - HPU available: False, using: 0 HPUs - Epoch 4999: 100%|██████████| 1/1 [00:00<00:00, 65.77it/s, v_num=70, gamma0_loss=151.0, gamma1_loss=148.0, D_loss=6.38e+5, mean_loss=2.13e+5] - - - -.. image:: tutorial_files/tutorial_5_8.png - - - -.. image:: tutorial_files/tutorial_5_9.png - - -We can clearly see that the solution has not been learned by the two -different solvers. Indeed the big problem is not in the optimization -strategy (i.e. the solver), but in the model used to solve the problem. -A simple ``FeedForward`` network can hardly handle multiscales if not -enough collocation points are used! - -We can also compute the :math:`l_2` relative error for the ``PINN`` and -``SAPINN`` solutions: - -.. code:: ipython3 - - # l2 loss from PINA losses - l2_loss = LpLoss(p=2, relative=True) - - # sample new test points - pts = pts = problem.spatial_domain.sample(100, 'grid') - print(f'Relative l2 error PINN {l2_loss(pinn(pts), problem.truth_solution(pts)).item():.2%}') - print(f'Relative l2 error SAPINN {l2_loss(sapinn(pts), problem.truth_solution(pts)).item():.2%}') - - -.. parsed-literal:: - - Relative l2 error PINN 95.76% - Relative l2 error SAPINN 124.26% - - -Which is indeed very high! - -Fourier Feature Embedding in PINA ---------------------------------- - -Fourier Feature Embedding is a way to transform the input features, to -help the network in learning multiscale variations in the output. It was -first introduced in `On the eigenvector bias of Fourier feature -networks: From regression to solving multi-scale PDEs with -physics-informed neural -networks `__ showing great -results for multiscale problems. The basic idea is to map the input -:math:`\mathbf{x}` into an embedding :math:`\tilde{\mathbf{x}}` where: - -.. math:: \tilde{\mathbf{x}} =\left[\cos\left( \mathbf{B} \mathbf{x} \right), \sin\left( \mathbf{B} \mathbf{x} \right)\right] - -and :math:`\mathbf{B}_{ij} \sim \mathcal{N}(0, \sigma^2)`. This simple -operation allow the network to learn on multiple scales! - -In PINA we already have implemented the feature as a ``layer`` called -```FourierFeatureEmbedding`` `__. -Below we will build the *Multi-scale Fourier Feature Architecture*. In -this architecture multiple Fourier feature embeddings (initialized with -different :math:`\sigma`) are applied to input coordinates and then -passed through the same fully-connected neural network, before the -outputs are finally concatenated with a linear layer. - -.. code:: ipython3 - - class MultiscaleFourierNet(torch.nn.Module): - def __init__(self): - super().__init__() - self.embedding1 = FourierFeatureEmbedding(input_dimension=1, - output_dimension=100, - sigma=1) - self.embedding2 = FourierFeatureEmbedding(input_dimension=1, - output_dimension=100, - sigma=10) - self.layers = FeedForward(input_dimensions=100, output_dimensions=100, layers=[100]) - self.final_layer = torch.nn.Linear(2*100, 1) - - def forward(self, x): - e1 = self.layers(self.embedding1(x)) - e2 = self.layers(self.embedding2(x)) - return self.final_layer(torch.cat([e1, e2], dim=-1)) - - MultiscaleFourierNet() - - - - -.. parsed-literal:: - - MultiscaleFourierNet( - (embedding1): FourierFeatureEmbedding() - (embedding2): FourierFeatureEmbedding() - (layers): FeedForward( - (model): Sequential( - (0): Linear(in_features=100, out_features=100, bias=True) - (1): Tanh() - (2): Linear(in_features=100, out_features=100, bias=True) - ) - ) - (final_layer): Linear(in_features=200, out_features=1, bias=True) - ) - - - -We will train the ``MultiscaleFourierNet`` with the ``PINN`` solver (and -feel free to try also with our PINN variants (``SAPINN``, ``GPINN``, -``CompetitivePINN``, …). - -.. code:: ipython3 - - multiscale_pinn = PINN(problem=problem, - model=MultiscaleFourierNet(), - scheduler=torch.optim.lr_scheduler.MultiStepLR, - scheduler_kwargs={'milestones' : [1000, 2000, 3000, 4000], 'gamma':0.9}) - trainer = Trainer(multiscale_pinn, max_epochs=5000, accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional) - trainer.train() - - -.. parsed-literal:: - - GPU available: True (mps), used: False - TPU available: False, using: 0 TPU cores - IPU available: False, using: 0 IPUs - HPU available: False, using: 0 HPUs - Epoch 4999: 100%|██████████| 1/1 [00:00<00:00, 72.21it/s, v_num=71, gamma0_loss=3.91e-5, gamma1_loss=3.91e-5, D_loss=0.000151, mean_loss=0.000113] - - -Let us now plot the solution and compute the relative :math:`l_2` again! - -.. code:: ipython3 - - # plot the solution - pl.plot(multiscale_pinn, title='Solution PINN with MultiscaleFourierNet') - - # sample new test points - pts = pts = problem.spatial_domain.sample(100, 'grid') - print(f'Relative l2 error PINN with MultiscaleFourierNet {l2_loss(multiscale_pinn(pts), problem.truth_solution(pts)).item():.2%}') - - - -.. image:: tutorial_files/tutorial_15_0.png - - -.. parsed-literal:: - - Relative l2 error PINN with MultiscaleFourierNet 2.72% - - -It is pretty clear that the network has learned the correct solution, -with also a very law error. Obviously a longer training and a more -expressive neural network could improve the results! - -What’s next? ------------- - -Congratulations on completing the one dimensional Poisson tutorial of -**PINA** using ``FourierFeatureEmbedding``! There are multiple -directions you can go now: - -1. Train the network for longer or with different layer sizes and assert - the finaly accuracy - -2. Understand the role of ``sigma`` in ``FourierFeatureEmbedding`` (see - original paper for a nice reference) - -3. Code the *Spatio-temporal multi-scale Fourier feature architecture* - for a more complex time dependent PDE (section 3 of the original - reference) - -4. Many more… diff --git a/docs/source/_rst/tutorials/tutorial13/tutorial_files/tutorial_15_0.png b/docs/source/_rst/tutorials/tutorial13/tutorial_files/tutorial_15_0.png deleted file mode 100644 index c6f0e508a..000000000 Binary files a/docs/source/_rst/tutorials/tutorial13/tutorial_files/tutorial_15_0.png and /dev/null differ diff --git a/docs/source/_rst/tutorials/tutorial13/tutorial_files/tutorial_5_8.png b/docs/source/_rst/tutorials/tutorial13/tutorial_files/tutorial_5_8.png deleted file mode 100644 index 470a5715a..000000000 Binary files a/docs/source/_rst/tutorials/tutorial13/tutorial_files/tutorial_5_8.png and /dev/null differ diff --git a/docs/source/_rst/tutorials/tutorial13/tutorial_files/tutorial_5_9.png b/docs/source/_rst/tutorials/tutorial13/tutorial_files/tutorial_5_9.png deleted file mode 100644 index 1cfc02b1c..000000000 Binary files a/docs/source/_rst/tutorials/tutorial13/tutorial_files/tutorial_5_9.png and /dev/null differ diff --git a/docs/source/_rst/tutorials/tutorial2/tutorial.rst b/docs/source/_rst/tutorials/tutorial2/tutorial.rst deleted file mode 100644 index 9ed0eae56..000000000 --- a/docs/source/_rst/tutorials/tutorial2/tutorial.rst +++ /dev/null @@ -1,385 +0,0 @@ -Tutorial: Two dimensional Poisson problem using Extra Features Learning -======================================================================= - -|Open In Colab| - -.. |Open In Colab| image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial2/tutorial.ipynb - - -This tutorial presents how to solve with Physics-Informed Neural -Networks (PINNs) a 2D Poisson problem with Dirichlet boundary -conditions. We will train with standard PINN’s training, and with -extrafeatures. For more insights on extrafeature learning please read -`An extended physics informed neural network for preliminary analysis of -parametric optimal control -problems `__. - -First of all, some useful imports. - -.. code:: ipython3 - - ## routine needed to run the notebook on Google Colab - try: - import google.colab - IN_COLAB = True - except: - IN_COLAB = False - if IN_COLAB: - !pip install "pina-mathlab" - - import torch - from torch.nn import Softplus - - from pina.problem import SpatialProblem - from pina.operators import laplacian - from pina.model import FeedForward - from pina.solvers import PINN - from pina.trainer import Trainer - from pina.plotter import Plotter - from pina.geometry import CartesianDomain - from pina.equation import Equation, FixedValue - from pina import Condition, LabelTensor - from pina.callbacks import MetricTracker - -The problem definition ----------------------- - -The two-dimensional Poisson problem is mathematically written as: - -.. math:: - - \begin{equation} - \begin{cases} - \Delta u = \sin{(\pi x)} \sin{(\pi y)} \text{ in } D, \\ - u = 0 \text{ on } \Gamma_1 \cup \Gamma_2 \cup \Gamma_3 \cup \Gamma_4, - \end{cases} - \end{equation} - -where :math:`D` is a square domain :math:`[0,1]^2`, and -:math:`\Gamma_i`, with :math:`i=1,...,4`, are the boundaries of the -square. - -The Poisson problem is written in **PINA** code as a class. The -equations are written as *conditions* that should be satisfied in the -corresponding domains. The *truth_solution* is the exact solution which -will be compared with the predicted one. - -.. code:: ipython3 - - class Poisson(SpatialProblem): - output_variables = ['u'] - spatial_domain = CartesianDomain({'x': [0, 1], 'y': [0, 1]}) - - def laplace_equation(input_, output_): - force_term = (torch.sin(input_.extract(['x'])*torch.pi) * - torch.sin(input_.extract(['y'])*torch.pi)) - laplacian_u = laplacian(output_, input_, components=['u'], d=['x', 'y']) - return laplacian_u - force_term - - # here we write the problem conditions - conditions = { - 'gamma1': Condition(location=CartesianDomain({'x': [0, 1], 'y': 1}), equation=FixedValue(0.)), - 'gamma2': Condition(location=CartesianDomain({'x': [0, 1], 'y': 0}), equation=FixedValue(0.)), - 'gamma3': Condition(location=CartesianDomain({'x': 1, 'y': [0, 1]}), equation=FixedValue(0.)), - 'gamma4': Condition(location=CartesianDomain({'x': 0, 'y': [0, 1]}), equation=FixedValue(0.)), - 'D': Condition(location=CartesianDomain({'x': [0, 1], 'y': [0, 1]}), equation=Equation(laplace_equation)), - } - - def poisson_sol(self, pts): - return -( - torch.sin(pts.extract(['x'])*torch.pi)* - torch.sin(pts.extract(['y'])*torch.pi) - )/(2*torch.pi**2) - - truth_solution = poisson_sol - - problem = Poisson() - - # let's discretise the domain - problem.discretise_domain(25, 'grid', locations=['D']) - problem.discretise_domain(25, 'grid', locations=['gamma1', 'gamma2', 'gamma3', 'gamma4']) - -Solving the problem with standard PINNs ---------------------------------------- - -After the problem, the feed-forward neural network is defined, through -the class ``FeedForward``. This neural network takes as input the -coordinates (in this case :math:`x` and :math:`y`) and provides the -unkwown field of the Poisson problem. The residual of the equations are -evaluated at several sampling points (which the user can manipulate -using the method ``CartesianDomain_pts``) and the loss minimized by the -neural network is the sum of the residuals. - -In this tutorial, the neural network is composed by two hidden layers of -10 neurons each, and it is trained for 1000 epochs with a learning rate -of 0.006 and :math:`l_2` weight regularization set to :math:`10^{-7}`. -These parameters can be modified as desired. We use the -``MetricTracker`` class to track the metrics during training. - -.. code:: ipython3 - - # make model + solver + trainer - model = FeedForward( - layers=[10, 10], - func=Softplus, - output_dimensions=len(problem.output_variables), - input_dimensions=len(problem.input_variables) - ) - pinn = PINN(problem, model, optimizer_kwargs={'lr':0.006, 'weight_decay':1e-8}) - trainer = Trainer(pinn, max_epochs=1000, callbacks=[MetricTracker()], accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional) - - # train - trainer.train() - -.. parsed-literal:: - - `Trainer.fit` stopped: `max_epochs=1000` reached. - - -.. parsed-literal:: - - Epoch 999: : 1it [00:00, 105.33it/s, v_num=3, gamma1_loss=5.29e-5, gamma2_loss=4.09e-5, gamma3_loss=4.73e-5, gamma4_loss=4.18e-5, D_loss=0.00134, mean_loss=0.000304] - - -Now the ``Plotter`` class is used to plot the results. The solution -predicted by the neural network is plotted on the left, the exact one is -represented at the center and on the right the error between the exact -and the predicted solutions is showed. - -.. code:: ipython3 - - plotter = Plotter() - plotter.plot(solver=pinn) - - - -.. image:: tutorial_files/tutorial_9_0.png - - -Solving the problem with extra-features PINNs ---------------------------------------------- - -Now, the same problem is solved in a different way. A new neural network -is now defined, with an additional input variable, named extra-feature, -which coincides with the forcing term in the Laplace equation. The set -of input variables to the neural network is: - -.. math:: - - \begin{equation} - [x, y, k(x, y)], \text{ with } k(x, y)=\sin{(\pi x)}\sin{(\pi y)}, - \end{equation} - -where :math:`x` and :math:`y` are the spatial coordinates and -:math:`k(x, y)` is the added feature. - -This feature is initialized in the class ``SinSin``, which needs to be -inherited by the ``torch.nn.Module`` class and to have the ``forward`` -method. After declaring such feature, we can just incorporate in the -``FeedForward`` class thanks to the ``extra_features`` argument. **NB**: -``extra_features`` always needs a ``list`` as input, you you have one -feature just encapsulated it in a class, as in the next cell. - -Finally, we perform the same training as before: the problem is -``Poisson``, the network is composed by the same number of neurons and -optimizer parameters are equal to previous test, the only change is the -new extra feature. - -.. code:: ipython3 - - class SinSin(torch.nn.Module): - """Feature: sin(x)*sin(y)""" - def __init__(self): - super().__init__() - - def forward(self, x): - t = (torch.sin(x.extract(['x'])*torch.pi) * - torch.sin(x.extract(['y'])*torch.pi)) - return LabelTensor(t, ['sin(x)sin(y)']) - - - # make model + solver + trainer - model_feat = FeedForward( - layers=[10, 10], - func=Softplus, - output_dimensions=len(problem.output_variables), - input_dimensions=len(problem.input_variables)+1 - ) - pinn_feat = PINN(problem, model_feat, extra_features=[SinSin()], optimizer_kwargs={'lr':0.006, 'weight_decay':1e-8}) - trainer_feat = Trainer(pinn_feat, max_epochs=1000, callbacks=[MetricTracker()], accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional) - - # train - trainer_feat.train() - -.. parsed-literal:: - - `Trainer.fit` stopped: `max_epochs=1000` reached. - - -.. parsed-literal:: - - Epoch 999: : 1it [00:00, 85.62it/s, v_num=4, gamma1_loss=2.54e-7, gamma2_loss=2.17e-7, gamma3_loss=1.94e-7, gamma4_loss=2.69e-7, D_loss=9.2e-6, mean_loss=2.03e-6] - - -The predicted and exact solutions and the error between them are -represented below. We can easily note that now our network, having -almost the same condition as before, is able to reach additional order -of magnitudes in accuracy. - -.. code:: ipython3 - - plotter.plot(solver=pinn_feat) - - - -.. image:: tutorial_files/tutorial_14_0.png - - -Solving the problem with learnable extra-features PINNs -------------------------------------------------------- - -We can still do better! - -Another way to exploit the extra features is the addition of learnable -parameter inside them. In this way, the added parameters are learned -during the training phase of the neural network. In this case, we use: - -.. math:: - - \begin{equation} - k(x, \mathbf{y}) = \beta \sin{(\alpha x)} \sin{(\alpha y)}, - \end{equation} - -where :math:`\alpha` and :math:`\beta` are the abovementioned -parameters. Their implementation is quite trivial: by using the class -``torch.nn.Parameter`` we cam define all the learnable parameters we -need, and they are managed by ``autograd`` module! - -.. code:: ipython3 - - class SinSinAB(torch.nn.Module): - """ """ - def __init__(self): - super().__init__() - self.alpha = torch.nn.Parameter(torch.tensor([1.0])) - self.beta = torch.nn.Parameter(torch.tensor([1.0])) - - - def forward(self, x): - t = ( - self.beta*torch.sin(self.alpha*x.extract(['x'])*torch.pi)* - torch.sin(self.alpha*x.extract(['y'])*torch.pi) - ) - return LabelTensor(t, ['b*sin(a*x)sin(a*y)']) - - - # make model + solver + trainer - model_lean= FeedForward( - layers=[10, 10], - func=Softplus, - output_dimensions=len(problem.output_variables), - input_dimensions=len(problem.input_variables)+1 - ) - pinn_lean = PINN(problem, model_lean, extra_features=[SinSinAB()], optimizer_kwargs={'lr':0.006, 'weight_decay':1e-8}) - trainer_learn = Trainer(pinn_lean, max_epochs=1000, accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional) - - # train - trainer_learn.train() - -.. parsed-literal:: - - `Trainer.fit` stopped: `max_epochs=1000` reached. - - -.. parsed-literal:: - - Epoch 999: : 1it [00:00, 85.94it/s, v_num=5, gamma1_loss=3.26e-8, gamma2_loss=7.84e-8, gamma3_loss=1.13e-7, gamma4_loss=3.02e-8, D_loss=2.66e-6, mean_loss=5.82e-7] - - -Umh, the final loss is not appreciabily better than previous model (with -static extra features), despite the usage of learnable parameters. This -is mainly due to the over-parametrization of the network: there are many -parameter to optimize during the training, and the model in unable to -understand automatically that only the parameters of the extra feature -(and not the weights/bias of the FFN) should be tuned in order to fit -our problem. A longer training can be helpful, but in this case the -faster way to reach machine precision for solving the Poisson problem is -removing all the hidden layers in the ``FeedForward``, keeping only the -:math:`\alpha` and :math:`\beta` parameters of the extra feature. - -.. code:: ipython3 - - # make model + solver + trainer - model_lean= FeedForward( - layers=[], - func=Softplus, - output_dimensions=len(problem.output_variables), - input_dimensions=len(problem.input_variables)+1 - ) - pinn_learn = PINN(problem, model_lean, extra_features=[SinSinAB()], optimizer_kwargs={'lr':0.01, 'weight_decay':1e-8}) - trainer_learn = Trainer(pinn_learn, max_epochs=1000, callbacks=[MetricTracker()], accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional) - - # train - trainer_learn.train() - -.. parsed-literal:: - - `Trainer.fit` stopped: `max_epochs=1000` reached. - - -.. parsed-literal:: - - Epoch 999: : 1it [00:00, 98.81it/s, v_num=6, gamma1_loss=2.55e-16, gamma2_loss=4.76e-17, gamma3_loss=2.55e-16, gamma4_loss=4.76e-17, D_loss=1.74e-13, mean_loss=3.5e-14] - - -In such a way, the model is able to reach a very high accuracy! Of -course, this is a toy problem for understanding the usage of extra -features: similar precision could be obtained if the extra features are -very similar to the true solution. The analyzed Poisson problem shows a -forcing term very close to the solution, resulting in a perfect problem -to address with such an approach. - -We conclude here by showing the graphical comparison of the unknown -field and the loss trend for all the test cases presented here: the -standard PINN, PINN with extra features, and PINN with learnable extra -features. - -.. code:: ipython3 - - plotter.plot(solver=pinn_learn) - - - -.. image:: tutorial_files/tutorial_21_0.png - - -Let us compare the training losses for the various types of training - -.. code:: ipython3 - - plotter.plot_loss(trainer, logy=True, label='Standard') - plotter.plot_loss(trainer_feat, logy=True,label='Static Features') - plotter.plot_loss(trainer_learn, logy=True, label='Learnable Features') - - - - -.. image:: tutorial_files/tutorial_23_0.png - - -What’s next? ------------- - -Nice you have completed the two dimensional Poisson tutorial of -**PINA**! There are multiple directions you can go now: - -1. Train the network for longer or with different layer sizes and assert - the finaly accuracy - -2. Propose new types of extrafeatures and see how they affect the - learning - -3. Exploit extrafeature training in more complex problems - -4. Many more… diff --git a/docs/source/_rst/tutorials/tutorial2/tutorial_files/tutorial_14_0.png b/docs/source/_rst/tutorials/tutorial2/tutorial_files/tutorial_14_0.png deleted file mode 100644 index 4974131c8..000000000 Binary files a/docs/source/_rst/tutorials/tutorial2/tutorial_files/tutorial_14_0.png and /dev/null differ diff --git a/docs/source/_rst/tutorials/tutorial2/tutorial_files/tutorial_21_0.png b/docs/source/_rst/tutorials/tutorial2/tutorial_files/tutorial_21_0.png deleted file mode 100644 index acaece688..000000000 Binary files a/docs/source/_rst/tutorials/tutorial2/tutorial_files/tutorial_21_0.png and /dev/null differ diff --git a/docs/source/_rst/tutorials/tutorial2/tutorial_files/tutorial_23_0.png b/docs/source/_rst/tutorials/tutorial2/tutorial_files/tutorial_23_0.png deleted file mode 100644 index 5960e46b7..000000000 Binary files a/docs/source/_rst/tutorials/tutorial2/tutorial_files/tutorial_23_0.png and /dev/null differ diff --git a/docs/source/_rst/tutorials/tutorial2/tutorial_files/tutorial_9_0.png b/docs/source/_rst/tutorials/tutorial2/tutorial_files/tutorial_9_0.png deleted file mode 100644 index 4dd8b3be5..000000000 Binary files a/docs/source/_rst/tutorials/tutorial2/tutorial_files/tutorial_9_0.png and /dev/null differ diff --git a/docs/source/_rst/tutorials/tutorial3/tutorial.rst b/docs/source/_rst/tutorials/tutorial3/tutorial.rst deleted file mode 100644 index 54172f423..000000000 --- a/docs/source/_rst/tutorials/tutorial3/tutorial.rst +++ /dev/null @@ -1,335 +0,0 @@ -Tutorial: Two dimensional Wave problem with hard constraint -=========================================================== - -|Open In Colab| - -.. |Open In Colab| image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial3/tutorial.ipynb - - -In this tutorial we present how to solve the wave equation using hard -constraint PINNs. For doing so we will build a costum ``torch`` model -and pass it to the ``PINN`` solver. - -First of all, some useful imports. - -.. code:: ipython3 - - ## routine needed to run the notebook on Google Colab - try: - import google.colab - IN_COLAB = True - except: - IN_COLAB = False - if IN_COLAB: - !pip install "pina-mathlab" - - import torch - - from pina.problem import SpatialProblem, TimeDependentProblem - from pina.operators import laplacian, grad - from pina.geometry import CartesianDomain - from pina.solvers import PINN - from pina.trainer import Trainer - from pina.equation import Equation - from pina.equation.equation_factory import FixedValue - from pina import Condition, Plotter - -The problem definition ----------------------- - -The problem is written in the following form: - -.. math:: - \begin{equation} - \begin{cases} - \Delta u(x,y,t) = \frac{\partial^2}{\partial t^2} u(x,y,t) \quad \text{in } D, \\\\ - u(x, y, t=0) = \sin(\pi x)\sin(\pi y), \\\\ - u(x, y, t) = 0 \quad \text{on } \Gamma_1 \cup \Gamma_2 \cup \Gamma_3 \cup \Gamma_4, - \end{cases} - \end{equation} - -where :math:`D` is a square domain :math:`[0,1]^2`, and -:math:`\Gamma_i`, with :math:`i=1,...,4`, are the boundaries of the -square, and the velocity in the standard wave equation is fixed to one. - -Now, the wave problem is written in PINA code as a class, inheriting -from ``SpatialProblem`` and ``TimeDependentProblem`` since we deal with -spatial, and time dependent variables. The equations are written as -``conditions`` that should be satisfied in the corresponding domains. -``truth_solution`` is the exact solution which will be compared with the -predicted one. - -.. code:: ipython3 - - class Wave(TimeDependentProblem, SpatialProblem): - output_variables = ['u'] - spatial_domain = CartesianDomain({'x': [0, 1], 'y': [0, 1]}) - temporal_domain = CartesianDomain({'t': [0, 1]}) - - def wave_equation(input_, output_): - u_t = grad(output_, input_, components=['u'], d=['t']) - u_tt = grad(u_t, input_, components=['dudt'], d=['t']) - nabla_u = laplacian(output_, input_, components=['u'], d=['x', 'y']) - return nabla_u - u_tt - - def initial_condition(input_, output_): - u_expected = (torch.sin(torch.pi*input_.extract(['x'])) * - torch.sin(torch.pi*input_.extract(['y']))) - return output_.extract(['u']) - u_expected - - conditions = { - 'gamma1': Condition(location=CartesianDomain({'x': [0, 1], 'y': 1, 't': [0, 1]}), equation=FixedValue(0.)), - 'gamma2': Condition(location=CartesianDomain({'x': [0, 1], 'y': 0, 't': [0, 1]}), equation=FixedValue(0.)), - 'gamma3': Condition(location=CartesianDomain({'x': 1, 'y': [0, 1], 't': [0, 1]}), equation=FixedValue(0.)), - 'gamma4': Condition(location=CartesianDomain({'x': 0, 'y': [0, 1], 't': [0, 1]}), equation=FixedValue(0.)), - 't0': Condition(location=CartesianDomain({'x': [0, 1], 'y': [0, 1], 't': 0}), equation=Equation(initial_condition)), - 'D': Condition(location=CartesianDomain({'x': [0, 1], 'y': [0, 1], 't': [0, 1]}), equation=Equation(wave_equation)), - } - - def wave_sol(self, pts): - return (torch.sin(torch.pi*pts.extract(['x'])) * - torch.sin(torch.pi*pts.extract(['y'])) * - torch.cos(torch.sqrt(torch.tensor(2.))*torch.pi*pts.extract(['t']))) - - truth_solution = wave_sol - - problem = Wave() - -Hard Constraint Model ---------------------- - -After the problem, a **torch** model is needed to solve the PINN. -Usually, many models are already implemented in **PINA**, but the user -has the possibility to build his/her own model in ``torch``. The hard -constraint we impose is on the boundary of the spatial domain. -Specifically, our solution is written as: - -.. math:: u_{\rm{pinn}} = xy(1-x)(1-y)\cdot NN(x, y, t), - -where :math:`NN` is the neural net output. This neural network takes as -input the coordinates (in this case :math:`x`, :math:`y` and :math:`t`) -and provides the unknown field :math:`u`. By construction, it is zero on -the boundaries. The residuals of the equations are evaluated at several -sampling points (which the user can manipulate using the method -``discretise_domain``) and the loss minimized by the neural network is -the sum of the residuals. - -.. code:: ipython3 - - class HardMLP(torch.nn.Module): - - def __init__(self, input_dim, output_dim): - super().__init__() - - self.layers = torch.nn.Sequential(torch.nn.Linear(input_dim, 40), - torch.nn.ReLU(), - torch.nn.Linear(40, 40), - torch.nn.ReLU(), - torch.nn.Linear(40, output_dim)) - - # here in the foward we implement the hard constraints - def forward(self, x): - hard = x.extract(['x'])*(1-x.extract(['x']))*x.extract(['y'])*(1-x.extract(['y'])) - return hard*self.layers(x) - -Train and Inference -------------------- - -In this tutorial, the neural network is trained for 1000 epochs with a -learning rate of 0.001 (default in ``PINN``). Training takes -approximately 3 minutes. - -.. code:: ipython3 - - # generate the data - problem.discretise_domain(1000, 'random', locations=['D', 't0', 'gamma1', 'gamma2', 'gamma3', 'gamma4']) - - # crete the solver - pinn = PINN(problem, HardMLP(len(problem.input_variables), len(problem.output_variables))) - - # create trainer and train - trainer = Trainer(pinn, max_epochs=1000, accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional) - trainer.train() - -.. parsed-literal:: - - `Trainer.fit` stopped: `max_epochs=1000` reached. - - -.. parsed-literal:: - - Epoch 999: : 1it [00:00, 68.69it/s, v_num=0, gamma1_loss=0.000, gamma2_loss=0.000, gamma3_loss=0.000, gamma4_loss=0.000, t0_loss=0.0419, D_loss=0.0307, mean_loss=0.0121] - - -Notice that the loss on the boundaries of the spatial domain is exactly -zero, as expected! After the training is completed one can now plot some -results using the ``Plotter`` class of **PINA**. - -.. code:: ipython3 - - plotter = Plotter() - - # plotting at fixed time t = 0.0 - print('Plotting at t=0') - plotter.plot(pinn, fixed_variables={'t': 0.0}) - - # plotting at fixed time t = 0.5 - print('Plotting at t=0.5') - plotter.plot(pinn, fixed_variables={'t': 0.5}) - - # plotting at fixed time t = 1. - print('Plotting at t=1') - plotter.plot(pinn, fixed_variables={'t': 1.0}) - - -.. parsed-literal:: - - Plotting at t=0 - - - -.. image:: tutorial_files/tutorial_13_1.png - - -.. parsed-literal:: - - Plotting at t=0.5 - - - -.. image:: tutorial_files/tutorial_13_3.png - - -.. parsed-literal:: - - Plotting at t=1 - - - -.. image:: tutorial_files/tutorial_13_5.png - - -The results are not so great, and we can clearly see that as time -progress the solution get worse…. Can we do better? - -A valid option is to impose the initial condition as hard constraint as -well. Specifically, our solution is written as: - -.. math:: u_{\rm{pinn}} = xy(1-x)(1-y)\cdot NN(x, y, t)\cdot t + \cos(\sqrt{2}\pi t)\sin(\pi x)\sin(\pi y), - -Let us build the network first - -.. code:: ipython3 - - class HardMLPtime(torch.nn.Module): - - def __init__(self, input_dim, output_dim): - super().__init__() - - self.layers = torch.nn.Sequential(torch.nn.Linear(input_dim, 40), - torch.nn.ReLU(), - torch.nn.Linear(40, 40), - torch.nn.ReLU(), - torch.nn.Linear(40, output_dim)) - - # here in the foward we implement the hard constraints - def forward(self, x): - hard_space = x.extract(['x'])*(1-x.extract(['x']))*x.extract(['y'])*(1-x.extract(['y'])) - hard_t = torch.sin(torch.pi*x.extract(['x'])) * torch.sin(torch.pi*x.extract(['y'])) * torch.cos(torch.sqrt(torch.tensor(2.))*torch.pi*x.extract(['t'])) - return hard_space * self.layers(x) * x.extract(['t']) + hard_t - -Now let’s train with the same configuration as thre previous test - -.. code:: ipython3 - - # generate the data - problem.discretise_domain(1000, 'random', locations=['D', 't0', 'gamma1', 'gamma2', 'gamma3', 'gamma4']) - - # crete the solver - pinn = PINN(problem, HardMLPtime(len(problem.input_variables), len(problem.output_variables))) - - # create trainer and train - trainer = Trainer(pinn, max_epochs=1000, accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional) - trainer.train() - - -.. parsed-literal:: - - `Trainer.fit` stopped: `max_epochs=1000` reached. - - -.. parsed-literal:: - - Epoch 999: : 1it [00:00, 45.78it/s, v_num=1, gamma1_loss=1.97e-15, gamma2_loss=0.000, gamma3_loss=2.14e-15, gamma4_loss=0.000, t0_loss=0.000, D_loss=1.25e-7, mean_loss=2.09e-8] - - -We can clearly see that the loss is way lower now. Let’s plot the -results - -.. code:: ipython3 - - plotter = Plotter() - - # plotting at fixed time t = 0.0 - print('Plotting at t=0') - plotter.plot(pinn, fixed_variables={'t': 0.0}) - - # plotting at fixed time t = 0.5 - print('Plotting at t=0.5') - plotter.plot(pinn, fixed_variables={'t': 0.5}) - - # plotting at fixed time t = 1. - print('Plotting at t=1') - plotter.plot(pinn, fixed_variables={'t': 1.0}) - - -.. parsed-literal:: - - Plotting at t=0 - - - -.. image:: tutorial_files/tutorial_19_1.png - - -.. parsed-literal:: - - Plotting at t=0.5 - - - -.. image:: tutorial_files/tutorial_19_3.png - - -.. parsed-literal:: - - Plotting at t=1 - - - -.. image:: tutorial_files/tutorial_19_5.png - - -We can see now that the results are way better! This is due to the fact -that previously the network was not learning correctly the initial -conditon, leading to a poor solution when the time evolved. By imposing -the initial condition the network is able to correctly solve the -problem. - -What’s next? ------------- - -Nice you have completed the two dimensional Wave tutorial of **PINA**! -There are multiple directions you can go now: - -1. Train the network for longer or with different layer sizes and assert - the finaly accuracy - -2. Propose new types of hard constraints in time, e.g.  - - .. math:: u_{\rm{pinn}} = xy(1-x)(1-y)\cdot NN(x, y, t)(1-\exp(-t)) + \cos(\sqrt{2}\pi t)sin(\pi x)\sin(\pi y), - -3. Exploit extrafeature training for model 1 and 2 - -4. 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The -implementation of the filter follows the original work `A Continuous -Convolutional Trainable Filter for Modelling Unstructured -Data `__. - -First of all we import the modules needed for the tutorial: - -.. code:: ipython3 - - ## routine needed to run the notebook on Google Colab - try: - import google.colab - IN_COLAB = True - except: - IN_COLAB = False - if IN_COLAB: - !pip install "pina-mathlab" - - import torch - import matplotlib.pyplot as plt - plt.style.use('tableau-colorblind10') - from pina.problem import AbstractProblem - from pina.solvers import SupervisedSolver - from pina.trainer import Trainer - from pina import Condition, LabelTensor - from pina.model.layers import ContinuousConvBlock - import torchvision # for MNIST dataset - from pina.model import FeedForward # for building AE and MNIST classification - -The tutorial is structured as follow: - -* `Continuous filter background <#continuous-filter-background>`__: understand how the convolutional filter works and how to use it. -* `Building a MNIST Classifier <#building-a-mnist-classifier>`__: show how to build a simple - classifier using the MNIST dataset and how to combine a continuous - convolutional layer with a feedforward neural network. -* `Building a Continuous Convolutional Autoencoder <#building-a-continuous-convolutional-autoencoder>`__: show - show to use the continuous filter to work with unstructured data for - autoencoding and up-sampling. - -Continuous filter background ----------------------------- - -As reported by the authors in the original paper: in contrast to -discrete convolution, continuous convolution is mathematically defined -as: - -.. math:: - - - \mathcal{I}_{\rm{out}}(\mathbf{x}) = \int_{\mathcal{X}} \mathcal{I}(\mathbf{x} + \mathbf{\tau}) \cdot \mathcal{K}(\mathbf{\tau}) d\mathbf{\tau}, - -where :math:`\mathcal{K} : \mathcal{X} \rightarrow \mathbb{R}` is the -*continuous filter* function, and -:math:`\mathcal{I} : \Omega \subset \mathbb{R}^N \rightarrow \mathbb{R}` -is the input function. The continuous filter function is approximated -using a FeedForward Neural Network, thus trainable during the training -phase. The way in which the integral is approximated can be different, -currently on **PINA** we approximate it using a simple sum, as suggested -by the authors. Thus, given :math:`\{\mathbf{x}_i\}_{i=1}^{n}` points in -:math:`\mathbb{R}^N` of the input function mapped on the -:math:`\mathcal{X}` filter domain, we approximate the above equation as: - -.. math:: - - - \mathcal{I}_{\rm{out}}(\mathbf{\tilde{x}}_i) = \sum_{{\mathbf{x}_i}\in\mathcal{X}} \mathcal{I}(\mathbf{x}_i + \mathbf{\tau}) \cdot \mathcal{K}(\mathbf{x}_i), - -where :math:`\mathbf{\tau} \in \mathcal{S}`, with :math:`\mathcal{S}` -the set of available strides, corresponds to the current stride position -of the filter, and :math:`\mathbf{\tilde{x}}_i` points are obtained by -taking the centroid of the filter position mapped on the :math:`\Omega` -domain. - -We will now try to pratically see how to work with the filter. From the -above definition we see that what is needed is: 1. A domain and a -function defined on that domain (the input) 2. A stride, corresponding -to the positions where the filter needs to be :math:`\rightarrow` -``stride`` variable in ``ContinuousConv`` 3. The filter rectangular -domain :math:`\rightarrow` ``filter_dim`` variable in ``ContinuousConv`` - -Input function -~~~~~~~~~~~~~~ - -The input function for the continuous filter is defined as a tensor of -shape: - -.. math:: [B \times N_{in} \times N \times D] - -\ where :math:`B` is the batch_size, :math:`N_{in}` is the number of -input fields, :math:`N` the number of points in the mesh, :math:`D` the -dimension of the problem. In particular: \* :math:`D` is the number of -spatial variables + 1. The last column must contain the field value. For -example for 2D problems :math:`D=3` and the tensor will be something -like ``[first coordinate, second coordinate, field value]`` \* -:math:`N_{in}` represents the number of vectorial function presented. -For example a vectorial function :math:`f = [f_1, f_2]` will have -:math:`N_{in}=2` - -Let’s see an example to clear the ideas. We will be verbose to explain -in details the input form. We wish to create the function: - -.. math:: - - - f(x, y) = [\sin(\pi x) \sin(\pi y), -\sin(\pi x) \sin(\pi y)] \quad (x,y)\in[0,1]\times[0,1] - -using a batch size of one. - -.. code:: ipython3 - - # batch size fixed to 1 - batch_size = 1 - - # points in the mesh fixed to 200 - N = 200 - - # vectorial 2 dimensional function, number_input_fileds=2 - number_input_fileds = 2 - - # 2 dimensional spatial variables, D = 2 + 1 = 3 - D = 3 - - # create the function f domain as random 2d points in [0, 1] - domain = torch.rand(size=(batch_size, number_input_fileds, N, D-1)) - print(f"Domain has shape: {domain.shape}") - - # create the functions - pi = torch.acos(torch.tensor([-1.])) # pi value - f1 = torch.sin(pi * domain[:, 0, :, 0]) * torch.sin(pi * domain[:, 0, :, 1]) - f2 = - torch.sin(pi * domain[:, 1, :, 0]) * torch.sin(pi * domain[:, 1, :, 1]) - - # stacking the input domain and field values - data = torch.empty(size=(batch_size, number_input_fileds, N, D)) - data[..., :-1] = domain # copy the domain - data[:, 0, :, -1] = f1 # copy first field value - data[:, 1, :, -1] = f1 # copy second field value - print(f"Filter input data has shape: {data.shape}") - - -.. parsed-literal:: - - Domain has shape: torch.Size([1, 2, 200, 2]) - Filter input data has shape: torch.Size([1, 2, 200, 3]) - - -Stride -~~~~~~ - -The stride is passed as a dictionary ``stride`` which tells the filter -where to go. Here is an example for the :math:`[0,1]\times[0,5]` domain: - -.. code:: python - - # stride definition - stride = {"domain": [1, 5], - "start": [0, 0], - "jump": [0.1, 0.3], - "direction": [1, 1], - } - -This tells the filter: - -1. ``domain``: square domain (the only implemented) :math:`[0,1]\times[0,5]`. The minimum value is always zero, - while the maximum is specified by the user -2. ``start``: start position - of the filter, coordinate :math:`(0, 0)` -3. ``jump``: the jumps of the - centroid of the filter to the next position :math:`(0.1, 0.3)` -4. ``direction``: the directions of the jump, with ``1 = right``, - ``0 = no jump``,\ ``-1 = left`` with respect to the current position - -**Note** - -We are planning to release the possibility to directly pass a list of -possible strides! - -Filter definition -~~~~~~~~~~~~~~~~~ - -Having defined all the previous blocks we are able to construct the -continuous filter. Suppose we would like to get an ouput with only one field, and let us -fix the filter dimension to be :math:`[0.1, 0.1]`. - -.. code:: ipython3 - - # filter dim - filter_dim = [0.1, 0.1] - - # stride - stride = {"domain": [1, 1], - "start": [0, 0], - "jump": [0.08, 0.08], - "direction": [1, 1], - } - - # creating the filter - cConv = ContinuousConvBlock(input_numb_field=number_input_fileds, - output_numb_field=1, - filter_dim=filter_dim, - stride=stride) - - -That’s it! In just one line of code we have created the continuous -convolutional filter. By default the ``pina.model.FeedForward`` neural -network is intitialised, more on the -`documentation `__. In -case the mesh doesn’t change during training we can set the ``optimize`` -flag equals to ``True``, to exploit optimizations for finding the points -to convolve. - -.. code:: ipython3 - - # creating the filter + optimization - cConv = ContinuousConvBlock(input_numb_field=number_input_fileds, - output_numb_field=1, - filter_dim=filter_dim, - stride=stride, - optimize=True) - - -Let’s try to do a forward pass - -.. code:: ipython3 - - print(f"Filter input data has shape: {data.shape}") - - #input to the filter - output = cConv(data) - - print(f"Filter output data has shape: {output.shape}") - - -.. parsed-literal:: - - Filter input data has shape: torch.Size([1, 2, 200, 3]) - Filter output data has shape: torch.Size([1, 1, 169, 3]) - - -If we don’t want to use the default ``FeedForward`` neural network, we -can pass a specified torch model in the ``model`` keyword as follow: - -.. code:: ipython3 - - class SimpleKernel(torch.nn.Module): - def __init__(self) -> None: - super().__init__() - self. model = torch.nn.Sequential( - torch.nn.Linear(2, 20), - torch.nn.ReLU(), - torch.nn.Linear(20, 20), - torch.nn.ReLU(), - torch.nn.Linear(20, 1)) - - def forward(self, x): - return self.model(x) - - - cConv = ContinuousConvBlock(input_numb_field=number_input_fileds, - output_numb_field=1, - filter_dim=filter_dim, - stride=stride, - optimize=True, - model=SimpleKernel) - - -Notice that we pass the class and not an already built object! - -Building a MNIST Classifier ---------------------------- - -Let’s see how we can build a MNIST classifier using a continuous -convolutional filter. We will use the MNIST dataset from PyTorch. In -order to keep small training times we use only 6000 samples for training -and 1000 samples for testing. - -.. code:: ipython3 - - from torch.utils.data import DataLoader, SubsetRandomSampler - - numb_training = 6000 # get just 6000 images for training - numb_testing= 1000 # get just 1000 images for training - seed = 111 # for reproducibility - batch_size = 8 # setting batch size - - # setting the seed - torch.manual_seed(seed) - - # downloading the dataset - train_data = torchvision.datasets.MNIST('./data/', train=True, download=True, - transform=torchvision.transforms.Compose([ - torchvision.transforms.ToTensor(), - torchvision.transforms.Normalize( - (0.1307,), (0.3081,)) - ])) - subsample_train_indices = torch.randperm(len(train_data))[:numb_training] - train_loader = DataLoader(train_data, batch_size=batch_size, - sampler=SubsetRandomSampler(subsample_train_indices)) - - test_data = torchvision.datasets.MNIST('./data/', train=False, download=True, - transform=torchvision.transforms.Compose([ - torchvision.transforms.ToTensor(), - torchvision.transforms.Normalize( - (0.1307,), (0.3081,)) - ])) - subsample_test_indices = torch.randperm(len(train_data))[:numb_testing] - test_loader = DataLoader(train_data, batch_size=batch_size, - sampler=SubsetRandomSampler(subsample_train_indices)) - -Let’s now build a simple classifier. The MNIST dataset is composed by -vectors of shape ``[batch, 1, 28, 28]``, but we can image them as one -field functions where the pixels :math:`ij` are the coordinate -:math:`x=i, y=j` in a :math:`[0, 27]\times[0,27]` domain, and the pixels -value are the field values. We just need a function to transform the -regular tensor in a tensor compatible for the continuous filter: - -.. code:: ipython3 - - def transform_input(x): - batch_size = x.shape[0] - dim_grid = tuple(x.shape[:-3:-1]) - - # creating the n dimensional mesh grid for a single channel image - values_mesh = [torch.arange(0, dim).float() for dim in dim_grid] - mesh = torch.meshgrid(values_mesh) - coordinates_mesh = [x.reshape(-1, 1) for x in mesh] - coordinates = torch.cat(coordinates_mesh, dim=1).unsqueeze( - 0).repeat((batch_size, 1, 1)).unsqueeze(1) - - return torch.cat((coordinates, x.flatten(2).unsqueeze(-1)), dim=-1) - - - # let's try it out - image, s = next(iter(train_loader)) - print(f"Original MNIST image shape: {image.shape}") - - image_transformed = transform_input(image) - print(f"Transformed MNIST image shape: {image_transformed.shape}") - - - -.. parsed-literal:: - - Original MNIST image shape: torch.Size([8, 1, 28, 28]) - Transformed MNIST image shape: torch.Size([8, 1, 784, 3]) - - -We can now build a simple classifier! We will use just one convolutional -filter followed by a feedforward neural network - -.. code:: ipython3 - - # setting the seed - torch.manual_seed(seed) - - class ContinuousClassifier(torch.nn.Module): - def __init__(self): - super().__init__() - - # number of classes for classification - numb_class = 10 - - # convolutional block - self.convolution = ContinuousConvBlock(input_numb_field=1, - output_numb_field=4, - stride={"domain": [27, 27], - "start": [0, 0], - "jumps": [4, 4], - "direction": [1, 1.], - }, - filter_dim=[4, 4], - optimize=True) - # feedforward net - self.nn = FeedForward(input_dimensions=196, - output_dimensions=numb_class, - layers=[120, 64], - func=torch.nn.ReLU) - - def forward(self, x): - # transform input + convolution - x = transform_input(x) - x = self.convolution(x) - # feed forward classification - return self.nn(x[..., -1].flatten(1)) - - - net = ContinuousClassifier() - -Let’s try to train it using a simple pytorch training loop. We train for -juts 1 epoch using Adam optimizer with a :math:`0.001` learning rate. - -.. code:: ipython3 - - # setting the seed - torch.manual_seed(seed) - - # optimizer and loss function - optimizer = torch.optim.Adam(net.parameters(), lr=0.001) - criterion = torch.nn.CrossEntropyLoss() - - for epoch in range(1): # loop over the dataset multiple times - - running_loss = 0.0 - for i, data in enumerate(train_loader, 0): - # get the inputs; data is a list of [inputs, labels] - inputs, labels = data - - # zero the parameter gradients - optimizer.zero_grad() - - # forward + backward + optimize - outputs = net(inputs) - loss = criterion(outputs, labels) - loss.backward() - optimizer.step() - - # print statistics - running_loss += loss.item() - if i % 50 == 49: - print( - f'batch [{i + 1}/{numb_training//batch_size}] loss[{running_loss / 500:.3f}]') - running_loss = 0.0 - - -.. parsed-literal:: - - batch [50/750] loss[0.161] - batch [100/750] loss[0.073] - batch [150/750] loss[0.063] - batch [200/750] loss[0.051] - batch [250/750] loss[0.044] - batch [300/750] loss[0.050] - batch [350/750] loss[0.053] - batch [400/750] loss[0.049] - batch [450/750] loss[0.046] - batch [500/750] loss[0.034] - batch [550/750] loss[0.036] - batch [600/750] loss[0.040] - batch [650/750] loss[0.028] - batch [700/750] loss[0.040] - batch [750/750] loss[0.040] - - -Let’s see the performance on the train set! - -.. code:: ipython3 - - correct = 0 - total = 0 - with torch.no_grad(): - for data in test_loader: - images, labels = data - # calculate outputs by running images through the network - outputs = net(images) - # the class with the highest energy is what we choose as prediction - _, predicted = torch.max(outputs.data, 1) - total += labels.size(0) - correct += (predicted == labels).sum().item() - - print( - f'Accuracy of the network on the 1000 test images: {(correct / total):.3%}') - - - -.. parsed-literal:: - - Accuracy of the network on the 1000 test images: 92.733% - - -As we can see we have very good performance for having traing only for 1 -epoch! Nevertheless, we are still using structured data… Let’s see how -we can build an autoencoder for unstructured data now. - -Building a Continuous Convolutional Autoencoder ------------------------------------------------ - -Just as toy problem, we will now build an autoencoder for the following -function :math:`f(x,y)=\sin(\pi x)\sin(\pi y)` on the unit circle domain -centered in :math:`(0.5, 0.5)`. We will also see the ability to -up-sample (once trained) the results without retraining. Let’s first -create the input and visualize it, we will use firstly a mesh of -:math:`100` points. - -.. code:: ipython3 - - # create inputs - def circle_grid(N=100): - """Generate points withing a unit 2D circle centered in (0.5, 0.5) - - :param N: number of points - :type N: float - :return: [x, y] array of points - :rtype: torch.tensor - """ - - PI = torch.acos(torch.zeros(1)).item() * 2 - R = 0.5 - centerX = 0.5 - centerY = 0.5 - - r = R * torch.sqrt(torch.rand(N)) - theta = torch.rand(N) * 2 * PI - - x = centerX + r * torch.cos(theta) - y = centerY + r * torch.sin(theta) - - return torch.stack([x, y]).T - - # create the grid - grid = circle_grid(500) - - # create input - input_data = torch.empty(size=(1, 1, grid.shape[0], 3)) - input_data[0, 0, :, :-1] = grid - input_data[0, 0, :, -1] = torch.sin(pi * grid[:, 0]) * torch.sin(pi * grid[:, 1]) - - # visualize data - plt.title("Training sample with 500 points") - plt.scatter(grid[:, 0], grid[:, 1], c=input_data[0, 0, :, -1]) - plt.colorbar() - plt.show() - - - - -.. image:: tutorial_files/tutorial_32_0.png - - -Let’s now build a simple autoencoder using the continuous convolutional -filter. The data is clearly unstructured and a simple convolutional -filter might not work without projecting or interpolating first. Let’s -first build and ``Encoder`` and ``Decoder`` class, and then a -``Autoencoder`` class that contains both. - -.. code:: ipython3 - - class Encoder(torch.nn.Module): - def __init__(self, hidden_dimension): - super().__init__() - - # convolutional block - self.convolution = ContinuousConvBlock(input_numb_field=1, - output_numb_field=2, - stride={"domain": [1, 1], - "start": [0, 0], - "jumps": [0.05, 0.05], - "direction": [1, 1.], - }, - filter_dim=[0.15, 0.15], - optimize=True) - # feedforward net - self.nn = FeedForward(input_dimensions=400, - output_dimensions=hidden_dimension, - layers=[240, 120]) - - def forward(self, x): - # convolution - x = self.convolution(x) - # feed forward pass - return self.nn(x[..., -1]) - - - class Decoder(torch.nn.Module): - def __init__(self, hidden_dimension): - super().__init__() - - # convolutional block - self.convolution = ContinuousConvBlock(input_numb_field=2, - output_numb_field=1, - stride={"domain": [1, 1], - "start": [0, 0], - "jumps": [0.05, 0.05], - "direction": [1, 1.], - }, - filter_dim=[0.15, 0.15], - optimize=True) - # feedforward net - self.nn = FeedForward(input_dimensions=hidden_dimension, - output_dimensions=400, - layers=[120, 240]) - - def forward(self, weights, grid): - # feed forward pass - x = self.nn(weights) - # transpose convolution - return torch.sigmoid(self.convolution.transpose(x, grid)) - - -Very good! Notice that in the ``Decoder`` class in the ``forward`` pass -we have used the ``.transpose()`` method of the -``ContinuousConvolution`` class. This method accepts the ``weights`` for -upsampling and the ``grid`` on where to upsample. Let’s now build the -autoencoder! We set the hidden dimension in the ``hidden_dimension`` -variable. We apply the sigmoid on the output since the field value is -between :math:`[0, 1]`. - -.. code:: ipython3 - - class Autoencoder(torch.nn.Module): - def __init__(self, hidden_dimension=10): - super().__init__() - - self.encoder = Encoder(hidden_dimension) - self.decoder = Decoder(hidden_dimension) - - def forward(self, x): - # saving grid for later upsampling - grid = x.clone().detach() - # encoder - weights = self.encoder(x) - # decoder - out = self.decoder(weights, grid) - return out - - net = Autoencoder() - -Let’s now train the autoencoder, minimizing the mean square error loss -and optimizing using Adam. We use the ``SupervisedSolver`` as solver, -and the problem is a simple problem created by inheriting from -``AbstractProblem``. It takes approximately two minutes to train on CPU. - -.. code:: ipython3 - - # define the problem - class CircleProblem(AbstractProblem): - input_variables = ['x', 'y', 'f'] - output_variables = input_variables - conditions = {'data' : Condition(input_points=LabelTensor(input_data, input_variables), output_points=LabelTensor(input_data, output_variables))} - - # define the solver - solver = SupervisedSolver(problem=CircleProblem(), model=net, loss=torch.nn.MSELoss()) - - # train - trainer = Trainer(solver, max_epochs=150, accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional) - trainer.train() - - -.. parsed-literal:: - - `Trainer.fit` stopped: `max_epochs=150` reached. - - -Let’s visualize the two solutions side by side! - -.. code:: ipython3 - - net.eval() - - # get output and detach from computational graph for plotting - output = net(input_data).detach() - - # visualize data - fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 3)) - pic1 = axes[0].scatter(grid[:, 0], grid[:, 1], c=input_data[0, 0, :, -1]) - axes[0].set_title("Real") - fig.colorbar(pic1) - plt.subplot(1, 2, 2) - pic2 = axes[1].scatter(grid[:, 0], grid[:, 1], c=output[0, 0, :, -1]) - axes[1].set_title("Autoencoder") - fig.colorbar(pic2) - plt.tight_layout() - plt.show() - - - - -.. image:: tutorial_files/tutorial_40_0.png - - -As we can see the two are really similar! We can compute the :math:`l_2` -error quite easily as well: - -.. code:: ipython3 - - def l2_error(input_, target): - return torch.linalg.norm(input_-target, ord=2)/torch.linalg.norm(input_, ord=2) - - - print(f'l2 error: {l2_error(input_data[0, 0, :, -1], output[0, 0, :, -1]):.2%}') - - -.. parsed-literal:: - - l2 error: 4.32% - - -More or less :math:`4\%` in :math:`l_2` error, which is really low -considering the fact that we use just **one** convolutional layer and a -simple feedforward to decrease the dimension. Let’s see now some -peculiarity of the filter. - -Filter for upsampling -~~~~~~~~~~~~~~~~~~~~~ - -Suppose we have already the hidden dimension and we want to upsample on -a differen grid with more points. Let’s see how to do it: - -.. code:: ipython3 - - # setting the seed - torch.manual_seed(seed) - - grid2 = circle_grid(1500) # triple number of points - input_data2 = torch.zeros(size=(1, 1, grid2.shape[0], 3)) - input_data2[0, 0, :, :-1] = grid2 - input_data2[0, 0, :, -1] = torch.sin(pi * - grid2[:, 0]) * torch.sin(pi * grid2[:, 1]) - - # get the hidden dimension representation from original input - latent = net.encoder(input_data) - - # upsample on the second input_data2 - output = net.decoder(latent, input_data2).detach() - - # show the picture - fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 3)) - pic1 = axes[0].scatter(grid2[:, 0], grid2[:, 1], c=input_data2[0, 0, :, -1]) - axes[0].set_title("Real") - fig.colorbar(pic1) - plt.subplot(1, 2, 2) - pic2 = axes[1].scatter(grid2[:, 0], grid2[:, 1], c=output[0, 0, :, -1]) - axes[1].set_title("Up-sampling") - fig.colorbar(pic2) - plt.tight_layout() - plt.show() - - - - -.. image:: tutorial_files/tutorial_45_0.png - - -As we can see we have a very good approximation of the original -function, even thought some noise is present. Let’s calculate the error -now: - -.. code:: ipython3 - - print(f'l2 error: {l2_error(input_data2[0, 0, :, -1], output[0, 0, :, -1]):.2%}') - - -.. parsed-literal:: - - l2 error: 8.49% - - -Autoencoding at different resolution -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -In the previous example we already had the hidden dimension (of original -input) and we used it to upsample. Sometimes however we have a more fine -mesh solution and we simply want to encode it. This can be done without -retraining! This procedure can be useful in case we have many points in -the mesh and just a smaller part of them are needed for training. Let’s -see the results of this: - -.. code:: ipython3 - - # setting the seed - torch.manual_seed(seed) - - grid2 = circle_grid(3500) # very fine mesh - input_data2 = torch.zeros(size=(1, 1, grid2.shape[0], 3)) - input_data2[0, 0, :, :-1] = grid2 - input_data2[0, 0, :, -1] = torch.sin(pi * - grid2[:, 0]) * torch.sin(pi * grid2[:, 1]) - - # get the hidden dimension representation from more fine mesh input - latent = net.encoder(input_data2) - - # upsample on the second input_data2 - output = net.decoder(latent, input_data2).detach() - - # show the picture - fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 3)) - pic1 = axes[0].scatter(grid2[:, 0], grid2[:, 1], c=input_data2[0, 0, :, -1]) - axes[0].set_title("Real") - fig.colorbar(pic1) - plt.subplot(1, 2, 2) - pic2 = axes[1].scatter(grid2[:, 0], grid2[:, 1], c=output[0, 0, :, -1]) - axes[1].set_title("Autoencoder not re-trained") - fig.colorbar(pic2) - plt.tight_layout() - plt.show() - - # calculate l2 error - print( - f'l2 error: {l2_error(input_data2[0, 0, :, -1], output[0, 0, :, -1]):.2%}') - - - - -.. image:: tutorial_files/tutorial_49_0.png - - -.. parsed-literal:: - - l2 error: 8.59% - - -What’s next? ------------- - -We have shown the basic usage of a convolutional filter. There are -additional extensions possible: - -1. Train using Physics Informed strategies - -2. Use the filter to build an unstructured convolutional autoencoder for - reduced order modelling - -3. 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Importing -``scipy`` is needed for input output operations. - -.. code:: ipython3 - - ## routine needed to run the notebook on Google Colab - try: - import google.colab - IN_COLAB = True - except: - IN_COLAB = False - if IN_COLAB: - !pip install "pina-mathlab" - !pip install scipy - # get the data - !wget https://github.com/mathLab/PINA/raw/refs/heads/master/tutorials/tutorial5/Data_Darcy.mat - - - # !pip install scipy # install scipy - from scipy import io - import torch - from pina.model import FNO, FeedForward # let's import some models - from pina import Condition, LabelTensor - from pina.solvers import SupervisedSolver - from pina.trainer import Trainer - from pina.problem import AbstractProblem - import matplotlib.pyplot as plt - plt.style.use('tableau-colorblind10') - -Data Generation ---------------- - -We will focus on solving the a specfic PDE, the **Darcy Flow** equation. -The Darcy PDE is a second order, elliptic PDE with the following form: - -.. math:: - - - -\nabla\cdot(k(x, y)\nabla u(x, y)) = f(x) \quad (x, y) \in D. - -Specifically, :math:`u` is the flow pressure, :math:`k` is the -permeability field and :math:`f` is the forcing function. The Darcy flow -can parameterize a variety of systems including flow through porous -media, elastic materials and heat conduction. Here you will define the -domain as a 2D unit square Dirichlet boundary conditions. The dataset is -taken from the authors original reference. - -.. code:: ipython3 - - # download the dataset - data = io.loadmat("Data_Darcy.mat") - - # extract data (we use only 100 data for train) - k_train = LabelTensor(torch.tensor(data['k_train'], dtype=torch.float).unsqueeze(-1), ['u0']) - u_train = LabelTensor(torch.tensor(data['u_train'], dtype=torch.float).unsqueeze(-1), ['u']) - k_test = LabelTensor(torch.tensor(data['k_test'], dtype=torch.float).unsqueeze(-1), ['u0']) - u_test= LabelTensor(torch.tensor(data['u_test'], dtype=torch.float).unsqueeze(-1), ['u']) - x = torch.tensor(data['x'], dtype=torch.float)[0] - y = torch.tensor(data['y'], dtype=torch.float)[0] - -Let’s visualize some data - -.. code:: ipython3 - - plt.subplot(1, 2, 1) - plt.title('permeability') - plt.imshow(k_train.squeeze(-1)[0]) - plt.subplot(1, 2, 2) - plt.title('field solution') - plt.imshow(u_train.squeeze(-1)[0]) - plt.show() - - - -.. image:: tutorial_files/tutorial_6_0.png - - -We now create the neural operator class. It is a very simple class, -inheriting from ``AbstractProblem``. - -.. code:: ipython3 - - class NeuralOperatorSolver(AbstractProblem): - input_variables = k_train.labels - output_variables = u_train.labels - conditions = {'data' : Condition(input_points=k_train, - output_points=u_train)} - - # make problem - problem = NeuralOperatorSolver() - -Solving the problem with a FeedForward Neural Network ------------------------------------------------------ - -We will first solve the problem using a Feedforward neural network. We -will use the ``SupervisedSolver`` for solving the problem, since we are -training using supervised learning. - -.. code:: ipython3 - - # make model - model = FeedForward(input_dimensions=1, output_dimensions=1) - - - # make solver - solver = SupervisedSolver(problem=problem, model=model) - - # make the trainer and train - trainer = Trainer(solver=solver, max_epochs=10, accelerator='cpu', enable_model_summary=False, batch_size=10) # we train on CPU and avoid model summary at beginning of training (optional) - trainer.train() - - - -.. parsed-literal:: - - GPU available: False, used: False - TPU available: False, using: 0 TPU cores - IPU available: False, using: 0 IPUs - HPU available: False, using: 0 HPUs - - -.. parsed-literal:: - - Epoch 9: : 100it [00:00, 357.28it/s, v_num=1, mean_loss=0.108] - -.. parsed-literal:: - - `Trainer.fit` stopped: `max_epochs=10` reached. - - -.. parsed-literal:: - - Epoch 9: : 100it [00:00, 354.81it/s, v_num=1, mean_loss=0.108] - - -The final loss is pretty high… We can calculate the error by importing -``LpLoss``. - -.. code:: ipython3 - - from pina.loss import LpLoss - - # make the metric - metric_err = LpLoss(relative=True) - - - err = float(metric_err(u_train.squeeze(-1), solver.neural_net(k_train).squeeze(-1)).mean())*100 - print(f'Final error training {err:.2f}%') - - err = float(metric_err(u_test.squeeze(-1), solver.neural_net(k_test).squeeze(-1)).mean())*100 - print(f'Final error testing {err:.2f}%') - - -.. parsed-literal:: - - Final error training 56.04% - Final error testing 56.01% - - -Solving the problem with a Fuorier Neural Operator (FNO) --------------------------------------------------------- - -We will now move to solve the problem using a FNO. Since we are learning -operator this approach is better suited, as we shall see. - -.. code:: ipython3 - - # make model - lifting_net = torch.nn.Linear(1, 24) - projecting_net = torch.nn.Linear(24, 1) - model = FNO(lifting_net=lifting_net, - projecting_net=projecting_net, - n_modes=8, - dimensions=2, - inner_size=24, - padding=8) - - - # make solver - solver = SupervisedSolver(problem=problem, model=model) - - # make the trainer and train - trainer = Trainer(solver=solver, max_epochs=10, accelerator='cpu', enable_model_summary=False, batch_size=10) # we train on CPU and avoid model summary at beginning of training (optional) - trainer.train() - - - -.. parsed-literal:: - - GPU available: False, used: False - TPU available: False, using: 0 TPU cores - IPU available: False, using: 0 IPUs - HPU available: False, using: 0 HPUs - - -.. parsed-literal:: - - Epoch 0: : 0it [00:00, ?it/s]Epoch 9: : 100it [00:02, 47.76it/s, v_num=4, mean_loss=0.00106] - -.. parsed-literal:: - - `Trainer.fit` stopped: `max_epochs=10` reached. - - -.. parsed-literal:: - - Epoch 9: : 100it [00:02, 47.65it/s, v_num=4, mean_loss=0.00106] - - -We can clearly see that the final loss is lower. Let’s see in testing.. -Notice that the number of parameters is way higher than a -``FeedForward`` network. We suggest to use GPU or TPU for a speed up in -training, when many data samples are used. - -.. code:: ipython3 - - err = float(metric_err(u_train.squeeze(-1), solver.neural_net(k_train).squeeze(-1)).mean())*100 - print(f'Final error training {err:.2f}%') - - err = float(metric_err(u_test.squeeze(-1), solver.neural_net(k_test).squeeze(-1)).mean())*100 - print(f'Final error testing {err:.2f}%') - - -.. parsed-literal:: - - Final error training 4.83% - Final error testing 5.16% - - -As we can see the loss is way lower! - -What’s next? ------------- - -We have made a very simple example on how to use the ``FNO`` for -learning neural operator. Currently in **PINA** we implement 1D/2D/3D -cases. We suggest to extend the tutorial using more complex problems and -train for longer, to see the full potential of neural operators. diff --git a/docs/source/_rst/tutorials/tutorial5/tutorial_files/tutorial_6_0.png b/docs/source/_rst/tutorials/tutorial5/tutorial_files/tutorial_6_0.png deleted file mode 100644 index fec83e2c2..000000000 Binary files a/docs/source/_rst/tutorials/tutorial5/tutorial_files/tutorial_6_0.png and /dev/null differ diff --git a/docs/source/_rst/tutorials/tutorial6/tutorial.rst b/docs/source/_rst/tutorials/tutorial6/tutorial.rst deleted file mode 100644 index d021adf8b..000000000 --- a/docs/source/_rst/tutorials/tutorial6/tutorial.rst +++ /dev/null @@ -1,330 +0,0 @@ -Tutorial: Building custom geometries with PINA ``Location`` class -================================================================= - -|Open In Colab| - -.. |Open In Colab| image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial6/tutorial.ipynb - -In this tutorial we will show how to use geometries in PINA. -Specifically, the tutorial will include how to create geometries and how -to visualize them. The topics covered are: - -- Creating CartesianDomains and EllipsoidDomains -- Getting the Union and Difference of Geometries -- Sampling points in the domain (and visualize them) - -We import the relevant modules first. - -.. code:: ipython3 - - ## routine needed to run the notebook on Google Colab - try: - import google.colab - IN_COLAB = True - except: - IN_COLAB = False - if IN_COLAB: - !pip install "pina-mathlab" - - import matplotlib.pyplot as plt - plt.style.use('tableau-colorblind10') - from pina.geometry import EllipsoidDomain, Difference, CartesianDomain, Union, SimplexDomain - from pina.label_tensor import LabelTensor - - def plot_scatter(ax, pts, title): - ax.title.set_text(title) - ax.scatter(pts.extract('x'), pts.extract('y'), color='blue', alpha=0.5) - -Built-in Geometries -------------------- - -We will create one cartesian and two ellipsoids. For the sake of -simplicity, we show here the 2-dimensional, but it’s trivial the -extension to 3D (and higher) cases. The geometries allows also the -generation of samples belonging to the boundary. So, we will create one -ellipsoid with the border and one without. - -.. code:: ipython3 - - cartesian = CartesianDomain({'x': [0, 2], 'y': [0, 2]}) - ellipsoid_no_border = EllipsoidDomain({'x': [1, 3], 'y': [1, 3]}) - ellipsoid_border = EllipsoidDomain({'x': [2, 4], 'y': [2, 4]}, sample_surface=True) - -The ``{'x': [0, 2], 'y': [0, 2]}`` are the bounds of the -``CartesianDomain`` being created. - -To visualize these shapes, we need to sample points on them. We will use -the ``sample`` method of the ``CartesianDomain`` and ``EllipsoidDomain`` -classes. This method takes a ``n`` argument which is the number of -points to sample. It also takes different modes to sample such as -random. - -.. code:: ipython3 - - cartesian_samples = cartesian.sample(n=1000, mode='random') - ellipsoid_no_border_samples = ellipsoid_no_border.sample(n=1000, mode='random') - ellipsoid_border_samples = ellipsoid_border.sample(n=1000, mode='random') - -We can see the samples of each of the geometries to see what we are -working with. - -.. code:: ipython3 - - print(f"Cartesian Samples: {cartesian_samples}") - print(f"Ellipsoid No Border Samples: {ellipsoid_no_border_samples}") - print(f"Ellipsoid Border Samples: {ellipsoid_border_samples}") - - -.. parsed-literal:: - - Cartesian Samples: labels(['x', 'y']) - LabelTensor([[[0.2300, 1.6698]], - [[1.7785, 0.4063]], - [[1.5143, 1.8979]], - ..., - [[0.0905, 1.4660]], - [[0.8176, 1.7357]], - [[0.0475, 0.0170]]]) - Ellipsoid No Border Samples: labels(['x', 'y']) - LabelTensor([[[1.9341, 2.0182]], - [[1.5503, 1.8426]], - [[2.0392, 1.7597]], - ..., - [[1.8976, 2.2859]], - [[1.8015, 2.0012]], - [[2.2713, 2.2355]]]) - Ellipsoid Border Samples: labels(['x', 'y']) - LabelTensor([[[3.3413, 3.9400]], - [[3.9573, 2.7108]], - [[3.8341, 2.4484]], - ..., - [[2.7251, 2.0385]], - [[3.8654, 2.4990]], - [[3.2292, 3.9734]]]) - - -Notice how these are all ``LabelTensor`` objects. You can read more -about these in the -`documentation `__. -At a very high level, they are tensors where each element in a tensor -has a label that we can access by doing ``.labels``. We can -also access the values of the tensor by doing -``.extract(['x'])``. - -We are now ready to visualize the samples using matplotlib. - -.. code:: ipython3 - - fig, axs = plt.subplots(1, 3, figsize=(16, 4)) - pts_list = [cartesian_samples, ellipsoid_no_border_samples, ellipsoid_border_samples] - title_list = ['Cartesian Domain', 'Ellipsoid Domain', 'Ellipsoid Border Domain'] - for ax, pts, title in zip(axs, pts_list, title_list): - plot_scatter(ax, pts, title) - - - -.. image:: tutorial_files/tutorial_10_0.png - - -We have now created, sampled, and visualized our first geometries! We -can see that the ``EllipsoidDomain`` with the border has a border around -it. We can also see that the ``EllipsoidDomain`` without the border is -just the ellipse. We can also see that the ``CartesianDomain`` is just a -square. - -Simplex Domain -~~~~~~~~~~~~~~ - -Among the built-in shapes, we quickly show here the usage of -``SimplexDomain``, which can be used for polygonal domains! - -.. code:: ipython3 - - import torch - spatial_domain = SimplexDomain( - [ - LabelTensor(torch.tensor([[0, 0]]), labels=["x", "y"]), - LabelTensor(torch.tensor([[1, 1]]), labels=["x", "y"]), - LabelTensor(torch.tensor([[0, 2]]), labels=["x", "y"]), - ] - ) - - spatial_domain2 = SimplexDomain( - [ - LabelTensor(torch.tensor([[ 0., -2.]]), labels=["x", "y"]), - LabelTensor(torch.tensor([[-.5, -.5]]), labels=["x", "y"]), - LabelTensor(torch.tensor([[-2., 0.]]), labels=["x", "y"]), - ] - ) - - pts = spatial_domain2.sample(100) - fig, axs = plt.subplots(1, 2, figsize=(16, 6)) - for domain, ax in zip([spatial_domain, spatial_domain2], axs): - pts = domain.sample(1000) - plot_scatter(ax, pts, 'Simplex Domain') - - - -.. image:: tutorial_files/tutorial_13_0.png - - -Boolean Operations ------------------- - -To create complex shapes we can use the boolean operations, for example -to merge two default geometries. We need to simply use the ``Union`` -class: it takes a list of geometries and returns the union of them. - -Let’s create three unions. Firstly, it will be a union of ``cartesian`` -and ``ellipsoid_no_border``. Next, it will be a union of -``ellipse_no_border`` and ``ellipse_border``. Lastly, it will be a union -of all three geometries. - -.. code:: ipython3 - - cart_ellipse_nb_union = Union([cartesian, ellipsoid_no_border]) - cart_ellipse_b_union = Union([cartesian, ellipsoid_border]) - three_domain_union = Union([cartesian, ellipsoid_no_border, ellipsoid_border]) - -We can of course sample points over the new geometries, by using the -``sample`` method as before. We highlihgt that the available sample -strategy here is only *random*. - -.. code:: ipython3 - - c_e_nb_u_points = cart_ellipse_nb_union.sample(n=2000, mode='random') - c_e_b_u_points = cart_ellipse_b_union.sample(n=2000, mode='random') - three_domain_union_points = three_domain_union.sample(n=3000, mode='random') - -We can plot the samples of each of the unions to see what we are working -with. - -.. code:: ipython3 - - fig, axs = plt.subplots(1, 3, figsize=(16, 4)) - pts_list = [c_e_nb_u_points, c_e_b_u_points, three_domain_union_points] - title_list = ['Cartesian with Ellipsoid No Border Union', 'Cartesian with Ellipsoid Border Union', 'Three Domain Union'] - for ax, pts, title in zip(axs, pts_list, title_list): - plot_scatter(ax, pts, title) - - - -.. image:: tutorial_files/tutorial_20_0.png - - -Now, we will find the differences of the geometries. We will find the -difference of ``cartesian`` and ``ellipsoid_no_border``. - -.. code:: ipython3 - - cart_ellipse_nb_difference = Difference([cartesian, ellipsoid_no_border]) - c_e_nb_d_points = cart_ellipse_nb_difference.sample(n=2000, mode='random') - - fig, ax = plt.subplots(1, 1, figsize=(8, 6)) - plot_scatter(ax, c_e_nb_d_points, 'Difference') - - - -.. image:: tutorial_files/tutorial_22_0.png - - -Create Custom Location ----------------------- - -We will take a look on how to create our own geometry. The one we will -try to make is a heart defined by the function - -.. math:: (x^2+y^2-1)^3-x^2y^3 \le 0 - -Let’s start by importing what we will need to create our own geometry -based on this equation. - -.. code:: ipython3 - - import torch - from pina import Location - from pina import LabelTensor - import random - -Next, we will create the ``Heart(Location)`` class and initialize it. - -.. code:: ipython3 - - class Heart(Location): - """Implementation of the Heart Domain.""" - - def __init__(self, sample_border=False): - super().__init__() - - -Because the ``Location`` class we are inherting from requires both a -``sample`` method and ``is_inside`` method, we will create them and just -add in “pass” for the moment. - -.. code:: ipython3 - - class Heart(Location): - """Implementation of the Heart Domain.""" - - def __init__(self, sample_border=False): - super().__init__() - - def is_inside(self): - pass - - def sample(self): - pass - -Now we have the skeleton for our ``Heart`` class. The ``sample`` -method is where most of the work is done so let’s fill it out. - -.. code:: ipython3 - - - class Heart(Location): - """Implementation of the Heart Domain.""" - - def __init__(self, sample_border=False): - super().__init__() - - def is_inside(self): - pass - - def sample(self, n, mode='random', variables='all'): - sampled_points = [] - - while len(sampled_points) < n: - x = torch.rand(1)*3.-1.5 - y = torch.rand(1)*3.-1.5 - if ((x**2 + y**2 - 1)**3 - (x**2)*(y**3)) <= 0: - sampled_points.append([x.item(), y.item()]) - - return LabelTensor(torch.tensor(sampled_points), labels=['x','y']) - -To create the Heart geometry we simply run: - -.. code:: ipython3 - - heart = Heart() - -To sample from the Heart geometry we simply run: - -.. code:: ipython3 - - pts_heart = heart.sample(1500) - - fig, ax = plt.subplots() - plot_scatter(ax, pts_heart, 'Heart Domain') - - - -.. image:: tutorial_files/tutorial_36_0.png - - -What’s next? ------------- - -We have made a very simple tutorial on how to build custom geometries -and use domain operation to compose base geometries. Now you can play -around with different geometries and build your own! diff --git a/docs/source/_rst/tutorials/tutorial6/tutorial_files/tutorial_10_0.png b/docs/source/_rst/tutorials/tutorial6/tutorial_files/tutorial_10_0.png deleted file mode 100644 index b253ffa17..000000000 Binary files a/docs/source/_rst/tutorials/tutorial6/tutorial_files/tutorial_10_0.png and /dev/null differ diff --git a/docs/source/_rst/tutorials/tutorial6/tutorial_files/tutorial_13_0.png b/docs/source/_rst/tutorials/tutorial6/tutorial_files/tutorial_13_0.png deleted file mode 100644 index a64e90b13..000000000 Binary files a/docs/source/_rst/tutorials/tutorial6/tutorial_files/tutorial_13_0.png and /dev/null differ diff --git a/docs/source/_rst/tutorials/tutorial6/tutorial_files/tutorial_20_0.png b/docs/source/_rst/tutorials/tutorial6/tutorial_files/tutorial_20_0.png deleted file mode 100644 index 42862ad68..000000000 Binary files a/docs/source/_rst/tutorials/tutorial6/tutorial_files/tutorial_20_0.png and /dev/null differ diff --git a/docs/source/_rst/tutorials/tutorial6/tutorial_files/tutorial_22_0.png b/docs/source/_rst/tutorials/tutorial6/tutorial_files/tutorial_22_0.png deleted file mode 100644 index 5a573bbdb..000000000 Binary files a/docs/source/_rst/tutorials/tutorial6/tutorial_files/tutorial_22_0.png and /dev/null differ diff --git a/docs/source/_rst/tutorials/tutorial6/tutorial_files/tutorial_36_0.png b/docs/source/_rst/tutorials/tutorial6/tutorial_files/tutorial_36_0.png deleted file mode 100644 index 85846024f..000000000 Binary files a/docs/source/_rst/tutorials/tutorial6/tutorial_files/tutorial_36_0.png and /dev/null differ diff --git a/docs/source/_rst/tutorials/tutorial7/tutorial.rst b/docs/source/_rst/tutorials/tutorial7/tutorial.rst deleted file mode 100644 index ac5ace30e..000000000 --- a/docs/source/_rst/tutorials/tutorial7/tutorial.rst +++ /dev/null @@ -1,240 +0,0 @@ -Tutorial: Resolution of an inverse problem -============================================ - -|Open In Colab| - -.. |Open In Colab| image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial7/tutorial.ipynb - -Introduction to the inverse problem -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -This tutorial shows how to solve an inverse Poisson problem with -Physics-Informed Neural Networks. The problem definition is that of a -Poisson problem with homogeneous boundary conditions and it reads: - -.. math:: - - \begin{equation} - \begin{cases} - \Delta u = e^{-2(x-\mu_1)^2-2(y-\mu_2)^2} \text{ in } \Omega\, ,\\ - u = 0 \text{ on }\partial \Omega,\\ - u(\mu_1, \mu_2) = \text{ data} - \end{cases} - \end{equation} - -where :math:`\Omega` is a square domain -:math:`[-2, 2] \times [-2, 2]`, and -:math:`\partial \Omega=\Gamma_1 \cup \Gamma_2 \cup \Gamma_3 \cup \Gamma_4` -is the union of the boundaries of the domain. - -This kind of problem, namely the “inverse problem”, has two main goals: - -* find the solution :math:`u` that satisfies the Poisson equation -* find the unknown parameters (:math:`\mu_1`, :math:`\mu_2`) that better fit some given data (third equation in the system above). - -In order to achieve both the goals we will need to define an -``InverseProblem`` in PINA. Let’s start with useful imports. - -.. code:: ipython3 - - ## routine needed to run the notebook on Google Colab - try: - import google.colab - IN_COLAB = True - except: - IN_COLAB = False - if IN_COLAB: - !pip install "pina-mathlab" - # get the data - !mkdir "data" - !wget "https://github.com/mathLab/PINA/raw/refs/heads/master/tutorials/tutorial7/data/pinn_solution_0.5_0.5" -O "data/pinn_solution_0.5_0.5" - !wget "https://github.com/mathLab/PINA/raw/refs/heads/master/tutorials/tutorial7/data/pts_0.5_0.5" -O "data/pts_0.5_0.5" - - - import matplotlib.pyplot as plt - plt.style.use('tableau-colorblind10') - import torch - from pytorch_lightning.callbacks import Callback - from pina.problem import SpatialProblem, InverseProblem - from pina.operators import laplacian - from pina.model import FeedForward - from pina.equation import Equation, FixedValue - from pina import Condition, Trainer - from pina.solvers import PINN - from pina.geometry import CartesianDomain - -Then, we import the pre-saved data, for (:math:`\mu_1`, -:math:`\mu_2`)=(:math:`0.5`, :math:`0.5`). These two values are the -optimal parameters that we want to find through the neural network -training. In particular, we import the ``input_points``\ (the spatial -coordinates), and the ``output_points`` (the corresponding :math:`u` -values evaluated at the ``input_points``). - -.. code:: ipython3 - - data_output = torch.load('data/pinn_solution_0.5_0.5').detach() - data_input = torch.load('data/pts_0.5_0.5') - -Moreover, let’s plot also the data points and the reference solution: -this is the expected output of the neural network. - -.. code:: ipython3 - - points = data_input.extract(['x', 'y']).detach().numpy() - truth = data_output.detach().numpy() - - plt.scatter(points[:, 0], points[:, 1], c=truth, s=8) - plt.axis('equal') - plt.colorbar() - plt.show() - - - -.. image:: tutorial_files/output_8_0.png - - -Inverse problem definition in PINA -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Then, we initialize the Poisson problem, that is inherited from the -``SpatialProblem`` and from the ``InverseProblem`` classes. We here have -to define all the variables, and the domain where our unknown parameters -(:math:`\mu_1`, :math:`\mu_2`) belong. Notice that the laplace equation -takes as inputs also the unknown variables, that will be treated as -parameters that the neural network optimizes during the training -process. - -.. code:: ipython3 - - ### Define ranges of variables - x_min = -2 - x_max = 2 - y_min = -2 - y_max = 2 - - class Poisson(SpatialProblem, InverseProblem): - ''' - Problem definition for the Poisson equation. - ''' - output_variables = ['u'] - spatial_domain = CartesianDomain({'x': [x_min, x_max], 'y': [y_min, y_max]}) - # define the ranges for the parameters - unknown_parameter_domain = CartesianDomain({'mu1': [-1, 1], 'mu2': [-1, 1]}) - - def laplace_equation(input_, output_, params_): - ''' - Laplace equation with a force term. - ''' - force_term = torch.exp( - - 2*(input_.extract(['x']) - params_['mu1'])**2 - - 2*(input_.extract(['y']) - params_['mu2'])**2) - delta_u = laplacian(output_, input_, components=['u'], d=['x', 'y']) - - return delta_u - force_term - - # define the conditions for the loss (boundary conditions, equation, data) - conditions = { - 'gamma1': Condition(location=CartesianDomain({'x': [x_min, x_max], - 'y': y_max}), - equation=FixedValue(0.0, components=['u'])), - 'gamma2': Condition(location=CartesianDomain({'x': [x_min, x_max], 'y': y_min - }), - equation=FixedValue(0.0, components=['u'])), - 'gamma3': Condition(location=CartesianDomain({'x': x_max, 'y': [y_min, y_max] - }), - equation=FixedValue(0.0, components=['u'])), - 'gamma4': Condition(location=CartesianDomain({'x': x_min, 'y': [y_min, y_max] - }), - equation=FixedValue(0.0, components=['u'])), - 'D': Condition(location=CartesianDomain({'x': [x_min, x_max], 'y': [y_min, y_max] - }), - equation=Equation(laplace_equation)), - 'data': Condition(input_points=data_input.extract(['x', 'y']), output_points=data_output) - } - - problem = Poisson() - -Then, we define the model of the neural network we want to use. Here we -used a model which impose hard constrains on the boundary conditions, as -also done in the Wave tutorial! - -.. code:: ipython3 - - model = FeedForward( - layers=[20, 20, 20], - func=torch.nn.Softplus, - output_dimensions=len(problem.output_variables), - input_dimensions=len(problem.input_variables) - ) - -After that, we discretize the spatial domain. - -.. code:: ipython3 - - problem.discretise_domain(20, 'grid', locations=['D'], variables=['x', 'y']) - problem.discretise_domain(1000, 'random', locations=['gamma1', 'gamma2', - 'gamma3', 'gamma4'], variables=['x', 'y']) - -Here, we define a simple callback for the trainer. We use this callback -to save the parameters predicted by the neural network during the -training. The parameters are saved every 100 epochs as ``torch`` tensors -in a specified directory (``tmp_dir`` in our case). The goal is to read -the saved parameters after training and plot their trend across the -epochs. - -.. code:: ipython3 - - # temporary directory for saving logs of training - tmp_dir = "tmp_poisson_inverse" - - class SaveParameters(Callback): - ''' - Callback to save the parameters of the model every 100 epochs. - ''' - def on_train_epoch_end(self, trainer, __): - if trainer.current_epoch % 100 == 99: - torch.save(trainer.solver.problem.unknown_parameters, '{}/parameters_epoch{}'.format(tmp_dir, trainer.current_epoch)) - -Then, we define the ``PINN`` object and train the solver using the -``Trainer``. - -.. code:: ipython3 - - ### train the problem with PINN - max_epochs = 5000 - pinn = PINN(problem, model, optimizer_kwargs={'lr':0.005}) - # define the trainer for the solver - trainer = Trainer(solver=pinn, accelerator='cpu', max_epochs=max_epochs, - default_root_dir=tmp_dir, callbacks=[SaveParameters()]) - trainer.train() - -One can now see how the parameters vary during the training by reading -the saved solution and plotting them. The plot shows that the parameters -stabilize to their true value before reaching the epoch :math:`1000`! - -.. code:: ipython3 - - epochs_saved = range(99, max_epochs, 100) - parameters = torch.empty((int(max_epochs/100), 2)) - for i, epoch in enumerate(epochs_saved): - params_torch = torch.load('{}/parameters_epoch{}'.format(tmp_dir, epoch)) - for e, var in enumerate(pinn.problem.unknown_variables): - parameters[i, e] = params_torch[var].data - - # Plot parameters - plt.close() - plt.plot(epochs_saved, parameters[:, 0], label='mu1', marker='o') - plt.plot(epochs_saved, parameters[:, 1], label='mu2', marker='s') - plt.ylim(-1, 1) - plt.grid() - plt.legend() - plt.xlabel('Epoch') - plt.ylabel('Parameter value') - plt.show() - - - -.. image:: tutorial_files/output_21_0.png - - diff --git a/docs/source/_rst/tutorials/tutorial7/tutorial_files/output_21_0.png b/docs/source/_rst/tutorials/tutorial7/tutorial_files/output_21_0.png deleted file mode 100644 index 39f313bf3..000000000 Binary files a/docs/source/_rst/tutorials/tutorial7/tutorial_files/output_21_0.png and /dev/null differ diff --git a/docs/source/_rst/tutorials/tutorial7/tutorial_files/output_8_0.png b/docs/source/_rst/tutorials/tutorial7/tutorial_files/output_8_0.png deleted file mode 100644 index 4f706c373..000000000 Binary files a/docs/source/_rst/tutorials/tutorial7/tutorial_files/output_8_0.png and /dev/null differ diff --git a/docs/source/_rst/tutorials/tutorial8/tutorial.rst b/docs/source/_rst/tutorials/tutorial8/tutorial.rst deleted file mode 100644 index 6be60b4e6..000000000 --- a/docs/source/_rst/tutorials/tutorial8/tutorial.rst +++ /dev/null @@ -1,403 +0,0 @@ -Tutorial: Reduced order model (POD-RBF or POD-NN) for parametric problems -========================================================================= - -|Open In Colab| - -.. |Open In Colab| image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial8/tutorial.ipynb - -The tutorial aims to show how to employ the **PINA** library in order to -apply a reduced order modeling technique [1]. Such methodologies have -several similarities with machine learning approaches, since the main -goal consists in predicting the solution of differential equations -(typically parametric PDEs) in a real-time fashion. - -In particular we are going to use the Proper Orthogonal Decomposition -with either Radial Basis Function Interpolation(POD-RBF) or Neural -Network (POD-NN) [2]. Here we basically perform a dimensional reduction -using the POD approach, and approximating the parametric solution -manifold (at the reduced space) using an interpolation (RBF) or a -regression technique (NN). In this example, we use a simple multilayer -perceptron, but the plenty of different architectures can be plugged as -well. - -References -^^^^^^^^^^ - -1. Rozza G., Stabile G., Ballarin F. (2022). Advanced Reduced Order - Methods and Applications in Computational Fluid Dynamics, Society for - Industrial and Applied Mathematics. -2. Hesthaven, J. S., & Ubbiali, S. (2018). Non-intrusive reduced order - modeling of nonlinear problems using neural networks. Journal of - Computational Physics, 363, 55-78. - -Let’s start with the necessary imports. It’s important to note the -minimum PINA version to run this tutorial is the ``0.1``. - -.. code:: ipython3 - - ## routine needed to run the notebook on Google Colab - try: - import google.colab - IN_COLAB = True - except: - IN_COLAB = False - if IN_COLAB: - !pip install "pina-mathlab" - - %matplotlib inline - - import matplotlib.pyplot as plt - plt.style.use('tableau-colorblind10') - import torch - import pina - - from pina.geometry import CartesianDomain - - from pina.problem import ParametricProblem - from pina.model.layers import PODBlock, RBFBlock - from pina import Condition, LabelTensor, Trainer - from pina.model import FeedForward - from pina.solvers import SupervisedSolver - - print(f'We are using PINA version {pina.__version__}') - - -.. parsed-literal:: - - We are using PINA version 0.1.1 - - -We exploit the `Smithers `__ library to -collect the parametric snapshots. In particular, we use the -``NavierStokesDataset`` class that contains a set of parametric -solutions of the Navier-Stokes equations in a 2D L-shape domain. The -parameter is the inflow velocity. The dataset is composed by 500 -snapshots of the velocity (along :math:`x`, :math:`y`, and the -magnitude) and pressure fields, and the corresponding parameter values. - -To visually check the snapshots, let’s plot also the data points and the -reference solution: this is the expected output of our model. - -.. code:: ipython3 - - from smithers.dataset import NavierStokesDataset - dataset = NavierStokesDataset() - - fig, axs = plt.subplots(1, 4, figsize=(14, 3)) - for ax, p, u in zip(axs, dataset.params[:4], dataset.snapshots['mag(v)'][:4]): - ax.tricontourf(dataset.triang, u, levels=16) - ax.set_title(f'$\mu$ = {p[0]:.2f}') - - - -.. image:: tutorial_files/tutorial_5_0.png - - -The *snapshots* - aka the numerical solutions computed for several -parameters - and the corresponding parameters are the only data we need -to train the model, in order to predict the solution for any new test -parameter. To properly validate the accuracy, we initially split the 500 -snapshots into the training dataset (90% of the original data) and the -testing one (the reamining 10%). It must be said that, to plug the -snapshots into **PINA**, we have to cast them to ``LabelTensor`` -objects. - -.. code:: ipython3 - - u = torch.tensor(dataset.snapshots['mag(v)']).float() - p = torch.tensor(dataset.params).float() - - p = LabelTensor(p, labels=['mu']) - u = LabelTensor(u, labels=[f's{i}' for i in range(u.shape[1])]) - - ratio_train_test = 0.9 - n = u.shape - n_train = int(u.shape[0] * ratio_train_test) - n_test = u - n_train - u_train, u_test = u[:n_train], u[n_train:] - p_train, p_test = p[:n_train], p[n_train:] - -It is now time to define the problem! We inherit from -``ParametricProblem`` (since the space invariant typically of this -methodology), just defining a simple *input-output* condition. - -.. code:: ipython3 - - class SnapshotProblem(ParametricProblem): - output_variables = [f's{i}' for i in range(u.shape[1])] - parameter_domain = CartesianDomain({'mu': [0, 100]}) - - conditions = { - 'io': Condition(input_points=p_train, output_points=u_train) - } - - poisson_problem = SnapshotProblem() - -We can then build a ``PODRBF`` model (using a Radial Basis Function -interpolation as approximation) and a ``PODNN`` approach (using an MLP -architecture as approximation). - -POD-RBF reduced order model ---------------------------- - -Then, we define the model we want to use, with the POD (``PODBlock``) -and the RBF (``RBFBlock``) objects. - -.. code:: ipython3 - - class PODRBF(torch.nn.Module): - """ - Proper orthogonal decomposition with Radial Basis Function interpolation model. - """ - - def __init__(self, pod_rank, rbf_kernel): - """ - - """ - super().__init__() - - self.pod = PODBlock(pod_rank) - self.rbf = RBFBlock(kernel=rbf_kernel) - - - def forward(self, x): - """ - Defines the computation performed at every call. - - :param x: The tensor to apply the forward pass. - :type x: torch.Tensor - :return: the output computed by the model. - :rtype: torch.Tensor - """ - coefficents = self.rbf(x) - return self.pod.expand(coefficents) - - def fit(self, p, x): - """ - Call the :meth:`pina.model.layers.PODBlock.fit` method of the - :attr:`pina.model.layers.PODBlock` attribute to perform the POD, - and the :meth:`pina.model.layers.RBFBlock.fit` method of the - :attr:`pina.model.layers.RBFBlock` attribute to fit the interpolation. - """ - self.pod.fit(x) - self.rbf.fit(p, self.pod.reduce(x)) - -We can then fit the model and ask it to predict the required field for -unseen values of the parameters. Note that this model does not need a -``Trainer`` since it does not include any neural network or learnable -parameters. - -.. code:: ipython3 - - pod_rbf = PODRBF(pod_rank=20, rbf_kernel='thin_plate_spline') - pod_rbf.fit(p_train, u_train) - -.. code:: ipython3 - - u_test_rbf = pod_rbf(p_test) - u_train_rbf = pod_rbf(p_train) - - relative_error_train = torch.norm(u_train_rbf - u_train)/torch.norm(u_train) - relative_error_test = torch.norm(u_test_rbf - u_test)/torch.norm(u_test) - - print('Error summary for POD-RBF model:') - print(f' Train: {relative_error_train.item():e}') - print(f' Test: {relative_error_test.item():e}') - - -.. parsed-literal:: - - Error summary for POD-RBF model: - Train: 1.287801e-03 - Test: 1.217041e-03 - - -POD-NN reduced order model --------------------------- - -.. code:: ipython3 - - class PODNN(torch.nn.Module): - """ - Proper orthogonal decomposition with neural network model. - """ - - def __init__(self, pod_rank, layers, func): - """ - - """ - super().__init__() - - self.pod = PODBlock(pod_rank) - self.nn = FeedForward( - input_dimensions=1, - output_dimensions=pod_rank, - layers=layers, - func=func - ) - - - def forward(self, x): - """ - Defines the computation performed at every call. - - :param x: The tensor to apply the forward pass. - :type x: torch.Tensor - :return: the output computed by the model. - :rtype: torch.Tensor - """ - coefficents = self.nn(x) - return self.pod.expand(coefficents) - - def fit_pod(self, x): - """ - Just call the :meth:`pina.model.layers.PODBlock.fit` method of the - :attr:`pina.model.layers.PODBlock` attribute. - """ - self.pod.fit(x) - -We highlight that the POD modes are directly computed by means of the -singular value decomposition (computed over the input data), and not -trained using the backpropagation approach. Only the weights of the MLP -are actually trained during the optimization loop. - -.. code:: ipython3 - - pod_nn = PODNN(pod_rank=20, layers=[10, 10, 10], func=torch.nn.Tanh) - pod_nn.fit_pod(u_train) - - pod_nn_stokes = SupervisedSolver( - problem=poisson_problem, - model=pod_nn, - optimizer=torch.optim.Adam, - optimizer_kwargs={'lr': 0.0001}) - -Now that we have set the ``Problem`` and the ``Model``, we have just to -train the model and use it for predicting the test snapshots. - -.. code:: ipython3 - - trainer = Trainer( - solver=pod_nn_stokes, - max_epochs=1000, - batch_size=100, - log_every_n_steps=5, - accelerator='cpu') - trainer.train() - - -.. parsed-literal:: - - GPU available: True (cuda), used: False - TPU available: False, using: 0 TPU cores - IPU available: False, using: 0 IPUs - HPU available: False, using: 0 HPUs - /u/a/aivagnes/anaconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/setup.py:187: GPU available but not used. You can set it by doing `Trainer(accelerator='gpu')`. - - | Name | Type | Params - ---------------------------------------- - 0 | _loss | MSELoss | 0 - 1 | _neural_net | Network | 460 - ---------------------------------------- - 460 Trainable params - 0 Non-trainable params - 460 Total params - 0.002 Total estimated model params size (MB) - /u/a/aivagnes/anaconda3/lib/python3.8/site-packages/torch/cuda/__init__.py:152: UserWarning: - Found GPU0 Quadro K600 which is of cuda capability 3.0. - PyTorch no longer supports this GPU because it is too old. - The minimum cuda capability supported by this library is 3.7. - - warnings.warn(old_gpu_warn % (d, name, major, minor, min_arch // 10, min_arch % 10)) - - - -.. parsed-literal:: - - Training: | | 0/? [00:00`__. - -First of all, some useful imports. - -.. code:: ipython3 - - ## routine needed to run the notebook on Google Colab - try: - import google.colab - IN_COLAB = True - except: - IN_COLAB = False - if IN_COLAB: - !pip install "pina-mathlab" - - import torch - import matplotlib.pyplot as plt - plt.style.use('tableau-colorblind10') - - from pina import Condition, Plotter - from pina.problem import SpatialProblem - from pina.operators import laplacian - from pina.model import FeedForward - from pina.model.layers import PeriodicBoundaryEmbedding # The PBC module - from pina.solvers import PINN - from pina.trainer import Trainer - from pina.geometry import CartesianDomain - from pina.equation import Equation - -The problem definition ----------------------- - -The one-dimensional Helmotz problem is mathematically written as: - -.. math:: - - - \begin{cases} - \frac{d^2}{dx^2}u(x) - \lambda u(x) -f(x) &= 0 \quad x\in(0,2)\\ - u^{(m)}(x=0) - u^{(m)}(x=2) &= 0 \quad m\in[0, 1, \cdots]\\ - \end{cases} - -In this case we are asking the solution to be :math:`C^{\infty}` -periodic with period :math:`2`, on the inifite domain -:math:`x\in(-\infty, \infty)`. Notice that the classical PINN would need -inifinite conditions to evaluate the PBC loss function, one for each -derivative, which is of course infeasable… A possible solution, -diverging from the original PINN formulation, is to use *coordinates -augmentation*. In coordinates augmentation you seek for a coordinates -transformation :math:`v` such that :math:`x\rightarrow v(x)` such that -the periodicity condition -:math:`u^{(m)}(x=0) - u^{(m)}(x=2) = 0 \quad, m\in[0, 1, \cdots]` is satisfied. - -For demonstration porpuses the problem specifics are -:math:`\lambda=-10\pi^2`, and -:math:`f(x)=-6\pi^2\sin(3\pi x)\cos(\pi x)` which gives a solution that -can be computed analytically :math:`u(x) = \sin(\pi x)\cos(3\pi x)`. - -.. code:: ipython3 - - class Helmotz(SpatialProblem): - output_variables = ['u'] - spatial_domain = CartesianDomain({'x': [0, 2]}) - - def helmotz_equation(input_, output_): - x = input_.extract('x') - u_xx = laplacian(output_, input_, components=['u'], d=['x']) - f = - 6.*torch.pi**2 * torch.sin(3*torch.pi*x)*torch.cos(torch.pi*x) - lambda_ = - 10. * torch.pi ** 2 - return u_xx - lambda_ * output_ - f - - # here we write the problem conditions - conditions = { - 'D': Condition(location=spatial_domain, - equation=Equation(helmotz_equation)), - } - - def helmotz_sol(self, pts): - return torch.sin(torch.pi * pts) * torch.cos(3. * torch.pi * pts) - - truth_solution = helmotz_sol - - problem = Helmotz() - - # let's discretise the domain - problem.discretise_domain(200, 'grid', locations=['D']) - -As usual the Helmotz problem is written in **PINA** code as a class. The -equations are written as ``conditions`` that should be satisfied in the -corresponding domains. The ``truth_solution`` is the exact solution -which will be compared with the predicted one. We used latin hypercube -sampling for choosing the collocation points. - -Solving the problem with a Periodic Network -------------------------------------------- - -Any :math:`\mathcal{C}^{\infty}` periodic function -:math:`u : \mathbb{R} \rightarrow \mathbb{R}` with period -:math:`L\in\mathbb{N}` can be constructed by composition of an arbitrary -smooth function :math:`f : \mathbb{R}^n \rightarrow \mathbb{R}` and a -given smooth periodic function -:math:`v : \mathbb{R} \rightarrow \mathbb{R}^n` with period :math:`L`, -that is :math:`u(x) = f(v(x))`. The formulation is generalizable for -arbitrary dimension, see `A method for representing periodic functions -and enforcing exactly periodic boundary conditions with deep neural -networks `__. - -In our case, we rewrite -:math:`v(x) = \left[1, \cos\left(\frac{2\pi}{L} x\right), \sin\left(\frac{2\pi}{L} x\right)\right]`, -i.e the coordinates augmentation, and -:math:`f(\cdot) = NN_{\theta}(\cdot)` i.e. a neural network. The -resulting neural network obtained by composing :math:`f` with :math:`v` -gives the PINN approximate solution, that is -:math:`u(x) \approx u_{\theta}(x)=NN_{\theta}(v(x))`. - -In **PINA** this translates in using the ``PeriodicBoundaryEmbedding`` layer for -:math:`v`, and any ``pina.model`` for :math:`NN_{\theta}`. Let’s see it -in action! - -.. code:: ipython3 - - # we encapsulate all modules in a torch.nn.Sequential container - model = torch.nn.Sequential(PeriodicBoundaryEmbedding(input_dimension=1, - periods=2), - FeedForward(input_dimensions=3, # output of PeriodicBoundaryEmbedding = 3 * input_dimension - output_dimensions=1, - layers=[10, 10])) - -As simple as that! Notice in higher dimension you can specify different -periods for all dimensions using a dictionary, -e.g. ``periods={'x':2, 'y':3, ...}`` would indicate a periodicity of -:math:`2` in :math:`x`, :math:`3` in :math:`y`, and so on… - -We will now sole the problem as usually with the ``PINN`` and -``Trainer`` class. - -.. code:: ipython3 - - pinn = PINN(problem=problem, model=model) - trainer = Trainer(pinn, max_epochs=5000, accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional) - trainer.train() - - -.. parsed-literal:: - - GPU available: True (mps), used: False - TPU available: False, using: 0 TPU cores - IPU available: False, using: 0 IPUs - HPU available: False, using: 0 HPUs - -.. parsed-literal:: - - `Trainer.fit` stopped: `max_epochs=5000` reached. - - -.. parsed-literal:: - - Epoch 4999: 100%|██████████| 1/1 [00:00<00:00, 155.47it/s, v_num=20, D_loss=0.0123, mean_loss=0.0123] - - -We are going to plot the solution now! - -.. code:: ipython3 - - pl = Plotter() - pl.plot(pinn) - - - -.. image:: tutorial_files/tutorial_11_0.png - - -Great, they overlap perfectly! This seeams a good result, considering -the simple neural network used to some this (complex) problem. We will -now test the neural network on the domain :math:`[-4, 4]` without -retraining. In principle the periodicity should be present since the -:math:`v` function ensures the periodicity in :math:`(-\infty, \infty)`. - -.. code:: ipython3 - - # plotting solution - with torch.no_grad(): - # Notice here we put [-4, 4]!!! - new_domain = CartesianDomain({'x' : [0, 4]}) - x = new_domain.sample(1000, mode='grid') - fig, axes = plt.subplots(1, 3, figsize=(15, 5)) - # Plot 1 - axes[0].plot(x, problem.truth_solution(x), label=r'$u(x)$', color='blue') - axes[0].set_title(r'True solution $u(x)$') - axes[0].legend(loc="upper right") - # Plot 2 - axes[1].plot(x, pinn(x), label=r'$u_{\theta}(x)$', color='green') - axes[1].set_title(r'PINN solution $u_{\theta}(x)$') - axes[1].legend(loc="upper right") - # Plot 3 - diff = torch.abs(problem.truth_solution(x) - pinn(x)) - axes[2].plot(x, diff, label=r'$|u(x) - u_{\theta}(x)|$', color='red') - axes[2].set_title(r'Absolute difference $|u(x) - u_{\theta}(x)|$') - axes[2].legend(loc="upper right") - # Adjust layout - plt.tight_layout() - # Show the plots - plt.show() - - - -.. image:: tutorial_files/tutorial_13_0.png - - -It is pretty clear that the network is periodic, with also the error -following a periodic pattern. Obviusly a longer training, and a more -expressive neural network could improve the results! - -What’s next? ------------- - -Nice you have completed the one dimensional Helmotz tutorial of -**PINA**! There are multiple directions you can go now: - -1. Train the network for longer or with different layer sizes and assert - the finaly accuracy - -2. Apply the ``PeriodicBoundaryEmbedding`` layer for a time-dependent problem (see - reference in the documentation) - -3. Exploit extrafeature training ? - -4. Many more… diff --git a/docs/source/_rst/tutorials/tutorial9/tutorial_files/tutorial_11_0.png b/docs/source/_rst/tutorials/tutorial9/tutorial_files/tutorial_11_0.png deleted file mode 100644 index baf10c71f..000000000 Binary files a/docs/source/_rst/tutorials/tutorial9/tutorial_files/tutorial_11_0.png and /dev/null differ diff --git a/docs/source/_rst/tutorials/tutorial9/tutorial_files/tutorial_13_0.png b/docs/source/_rst/tutorials/tutorial9/tutorial_files/tutorial_13_0.png deleted file mode 100644 index 4178e8274..000000000 Binary files a/docs/source/_rst/tutorials/tutorial9/tutorial_files/tutorial_13_0.png and /dev/null differ diff --git a/docs/source/_team.rst b/docs/source/_team.rst index 973274d1b..287f11fcc 100644 --- a/docs/source/_team.rst +++ b/docs/source/_team.rst @@ -1,10 +1,14 @@ PINA Team ============== -PINA is currently developed in the `SISSA MathLab `_, in collaboration with `Fast Computing `_. +**PINA** is currently developed in the `SISSA MathLab `_, in collaboration with `Fast Computing `_. -A significant part of PINA has been written either as a by-product for other projects people were funded for, or by people on university-funded positions. -There are probably many of such projects that have led to some development of PINA. We are very grateful for this support! +.. figure:: index_files/fast_mathlab.png + :align: center + :width: 500 + +A significant part of **PINA** has been written either as a by-product for other projects people were funded for, or by people on university-funded positions. +There are probably many of such projects that have led to some development of **PINA**. We are very grateful for this support! In particular, we acknowledge the following sources of support with great gratitude: * `H2020 ERC CoG 2015 AROMA-CFD project 681447 `_, P.I. Professor `Prof. Gianluigi Rozza `_ at `SISSA MathLab `_. @@ -12,11 +16,12 @@ In particular, we acknowledge the following sources of support with great gratit .. figure:: index_files/foudings.png :align: center - :width: 400 + :width: 500 We also acknowledge the contribuition of `Maria Strazzullo `_ in the early developments of the package. A special -thank goeas to all the students and researchers from different universities which contributed to the package. Finally we warmly thank all the -`contributors `_! +thank goeas to all the students and researchers from different universities which contributed to the package. +Finally we warmly thank all the +`contributors `_ which are the real heart of **PINA**! .. figure:: index_files/university_dev_pina.png :align: center diff --git a/docs/source/_templates/layout.html b/docs/source/_templates/layout.html new file mode 100644 index 000000000..c1bc42107 --- /dev/null +++ b/docs/source/_templates/layout.html @@ -0,0 +1,17 @@ +{% extends "!layout.html" %} + +{%- block footer %} + +{%- endblock %} \ No newline at end of file diff --git a/docs/source/_tutorial.rst b/docs/source/_tutorial.rst new file mode 100644 index 000000000..745e575ba --- /dev/null +++ b/docs/source/_tutorial.rst @@ -0,0 +1,35 @@ +PINA Tutorials +====================== + + +In this folder we collect useful tutorials in order to understand the principles and the potential of **PINA**. + +Getting started with PINA +------------------------- + +- `Introduction to PINA for Physics Informed Neural Networks training `_ +- `Introduction to PINA Equation class `_ +- `PINA and PyTorch Lightning, training tips and visualizations `_ +- `Building custom geometries with PINA Location class `_ + + +Physics Informed Neural Networks +-------------------------------- + +- `Two dimensional Poisson problem using Extra Features Learning `_ +- `Two dimensional Wave problem with hard constraint `_ +- `Resolution of a 2D Poisson inverse problem `_ +- `Periodic Boundary Conditions for Helmotz Equation `_ +- `Multiscale PDE learning with Fourier Feature Network `_ + +Neural Operator Learning +------------------------ + +- `Two dimensional Darcy flow using the Fourier Neural Operator `_ +- `Time dependent Kuramoto Sivashinsky equation using the Averaging Neural Operator `_ + +Supervised Learning +------------------- + +- `Unstructured convolutional autoencoder via continuous convolution `_ +- `POD-RBF and POD-NN for reduced order modeling `_ diff --git a/docs/source/conf.py b/docs/source/conf.py index d561030f0..9cc6f7454 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- # -# pydmd documentation build configuration file, created by +# PINA documentation build configuration file, created by # sphinx-quickstart on Mon Jun 22 16:09:40 2015. # # This file is execfile()d with the current directory set to its @@ -14,139 +14,88 @@ import sys import os -import sphinx_rtd_theme -import pina +import time +import importlib.metadata -# -- Project information ----------------------------------------------------- -project = pina.__project__ -copyright = pina.__copyright__ -author = pina.__author__ -version = pina.__version__ +# -- Project information ----------------------------------------------------- +_DISTRIBUTION_METADATA = importlib.metadata.metadata("pina-mathlab") +project = _DISTRIBUTION_METADATA["Name"] +copyright = f'2021-{time.strftime("%Y")}' +author = "PINA Contributors" +version = _DISTRIBUTION_METADATA["Version"] -sys.path.insert(0, os.path.abspath('../sphinx_extensions')) # extension to remove paramref link from lightinig +sys.path.insert(0, os.path.abspath("../sphinx_extensions")) # -- General configuration ------------------------------------------------ -# If your documentation needs a minimal Sphinx version, state it here. -# needs_sphinx = '1.4' -# if needs_sphinx > sphinx.__display_version__: -# message = 'This project needs at least Sphinx -# v{0!s}'.format(needs_sphinx) -# raise VersionRequirementError(message) - -# Add any Sphinx extension module names here, as strings. They can be -# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom -# ones. extensions = [ - 'sphinx.ext.autodoc', - 'sphinx.ext.autosummary', - 'sphinx.ext.doctest', - 'sphinx.ext.napoleon', - 'sphinx.ext.intersphinx', - 'sphinx.ext.todo', - 'sphinx.ext.coverage', - 'sphinx.ext.viewcode', - 'sphinx.ext.mathjax', - 'sphinx.ext.intersphinx', - 'paramref_extension', # this extension is made to remove paramref links from lightining doc - 'sphinx_copybutton', - 'sphinx_design' + "sphinx.ext.autodoc", + "sphinx.ext.autosummary", + "sphinx.ext.doctest", + "sphinx.ext.napoleon", + "sphinx.ext.intersphinx", + "sphinx.ext.todo", + "sphinx.ext.coverage", + "sphinx.ext.viewcode", + "sphinx.ext.mathjax", + "sphinx.ext.intersphinx", + "paramref_extension", # this extension is made to remove paramref links from lightining doc + "sphinx_copybutton", + "sphinx_design", ] -# The root document. -root_doc = 'index' - # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This pattern also affects html_static_path and html_extra_path. -exclude_patterns = ['_build', 'docstrings', 'nextgen', 'Thumbs.db', '.DS_Store'] +exclude_patterns = ["build", "docstrings", "nextgen", "Thumbs.db", ".DS_Store"] # The reST default role (used for this markup: `text`) to use for all documents. -#default_role = 'literal' +default_role = "literal" # Generate the API documentation when building autosummary_generate = True numpydoc_show_class_members = False intersphinx_mapping = { - 'python': ('http://docs.python.org/3', None), - # 'numpy': ('http://docs.scipy.org/doc/numpy/', None), - # 'scipy': ('http://docs.scipy.org/doc/scipy/reference/', None), - 'matplotlib': ('https://matplotlib.org/stable', None), - 'torch': ('https://pytorch.org/docs/stable/', None), - 'pytorch_lightning': ("https://lightning.ai/docs/pytorch/stable/", None), - } - -nitpicky = True -nitpick_ignore = [ - ('py:meth', 'pytorch_lightning.core.module.LightningModule.log'), - ('py:meth', 'pytorch_lightning.core.module.LightningModule.log_dict'), - ('py:exc', 'MisconfigurationException'), - ('py:func', 'torch.inference_mode'), - ('py:func', 'torch.no_grad'), - ('py:class', 'torch.utils.data.DistributedSampler'), - ('py:class', 'pina.model.layers.convolution.BaseContinuousConv'), - ('py:class', 'Module'), - ('py:class', 'torch.nn.modules.loss._Loss'), # TO FIX - ('py:class', 'torch.optim.LRScheduler'), # TO FIX - - ] - + "python": ("http://docs.python.org/3", None), + "matplotlib": ("https://matplotlib.org/stable", None), + "torch": ("https://pytorch.org/docs/stable/", None), + "lightning.pytorch": ("https://lightning.ai/docs/pytorch/stable/", None), + "torch_geometric": ( + "https://pytorch-geometric.readthedocs.io/en/latest/", + None, + ), +} # Add any paths that contain templates here, relative to this directory. -templates_path = ['_templates'] +templates_path = ["_templates"] # The suffix(es) of source filenames. -# You can specify multiple suffix as a list of string: -# source_suffix = ['.rst', '.md'] -source_suffix = '.rst' - -# The encoding of source files. -# source_encoding = 'utf-8-sig' +source_suffix = ".rst" # The master toctree document. -master_doc = 'index' - -# General information about the project. -project = pina.__project__ -copyright = pina.__copyright__ -author = pina.__author__ +master_doc = "index" # autoclass -autoclass_content = 'both' +autoclass_content = "both" # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. -# -# The short X.Y version. -version = pina.__version__ -# The full version, including alpha/beta/rc tags. release = version # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. -# # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. -language = 'en' - -# There are two options for replacing |today|: either, you set today to some -# non-false value, then it is used: -# today = '' -# Else, today_fmt is used as the format for a strftime call. -# today_fmt = '%B %d, %Y' +language = "en" # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. exclude_patterns = [] -# The reST default role (used for this markup: `text`) to use for all -# documents. -# default_role = None - # If true, '()' will be appended to :func: etc. cross-reference text. add_function_parentheses = True @@ -154,51 +103,23 @@ # unit titles (such as .. function::). add_module_names = False -# If true, sectionauthor and moduleauthor directives will be shown in the -# output. They are ignored by default. -# show_authors = False - # The name of the Pygments (syntax highlighting) style to use. -pygments_style = 'sphinx' +pygments_style = "sphinx" # A list of ignored prefixes for module index sortins as "systems = False # If true, `todo` and `todoList` produce output, else they produce nothing. todo_include_todos = True - -# -- Options for viewcode extension --------------------------------------- - -# Follow alias objects that are imported from another module such as functions, -# classes and attributes. As side effects, this option ... ??? -# If false, ... ???. -# The default is True. -viewcode_import = True - - # -- Options for HTML output ---------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. -html_theme = 'pydata_sphinx_theme' +html_theme = "pydata_sphinx_theme" # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. -# html_theme_options = {} - -# Add any paths that contain custom themes here, relative to this directory. -html_theme_path = [sphinx_rtd_theme.get_html_theme_path()] - -# The name for this set of Sphinx documents. If None, it defaults to -# " v documentation". -# html_title = None - -# A shorter title for the navigation bar. Default is the same as html_title. -# html_short_title = None - -# The name of an image file (relative to this directory) to place at the top -# of the sidebar. html_logo = "index_files/PINA_logo.png" html_theme_options = { "icon_links": [ @@ -206,7 +127,7 @@ "name": "GitHub", "url": "https://github.com/mathLab/PINA", "icon": "fab fa-github", - "type": "fontawesome", + "type": "fontawesome", }, { "name": "Twitter", @@ -216,7 +137,7 @@ }, { "name": "Email", - "url": "mailto:pina.mathlab@gmail.com", + "url": "mailto:pina.mathlab@gmail.com", "icon": "fas fa-envelope", "type": "fontawesome", }, @@ -227,137 +148,70 @@ "header_links_before_dropdown": 8, } -# The name of an image file (within the static path) to use as favicon of the -# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 -# pixels large. -# html_favicon = None - -# Add any paths that contain custom static files (such as style sheets) here, -# relative to this directory. They are copied after the builtin static files,# so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ['_static'] -html_css_files = [ - '/css/custom.css', -] -# Add any extra paths that contain custom files (such as robots.txt or -# .htaccess) here, relative to this directory. These files are copied -# directly to the root of the documentation. -# html_extra_path = ['_tutorial'] +html_context = { + "default_mode": "light", +} # If not ''i, a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. -html_last_updated_fmt = '%b %d, %Y' - -# If true, SmartyPants will be used to convert quotes and dashes to -# typographically correct entities. -# html_use_smartypants = True - -# Custom sidebar templates, maps document names to template names. -# html_sidebars = {} - -# Additional templates that should be rendered to pages, maps page names to -# template names. -# html_additional_pages = {} - -# If false, no module index is generated. -# html_domain_indices = True +html_last_updated_fmt = "%b %d, %Y" # If false, no index is generated. html_use_index = True -# If true, the index is split into individual pages for each letter. -# html_split_index = False - # If true, links to the reST sources are added to the pages. html_show_sourcelink = True -# If true, "Created using Sphinx" is shown in the HTML footer. Default is True. -# html_show_sphinx = True - # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. html_show_copyright = True -# If true, an OpenSearch description file will be output, and all pages will -# contain a tag referring to it. The value of this option must be the -# base URL from which the finished HTML is served. -# html_use_opensearch = '' - -# This is the file name suffix for HTML files (e.g. ".xhtml"). -# html_file_suffix = None - -# Language to be used for generating the HTML full-text search index. -# Sphinx supports the following languages: -# 'da', 'de', 'en', 'es', 'fi', 'fr', 'hu', 'it', 'ja' -# 'nl', 'no', 'pt', 'ro', 'ru', 'sv', 'tr' -# html_search_language = 'en' - -# A dictionary with options for the search language support, empty by default. -# Now only 'ja' uses this config value -# html_search_options = {'type': 'default'} - -# The name of a javascript file (relative to the configuration directory) that -# implements a search results scorer. If empty, the default will be used. -# html_search_scorer = 'scorer.js' - # Output file base name for HTML help builder. -htmlhelp_basename = 'pinadoc' +htmlhelp_basename = "pinadoc" + +# Link to external html files +html_extra_path = ["tutorials"] + +# Avoid side bar for html files +html_sidebars = { + "_tutorial": [], + "_team": [], + "_cite": [], + "_contributing": [], + "_installation": [], + "_LICENSE": [], +} # -- Options for LaTeX output --------------------------------------------- latex_elements = { # The paper size ('letterpaper' or 'a4paper'). - 'papersize': 'a4paper', - + "papersize": "a4paper", # The font size ('10pt', '11pt' or '12pt'). - 'pointsize': '20pt', - + "pointsize": "20pt", # Additional stuff for the LaTeX preamble. - 'preamble': '', - + "preamble": "", # Latex figure (float) alignment - 'figure_align': 'htbp', + "figure_align": "htbp", } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ - (master_doc, 'pina.tex', u'PINA Documentation', - u'PINA contributors', 'manual'), + ( + master_doc, + "pina.tex", + "PINA Documentation", + "PINA contributors", + "manual", + ), ] -# The name of an image file (relative to this directory) to place at the top of -# the title page. -# latex_logo = None - -# For "manual" documents, if this is true, then toplevel headings are parts, -# not chapters. -# latex_use_parts = False - -# If true, show page references after internal links. -# latex_show_pagerefs = False - -# If true, show URL addresses after external links. -# latex_show_urls = False - -# Documents to append as an appendix to all manuals. -# latex_appendices = [] - -# If false, no module index is generated. -# latex_domain_indices = True - - # -- Options for manual page output --------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). -man_pages = [ - (master_doc, 'pina', u'PINA Documentation', - [author], 1) -] - -# If true, show URL addresses after external links. -# man_show_urls = False - +man_pages = [(master_doc, "pina", "PINA Documentation", [author], 1)] # -- Options for Texinfo output ------------------------------------------- @@ -365,20 +219,19 @@ # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ - (master_doc, 'pina', u'PINA Documentation', - author, 'pina', 'One line description of project.', - 'Miscellaneous'), + ( + master_doc, + "pina", + "PINA Documentation", + author, + "pina", + "Miscellaneous", + ), ] -# Documents to append as an appendix to all manuals. -# texinfo_appendices = [] - -# If false, no module index is generated. -# texinfo_domain_indices = True - -# How to display URL addresses: 'footnote', 'no', or 'inline'. -# texinfo_show_urls = 'footnote' - # If true, do not generate a @detailmenu in the "Top" node's menu. # texinfo_no_detailmenu = False -autodoc_member_order = 'bysource' +autodoc_member_order = "bysource" + +# Do consider meth ending with _ (needed for in-place methods of torch) +strip_signature_backslash = True diff --git a/docs/source/index.rst b/docs/source/index.rst index c84307923..fbebe0aff 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -9,32 +9,32 @@ Welcome to PINA’s documentation! .. grid-item:: .. image:: index_files/tutorial_13_3.png - :target: _rst/tutorials/tutorial2/tutorial.html + :target: tutorial2/tutorial.html .. grid-item:: .. image:: index_files/tutorial_32_0.png - :target: _rst/tutorials/tutorial4/tutorial.html + :target: tutorial4/tutorial.html .. grid-item:: .. image:: index_files/tutorial_13_01.png - :target: _rst/tutorials/tutorial9/tutorial.html + :target: tutorial9/tutorial.html .. grid-item:: .. image:: index_files/tutorial_36_0.png - :target: _rst/tutorials/tutorial6/tutorial.html + :target: tutorial6/tutorial.html .. grid-item:: .. image:: index_files/tutorial_15_0.png - :target: _rst/tutorials/tutorial13/tutorial.html + :target: tutorial13/tutorial.html .. grid-item:: .. image:: index_files/tutorial_5_0.png - :target: _rst/tutorials/tutorial10/tutorial.html + :target: tutorial10/tutorial.html .. grid:: 1 1 3 3 @@ -45,7 +45,7 @@ Welcome to PINA’s documentation! an open-source Python library providing an intuitive interface for solving differential equations using PINNs, NOs or both together. - Based on `PyTorch `_ and `PyTorchLightning `_, **PINA** offers a simple and intuitive way to formalize a specific (differential) problem + Based on `PyTorch `_, `PyTorchLightning `_, and `PyG `_, **PINA** offers a simple and intuitive way to formalize a specific (differential) problem and solve it using neural networks . The approximated solution of a differential equation can be implemented using PINA in a few lines of code thanks to the intuitive and user-friendly interface. @@ -63,11 +63,11 @@ Welcome to PINA’s documentation! .. toctree:: :maxdepth: 1 - Installing <_rst/_installation> - Tutorial <_rst/_tutorial> API <_rst/_code> + Tutorial <_tutorial> + Installing <_installation> Team & Foundings <_team.rst> - Contributing <_rst/_contributing> + Contributing <_contributing> License <_LICENSE.rst> Cite PINA <_cite.rst> diff --git a/docs/source/index_files/fast_mathlab.png b/docs/source/index_files/fast_mathlab.png new file mode 100644 index 000000000..cccce6512 Binary files /dev/null and b/docs/source/index_files/fast_mathlab.png differ diff --git a/docs/source/tutorials/tutorial1/tutorial.html b/docs/source/tutorials/tutorial1/tutorial.html new file mode 100644 index 000000000..7dea1b1d5 --- /dev/null +++ b/docs/source/tutorials/tutorial1/tutorial.html @@ -0,0 +1,8324 @@ + + + + + +tutorial + + + + + + + + + + + + +
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+ + + diff --git a/docs/sphinx_extensions/paramref_extension.py b/docs/sphinx_extensions/paramref_extension.py index 3b722845a..e4f939675 100644 --- a/docs/sphinx_extensions/paramref_extension.py +++ b/docs/sphinx_extensions/paramref_extension.py @@ -1,11 +1,12 @@ from docutils import nodes from docutils.parsers.rst.roles import register_local_role + def paramref_role(name, rawtext, text, lineno, inliner, options={}, content=[]): # Simply replace :paramref: with :param: new_role = nodes.literal(text=text[1:]) return [new_role], [] -def setup(app): - register_local_role('paramref', paramref_role) +def setup(app): + register_local_role("paramref", paramref_role) diff --git a/examples/problems/burgers.py b/examples/problems/burgers.py deleted file mode 100644 index 5da9ccb47..000000000 --- a/examples/problems/burgers.py +++ /dev/null @@ -1,53 +0,0 @@ -""" Burgers' problem. """ - - -# ===================================================== # -# # -# This script implements the one dimensional Burger # -# problem. The Burgers1D class is defined inheriting # -# from TimeDependentProblem, SpatialProblem and we # -# denote: # -# u --> field variable # -# x --> spatial variable # -# t --> temporal variable # -# # -# ===================================================== # - - -import torch -from pina.geometry import CartesianDomain -from pina import Condition -from pina.problem import TimeDependentProblem, SpatialProblem -from pina.operators import grad -from pina.equation import FixedValue, Equation - - -class Burgers1D(TimeDependentProblem, SpatialProblem): - - # define the burger equation - def burger_equation(input_, output_): - du = grad(output_, input_) - ddu = grad(du, input_, components=['dudx']) - return ( - du.extract(['dudt']) + - output_.extract(['u'])*du.extract(['dudx']) - - (0.01/torch.pi)*ddu.extract(['ddudxdx']) - ) - - # define initial condition - def initial_condition(input_, output_): - u_expected = -torch.sin(torch.pi*input_.extract(['x'])) - return output_.extract(['u']) - u_expected - - # assign output/ spatial and temporal variables - output_variables = ['u'] - spatial_domain = CartesianDomain({'x': [-1, 1]}) - temporal_domain = CartesianDomain({'t': [0, 1]}) - - # problem condition statement - conditions = { - 'gamma1': Condition(location=CartesianDomain({'x': -1, 't': [0, 1]}), equation=FixedValue(0.)), - 'gamma2': Condition(location=CartesianDomain({'x': 1, 't': [0, 1]}), equation=FixedValue(0.)), - 't0': Condition(location=CartesianDomain({'x': [-1, 1], 't': 0}), equation=Equation(initial_condition)), - 'D': Condition(location=CartesianDomain({'x': [-1, 1], 't': [0, 1]}), equation=Equation(burger_equation)), - } \ No newline at end of file diff --git a/examples/problems/first_order_ode.py b/examples/problems/first_order_ode.py deleted file mode 100644 index 802b85bfe..000000000 --- a/examples/problems/first_order_ode.py +++ /dev/null @@ -1,52 +0,0 @@ -""" Simple ODE problem. """ - - -# ===================================================== # -# # -# This script implements a simple first order ode. # -# The FirstOrderODE class is defined inheriting from # -# SpatialProblem. We denote: # -# y --> field variable # -# x --> spatial variable # -# # -# The equation is: # -# dy(x)/dx + y(x) = x # -# # -# ===================================================== # - - -from pina.problem import SpatialProblem -from pina import Condition -from pina.geometry import CartesianDomain -from pina.operators import grad -from pina.equation import Equation, FixedValue -import torch - - -class FirstOrderODE(SpatialProblem): - - # variable domain range - x_rng = [0., 5.] - # field variable - output_variables = ['y'] - # create domain - spatial_domain = CartesianDomain({'x': x_rng}) - - # define the ode - def ode(input_, output_): - y = output_ - x = input_ - return grad(y, x) + y - x - - # define real solution - def solution(self, input_): - x = input_ - return x - 1.0 + 2*torch.exp(-x) - - # define problem conditions - conditions = { - 'BC': Condition(location=CartesianDomain({'x': x_rng[0]}), equation=FixedValue(1.)), - 'D': Condition(location=CartesianDomain({'x': x_rng}), equation=Equation(ode)), - } - - truth_solution = solution \ No newline at end of file diff --git a/examples/problems/parametric_elliptic_optimal_control.py b/examples/problems/parametric_elliptic_optimal_control.py deleted file mode 100644 index 9d88b497a..000000000 --- a/examples/problems/parametric_elliptic_optimal_control.py +++ /dev/null @@ -1,75 +0,0 @@ -""" Poisson OCP problem. """ - - -from pina import Condition -from pina.geometry import CartesianDomain -from pina.equation import SystemEquation, FixedValue -from pina.problem import SpatialProblem, ParametricProblem -from pina.operators import laplacian - -# ===================================================== # -# # -# This script implements the two dimensional # -# Parametric Elliptic Optimal Control problem. # -# The ParametricEllipticOptimalControl class is # -# inherited from TimeDependentProblem, SpatialProblem # -# and we denote: # -# u --> field variable # -# p --> field variable # -# y --> field variable # -# x1, x2 --> spatial variables # -# mu, alpha --> problem parameters # -# # -# More info in https://arxiv.org/pdf/2110.13530.pdf # -# Section 4.2 of the article # -# ===================================================== # - - -class ParametricEllipticOptimalControl(SpatialProblem, ParametricProblem): - - # setting spatial variables ranges - xmin, xmax, ymin, ymax = -1, 1, -1, 1 - x_range = [xmin, xmax] - y_range = [ymin, ymax] - # setting parameters range - amin, amax = 0.01, 1 - mumin, mumax = 0.5, 3 - mu_range = [mumin, mumax] - a_range = [amin, amax] - # setting field variables - output_variables = ['u', 'y', 'z'] - # setting spatial and parameter domain - spatial_domain = CartesianDomain({'x1': x_range, 'x2': y_range}) - parameter_domain = CartesianDomain({'mu': mu_range, 'alpha': a_range}) - - # equation terms as in https://arxiv.org/pdf/2110.13530.pdf - def term1(input_, output_): - laplace_z = laplacian(output_, input_, components=['z'], d=['x1', 'x2']) - return output_.extract(['y']) - input_.extract(['mu']) - laplace_z - - def term2(input_, output_): - laplace_y = laplacian(output_, input_, components=['y'], d=['x1', 'x2']) - return - laplace_y - output_.extract(['u']) - - - # setting problem condition formulation - conditions = { - 'gamma1': Condition( - location=CartesianDomain({'x1': x_range, 'x2': 1, 'mu': mu_range, 'alpha': a_range}), - equation=FixedValue(0, ['y',])), - 'gamma2': Condition( - location=CartesianDomain({'x1': x_range, 'x2': -1, 'mu': mu_range, 'alpha': a_range}), - equation=FixedValue(0, ['y', 'z'])), - 'gamma3': Condition( - location=CartesianDomain({'x1': 1, 'x2': y_range, 'mu': mu_range, 'alpha': a_range}), - equation=FixedValue(0, ['y', 'z'])), - 'gamma4': Condition( - location=CartesianDomain({'x1': -1, 'x2': y_range, 'mu': mu_range, 'alpha': a_range}), - equation=FixedValue(0, ['y', 'z'])), - 'D': Condition( - location=CartesianDomain( - {'x1': x_range, 'x2': y_range, - 'mu': mu_range, 'alpha': a_range - }), - equation=SystemEquation([term1, term2])), - } \ No newline at end of file diff --git a/examples/problems/parametric_poisson.py b/examples/problems/parametric_poisson.py deleted file mode 100644 index 58867d5bb..000000000 --- a/examples/problems/parametric_poisson.py +++ /dev/null @@ -1,55 +0,0 @@ -""" Parametric Poisson problem. """ - - -# ===================================================== # -# # -# This script implements the two dimensional # -# Parametric Poisson problem. The ParametricPoisson # -# class is defined inheriting from SpatialProblem and # -# ParametricProblem. We denote: # -# u --> field variable # -# x,y --> spatial variables # -# mu1, mu2 --> parameter variables # -# # -# ===================================================== # - - -from pina.geometry import CartesianDomain -from pina.problem import SpatialProblem, ParametricProblem -from pina.operators import laplacian -from pina.equation import FixedValue, Equation -from pina import Condition -import torch - -class ParametricPoisson(SpatialProblem, ParametricProblem): - - # assign output/ spatial and parameter variables - output_variables = ['u'] - spatial_domain = CartesianDomain({'x': [-1, 1], 'y': [-1, 1]}) - parameter_domain = CartesianDomain({'mu1': [-1, 1], 'mu2': [-1, 1]}) - - # define the laplace equation - def laplace_equation(input_, output_): - force_term = torch.exp( - - 2*(input_.extract(['x']) - input_.extract(['mu1']))**2 - - 2*(input_.extract(['y']) - input_.extract(['mu2']))**2) - return laplacian(output_.extract(['u']), input_, d=['x','y']) - force_term - - # problem condition statement - conditions = { - 'gamma1': Condition( - location=CartesianDomain({'x': [-1, 1], 'y': 1, 'mu1': [-1, 1], 'mu2': [-1, 1]}), - equation=FixedValue(0.)), - 'gamma2': Condition( - location=CartesianDomain({'x': [-1, 1], 'y': -1, 'mu1': [-1, 1], 'mu2': [-1, 1]}), - equation=FixedValue(0.)), - 'gamma3': Condition( - location=CartesianDomain({'x': 1, 'y': [-1, 1], 'mu1': [-1, 1], 'mu2': [-1, 1]}), - equation=FixedValue(0.)), - 'gamma4': Condition( - location=CartesianDomain({'x': -1, 'y': [-1, 1], 'mu1': [-1, 1], 'mu2': [-1, 1]}), - equation=FixedValue(0.)), - 'D': Condition( - location=CartesianDomain({'x': [-1, 1], 'y': [-1, 1], 'mu1': [-1, 1], 'mu2': [-1, 1]}), - equation=Equation(laplace_equation)), - } diff --git a/examples/problems/poisson.py b/examples/problems/poisson.py deleted file mode 100644 index c817787bd..000000000 --- a/examples/problems/poisson.py +++ /dev/null @@ -1,57 +0,0 @@ -""" Poisson problem. """ - - -# ===================================================== # -# # -# This script implements the two dimensional # -# Poisson problem. The Poisson class is defined # -# inheriting from SpatialProblem. We denote: # -# u --> field variable # -# x,y --> spatial variables # -# # -# ===================================================== # - - -import torch -from pina.geometry import CartesianDomain -from pina import Condition -from pina.problem import SpatialProblem -from pina.operators import laplacian -from pina.equation import FixedValue, Equation - - -class Poisson(SpatialProblem): - output_variables = ['u'] - spatial_domain = CartesianDomain({'x': [0, 1], 'y': [0, 1]}) - - def laplace_equation(input_, output_): - force_term = (torch.sin(input_.extract(['x'])*torch.pi) * - torch.sin(input_.extract(['y'])*torch.pi)) - nabla_u = laplacian(output_.extract(['u']), input_) - return nabla_u - force_term - - conditions = { - 'gamma1': Condition( - location=CartesianDomain({'x': [0, 1], 'y': 1}), - equation=FixedValue(0.0)), - 'gamma2': Condition( - location=CartesianDomain({'x': [0, 1], 'y': 0}), - equation=FixedValue(0.0)), - 'gamma3': Condition( - location=CartesianDomain({'x': 1, 'y': [0, 1]}), - equation=FixedValue(0.0)), - 'gamma4': Condition( - location=CartesianDomain({'x': 0, 'y': [0, 1]}), - equation=FixedValue(0.0)), - 'D': Condition( - location=CartesianDomain({'x': [0, 1], 'y': [0, 1]}), - equation=Equation(laplace_equation)), - } - - def poisson_sol(self, pts): - return -( - torch.sin(pts.extract(['x'])*torch.pi) * - torch.sin(pts.extract(['y'])*torch.pi) - )/(2*torch.pi**2) - - truth_solution = poisson_sol diff --git a/examples/problems/stokes.py b/examples/problems/stokes.py deleted file mode 100644 index f136d64ad..000000000 --- a/examples/problems/stokes.py +++ /dev/null @@ -1,59 +0,0 @@ -""" Steady Stokes Problem """ - -import torch -from pina.problem import SpatialProblem -from pina.operators import laplacian, grad, div -from pina import Condition, LabelTensor -from pina.geometry import CartesianDomain -from pina.equation import SystemEquation, Equation - -# ===================================================== # -# # -# This script implements the two dimensional # -# Stokes problem. The Stokes class is defined # -# inheriting from SpatialProblem. We denote: # -# ux --> field variable velocity along x # -# uy --> field variable velocity along y # -# p --> field variable pressure # -# x,y --> spatial variables # -# # -# ===================================================== # - -class Stokes(SpatialProblem): - - # assign output/ spatial variables - output_variables = ['ux', 'uy', 'p'] - spatial_domain = CartesianDomain({'x': [-2, 2], 'y': [-1, 1]}) - - # define the momentum equation - def momentum(input_, output_): - delta_ = torch.hstack((LabelTensor(laplacian(output_.extract(['ux']), input_), ['x']), - LabelTensor(laplacian(output_.extract(['uy']), input_), ['y']))) - return - delta_ + grad(output_.extract(['p']), input_) - - def continuity(input_, output_): - return div(output_.extract(['ux', 'uy']), input_) - - # define the inlet velocity - def inlet(input_, output_): - value = 2 * (1 - input_.extract(['y'])**2) - return output_.extract(['ux']) - value - - # define the outlet pressure - def outlet(input_, output_): - value = 0.0 - return output_.extract(['p']) - value - - # define the wall condition - def wall(input_, output_): - value = 0.0 - return output_.extract(['ux', 'uy']) - value - - # problem condition statement - conditions = { - 'gamma_top': Condition(location=CartesianDomain({'x': [-2, 2], 'y': 1}), equation=Equation(wall)), - 'gamma_bot': Condition(location=CartesianDomain({'x': [-2, 2], 'y': -1}), equation=Equation(wall)), - 'gamma_out': Condition(location=CartesianDomain({'x': 2, 'y': [-1, 1]}), equation=Equation(outlet)), - 'gamma_in': Condition(location=CartesianDomain({'x': -2, 'y': [-1, 1]}), equation=Equation(inlet)), - 'D': Condition(location=CartesianDomain({'x': [-2, 2], 'y': [-1, 1]}), equation=SystemEquation([momentum, continuity])) - } diff --git a/examples/problems/wave.py b/examples/problems/wave.py deleted file mode 100644 index cce94da68..000000000 --- a/examples/problems/wave.py +++ /dev/null @@ -1,57 +0,0 @@ -""" Wave equation Problem """ - - -import torch -from pina.geometry import CartesianDomain -from pina import Condition -from pina.problem import SpatialProblem, TimeDependentProblem -from pina.operators import laplacian, grad -from pina.equation import FixedValue, Equation - - -# ===================================================== # -# # -# This script implements the two dimensional # -# Wave equation. The Wave class is defined inheriting # -# from SpatialProblem and TimeDependentProblem. Let # -# u --> field variable # -# x,y --> spatial variables # -# t --> temporal variables # -# the velocity coefficient is set to one. # -# # -# ===================================================== # - - - -class Wave(TimeDependentProblem, SpatialProblem): - output_variables = ['u'] - spatial_domain = CartesianDomain({'x': [0, 1], 'y': [0, 1]}) - temporal_domain = CartesianDomain({'t': [0, 1]}) - - def wave_equation(input_, output_): - u_t = grad(output_, input_, components=['u'], d=['t']) - u_tt = grad(u_t, input_, components=['dudt'], d=['t']) - nabla_u = laplacian(output_, input_, components=['u'], d=['x', 'y']) - return nabla_u - u_tt - - def initial_condition(input_, output_): - u_expected = (torch.sin(torch.pi*input_.extract(['x'])) * - torch.sin(torch.pi*input_.extract(['y']))) - return output_.extract(['u']) - u_expected - - conditions = { - 'gamma1': Condition(location=CartesianDomain({'x': [0, 1], 'y': 1, 't': [0, 1]}), equation=FixedValue(0.)), - 'gamma2': Condition(location=CartesianDomain({'x': [0, 1], 'y': 0, 't': [0, 1]}), equation=FixedValue(0.)), - 'gamma3': Condition(location=CartesianDomain({'x': 1, 'y': [0, 1], 't': [0, 1]}), equation=FixedValue(0.)), - 'gamma4': Condition(location=CartesianDomain({'x': 0, 'y': [0, 1], 't': [0, 1]}), equation=FixedValue(0.)), - 't0': Condition(location=CartesianDomain({'x': [0, 1], 'y': [0, 1], 't': 0}), equation=Equation(initial_condition)), - 'D': Condition(location=CartesianDomain({'x': [0, 1], 'y': [0, 1], 't': [0, 1]}), equation=Equation(wave_equation)), - } - - def wave_sol(self, pts): - sqrt_2 = torch.sqrt(torch.tensor(2.)) - return (torch.sin(torch.pi*pts.extract(['x'])) * - torch.sin(torch.pi*pts.extract(['y'])) * - torch.cos(sqrt_2*torch.pi*pts.extract(['t']))) - - truth_solution = wave_sol \ No newline at end of file diff --git a/examples/run_burgers.py b/examples/run_burgers.py deleted file mode 100644 index 10f217a29..000000000 --- a/examples/run_burgers.py +++ /dev/null @@ -1,73 +0,0 @@ -""" Run PINA on Burgers equation. """ - -import argparse -import torch -from torch.nn import Softplus - -from pina import LabelTensor -from pina.model import FeedForward -from pina.solvers import PINN -from pina.plotter import Plotter -from pina.trainer import Trainer -from problems.burgers import Burgers1D - - -class myFeature(torch.nn.Module): - """ - Feature: sin(pi*x) - """ - - def __init__(self): - super(myFeature, self).__init__() - - def forward(self, x): - return LabelTensor(torch.sin(torch.pi * x.extract(['x'])), ['sin(x)']) - - -if __name__ == "__main__": - - parser = argparse.ArgumentParser(description="Run PINA") - parser.add_argument("--load", help="directory to save or load file", type=str) - parser.add_argument("--features", help="extra features", type=int) - parser.add_argument("--epochs", help="extra features", type=int, default=1000) - args = parser.parse_args() - - if args.features is None: - args.features = 0 - - # extra features - feat = [myFeature()] if args.features else [] - - # create problem and discretise domain - burgers_problem = Burgers1D() - burgers_problem.discretise_domain(n=200, mode='grid', variables = 't', locations=['D']) - burgers_problem.discretise_domain(n=20, mode='grid', variables = 'x', locations=['D']) - burgers_problem.discretise_domain(n=150, mode='random', locations=['gamma1', 'gamma2', 't0']) - - # create model - model = FeedForward( - layers=[30, 20, 10, 5], - output_dimensions=len(burgers_problem.output_variables), - input_dimensions=len(burgers_problem.input_variables) + len(feat), - func=Softplus - ) - - # create solver - pinn = PINN( - problem=burgers_problem, - model=model, - extra_features=feat, - optimizer_kwargs={'lr' : 0.006} - ) - - # create trainer - directory = 'pina.burger_extrafeats_{}'.format(bool(args.features)) - trainer = Trainer(solver=pinn, accelerator='cpu', max_epochs=args.epochs, default_root_dir=directory) - - - if args.load: - pinn = PINN.load_from_checkpoint(checkpoint_path=args.load, problem=burgers_problem, model=model) - plotter = Plotter() - plotter.plot(pinn) - else: - trainer.train() diff --git a/examples/run_first_order_ode.py b/examples/run_first_order_ode.py deleted file mode 100644 index b41b47062..000000000 --- a/examples/run_first_order_ode.py +++ /dev/null @@ -1,53 +0,0 @@ -""" Run PINA on ODE equation. """ -import argparse -import torch -from torch.nn import Softplus - -from pina.model import FeedForward -from pina.solvers import PINN -from pina.plotter import Plotter -from pina.trainer import Trainer -from problems.first_order_ode import FirstOrderODE - - - -if __name__ == "__main__": - - parser = argparse.ArgumentParser(description="Run PINA") - parser.add_argument("--load", help="directory to save or load file", type=str) - parser.add_argument("--epochs", help="extra features", type=int, default=3000) - args = parser.parse_args() - - - # create problem and discretise domain - problem = FirstOrderODE() - problem.discretise_domain(n=500, mode='grid', variables = 'x', locations=['D']) - problem.discretise_domain(n=1, mode='grid', variables = 'x', locations=['BC']) - - # create model - model = FeedForward( - layers=[10, 10], - output_dimensions=len(problem.output_variables), - input_dimensions=len(problem.input_variables), - func=Softplus - ) - - # create solver - pinn = PINN( - problem=problem, - model=model, - extra_features=None, - optimizer_kwargs={'lr' : 0.001} - ) - - # create trainer - directory = 'pina.ode' - trainer = Trainer(solver=pinn, accelerator='cpu', max_epochs=args.epochs, default_root_dir=directory) - - - if args.load: - pinn = PINN.load_from_checkpoint(checkpoint_path=args.load, problem=problem, model=model) - plotter = Plotter() - plotter.plot(pinn) - else: - trainer.train() \ No newline at end of file diff --git a/examples/run_parametric_elliptic_optimal.py b/examples/run_parametric_elliptic_optimal.py deleted file mode 100644 index 564fc5833..000000000 --- a/examples/run_parametric_elliptic_optimal.py +++ /dev/null @@ -1,89 +0,0 @@ -import argparse -import numpy as np -import torch -from torch.nn import Softplus - -from pina import LabelTensor -from pina.solvers import PINN -from pina.model import MultiFeedForward, FeedForward -from pina.plotter import Plotter -from pina.trainer import Trainer -from problems.parametric_elliptic_optimal_control import ( - ParametricEllipticOptimalControl) - - -class myFeature(torch.nn.Module): - """ - Feature: sin(x) - """ - - def __init__(self): - super(myFeature, self).__init__() - - def forward(self, x): - t = (-x.extract(['x1'])**2+1) * (-x.extract(['x2'])**2+1) - return LabelTensor(t, ['k0']) - - -class PIArch(MultiFeedForward): - - def __init__(self, dff_dict): - super().__init__(dff_dict) - - def forward(self, x): - out = self.uy(x) - out.labels = ['u', 'y'] - z = LabelTensor( - (out.extract(['u']) * x.extract(['alpha'])), ['z']) - return out.append(z) - -if __name__ == "__main__": - - parser = argparse.ArgumentParser(description="Run PINA") - parser.add_argument("--load", help="directory to save or load file", type=str) - parser.add_argument("--features", help="extra features", type=int) - parser.add_argument("--epochs", help="extra features", type=int, default=1000) - args = parser.parse_args() - - if args.features is None: - args.features = 0 - - # extra features - feat = [myFeature()] if args.features else [] - args = parser.parse_args() - - # create problem and discretise domain - opc = ParametricEllipticOptimalControl() - opc.discretise_domain(n= 900, mode='random', variables=['x1', 'x2'], locations=['D']) - opc.discretise_domain(n= 5, mode='random', variables=['mu', 'alpha'], locations=['D']) - opc.discretise_domain(n= 200, mode='random', variables=['x1', 'x2'], locations=['gamma1', 'gamma2', 'gamma3', 'gamma4']) - opc.discretise_domain(n= 5, mode='random', variables=['mu', 'alpha'], locations=['gamma1', 'gamma2', 'gamma3', 'gamma4']) - - # create model - model = PIArch( - { - 'uy': { - 'input_dimensions': 4 + len(feat), - 'output_dimensions': 2, - 'layers': [40, 40, 20], - 'func': Softplus, - }, - } - ) - - # create PINN - pinn = PINN(problem=opc, model=model, optimizer_kwargs={'lr' : 0.002}, extra_features=feat) - - # create trainer - directory = 'pina.parametric_optimal_control_{}'.format(bool(args.features)) - trainer = Trainer(solver=pinn, accelerator='cpu', max_epochs=args.epochs, default_root_dir=directory) - - - if args.load: - pinn = PINN.load_from_checkpoint(checkpoint_path=args.load, problem=opc, model=model, extra_features=feat) - plotter = Plotter() - plotter.plot(pinn, fixed_variables={'mu' : 3 , 'alpha' : 1}, components='u') - plotter.plot(pinn, fixed_variables={'mu' : 3 , 'alpha' : 1}, components='z') - plotter.plot(pinn, fixed_variables={'mu' : 3 , 'alpha' : 1}, components='y') - else: - trainer.train() diff --git a/examples/run_parametric_poisson.py b/examples/run_parametric_poisson.py deleted file mode 100644 index 1c713666d..000000000 --- a/examples/run_parametric_poisson.py +++ /dev/null @@ -1,73 +0,0 @@ -import argparse -import torch -from torch.nn import Softplus -from pina import Plotter, LabelTensor, Trainer -from pina.solvers import PINN -from pina.model import FeedForward -from problems.parametric_poisson import ParametricPoisson - - -class myFeature(torch.nn.Module): - """ - """ - def __init__(self): - super(myFeature, self).__init__() - - def forward(self, x): - t = ( - torch.exp( - - 2*(x.extract(['x']) - x.extract(['mu1']))**2 - - 2*(x.extract(['y']) - x.extract(['mu2']))**2 - ) - ) - return LabelTensor(t, ['k0']) - - -if __name__ == "__main__": - - parser = argparse.ArgumentParser(description="Run PINA") - parser.add_argument("--load", help="directory to save or load file", type=str) - parser.add_argument("--features", help="extra features", type=int) - parser.add_argument("--epochs", help="extra features", type=int, default=1000) - args = parser.parse_args() - - if args.features is None: - args.features = 0 - - # extra features - feat = [myFeature()] if args.features else [] - - # create problem and discretise domain - ppoisson_problem = ParametricPoisson() - ppoisson_problem.discretise_domain(n=100, mode='random', variables = ['x', 'y'], locations=['D']) - ppoisson_problem.discretise_domain(n=100, mode='random', variables = ['mu1', 'mu2'], locations=['D']) - ppoisson_problem.discretise_domain(n=20, mode='random', variables = ['x', 'y'], locations=['gamma1', 'gamma2', 'gamma3', 'gamma4']) - ppoisson_problem.discretise_domain(n=5, mode='random', variables = ['mu1', 'mu2'], locations=['gamma1', 'gamma2', 'gamma3', 'gamma4']) - - # create model - model = FeedForward( - layers=[10, 10, 10], - output_dimensions=len(ppoisson_problem.output_variables), - input_dimensions=len(ppoisson_problem.input_variables) + len(feat), - func=Softplus - ) - - # create solver - pinn = PINN( - problem=ppoisson_problem, - model=model, - extra_features=feat, - optimizer_kwargs={'lr' : 0.006} - ) - - # create trainer - directory = 'pina.parametric_poisson_extrafeats_{}'.format(bool(args.features)) - trainer = Trainer(solver=pinn, accelerator='cpu', max_epochs=args.epochs, default_root_dir=directory) - - - if args.load: - pinn = PINN.load_from_checkpoint(checkpoint_path=args.load, problem=ppoisson_problem, model=model, extra_features=feat) - plotter = Plotter() - plotter.plot(pinn, fixed_variables={'mu1': 1, 'mu2': -1}) - else: - trainer.train() diff --git a/examples/run_poisson.py b/examples/run_poisson.py deleted file mode 100644 index 390e042ca..000000000 --- a/examples/run_poisson.py +++ /dev/null @@ -1,73 +0,0 @@ -""" Run PINA on ODE equation. """ -import argparse -import torch -from torch.nn import Softplus - -from pina import LabelTensor -from pina.model import FeedForward -from pina.solvers import PINN -from pina.plotter import Plotter -from pina.trainer import Trainer -from problems.poisson import Poisson - - -class myFeature(torch.nn.Module): - """ - Feature: sin(x) - """ - - def __init__(self): - super(myFeature, self).__init__() - - def forward(self, x): - t = (torch.sin(x.extract(['x'])*torch.pi) * - torch.sin(x.extract(['y'])*torch.pi)) - return LabelTensor(t, ['sin(x)sin(y)']) - -if __name__ == "__main__": - - parser = argparse.ArgumentParser(description="Run PINA") - parser.add_argument("--load", help="directory to save or load file", type=str) - parser.add_argument("--features", help="extra features", type=int) - parser.add_argument("--epochs", help="extra features", type=int, default=1000) - args = parser.parse_args() - - if args.features is None: - args.features = 0 - - # extra features - feat = [myFeature()] if args.features else [] - args = parser.parse_args() - - # create problem and discretise domain - problem = Poisson() - problem.discretise_domain(n=20, mode='grid', locations=['D']) - problem.discretise_domain(n=100, mode='random', locations=['gamma1', 'gamma2', 'gamma3', 'gamma4']) - - # create model - model = FeedForward( - layers=[10, 10], - output_dimensions=len(problem.output_variables), - input_dimensions=len(problem.input_variables) + len(feat), - func=Softplus - ) - - # create solver - pinn = PINN( - problem=problem, - model=model, - extra_features=feat, - optimizer_kwargs={'lr' : 0.001} - ) - - # create trainer - directory = 'pina.parametric_poisson_extrafeats_{}'.format(bool(args.features)) - trainer = Trainer(solver=pinn, accelerator='cpu', max_epochs=args.epochs, default_root_dir=directory) - - - if args.load: - pinn = PINN.load_from_checkpoint(checkpoint_path=args.load, problem=problem, model=model, extra_features=feat) - plotter = Plotter() - plotter.plot(pinn) - else: - trainer.train() \ No newline at end of file diff --git a/examples/run_poisson_deeponet.py b/examples/run_poisson_deeponet.py deleted file mode 100644 index 3e577a612..000000000 --- a/examples/run_poisson_deeponet.py +++ /dev/null @@ -1,75 +0,0 @@ -import argparse -import torch -from pina import Plotter, LabelTensor, Trainer -from pina.solvers import PINN -from pina.model import DeepONet, FeedForward -from problems.parametric_poisson import ParametricPoisson - - -class myFeature(torch.nn.Module): - """ - """ - def __init__(self): - super(myFeature, self).__init__() - - def forward(self, x): - t = ( - torch.exp( - - 2*(x.extract(['x']) - x.extract(['mu1']))**2 - - 2*(x.extract(['y']) - x.extract(['mu2']))**2 - ) - ) - return LabelTensor(t, ['k0']) - - -if __name__ == "__main__": - - parser = argparse.ArgumentParser(description="Run PINA") - parser.add_argument("--load", help="directory to save or load file", type=str) - parser.add_argument("--epochs", help="extra features", type=int, default=1000) - args = parser.parse_args() - - - # create problem and discretise domain - ppoisson_problem = ParametricPoisson() - ppoisson_problem.discretise_domain(n=100, mode='random', variables = ['x', 'y'], locations=['D']) - ppoisson_problem.discretise_domain(n=100, mode='random', variables = ['mu1', 'mu2'], locations=['D']) - ppoisson_problem.discretise_domain(n=20, mode='random', variables = ['x', 'y'], locations=['gamma1', 'gamma2', 'gamma3', 'gamma4']) - ppoisson_problem.discretise_domain(n=5, mode='random', variables = ['mu1', 'mu2'], locations=['gamma1', 'gamma2', 'gamma3', 'gamma4']) - - # create model - trunck = FeedForward( - layers=[40, 40], - output_dimensions=1, - input_dimensions=2, - func=torch.nn.ReLU - ) - branch = FeedForward( - layers=[40, 40], - output_dimensions=1, - input_dimensions=2, - func=torch.nn.ReLU - ) - model = DeepONet(branch_net=branch, - trunk_net=trunck, - input_indeces_branch_net=['x', 'y'], - input_indeces_trunk_net=['mu1', 'mu2']) - - # create solver - pinn = PINN( - problem=ppoisson_problem, - model=model, - optimizer_kwargs={'lr' : 0.006} - ) - - # create trainer - directory = 'pina.parametric_poisson_deeponet' - trainer = Trainer(solver=pinn, accelerator='cpu', max_epochs=args.epochs, default_root_dir=directory) - - - if args.load: - pinn = PINN.load_from_checkpoint(checkpoint_path=args.load, problem=ppoisson_problem, model=model) - plotter = Plotter() - plotter.plot(pinn, fixed_variables={'mu1': 1, 'mu2': -1}) - else: - trainer.train() diff --git a/examples/run_stokes.py b/examples/run_stokes.py deleted file mode 100644 index 54b2aecc3..000000000 --- a/examples/run_stokes.py +++ /dev/null @@ -1,52 +0,0 @@ -import argparse -from torch.nn import Softplus - -from pina import Plotter, Trainer -from pina.model import FeedForward -from pina.solvers import PINN -from problems.stokes import Stokes - -if __name__ == "__main__": - - parser = argparse.ArgumentParser(description="Run PINA") - parser = argparse.ArgumentParser(description="Run PINA") - parser.add_argument("--load", help="directory to save or load file", type=str) - parser.add_argument("--epochs", help="extra features", type=int, default=1000) - args = parser.parse_args() - - - # create problem and discretise domain - stokes_problem = Stokes() - stokes_problem.discretise_domain(n=1000, locations=['gamma_top', 'gamma_bot', 'gamma_in', 'gamma_out']) - stokes_problem.discretise_domain(n=2000, locations=['D']) - - # make the model - model = FeedForward( - layers=[10, 10, 10, 10], - output_dimensions=len(stokes_problem.output_variables), - input_dimensions=len(stokes_problem.input_variables), - func=Softplus, - ) - - # make the pinn - pinn = PINN( - stokes_problem, - model, - optimizer_kwargs={'lr' : 0.001} - ) - - # create trainer - directory = 'pina.navier_stokes' - trainer = Trainer(solver=pinn, accelerator='cpu', max_epochs=args.epochs, default_root_dir=directory) - - - if args.load: - pinn = PINN.load_from_checkpoint(checkpoint_path=args.load, problem=stokes_problem, model=model) - plotter = Plotter() - plotter.plot(pinn, components='ux') - plotter.plot(pinn, components='uy') - plotter.plot(pinn, components='p') - else: - trainer.train() - - diff --git a/examples/run_wave.py b/examples/run_wave.py deleted file mode 100644 index 2d4b4e6e4..000000000 --- a/examples/run_wave.py +++ /dev/null @@ -1,64 +0,0 @@ -""" Run PINA on Burgers equation. """ - -import argparse -import torch -from torch.nn import Softplus - -from pina import LabelTensor -from pina.model import FeedForward -from pina.solvers import PINN -from pina.plotter import Plotter -from pina.trainer import Trainer -from problems.wave import Wave - -class HardMLP(torch.nn.Module): - - def __init__(self, **kwargs): - super().__init__() - - self.layers = FeedForward(**kwargs) - - # here in the foward we implement the hard constraints - def forward(self, x): - hard_space = x.extract(['x'])*(1-x.extract(['x']))*x.extract(['y'])*(1-x.extract(['y'])) - hard_t = torch.sin(torch.pi*x.extract(['x'])) * torch.sin(torch.pi*x.extract(['y'])) * torch.cos(torch.sqrt(torch.tensor(2.))*torch.pi*x.extract(['t'])) - return hard_space * self.layers(x) * x.extract(['t']) + hard_t - -if __name__ == "__main__": - - parser = argparse.ArgumentParser(description="Run PINA") - parser.add_argument("--load", help="directory to save or load file", type=str) - parser.add_argument("--epochs", help="extra features", type=int, default=1000) - args = parser.parse_args() - - - # create problem and discretise domain - wave_problem = Wave() - wave_problem.discretise_domain(1000, 'random', locations=['D', 't0', 'gamma1', 'gamma2', 'gamma3', 'gamma4']) - - # create model - model = HardMLP( - layers=[40, 40, 40], - output_dimensions=len(wave_problem.output_variables), - input_dimensions=len(wave_problem.input_variables), - func=Softplus - ) - - # create solver - pinn = PINN( - problem=wave_problem, - model=model, - optimizer_kwargs={'lr' : 0.006} - ) - - # create trainer - directory = 'pina.wave' - trainer = Trainer(solver=pinn, accelerator='cpu', max_epochs=args.epochs, default_root_dir=directory) - - - if args.load: - pinn = PINN.load_from_checkpoint(checkpoint_path=args.load, problem=wave_problem, model=model) - plotter = Plotter() - plotter.plot(pinn) - else: - trainer.train() diff --git a/pina/__init__.py b/pina/__init__.py index 730b2ead4..2cbe7f3bb 100644 --- a/pina/__init__.py +++ b/pina/__init__.py @@ -1,18 +1,18 @@ +"""Module for the Pina library.""" + __all__ = [ - "PINN", "Trainer", "LabelTensor", - "Plotter", "Condition", - "SamplePointDataset", - "SamplePointLoader", + "PinaDataModule", + "Graph", + "SolverInterface", + "MultiSolverInterface", ] -from .meta import * from .label_tensor import LabelTensor -from .solvers.solver import SolverInterface +from .graph import Graph +from .solver import SolverInterface, MultiSolverInterface from .trainer import Trainer -from .plotter import Plotter -from .condition import Condition -from .dataset import SamplePointDataset -from .dataset import SamplePointLoader +from .condition.condition import Condition +from .data import PinaDataModule diff --git a/pina/adaptive_function/__init__.py b/pina/adaptive_function/__init__.py new file mode 100644 index 000000000..d53c5f368 --- /dev/null +++ b/pina/adaptive_function/__init__.py @@ -0,0 +1,33 @@ +"""Adaptive Activation Functions Module.""" + +__all__ = [ + "AdaptiveActivationFunctionInterface", + "AdaptiveReLU", + "AdaptiveSigmoid", + "AdaptiveTanh", + "AdaptiveSiLU", + "AdaptiveMish", + "AdaptiveELU", + "AdaptiveCELU", + "AdaptiveGELU", + "AdaptiveSoftmin", + "AdaptiveSoftmax", + "AdaptiveSIREN", + "AdaptiveExp", +] + +from .adaptive_function import ( + AdaptiveReLU, + AdaptiveSigmoid, + AdaptiveTanh, + AdaptiveSiLU, + AdaptiveMish, + AdaptiveELU, + AdaptiveCELU, + AdaptiveGELU, + AdaptiveSoftmin, + AdaptiveSoftmax, + AdaptiveSIREN, + AdaptiveExp, +) +from .adaptive_function_interface import AdaptiveActivationFunctionInterface diff --git a/pina/adaptive_functions/adaptive_func.py b/pina/adaptive_function/adaptive_function.py similarity index 93% rename from pina/adaptive_functions/adaptive_func.py rename to pina/adaptive_function/adaptive_function.py index 30966f1fc..e6f86a549 100644 --- a/pina/adaptive_functions/adaptive_func.py +++ b/pina/adaptive_function/adaptive_function.py @@ -1,8 +1,8 @@ -""" Module for adaptive functions. """ +"""Module for the Adaptive Functions.""" import torch from ..utils import check_consistency -from .adaptive_func_interface import AdaptiveActivationFunctionInterface +from .adaptive_function_interface import AdaptiveActivationFunctionInterface class AdaptiveReLU(AdaptiveActivationFunctionInterface): @@ -15,7 +15,7 @@ class AdaptiveReLU(AdaptiveActivationFunctionInterface): is defined as: .. math:: - \text{ReLU}_{\text{adaptive}}({x}) = \alpha\,\text{ReLU}(\beta{x}+\gamma), + \text{ReLU}_{\text{adaptive}}({x})=\alpha\,\text{ReLU}(\beta{x}+\gamma), where :math:`\alpha,\,\beta,\,\gamma` are trainable parameters, and the ReLU function is defined as: @@ -50,13 +50,15 @@ class AdaptiveSigmoid(AdaptiveActivationFunctionInterface): r""" Adaptive trainable :class:`~torch.nn.Sigmoid` activation function. - Given the function :math:`\text{Sigmoid}:\mathbb{R}^n\rightarrow\mathbb{R}^n`, + Given the function + :math:`\text{Sigmoid}:\mathbb{R}^n\rightarrow\mathbb{R}^n`, the adaptive function :math:`\text{Sigmoid}_{\text{adaptive}}:\mathbb{R}^n\rightarrow\mathbb{R}^n` is defined as: .. math:: - \text{Sigmoid}_{\text{adaptive}}({x}) = \alpha\,\text{Sigmoid}(\beta{x}+\gamma), + \text{Sigmoid}_{\text{adaptive}}({x})= + \alpha\,\text{Sigmoid}(\beta{x}+\gamma), where :math:`\alpha,\,\beta,\,\gamma` are trainable parameters, and the Sigmoid function is defined as: @@ -97,7 +99,7 @@ class AdaptiveTanh(AdaptiveActivationFunctionInterface): is defined as: .. math:: - \text{Tanh}_{\text{adaptive}}({x}) = \alpha\,\text{Tanh}(\beta{x}+\gamma), + \text{Tanh}_{\text{adaptive}}({x})=\alpha\,\text{Tanh}(\beta{x}+\gamma), where :math:`\alpha,\,\beta,\,\gamma` are trainable parameters, and the Tanh function is defined as: @@ -138,7 +140,7 @@ class AdaptiveSiLU(AdaptiveActivationFunctionInterface): is defined as: .. math:: - \text{SiLU}_{\text{adaptive}}({x}) = \alpha\,\text{SiLU}(\beta{x}+\gamma), + \text{SiLU}_{\text{adaptive}}({x})=\alpha\,\text{SiLU}(\beta{x}+\gamma), where :math:`\alpha,\,\beta,\,\gamma` are trainable parameters, and the SiLU function is defined as: @@ -180,7 +182,7 @@ class AdaptiveMish(AdaptiveActivationFunctionInterface): is defined as: .. math:: - \text{Mish}_{\text{adaptive}}({x}) = \alpha\,\text{Mish}(\beta{x}+\gamma), + \text{Mish}_{\text{adaptive}}({x})=\alpha\,\text{Mish}(\beta{x}+\gamma), where :math:`\alpha,\,\beta,\,\gamma` are trainable parameters, and the Mish function is defined as: @@ -265,7 +267,7 @@ class AdaptiveCELU(AdaptiveActivationFunctionInterface): is defined as: .. math:: - \text{CELU}_{\text{adaptive}}({x}) = \alpha\,\text{CELU}(\beta{x}+\gamma), + \text{CELU}_{\text{adaptive}}({x})=\alpha\,\text{CELU}(\beta{x}+\gamma), where :math:`\alpha,\,\beta,\,\gamma` are trainable parameters, and the CELU function is defined as: @@ -306,13 +308,13 @@ class AdaptiveGELU(AdaptiveActivationFunctionInterface): is defined as: .. math:: - \text{GELU}_{\text{adaptive}}({x}) = \alpha\,\text{GELU}(\beta{x}+\gamma), + \text{GELU}_{\text{adaptive}}({x})=\alpha\,\text{GELU}(\beta{x}+\gamma), where :math:`\alpha,\,\beta,\,\gamma` are trainable parameters, and the GELU function is defined as: .. math:: - \text{GELU}(x) = 0.5 * x * (1 + \text{Tanh}(\sqrt{2 / \pi} * (x + 0.044715 * x^3))) + \text{GELU}(x)=0.5*x*(1+\text{Tanh}(\sqrt{2 / \pi}*(x+0.044715*x^3))) .. seealso:: @@ -342,13 +344,15 @@ class AdaptiveSoftmin(AdaptiveActivationFunctionInterface): r""" Adaptive trainable :class:`~torch.nn.Softmin` activation function. - Given the function :math:`\text{Softmin}:\mathbb{R}^n\rightarrow\mathbb{R}^n`, + Given the function + :math:`\text{Softmin}:\mathbb{R}^n\rightarrow\mathbb{R}^n`, the adaptive function :math:`\text{Softmin}_{\text{adaptive}}:\mathbb{R}^n\rightarrow\mathbb{R}^n` is defined as: .. math:: - \text{Softmin}_{\text{adaptive}}({x}) = \alpha\,\text{Softmin}(\beta{x}+\gamma), + \text{Softmin}_{\text{adaptive}}({x})=\alpha\, + \text{Softmin}(\beta{x}+\gamma), where :math:`\alpha,\,\beta,\,\gamma` are trainable parameters, and the Softmin function is defined as: @@ -383,13 +387,15 @@ class AdaptiveSoftmax(AdaptiveActivationFunctionInterface): r""" Adaptive trainable :class:`~torch.nn.Softmax` activation function. - Given the function :math:`\text{Softmax}:\mathbb{R}^n\rightarrow\mathbb{R}^n`, + Given the function + :math:`\text{Softmax}:\mathbb{R}^n\rightarrow\mathbb{R}^n`, the adaptive function :math:`\text{Softmax}_{\text{adaptive}}:\mathbb{R}^n\rightarrow\mathbb{R}^n` is defined as: .. math:: - \text{Softmax}_{\text{adaptive}}({x}) = \alpha\,\text{Softmax}(\beta{x}+\gamma), + \text{Softmax}_{\text{adaptive}}({x})=\alpha\, + \text{Softmax}(\beta{x}+\gamma), where :math:`\alpha,\,\beta,\,\gamma` are trainable parameters, and the Softmax function is defined as: diff --git a/pina/adaptive_functions/adaptive_func_interface.py b/pina/adaptive_function/adaptive_function_interface.py similarity index 92% rename from pina/adaptive_functions/adaptive_func_interface.py rename to pina/adaptive_function/adaptive_function_interface.py index a12b78b67..a655fdbd7 100644 --- a/pina/adaptive_functions/adaptive_func_interface.py +++ b/pina/adaptive_function/adaptive_function_interface.py @@ -1,15 +1,13 @@ -""" Module for adaptive functions. """ +"""Module for the Adaptive Function interface.""" -import torch - -from pina.utils import check_consistency from abc import ABCMeta +import torch +from ..utils import check_consistency, is_function class AdaptiveActivationFunctionInterface(torch.nn.Module, metaclass=ABCMeta): r""" - The - :class:`~pina.adaptive_functions.adaptive_func_interface.AdaptiveActivationFunctionInterface` + The :class:`AdaptiveActivationFunctionInterface` class makes a :class:`torch.nn.Module` activation function into an adaptive trainable activation function. If one wants to create an adpative activation function, this class must be use as base class. @@ -104,9 +102,6 @@ def __init__(self, alpha=None, beta=None, gamma=None, fixed=None): else: self.register_buffer("gamma", gamma) - # storing the activation - self._func = None - def forward(self, x): """ Define the computation performed at every call. @@ -144,3 +139,13 @@ def func(self): The callable activation function. """ return self._func + + @func.setter + def func(self, value): + """ + Set the activation function. + """ + if not is_function(value): + raise TypeError("The function must be callable.") + self._func = value + return self._func diff --git a/pina/adaptive_functions/__init__.py b/pina/adaptive_functions/__init__.py index 0fa0ecd9e..6df3338c0 100644 --- a/pina/adaptive_functions/__init__.py +++ b/pina/adaptive_functions/__init__.py @@ -1,31 +1,16 @@ -__all__ = [ - "AdaptiveActivationFunctionInterface", - "AdaptiveReLU", - "AdaptiveSigmoid", - "AdaptiveTanh", - "AdaptiveSiLU", - "AdaptiveMish", - "AdaptiveELU", - "AdaptiveCELU", - "AdaptiveGELU", - "AdaptiveSoftmin", - "AdaptiveSoftmax", - "AdaptiveSIREN", - "AdaptiveExp", -] +"""Old module for adaptive functions. Deprecated in 0.2.0.""" -from .adaptive_func import ( - AdaptiveReLU, - AdaptiveSigmoid, - AdaptiveTanh, - AdaptiveSiLU, - AdaptiveMish, - AdaptiveELU, - AdaptiveCELU, - AdaptiveGELU, - AdaptiveSoftmin, - AdaptiveSoftmax, - AdaptiveSIREN, - AdaptiveExp, +import warnings + +from ..adaptive_function import * +from ..utils import custom_warning_format + +# back-compatibility 0.1 +# Set the custom format for warnings +warnings.formatwarning = custom_warning_format +warnings.filterwarnings("always", category=DeprecationWarning) +warnings.warn( + "'pina.adaptive_functions' is deprecated and will be removed " + "in future versions. Please use 'pina.adaptive_function' instead.", + DeprecationWarning, ) -from .adaptive_func_interface import AdaptiveActivationFunctionInterface diff --git a/pina/callback/__init__.py b/pina/callback/__init__.py new file mode 100644 index 000000000..421071a2c --- /dev/null +++ b/pina/callback/__init__.py @@ -0,0 +1,14 @@ +"""Module for the Pina Callbacks.""" + +__all__ = [ + "SwitchOptimizer", + "R3Refinement", + "MetricTracker", + "PINAProgressBar", + "LinearWeightUpdate", +] + +from .optimizer_callback import SwitchOptimizer +from .adaptive_refinement_callback import R3Refinement +from .processing_callback import MetricTracker, PINAProgressBar +from .linear_weight_update_callback import LinearWeightUpdate diff --git a/pina/callback/adaptive_refinement_callback.py b/pina/callback/adaptive_refinement_callback.py new file mode 100644 index 000000000..84ac0cfcc --- /dev/null +++ b/pina/callback/adaptive_refinement_callback.py @@ -0,0 +1,181 @@ +"""Module for the R3Refinement callback.""" + +import importlib.metadata +import torch +from lightning.pytorch.callbacks import Callback +from ..label_tensor import LabelTensor +from ..utils import check_consistency + + +class R3Refinement(Callback): + """ + PINA Implementation of an R3 Refinement Callback. + """ + + def __init__(self, sample_every): + """ + This callback implements the R3 (Retain-Resample-Release) routine for + sampling new points based on adaptive search. + The algorithm incrementally accumulates collocation points in regions + of high PDE residuals, and releases those with low residuals. + Points are sampled uniformly in all regions where sampling is needed. + + .. seealso:: + + Original Reference: Daw, Arka, et al. *Mitigating Propagation + Failures in Physics-informed Neural Networks + using Retain-Resample-Release (R3) Sampling. (2023)*. + DOI: `10.48550/arXiv.2207.02338 + `_ + + :param int sample_every: Frequency for sampling. + :raises ValueError: If `sample_every` is not an integer. + + Example: + >>> r3_callback = R3Refinement(sample_every=5) + """ + raise NotImplementedError( + "R3Refinement callback is being refactored in the pina " + f"{importlib.metadata.metadata('pina-mathlab')['Version']} " + "version. Please use version 0.1 if R3Refinement is required." + ) + + # super().__init__() + + # # sample every + # check_consistency(sample_every, int) + # self._sample_every = sample_every + # self._const_pts = None + + # def _compute_residual(self, trainer): + # """ + # Computes the residuals for a PINN object. + + # :return: the total loss, and pointwise loss. + # :rtype: tuple + # """ + + # # extract the solver and device from trainer + # solver = trainer.solver + # device = trainer._accelerator_connector._accelerator_flag + # precision = trainer.precision + # if precision == "64-true": + # precision = torch.float64 + # elif precision == "32-true": + # precision = torch.float32 + # else: + # raise RuntimeError( + # "Currently R3Refinement is only implemented " + # "for precision '32-true' and '64-true', set " + # "Trainer precision to match one of the " + # "available precisions." + # ) + + # # compute residual + # res_loss = {} + # tot_loss = [] + # for location in self._sampling_locations: + # condition = solver.problem.conditions[location] + # pts = solver.problem.input_pts[location] + # # send points to correct device + # pts = pts.to(device=device, dtype=precision) + # pts = pts.requires_grad_(True) + # pts.retain_grad() + # # PINN loss: equation evaluated only for sampling locations + # target = condition.equation.residual(pts, solver.forward(pts)) + # res_loss[location] = torch.abs(target).as_subclass(torch.Tensor) + # tot_loss.append(torch.abs(target)) + + # print(tot_loss) + + # return torch.vstack(tot_loss), res_loss + + # def _r3_routine(self, trainer): + # """ + # R3 refinement main routine. + + # :param Trainer trainer: PINA Trainer. + # """ + # # compute residual (all device possible) + # tot_loss, res_loss = self._compute_residual(trainer) + # tot_loss = tot_loss.as_subclass(torch.Tensor) + + # # !!!!!! From now everything is performed on CPU !!!!!! + + # # average loss + # avg = (tot_loss.mean()).to("cpu") + # old_pts = {} # points to be retained + # for location in self._sampling_locations: + # pts = trainer._model.problem.input_pts[location] + # labels = pts.labels + # pts = pts.cpu().detach().as_subclass(torch.Tensor) + # residuals = res_loss[location].cpu() + # mask = (residuals > avg).flatten() + # if any(mask): # append residuals greater than average + # pts = (pts[mask]).as_subclass(LabelTensor) + # pts.labels = labels + # old_pts[location] = pts + # numb_pts = self._const_pts[location] - len(old_pts[location]) + # # sample new points + # trainer._model.problem.discretise_domain( + # numb_pts, "random", locations=[location] + # ) + + # else: # if no res greater than average, samples all uniformly + # numb_pts = self._const_pts[location] + # # sample new points + # trainer._model.problem.discretise_domain( + # numb_pts, "random", locations=[location] + # ) + # # adding previous population points + # trainer._model.problem.add_points(old_pts) + + # # update dataloader + # trainer._create_or_update_loader() + + # def on_train_start(self, trainer, _): + # """ + # Callback function called at the start of training. + + # This method extracts the locations for sampling from the problem + # conditions and calculates the total population. + + # :param trainer: The trainer object managing the training process. + # :type trainer: pytorch_lightning.Trainer + # :param _: Placeholder argument (not used). + + # :return: None + # :rtype: None + # """ + # # extract locations for sampling + # problem = trainer.solver.problem + # locations = [] + # for condition_name in problem.conditions: + # condition = problem.conditions[condition_name] + # if hasattr(condition, "location"): + # locations.append(condition_name) + # self._sampling_locations = locations + + # # extract total population + # const_pts = {} # for each location, store the pts to keep constant + # for location in self._sampling_locations: + # pts = trainer._model.problem.input_pts[location] + # const_pts[location] = len(pts) + # self._const_pts = const_pts + + # def on_train_epoch_end(self, trainer, __): + # """ + # Callback function called at the end of each training epoch. + + # This method triggers the R3 routine for refinement if the current + # epoch is a multiple of `_sample_every`. + + # :param trainer: The trainer object managing the training process. + # :type trainer: pytorch_lightning.Trainer + # :param __: Placeholder argument (not used). + + # :return: None + # :rtype: None + # """ + # if trainer.current_epoch % self._sample_every == 0: + # self._r3_routine(trainer) diff --git a/pina/callback/linear_weight_update_callback.py b/pina/callback/linear_weight_update_callback.py new file mode 100644 index 000000000..ae25ca158 --- /dev/null +++ b/pina/callback/linear_weight_update_callback.py @@ -0,0 +1,87 @@ +"""Module for the LinearWeightUpdate callback.""" + +import warnings +from lightning.pytorch.callbacks import Callback +from ..utils import check_consistency +from ..loss import ScalarWeighting + + +class LinearWeightUpdate(Callback): + """ + Callback to linearly adjust the weight of a condition from an + initial value to a target value over a specified number of epochs. + """ + + def __init__( + self, target_epoch, condition_name, initial_value, target_value + ): + """ + Callback initialization. + + :param int target_epoch: The epoch at which the weight of the condition + should reach the target value. + :param str condition_name: The name of the condition whose weight + should be adjusted. + :param float initial_value: The initial value of the weight. + :param float target_value: The target value of the weight. + """ + super().__init__() + self.target_epoch = target_epoch + self.condition_name = condition_name + self.initial_value = initial_value + self.target_value = target_value + + # Check consistency + check_consistency(self.target_epoch, int, subclass=False) + check_consistency(self.condition_name, str, subclass=False) + check_consistency(self.initial_value, (float, int), subclass=False) + check_consistency(self.target_value, (float, int), subclass=False) + + def on_train_start(self, trainer, pl_module): + """ + Initialize the weight of the condition to the specified `initial_value`. + + :param Trainer trainer: A :class:`~pina.trainer.Trainer` instance. + :param SolverInterface pl_module: A + :class:`~pina.solver.solver.SolverInterface` instance. + """ + # Check that the target epoch is valid + if not 0 < self.target_epoch <= trainer.max_epochs: + raise ValueError( + "`target_epoch` must be greater than 0" + " and less than or equal to `max_epochs`." + ) + + # Check that the condition is a problem condition + if self.condition_name not in pl_module.problem.conditions: + raise ValueError( + f"`{self.condition_name}` must be a problem condition." + ) + + # Check that the initial value is not equal to the target value + if self.initial_value == self.target_value: + warnings.warn( + "`initial_value` is equal to `target_value`. " + "No effective adjustment will be performed.", + UserWarning, + ) + + # Check that the weighting schema is ScalarWeighting + if not isinstance(pl_module.weighting, ScalarWeighting): + raise ValueError("The weighting schema must be ScalarWeighting.") + + # Initialize the weight of the condition + pl_module.weighting.weights[self.condition_name] = self.initial_value + + def on_train_epoch_start(self, trainer, pl_module): + """ + Adjust at each epoch the weight of the condition. + + :param Trainer trainer: A :class:`~pina.trainer.Trainer` instance. + :param SolverInterface pl_module: A + :class:`~pina.solver.solver.SolverInterface` instance. + """ + if 0 < trainer.current_epoch <= self.target_epoch: + pl_module.weighting.weights[self.condition_name] += ( + self.target_value - self.initial_value + ) / (self.target_epoch - 1) diff --git a/pina/callback/optimizer_callback.py b/pina/callback/optimizer_callback.py new file mode 100644 index 000000000..fb2770a43 --- /dev/null +++ b/pina/callback/optimizer_callback.py @@ -0,0 +1,65 @@ +"""Module for the SwitchOptimizer callback.""" + +from lightning.pytorch.callbacks import Callback +from ..optim import TorchOptimizer +from ..utils import check_consistency + + +class SwitchOptimizer(Callback): + """ + PINA Implementation of a Lightning Callback to switch optimizer during + training. + """ + + def __init__(self, new_optimizers, epoch_switch): + """ + This callback allows switching between different optimizers during + training, enabling the exploration of multiple optimization strategies + without interrupting the training process. + + :param new_optimizers: The model optimizers to switch to. Can be a + single :class:`torch.optim.Optimizer` instance or a list of them + for multiple model solver. + :type new_optimizers: pina.optim.TorchOptimizer | list + :param epoch_switch: The epoch at which the optimizer switch occurs. + :type epoch_switch: int + + Example: + >>> switch_callback = SwitchOptimizer(new_optimizers=optimizer, + >>> epoch_switch=10) + """ + super().__init__() + + if epoch_switch < 1: + raise ValueError("epoch_switch must be greater than one.") + + if not isinstance(new_optimizers, list): + new_optimizers = [new_optimizers] + + # check type consistency + for optimizer in new_optimizers: + check_consistency(optimizer, TorchOptimizer) + check_consistency(epoch_switch, int) + # save new optimizers + self._new_optimizers = new_optimizers + self._epoch_switch = epoch_switch + + def on_train_epoch_start(self, trainer, __): + """ + Switch the optimizer at the start of the specified training epoch. + + :param trainer: The trainer object managing the training process. + :type trainer: pytorch_lightning.Trainer + :param _: Placeholder argument (not used). + + :return: None + :rtype: None + """ + if trainer.current_epoch == self._epoch_switch: + optims = [] + + for idx, optim in enumerate(self._new_optimizers): + optim.hook(trainer.solver._pina_models[idx].parameters()) + optims.append(optim) + + trainer.solver._pina_optimizers = optims diff --git a/pina/callback/processing_callback.py b/pina/callback/processing_callback.py new file mode 100644 index 000000000..244c7266d --- /dev/null +++ b/pina/callback/processing_callback.py @@ -0,0 +1,177 @@ +"""Module for the Processing Callbacks.""" + +import copy +import torch + +from lightning.pytorch.callbacks import Callback, TQDMProgressBar +from lightning.pytorch.callbacks.progress.progress_bar import ( + get_standard_metrics, +) +from pina.utils import check_consistency + + +class MetricTracker(Callback): + """ + Lightning Callback for Metric Tracking. + """ + + def __init__(self, metrics_to_track=None): + """ + Tracks specified metrics during training. + + :param metrics_to_track: List of metrics to track. + Defaults to train loss. + :type metrics_to_track: list[str], optional + """ + super().__init__() + self._collection = [] + # Default to tracking 'train_loss' if not specified + self.metrics_to_track = metrics_to_track + + def setup(self, trainer, pl_module, stage): + """ + Called when fit, validate, test, predict, or tune begins. + + :param Trainer trainer: A :class:`~pina.trainer.Trainer` instance. + :param SolverInterface pl_module: A + :class:`~pina.solver.solver.SolverInterface` instance. + :param str stage: Either 'fit', 'test' or 'predict'. + """ + if self.metrics_to_track is None and trainer.batch_size is None: + self.metrics_to_track = ["train_loss"] + elif self.metrics_to_track is None: + self.metrics_to_track = ["train_loss_epoch"] + return super().setup(trainer, pl_module, stage) + + def on_train_epoch_end(self, trainer, pl_module): + """ + Collect and track metrics at the end of each training epoch. + + :param trainer: The trainer object managing the training process. + :type trainer: pytorch_lightning.Trainer + :param pl_module: The model being trained (not used here). + """ + # Track metrics after the first epoch onwards + if trainer.current_epoch > 0: + # Append only the tracked metrics to avoid unnecessary data + tracked_metrics = { + k: v + for k, v in trainer.logged_metrics.items() + if k in self.metrics_to_track + } + self._collection.append(copy.deepcopy(tracked_metrics)) + + @property + def metrics(self): + """ + Aggregate collected metrics over all epochs. + + :return: A dictionary containing aggregated metric values. + :rtype: dict + """ + if not self._collection: + return {} + + # Get intersection of keys across all collected dictionaries + common_keys = set(self._collection[0]).intersection( + *self._collection[1:] + ) + + # Stack the metric values for common keys and return + return { + k: torch.stack([dic[k] for dic in self._collection]) + for k in common_keys + if k in self.metrics_to_track + } + + +class PINAProgressBar(TQDMProgressBar): + """ + PINA Implementation of a Lightning Callback for enriching the progress bar. + """ + + BAR_FORMAT = ( + "{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, " + "{rate_noinv_fmt}{postfix}]" + ) + + def __init__(self, metrics="val", **kwargs): + """ + This class enables the display of only relevant metrics during training. + + :param metrics: Logged metrics to be shown during the training. + Must be a subset of the conditions keys defined in + :obj:`pina.condition.Condition`. + :type metrics: str | list(str) | tuple(str) + + :Keyword Arguments: + The additional keyword arguments specify the progress bar and can be + choosen from the `pytorch-lightning TQDMProgressBar API + `_ + + Example: + >>> pbar = PINAProgressBar(['mean']) + >>> # ... Perform training ... + >>> trainer = Trainer(solver, callbacks=[pbar]) + """ + super().__init__(**kwargs) + # check consistency + if not isinstance(metrics, (list, tuple)): + metrics = [metrics] + check_consistency(metrics, str) + self._sorted_metrics = metrics + + def get_metrics(self, trainer, pl_module): + r"""Combine progress bar metrics collected from the trainer with + standard metrics from get_standard_metrics. + Override this method to customize the items shown in the progress bar. + The progress bar metrics are sorted according to ``metrics``. + + Here is an example of how to override the defaults: + + .. code-block:: python + + def get_metrics(self, trainer, model): + # don't show the version number + items = super().get_metrics(trainer, model) + items.pop("v_num", None) + return items + + :return: Dictionary with the items to be displayed in the progress bar. + :rtype: tuple(dict) + """ + standard_metrics = get_standard_metrics(trainer) + pbar_metrics = trainer.progress_bar_metrics + if pbar_metrics: + pbar_metrics = { + key: pbar_metrics[key] + for key in pbar_metrics + if key in self._sorted_metrics + } + return {**standard_metrics, **pbar_metrics} + + def setup(self, trainer, pl_module, stage): + """ + Check that the initialized metrics are available and correctly logged. + + :param trainer: The trainer object managing the training process. + :type trainer: pytorch_lightning.Trainer + :param pl_module: Placeholder argument. + """ + # Check if all keys in sort_keys are present in the dictionary + for key in self._sorted_metrics: + if ( + key not in trainer.solver.problem.conditions.keys() + and key != "train" + and key != "val" + ): + raise KeyError(f"Key '{key}' is not present in the dictionary") + # add the loss pedix + if trainer.batch_size is not None: + pedix = "_loss_epoch" + else: + pedix = "_loss" + self._sorted_metrics = [ + metric + pedix for metric in self._sorted_metrics + ] + return super().setup(trainer, pl_module, stage) diff --git a/pina/callbacks/__init__.py b/pina/callbacks/__init__.py index e1eaf825e..69f8782f6 100644 --- a/pina/callbacks/__init__.py +++ b/pina/callbacks/__init__.py @@ -1,10 +1,16 @@ -__all__ = [ - "SwitchOptimizer", - "R3Refinement", - "MetricTracker", - "PINAProgressBar", -] +"""Old module for callbacks. Deprecated in 0.2.0.""" -from .optimizer_callbacks import SwitchOptimizer -from .adaptive_refinment_callbacks import R3Refinement -from .processing_callbacks import MetricTracker, PINAProgressBar +import warnings + +from ..callback import * +from ..utils import custom_warning_format + +# back-compatibility 0.1 +# Set the custom format for warnings +warnings.formatwarning = custom_warning_format +warnings.filterwarnings("always", category=DeprecationWarning) +warnings.warn( + "'pina.callbacks' is deprecated and will be removed " + "in future versions. Please use 'pina.callback' instead.", + DeprecationWarning, +) diff --git a/pina/callbacks/adaptive_refinment_callbacks.py b/pina/callbacks/adaptive_refinment_callbacks.py deleted file mode 100644 index 5af2cc859..000000000 --- a/pina/callbacks/adaptive_refinment_callbacks.py +++ /dev/null @@ -1,172 +0,0 @@ -"""PINA Callbacks Implementations""" - -import torch -from pytorch_lightning.callbacks import Callback -from ..label_tensor import LabelTensor -from ..utils import check_consistency - - -class R3Refinement(Callback): - - def __init__(self, sample_every): - """ - PINA Implementation of an R3 Refinement Callback. - - This callback implements the R3 (Retain-Resample-Release) routine for - sampling new points based on adaptive search. - The algorithm incrementally accumulates collocation points in regions - of high PDE residuals, and releases those - with low residuals. Points are sampled uniformly in all regions - where sampling is needed. - - .. seealso:: - - Original Reference: Daw, Arka, et al. *Mitigating Propagation - Failures in Physics-informed Neural Networks - using Retain-Resample-Release (R3) Sampling. (2023)*. - DOI: `10.48550/arXiv.2207.02338 - `_ - - :param int sample_every: Frequency for sampling. - :raises ValueError: If `sample_every` is not an integer. - - Example: - >>> r3_callback = R3Refinement(sample_every=5) - """ - super().__init__() - - # sample every - check_consistency(sample_every, int) - self._sample_every = sample_every - self._const_pts = None - - def _compute_residual(self, trainer): - """ - Computes the residuals for a PINN object. - - :return: the total loss, and pointwise loss. - :rtype: tuple - """ - - # extract the solver and device from trainer - solver = trainer._model - device = trainer._accelerator_connector._accelerator_flag - precision = trainer.precision - if precision == "64-true": - precision = torch.float64 - elif precision == "32-true": - precision = torch.float32 - else: - raise RuntimeError( - "Currently R3Refinement is only implemented " - "for precision '32-true' and '64-true', set " - "Trainer precision to match one of the " - "available precisions." - ) - - # compute residual - res_loss = {} - tot_loss = [] - for location in self._sampling_locations: - condition = solver.problem.conditions[location] - pts = solver.problem.input_pts[location] - # send points to correct device - pts = pts.to(device=device, dtype=precision) - pts = pts.requires_grad_(True) - pts.retain_grad() - # PINN loss: equation evaluated only for sampling locations - target = condition.equation.residual(pts, solver.forward(pts)) - res_loss[location] = torch.abs(target).as_subclass(torch.Tensor) - tot_loss.append(torch.abs(target)) - - return torch.vstack(tot_loss), res_loss - - def _r3_routine(self, trainer): - """ - R3 refinement main routine. - - :param Trainer trainer: PINA Trainer. - """ - # compute residual (all device possible) - tot_loss, res_loss = self._compute_residual(trainer) - tot_loss = tot_loss.as_subclass(torch.Tensor) - - # !!!!!! From now everything is performed on CPU !!!!!! - - # average loss - avg = (tot_loss.mean()).to("cpu") - old_pts = {} # points to be retained - for location in self._sampling_locations: - pts = trainer._model.problem.input_pts[location] - labels = pts.labels - pts = pts.cpu().detach().as_subclass(torch.Tensor) - residuals = res_loss[location].cpu() - mask = (residuals > avg).flatten() - if any(mask): # append residuals greater than average - pts = (pts[mask]).as_subclass(LabelTensor) - pts.labels = labels - old_pts[location] = pts - numb_pts = self._const_pts[location] - len(old_pts[location]) - # sample new points - trainer._model.problem.discretise_domain( - numb_pts, "random", locations=[location] - ) - - else: # if no res greater than average, samples all uniformly - numb_pts = self._const_pts[location] - # sample new points - trainer._model.problem.discretise_domain( - numb_pts, "random", locations=[location] - ) - # adding previous population points - trainer._model.problem.add_points(old_pts) - - # update dataloader - trainer._create_or_update_loader() - - def on_train_start(self, trainer, _): - """ - Callback function called at the start of training. - - This method extracts the locations for sampling from the problem - conditions and calculates the total population. - - :param trainer: The trainer object managing the training process. - :type trainer: pytorch_lightning.Trainer - :param _: Placeholder argument (not used). - - :return: None - :rtype: None - """ - # extract locations for sampling - problem = trainer._model.problem - locations = [] - for condition_name in problem.conditions: - condition = problem.conditions[condition_name] - if hasattr(condition, "location"): - locations.append(condition_name) - self._sampling_locations = locations - - # extract total population - const_pts = {} # for each location, store the # of pts to keep constant - for location in self._sampling_locations: - pts = trainer._model.problem.input_pts[location] - const_pts[location] = len(pts) - self._const_pts = const_pts - - def on_train_epoch_end(self, trainer, __): - """ - Callback function called at the end of each training epoch. - - This method triggers the R3 routine for refinement if the current - epoch is a multiple of `_sample_every`. - - :param trainer: The trainer object managing the training process. - :type trainer: pytorch_lightning.Trainer - :param __: Placeholder argument (not used). - - :return: None - :rtype: None - """ - if trainer.current_epoch % self._sample_every == 0: - self._r3_routine(trainer) diff --git a/pina/callbacks/optimizer_callbacks.py b/pina/callbacks/optimizer_callbacks.py deleted file mode 100644 index c11db8894..000000000 --- a/pina/callbacks/optimizer_callbacks.py +++ /dev/null @@ -1,85 +0,0 @@ -"""PINA Callbacks Implementations""" - -from pytorch_lightning.callbacks import Callback -import torch -from ..utils import check_consistency - - -class SwitchOptimizer(Callback): - - def __init__(self, new_optimizers, new_optimizers_kwargs, epoch_switch): - """ - PINA Implementation of a Lightning Callback to switch optimizer during training. - - This callback allows for switching between different optimizers during training, enabling - the exploration of multiple optimization strategies without the need to stop training. - - :param new_optimizers: The model optimizers to switch to. Can be a single - :class:`torch.optim.Optimizer` or a list of them for multiple model solvers. - :type new_optimizers: torch.optim.Optimizer | list - :param new_optimizers_kwargs: The keyword arguments for the new optimizers. Can be a single dictionary - or a list of dictionaries corresponding to each optimizer. - :type new_optimizers_kwargs: dict | list - :param epoch_switch: The epoch at which to switch to the new optimizer. - :type epoch_switch: int - - :raises ValueError: If `epoch_switch` is less than 1 or if there is a mismatch in the number of - optimizers and their corresponding keyword argument dictionaries. - - Example: - >>> switch_callback = SwitchOptimizer(new_optimizers=[optimizer1, optimizer2], - >>> new_optimizers_kwargs=[{'lr': 0.001}, {'lr': 0.01}], - >>> epoch_switch=10) - """ - super().__init__() - - # check type consistency - check_consistency(new_optimizers, torch.optim.Optimizer, subclass=True) - check_consistency(new_optimizers_kwargs, dict) - check_consistency(epoch_switch, int) - - if epoch_switch < 1: - raise ValueError("epoch_switch must be greater than one.") - - if not isinstance(new_optimizers, list): - new_optimizers = [new_optimizers] - new_optimizers_kwargs = [new_optimizers_kwargs] - len_optimizer = len(new_optimizers) - len_optimizer_kwargs = len(new_optimizers_kwargs) - - if len_optimizer_kwargs != len_optimizer: - raise ValueError( - "You must define one dictionary of keyword" - " arguments for each optimizers." - f" Got {len_optimizer} optimizers, and" - f" {len_optimizer_kwargs} dicitionaries" - ) - - # save new optimizers - self._new_optimizers = new_optimizers - self._new_optimizers_kwargs = new_optimizers_kwargs - self._epoch_switch = epoch_switch - - def on_train_epoch_start(self, trainer, __): - """ - Callback function to switch optimizer at the start of each training epoch. - - :param trainer: The trainer object managing the training process. - :type trainer: pytorch_lightning.Trainer - :param _: Placeholder argument (not used). - - :return: None - :rtype: None - """ - if trainer.current_epoch == self._epoch_switch: - optims = [] - for idx, (optim, optim_kwargs) in enumerate( - zip(self._new_optimizers, self._new_optimizers_kwargs) - ): - optims.append( - optim( - trainer._model.models[idx].parameters(), **optim_kwargs - ) - ) - - trainer.optimizers = optims diff --git a/pina/callbacks/processing_callbacks.py b/pina/callbacks/processing_callbacks.py deleted file mode 100644 index a70218eb1..000000000 --- a/pina/callbacks/processing_callbacks.py +++ /dev/null @@ -1,161 +0,0 @@ -"""PINA Callbacks Implementations""" - -from pytorch_lightning.core.module import LightningModule -from pytorch_lightning.trainer.trainer import Trainer -import torch -import copy - -from pytorch_lightning.callbacks import Callback, TQDMProgressBar -from lightning.pytorch.callbacks.progress.progress_bar import ( - get_standard_metrics, -) -from pina.utils import check_consistency - - -class MetricTracker(Callback): - - def __init__(self): - """ - PINA Implementation of a Lightning Callback for Metric Tracking. - - This class provides functionality to track relevant metrics during - the training process. - - :ivar _collection: A list to store collected metrics after each - training epoch. - - :param trainer: The trainer object managing the training process. - :type trainer: pytorch_lightning.Trainer - - :return: A dictionary containing aggregated metric values. - :rtype: dict - - Example: - >>> tracker = MetricTracker() - >>> # ... Perform training ... - >>> metrics = tracker.metrics - """ - super().__init__() - self._collection = [] - - def on_train_epoch_end(self, trainer, pl_module): - """ - Collect and track metrics at the end of each training epoch. - - :param trainer: The trainer object managing the training process. - :type trainer: pytorch_lightning.Trainer - :param pl_module: Placeholder argument. - """ - super().on_train_epoch_end(trainer, pl_module) - if trainer.current_epoch > 0: - self._collection.append( - copy.deepcopy(trainer.logged_metrics) - ) # track them - - @property - def metrics(self): - """ - Aggregate collected metrics during training. - - :return: A dictionary containing aggregated metric values. - :rtype: dict - """ - common_keys = set.intersection(*map(set, self._collection)) - v = { - k: torch.stack([dic[k] for dic in self._collection]) - for k in common_keys - } - return v - - -class PINAProgressBar(TQDMProgressBar): - - BAR_FORMAT = "{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_noinv_fmt}{postfix}]" - - def __init__(self, metrics="mean", **kwargs): - """ - PINA Implementation of a Lightning Callback for enriching the progress - bar. - - This class provides functionality to display only relevant metrics - during the training process. - - :param metrics: Logged metrics to display during the training. It should - be a subset of the conditions keys defined in - :obj:`pina.condition.Condition`. - :type metrics: str | list(str) | tuple(str) - - :Keyword Arguments: - The additional keyword arguments specify the progress bar - and can be choosen from the `pytorch-lightning - TQDMProgressBar API `_ - - Example: - >>> pbar = PINAProgressBar(['mean']) - >>> # ... Perform training ... - >>> trainer = Trainer(solver, callbacks=[pbar]) - """ - super().__init__(**kwargs) - # check consistency - if not isinstance(metrics, (list, tuple)): - metrics = [metrics] - check_consistency(metrics, str) - self._sorted_metrics = metrics - - def get_metrics(self, trainer, pl_module): - r"""Combines progress bar metrics collected from the trainer with - standard metrics from get_standard_metrics. - Implement this to override the items displayed in the progress bar. - The progress bar metrics are sorted according to ``metrics``. - - Here is an example of how to override the defaults: - - .. code-block:: python - - def get_metrics(self, trainer, model): - # don't show the version number - items = super().get_metrics(trainer, model) - items.pop("v_num", None) - return items - - :return: Dictionary with the items to be displayed in the progress bar. - :rtype: tuple(dict) - - """ - standard_metrics = get_standard_metrics(trainer) - pbar_metrics = trainer.progress_bar_metrics - if pbar_metrics: - pbar_metrics = { - key: pbar_metrics[key] for key in self._sorted_metrics - } - duplicates = list(standard_metrics.keys() & pbar_metrics.keys()) - if duplicates: - rank_zero_warn( - f"The progress bar already tracks a metric with the name(s) '{', '.join(duplicates)}' and" - f" `self.log('{duplicates[0]}', ..., prog_bar=True)` will overwrite this value. " - " If this is undesired, change the name or override `get_metrics()` in the progress bar callback.", - ) - - return {**standard_metrics, **pbar_metrics} - - def on_fit_start(self, trainer, pl_module): - """ - Check that the metrics defined in the initialization are available, - i.e. are correctly logged. - - :param trainer: The trainer object managing the training process. - :type trainer: pytorch_lightning.Trainer - :param pl_module: Placeholder argument. - """ - # Check if all keys in sort_keys are present in the dictionary - for key in self._sorted_metrics: - if ( - key not in trainer.solver.problem.conditions.keys() - and key != "mean" - ): - raise KeyError(f"Key '{key}' is not present in the dictionary") - # add the loss pedix - self._sorted_metrics = [ - metric + "_loss" for metric in self._sorted_metrics - ] - return super().on_fit_start(trainer, pl_module) diff --git a/pina/collector.py b/pina/collector.py new file mode 100644 index 000000000..db7296f3d --- /dev/null +++ b/pina/collector.py @@ -0,0 +1,133 @@ +"""Module for the Collector class.""" + +from .graph import Graph +from .utils import check_consistency + + +class Collector: + """ + Collector class for retrieving data from different conditions in the + problem. + """ + + def __init__(self, problem): + """ + Initialize the Collector class, by creating a hook between the collector + and the problem and initializing the data collections (dictionary where + data will be stored). + + :param pina.problem.abstract_problem.AbstractProblem problem: The + problem to collect data from. + """ + # creating a hook between collector and problem + self.problem = problem + + # those variables are used for the dataloading + self._data_collections = {name: {} for name in self.problem.conditions} + self.conditions_name = dict(enumerate(self.problem.conditions)) + + # variables used to check that all conditions are sampled + self._is_conditions_ready = { + name: False for name in self.problem.conditions + } + self.full = False + + @property + def full(self): + """ + Returns ``True`` if the collector is full. The collector is considered + full if all conditions have entries in the ``data_collection`` + dictionary. + + :return: ``True`` if all conditions are ready, ``False`` otherwise. + :rtype: bool + """ + + return all(self._is_conditions_ready.values()) + + @full.setter + def full(self, value): + """ + Set the ``_full`` variable. + + :param bool value: The value to set the ``_full`` variable. + """ + + check_consistency(value, bool) + self._full = value + + @property + def data_collections(self): + """ + Return the data collections (dictionary where data is stored). + + :return: The data collections where the data is stored. + :rtype: dict + """ + + return self._data_collections + + @property + def problem(self): + """ + Problem connected to the collector. + + :return: The problem from which the data is collected. + :rtype: pina.problem.abstract_problem.AbstractProblem + """ + return self._problem + + @problem.setter + def problem(self, value): + """ + Set the problem connected to the collector. + + :param pina.problem.abstract_problem.AbstractProblem value: The problem + to connect to the collector. + """ + + self._problem = value + + def store_fixed_data(self): + """ + Store inside data collections the fixed data of the problem. These comes + from the conditions that do not require sampling. + """ + + # loop over all conditions + for condition_name, condition in self.problem.conditions.items(): + # if the condition is not ready and domain is not attribute + # of condition, we get and store the data + if (not self._is_conditions_ready[condition_name]) and ( + not hasattr(condition, "domain") + ): + # get data + keys = condition.__slots__ + values = [getattr(condition, name) for name in keys] + values = [ + value.data if isinstance(value, Graph) else value + for value in values + ] + self.data_collections[condition_name] = dict(zip(keys, values)) + # condition now is ready + self._is_conditions_ready[condition_name] = True + + def store_sample_domains(self): + """ + Store inside data collections the sampled data of the problem. These + comes from the conditions that require sampling (e.g. + :class:`~pina.condition.domain_equation_condition.\ + DomainEquationCondition`). + """ + + for condition_name in self.problem.conditions: + condition = self.problem.conditions[condition_name] + if not hasattr(condition, "domain"): + continue + + samples = self.problem.discretised_domains[condition.domain] + + self.data_collections[condition_name] = { + "input": samples, + "equation": condition.equation, + } diff --git a/pina/condition.py b/pina/condition.py deleted file mode 100644 index 5125fe084..000000000 --- a/pina/condition.py +++ /dev/null @@ -1,97 +0,0 @@ -""" Condition module. """ - -from .label_tensor import LabelTensor -from .geometry import Location -from .equation.equation import Equation - - -def dummy(a): - """Dummy function for testing purposes.""" - return None - - -class Condition: - """ - The class ``Condition`` is used to represent the constraints (physical - equations, boundary conditions, etc.) that should be satisfied in the - problem at hand. Condition objects are used to formulate the PINA :obj:`pina.problem.abstract_problem.AbstractProblem` object. - Conditions can be specified in three ways: - - 1. By specifying the input and output points of the condition; in such a - case, the model is trained to produce the output points given the input - points. - - 2. By specifying the location and the equation of the condition; in such - a case, the model is trained to minimize the equation residual by - evaluating it at some samples of the location. - - 3. By specifying the input points and the equation of the condition; in - such a case, the model is trained to minimize the equation residual by - evaluating it at the passed input points. - - Example:: - - >>> example_domain = Span({'x': [0, 1], 'y': [0, 1]}) - >>> def example_dirichlet(input_, output_): - >>> value = 0.0 - >>> return output_.extract(['u']) - value - >>> example_input_pts = LabelTensor( - >>> torch.tensor([[0, 0, 0]]), ['x', 'y', 'z']) - >>> example_output_pts = LabelTensor(torch.tensor([[1, 2]]), ['a', 'b']) - >>> - >>> Condition( - >>> input_points=example_input_pts, - >>> output_points=example_output_pts) - >>> Condition( - >>> location=example_domain, - >>> equation=example_dirichlet) - >>> Condition( - >>> input_points=example_input_pts, - >>> equation=example_dirichlet) - - """ - - __slots__ = [ - "input_points", - "output_points", - "location", - "equation", - "data_weight", - ] - - def _dictvalue_isinstance(self, dict_, key_, class_): - """Check if the value of a dictionary corresponding to `key` is an instance of `class_`.""" - if key_ not in dict_.keys(): - return True - - return isinstance(dict_[key_], class_) - - def __init__(self, *args, **kwargs): - """ - Constructor for the `Condition` class. - """ - self.data_weight = kwargs.pop("data_weight", 1.0) - - if len(args) != 0: - raise ValueError( - f"Condition takes only the following keyword arguments: {Condition.__slots__}." - ) - - if ( - sorted(kwargs.keys()) != sorted(["input_points", "output_points"]) - and sorted(kwargs.keys()) != sorted(["location", "equation"]) - and sorted(kwargs.keys()) != sorted(["input_points", "equation"]) - ): - raise ValueError(f"Invalid keyword arguments {kwargs.keys()}.") - - if not self._dictvalue_isinstance(kwargs, "input_points", LabelTensor): - raise TypeError("`input_points` must be a torch.Tensor.") - if not self._dictvalue_isinstance(kwargs, "output_points", LabelTensor): - raise TypeError("`output_points` must be a torch.Tensor.") - if not self._dictvalue_isinstance(kwargs, "location", Location): - raise TypeError("`location` must be a Location.") - if not self._dictvalue_isinstance(kwargs, "equation", Equation): - raise TypeError("`equation` must be a Equation.") - - for key, value in kwargs.items(): - setattr(self, key, value) diff --git a/pina/condition/__init__.py b/pina/condition/__init__.py new file mode 100644 index 000000000..4e57811fb --- /dev/null +++ b/pina/condition/__init__.py @@ -0,0 +1,39 @@ +"""Module for PINA Conditions classes.""" + +__all__ = [ + "Condition", + "ConditionInterface", + "DomainEquationCondition", + "InputTargetCondition", + "TensorInputTensorTargetCondition", + "TensorInputGraphTargetCondition", + "GraphInputTensorTargetCondition", + "GraphInputGraphTargetCondition", + "InputEquationCondition", + "InputTensorEquationCondition", + "InputGraphEquationCondition", + "DataCondition", + "GraphDataCondition", + "TensorDataCondition", +] + +from .condition_interface import ConditionInterface +from .condition import Condition +from .domain_equation_condition import DomainEquationCondition +from .input_target_condition import ( + InputTargetCondition, + TensorInputTensorTargetCondition, + TensorInputGraphTargetCondition, + GraphInputTensorTargetCondition, + GraphInputGraphTargetCondition, +) +from .input_equation_condition import ( + InputEquationCondition, + InputTensorEquationCondition, + InputGraphEquationCondition, +) +from .data_condition import ( + DataCondition, + GraphDataCondition, + TensorDataCondition, +) diff --git a/pina/condition/condition.py b/pina/condition/condition.py new file mode 100644 index 000000000..05a377eab --- /dev/null +++ b/pina/condition/condition.py @@ -0,0 +1,151 @@ +"""Module for the Condition class.""" + +import warnings +from .data_condition import DataCondition +from .domain_equation_condition import DomainEquationCondition +from .input_equation_condition import InputEquationCondition +from .input_target_condition import InputTargetCondition +from ..utils import custom_warning_format + +# Set the custom format for warnings +warnings.formatwarning = custom_warning_format +warnings.filterwarnings("always", category=DeprecationWarning) + + +def warning_function(new, old): + """Handle the deprecation warning. + + :param new: Object to use instead of the old one. + :type new: str + :param old: Object to deprecate. + :type old: str + """ + warnings.warn( + f"'{old}' is deprecated and will be removed " + f"in future versions. Please use '{new}' instead.", + DeprecationWarning, + ) + + +class Condition: + """ + Represents constraints (such as physical equations, boundary conditions, + etc.) that must be satisfied in a given problem. Condition objects are used + to formulate the PINA + :class:`~pina.problem.abstract_problem.AbstractProblem` object. + + There are different types of conditions: + + - :class:`~pina.condition.input_target_condition.InputTargetCondition`: + Defined by specifying both the input and the target of the condition. In + this case, the model is trained to produce the target given the input. The + input and output data must be one of the :class:`torch.Tensor`, + :class:`~pina.label_tensor.LabelTensor`, + :class:`~torch_geometric.data.Data`, or :class:`~pina.graph.Graph`. + Different implementations exist depending on the type of input and target. + For more details, see + :class:`~pina.condition.input_target_condition.InputTargetCondition`. + + - :class:`~pina.condition.domain_equation_condition.DomainEquationCondition` + : Defined by specifying both the domain and the equation of the condition. + Here, the model is trained to minimize the equation residual by evaluating + it at sampled points within the domain. + + - :class:`~pina.condition.input_equation_condition.InputEquationCondition`: + Defined by specifying the input and the equation of the condition. In this + case, the model is trained to minimize the equation residual by evaluating + it at the provided input. The input must be either a + :class:`~pina.label_tensor.LabelTensor` or a :class:`~pina.graph.Graph`. + Different implementations exist depending on the type of input. For more + details, see + :class:`~pina.condition.input_equation_condition.InputEquationCondition`. + + - :class:`~pina.condition.data_condition.DataCondition`: + Defined by specifying only the input. In this case, the model is trained + with an unsupervised custom loss while using the provided data during + training. The input data must be one of :class:`torch.Tensor`, + :class:`~pina.label_tensor.LabelTensor`, + :class:`~torch_geometric.data.Data`, or :class:`~pina.graph.Graph`. + Additionally, conditional variables can be provided when the model + depends on extra parameters. These conditional variables must be either + :class:`torch.Tensor` or :class:`~pina.label_tensor.LabelTensor`. + Different implementations exist depending on the type of input. + For more details, see + :class:`~pina.condition.data_condition.DataCondition`. + + :Example: + + >>> from pina import Condition + >>> condition = Condition( + ... input=input, + ... target=target + ... ) + >>> condition = Condition( + ... domain=location, + ... equation=equation + ... ) + >>> condition = Condition( + ... input=input, + ... equation=equation + ... ) + >>> condition = Condition( + ... input=data, + ... conditional_variables=conditional_variables + ... ) + + """ + + __slots__ = list( + set( + InputTargetCondition.__slots__ + + InputEquationCondition.__slots__ + + DomainEquationCondition.__slots__ + + DataCondition.__slots__ + ) + ) + + def __new__(cls, *args, **kwargs): + """ + Instantiate the appropriate Condition object based on the keyword + arguments passed. + + :raises ValueError: If no keyword arguments are passed. + :raises ValueError: If the keyword arguments are invalid. + :return: The appropriate Condition object. + :rtype: ConditionInterface + """ + + if len(args) != 0: + raise ValueError( + "Condition takes only the following keyword " + f"arguments: {Condition.__slots__}." + ) + + # back-compatibility 0.1 + keys = list(kwargs.keys()) + if "location" in keys: + kwargs["domain"] = kwargs.pop("location") + warning_function(new="domain", old="location") + + if "input_points" in keys: + kwargs["input"] = kwargs.pop("input_points") + warning_function(new="input", old="input_points") + + if "output_points" in keys: + kwargs["target"] = kwargs.pop("output_points") + warning_function(new="target", old="output_points") + + sorted_keys = sorted(kwargs.keys()) + if sorted_keys == sorted(InputTargetCondition.__slots__): + return InputTargetCondition(**kwargs) + if sorted_keys == sorted(InputEquationCondition.__slots__): + return InputEquationCondition(**kwargs) + if sorted_keys == sorted(DomainEquationCondition.__slots__): + return DomainEquationCondition(**kwargs) + if ( + sorted_keys == sorted(DataCondition.__slots__) + or sorted_keys[0] == DataCondition.__slots__[0] + ): + return DataCondition(**kwargs) + + raise ValueError(f"Invalid keyword arguments {kwargs.keys()}.") diff --git a/pina/condition/condition_interface.py b/pina/condition/condition_interface.py new file mode 100644 index 000000000..9869c1e0c --- /dev/null +++ b/pina/condition/condition_interface.py @@ -0,0 +1,117 @@ +"""Module for the Condition interface.""" + +from abc import ABCMeta +from torch_geometric.data import Data +from ..label_tensor import LabelTensor +from ..graph import Graph + + +class ConditionInterface(metaclass=ABCMeta): + """ + Abstract class which defines a common interface for all the conditions. + It defined a common interface for all the conditions. + + """ + + def __init__(self): + """ + Initialize the ConditionInterface object. + """ + + self._problem = None + + @property + def problem(self): + """ + Return the problem to which the condition is associated. + + :return: Problem to which the condition is associated. + :rtype: ~pina.problem.abstract_problem.AbstractProblem + """ + return self._problem + + @problem.setter + def problem(self, value): + """ + Set the problem to which the condition is associated. + + :param pina.problem.abstract_problem.AbstractProblem value: Problem to + which the condition is associated + """ + self._problem = value + + @staticmethod + def _check_graph_list_consistency(data_list): + """ + Check the consistency of the list of Data/Graph objects. It performs + the following checks: + + 1. All elements in the list must be of the same type (either Data or + Graph). + 2. All elements in the list must have the same keys. + 3. The type of each tensor must be consistent across all elements in + the list. + 4. If the tensor is a LabelTensor, the labels must be consistent across + all elements in the list. + + :param data_list: List of Data/Graph objects to check + :type data_list: list[Data] | list[Graph] | tuple[Data] | tuple[Graph] + + :raises ValueError: If the input types are invalid. + :raises ValueError: If all elements in the list do not have the same + keys. + :raises ValueError: If the type of each tensor is not consistent across + all elements in the list. + :raises ValueError: If the labels of the LabelTensors are not consistent + across all elements in the list. + """ + + # If the data is a Graph or Data object, return (do not need to check + # anything) + if isinstance(data_list, (Graph, Data)): + return + + # check all elements in the list are of the same type + if not all(isinstance(i, (Graph, Data)) for i in data_list): + raise ValueError( + "Invalid input types. " + "Please provide either Data or Graph objects." + ) + data = data_list[0] + # Store the keys of the first element in the list + keys = sorted(list(data.keys())) + + # Store the type of each tensor inside first element Data/Graph object + data_types = {name: tensor.__class__ for name, tensor in data.items()} + + # Store the labels of each LabelTensor inside first element Data/Graph + # object + labels = { + name: tensor.labels + for name, tensor in data.items() + if isinstance(tensor, LabelTensor) + } + + # Iterate over the list of Data/Graph objects + for data in data_list[1:]: + # Check if the keys of the current element are the same as the first + # element + if sorted(list(data.keys())) != keys: + raise ValueError( + "All elements in the list must have the same keys." + ) + for name, tensor in data.items(): + # Check if the type of each tensor inside the current element + # is the same as the first element + if tensor.__class__ is not data_types[name]: + raise ValueError( + f"Data {name} must be a {data_types[name]}, got " + f"{tensor.__class__}" + ) + # If the tensor is a LabelTensor, check if the labels are the + # same as the first element + if isinstance(tensor, LabelTensor): + if tensor.labels != labels[name]: + raise ValueError( + "LabelTensor must have the same labels" + ) diff --git a/pina/condition/data_condition.py b/pina/condition/data_condition.py new file mode 100644 index 000000000..4ecd0aefb --- /dev/null +++ b/pina/condition/data_condition.py @@ -0,0 +1,94 @@ +"""Module for the DataCondition class.""" + +import torch +from torch_geometric.data import Data +from .condition_interface import ConditionInterface +from ..label_tensor import LabelTensor +from ..graph import Graph + + +class DataCondition(ConditionInterface): + """ + Condition defined by input data and conditional variables. It can be used + in unsupervised learning problems. Based on the type of the input, + different condition implementations are available: + + - :class:`TensorDataCondition`: For :class:`torch.Tensor` or + :class:`~pina.label_tensor.LabelTensor` input data. + - :class:`GraphDataCondition`: For :class:`~pina.graph.Graph` or + :class:`~torch_geometric.data.Data` input data. + """ + + __slots__ = ["input", "conditional_variables"] + _avail_input_cls = (torch.Tensor, LabelTensor, Data, Graph, list, tuple) + _avail_conditional_variables_cls = (torch.Tensor, LabelTensor) + + def __new__(cls, input, conditional_variables=None): + """ + Instantiate the appropriate subclass of :class:`DataCondition` based on + the type of ``input``. + + :param input: Input data for the condition. + :type input: torch.Tensor | LabelTensor | Graph | + Data | list[Graph] | list[Data] | tuple[Graph] | tuple[Data] + :param conditional_variables: Conditional variables for the condition. + :type conditional_variables: torch.Tensor | LabelTensor, optional + :return: Subclass of DataCondition. + :rtype: pina.condition.data_condition.TensorDataCondition | + pina.condition.data_condition.GraphDataCondition + + :raises ValueError: If input is not of type :class:`torch.Tensor`, + :class:`~pina.label_tensor.LabelTensor`, :class:`~pina.graph.Graph`, + or :class:`~torch_geometric.data.Data`. + """ + + if cls != DataCondition: + return super().__new__(cls) + if isinstance(input, (torch.Tensor, LabelTensor)): + subclass = TensorDataCondition + return subclass.__new__(subclass, input, conditional_variables) + + if isinstance(input, (Graph, Data, list, tuple)): + cls._check_graph_list_consistency(input) + subclass = GraphDataCondition + return subclass.__new__(subclass, input, conditional_variables) + + raise ValueError( + "Invalid input types. " + "Please provide either torch_geometric.data.Data or Graph objects." + ) + + def __init__(self, input, conditional_variables=None): + """ + Initialize the object by storing the input and conditional + variables (if any). + + :param input: Input data for the condition. + :type input: torch.Tensor | LabelTensor | Graph | Data | list[Graph] | + list[Data] | tuple[Graph] | tuple[Data] + :param conditional_variables: Conditional variables for the condition. + :type conditional_variables: torch.Tensor | LabelTensor + + .. note:: + If ``input`` consists of a list of :class:`~pina.graph.Graph` or + :class:`~torch_geometric.data.Data`, all elements must have the same + structure (keys and data types) + """ + + super().__init__() + self.input = input + self.conditional_variables = conditional_variables + + +class TensorDataCondition(DataCondition): + """ + DataCondition for :class:`torch.Tensor` or + :class:`~pina.label_tensor.LabelTensor` input data + """ + + +class GraphDataCondition(DataCondition): + """ + DataCondition for :class:`~pina.graph.Graph` or + :class:`~torch_geometric.data.Data` input data + """ diff --git a/pina/condition/domain_equation_condition.py b/pina/condition/domain_equation_condition.py new file mode 100644 index 000000000..ee2b5074e --- /dev/null +++ b/pina/condition/domain_equation_condition.py @@ -0,0 +1,38 @@ +"""Module for the DomainEquationCondition class.""" + +from .condition_interface import ConditionInterface +from ..utils import check_consistency +from ..domain import DomainInterface +from ..equation.equation_interface import EquationInterface + + +class DomainEquationCondition(ConditionInterface): + """ + Condition defined by a domain and an equation. It can be used in Physics + Informed problems. Before using this condition, make sure that input data + are correctly sampled from the domain. + """ + + __slots__ = ["domain", "equation"] + + def __init__(self, domain, equation): + """ + Initialise the object by storing the domain and equation. + + :param DomainInterface domain: Domain object containing the domain data. + :param EquationInterface equation: Equation object containing the + equation data. + """ + super().__init__() + self.domain = domain + self.equation = equation + + def __setattr__(self, key, value): + if key == "domain": + check_consistency(value, (DomainInterface, str)) + DomainEquationCondition.__dict__[key].__set__(self, value) + elif key == "equation": + check_consistency(value, (EquationInterface)) + DomainEquationCondition.__dict__[key].__set__(self, value) + elif key in ("_problem"): + super().__setattr__(key, value) diff --git a/pina/condition/input_equation_condition.py b/pina/condition/input_equation_condition.py new file mode 100644 index 000000000..a803a8815 --- /dev/null +++ b/pina/condition/input_equation_condition.py @@ -0,0 +1,131 @@ +"""Module for the InputEquationCondition class and its subclasses.""" + +from torch_geometric.data import Data +from .condition_interface import ConditionInterface +from ..label_tensor import LabelTensor +from ..graph import Graph +from ..utils import check_consistency +from ..equation.equation_interface import EquationInterface + + +class InputEquationCondition(ConditionInterface): + """ + Condition defined by input data and an equation. This condition can be + used in a Physics Informed problems. Based on the type of the input, + different condition implementations are available: + + - :class:`InputTensorEquationCondition`: For \ + :class:`~pina.label_tensor.LabelTensor` input data. + - :class:`InputGraphEquationCondition`: For :class:`~pina.graph.Graph` \ + input data. + """ + + __slots__ = ["input", "equation"] + _avail_input_cls = (LabelTensor, Graph, list, tuple) + _avail_equation_cls = EquationInterface + + def __new__(cls, input, equation): + """ + Instantiate the appropriate subclass of :class:`InputEquationCondition` + based on the type of ``input``. + + :param input: Input data for the condition. + :type input: LabelTensor | Graph | list[Graph] | tuple[Graph] + :param EquationInterface equation: Equation object containing the + equation function. + :return: Subclass of InputEquationCondition, based on the input type. + :rtype: pina.condition.input_equation_condition. + InputTensorEquationCondition | + pina.condition.input_equation_condition.InputGraphEquationCondition + + :raises ValueError: If input is not of type + :class:`~pina.label_tensor.LabelTensor`, :class:`~pina.graph.Graph`. + """ + + # If the class is already a subclass, return the instance + if cls != InputEquationCondition: + return super().__new__(cls) + + # Instanciate the correct subclass + if isinstance(input, (Graph, Data, list, tuple)): + subclass = InputGraphEquationCondition + cls._check_graph_list_consistency(input) + subclass._check_label_tensor(input) + return subclass.__new__(subclass, input, equation) + if isinstance(input, LabelTensor): + subclass = InputTensorEquationCondition + return subclass.__new__(subclass, input, equation) + + # If the input is not a LabelTensor or a Graph object raise an error + raise ValueError( + "The input data object must be a LabelTensor or a Graph object." + ) + + def __init__(self, input, equation): + """ + Initialize the object by storing the input data and equation object. + + :param input: Input data for the condition. + :type input: LabelTensor | Graph | + list[Graph] | tuple[Graph] + :param EquationInterface equation: Equation object containing the + equation function. + + .. note:: + If ``input`` consists of a list of :class:`~pina.graph.Graph` or + :class:`~torch_geometric.data.Data`, all elements must have the same + structure (keys and data types) + """ + + super().__init__() + self.input = input + self.equation = equation + + def __setattr__(self, key, value): + if key == "input": + check_consistency(value, self._avail_input_cls) + InputEquationCondition.__dict__[key].__set__(self, value) + elif key == "equation": + check_consistency(value, self._avail_equation_cls) + InputEquationCondition.__dict__[key].__set__(self, value) + elif key in ("_problem"): + super().__setattr__(key, value) + + +class InputTensorEquationCondition(InputEquationCondition): + """ + InputEquationCondition subclass for :class:`~pina.label_tensor.LabelTensor` + input data. + """ + + +class InputGraphEquationCondition(InputEquationCondition): + """ + InputEquationCondition subclass for :class:`~pina.graph.Graph` input data. + """ + + @staticmethod + def _check_label_tensor(input): + """ + Check if at least one :class:`~pina.label_tensor.LabelTensor` is present + in the :class:`~pina.graph.Graph` object. + + :param input: Input data. + :type input: torch.Tensor | Graph | Data + + :raises ValueError: If the input data object does not contain at least + one LabelTensor. + """ + + # Store the fist element of the list/tuple if input is a list/tuple + # it is anougth to check the first element because all elements must + # have the same type and structure (already checked) + data = input[0] if isinstance(input, (list, tuple)) else input + + # Check if the input data contains at least one LabelTensor + for v in data.values(): + if isinstance(v, LabelTensor): + return + raise ValueError( + "The input data object must contain at least one LabelTensor." + ) diff --git a/pina/condition/input_target_condition.py b/pina/condition/input_target_condition.py new file mode 100644 index 000000000..d39fb28ca --- /dev/null +++ b/pina/condition/input_target_condition.py @@ -0,0 +1,158 @@ +""" +This module contains condition classes for supervised learning tasks. +""" + +import torch +from torch_geometric.data import Data +from ..label_tensor import LabelTensor +from ..graph import Graph +from .condition_interface import ConditionInterface + + +class InputTargetCondition(ConditionInterface): + """ + Condition defined by input and target data. This condition can be used in + both supervised learning and Physics-informed problems. Based on the type of + the input and target, different condition implementations are available: + + - :class:`TensorInputTensorTargetCondition`: For :class:`torch.Tensor` or \ + :class:`~pina.label_tensor.LabelTensor` input and target data. + - :class:`TensorInputGraphTargetCondition`: For :class:`torch.Tensor` or \ + :class:`~pina.label_tensor.LabelTensor` input and \ + :class:`~pina.graph.Graph` or :class:`torch_geometric.data.Data` \ + target data. + - :class:`GraphInputTensorTargetCondition`: For :class:`~pina.graph.Graph` \ + or :class:`~torch_geometric.data.Data` input and :class:`torch.Tensor` \ + or :class:`~pina.label_tensor.LabelTensor` target data. + - :class:`GraphInputGraphTargetCondition`: For :class:`~pina.graph.Graph` \ + or :class:`~torch_geometric.data.Data` input and target data. + """ + + __slots__ = ["input", "target"] + _avail_input_cls = (torch.Tensor, LabelTensor, Data, Graph, list, tuple) + _avail_output_cls = (torch.Tensor, LabelTensor, Data, Graph, list, tuple) + + def __new__(cls, input, target): + """ + Instantiate the appropriate subclass of InputTargetCondition based on + the types of input and target data. + + :param input: Input data for the condition. + :type input: torch.Tensor | LabelTensor | Graph | Data | list[Graph] | + list[Data] | tuple[Graph] | tuple[Data] + :param target: Target data for the condition. + :type target: torch.Tensor | LabelTensor | Graph | Data | list[Graph] | + list[Data] | tuple[Graph] | tuple[Data] + :return: Subclass of InputTargetCondition + :rtype: pina.condition.input_target_condition. + TensorInputTensorTargetCondition | + pina.condition.input_target_condition. + TensorInputGraphTargetCondition | + pina.condition.input_target_condition. + GraphInputTensorTargetCondition | + pina.condition.input_target_condition.GraphInputGraphTargetCondition + + :raises ValueError: If ``input`` and/or ``target`` are not of type + :class:`torch.Tensor`, :class:`~pina.label_tensor.LabelTensor`, + :class:`~pina.graph.Graph`, or :class:`~torch_geometric.data.Data`. + """ + if cls != InputTargetCondition: + return super().__new__(cls) + + if isinstance(input, (torch.Tensor, LabelTensor)) and isinstance( + target, (torch.Tensor, LabelTensor) + ): + subclass = TensorInputTensorTargetCondition + return subclass.__new__(subclass, input, target) + if isinstance(input, (torch.Tensor, LabelTensor)) and isinstance( + target, (Graph, Data, list, tuple) + ): + cls._check_graph_list_consistency(target) + subclass = TensorInputGraphTargetCondition + return subclass.__new__(subclass, input, target) + + if isinstance(input, (Graph, Data, list, tuple)) and isinstance( + target, (torch.Tensor, LabelTensor) + ): + cls._check_graph_list_consistency(input) + subclass = GraphInputTensorTargetCondition + return subclass.__new__(subclass, input, target) + + if isinstance(input, (Graph, Data, list, tuple)) and isinstance( + target, (Graph, Data, list, tuple) + ): + cls._check_graph_list_consistency(input) + cls._check_graph_list_consistency(target) + subclass = GraphInputGraphTargetCondition + return subclass.__new__(subclass, input, target) + + raise ValueError( + "Invalid input/target types. " + "Please provide either torch_geometric.data.Data, Graph, " + "LabelTensor or torch.Tensor objects." + ) + + def __init__(self, input, target): + """ + Initialize the object by storing the ``input`` and ``target`` data. + + :param input: Input data for the condition. + :type input: torch.Tensor | LabelTensor | Graph | Data | list[Graph] | + list[Data] | tuple[Graph] | tuple[Data] + :param target: Target data for the condition. + :type target: torch.Tensor | LabelTensor | Graph | Data | list[Graph] | + list[Data] | tuple[Graph] | tuple[Data] + + .. note:: + If either input or target consists of a list of + :class:~pina.graph.Graph or :class:~torch_geometric.data.Data + objects, all elements must have the same structure (matching + keys and data types). + """ + + super().__init__() + self._check_input_target_len(input, target) + self.input = input + self.target = target + + @staticmethod + def _check_input_target_len(input, target): + if isinstance(input, (Graph, Data)) or isinstance( + target, (Graph, Data) + ): + return + if len(input) != len(target): + raise ValueError( + "The input and target lists must have the same length." + ) + + +class TensorInputTensorTargetCondition(InputTargetCondition): + """ + InputTargetCondition subclass for :class:`torch.Tensor` or + :class:`~pina.label_tensor.LabelTensor` ``input`` and ``target`` data. + """ + + +class TensorInputGraphTargetCondition(InputTargetCondition): + """ + InputTargetCondition subclass for :class:`torch.Tensor` or + :class:`~pina.label_tensor.LabelTensor` ``input`` and + :class:`~pina.graph.Graph` or :class:`~torch_geometric.data.Data` `target` + data. + """ + + +class GraphInputTensorTargetCondition(InputTargetCondition): + """ + InputTargetCondition subclass for :class:`~pina.graph.Graph` o + :class:`~torch_geometric.data.Data` ``input`` and :class:`torch.Tensor` or + :class:`~pina.label_tensor.LabelTensor` ``target`` data. + """ + + +class GraphInputGraphTargetCondition(InputTargetCondition): + """ + InputTargetCondition subclass for :class:`~pina.graph.Graph`/ + :class:`~torch_geometric.data.Data` ``input`` and ``target`` data. + """ diff --git a/pina/data/__init__.py b/pina/data/__init__.py new file mode 100644 index 000000000..70e100011 --- /dev/null +++ b/pina/data/__init__.py @@ -0,0 +1,7 @@ +"""Module for data, data module, and dataset.""" + +__all__ = ["PinaDataModule", "PinaDataset"] + + +from .data_module import PinaDataModule +from .dataset import PinaDataset diff --git a/pina/data/data_module.py b/pina/data/data_module.py new file mode 100644 index 000000000..349d74d0d --- /dev/null +++ b/pina/data/data_module.py @@ -0,0 +1,664 @@ +""" +This module contains the PinaDataModule class, which extends the +LightningDataModule class to allow proper creation and management of +different types of Datasets defined in PINA. +""" + +import warnings +from lightning.pytorch import LightningDataModule +import torch +from torch_geometric.data import Data +from torch.utils.data import DataLoader, SequentialSampler, RandomSampler +from torch.utils.data.distributed import DistributedSampler +from ..label_tensor import LabelTensor +from .dataset import PinaDatasetFactory, PinaTensorDataset +from ..collector import Collector + + +class DummyDataloader: + + def __init__(self, dataset): + """ + Prepare a dataloader object that returns the entire dataset in a single + batch. Depending on the number of GPUs, the dataset is managed + as follows: + + - **Distributed Environment** (multiple GPUs): Divides dataset across + processes using the rank and world size. Fetches only portion of + data corresponding to the current process. + - **Non-Distributed Environment** (single GPU): Fetches the entire + dataset. + + :param PinaDataset dataset: The dataset object to be processed. + + .. note:: + This dataloader is used when the batch size is ``None``. + """ + + if ( + torch.distributed.is_available() + and torch.distributed.is_initialized() + ): + rank = torch.distributed.get_rank() + world_size = torch.distributed.get_world_size() + if len(dataset) < world_size: + raise RuntimeError( + "Dimension of the dataset smaller than world size." + " Increase the size of the partition or use a single GPU" + ) + idx, i = [], rank + while i < len(dataset): + idx.append(i) + i += world_size + self.dataset = dataset.fetch_from_idx_list(idx) + else: + self.dataset = dataset.get_all_data() + + def __iter__(self): + return self + + def __len__(self): + return 1 + + def __next__(self): + return self.dataset + + +class Collator: + """ + This callable class is used to collate the data points fetched from the + dataset. The collation is performed based on the type of dataset used and + on the batching strategy. + """ + + def __init__( + self, max_conditions_lengths, automatic_batching, dataset=None + ): + """ + Initialize the object, setting the collate function based on whether + automatic batching is enabled or not. + + :param dict max_conditions_lengths: ``dict`` containing the maximum + number of data points to consider in a single batch for + each condition. + :param bool automatic_batching: Whether automatic PyTorch batching is + enabled or not. For more information, see the + :class:`~pina.data.data_module.PinaDataModule` class. + :param PinaDataset dataset: The dataset where the data is stored. + """ + + self.max_conditions_lengths = max_conditions_lengths + # Set the collate function based on the batching strategy + # collate_pina_dataloader is used when automatic batching is disabled + # collate_torch_dataloader is used when automatic batching is enabled + self.callable_function = ( + self._collate_torch_dataloader + if automatic_batching + else (self._collate_pina_dataloader) + ) + self.dataset = dataset + + # Set the function which performs the actual collation + if isinstance(self.dataset, PinaTensorDataset): + # If the dataset is a PinaTensorDataset, use this collate function + self._collate = self._collate_tensor_dataset + else: + # If the dataset is a PinaDataset, use this collate function + self._collate = self._collate_graph_dataset + + def _collate_pina_dataloader(self, batch): + """ + Function used to create a batch when automatic batching is disabled. + + :param list[int] batch: List of integers representing the indices of + the data points to be fetched. + :return: Dictionary containing the data points fetched from the dataset. + :rtype: dict + """ + # Call the fetch_from_idx_list method of the dataset + return self.dataset.fetch_from_idx_list(batch) + + def _collate_torch_dataloader(self, batch): + """ + Function used to collate the batch + + :param list[dict] batch: List of retrieved data. + :return: Dictionary containing the data points fetched from the dataset, + collated. + :rtype: dict + """ + + batch_dict = {} + if isinstance(batch, dict): + return batch + conditions_names = batch[0].keys() + # Condition names + for condition_name in conditions_names: + single_cond_dict = {} + condition_args = batch[0][condition_name].keys() + for arg in condition_args: + data_list = [ + batch[idx][condition_name][arg] + for idx in range( + min( + len(batch), + self.max_conditions_lengths[condition_name], + ) + ) + ] + single_cond_dict[arg] = self._collate(data_list) + + batch_dict[condition_name] = single_cond_dict + return batch_dict + + @staticmethod + def _collate_tensor_dataset(data_list): + """ + Function used to collate the data when the dataset is a + :class:`~pina.data.dataset.PinaTensorDataset`. + + :param data_list: Elements to be collated. + :type data_list: list[torch.Tensor] | list[LabelTensor] + :return: Batch of data. + :rtype: dict + + :raises RuntimeError: If the data is not a :class:`torch.Tensor` or a + :class:`~pina.label_tensor.LabelTensor`. + """ + + if isinstance(data_list[0], LabelTensor): + return LabelTensor.stack(data_list) + if isinstance(data_list[0], torch.Tensor): + return torch.stack(data_list) + raise RuntimeError("Data must be Tensors or LabelTensor ") + + def _collate_graph_dataset(self, data_list): + """ + Function used to collate data when the dataset is a + :class:`~pina.data.dataset.PinaGraphDataset`. + + :param data_list: Elememts to be collated. + :type data_list: list[Data] | list[Graph] + :return: Batch of data. + :rtype: dict + + :raises RuntimeError: If the data is not a + :class:`~torch_geometric.data.Data` or a :class:`~pina.graph.Graph`. + """ + if isinstance(data_list[0], LabelTensor): + return LabelTensor.cat(data_list) + if isinstance(data_list[0], torch.Tensor): + return torch.cat(data_list) + if isinstance(data_list[0], Data): + return self.dataset.create_batch(data_list) + raise RuntimeError( + "Data must be Tensors or LabelTensor or pyG " + "torch_geometric.data.Data" + ) + + def __call__(self, batch): + """ + Perform the collation of data fetched from the dataset. The behavoior + of the function is set based on the batching strategy during class + initialization. + + :param batch: List of retrieved data or sampled indices. + :type batch: list[int] | list[dict] + :return: Dictionary containing colleted data fetched from the dataset. + :rtype: dict + """ + + return self.callable_function(batch) + + +class PinaSampler: + """ + This class is used to create the sampler instance based on the shuffle + parameter and the environment in which the code is running. + """ + + def __new__(cls, dataset, shuffle): + """ + Instantiate and initialize the sampler. + + :param PinaDataset dataset: The dataset from which to sample. + :param bool shuffle: Whether to shuffle the dataset. + :return: The sampler instance. + :rtype: :class:`torch.utils.data.Sampler` + """ + + if ( + torch.distributed.is_available() + and torch.distributed.is_initialized() + ): + sampler = DistributedSampler(dataset, shuffle=shuffle) + else: + if shuffle: + sampler = RandomSampler(dataset) + else: + sampler = SequentialSampler(dataset) + return sampler + + +class PinaDataModule(LightningDataModule): + """ + This class extends :class:`~lightning.pytorch.core.LightningDataModule`, + allowing proper creation and management of different types of datasets + defined in PINA. + """ + + def __init__( + self, + problem, + train_size=0.7, + test_size=0.2, + val_size=0.1, + batch_size=None, + shuffle=True, + repeat=False, + automatic_batching=None, + num_workers=0, + pin_memory=False, + ): + """ + Initialize the object and creating datasets based on the input problem. + + :param AbstractProblem problem: The problem containing the data on which + to create the datasets and dataloaders. + :param float train_size: Fraction of elements in the training split. It + must be in the range [0, 1]. + :param float test_size: Fraction of elements in the test split. It must + be in the range [0, 1]. + :param float val_size: Fraction of elements in the validation split. It + must be in the range [0, 1]. + :param int batch_size: The batch size used for training. If ``None``, + the entire dataset is returned in a single batch. + Default is ``None``. + :param bool shuffle: Whether to shuffle the dataset before splitting. + Default ``True``. + :param bool repeat: If ``True``, in case of batch size larger than the + number of elements in a specific condition, the elements are + repeated until the batch size is reached. If ``False``, the number + of elements in the batch is the minimum between the batch size and + the number of elements in the condition. Default is ``False``. + :param automatic_batching: If ``True``, automatic PyTorch batching + is performed, which consists of extracting one element at a time + from the dataset and collating them into a batch. This is useful + when the dataset is too large to fit into memory. On the other hand, + if ``False``, the items are retrieved from the dataset all at once + avoind the overhead of collating them into a batch and reducing the + ``__getitem__`` calls to the dataset. This is useful when the + dataset fits into memory. Avoid using automatic batching when + ``batch_size`` is large. Default is ``False``. + :param int num_workers: Number of worker threads for data loading. + Default ``0`` (serial loading). + :param bool pin_memory: Whether to use pinned memory for faster data + transfer to GPU. Default ``False``. + + :raises ValueError: If at least one of the splits is negative. + :raises ValueError: If the sum of the splits is different from 1. + + .. seealso:: + For more information on multi-process data loading, see: + https://pytorch.org/docs/stable/data.html#multi-process-data-loading + + For details on memory pinning, see: + https://pytorch.org/docs/stable/data.html#memory-pinning + """ + super().__init__() + + # Store fixed attributes + self.batch_size = batch_size + self.shuffle = shuffle + self.repeat = repeat + self.automatic_batching = automatic_batching + + # If batch size is None, num_workers has no effect + if batch_size is None and num_workers != 0: + warnings.warn( + "Setting num_workers when batch_size is None has no effect on " + "the DataLoading process." + ) + self.num_workers = 0 + else: + self.num_workers = num_workers + + # If batch size is None, pin_memory has no effect + if batch_size is None and pin_memory: + warnings.warn( + "Setting pin_memory to True has no effect when " + "batch_size is None." + ) + self.pin_memory = False + else: + self.pin_memory = pin_memory + + # Collect data + collector = Collector(problem) + collector.store_fixed_data() + collector.store_sample_domains() + + # Check if the splits are correct + self._check_slit_sizes(train_size, test_size, val_size) + + # Split input data into subsets + splits_dict = {} + if train_size > 0: + splits_dict["train"] = train_size + self.train_dataset = None + else: + # Use the super method to create the train dataloader which + # raises NotImplementedError + self.train_dataloader = super().train_dataloader + if test_size > 0: + splits_dict["test"] = test_size + self.test_dataset = None + else: + # Use the super method to create the train dataloader which + # raises NotImplementedError + self.test_dataloader = super().test_dataloader + if val_size > 0: + splits_dict["val"] = val_size + self.val_dataset = None + else: + # Use the super method to create the train dataloader which + # raises NotImplementedError + self.val_dataloader = super().val_dataloader + + self.collector_splits = self._create_splits(collector, splits_dict) + self.transfer_batch_to_device = self._transfer_batch_to_device + + def setup(self, stage=None): + """ + Create the dataset objects for the given stage. + If the stage is "fit", the training and validation datasets are created. + If the stage is "test", the testing dataset is created. + + :param str stage: The stage for which to perform the dataset setup. + + :raises ValueError: If the stage is neither "fit" nor "test". + """ + if stage == "fit" or stage is None: + self.train_dataset = PinaDatasetFactory( + self.collector_splits["train"], + max_conditions_lengths=self.find_max_conditions_lengths( + "train" + ), + automatic_batching=self.automatic_batching, + ) + if "val" in self.collector_splits.keys(): + self.val_dataset = PinaDatasetFactory( + self.collector_splits["val"], + max_conditions_lengths=self.find_max_conditions_lengths( + "val" + ), + automatic_batching=self.automatic_batching, + ) + elif stage == "test": + self.test_dataset = PinaDatasetFactory( + self.collector_splits["test"], + max_conditions_lengths=self.find_max_conditions_lengths("test"), + automatic_batching=self.automatic_batching, + ) + else: + raise ValueError("stage must be either 'fit' or 'test'.") + + @staticmethod + def _split_condition(single_condition_dict, splits_dict): + """ + Split the condition into different stages. + + :param dict single_condition_dict: The condition to be split. + :param dict splits_dict: The dictionary containing the number of + elements in each stage. + :return: A dictionary containing the split condition. + :rtype: dict + """ + + len_condition = len(single_condition_dict["input"]) + + lengths = [ + int(len_condition * length) for length in splits_dict.values() + ] + + remainder = len_condition - sum(lengths) + for i in range(remainder): + lengths[i % len(lengths)] += 1 + + splits_dict = { + k: max(1, v) for k, v in zip(splits_dict.keys(), lengths) + } + to_return_dict = {} + offset = 0 + + for stage, stage_len in splits_dict.items(): + to_return_dict[stage] = { + k: v[offset : offset + stage_len] + for k, v in single_condition_dict.items() + if k != "equation" + # Equations are NEVER dataloaded + } + if offset + stage_len >= len_condition: + offset = len_condition - 1 + continue + offset += stage_len + return to_return_dict + + def _create_splits(self, collector, splits_dict): + """ + Create the dataset objects putting data in the correct splits. + + :param Collector collector: The collector object containing the data. + :param dict splits_dict: The dictionary containing the number of + elements in each stage. + :return: The dictionary containing the dataset objects. + :rtype: dict + """ + + # ----------- Auxiliary function ------------ + def _apply_shuffle(condition_dict, len_data): + idx = torch.randperm(len_data) + for k, v in condition_dict.items(): + if k == "equation": + continue + if isinstance(v, list): + condition_dict[k] = [v[i] for i in idx] + elif isinstance(v, LabelTensor): + condition_dict[k] = LabelTensor(v.tensor[idx], v.labels) + elif isinstance(v, torch.Tensor): + condition_dict[k] = v[idx] + else: + raise ValueError(f"Data type {type(v)} not supported") + + # ----------- End auxiliary function ------------ + + split_names = list(splits_dict.keys()) + dataset_dict = {name: {} for name in split_names} + for ( + condition_name, + condition_dict, + ) in collector.data_collections.items(): + len_data = len(condition_dict["input"]) + if self.shuffle: + _apply_shuffle(condition_dict, len_data) + for key, data in self._split_condition( + condition_dict, splits_dict + ).items(): + dataset_dict[key].update({condition_name: data}) + return dataset_dict + + def _create_dataloader(self, split, dataset): + """ " + Create the dataloader for the given split. + + :param str split: The split on which to create the dataloader. + :param str dataset: The dataset to be used for the dataloader. + :return: The dataloader for the given split. + :rtype: torch.utils.data.DataLoader + """ + + shuffle = self.shuffle if split == "train" else False + # Suppress the warning about num_workers. + # In many cases, especially for PINNs, + # serial data loading can outperform parallel data loading. + warnings.filterwarnings( + "ignore", + message=( + "The '(train|val|test)_dataloader' does not have many workers " + "which may be a bottleneck." + ), + module="lightning.pytorch.trainer.connectors.data_connector", + ) + # Use custom batching (good if batch size is large) + if self.batch_size is not None: + sampler = PinaSampler(dataset, shuffle) + if self.automatic_batching: + collate = Collator( + self.find_max_conditions_lengths(split), + self.automatic_batching, + dataset=dataset, + ) + else: + collate = Collator( + None, self.automatic_batching, dataset=dataset + ) + return DataLoader( + dataset, + self.batch_size, + collate_fn=collate, + sampler=sampler, + num_workers=self.num_workers, + ) + dataloader = DummyDataloader(dataset) + dataloader.dataset = self._transfer_batch_to_device( + dataloader.dataset, self.trainer.strategy.root_device, 0 + ) + self.transfer_batch_to_device = self._transfer_batch_to_device_dummy + return dataloader + + def find_max_conditions_lengths(self, split): + """ + Define the maximum length for each conditions. + + :param dict split: The split of the dataset. + :return: The maximum length per condition. + :rtype: dict + """ + + max_conditions_lengths = {} + for k, v in self.collector_splits[split].items(): + if self.batch_size is None: + max_conditions_lengths[k] = len(v["input"]) + elif self.repeat: + max_conditions_lengths[k] = self.batch_size + else: + max_conditions_lengths[k] = min( + len(v["input"]), self.batch_size + ) + return max_conditions_lengths + + def val_dataloader(self): + """ + Create the validation dataloader. + + :return: The validation dataloader + :rtype: torch.utils.data.DataLoader + """ + return self._create_dataloader("val", self.val_dataset) + + def train_dataloader(self): + """ + Create the training dataloader + + :return: The training dataloader + :rtype: torch.utils.data.DataLoader + """ + return self._create_dataloader("train", self.train_dataset) + + def test_dataloader(self): + """ + Create the testing dataloader + + :return: The testing dataloader + :rtype: torch.utils.data.DataLoader + """ + return self._create_dataloader("test", self.test_dataset) + + @staticmethod + def _transfer_batch_to_device_dummy(batch, device, dataloader_idx): + """ + Transfer the batch to the device. This method is used when the batch + size is None: batch has already been transferred to the device. + + :param list[tuple] batch: List of tuple where the first element of the + tuple is the condition name and the second element is the data. + :param torch.device device: Device to which the batch is transferred. + :param int dataloader_idx: Index of the dataloader. + :return: The batch transferred to the device. + :rtype: list[tuple] + """ + + return batch + + def _transfer_batch_to_device(self, batch, device, dataloader_idx): + """ + Transfer the batch to the device. This method is called in the + training loop and is used to transfer the batch to the device. + + :param dict batch: The batch to be transferred to the device. + :param torch.device device: The device to which the batch is + transferred. + :param int dataloader_idx: The index of the dataloader. + :return: The batch transferred to the device. + :rtype: list[tuple] + """ + + batch = [ + ( + k, + super(LightningDataModule, self).transfer_batch_to_device( + v, device, dataloader_idx + ), + ) + for k, v in batch.items() + ] + + return batch + + @staticmethod + def _check_slit_sizes(train_size, test_size, val_size): + """ + Check if the splits are correct. The splits sizes must be positive and + the sum of the splits must be 1. + + :param float train_size: The size of the training split. + :param float test_size: The size of the testing split. + :param float val_size: The size of the validation split. + + :raises ValueError: If at least one of the splits is negative. + :raises ValueError: If the sum of the splits is different + from 1. + """ + + if train_size < 0 or test_size < 0 or val_size < 0: + raise ValueError("The splits must be positive") + if abs(train_size + test_size + val_size - 1) > 1e-6: + raise ValueError("The sum of the splits must be 1") + + @property + def input(self): + """ + Return all the input points coming from all the datasets. + + :return: The input points for training. + :rtype: dict + """ + + to_return = {} + if hasattr(self, "train_dataset") and self.train_dataset is not None: + to_return["train"] = self.train_dataset.input + if hasattr(self, "val_dataset") and self.val_dataset is not None: + to_return["val"] = self.val_dataset.input + if hasattr(self, "test_dataset") and self.test_dataset is not None: + to_return["test"] = self.test_dataset.input + return to_return diff --git a/pina/data/dataset.py b/pina/data/dataset.py new file mode 100644 index 000000000..54c15564d --- /dev/null +++ b/pina/data/dataset.py @@ -0,0 +1,308 @@ +"""Module for the PINA dataset classes.""" + +from abc import abstractmethod, ABC +from torch.utils.data import Dataset +from torch_geometric.data import Data +from ..graph import Graph, LabelBatch + + +class PinaDatasetFactory: + """ + Factory class for the PINA dataset. + + Depending on the data type inside the conditions, it instanciate an object + belonging to the appropriate subclass of + :class:`~pina.data.dataset.PinaDataset`. The possible subclasses are: + + - :class:`~pina.data.dataset.PinaTensorDataset`, for handling \ + :class:`torch.Tensor` and :class:`~pina.label_tensor.LabelTensor` data. + - :class:`~pina.data.dataset.PinaGraphDataset`, for handling \ + :class:`~pina.graph.Graph` and :class:`~torch_geometric.data.Data` data. + """ + + def __new__(cls, conditions_dict, **kwargs): + """ + Instantiate the appropriate subclass of + :class:`~pina.data.dataset.PinaDataset`. + + If a graph is present in the conditions, returns a + :class:`~pina.data.dataset.PinaGraphDataset`, otherwise returns a + :class:`~pina.data.dataset.PinaTensorDataset`. + + :param dict conditions_dict: Dictionary containing all the conditions + to be included in the dataset instance. + :return: A subclass of :class:`~pina.data.dataset.PinaDataset`. + :rtype: PinaTensorDataset | PinaGraphDataset + + :raises ValueError: If an empty dictionary is provided. + """ + + # Check if conditions_dict is empty + if len(conditions_dict) == 0: + raise ValueError("No conditions provided") + + # Check is a Graph is present in the conditions + is_graph = cls._is_graph_dataset(conditions_dict) + if is_graph: + # If a Graph is present, return a PinaGraphDataset + return PinaGraphDataset(conditions_dict, **kwargs) + # If no Graph is present, return a PinaTensorDataset + return PinaTensorDataset(conditions_dict, **kwargs) + + @staticmethod + def _is_graph_dataset(conditions_dict): + """ + Check if a graph is present in the conditions (at least one time). + + :param conditions_dict: Dictionary containing the conditions. + :type conditions_dict: dict + :return: True if a graph is present in the conditions, False otherwise. + :rtype: bool + """ + + # Iterate over the conditions dictionary + for v in conditions_dict.values(): + # Iterate over the values of the current condition + for cond in v.values(): + # Check if the current value is a list of Data objects + if isinstance(cond, (Data, Graph, list, tuple)): + return True + return False + + +class PinaDataset(Dataset, ABC): + """ + Abstract class for the PINA dataset which extends the PyTorch + :class:`~torch.utils.data.Dataset` class. It defines the common interface + for :class:`~pina.data.dataset.PinaTensorDataset` and + :class:`~pina.data.dataset.PinaGraphDataset` classes. + """ + + def __init__( + self, conditions_dict, max_conditions_lengths, automatic_batching + ): + """ + Initialize the instance by storing the conditions dictionary, the + maximum number of items per conditions to consider, and the automatic + batching flag. + + :param dict conditions_dict: A dictionary mapping condition names to + their respective data. Each key represents a condition name, and the + corresponding value is a dictionary containing the associated data. + :param dict max_conditions_lengths: Maximum number of data points that + can be included in a single batch per condition. + :param bool automatic_batching: Indicates whether PyTorch automatic + batching is enabled in + :class:`~pina.data.data_module.PinaDataModule`. + """ + + # Store the conditions dictionary + self.conditions_dict = conditions_dict + # Store the maximum number of conditions to consider + self.max_conditions_lengths = max_conditions_lengths + # Store length of each condition + self.conditions_length = { + k: len(v["input"]) for k, v in self.conditions_dict.items() + } + # Store the maximum length of the dataset + self.length = max(self.conditions_length.values()) + # Dynamically set the getitem function based on automatic batching + if automatic_batching: + self._getitem_func = self._getitem_int + else: + self._getitem_func = self._getitem_dummy + + def _get_max_len(self): + """ + Returns the length of the longest condition in the dataset. + + :return: Length of the longest condition in the dataset. + :rtype: int + """ + + max_len = 0 + for condition in self.conditions_dict.values(): + max_len = max(max_len, len(condition["input"])) + return max_len + + def __len__(self): + return self.length + + def __getitem__(self, idx): + return self._getitem_func(idx) + + def _getitem_dummy(self, idx): + """ + Return the index itself. This is used when automatic batching is + disabled to postpone the data retrieval to the dataloader. + + :param int idx: Index. + :return: Index. + :rtype: int + """ + + # If automatic batching is disabled, return the data at the given index + return idx + + def _getitem_int(self, idx): + """ + Return the data at the given index in the dataset. This is used when + automatic batching is enabled. + + :param int idx: Index. + :return: A dictionary containing the data at the given index. + :rtype: dict + """ + + # If automatic batching is enabled, return the data at the given index + return { + k: {k_data: v[k_data][idx % len(v["input"])] for k_data in v.keys()} + for k, v in self.conditions_dict.items() + } + + def get_all_data(self): + """ + Return all data in the dataset. + + :return: A dictionary containing all the data in the dataset. + :rtype: dict + """ + + index = list(range(len(self))) + return self.fetch_from_idx_list(index) + + def fetch_from_idx_list(self, idx): + """ + Return data from the dataset given a list of indices. + + :param list[int] idx: List of indices. + :return: A dictionary containing the data at the given indices. + :rtype: dict + """ + + to_return_dict = {} + for condition, data in self.conditions_dict.items(): + # Get the indices for the current condition + cond_idx = idx[: self.max_conditions_lengths[condition]] + # Get the length of the current condition + condition_len = self.conditions_length[condition] + # If the length of the dataset is greater than the length of the + # current condition, repeat the indices + if self.length > condition_len: + cond_idx = [idx % condition_len for idx in cond_idx] + # Retrieve the data from the current condition + to_return_dict[condition] = self._retrive_data(data, cond_idx) + return to_return_dict + + @abstractmethod + def _retrive_data(self, data, idx_list): + """ + Abstract method to retrieve data from the dataset given a list of + indices. + """ + + +class PinaTensorDataset(PinaDataset): + """ + Dataset class for the PINA dataset with :class:`torch.Tensor` and + :class:`~pina.label_tensor.LabelTensor` data. + """ + + # Override _retrive_data method for torch.Tensor data + def _retrive_data(self, data, idx_list): + """ + Retrieve data from the dataset given a list of indices. + + :param dict data: Dictionary containing the data + (only :class:`torch.Tensor` or + :class:`~pina.label_tensor.LabelTensor`). + :param list[int] idx_list: indices to retrieve. + :return: Dictionary containing the data at the given indices. + :rtype: dict + """ + + return {k: v[idx_list] for k, v in data.items()} + + @property + def input(self): + """ + Return the input data for the dataset. + + :return: Dictionary containing the input points. + :rtype: dict + """ + return {k: v["input"] for k, v in self.conditions_dict.items()} + + +class PinaGraphDataset(PinaDataset): + """ + Dataset class for the PINA dataset with :class:`~torch_geometric.data.Data` + and :class:`~pina.graph.Graph` data. + """ + + def _create_graph_batch(self, data): + """ + Create a LabelBatch object from a list of + :class:`~torch_geometric.data.Data` objects. + + :param data: List of items to collate in a single batch. + :type data: list[Data] | list[Graph] + :return: LabelBatch object all the graph collated in a single batch + disconnected graphs. + :rtype: LabelBatch + """ + batch = LabelBatch.from_data_list(data) + return batch + + def _create_tensor_batch(self, data): + """ + Reshape properly ``data`` tensor to be processed handle by the graph + based models. + + :param data: torch.Tensor object of shape ``(N, ...)`` where ``N`` is + the number of data objects. + :type data: torch.Tensor | LabelTensor + :return: Reshaped tensor object. + :rtype: torch.Tensor | LabelTensor + """ + out = data.reshape(-1, *data.shape[2:]) + return out + + def create_batch(self, data): + """ + Create a Batch object from a list of :class:`~torch_geometric.data.Data` + objects. + + :param data: List of items to collate in a single batch. + :type data: list[Data] | list[Graph] + :return: Batch object. + :rtype: :class:`~torch_geometric.data.Batch` + | :class:`~pina.graph.LabelBatch` + """ + + if isinstance(data[0], Data): + return self._create_graph_batch(data) + return self._create_tensor_batch(data) + + # Override _retrive_data method for graph handling + def _retrive_data(self, data, idx_list): + """ + Retrieve data from the dataset given a list of indices. + + :param dict data: Dictionary containing the data. + :param list[int] idx_list: List of indices to retrieve. + :return: Dictionary containing the data at the given indices. + :rtype: dict + """ + + # Return the data from the current condition + # If the data is a list of Data objects, create a Batch object + # If the data is a list of torch.Tensor objects, create a torch.Tensor + return { + k: ( + self._create_graph_batch([v[i] for i in idx_list]) + if isinstance(v, list) + else self._create_tensor_batch(v[idx_list]) + ) + for k, v in data.items() + } diff --git a/pina/dataset.py b/pina/dataset.py deleted file mode 100644 index c6a8d29e4..000000000 --- a/pina/dataset.py +++ /dev/null @@ -1,259 +0,0 @@ -from torch.utils.data import Dataset -import torch -from pina import LabelTensor - - -class SamplePointDataset(Dataset): - """ - This class is used to create a dataset of sample points. - """ - - def __init__(self, problem, device) -> None: - """ - :param dict input_pts: The input points. - """ - super().__init__() - pts_list = [] - self.condition_names = [] - - for name, condition in problem.conditions.items(): - if not hasattr(condition, "output_points"): - pts_list.append(problem.input_pts[name]) - self.condition_names.append(name) - - self.pts = LabelTensor.vstack(pts_list) - - if self.pts != []: - self.condition_indeces = torch.cat( - [ - torch.tensor([i] * len(pts_list[i])) - for i in range(len(self.condition_names)) - ], - dim=0, - ) - else: # if there are no sample points - self.condition_indeces = torch.tensor([]) - self.pts = torch.tensor([]) - - self.pts = self.pts.to(device) - self.condition_indeces = self.condition_indeces.to(device) - - def __len__(self): - return self.pts.shape[0] - - -class DataPointDataset(Dataset): - - def __init__(self, problem, device) -> None: - super().__init__() - input_list = [] - output_list = [] - self.condition_names = [] - - for name, condition in problem.conditions.items(): - if hasattr(condition, "output_points"): - input_list.append(problem.conditions[name].input_points) - output_list.append(problem.conditions[name].output_points) - self.condition_names.append(name) - - self.input_pts = LabelTensor.vstack(input_list) - self.output_pts = LabelTensor.vstack(output_list) - - if self.input_pts != []: - self.condition_indeces = torch.cat( - [ - torch.tensor([i] * len(input_list[i])) - for i in range(len(self.condition_names)) - ], - dim=0, - ) - else: # if there are no data points - self.condition_indeces = torch.tensor([]) - self.input_pts = torch.tensor([]) - self.output_pts = torch.tensor([]) - - self.input_pts = self.input_pts.to(device) - self.output_pts = self.output_pts.to(device) - self.condition_indeces = self.condition_indeces.to(device) - - def __len__(self): - return self.input_pts.shape[0] - - -class SamplePointLoader: - """ - This class is used to create a dataloader to use during the training. - - :var condition_names: The names of the conditions. The order is consistent - with the condition indeces in the batches. - :vartype condition_names: list[str] - """ - - def __init__( - self, sample_dataset, data_dataset, batch_size=None, shuffle=True - ) -> None: - """ - Constructor. - - :param SamplePointDataset sample_pts: The sample points dataset. - :param int batch_size: The batch size. If ``None``, the batch size is - set to the number of sample points. Default is ``None``. - :param bool shuffle: If ``True``, the sample points are shuffled. - Default is ``True``. - """ - if not isinstance(sample_dataset, SamplePointDataset): - raise TypeError( - f"Expected SamplePointDataset, got {type(sample_dataset)}" - ) - if not isinstance(data_dataset, DataPointDataset): - raise TypeError( - f"Expected DataPointDataset, got {type(data_dataset)}" - ) - - self.n_data_conditions = len(data_dataset.condition_names) - self.n_phys_conditions = len(sample_dataset.condition_names) - data_dataset.condition_indeces += self.n_phys_conditions - - self._prepare_sample_dataset(sample_dataset, batch_size, shuffle) - self._prepare_data_dataset(data_dataset, batch_size, shuffle) - - self.condition_names = ( - sample_dataset.condition_names + data_dataset.condition_names - ) - - self.batch_list = [] - for i in range(len(self.batch_sample_pts)): - self.batch_list.append(("sample", i)) - - for i in range(len(self.batch_input_pts)): - self.batch_list.append(("data", i)) - - if shuffle: - self.random_idx = torch.randperm(len(self.batch_list)) - else: - self.random_idx = torch.arange(len(self.batch_list)) - - def _prepare_data_dataset(self, dataset, batch_size, shuffle): - """ - Prepare the dataset for data points. - - :param SamplePointDataset dataset: The dataset. - :param int batch_size: The batch size. - :param bool shuffle: If ``True``, the sample points are shuffled. - """ - self.sample_dataset = dataset - - if len(dataset) == 0: - self.batch_data_conditions = [] - self.batch_input_pts = [] - self.batch_output_pts = [] - return - - if batch_size is None: - batch_size = len(dataset) - batch_num = len(dataset) // batch_size - if len(dataset) % batch_size != 0: - batch_num += 1 - - output_labels = dataset.output_pts.labels - input_labels = dataset.input_pts.labels - self.tensor_conditions = dataset.condition_indeces - - if shuffle: - idx = torch.randperm(dataset.input_pts.shape[0]) - self.input_pts = dataset.input_pts[idx] - self.output_pts = dataset.output_pts[idx] - self.tensor_conditions = dataset.condition_indeces[idx] - - self.batch_input_pts = torch.tensor_split(dataset.input_pts, batch_num) - self.batch_output_pts = torch.tensor_split( - dataset.output_pts, batch_num - ) - - for i in range(len(self.batch_input_pts)): - self.batch_input_pts[i].labels = input_labels - self.batch_output_pts[i].labels = output_labels - - self.batch_data_conditions = torch.tensor_split( - self.tensor_conditions, batch_num - ) - - def _prepare_sample_dataset(self, dataset, batch_size, shuffle): - """ - Prepare the dataset for sample points. - - :param DataPointDataset dataset: The dataset. - :param int batch_size: The batch size. - :param bool shuffle: If ``True``, the sample points are shuffled. - """ - - self.sample_dataset = dataset - if len(dataset) == 0: - self.batch_sample_conditions = [] - self.batch_sample_pts = [] - return - - if batch_size is None: - batch_size = len(dataset) - - batch_num = len(dataset) // batch_size - if len(dataset) % batch_size != 0: - batch_num += 1 - - self.tensor_pts = dataset.pts - self.tensor_conditions = dataset.condition_indeces - - # if shuffle: - # idx = torch.randperm(self.tensor_pts.shape[0]) - # self.tensor_pts = self.tensor_pts[idx] - # self.tensor_conditions = self.tensor_conditions[idx] - - self.batch_sample_pts = torch.tensor_split(self.tensor_pts, batch_num) - for i in range(len(self.batch_sample_pts)): - self.batch_sample_pts[i].labels = dataset.pts.labels - - self.batch_sample_conditions = torch.tensor_split( - self.tensor_conditions, batch_num - ) - - def __iter__(self): - """ - Return an iterator over the points. Any element of the iterator is a - dictionary with the following keys: - - ``pts``: The input sample points. It is a LabelTensor with the - shape ``(batch_size, input_dimension)``. - - ``output``: The output sample points. This key is present only - if data conditions are present. It is a LabelTensor with the - shape ``(batch_size, output_dimension)``. - - ``condition``: The integer condition indeces. It is a tensor - with the shape ``(batch_size, )`` of type ``torch.int64`` and - indicates for any ``pts`` the corresponding problem condition. - - :return: An iterator over the points. - :rtype: iter - """ - # for i in self.random_idx: - for i in range(len(self.batch_list)): - type_, idx_ = self.batch_list[i] - - if type_ == "sample": - d = { - "pts": self.batch_sample_pts[idx_].requires_grad_(True), - "condition": self.batch_sample_conditions[idx_], - } - else: - d = { - "pts": self.batch_input_pts[idx_].requires_grad_(True), - "output": self.batch_output_pts[idx_], - "condition": self.batch_data_conditions[idx_], - } - yield d - - def __len__(self): - """ - Return the number of batches. - - :return: The number of batches. - :rtype: int - """ - return len(self.batch_list) diff --git a/pina/domain/__init__.py b/pina/domain/__init__.py new file mode 100644 index 000000000..cf0f03b90 --- /dev/null +++ b/pina/domain/__init__.py @@ -0,0 +1,23 @@ +"""Module to create and handle domains.""" + +__all__ = [ + "DomainInterface", + "CartesianDomain", + "EllipsoidDomain", + "Union", + "Intersection", + "Exclusion", + "Difference", + "OperationInterface", + "SimplexDomain", +] + +from .domain_interface import DomainInterface +from .cartesian import CartesianDomain +from .ellipsoid import EllipsoidDomain +from .exclusion_domain import Exclusion +from .intersection_domain import Intersection +from .union_domain import Union +from .difference_domain import Difference +from .operation_interface import OperationInterface +from .simplex import SimplexDomain diff --git a/pina/geometry/cartesian.py b/pina/domain/cartesian.py similarity index 59% rename from pina/geometry/cartesian.py rename to pina/domain/cartesian.py index 11354b62f..4e6f3b9b0 100644 --- a/pina/geometry/cartesian.py +++ b/pina/domain/cartesian.py @@ -1,19 +1,26 @@ +"""Module for the Cartesian Domain.""" + import torch -from .location import Location +from .domain_interface import DomainInterface from ..label_tensor import LabelTensor from ..utils import torch_lhs, chebyshev_roots -class CartesianDomain(Location): - """PINA implementation of Hypercube domain.""" +class CartesianDomain(DomainInterface): + """ + Implementation of the hypercube domain. + """ def __init__(self, cartesian_dict): """ - :param cartesian_dict: A dictionary with dict-key a string representing - the input variables for the pinn, and dict-value a list with - the domain extrema. - :type cartesian_dict: dict + Initialization of the :class:`CartesianDomain` class. + + :param dict cartesian_dict: A dictionary where the keys are the + variable names and the values are the domain extrema. The domain + extrema can be either a list with two elements or a single number. + If the domain extrema is a single number, the variable is fixed to + that value. :Example: >>> spatial_domain = CartesianDomain({'x': [0, 1], 'y': [0, 1]}) @@ -29,19 +36,32 @@ def __init__(self, cartesian_dict): else: raise TypeError + @property + def sample_modes(self): + """ + List of available sampling modes. + + :return: List of available sampling modes. + :rtype: list[str] + """ + return ["random", "grid", "lh", "chebyshev", "latin"] + @property def variables(self): - """Spatial variables. + """ + List of variables of the domain. - :return: Spatial variables defined in ``__init__()`` + :return: List of variables of the domain. :rtype: list[str] """ return sorted(list(self.fixed_.keys()) + list(self.range_.keys())) def update(self, new_domain): - """Adding new dimensions on the ``CartesianDomain`` + """ + Add new dimensions to an existing :class:`CartesianDomain` object. - :param CartesianDomain new_domain: A new ``CartesianDomain`` object to merge + :param CartesianDomain new_domain: New domain to be added to an existing + :class:`CartesianDomain` object. :Example: >>> spatial_domain = CartesianDomain({'x': [0, 1], 'y': [0, 1]}) @@ -56,24 +76,20 @@ def update(self, new_domain): self.range_.update(new_domain.range_) def _sample_range(self, n, mode, bounds): - """Rescale the samples to the correct bounds + """ + Rescale the samples to fit within the specified bounds. - :param n: Number of points to sample, see Note below - for reference. - :type n: int - :param mode: Mode for sampling, defaults to ``random``. - Available modes include: random sampling, ``random``; - latin hypercube sampling, ``latin`` or ``lh``; - chebyshev sampling, ``chebyshev``; grid sampling ``grid``. - :type mode: str - :param bounds: Bounds to rescale the samples. - :type bounds: torch.Tensor + :param int n: Number of points to sample. + :param str mode: Sampling method. Default is ``random``. + :param torch.Tensor bounds: Bounds of the domain. + :raises RuntimeError: Wrong bounds initialization. + :raises ValueError: Invalid sampling mode. :return: Rescaled sample points. :rtype: torch.Tensor """ dim = bounds.shape[0] if mode in ["chebyshev", "grid"] and dim != 1: - raise RuntimeError("Something wrong in Span...") + raise RuntimeError("Wrong bounds initialization") if mode == "random": pts = torch.rand(size=(n, dim)) @@ -81,45 +97,42 @@ def _sample_range(self, n, mode, bounds): pts = chebyshev_roots(n).mul(0.5).add(0.5).reshape(-1, 1) elif mode == "grid": pts = torch.linspace(0, 1, n).reshape(-1, 1) - # elif mode == 'lh' or mode == 'latin': elif mode in ["lh", "latin"]: pts = torch_lhs(n, dim) + else: + raise ValueError("Invalid mode") - pts *= bounds[:, 1] - bounds[:, 0] - pts += bounds[:, 0] - - return pts + return pts * (bounds[:, 1] - bounds[:, 0]) + bounds[:, 0] def sample(self, n, mode="random", variables="all"): - """Sample routine. + """ + Sampling routine. - :param n: Number of points to sample, see Note below - for reference. - :type n: int - :param mode: Mode for sampling, defaults to ``random``. - Available modes include: random sampling, ``random``; + :param int n: Number of points to sample, see Note below for reference. + :param str mode: Sampling method. Default is ``random``. + Available modes: random sampling, ``random``; latin hypercube sampling, ``latin`` or ``lh``; chebyshev sampling, ``chebyshev``; grid sampling ``grid``. - :type mode: str - :param variables: pinn variable to be sampled, defaults to ``all``. - :type variables: str | list[str] - :return: Returns ``LabelTensor`` of n sampled points. + :param list[str] variables: variables to be sampled. Default is ``all``. + :return: Sampled points. :rtype: LabelTensor .. note:: - The total number of points sampled in case of multiple variables - is not ``n``, and it depends on the chosen ``mode``. If ``mode`` is - 'grid' or ``chebyshev``, the points are sampled independentely - across the variables and the results crossed together, i.e. the - final number of points is ``n`` to the power of the number of - variables. If 'mode' is 'random', ``lh`` or ``latin``, the variables - are sampled all together, and the final number of points + When multiple variables are involved, the total number of sampled + points may differ from ``n``, depending on the chosen ``mode``. + If ``mode`` is ``grid`` or ``chebyshev``, points are sampled + independently for each variable and then combined, resulting in a + total number of points equal to ``n`` raised to the power of the + number of variables. If 'mode' is 'random', ``lh`` or ``latin``, + all variables are sampled together, and the total number of points + remains ``n``. .. warning:: - The extrema values of Span are always sampled only for ``grid`` mode. + The extrema of CartesianDomain are only sampled when using the + ``grid`` mode. :Example: - >>> spatial_domain = Span({'x': [0, 1], 'y': [0, 1]}) + >>> spatial_domain = CartesianDomain({'x': [0, 1], 'y': [0, 1]}) >>> spatial_domain.sample(n=4, mode='random') tensor([[0.0108, 0.7643], [0.4477, 0.8015], @@ -145,23 +158,31 @@ def sample(self, n, mode="random", variables="all"): """ def _1d_sampler(n, mode, variables): - """Sample independentely the variables and cross the results""" + """ + Sample each variable independently. + + :param int n: Number of points to sample. + :param str mode: Sampling method. + :param list[str] variables: variables to be sampled. + :return: Sampled points. + :rtype: list[LabelTensor] + """ tmp = [] for variable in variables: - if variable in self.range_.keys(): + if variable in self.range_: bound = torch.tensor([self.range_[variable]]) pts_variable = self._sample_range(n, mode, bound) pts_variable = pts_variable.as_subclass(LabelTensor) pts_variable.labels = [variable] tmp.append(pts_variable) - - result = tmp[0] - for i in tmp[1:]: - result = result.append(i, mode="cross") + if tmp: + result = tmp[0] + for i in tmp[1:]: + result = result.append(i, mode="cross") for variable in variables: - if variable in self.fixed_.keys(): + if variable in self.fixed_: value = self.fixed_[variable] pts_variable = torch.tensor([[value]]).repeat( result.shape[0], 1 @@ -174,19 +195,14 @@ def _1d_sampler(n, mode, variables): return result def _Nd_sampler(n, mode, variables): - """Sample all the variables together - - :param n: Number of points to sample. - :type n: int - :param mode: Mode for sampling, defaults to ``random``. - Available modes include: random sampling, ``random``; - latin hypercube sampling, ``latin`` or ``lh``; - chebyshev sampling, ``chebyshev``; grid sampling ``grid``. - :type mode: str. - :param variables: pinn variable to be sampled, defaults to ``all``. - :type variables: str or list[str]. - :return: Sample points. - :rtype: list[torch.Tensor] + """ + Sample all variables together. + + :param int n: Number of points to sample. + :param str mode: Sampling method. + :param list[str] variables: variables to be sampled. + :return: Sampled points. + :rtype: list[LabelTensor] """ pairs = [(k, v) for k, v in self.range_.items() if k in variables] keys, values = map(list, zip(*pairs)) @@ -196,7 +212,7 @@ def _Nd_sampler(n, mode, variables): result.labels = keys for variable in variables: - if variable in self.fixed_.keys(): + if variable in self.fixed_: value = self.fixed_[variable] pts_variable = torch.tensor([[value]]).repeat( result.shape[0], 1 @@ -208,18 +224,17 @@ def _Nd_sampler(n, mode, variables): return result def _single_points_sample(n, variables): - """Sample a single point in one dimension. + """ + Sample a single point in one dimension. - :param n: Number of points to sample. - :type n: int - :param variables: Variables to sample from. - :type variables: list[str] - :return: Sample points. + :param int n: Number of points to sample. + :param list[str] variables: variables to be sampled. + :return: Sampled points. :rtype: list[torch.Tensor] """ tmp = [] for variable in variables: - if variable in self.fixed_.keys(): + if variable in self.fixed_: value = self.fixed_[variable] pts_variable = torch.tensor([[value]]).repeat(n, 1) pts_variable = pts_variable.as_subclass(LabelTensor) @@ -239,23 +254,24 @@ def _single_points_sample(n, variables): if self.fixed_ and (not self.range_): return _single_points_sample(n, variables) + if isinstance(variables, str) and variables in self.fixed_: + return _single_points_sample(n, variables) if mode in ["grid", "chebyshev"]: return _1d_sampler(n, mode, variables).extract(variables) - elif mode in ["random", "lh", "latin"]: + if mode in ["random", "lh", "latin"]: return _Nd_sampler(n, mode, variables).extract(variables) - else: - raise ValueError(f"mode={mode} is not valid.") + raise ValueError(f"mode={mode} is not valid.") def is_inside(self, point, check_border=False): - """Check if a point is inside the ellipsoid. - - :param point: Point to be checked - :type point: LabelTensor - :param check_border: Check if the point is also on the frontier - of the hypercube, default ``False``. - :type check_border: bool - :return: Returning ``True`` if the point is inside, ``False`` otherwise. + """ + Check if a point is inside the hypercube. + + :param LabelTensor point: Point to be checked. + :param bool check_border: If ``True``, the border is considered inside + the hypercube. Default is ``False``. + :return: ``True`` if the point is inside the domain, + ``False`` otherwise. :rtype: bool """ is_inside = [] diff --git a/pina/geometry/difference_domain.py b/pina/domain/difference_domain.py similarity index 59% rename from pina/geometry/difference_domain.py rename to pina/domain/difference_domain.py index d2ba414f0..4ea7b5278 100644 --- a/pina/geometry/difference_domain.py +++ b/pina/domain/difference_domain.py @@ -1,4 +1,4 @@ -"""Module for Difference class.""" +"""Module for the Difference Operation.""" import torch from .operation_interface import OperationInterface @@ -6,42 +6,47 @@ class Difference(OperationInterface): + r""" + Implementation of the difference operation between of a list of domains. - def __init__(self, geometries): - r""" - PINA implementation of Difference of Domains. - Given two sets :math:`A` and :math:`B` then the - domain difference is defined as: + Given two sets :math:`A` and :math:`B`, define the difference of the two + sets as: - .. math:: - A - B = \{x \mid x \in A \land x \not\in B\}, + .. math:: + A - B = \{x \mid x \in A \land x \not\in B\}, - with :math:`x` a point in :math:`\mathbb{R}^N` and :math:`N` - the dimension of the geometry space. + where :math:`x` is a point in :math:`\mathbb{R}^N`. + """ - :param list geometries: A list of geometries from ``pina.geometry`` - such as ``EllipsoidDomain`` or ``CartesianDomain``. The first - geometry in the list is the geometry from which points are - sampled. The rest of the geometries are the geometries that - are excluded from the first geometry to find the difference. + def __init__(self, geometries): + """ + Initialization of the :class:`Difference` class. + + :param list[DomainInterface] geometries: A list of instances of the + :class:`~pina.domain.domain_interface.DomainInterface` class on + which the difference operation is performed. The first domain in the + list serves as the base from which points are sampled, while the + remaining domains define the regions to be excluded from the base + domain to compute the difference. :Example: >>> # Create two ellipsoid domains >>> ellipsoid1 = EllipsoidDomain({'x': [-1, 1], 'y': [-1, 1]}) >>> ellipsoid2 = EllipsoidDomain({'x': [0, 2], 'y': [0, 2]}) - >>> # Create a Difference of the ellipsoid domains + >>> # Define the difference between the domains >>> difference = Difference([ellipsoid1, ellipsoid2]) """ super().__init__(geometries) def is_inside(self, point, check_border=False): """ - Check if a point is inside the ``Difference`` domain. + Check if a point is inside the resulting domain. - :param point: Point to be checked. - :type point: torch.Tensor - :param bool check_border: If ``True``, the border is considered inside. - :return: ``True`` if the point is inside the Exclusion domain, ``False`` otherwise. + :param LabelTensor point: Point to be checked. + :param bool check_border: If ``True``, the border is considered inside + the domain. Default is ``False``. + :return: ``True`` if the point is inside the domain, + ``False`` otherwise. :rtype: bool """ for geometry in self.geometries[1:]: @@ -51,20 +56,21 @@ def is_inside(self, point, check_border=False): def sample(self, n, mode="random", variables="all"): """ - Sample routine for ``Difference`` domain. - - :param int n: Number of points to sample in the shape. - :param str mode: Mode for sampling, defaults to ``random``. Available modes include: ``random``. - :param variables: Variables to be sampled, defaults to ``all``. - :type variables: str | list[str] - :return: Returns ``LabelTensor`` of n sampled points. + Sampling routine. + + :param int n: Number of points to sample. + :param str mode: Sampling method. Default is ``random``. + Available modes: random sampling, ``random``; + :param list[str] variables: variables to be sampled. Default is ``all``. + :raises NotImplementedError: If the sampling method is not implemented. + :return: Sampled points. :rtype: LabelTensor :Example: >>> # Create two Cartesian domains >>> cartesian1 = CartesianDomain({'x': [0, 2], 'y': [0, 2]}) >>> cartesian2 = CartesianDomain({'x': [1, 3], 'y': [1, 3]}) - >>> # Create a Difference of the ellipsoid domains + >>> # Define the difference between the domains >>> difference = Difference([cartesian1, cartesian2]) >>> # Sampling >>> difference.sample(n=5) @@ -77,7 +83,7 @@ def sample(self, n, mode="random", variables="all"): 5 """ - if mode != "random": + if mode not in self.sample_modes: raise NotImplementedError( f"{mode} is not a valid mode for sampling." ) diff --git a/pina/domain/domain_interface.py b/pina/domain/domain_interface.py new file mode 100644 index 000000000..7f693e3da --- /dev/null +++ b/pina/domain/domain_interface.py @@ -0,0 +1,61 @@ +"""Module for the Domain Interface.""" + +from abc import ABCMeta, abstractmethod + + +class DomainInterface(metaclass=ABCMeta): + """ + Abstract base class for geometric domains. All specific domain types should + inherit from this class. + """ + + available_sampling_modes = ["random", "grid", "lh", "chebyshev", "latin"] + + @property + @abstractmethod + def sample_modes(self): + """ + Abstract method defining sampling methods. + """ + + @property + @abstractmethod + def variables(self): + """ + Abstract method returning the domain variables. + """ + + @sample_modes.setter + def sample_modes(self, values): + """ + Setter for the sample_modes property. + + :param values: Sampling modes to be set. + :type values: str | list[str] + :raises TypeError: Invalid sampling mode. + """ + if not isinstance(values, (list, tuple)): + values = [values] + for value in values: + if value not in DomainInterface.available_sampling_modes: + raise TypeError( + f"mode {value} not valid. Expected at least " + "one in " + f"{DomainInterface.available_sampling_modes}." + ) + + @abstractmethod + def sample(self): + """ + Abstract method for the sampling routine. + """ + + @abstractmethod + def is_inside(self, point, check_border=False): + """ + Abstract method for checking if a point is inside the domain. + + :param LabelTensor point: Point to be checked. + :param bool check_border: If ``True``, the border is considered inside + the domain. Default is ``False``. + """ diff --git a/pina/geometry/ellipsoid.py b/pina/domain/ellipsoid.py similarity index 64% rename from pina/geometry/ellipsoid.py rename to pina/domain/ellipsoid.py index 2baea5324..4b75be8e2 100644 --- a/pina/geometry/ellipsoid.py +++ b/pina/domain/ellipsoid.py @@ -1,39 +1,42 @@ -import torch +"""Module for the Ellipsoid Domain.""" -from .location import Location +import torch +from .domain_interface import DomainInterface from ..label_tensor import LabelTensor from ..utils import check_consistency -class EllipsoidDomain(Location): - """PINA implementation of Ellipsoid domain.""" +class EllipsoidDomain(DomainInterface): + """ + Implementation of the ellipsoid domain. + """ def __init__(self, ellipsoid_dict, sample_surface=False): - """PINA implementation of Ellipsoid domain. - - :param ellipsoid_dict: A dictionary with dict-key a string representing - the input variables for the pinn, and dict-value a list with - the domain extrema. - :type ellipsoid_dict: dict - :param sample_surface: A variable for choosing sample strategies. If - ``sample_surface=True`` only samples on the ellipsoid surface - frontier are taken. If ``sample_surface=False`` only samples on - the ellipsoid interior are taken, defaults to ``False``. - :type sample_surface: bool + """ + Initialization of the :class:`EllipsoidDomain` class. + + :param dict ellipsoid_dict: A dictionary where the keys are the variable + names and the values are the domain extrema. + :param bool sample_surface: A flag to choose the sampling strategy. + If ``True``, samples are taken from the surface of the ellipsoid. + If ``False``, samples are taken from the interior of the ellipsoid. + Default is ``False``. + :raises TypeError: If the input dictionary is not correctly formatted. .. warning:: - Sampling for dimensions greater or equal to 10 could result - in a shrinking of the ellipsoid, which degrades the quality - of the samples. For dimensions higher than 10, other algorithms - for sampling should be used, such as: Dezert, Jean, and Christian - Musso. "An efficient method for generating points uniformly - distributed in hyperellipsoids." Proceedings of the Workshop on - Estimation, Tracking and Fusion: A Tribute to Yaakov Bar-Shalom. - Vol. 7. No. 8. 2001. + Sampling for dimensions greater or equal to 10 could result in a + shrinkage of the ellipsoid, which degrades the quality of the + samples. For dimensions higher than 10, see the following reference. + + .. seealso:: + **Original reference**: Dezert, Jean, and Musso, Christian. + *An efficient method for generating points uniformly distributed + in hyperellipsoids.* + Proceedings of the Workshop on Estimation, Tracking and Fusion: + A Tribute to Yaakov Bar-Shalom. 2001. :Example: >>> spatial_domain = Ellipsoid({'x':[-1, 1], 'y':[-1,1]}) - """ self.fixed_ = {} self.range_ = {} @@ -55,7 +58,6 @@ def __init__(self, ellipsoid_dict, sample_surface=False): # perform operation only for not fixed variables (if any) if self.range_: - # convert dict vals to torch [dim, 2] matrix list_dict_vals = list(self.range_.values()) tmp = torch.tensor(list_dict_vals, dtype=torch.float) @@ -71,32 +73,43 @@ def __init__(self, ellipsoid_dict, sample_surface=False): self._centers = dict(zip(self.range_.keys(), centers.tolist())) self._axis = dict(zip(self.range_.keys(), ellipsoid_axis.tolist())) + @property + def sample_modes(self): + """ + List of available sampling modes. + + :return: List of available sampling modes. + :rtype: list[str] + """ + return ["random"] + @property def variables(self): - """Spatial variables. + """ + List of variables of the domain. - :return: Spatial variables defined in '__init__()' + :return: List of variables of the domain. :rtype: list[str] """ return sorted(list(self.fixed_.keys()) + list(self.range_.keys())) def is_inside(self, point, check_border=False): - """Check if a point is inside the ellipsoid domain. + """ + Check if a point is inside the ellipsoid. + + :param LabelTensor point: Point to be checked. + :param bool check_border: If ``True``, the border is considered inside + the ellipsoid. Default is ``False``. + :raises ValueError: If the labels of the point are different from those + passed in the ``__init__`` method. + :return: ``True`` if the point is inside the domain, + ``False`` otherwise. + :rtype: bool .. note:: - When ``sample_surface`` in the ``__init()__`` - is set to ``True``, then the method only checks - points on the surface, and not inside the domain. - - :param point: Point to be checked. - :type point: LabelTensor - :param check_border: Check if the point is also on the frontier - of the ellipsoid, default ``False``. - :type check_border: bool - :return: Returning True if the point is inside, ``False`` otherwise. - :rtype: bool + When ``sample_surface=True`` in the ``__init__`` method, this method + checks only those points lying on the surface of the ellipsoid. """ - # small check that point is labeltensor check_consistency(point, LabelTensor) @@ -110,7 +123,7 @@ def is_inside(self, point, check_border=False): tmp = torch.tensor(list_dict_vals, dtype=torch.float) centers = LabelTensor(tmp.reshape(1, -1), self.variables) - if not all([i in ax_sq.labels for i in point.labels]): + if not all(i in ax_sq.labels for i in point.labels): raise ValueError( "point labels different from constructor" f" dictionary labels. Got {point.labels}," @@ -136,15 +149,12 @@ def is_inside(self, point, check_border=False): return bool(eqn < 0) def _sample_range(self, n, mode, variables): - """Rescale the samples to the correct bounds. - - :param n: Number of points to sample in the ellipsoid. - :type n: int - :param mode: Mode for sampling, defaults to ``random``. - Available modes include: random sampling, ``random``. - :type mode: str, optional - :param variables: Variables to be rescaled in the samples. - :type variables: torch.Tensor + """ + Rescale the samples to fit within the specified bounds. + + :param int n: Number of points to sample. + :param str mode: Sampling method. Default is ``random``. + :param list[str] variables: variables whose samples must be rescaled. :return: Rescaled sample points. :rtype: torch.Tensor """ @@ -196,18 +206,20 @@ def _sample_range(self, n, mode, variables): return pts def sample(self, n, mode="random", variables="all"): - """Sample routine. - - :param int n: Number of points to sample in the shape. - :param str mode: Mode for sampling, defaults to ``random``. Available modes include: ``random``. - :param variables: Variables to be sampled, defaults to ``all``. - :type variables: str | list[str] - :return: Returns ``LabelTensor`` of n sampled points. + """ + Sampling routine. + + :param int n: Number of points to sample. + :param str mode: Sampling method. Default is ``random``. + Available modes: random sampling, ``random``. + :param list[str] variables: variables to be sampled. Default is ``all``. + :raises NotImplementedError: If the sampling mode is not implemented. + :return: Sampled points. :rtype: LabelTensor :Example: - >>> elips = Ellipsoid({'x':[1, 0], 'y':1}) - >>> elips.sample(n=6) + >>> ellipsoid = Ellipsoid({'x':[1, 0], 'y':1}) + >>> ellipsoid.sample(n=6) tensor([[0.4872, 1.0000], [0.2977, 1.0000], [0.0422, 1.0000], @@ -217,19 +229,14 @@ def sample(self, n, mode="random", variables="all"): """ def _Nd_sampler(n, mode, variables): - """Sample all the variables together - - :param n: Number of points to sample. - :type n: int - :param mode: Mode for sampling, defaults to ``random``. - Available modes include: random sampling, ``random``; - latin hypercube sampling, 'latin' or 'lh'; - chebyshev sampling, 'chebyshev'; grid sampling 'grid'. - :type mode: str, optional. - :param variables: pinn variable to be sampled, defaults to ``all``. - :type variables: str or list[str], optional. - :return: Sample points. - :rtype: list[torch.Tensor] + """ + Sample all variables together. + + :param int n: Number of points to sample. + :param str mode: Sampling method. + :param list[str] variables: variables to be sampled. + :return: Sampled points. + :rtype: list[LabelTensor] """ pairs = [(k, v) for k, v in self.range_.items() if k in variables] keys, _ = map(list, zip(*pairs)) @@ -239,7 +246,7 @@ def _Nd_sampler(n, mode, variables): result.labels = keys for variable in variables: - if variable in self.fixed_.keys(): + if variable in self.fixed_: value = self.fixed_[variable] pts_variable = torch.tensor([[value]]).repeat( result.shape[0], 1 @@ -251,18 +258,17 @@ def _Nd_sampler(n, mode, variables): return result def _single_points_sample(n, variables): - """Sample a single point in one dimension. + """ + Sample a single point in one dimension. - :param n: Number of points to sample. - :type n: int - :param variables: Variables to sample from. - :type variables: list[str] - :return: Sample points. + :param int n: Number of points to sample. + :param list[str] variables: variables to be sampled. + :return: Sampled points. :rtype: list[torch.Tensor] """ tmp = [] for variable in variables: - if variable in self.fixed_.keys(): + if variable in self.fixed_: value = self.fixed_[variable] pts_variable = torch.tensor([[value]]).repeat(n, 1) pts_variable = pts_variable.as_subclass(LabelTensor) @@ -283,10 +289,7 @@ def _single_points_sample(n, variables): if self.fixed_ and (not self.range_): return _single_points_sample(n, variables).extract(variables) - if variables == "all": - variables = self.variables - - if mode in ["random"]: + if mode in self.sample_modes: return _Nd_sampler(n, mode, variables).extract(variables) - else: - raise NotImplementedError(f"mode={mode} is not implemented.") + + raise NotImplementedError(f"mode={mode} is not implemented.") diff --git a/pina/geometry/exclusion_domain.py b/pina/domain/exclusion_domain.py similarity index 65% rename from pina/geometry/exclusion_domain.py rename to pina/domain/exclusion_domain.py index ed63db314..4a61e415d 100644 --- a/pina/geometry/exclusion_domain.py +++ b/pina/domain/exclusion_domain.py @@ -1,45 +1,51 @@ -"""Module for Exclusion class. """ +"""Module for the Exclusion Operation.""" +import random import torch from ..label_tensor import LabelTensor -import random from .operation_interface import OperationInterface class Exclusion(OperationInterface): + r""" + Implementation of the exclusion operation between of a list of domains. - def __init__(self, geometries): - r""" - PINA implementation of Exclusion of Domains. - Given two sets :math:`A` and :math:`B` then the - domain difference is defined as: + Given two sets :math:`A` and :math:`B`, define the exclusion of the two + sets as: + + .. math:: + A \setminus B = \{x \mid x \in A \land x \in B \land + x \not\in(A \lor B)\}, - .. math:: - A \setminus B = \{x \mid x \in A \land x \in B \land x \not\in (A \lor B)\}, + where :math:`x` is a point in :math:`\mathbb{R}^N`. + """ - with :math:`x` a point in :math:`\mathbb{R}^N` and :math:`N` - the dimension of the geometry space. + def __init__(self, geometries): + """ + Initialization of the :class:`Exclusion` class. - :param list geometries: A list of geometries from ``pina.geometry`` - such as ``EllipsoidDomain`` or ``CartesianDomain``. + :param list[DomainInterface] geometries: A list of instances of the + :class:`~pina.domain.domain_interface.DomainInterface` class on + which the exclusion operation is performed. :Example: >>> # Create two ellipsoid domains >>> ellipsoid1 = EllipsoidDomain({'x': [-1, 1], 'y': [-1, 1]}) >>> ellipsoid2 = EllipsoidDomain({'x': [0, 2], 'y': [0, 2]}) - >>> # Create a Exclusion of the ellipsoid domains + >>> # Define the exclusion between the domains >>> exclusion = Exclusion([ellipsoid1, ellipsoid2]) """ super().__init__(geometries) def is_inside(self, point, check_border=False): """ - Check if a point is inside the ``Exclusion`` domain. + Check if a point is inside the resulting domain. - :param point: Point to be checked. - :type point: torch.Tensor - :param bool check_border: If ``True``, the border is considered inside. - :return: ``True`` if the point is inside the Exclusion domain, ``False`` otherwise. + :param LabelTensor point: Point to be checked. + :param bool check_border: If ``True``, the border is considered inside + the domain. Default is ``False``. + :return: ``True`` if the point is inside the domain, + ``False`` otherwise. :rtype: bool """ flag = 0 @@ -50,20 +56,21 @@ def is_inside(self, point, check_border=False): def sample(self, n, mode="random", variables="all"): """ - Sample routine for ``Exclusion`` domain. - - :param int n: Number of points to sample in the shape. - :param str mode: Mode for sampling, defaults to ``random``. Available modes include: ``random``. - :param variables: Variables to be sampled, defaults to ``all``. - :type variables: str | list[str] - :return: Returns ``LabelTensor`` of n sampled points. + Sampling routine. + + :param int n: Number of points to sample. + :param str mode: Sampling method. Default is ``random``. + Available modes: random sampling, ``random``; + :param list[str] variables: variables to be sampled. Default is ``all``. + :raises NotImplementedError: If the sampling method is not implemented. + :return: Sampled points. :rtype: LabelTensor :Example: >>> # Create two Cartesian domains >>> cartesian1 = CartesianDomain({'x': [0, 2], 'y': [0, 2]}) >>> cartesian2 = CartesianDomain({'x': [1, 3], 'y': [1, 3]}) - >>> # Create a Exclusion of the ellipsoid domains + >>> # Define the exclusion between the domains >>> Exclusion = Exclusion([cartesian1, cartesian2]) >>> # Sample >>> Exclusion.sample(n=5) @@ -74,16 +81,16 @@ def sample(self, n, mode="random", variables="all"): [0.1978, 0.3526]]) >>> len(Exclusion.sample(n=5) 5 - """ - if mode != "random": + if mode not in self.sample_modes: raise NotImplementedError( f"{mode} is not a valid mode for sampling." ) sampled = [] - # calculate the number of points to sample for each geometry and the remainder. + # calculate the number of points to sample for each geometry and the + # remainder. remainder = n % len(self.geometries) num_points = n // len(self.geometries) diff --git a/pina/geometry/intersection_domain.py b/pina/domain/intersection_domain.py similarity index 64% rename from pina/geometry/intersection_domain.py rename to pina/domain/intersection_domain.py index b40d36950..0921ff381 100644 --- a/pina/geometry/intersection_domain.py +++ b/pina/domain/intersection_domain.py @@ -1,47 +1,50 @@ -"""Module for Intersection class. """ +"""Module for the Intersection Operation.""" +import random import torch from ..label_tensor import LabelTensor from .operation_interface import OperationInterface -import random class Intersection(OperationInterface): + r""" + Implementation of the intersection operation between of a list of domains. - def __init__(self, geometries): - r""" - PINA implementation of Intersection of Domains. - Given two sets :math:`A` and :math:`B` then the - domain difference is defined as: + Given two sets :math:`A` and :math:`B`, define the intersection of the two + sets as: + + .. math:: + A \cap B = \{x \mid x \in A \land x \in B\}, - .. math:: - A \cap B = \{x \mid x \in A \land x \in B\}, + where :math:`x` is a point in :math:`\mathbb{R}^N`. + """ - with :math:`x` a point in :math:`\mathbb{R}^N` and :math:`N` - the dimension of the geometry space. + def __init__(self, geometries): + """ + Initialization of the :class:`Intersection` class. - :param list geometries: A list of geometries from ``pina.geometry`` - such as ``EllipsoidDomain`` or ``CartesianDomain``. The intersection - will be taken between all the geometries in the list. The resulting - geometry will be the intersection of all the geometries in the list. + :param list[DomainInterface] geometries: A list of instances of the + :class:`~pina.domain.domain_interface.DomainInterface` class on + which the intersection operation is performed. :Example: >>> # Create two ellipsoid domains >>> ellipsoid1 = EllipsoidDomain({'x': [-1, 1], 'y': [-1, 1]}) >>> ellipsoid2 = EllipsoidDomain({'x': [0, 2], 'y': [0, 2]}) - >>> # Create a Intersection of the ellipsoid domains + >>> # Define the intersection of the domains >>> intersection = Intersection([ellipsoid1, ellipsoid2]) """ super().__init__(geometries) def is_inside(self, point, check_border=False): """ - Check if a point is inside the ``Intersection`` domain. + Check if a point is inside the resulting domain. - :param point: Point to be checked. - :type point: torch.Tensor - :param bool check_border: If ``True``, the border is considered inside. - :return: ``True`` if the point is inside the Intersection domain, ``False`` otherwise. + :param LabelTensor point: Point to be checked. + :param bool check_border: If ``True``, the border is considered inside + the domain. Default is ``False``. + :return: ``True`` if the point is inside the domain, + ``False`` otherwise. :rtype: bool """ flag = 0 @@ -52,20 +55,21 @@ def is_inside(self, point, check_border=False): def sample(self, n, mode="random", variables="all"): """ - Sample routine for ``Intersection`` domain. - - :param int n: Number of points to sample in the shape. - :param str mode: Mode for sampling, defaults to ``random``. Available modes include: ``random``. - :param variables: Variables to be sampled, defaults to ``all``. - :type variables: str | list[str] - :return: Returns ``LabelTensor`` of n sampled points. + Sampling routine. + + :param int n: Number of points to sample. + :param str mode: Sampling method. Default is ``random``. + Available modes: random sampling, ``random``; + :param list[str] variables: variables to be sampled. Default is ``all``. + :raises NotImplementedError: If the sampling method is not implemented. + :return: Sampled points. :rtype: LabelTensor :Example: >>> # Create two Cartesian domains >>> cartesian1 = CartesianDomain({'x': [0, 2], 'y': [0, 2]}) >>> cartesian2 = CartesianDomain({'x': [1, 3], 'y': [1, 3]}) - >>> # Create a Intersection of the ellipsoid domains + >>> # Define the intersection of the domains >>> intersection = Intersection([cartesian1, cartesian2]) >>> # Sample >>> intersection.sample(n=5) @@ -76,16 +80,16 @@ def sample(self, n, mode="random", variables="all"): [1.9902, 1.4458]]) >>> len(intersection.sample(n=5) 5 - """ - if mode != "random": + if mode not in self.sample_modes: raise NotImplementedError( f"{mode} is not a valid mode for sampling." ) sampled = [] - # calculate the number of points to sample for each geometry and the remainder. + # calculate the number of points to sample for each geometry and the + # remainder. remainder = n % len(self.geometries) num_points = n // len(self.geometries) diff --git a/pina/domain/operation_interface.py b/pina/domain/operation_interface.py new file mode 100644 index 000000000..8cce9698a --- /dev/null +++ b/pina/domain/operation_interface.py @@ -0,0 +1,90 @@ +"""Module for the Operation Interface.""" + +from abc import ABCMeta, abstractmethod +from .domain_interface import DomainInterface +from ..utils import check_consistency + + +class OperationInterface(DomainInterface, metaclass=ABCMeta): + """ + Abstract class for set operations defined on geometric domains. + """ + + def __init__(self, geometries): + """ + Initialization of the :class:`OperationInterface` class. + + :param list[DomainInterface] geometries: A list of instances of the + :class:`~pina.domain.domain_interface.DomainInterface` class on + which the set operation is performed. + """ + # check consistency geometries + check_consistency(geometries, DomainInterface) + + # check we are passing always different + # geometries with the same labels. + self._check_dimensions(geometries) + + # assign geometries + self._geometries = geometries + + @property + def sample_modes(self): + """ + List of available sampling modes. + + :return: List of available sampling modes. + :rtype: list[str] + """ + return ["random"] + + @property + def geometries(self): + """ + The domains on which to perform the set operation. + + :return: The domains on which to perform the set operation. + :rtype: list[DomainInterface] + """ + return self._geometries + + @property + def variables(self): + """ + List of variables of the domain. + + :return: List of variables of the domain. + :rtype: list[str] + """ + variables = [] + for geom in self.geometries: + variables += geom.variables + return sorted(list(set(variables))) + + @abstractmethod + def is_inside(self, point, check_border=False): + """ + Abstract method to check if a point lies inside the resulting domain + after performing the set operation. + + :param LabelTensor point: Point to be checked. + :param bool check_border: If ``True``, the border is considered inside + the resulting domain. Default is ``False``. + :return: ``True`` if the point is inside the domain, + ``False`` otherwise. + :rtype: bool + """ + + def _check_dimensions(self, geometries): + """ + Check if the dimensions of the geometries are consistent. + + :param list[DomainInterface] geometries: Domains to be checked. + :raises NotImplementedError: If the dimensions of the geometries are not + consistent. + """ + for geometry in geometries: + if geometry.variables != geometries[0].variables: + raise NotImplementedError( + "The geometries need to have same dimensions and labels." + ) diff --git a/pina/geometry/simplex.py b/pina/domain/simplex.py similarity index 63% rename from pina/geometry/simplex.py rename to pina/domain/simplex.py index b04ad537f..cc496daee 100644 --- a/pina/geometry/simplex.py +++ b/pina/domain/simplex.py @@ -1,32 +1,35 @@ +"""Module for the Simplex Domain.""" + import torch -from .location import Location -from pina.geometry import CartesianDomain -from pina import LabelTensor +from .domain_interface import DomainInterface +from .cartesian import CartesianDomain +from ..label_tensor import LabelTensor from ..utils import check_consistency -class SimplexDomain(Location): - """PINA implementation of a Simplex.""" +class SimplexDomain(DomainInterface): + """ + Implementation of the simplex domain. + """ def __init__(self, simplex_matrix, sample_surface=False): """ - :param simplex_matrix: A matrix of LabelTensor objects representing - a vertex of the simplex (a tensor), and the coordinates of the - point (a list of labels). - - :type simplex_matrix: list[LabelTensor] - :param sample_surface: A variable for choosing sample strategies. If - ``sample_surface=True`` only samples on the Simplex surface - frontier are taken. If ``sample_surface=False``, no such criteria - is followed. - - :type sample_surface: bool + Initialization of the :class:`SimplexDomain` class. + + :param list[LabelTensor] simplex_matrix: A matrix representing the + vertices of the simplex. + :param bool sample_surface: A flag to choose the sampling strategy. + If ``True``, samples are taken only from the surface of the simplex. + If ``False``, samples are taken from the interior of the simplex. + Default is ``False``. + :raises ValueError: If the labels of the vertices don't match. + :raises ValueError: If the number of vertices is not equal to the + dimension of the simplex plus one. .. warning:: - Sampling for dimensions greater or equal to 10 could result - in a shrinking of the simplex, which degrades the quality - of the samples. For dimensions higher than 10, other algorithms - for sampling should be used. + Sampling for dimensions greater or equal to 10 could result in a + shrinkage of the simplex, which degrades the quality of the samples. + For dimensions higher than 10, use other sampling algorithms. :Example: >>> spatial_domain = SimplexDomain( @@ -51,72 +54,79 @@ def __init__(self, simplex_matrix, sample_surface=False): # check consistency of labels matrix_labels = simplex_matrix[0].labels if not all(vertex.labels == matrix_labels for vertex in simplex_matrix): - raise ValueError(f"Labels don't match.") + raise ValueError("Labels don't match.") # check consistency dimensions dim_simplex = len(matrix_labels) if len(simplex_matrix) != dim_simplex + 1: raise ValueError( - "An n-dimensional simplex is composed by n + 1 tensors of dimension n." + "An n-dimensional simplex is composed by n + 1 tensors of " + "dimension n." ) # creating vertices matrix self._vertices_matrix = LabelTensor.vstack(simplex_matrix) # creating basis vectors for simplex - # self._vectors_shifted = ( - # (self._vertices_matrix.T)[:, :-1] - (self._vertices_matrix.T)[:, None, -1] - # ) ### TODO: Remove after checking - vert = self._vertices_matrix self._vectors_shifted = (vert[:-1] - vert[-1]).T # build cartesian_bound self._cartesian_bound = self._build_cartesian(self._vertices_matrix) + @property + def sample_modes(self): + """ + List of available sampling modes. + + :return: List of available sampling modes. + :rtype: list[str] + """ + return ["random"] + @property def variables(self): + """ + List of variables of the domain. + + :return: List of variables of the domain. + :rtype: list[str] + """ return sorted(self._vertices_matrix.labels) def _build_cartesian(self, vertices): """ - Build Cartesian border for Simplex domain to be used in sampling. - :param vertex_matrix: matrix of vertices - :type vertices: list[list] - :return: Cartesian border for triangular domain + Build the cartesian border for a simplex domain to be used in sampling. + + :param list[LabelTensor] vertices: list of vertices defining the domain. + :return: The cartesian border for the simplex domain. :rtype: CartesianDomain """ span_dict = {} - - for i, coord in enumerate(self.variables): - sorted_vertices = sorted(vertices, key=lambda vertex: vertex[i]) + for coord in self.variables: + sorted_vertices = torch.sort(vertices[coord].tensor.squeeze()) # respective coord bounded by the lowest and highest values span_dict[coord] = [ - float(sorted_vertices[0][i]), - float(sorted_vertices[-1][i]), + float(sorted_vertices.values[0]), + float(sorted_vertices.values[-1]), ] return CartesianDomain(span_dict) def is_inside(self, point, check_border=False): """ - Check if a point is inside the simplex. - Uses the algorithm described involving barycentric coordinates: - https://en.wikipedia.org/wiki/Barycentric_coordinate_system. - - :param point: Point to be checked. - :type point: LabelTensor - :param check_border: Check if the point is also on the frontier - of the simplex, default ``False``. - :type check_border: bool - :return: Returning ``True`` if the point is inside, ``False`` otherwise. + Check if a point is inside the simplex. It uses an algorithm involving + barycentric coordinates. + + :param LabelTensor point: Point to be checked. + :param check_border: If ``True``, the border is considered inside + the simplex. Default is ``False``. + :raises ValueError: If the labels of the point are different from those + passed in the ``__init__`` method. + :return: ``True`` if the point is inside the domain, + ``False`` otherwise. :rtype: bool - - .. note:: - When ``sample_surface`` in the ``__init()__`` - is set to ``True``, then the method only checks - points on the surface, and not inside the domain. """ if not all(label in self.variables for label in point.labels): @@ -146,13 +156,13 @@ def is_inside(self, point, check_border=False): def _sample_interior_randomly(self, n, variables): """ - Randomly sample points inside a simplex of arbitrary - dimension, without the boundary. - :param int n: Number of points to sample in the shape. - :param variables: pinn variable to be sampled, defaults to ``all``. - :type variables: str or list[str], optional - :return: Returns tensor of n sampled points. - :rtype: torch.Tensor + Sample at random points from the interior of the simplex. Boundaries are + excluded from this sampling routine. + + :param int n: Number of points to sample. + :param list[str] variables: variables to be sampled. + :return: Sampled points. + :rtype: list[torch.Tensor] """ # =============== For Developers ================ # @@ -177,10 +187,10 @@ def _sample_interior_randomly(self, n, variables): def _sample_boundary_randomly(self, n): """ - Randomly sample points on the boundary of a simplex - of arbitrary dimensions. - :param int n: Number of points to sample in the shape. - :return: Returns tensor of n sampled points + Sample at random points from the boundary of the simplex. + + :param int n: Number of points to sample. + :return: Sampled points. :rtype: torch.Tensor """ @@ -216,19 +226,19 @@ def _sample_boundary_randomly(self, n): def sample(self, n, mode="random", variables="all"): """ - Sample n points from Simplex domain. - - :param int n: Number of points to sample in the shape. - :param str mode: Mode for sampling, defaults to ``random``. Available modes include: ``random``. - :param variables: Variables to be sampled, defaults to ``all``. - :type variables: str | list[str] - :return: Returns ``LabelTensor`` of n sampled points. + Sampling routine. + + :param int n: Number of points to sample. + :param str mode: Sampling method. Default is ``random``. + Available modes: random sampling, ``random``. + :param list[str] variables: variables to be sampled. Default is ``all``. + :raises NotImplementedError: If the sampling method is not implemented. + :return: Sampled points. :rtype: LabelTensor .. warning:: - When ``sample_surface = True`` in the initialization, all - the variables are sampled, despite passing different once - in ``variables``. + When ``sample_surface=True``, all variables are sampled, + ignoring the ``variables`` parameter. """ if variables == "all": @@ -236,7 +246,7 @@ def sample(self, n, mode="random", variables="all"): elif isinstance(variables, (list, tuple)): variables = sorted(variables) - if mode in ["random"]: + if mode in self.sample_modes: if self._sample_surface: sample_pts = self._sample_boundary_randomly(n) else: diff --git a/pina/geometry/union_domain.py b/pina/domain/union_domain.py similarity index 58% rename from pina/geometry/union_domain.py rename to pina/domain/union_domain.py index da2ead90d..5c3e96f3f 100644 --- a/pina/geometry/union_domain.py +++ b/pina/domain/union_domain.py @@ -1,48 +1,58 @@ -"""Module for Union class. """ +"""Module for the Union Operation.""" +import random import torch from .operation_interface import OperationInterface from ..label_tensor import LabelTensor -import random class Union(OperationInterface): + r""" + Implementation of the union operation between of a list of domains. - def __init__(self, geometries): - r""" - PINA implementation of Unions of Domains. - Given two sets :math:`A` and :math:`B` then the - domain difference is defined as: + Given two sets :math:`A` and :math:`B`, define the union of the two sets as: - .. math:: - A \cup B = \{x \mid x \in A \lor x \in B\}, + .. math:: + A \cup B = \{x \mid x \in A \lor x \in B\}, - with :math:`x` a point in :math:`\mathbb{R}^N` and :math:`N` - the dimension of the geometry space. + where :math:`x` is a point in :math:`\mathbb{R}^N`. + """ + + def __init__(self, geometries): + """ + Initialization of the :class:`Union` class. - :param list geometries: A list of geometries from ``pina.geometry`` - such as ``EllipsoidDomain`` or ``CartesianDomain``. + :param list[DomainInterface] geometries: A list of instances of the + :class:`~pina.domain.domain_interface.DomainInterface` class on + which the union operation is performed. :Example: >>> # Create two ellipsoid domains >>> ellipsoid1 = EllipsoidDomain({'x': [-1, 1], 'y': [-1, 1]}) >>> ellipsoid2 = EllipsoidDomain({'x': [0, 2], 'y': [0, 2]}) - >>> # Create a union of the ellipsoid domains - >>> union = GeometryUnion([ellipsoid1, ellipsoid2]) - + >>> # Define the union of the domains + >>> union = Union([ellipsoid1, ellipsoid2]) """ super().__init__(geometries) + @property + def sample_modes(self): + """ + List of available sampling modes. + """ + self.sample_modes = list( + set(geom.sample_modes for geom in self.geometries) + ) + def is_inside(self, point, check_border=False): """ - Check if a point is inside the ``Union`` domain. - - :param point: Point to be checked. - :type point: LabelTensor - :param check_border: Check if the point is also on the frontier - of the ellipsoid, default ``False``. - :type check_border: bool - :return: Returning ``True`` if the point is inside, ``False`` otherwise. + Check if a point is inside the resulting domain. + + :param LabelTensor point: Point to be checked. + :param bool check_border: If ``True``, the border is considered inside + the domain. Default is ``False``. + :return: ``True`` if the point is inside the domain, + ``False`` otherwise. :rtype: bool """ for geometry in self.geometries: @@ -52,20 +62,20 @@ def is_inside(self, point, check_border=False): def sample(self, n, mode="random", variables="all"): """ - Sample routine for ``Union`` domain. + Sampling routine. - :param int n: Number of points to sample in the shape. - :param str mode: Mode for sampling, defaults to ``random``. Available modes include: ``random``. - :param variables: Variables to be sampled, defaults to ``all``. - :type variables: str | list[str] - :return: Returns ``LabelTensor`` of n sampled points. + :param int n: Number of points to sample. + :param str mode: Sampling method. Default is ``random``. + Available modes: random sampling, ``random``; + :param list[str] variables: variables to be sampled. Default is ``all``. + :return: Sampled points. :rtype: LabelTensor :Example: - >>> # Create two ellipsoid domains + >>> # Create two cartesian domains >>> cartesian1 = CartesianDomain({'x': [0, 2], 'y': [0, 2]}) >>> cartesian2 = CartesianDomain({'x': [1, 3], 'y': [1, 3]}) - >>> # Create a union of the ellipsoid domains + >>> # Define the union of the domains >>> union = Union([cartesian1, cartesian2]) >>> # Sample >>> union.sample(n=5) @@ -79,7 +89,8 @@ def sample(self, n, mode="random", variables="all"): """ sampled_points = [] - # calculate the number of points to sample for each geometry and the remainder + # calculate the number of points to sample for each geometry and the + # remainder remainder = n % len(self.geometries) num_points = n // len(self.geometries) @@ -97,7 +108,8 @@ def sample(self, n, mode="random", variables="all"): num_points + int(i < remainder), mode, variables ) ) - # in case number of sampled points is smaller than the number of geometries + # in case number of sampled points is smaller than the number of + # geometries if len(sampled_points) >= n: break diff --git a/pina/equation/__init__.py b/pina/equation/__init__.py index d9961b486..07ab74239 100644 --- a/pina/equation/__init__.py +++ b/pina/equation/__init__.py @@ -1,3 +1,5 @@ +"""Module to define equations and systems of equations.""" + __all__ = [ "SystemEquation", "Equation", diff --git a/pina/equation/equation.py b/pina/equation/equation.py index 3a8f4b1a3..60b538e11 100644 --- a/pina/equation/equation.py +++ b/pina/equation/equation.py @@ -1,19 +1,23 @@ -""" Module for Equation. """ +"""Module for the Equation.""" from .equation_interface import EquationInterface class Equation(EquationInterface): + """ + Implementation of the Equation class. Every ``equation`` passed to a + :class:`~pina.condition.condition.Condition` object must be either an + instance of :class:`Equation` or + :class:`~pina.equation.system_equation.SystemEquation`. + """ def __init__(self, equation): """ - Equation class for specifing any equation in PINA. - Each ``equation`` passed to a ``Condition`` object - must be an ``Equation`` or ``SystemEquation``. + Initialization of the :class:`Equation` class. - :param equation: A ``torch`` callable equation to - evaluate the residual. - :type equation: Callable + :param Callable equation: A ``torch`` callable function used to compute + the residual of a mathematical equation. + :raises ValueError: If the equation is not a callable function. """ if not callable(equation): raise ValueError( @@ -25,20 +29,17 @@ def __init__(self, equation): def residual(self, input_, output_, params_=None): """ - Residual computation of the equation. - - :param LabelTensor input_: Input points to evaluate the equation. - :param LabelTensor output_: Output vectors given by a model (e.g, - a ``FeedForward`` model). - :param dict params_: Dictionary of parameters related to the inverse - problem (if any). - If the equation is not related to an ``InverseProblem``, the - parameters are initialized to ``None`` and the residual is - computed as ``equation(input_, output_)``. - Otherwise, the parameters are automatically initialized in the - ranges specified by the user. - - :return: The residual evaluation of the specified equation. + Compute the residual of the equation. + + :param LabelTensor input_: Input points where the equation is evaluated. + :param LabelTensor output_: Output tensor, eventually produced by a + :class:`torch.nn.Module` instance. + :param dict params_: Dictionary of unknown parameters, associated with a + :class:`~pina.problem.inverse_problem.InverseProblem` instance. + If the equation is not related to a + :class:`~pina.problem.inverse_problem.InverseProblem` instance, the + parameters must be initialized to ``None``. Default is ``None``. + :return: The computed residual of the equation. :rtype: LabelTensor """ if params_ is None: diff --git a/pina/equation/equation_factory.py b/pina/equation/equation_factory.py index 5921b1f73..879990ae9 100644 --- a/pina/equation/equation_factory.py +++ b/pina/equation/equation_factory.py @@ -1,26 +1,37 @@ -""" Module """ +"""Module for defining various general equations.""" from .equation import Equation -from ..operators import grad, div, laplacian +from ..operator import grad, div, laplacian class FixedValue(Equation): + """ + Equation to enforce a fixed value. Can be used to enforce Dirichlet Boundary + conditions. + """ def __init__(self, value, components=None): """ - Fixed Value Equation class. This class can be - used to enforced a fixed value for a specific - condition, e.g. Dirichlet Boundary conditions. - - :param float value: Value to be mantained fixed. - :param list(str) components: the name of the output - variables to calculate the gradient for. It should - be a subset of the output labels. If ``None``, - all the output variables are considered. + Initialization of the :class:`FixedValue` class. + + :param float value: The fixed value to be enforced. + :param list[str] components: The name of the output variables for which + the fixed value condition is applied. It should be a subset of the + output labels. If ``None``, all output variables are considered. Default is ``None``. """ def equation(input_, output_): + """ + Definition of the equation to enforce a fixed value. + + :param LabelTensor input_: Input points where the equation is + evaluated. + :param LabelTensor output_: Output tensor, eventually produced by a + :class:`torch.nn.Module` instance. + :return: The computed residual of the equation. + :rtype: LabelTensor + """ if components is None: return output_ - value return output_.extract(components) - value @@ -29,77 +40,106 @@ def equation(input_, output_): class FixedGradient(Equation): + """ + Equation to enforce a fixed gradient for a specific condition. + """ def __init__(self, value, components=None, d=None): """ - Fixed Gradient Equation class. This class can be - used to enforced a fixed gradient for a specific - condition. - - :param float value: Value to be mantained fixed. - :param list(str) components: the name of the output - variables to calculate the gradient for. It should - be a subset of the output labels. If ``None``, - all the output variables are considered. + Initialization of the :class:`FixedGradient` class. + + :param float value: The fixed value to be enforced to the gradient. + :param list[str] components: The name of the output variables for which + the fixed gradient condition is applied. It should be a subset of + the output labels. If ``None``, all output variables are considered. + Default is ``None``. + :param list[str] d: The name of the input variables on which the + gradient is computed. It should be a subset of the input labels. + If ``None``, all the input variables are considered. Default is ``None``. - :param list(str) d: the name of the input variables on - which the gradient is calculated. d should be a subset - of the input labels. If ``None``, all the input variables - are considered. Default is ``None``. """ def equation(input_, output_): + """ + Definition of the equation to enforce a fixed gradient. + + :param LabelTensor input_: Input points where the equation is + evaluated. + :param LabelTensor output_: Output tensor, eventually produced by a + :class:`torch.nn.Module` instance. + :return: The computed residual of the equation. + :rtype: LabelTensor + """ return grad(output_, input_, components=components, d=d) - value super().__init__(equation) class FixedFlux(Equation): + """ + Equation to enforce a fixed flux, or divergence, for a specific condition. + """ def __init__(self, value, components=None, d=None): """ - Fixed Flux Equation class. This class can be - used to enforced a fixed flux for a specific - condition. - - :param float value: Value to be mantained fixed. - :param list(str) components: the name of the output - variables to calculate the flux for. It should - be a subset of the output labels. If ``None``, - all the output variables are considered. + Initialization of the :class:`FixedFlux` class. + + :param float value: The fixed value to be enforced to the flux. + :param list[str] components: The name of the output variables for which + the fixed flux condition is applied. It should be a subset of the + output labels. If ``None``, all output variables are considered. Default is ``None``. - :param list(str) d: the name of the input variables on - which the flux is calculated. d should be a subset - of the input labels. If ``None``, all the input variables - are considered. Default is ``None``. + :param list[str] d: The name of the input variables on which the flux + is computed. It should be a subset of the input labels. If ``None``, + all the input variables are considered. Default is ``None``. """ def equation(input_, output_): + """ + Definition of the equation to enforce a fixed flux. + + :param LabelTensor input_: Input points where the equation is + evaluated. + :param LabelTensor output_: Output tensor, eventually produced by a + :class:`torch.nn.Module` instance. + :return: The computed residual of the equation. + :rtype: LabelTensor + """ return div(output_, input_, components=components, d=d) - value super().__init__(equation) class Laplace(Equation): + """ + Equation to enforce a null laplacian for a specific condition. + """ def __init__(self, components=None, d=None): """ - Laplace Equation class. This class can be - used to enforced a Laplace equation for a specific - condition (force term set to zero). - - :param list(str) components: the name of the output - variables to calculate the flux for. It should - be a subset of the output labels. If ``None``, - all the output variables are considered. + Initialization of the :class:`Laplace` class. + + :param list[str] components: The name of the output variables for which + the null laplace condition is applied. It should be a subset of the + output labels. If ``None``, all output variables are considered. + Default is ``None``. + :param list[str] d: The name of the input variables on which the + laplacian is computed. It should be a subset of the input labels. + If ``None``, all the input variables are considered. Default is ``None``. - :param list(str) d: the name of the input variables on - which the flux is calculated. d should be a subset - of the input labels. If ``None``, all the input variables - are considered. Default is ``None``. """ def equation(input_, output_): + """ + Definition of the equation to enforce a null laplacian. + + :param LabelTensor input_: Input points where the equation is + evaluated. + :param LabelTensor output_: Output tensor, eventually produced by a + :class:`torch.nn.Module` instance. + :return: The computed residual of the equation. + :rtype: LabelTensor + """ return laplacian(output_, input_, components=components, d=d) super().__init__(equation) diff --git a/pina/equation/equation_interface.py b/pina/equation/equation_interface.py index c64c180cd..f1cc74754 100644 --- a/pina/equation/equation_interface.py +++ b/pina/equation/equation_interface.py @@ -1,28 +1,35 @@ -""" Module for EquationInterface class """ +"""Module for the Equation Interface.""" from abc import ABCMeta, abstractmethod class EquationInterface(metaclass=ABCMeta): """ - The abstract `AbstractProblem` class. All the class defining a PINA Problem - should be inheritied from this class. + Abstract base class for equations. - In the definition of a PINA problem, the fundamental elements are: - the output variables, the condition(s), and the domain(s) where the - conditions are applied. + Equations in PINA simplify the training process. When defining a problem, + each equation passed to a :class:`~pina.condition.condition.Condition` + object must be either an :class:`~pina.equation.equation.Equation` or a + :class:`~pina.equation.system_equation.SystemEquation` instance. + + An :class:`~pina.equation.equation.Equation` is a wrapper for a callable + function, while :class:`~pina.equation.system_equation.SystemEquation` + wraps a list of callable functions. To streamline code writing, PINA + provides a diverse set of pre-implemented equations, such as + :class:`~pina.equation.equation_factory.FixedValue`, + :class:`~pina.equation.equation_factory.FixedGradient`, and many others. """ @abstractmethod def residual(self, input_, output_, params_): """ - Residual computation of the equation. + Abstract method to compute the residual of an equation. - :param LabelTensor input_: Input points to evaluate the equation. - :param LabelTensor output_: Output vectors given by my model (e.g., a ``FeedForward`` model). - :param dict params_: Dictionary of unknown parameters, eventually - related to an ``InverseProblem``. - :return: The residual evaluation of the specified equation. + :param LabelTensor input_: Input points where the equation is evaluated. + :param LabelTensor output_: Output tensor, eventually produced by a + :class:`torch.nn.Module` instance. + :param dict params_: Dictionary of unknown parameters, associated with a + :class:`~pina.problem.inverse_problem.InverseProblem` instance. + :return: The computed residual of the equation. :rtype: LabelTensor """ - pass diff --git a/pina/equation/system_equation.py b/pina/equation/system_equation.py index bf54abd51..d51ba9408 100644 --- a/pina/equation/system_equation.py +++ b/pina/equation/system_equation.py @@ -1,29 +1,33 @@ -""" Module for SystemEquation. """ +"""Module for the System of Equation.""" import torch +from .equation_interface import EquationInterface from .equation import Equation from ..utils import check_consistency -class SystemEquation(Equation): +class SystemEquation(EquationInterface): + """ + Implementation of the System of Equations. Every ``equation`` passed to a + :class:`~pina.condition.condition.Condition` object must be either a + :class:`~pina.equation.equation.Equation` or a + :class:`~pina.equation.system_equation.SystemEquation` instance. + """ def __init__(self, list_equation, reduction=None): """ - System of Equation class for specifing any system - of equations in PINA. - Each ``equation`` passed to a ``Condition`` object - must be an ``Equation`` or ``SystemEquation``. - A ``SystemEquation`` is specified by a list of - equations. + Initialization of the :class:`SystemEquation` class. - :param Callable equation: A ``torch`` callable equation to - evaluate the residual - :param str reduction: Specifies the reduction to apply to the output: - None | ``mean`` | ``sum`` | callable. None: no reduction - will be applied, ``mean``: the output sum will be divided - by the number of elements in the output, ``sum``: the output will - be summed. *callable* is a callable function to perform reduction, - no checks guaranteed. Default: None. + :param Callable equation: A ``torch`` callable function used to compute + the residual of a mathematical equation. + :param str reduction: The reduction method to aggregate the residuals of + each equation. Available options are: ``None``, ``mean``, ``sum``, + ``callable``. + If ``None``, no reduction is applied. If ``mean``, the output sum is + divided by the number of elements in the output. If ``sum``, the + output is summed. ``callable`` is a user-defined callable function + to perform reduction, no checks guaranteed. Default is ``None``. + :raises NotImplementedError: If the reduction is not implemented. """ check_consistency([list_equation], list) @@ -37,7 +41,7 @@ def __init__(self, list_equation, reduction=None): self.reduction = torch.mean elif reduction == "sum": self.reduction = torch.sum - elif (reduction == None) or callable(reduction): + elif (reduction is None) or callable(reduction): self.reduction = reduction else: raise NotImplementedError( @@ -46,22 +50,21 @@ def __init__(self, list_equation, reduction=None): def residual(self, input_, output_, params_=None): """ - Residual computation for the equations of the system. + Compute the residual for each equation in the system of equations and + aggregate it according to the ``reduction`` specified in the + ``__init__`` method. - :param LabelTensor input_: Input points to evaluate the system of - equations. - :param LabelTensor output_: Output vectors given by a model (e.g, - a ``FeedForward`` model). - :param dict params_: Dictionary of parameters related to the inverse - problem (if any). - If the equation is not related to an ``InverseProblem``, the - parameters are initialized to ``None`` and the residual is - computed as ``equation(input_, output_)``. - Otherwise, the parameters are automatically initialized in the - ranges specified by the user. + :param LabelTensor input_: Input points where each equation of the + system is evaluated. + :param LabelTensor output_: Output tensor, eventually produced by a + :class:`torch.nn.Module` instance. + :param dict params_: Dictionary of unknown parameters, associated with a + :class:`~pina.problem.inverse_problem.InverseProblem` instance. + If the equation is not related to a + :class:`~pina.problem.inverse_problem.InverseProblem` instance, the + parameters must be initialized to ``None``. Default is ``None``. - :return: The residual evaluation of the specified system of equations, - aggregated by the ``reduction`` defined in the ``__init__``. + :return: The aggregated residuals of the system of equations. :rtype: LabelTensor """ residual = torch.hstack( diff --git a/pina/geometry/__init__.py b/pina/geometry/__init__.py index 963136a3e..762820ac6 100644 --- a/pina/geometry/__init__.py +++ b/pina/geometry/__init__.py @@ -1,21 +1,20 @@ -__all__ = [ - "Location", - "CartesianDomain", - "EllipsoidDomain", - "Union", - "Intersection", - "Exclusion", - "Difference", - "OperationInterface", - "SimplexDomain", -] +"""Old module for geometry classes and functions. Deprecated in 0.2.0.""" -from .location import Location -from .cartesian import CartesianDomain -from .ellipsoid import EllipsoidDomain -from .exclusion_domain import Exclusion -from .intersection_domain import Intersection -from .union_domain import Union -from .difference_domain import Difference -from .operation_interface import OperationInterface -from .simplex import SimplexDomain +import warnings + +from ..domain import * +from ..utils import custom_warning_format + +# back-compatibility 0.1 +# creating alias +Location = DomainInterface + +# Set the custom format for warnings +warnings.formatwarning = custom_warning_format +warnings.filterwarnings("always", category=DeprecationWarning) +warnings.warn( + "'pina.geometry' is deprecated and will be removed " + "in future versions. Please use 'pina.domain' instead. " + "Location moved to DomainInferface object.", + DeprecationWarning, +) diff --git a/pina/geometry/location.py b/pina/geometry/location.py deleted file mode 100644 index a22dfe13f..000000000 --- a/pina/geometry/location.py +++ /dev/null @@ -1,31 +0,0 @@ -"""Module for Location class.""" - -from abc import ABCMeta, abstractmethod - - -class Location(metaclass=ABCMeta): - """ - Abstract Location class. - Any geometry entity should inherit from this class. - """ - - @abstractmethod - def sample(self): - """ - Abstract method for sampling a point from the location. To be - implemented in the child class. - """ - pass - - @abstractmethod - def is_inside(self, point, check_border=False): - """ - Abstract method for checking if a point is inside the location. To be - implemented in the child class. - - :param torch.Tensor point: A tensor point to be checked. - :param bool check_border: A boolean that determines whether the border - of the location is considered checked to be considered inside or - not. Defaults to ``False``. - """ - pass diff --git a/pina/geometry/operation_interface.py b/pina/geometry/operation_interface.py deleted file mode 100644 index 4f7709b9a..000000000 --- a/pina/geometry/operation_interface.py +++ /dev/null @@ -1,68 +0,0 @@ -""" Module for OperationInterface class. """ - -from .location import Location -from ..utils import check_consistency -from abc import ABCMeta, abstractmethod - - -class OperationInterface(Location, metaclass=ABCMeta): - - def __init__(self, geometries): - """ - Abstract set operation class. Any geometry operation entity must inherit from this class. - - :param list geometries: A list of geometries from ``pina.geometry`` - such as ``EllipsoidDomain`` or ``CartesianDomain``. - """ - # check consistency geometries - check_consistency(geometries, Location) - - # check we are passing always different - # geometries with the same labels. - self._check_dimensions(geometries) - - # assign geometries - self._geometries = geometries - - @property - def geometries(self): - """ - The geometries to perform set operation. - """ - return self._geometries - - @property - def variables(self): - """ - Spatial variables of the domain. - - :return: All the variables defined in ``__init__`` in order. - :rtype: list[str] - """ - return self.geometries[0].variables - - @abstractmethod - def is_inside(self, point, check_border=False): - """ - Check if a point is inside the resulting domain after - a set operation is applied. - - :param point: Point to be checked. - :type point: torch.Tensor - :param bool check_border: If ``True``, the border is considered inside. - :return: ``True`` if the point is inside the Intersection domain, ``False`` otherwise. - :rtype: bool - """ - pass - - def _check_dimensions(self, geometries): - """Check if the dimensions of the geometries are consistent. - - :param geometries: Geometries to be checked. - :type geometries: list[Location] - """ - for geometry in geometries: - if geometry.variables != geometries[0].variables: - raise NotImplementedError( - f"The geometries need to have same dimensions and labels." - ) diff --git a/pina/graph.py b/pina/graph.py new file mode 100644 index 000000000..1340ed69a --- /dev/null +++ b/pina/graph.py @@ -0,0 +1,421 @@ +"""Module to build Graph objects and perform operations on them.""" + +import torch +from torch_geometric.data import Data, Batch +from torch_geometric.utils import to_undirected +from .label_tensor import LabelTensor +from .utils import check_consistency, is_function + + +class Graph(Data): + """ + Extends :class:`~torch_geometric.data.Data` class to include additional + checks and functionlities. + """ + + def __new__( + cls, + **kwargs, + ): + """ + Create a new instance of the :class:`~pina.graph.Graph` class by + checking the consistency of the input data and storing the attributes. + + :param dict kwargs: Parameters used to initialize the + :class:`~pina.graph.Graph` object. + :return: A new instance of the :class:`~pina.graph.Graph` class. + :rtype: Graph + """ + # create class instance + instance = Data.__new__(cls) + + # check the consistency of types defined in __init__, the others are not + # checked (as in pyg Data object) + instance._check_type_consistency(**kwargs) + + return instance + + def __init__( + self, + x=None, + edge_index=None, + pos=None, + edge_attr=None, + undirected=False, + **kwargs, + ): + """ + Initialize the object by setting the node features, edge index, + edge attributes, and positions. The edge index is preprocessed to make + the graph undirected if required. For more details, see the + :meth:`torch_geometric.data.Data` + + :param x: Optional tensor of node features ``(N, F)`` where ``F`` is the + number of features per node. + :type x: torch.Tensor, LabelTensor + :param torch.Tensor edge_index: A tensor of shape ``(2, E)`` + representing the indices of the graph's edges. + :param pos: A tensor of shape ``(N, D)`` representing the positions of + ``N`` points in ``D``-dimensional space. + :type pos: torch.Tensor | LabelTensor + :param edge_attr: Optional tensor of edge_featured ``(E, F')`` where + ``F'`` is the number of edge features + :type edge_attr: torch.Tensor | LabelTensor + :param bool undirected: Whether to make the graph undirected + :param dict kwargs: Additional keyword arguments passed to the + :class:`~torch_geometric.data.Data` class constructor. + """ + # preprocessing + self._preprocess_edge_index(edge_index, undirected) + + # calling init + super().__init__( + x=x, edge_index=edge_index, edge_attr=edge_attr, pos=pos, **kwargs + ) + + def _check_type_consistency(self, **kwargs): + """ + Check the consistency of the types of the input data. + + :param dict kwargs: Attributes to be checked for consistency. + """ + # default types, specified in cls.__new__, by default they are Nont + # if specified in **kwargs they get override + x, pos, edge_index, edge_attr = None, None, None, None + if "pos" in kwargs: + pos = kwargs["pos"] + self._check_pos_consistency(pos) + if "edge_index" in kwargs: + edge_index = kwargs["edge_index"] + self._check_edge_index_consistency(edge_index) + if "x" in kwargs: + x = kwargs["x"] + self._check_x_consistency(x, pos) + if "edge_attr" in kwargs: + edge_attr = kwargs["edge_attr"] + self._check_edge_attr_consistency(edge_attr, edge_index) + if "undirected" in kwargs: + undirected = kwargs["undirected"] + check_consistency(undirected, bool) + + @staticmethod + def _check_pos_consistency(pos): + """ + Check if the position tensor is consistent. + :param torch.Tensor pos: The position tensor. + :raises ValueError: If the position tensor is not consistent. + """ + if pos is not None: + check_consistency(pos, (torch.Tensor, LabelTensor)) + if pos.ndim != 2: + raise ValueError("pos must be a 2D tensor.") + + @staticmethod + def _check_edge_index_consistency(edge_index): + """ + Check if the edge index is consistent. + + :param torch.Tensor edge_index: The edge index tensor. + :raises ValueError: If the edge index tensor is not consistent. + """ + check_consistency(edge_index, (torch.Tensor, LabelTensor)) + if edge_index.ndim != 2: + raise ValueError("edge_index must be a 2D tensor.") + if edge_index.size(0) != 2: + raise ValueError("edge_index must have shape [2, num_edges].") + + @staticmethod + def _check_edge_attr_consistency(edge_attr, edge_index): + """ + Check if the edge attribute tensor is consistent in type and shape + with the edge index. + + :param edge_attr: The edge attribute tensor. + :type edge_attr: torch.Tensor | LabelTensor + :param torch.Tensor edge_index: The edge index tensor. + :raises ValueError: If the edge attribute tensor is not consistent. + """ + if edge_attr is not None: + check_consistency(edge_attr, (torch.Tensor, LabelTensor)) + if edge_attr.ndim != 2: + raise ValueError("edge_attr must be a 2D tensor.") + if edge_attr.size(0) != edge_index.size(1): + raise ValueError( + "edge_attr must have shape " + "[num_edges, num_edge_features], expected " + f"num_edges {edge_index.size(1)} " + f"got {edge_attr.size(0)}." + ) + + @staticmethod + def _check_x_consistency(x, pos=None): + """ + Check if the input tensor x is consistent with the position tensor + `pos`. + + :param x: The input tensor. + :type x: torch.Tensor | LabelTensor + :param pos: The position tensor. + :type pos: torch.Tensor | LabelTensor + :raises ValueError: If the input tensor is not consistent. + """ + if x is not None: + check_consistency(x, (torch.Tensor, LabelTensor)) + if x.ndim != 2: + raise ValueError("x must be a 2D tensor.") + if pos is not None: + if x.size(0) != pos.size(0): + raise ValueError("Inconsistent number of nodes.") + + @staticmethod + def _preprocess_edge_index(edge_index, undirected): + """ + Preprocess the edge index to make the graph undirected (if required). + + :param torch.Tensor edge_index: The edge index. + :param bool undirected: Whether the graph is undirected. + :return: The preprocessed edge index. + :rtype: torch.Tensor + """ + if undirected: + edge_index = to_undirected(edge_index) + return edge_index + + def extract(self, labels, attr="x"): + """ + Perform extraction of labels from the attribute specified by `attr`. + + :param labels: Labels to extract + :type labels: list[str] | tuple[str] | str | dict + :return: Batch object with extraction performed on x + :rtype: PinaBatch + """ + # Extract labels from LabelTensor object + tensor = getattr(self, attr).extract(labels) + # Set the extracted tensor as the new attribute + setattr(self, attr, tensor) + return self + + +class GraphBuilder: + """ + A class that allows an easy definition of :class:`Graph` instances. + """ + + def __new__( + cls, + pos, + edge_index, + x=None, + edge_attr=False, + custom_edge_func=None, + **kwargs, + ): + """ + Compute the edge attributes and create a new instance of the + :class:`~pina.graph.Graph` class. + + :param pos: A tensor of shape ``(N, D)`` representing the positions of + ``N`` points in ``D``-dimensional space. + :type pos: torch.Tensor or LabelTensor + :param edge_index: A tensor of shape ``(2, E)`` representing the indices + of the graph's edges. + :type edge_index: torch.Tensor + :param x: Optional tensor of node features of shape ``(N, F)``, where + ``F`` is the number of features per node. + :type x: torch.Tensor | LabelTensor, optional + :param edge_attr: Optional tensor of edge attributes of shape ``(E, F)`` + , where ``F`` is the number of features per edge. + :type edge_attr: torch.Tensor, optional + :param custom_edge_func: A custom function to compute edge attributes. + If provided, overrides ``edge_attr``. + :type custom_edge_func: Callable, optional + :param kwargs: Additional keyword arguments passed to the + :class:`~pina.graph.Graph` class constructor. + :return: A :class:`~pina.graph.Graph` instance constructed using the + provided information. + :rtype: Graph + """ + edge_attr = cls._create_edge_attr( + pos, edge_index, edge_attr, custom_edge_func or cls._build_edge_attr + ) + return Graph( + x=x, + edge_index=edge_index, + edge_attr=edge_attr, + pos=pos, + **kwargs, + ) + + @staticmethod + def _create_edge_attr(pos, edge_index, edge_attr, func): + """ + Create the edge attributes based on the input parameters. + + :param pos: Positions of the points. + :type pos: torch.Tensor | LabelTensor + :param torch.Tensor edge_index: Edge indices. + :param bool edge_attr: Whether to compute the edge attributes. + :param Callable func: Function to compute the edge attributes. + :raises ValueError: If ``func`` is not a function. + :return: The edge attributes. + :rtype: torch.Tensor | LabelTensor | None + """ + check_consistency(edge_attr, bool) + if edge_attr: + if is_function(func): + return func(pos, edge_index) + raise ValueError("custom_edge_func must be a function.") + return None + + @staticmethod + def _build_edge_attr(pos, edge_index): + """ + Default function to compute the edge attributes. + + :param pos: Positions of the points. + :type pos: torch.Tensor | LabelTensor + :param torch.Tensor edge_index: Edge indices. + :return: The edge attributes. + :rtype: torch.Tensor + """ + return ( + (pos[edge_index[0]] - pos[edge_index[1]]) + .abs() + .as_subclass(torch.Tensor) + ) + + +class RadiusGraph(GraphBuilder): + """ + Extends the :class:`~pina.graph.GraphBuilder` class to compute + ``edge_index`` based on a radius. Each point is connected to all the points + within the radius. + """ + + def __new__(cls, pos, radius, **kwargs): + """ + Instantiate the :class:`~pina.graph.Graph` class by computing the + ``edge_index`` based on the radius provided. + + :param pos: A tensor of shape ``(N, D)`` representing the positions of + ``N`` points in ``D``-dimensional space. + :type pos: torch.Tensor | LabelTensor + :param float radius: The radius within which points are connected. + :param dict kwargs: The additional keyword arguments to be passed to + :class:`GraphBuilder` and :class:`Graph` classes. + :return: A :class:`~pina.graph.Graph` instance with the computed + ``edge_index``. + :rtype: Graph + """ + edge_index = cls.compute_radius_graph(pos, radius) + return super().__new__(cls, pos=pos, edge_index=edge_index, **kwargs) + + @staticmethod + def compute_radius_graph(points, radius): + """ + Computes the ``edge_index`` based on the radius. Each point is connected + to all the points within the radius. + + :param points: A tensor of shape ``(N, D)`` representing the positions + of ``N`` points in ``D``-dimensional space. + :type points: torch.Tensor | LabelTensor + :param float radius: The radius within which points are connected. + :return: A tensor of shape ``(2, E)``, with ``E`` number of edges, + representing the edge indices of the graph. + :rtype: torch.Tensor + """ + dist = torch.cdist(points, points, p=2) + return ( + torch.nonzero(dist <= radius, as_tuple=False) + .t() + .as_subclass(torch.Tensor) + ) + + +class KNNGraph(GraphBuilder): + """ + Extends the :class:`~pina.graph.GraphBuilder` class to compute + ``edge_index`` based on a K-nearest neighbors algorithm. + """ + + def __new__(cls, pos, neighbours, **kwargs): + """ + Instantiate the :class:`~pina.graph.Graph` class by computing the + ``edge_index`` based on the K-nearest neighbors algorithm. + + :param pos: A tensor of shape ``(N, D)`` representing the positions of + ``N`` points in ``D``-dimensional space. + :type pos: torch.Tensor | LabelTensor + :param int neighbours: The number of nearest neighbors to consider when + building the graph. + :param dict kwargs: The additional keyword arguments to be passed to + :class:`GraphBuilder` and :class:`Graph` classes. + + :return: A :class:`~pina.graph.Graph` instance with the computed + ``edge_index``. + :rtype: Graph + """ + + edge_index = cls.compute_knn_graph(pos, neighbours) + return super().__new__(cls, pos=pos, edge_index=edge_index, **kwargs) + + @staticmethod + def compute_knn_graph(points, neighbours): + """ + Computes the ``edge_index`` based on the K-nearest neighbors algorithm. + + :param points: A tensor of shape ``(N, D)`` representing the positions + of ``N`` points in ``D``-dimensional space. + :type points: torch.Tensor | LabelTensor + :param int neighbours: The number of nearest neighbors to consider when + building the graph. + :return: A tensor of shape ``(2, E)``, with ``E`` number of edges, + representing the edge indices of the graph. + :rtype: torch.Tensor + """ + + dist = torch.cdist(points, points, p=2) + knn_indices = torch.topk(dist, k=neighbours + 1, largest=False).indices[ + :, 1: + ] + row = torch.arange(points.size(0)).repeat_interleave(neighbours) + col = knn_indices.flatten() + return torch.stack([row, col], dim=0).as_subclass(torch.Tensor) + + +class LabelBatch(Batch): + """ + Extends the :class:`~torch_geometric.data.Batch` class to include + :class:`~pina.label_tensor.LabelTensor` objects. + """ + + @classmethod + def from_data_list(cls, data_list): + """ + Create a Batch object from a list of :class:`~torch_geometric.data.Data` + or :class:`~pina.graph.Graph` objects. + + :param data_list: List of :class:`~torch_geometric.data.Data` or + :class:`~pina.graph.Graph` objects. + :type data_list: list[Data] | list[Graph] + :return: A :class:`~torch_geometric.data.Batch` object containing + the input data. + :rtype: :class:`~torch_geometric.data.Batch` + """ + # Store the labels of Data/Graph objects (all data have the same labels) + # If the data do not contain labels, labels is an empty dictionary, + # therefore the labels are not stored + labels = { + k: v.labels + for k, v in data_list[0].items() + if isinstance(v, LabelTensor) + } + + # Create a Batch object from the list of Data objects + batch = super().from_data_list(data_list) + + # Put the labels back in the Batch object + for k, v in labels.items(): + batch[k].labels = v + return batch diff --git a/pina/label_tensor.py b/pina/label_tensor.py index c8a41f7b4..3ff1e79d2 100644 --- a/pina/label_tensor.py +++ b/pina/label_tensor.py @@ -1,318 +1,756 @@ -""" Module for LabelTensor """ +"""Module for LabelTensor""" -from copy import deepcopy +from copy import copy, deepcopy import torch from torch import Tensor class LabelTensor(torch.Tensor): - """Torch tensor with a label for any column.""" + """ + Extension of the :class:`torch.Tensor` class that includes labels for + each dimension. + """ @staticmethod def __new__(cls, x, labels, *args, **kwargs): + """ + Create a new instance of the :class:`~pina.label_tensor.LabelTensor` + class. + + :param torch.Tensor x: :class:`torch.tensor` instance to be casted as a + :class:`~pina.label_tensor.LabelTensor`. + :param labels: Labels to assign to the tensor. + :type labels: str | list[str] | dict + :return: The instance of the :class:`~pina.label_tensor.LabelTensor` + class. + :rtype: LabelTensor + """ + + if isinstance(x, LabelTensor): + return x return super().__new__(cls, x, *args, **kwargs) + @property + def tensor(self): + """ + Returns the tensor part of the :class:`~pina.label_tensor.LabelTensor` + object. + + :return: Tensor part of the :class:`~pina.label_tensor.LabelTensor`. + :rtype: torch.Tensor + """ + + return self.as_subclass(Tensor) + def __init__(self, x, labels): """ - Construct a `LabelTensor` by passing a tensor and a list of column - labels. Such labels uniquely identify the columns of the tensor, - allowing for an easier manipulation. + Initialize the :class:`~pina.label_tensor.LabelTensor` instance, by + checking the consistency of the labels and the tensor. Specifically, the + labels must match the following conditions: - :param torch.Tensor x: The data tensor. - :param labels: The labels of the columns. - :type labels: str | list(str) | tuple(str) + - At each dimension, the number of labels must match the size of the \ + dimension. + - At each dimension, the labels must be unique. + + The labels can be passed in the following formats: :Example: >>> from pina import LabelTensor - >>> tensor = LabelTensor(torch.rand((2000, 3)), ['a', 'b', 'c']) - >>> tensor - tensor([[6.7116e-02, 4.8892e-01, 8.9452e-01], - [9.2392e-01, 8.2065e-01, 4.1986e-04], - [8.9266e-01, 5.5446e-01, 6.3500e-01], - ..., - [5.8194e-01, 9.4268e-01, 4.1841e-01], - [1.0246e-01, 9.5179e-01, 3.7043e-02], - [9.6150e-01, 8.0656e-01, 8.3824e-01]]) - >>> tensor.extract('a') - tensor([[0.0671], - [0.9239], - [0.8927], - ..., - [0.5819], - [0.1025], - [0.9615]]) - >>> tensor['a'] - tensor([[0.0671], - [0.9239], - [0.8927], - ..., - [0.5819], - [0.1025], - [0.9615]]) - >>> tensor.extract(['a', 'b']) - tensor([[0.0671, 0.4889], - [0.9239, 0.8207], - [0.8927, 0.5545], - ..., - [0.5819, 0.9427], - [0.1025, 0.9518], - [0.9615, 0.8066]]) - >>> tensor.extract(['b', 'a']) - tensor([[0.4889, 0.0671], - [0.8207, 0.9239], - [0.5545, 0.8927], - ..., - [0.9427, 0.5819], - [0.9518, 0.1025], - [0.8066, 0.9615]]) - """ - if x.ndim == 1: - x = x.reshape(-1, 1) - - if isinstance(labels, str): - labels = [labels] + >>> tensor = LabelTensor( + >>> torch.rand((2000, 3)), + ... {1: {"name": "space", "dof": ['a', 'b', 'c']) + >>> tensor = LabelTensor( + >>> torch.rand((2000, 3)), + ... ["a", "b", "c"]) + + The keys of the dictionary are the dimension indices, and the values are + dictionaries containing the labels and the name of the dimension. If + the labels are passed as a list, these are assigned to the last + dimension. + + :param torch.Tensor x: The tensor to be casted as a + :class:`~pina.label_tensor.LabelTensor`. + :param labels: Labels to assign to the tensor. + :type labels: str | list[str] | dict + :raises ValueError: If the labels are not consistent with the tensor. + """ + super().__init__() + if labels is not None: + self.labels = labels + else: + self._labels = {} - if len(labels) != x.shape[-1]: - raise ValueError( - "the tensor has not the same number of columns of " - "the passed labels." - ) - self._labels = labels + @property + def full_labels(self): + """ + Returns the full labels of the tensor, even for the dimensions that are + not labeled. - def __deepcopy__(self, __): + :return: The full labels of the tensor + :rtype: dict """ - Implements deepcopy for label tensor. By default it stores the - current labels and use the :meth:`~torch._tensor.Tensor.__deepcopy__` - method for creating a new :class:`pina.label_tensor.LabelTensor`. + to_return_dict = {} + shape_tensor = self.shape + for i, value in enumerate(shape_tensor): + if i in self._labels: + to_return_dict[i] = self._labels[i] + else: + to_return_dict[i] = {"dof": range(value), "name": i} + return to_return_dict - :param __: Placeholder parameter. - :type __: None - :return: The deep copy of the :class:`pina.label_tensor.LabelTensor`. - :rtype: LabelTensor + @property + def stored_labels(self): + """ + Returns the labels stored inside the instance. + + :return: The labels stored inside the instance. + :rtype: dict """ - labels = self.labels - copy_tensor = deepcopy(self.tensor) - return LabelTensor(copy_tensor, labels) + return self._labels @property def labels(self): - """Property decorator for labels + """ + Returns the labels of the last dimension of the instance. - :return: labels of self + :return: labels of last dimension :rtype: list """ - return self._labels + if self.ndim - 1 in self._labels: + return self._labels[self.ndim - 1]["dof"] + return None @labels.setter def labels(self, labels): - if len(labels) != self.shape[self.ndim - 1]: # small check - raise ValueError( - "The tensor has not the same number of columns of " - "the passed labels." - ) + """ + Set labels stored insider the instance by checking the type of the + input labels and handling it accordingly. The following types are + accepted: - self._labels = labels # assign the label + - **list**: The list of labels is assigned to the last dimension. + - **dict**: The dictionary of labels is assigned to the tensor. + - **str**: The string is assigned to the last dimension. - @staticmethod - def vstack(label_tensors): + :param labels: Labels to assign to the class variable _labels. + :type labels: str | list[str] | dict """ - Stack tensors vertically. For more details, see - :meth:`torch.vstack`. - :param list(LabelTensor) label_tensors: the tensors to stack. They need - to have equal labels. - :return: the stacked tensor - :rtype: LabelTensor + if not hasattr(self, "_labels"): + self._labels = {} + if isinstance(labels, dict): + self._init_labels_from_dict(labels) + elif isinstance(labels, (list, range)): + self._init_labels_from_list(labels) + elif isinstance(labels, str): + labels = [labels] + self._init_labels_from_list(labels) + else: + raise ValueError("labels must be list, dict or string.") + + def _init_labels_from_dict(self, labels): """ - if len(label_tensors) == 0: - return [] + Store the internal label representation according to the values + passed as input. - all_labels = [label for lt in label_tensors for label in lt.labels] - if set(all_labels) != set(label_tensors[0].labels): - raise RuntimeError("The tensors to stack have different labels") + :param dict labels: The label(s) to update. + :raises ValueError: If the dof list contains duplicates or the number of + dof does not match the tensor shape. + """ - labels = label_tensors[0].labels - tensors = [lt.extract(labels) for lt in label_tensors] - return LabelTensor(torch.vstack(tensors), labels) + tensor_shape = self.shape + + def validate_dof(dof_list, dim_size): + """Validate the 'dof' list for uniqueness and size.""" + if len(dof_list) != len(set(dof_list)): + raise ValueError("dof must be unique") + if len(dof_list) != dim_size: + raise ValueError( + f"Number of dof ({len(dof_list)}) does not match " + f"tensor shape ({dim_size})" + ) + + for dim, label in labels.items(): + if isinstance(label, dict): + if "name" not in label: + label["name"] = dim + if "dof" not in label: + label["dof"] = range(tensor_shape[dim]) + if "dof" in label and "name" in label: + dof = label["dof"] + dof_list = dof if isinstance(dof, (list, range)) else [dof] + if not isinstance(dof_list, (list, range)): + raise ValueError( + f"'dof' should be a list or range, not" + f" {type(dof_list)}" + ) + validate_dof(dof_list, tensor_shape[dim]) + else: + raise ValueError( + "Labels dictionary must contain either " + " both 'name' and 'dof' keys" + ) + else: + raise ValueError( + f"Invalid label format for {dim}: Expected " + f"list or dictionary, got {type(label)}" + ) + + # Assign validated label data to internal labels + self._labels[dim] = label + + def _init_labels_from_list(self, labels): + """ + Given a list of dof, this method update the internal label + representation by assigning the dof to the last dimension. - def clone(self, *args, **kwargs): + :param labels: The label(s) to update. + :type labels: list """ - Clone the LabelTensor. For more details, see - :meth:`torch.Tensor.clone`. - :return: A copy of the tensor. + # Create a dict with labels + last_dim_labels = { + self.ndim - 1: {"dof": labels, "name": self.ndim - 1} + } + self._init_labels_from_dict(last_dim_labels) + + def extract(self, labels_to_extract): + """ + Extract the subset of the original tensor by returning all the positions + corresponding to the passed ``label_to_extract``. If + ``label_to_extract`` is a dictionary, the keys are the dimension names + and the values are the labels to extract. If a single label or a list + of labels is passed, the last dimension is considered. + + :Example: + >>> from pina import LabelTensor + >>> labels = {1: {'dof': ["a", "b", "c"], 'name': 'space'}} + >>> tensor = LabelTensor(torch.rand((2000, 3)), labels) + >>> tensor.extract("a") + >>> tensor.extract(["a", "b"]) + >>> tensor.extract({"space": ["a", "b"]}) + + :param labels_to_extract: The label(s) to extract. + :type labels_to_extract: str | list[str] | tuple[str] | dict + :return: The extracted tensor with the updated labels. :rtype: LabelTensor + + :raises TypeError: Labels are not ``str``, ``list[str]`` or ``dict`` + properly setted. + :raises ValueError: Label to extract is not in the labels ``list``. """ - # # used before merging - # try: - # out = LabelTensor(super().clone(*args, **kwargs), self.labels) - # except: - # out = super().clone(*args, **kwargs) - out = LabelTensor(super().clone(*args, **kwargs), self.labels) - return out - def to(self, *args, **kwargs): + def get_label_indices(dim_labels, labels_te): + if isinstance(labels_te, (int, str)): + labels_te = [labels_te] + return ( + [dim_labels.index(label) for label in labels_te] + if len(labels_te) > 1 + else slice( + dim_labels.index(labels_te[0]), + dim_labels.index(labels_te[0]) + 1, + ) + ) + + # Ensure labels_to_extract is a list or dict + if isinstance(labels_to_extract, (str, int)): + labels_to_extract = [labels_to_extract] + + labels = copy(self._labels) + + # Get the dimension names and the respective dimension index + dim_names = {labels[dim]["name"]: dim for dim in labels} + ndim = super().ndim + tensor = self.tensor.as_subclass(torch.Tensor) + + # Convert list/tuple to a dict for the last dimension if applicable + if isinstance(labels_to_extract, (list, tuple)): + last_dim = ndim - 1 + dim_name = labels[last_dim]["name"] + labels_to_extract = {dim_name: list(labels_to_extract)} + + # Validate the labels_to_extract type + if not isinstance(labels_to_extract, dict): + raise ValueError( + "labels_to_extract must be a string, list, or dictionary." + ) + + # Perform the extraction for each specified dimension + for dim_name, labels_te in labels_to_extract.items(): + if dim_name not in dim_names: + raise ValueError( + f"Cannot extract labels for dimension '{dim_name}' as it is" + f" not present in the original labels." + ) + + idx_dim = dim_names[dim_name] + dim_labels = labels[idx_dim]["dof"] + indices = get_label_indices(dim_labels, labels_te) + + extractor = [slice(None)] * ndim + extractor[idx_dim] = indices + tensor = tensor[tuple(extractor)] + + labels[idx_dim] = {"dof": labels_te, "name": dim_name} + + return LabelTensor(tensor, labels) + + def __str__(self): """ - Performs Tensor dtype and/or device conversion. For more details, see - :meth:`torch.Tensor.to`. + The string representation of the + :class:`~pina.label_tensor.LabelTensor`. + + :return: String representation of the + :class:`~pina.label_tensor.LabelTensor` instance. + :rtype: str """ - tmp = super().to(*args, **kwargs) - new = self.__class__.clone(self) - new.data = tmp.data - return new - def select(self, *args, **kwargs): + s = "" + for key, value in self._labels.items(): + s += f"{key}: {value}\n" + s += "\n" + s += self.tensor.__str__() + return s + + @staticmethod + def cat(tensors, dim=0): """ - Performs Tensor selection. For more details, see :meth:`torch.Tensor.select`. + Concatenate a list of tensors along a specified dimension. For more + details, see :meth:`torch.cat`. + + :param list[LabelTensor] tensors: + :class:`~pina.label_tensor.LabelTensor` instances to concatenate + :param int dim: Dimensions on which you want to perform the operation + (default is 0) + :return: A new :class:`LabelTensor` instance obtained by concatenating + the input instances. + + :rtype: LabelTensor + :raises ValueError: either number dof or dimensions names differ. """ - tmp = super().select(*args, **kwargs) - tmp._labels = self._labels - return tmp - def cuda(self, *args, **kwargs): + if not tensors: + return [] # Handle empty list + if len(tensors) == 1: + return tensors[0] # Return single tensor as-is + + # Perform concatenation + cat_tensor = torch.cat(tensors, dim=dim) + tensors_labels = [tensor.stored_labels for tensor in tensors] + + # Check label consistency across tensors, excluding the + # concatenation dimension + for key in tensors_labels[0]: + if key != dim: + if any( + tensors_labels[i][key] != tensors_labels[0][key] + for i in range(len(tensors_labels)) + ): + raise RuntimeError( + f"Tensors must have the same labels along all " + f"dimensions except {dim}." + ) + + # Copy and update the 'dof' for the concatenation dimension + cat_labels = {k: copy(v) for k, v in tensors_labels[0].items()} + + # Update labels if the concatenation dimension has labels + if dim in tensors[0].stored_labels: + if dim in cat_labels: + cat_dofs = [label[dim]["dof"] for label in tensors_labels] + cat_labels[dim]["dof"] = sum(cat_dofs, []) + else: + cat_labels = tensors[0].stored_labels + + # Assign updated labels to the concatenated tensor + cat_tensor._labels = cat_labels + return cat_tensor + + @staticmethod + def stack(tensors): """ - Send Tensor to cuda. For more details, see :meth:`torch.Tensor.cuda`. + Stacks a list of tensors along a new dimension. For more details, see + :meth:`torch.stack`. + + :param list[LabelTensor] tensors: A list of tensors to stack. + All tensors must have the same shape. + :return: A new :class:`~pina.label_tensor.LabelTensor` instance obtained + by stacking the input tensors. + :rtype: LabelTensor """ - tmp = super().cuda(*args, **kwargs) - new = self.__class__.clone(self) - new.data = tmp.data - return new - def cpu(self, *args, **kwargs): + # Perform stacking in torch + new_tensor = torch.stack(tensors) + + # Increase labels keys by 1 + labels = tensors[0]._labels + labels = {key + 1: value for key, value in labels.items()} + new_tensor._labels = labels + return new_tensor + + def requires_grad_(self, mode=True): """ - Send Tensor to cpu. For more details, see :meth:`torch.Tensor.cpu`. + Override the :meth:`~torch.Tensor.requires_grad_` method to handle + the labels in the new tensor. + For more details, see :meth:`~torch.Tensor.requires_grad_`. + + :param bool mode: A boolean value indicating whether the tensor should + track gradients.If `True`, the tensor will track gradients; + if `False`, it will not. + :return: The :class:`~pina.label_tensor.LabelTensor` itself with the + updated ``requires_grad`` state and retained labels. + :rtype: LabelTensor """ - tmp = super().cpu(*args, **kwargs) - new = self.__class__.clone(self) - new.data = tmp.data - return new - def extract(self, label_to_extract): + lt = super().requires_grad_(mode) + lt._labels = self._labels + return lt + + @property + def dtype(self): """ - Extract the subset of the original tensor by returning all the columns - corresponding to the passed ``label_to_extract``. + Give the ``dtype`` of the tensor. For more details, see + :meth:`torch.dtype`. - :param label_to_extract: The label(s) to extract. - :type label_to_extract: str | list(str) | tuple(str) - :raises TypeError: Labels are not ``str``. - :raises ValueError: Label to extract is not in the labels ``list``. + :return: The data type of the tensor. + :rtype: torch.dtype """ - if isinstance(label_to_extract, str): - label_to_extract = [label_to_extract] - elif isinstance(label_to_extract, (tuple, list)): # TODO - pass - else: - raise TypeError( - "`label_to_extract` should be a str, or a str iterator" - ) + return super().dtype - indeces = [] - for f in label_to_extract: - try: - indeces.append(self.labels.index(f)) - except ValueError: - raise ValueError(f"`{f}` not in the labels list") + def to(self, *args, **kwargs): + """ + Performs Tensor dtype and/or device conversion. For more details, see + :meth:`torch.Tensor.to`. - new_data = super(Tensor, self.T).__getitem__(indeces).T - new_labels = [self.labels[idx] for idx in indeces] + :return: A new :class:`~pina.label_tensor.LabelTensor` instance with the + updated dtype and/or device and retained labels. + :rtype: LabelTensor + """ - extracted_tensor = new_data.as_subclass(LabelTensor) - extracted_tensor.labels = new_labels + lt = super().to(*args, **kwargs) + lt._labels = self._labels + return lt - return extracted_tensor + def clone(self, *args, **kwargs): + """ + Clone the :class:`~pina.label_tensor.LabelTensor`. For more details, see + :meth:`torch.Tensor.clone`. - def detach(self): - detached = super().detach() - if hasattr(self, "_labels"): - detached._labels = self._labels - return detached + :return: A new :class:`~pina.label_tensor.LabelTensor` instance with the + same data and labels but allocated in a different memory location. + :rtype: LabelTensor + """ - def requires_grad_(self, mode=True): - lt = super().requires_grad_(mode) - lt.labels = self.labels - return lt + out = LabelTensor( + super().clone(*args, **kwargs), deepcopy(self._labels) + ) + return out - def append(self, lt, mode="std"): + def append(self, tensor, mode="std"): """ - Return a copy of the merged tensors. - - :param LabelTensor lt: The tensor to merge. - :param str mode: {'std', 'first', 'cross'} - :return: The merged tensors. + Appends a given tensor to the current tensor along the last dimension. + This method supports two types of appending operations: + + 1. **Standard append** ("std"): Concatenates the input tensor with the \ + current tensor along the last dimension. + 2. **Cross append** ("cross"): Creates a cross-product of the current \ + tensor and the input tensor. + + :param tensor: The tensor to append to the current tensor. + :type tensor: LabelTensor + :param mode: The append mode to use. Defaults to ``st``. + :type mode: str, optional + :return: A new :class:`LabelTensor` instance obtained by appending the + input tensor. :rtype: LabelTensor + + :raises ValueError: If the mode is not "std" or "cross". """ - if set(self.labels).intersection(lt.labels): - raise RuntimeError("The tensors to merge have common labels") - new_labels = self.labels + lt.labels if mode == "std": - new_tensor = torch.cat((self, lt), dim=1) - elif mode == "first": - raise NotImplementedError - elif mode == "cross": + # Call cat on last dimension + new_label_tensor = LabelTensor.cat( + [self, tensor], dim=self.ndim - 1 + ) + return new_label_tensor + if mode == "cross": + # Crete tensor and call cat on last dimension tensor1 = self - tensor2 = lt + tensor2 = tensor n1 = tensor1.shape[0] n2 = tensor2.shape[0] - tensor1 = LabelTensor(tensor1.repeat(n2, 1), labels=tensor1.labels) tensor2 = LabelTensor( tensor2.repeat_interleave(n1, dim=0), labels=tensor2.labels ) - new_tensor = torch.cat((tensor1, tensor2), dim=1) + new_label_tensor = LabelTensor.cat( + [tensor1, tensor2], dim=self.ndim - 1 + ) + return new_label_tensor + raise ValueError('mode must be either "std" or "cross"') - new_tensor = new_tensor.as_subclass(LabelTensor) - new_tensor.labels = new_labels - return new_tensor + @staticmethod + def vstack(tensors): + """ + Stack tensors vertically. For more details, see :meth:`torch.vstack`. - def __getitem__(self, index): + :param list of LabelTensor label_tensors: The + :class:`~pina.label_tensor.LabelTensor` instances to stack. They + need to have equal labels. + :return: A new :class:`~pina.label_tensor.LabelTensor` instance obtained + by stacking the input tensors vertically. + :rtype: LabelTensor + """ + + return LabelTensor.cat(tensors, dim=0) + + # This method is used to update labels + def _update_single_label( + self, old_labels, to_update_labels, index, dim, to_update_dim + ): """ - Return a copy of the selected tensor. + Update the labels of the tensor based on the index (or list of indices). + + :param dict old_labels: Labels from which retrieve data. + :param dict to_update_labels: Labels to update. + :param index: Index of dof to retain. + :type index: int | slice | list[int] | tuple[int] | torch.Tensor + :param int dim: The dimension to update. + + :raises: ValueError: If the index type is not supported. """ + old_dof = old_labels[to_update_dim]["dof"] + label_name = old_labels[dim]["name"] + # Handle slicing + if isinstance(index, slice): + to_update_labels[dim] = {"dof": old_dof[index], "name": label_name} + # Handle single integer index + elif isinstance(index, int): + to_update_labels[dim] = { + "dof": [old_dof[index]], + "name": label_name, + } + # Handle lists or tensors + elif isinstance(index, (list, torch.Tensor)): + # Handle list of bools + if isinstance(index, torch.Tensor) and index.dtype == torch.bool: + index = index.nonzero().squeeze() + to_update_labels[dim] = { + "dof": ( + [old_dof[i] for i in index] + if isinstance(old_dof, list) + else index + ), + "name": label_name, + } + else: + raise NotImplementedError( + f"Unsupported index type: {type(index)}. Expected slice, int, " + f"list, or torch.Tensor." + ) + + def __getitem__(self, index): + """ " + Override the __getitem__ method to handle the labels of the + :class:`~pina.label_tensor.LabelTensor` instance. It first performs + __getitem__ operation on the :class:`torch.Tensor` part of the instance, + then updates the labels based on the index. + + :param index: The index used to access the item + :type index: int | str | tuple of int | list ot int | torch.Tensor + :return: A new :class:`~pina.label_tensor.LabelTensor` instance obtained + `__getitem__` operation on :class:`torch.Tensor` part of the + instance, with the updated labels. + :rtype: LabelTensor + + :raises KeyError: If an invalid label index is provided. + :raises IndexError: If an invalid index is accessed in the tensor. + """ + + # Handle string index if isinstance(index, str) or ( isinstance(index, (tuple, list)) - and all(isinstance(a, str) for a in index) + and all(isinstance(i, str) for i in index) ): return self.extract(index) - selected_lt = super(Tensor, self).__getitem__(index) - - try: - len_index = len(index) - except TypeError: - len_index = 1 - - if isinstance(index, int) or len_index == 1: - if selected_lt.ndim == 1: - selected_lt = selected_lt.reshape(1, -1) - if hasattr(self, "labels"): - selected_lt.labels = self.labels - elif len_index == 2: - if selected_lt.ndim == 1: - selected_lt = selected_lt.reshape(-1, 1) - if hasattr(self, "labels"): - if isinstance(index[1], list): - selected_lt.labels = [self.labels[i] for i in index[1]] - else: - selected_lt.labels = self.labels[index[1]] - else: - selected_lt.labels = self.labels + # Retrieve selected tensor and labels + selected_tensor = super().__getitem__(index) + if not hasattr(self, "_labels"): + return selected_tensor + + original_labels = self._labels + updated_labels = copy(original_labels) + + # Ensure the index is iterable + if not isinstance(index, tuple): + index = [index] + + # Update labels based on the index + offset = 0 + for dim, idx in enumerate(index): + if dim in self.stored_labels: + if isinstance(idx, int): + selected_tensor = selected_tensor.unsqueeze(dim) + if idx != slice(None): + self._update_single_label( + original_labels, updated_labels, idx, dim, offset + ) + else: + # Adjust label keys if dimension is reduced (case of integer + # index on a non-labeled dimension) + if isinstance(idx, int): + updated_labels = { + key - 1 if key > dim else key: value + for key, value in updated_labels.items() + } + continue + offset += 1 + + # Update the selected tensor's labels + selected_tensor._labels = updated_labels + return selected_tensor + + def sort_labels(self, dim=None): + """ + Sort the labels along the specified dimension and apply. It applies the + same sorting to the tensor part of the instance. - return selected_lt + :param int dim: The dimension along which to sort the labels. + If ``None``, the last dimension is used. + :return: A new tensor with sorted labels along the specified dimension. + :rtype: LabelTensor + """ - @property - def tensor(self): - return self.as_subclass(Tensor) + def arg_sort(lst): + return sorted(range(len(lst)), key=lambda x: lst[x]) + + if dim is None: + dim = self.ndim - 1 + if self.shape[dim] == 1: + return self + labels = self.stored_labels[dim]["dof"] + sorted_index = arg_sort(labels) + # Define an indexer to sort the tensor along the specified dimension + indexer = [slice(None)] * self.ndim + # Assigned the sorted index to the specified dimension + indexer[dim] = sorted_index + return self[tuple(indexer)] + + def __deepcopy__(self, memo): + """ + Creates a deep copy of the object. For more details, see + :meth:`copy.deepcopy`. - def __len__(self) -> int: - return super().__len__() + :param memo: LabelTensor object to be copied. + :type memo: LabelTensor + :return: A deep copy of the original LabelTensor object. + :rtype: LabelTensor + """ - def __str__(self): - if hasattr(self, "labels"): - s = f"labels({str(self.labels)})\n" - else: - s = "no labels\n" - s += super().__str__() - return s + cls = self.__class__ + result = cls(deepcopy(self.tensor), deepcopy(self.stored_labels)) + return result + + def permute(self, *dims): + """ + Permutes the dimensions of the tensor and the associated labels + accordingly. For more details, see :meth:`torch.Tensor.permute`. + + :param dims: The dimensions to permute the tensor to. + :type dims: tuple[int] | list[int] + :return: A new object with permuted dimensions and reordered labels. + :rtype: LabelTensor + """ + # Call the base class permute method + tensor = super().permute(*dims) + + # Update lables + labels = self._labels + keys_list = list(*dims) + labels = {keys_list.index(k): v for k, v in labels.items()} + + # Assign labels to the new tensor + tensor._labels = labels + return tensor + + def detach(self): + """ + Detaches the tensor from the computation graph and retains the stored + labels. For more details, see :meth:`torch.Tensor.detach`. + + :return: A new tensor detached from the computation graph. + :rtype: LabelTensor + """ + + lt = super().detach() + + # Copy the labels to the new tensor only if present + if hasattr(self, "_labels"): + lt._labels = self.stored_labels + return lt + + @staticmethod + def summation(tensors): + """ + Computes the summation of a list of + :class:`~pina.label_tensor.LabelTensor` instances. + + + :param list[LabelTensor] tensors: A list of tensors to sum. All + tensors must have the same shape and labels. + :return: A new `LabelTensor` containing the element-wise sum of the + input tensors. + :rtype: LabelTensor + + :raises ValueError: If the input `tensors` list is empty. + :raises RuntimeError: If the tensors have different shapes and/or + mismatched labels. + """ + + if not tensors: + raise ValueError("The tensors list must not be empty.") + + if len(tensors) == 1: + return tensors[0] + + # Initialize result tensor and labels + data = torch.zeros_like(tensors[0].tensor).to(tensors[0].device) + last_dim_labels = [] + + # Accumulate tensors + for tensor in tensors: + data += tensor.tensor + last_dim_labels.append(tensor.labels) + + # Construct last dimension labels + last_dim_labels = ["+".join(items) for items in zip(*last_dim_labels)] + + # Update the labels for the resulting tensor + labels = {k: copy(v) for k, v in tensors[0].stored_labels.items()} + labels[tensors[0].ndim - 1] = { + "dof": last_dim_labels, + "name": tensors[0].name, + } + + return LabelTensor(data, labels) + + def reshape(self, *shape): + """ + Override the reshape method to update the labels of the tensor. + For more details, see :meth:`torch.Tensor.reshape`. + + :param tuple of int shape: The new shape of the tensor. + :return: A new :class:`~pina.label_tensor.LabelTensor` instance with the + updated shape and labels. + :rtype: LabelTensor + """ + + # As for now the reshape method is used only in the context of the + # dataset, the labels are not + tensor = super().reshape(*shape) + if not hasattr(self, "_labels") or shape != (-1, *self.shape[2:]): + return tensor + tensor.labels = self.labels + return tensor diff --git a/pina/loss.py b/pina/loss.py deleted file mode 100644 index 3cbf88880..000000000 --- a/pina/loss.py +++ /dev/null @@ -1,209 +0,0 @@ -""" Module for Loss class """ - -from abc import ABCMeta, abstractmethod -from torch.nn.modules.loss import _Loss -import torch -from .utils import check_consistency - -__all__ = ["LossInterface", "LpLoss", "PowerLoss"] - - -class LossInterface(_Loss, metaclass=ABCMeta): - """ - The abstract ``LossInterface`` class. All the class defining a PINA Loss - should be inheritied from this class. - """ - - def __init__(self, reduction="mean"): - """ - :param str reduction: Specifies the reduction to apply to the output: - ``none`` | ``mean`` | ``sum``. When ``none``: no reduction - will be applied, ``mean``: the sum of the output will be divided - by the number of elements in the output, ``sum``: the output will - be summed. Note: ``size_average`` and ``reduce`` are in the - process of being deprecated, and in the meantime, specifying either of - those two args will override ``reduction``. Default: ``mean``. - """ - super().__init__(reduction=reduction, size_average=None, reduce=None) - - @abstractmethod - def forward(self, input, target): - """Forward method for loss function. - - :param torch.Tensor input: Input tensor from real data. - :param torch.Tensor target: Model tensor output. - :return: Loss evaluation. - :rtype: torch.Tensor - """ - pass - - def _reduction(self, loss): - """Simple helper function to check reduction - - :param reduction: Specifies the reduction to apply to the output: - ``none`` | ``mean`` | ``sum``. When ``none``: no reduction - will be applied, ``mean``: the sum of the output will be divided - by the number of elements in the output, ``sum``: the output will - be summed. Note: ``size_average`` and ``reduce`` are in the - process of being deprecated, and in the meantime, specifying either of - those two args will override ``reduction``. Default: ``mean``. - :type reduction: str - :param loss: Loss tensor for each element. - :type loss: torch.Tensor - :return: Reduced loss. - :rtype: torch.Tensor - """ - if self.reduction == "none": - ret = loss - elif self.reduction == "mean": - ret = torch.mean(loss, keepdim=True, dim=-1) - elif self.reduction == "sum": - ret = torch.sum(loss, keepdim=True, dim=-1) - else: - raise ValueError(self.reduction + " is not valid") - return ret - - -class LpLoss(LossInterface): - r""" - The Lp loss implementation class. Creates a criterion that measures - the Lp error between each element in the input :math:`x` and - target :math:`y`. - - The unreduced (i.e. with ``reduction`` set to ``none``) loss can - be described as: - - .. math:: - \ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad - l_n = \left[\sum_{i=1}^{D} \left| x_n^i - y_n^i \right|^p \right], - - If ``'relative'`` is set to true: - - .. math:: - \ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad - l_n = \frac{ [\sum_{i=1}^{D} | x_n^i - y_n^i|^p] }{[\sum_{i=1}^{D}|y_n^i|^p]}, - - where :math:`N` is the batch size. If ``reduction`` is not ``none`` - (default ``mean``), then: - - .. math:: - \ell(x, y) = - \begin{cases} - \operatorname{mean}(L), & \text{if reduction} = \text{`mean';}\\ - \operatorname{sum}(L), & \text{if reduction} = \text{`sum'.} - \end{cases} - - :math:`x` and :math:`y` are tensors of arbitrary shapes with a total - of :math:`n` elements each. - - The sum operation still operates over all the elements, and divides by :math:`n`. - - The division by :math:`n` can be avoided if one sets ``reduction`` to ``sum``. - """ - - def __init__(self, p=2, reduction="mean", relative=False): - """ - :param int p: Degree of Lp norm. It specifies the type of norm to - be calculated. See `list of possible orders in torch linalg - `_ to - for possible degrees. Default 2 (euclidean norm). - :param str reduction: Specifies the reduction to apply to the output: - ``none`` | ``mean`` | ``sum``. ``none``: no reduction - will be applied, ``mean``: the sum of the output will be divided - by the number of elements in the output, ``sum``: the output will - be summed. - :param bool relative: Specifies if relative error should be computed. - """ - super().__init__(reduction=reduction) - - # check consistency - check_consistency(p, (str, int, float)) - check_consistency(relative, bool) - - self.p = p - self.relative = relative - - def forward(self, input, target): - """Forward method for loss function. - - :param torch.Tensor input: Input tensor from real data. - :param torch.Tensor target: Model tensor output. - :return: Loss evaluation. - :rtype: torch.Tensor - """ - loss = torch.linalg.norm((input - target), ord=self.p, dim=-1) - if self.relative: - loss = loss / torch.linalg.norm(input, ord=self.p, dim=-1) - return self._reduction(loss) - - -class PowerLoss(LossInterface): - r""" - The PowerLoss loss implementation class. Creates a criterion that measures - the error between each element in the input :math:`x` and - target :math:`y` powered to a specific integer. - - The unreduced (i.e. with ``reduction`` set to ``none``) loss can - be described as: - - .. math:: - \ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad - l_n = \frac{1}{D}\left[\sum_{i=1}^{D} \left| x_n^i - y_n^i \right|^p \right], - - If ``'relative'`` is set to true: - - .. math:: - \ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad - l_n = \frac{ \sum_{i=1}^{D} | x_n^i - y_n^i|^p }{\sum_{i=1}^{D}|y_n^i|^p}, - - where :math:`N` is the batch size. If ``reduction`` is not ``none`` - (default ``mean``), then: - - .. math:: - \ell(x, y) = - \begin{cases} - \operatorname{mean}(L), & \text{if reduction} = \text{`mean';}\\ - \operatorname{sum}(L), & \text{if reduction} = \text{`sum'.} - \end{cases} - - :math:`x` and :math:`y` are tensors of arbitrary shapes with a total - of :math:`n` elements each. - - The sum operation still operates over all the elements, and divides by :math:`n`. - - The division by :math:`n` can be avoided if one sets ``reduction`` to ``sum``. - """ - - def __init__(self, p=2, reduction="mean", relative=False): - """ - :param int p: Degree of Lp norm. It specifies the type of norm to - be calculated. See `list of possible orders in torch linalg - `_ to - see the possible degrees. Default 2 (euclidean norm). - :param str reduction: Specifies the reduction to apply to the output: - ``none`` | ``mean`` | ``sum``. When ``none``: no reduction - will be applied, ``mean``: the sum of the output will be divided - by the number of elements in the output, ``sum``: the output will - be summed. - :param bool relative: Specifies if relative error should be computed. - """ - super().__init__(reduction=reduction) - - # check consistency - check_consistency(p, (str, int, float)) - self.p = p - check_consistency(relative, bool) - self.relative = relative - - def forward(self, input, target): - """Forward method for loss function. - - :param torch.Tensor input: Input tensor from real data. - :param torch.Tensor target: Model tensor output. - :return: Loss evaluation. - :rtype: torch.Tensor - """ - loss = torch.abs((input - target)).pow(self.p).mean(-1) - if self.relative: - loss = loss / torch.abs(input).pow(self.p).mean(-1) - return self._reduction(loss) diff --git a/pina/loss/__init__.py b/pina/loss/__init__.py new file mode 100644 index 000000000..2f15c6db9 --- /dev/null +++ b/pina/loss/__init__.py @@ -0,0 +1,17 @@ +"""Module for loss functions and weighting functions.""" + +__all__ = [ + "LossInterface", + "LpLoss", + "PowerLoss", + "WeightingInterface", + "ScalarWeighting", + "NeuralTangentKernelWeighting", +] + +from .loss_interface import LossInterface +from .power_loss import PowerLoss +from .lp_loss import LpLoss +from .weighting_interface import WeightingInterface +from .scalar_weighting import ScalarWeighting +from .ntk_weighting import NeuralTangentKernelWeighting diff --git a/pina/loss/loss_interface.py b/pina/loss/loss_interface.py new file mode 100644 index 000000000..728c9f77e --- /dev/null +++ b/pina/loss/loss_interface.py @@ -0,0 +1,52 @@ +"""Module for the Loss Interface.""" + +from abc import ABCMeta, abstractmethod +from torch.nn.modules.loss import _Loss +import torch + + +class LossInterface(_Loss, metaclass=ABCMeta): + """ + Abstract base class for all losses. All classes defining a loss function + should inherit from this interface. + """ + + def __init__(self, reduction="mean"): + """ + Initialization of the :class:`LossInterface` class. + + :param str reduction: The reduction method for the loss. + Available options: ``none``, ``mean``, ``sum``. + If ``none``, no reduction is applied. If ``mean``, the sum of the + loss values is divided by the number of values. If ``sum``, the loss + values are summed. Default is ``mean``. + """ + super().__init__(reduction=reduction, size_average=None, reduce=None) + + @abstractmethod + def forward(self, input, target): + """ + Forward method of the loss function. + + :param torch.Tensor input: Input tensor from real data. + :param torch.Tensor target: Model tensor output. + """ + + def _reduction(self, loss): + """ + Apply the reduction to the loss. + + :param torch.Tensor loss: The tensor containing the pointwise losses. + :raises ValueError: If the reduction method is not valid. + :return: Reduced loss. + :rtype: torch.Tensor + """ + if self.reduction == "none": + ret = loss + elif self.reduction == "mean": + ret = torch.mean(loss, keepdim=True, dim=-1) + elif self.reduction == "sum": + ret = torch.sum(loss, keepdim=True, dim=-1) + else: + raise ValueError(self.reduction + " is not valid") + return ret diff --git a/pina/loss/lp_loss.py b/pina/loss/lp_loss.py new file mode 100644 index 000000000..f535a5b6f --- /dev/null +++ b/pina/loss/lp_loss.py @@ -0,0 +1,75 @@ +"""Module for the LpLoss class.""" + +import torch + +from ..utils import check_consistency +from .loss_interface import LossInterface + + +class LpLoss(LossInterface): + r""" + Implementation of the Lp Loss. It defines a criterion to measures the + pointwise Lp error between values in the input :math:`x` and values in the + target :math:`y`. + + If ``reduction`` is set to ``none``, the loss can be written as: + + .. math:: + \ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad + l_n = \left[\sum_{i=1}^{D} \left| x_n^i - y_n^i \right|^p \right], + + If ``relative`` is set to ``True``, the relative Lp error is computed: + + .. math:: + \ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad + l_n = \frac{ [\sum_{i=1}^{D} | x_n^i - y_n^i|^p] } + {[\sum_{i=1}^{D}|y_n^i|^p]}, + + where :math:`N` is the batch size. + + If ``reduction`` is not ``none``, then: + + .. math:: + \ell(x, y) = + \begin{cases} + \operatorname{mean}(L), & \text{if reduction} = \text{`mean';}\\ + \operatorname{sum}(L), & \text{if reduction} = \text{`sum'.} + \end{cases} + """ + + def __init__(self, p=2, reduction="mean", relative=False): + """ + Initialization of the :class:`LpLoss` class. + + :param int p: Degree of the Lp norm. It specifies the norm to be + computed. Default is ``2`` (euclidean norm). + :param str reduction: The reduction method for the loss. + Available options: ``none``, ``mean``, ``sum``. + If ``none``, no reduction is applied. If ``mean``, the sum of the + loss values is divided by the number of values. If ``sum``, the loss + values are summed. Default is ``mean``. + :param bool relative: If ``True``, the relative error is computed. + Default is ``False``. + """ + super().__init__(reduction=reduction) + + # check consistency + check_consistency(p, (str, int, float)) + check_consistency(relative, bool) + + self.p = p + self.relative = relative + + def forward(self, input, target): + """ + Forward method of the loss function. + + :param torch.Tensor input: Input tensor from real data. + :param torch.Tensor target: Model tensor output. + :return: Loss evaluation. + :rtype: torch.Tensor + """ + loss = torch.linalg.norm((input - target), ord=self.p, dim=-1) + if self.relative: + loss = loss / torch.linalg.norm(input, ord=self.p, dim=-1) + return self._reduction(loss) diff --git a/pina/loss/ntk_weighting.py b/pina/loss/ntk_weighting.py new file mode 100644 index 000000000..d8c947f06 --- /dev/null +++ b/pina/loss/ntk_weighting.py @@ -0,0 +1,71 @@ +"""Module for Neural Tangent Kernel Class""" + +import torch +from torch.nn import Module +from .weighting_interface import WeightingInterface +from ..utils import check_consistency + + +class NeuralTangentKernelWeighting(WeightingInterface): + """ + A neural tangent kernel scheme for weighting different losses to + boost the convergence. + + .. seealso:: + + **Original reference**: Wang, Sifan, Xinling Yu, and + Paris Perdikaris. *When and why PINNs fail to train: + A neural tangent kernel perspective*. Journal of + Computational Physics 449 (2022): 110768. + DOI: `10.1016 `_. + + """ + + def __init__(self, model, alpha=0.5): + """ + Initialization of the :class:`NeuralTangentKernelWeighting` class. + + :param torch.nn.Module model: The neural network model. + :param float alpha: The alpha parameter. + + :raises ValueError: If ``alpha`` is not between 0 and 1 (inclusive). + """ + + super().__init__() + check_consistency(alpha, float) + check_consistency(model, Module) + if alpha < 0 or alpha > 1: + raise ValueError("alpha should be a value between 0 and 1") + self.alpha = alpha + self.model = model + self.weights = {} + self.default_value_weights = 1 + + def aggregate(self, losses): + """ + Weight the losses according to the Neural Tangent Kernel + algorithm. + + :param dict(torch.Tensor) input: The dictionary of losses. + :return: The losses aggregation. It should be a scalar Tensor. + :rtype: torch.Tensor + """ + losses_norm = {} + for condition in losses: + losses[condition].backward(retain_graph=True) + grads = [] + for param in self.model.parameters(): + grads.append(param.grad.view(-1)) + grads = torch.cat(grads) + losses_norm[condition] = torch.norm(grads) + self.weights = { + condition: self.alpha + * self.weights.get(condition, self.default_value_weights) + + (1 - self.alpha) + * losses_norm[condition] + / sum(losses_norm.values()) + for condition in losses + } + return sum( + self.weights[condition] * loss for condition, loss in losses.items() + ) diff --git a/pina/loss/power_loss.py b/pina/loss/power_loss.py new file mode 100644 index 000000000..1edbf4f86 --- /dev/null +++ b/pina/loss/power_loss.py @@ -0,0 +1,76 @@ +"""Module for the PowerLoss class.""" + +import torch + +from ..utils import check_consistency +from .loss_interface import LossInterface + + +class PowerLoss(LossInterface): + r""" + Implementation of the Power Loss. It defines a criterion to measures the + pointwise error between values in the input :math:`x` and values in the + target :math:`y`. + + If ``reduction`` is set to ``none``, the loss can be written as: + + .. math:: + \ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad + l_n = \frac{1}{D}\left[\sum_{i=1}^{D} + \left| x_n^i - y_n^i \right|^p\right], + + If ``relative`` is set to ``True``, the relative error is computed: + + .. math:: + \ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad + l_n = \frac{ \sum_{i=1}^{D} | x_n^i - y_n^i|^p } + {\sum_{i=1}^{D}|y_n^i|^p}, + + where :math:`N` is the batch size. + + If ``reduction`` is not ``none``, then: + + .. math:: + \ell(x, y) = + \begin{cases} + \operatorname{mean}(L), & \text{if reduction} = \text{`mean';}\\ + \operatorname{sum}(L), & \text{if reduction} = \text{`sum'.} + \end{cases} + """ + + def __init__(self, p=2, reduction="mean", relative=False): + """ + Initialization of the :class:`PowerLoss` class. + + :param int p: Degree of the Lp norm. It specifies the norm to be + computed. Default is ``2`` (euclidean norm). + :param str reduction: The reduction method for the loss. + Available options: ``none``, ``mean``, ``sum``. + If ``none``, no reduction is applied. If ``mean``, the sum of the + loss values is divided by the number of values. If ``sum``, the loss + values are summed. Default is ``mean``. + :param bool relative: If ``True``, the relative error is computed. + Default is ``False``. + """ + super().__init__(reduction=reduction) + + # check consistency + check_consistency(p, (str, int, float)) + check_consistency(relative, bool) + + self.p = p + self.relative = relative + + def forward(self, input, target): + """ + Forward method of the loss function. + + :param torch.Tensor input: Input tensor from real data. + :param torch.Tensor target: Model tensor output. + :return: Loss evaluation. + :rtype: torch.Tensor + """ + loss = torch.abs((input - target)).pow(self.p).mean(-1) + if self.relative: + loss = loss / torch.abs(input).pow(self.p).mean(-1) + return self._reduction(loss) diff --git a/pina/loss/scalar_weighting.py b/pina/loss/scalar_weighting.py new file mode 100644 index 000000000..6bc093c7d --- /dev/null +++ b/pina/loss/scalar_weighting.py @@ -0,0 +1,59 @@ +"""Module for the Scalar Weighting.""" + +from .weighting_interface import WeightingInterface +from ..utils import check_consistency + + +class _NoWeighting(WeightingInterface): + """ + Weighting scheme that does not apply any weighting to the losses. + """ + + def aggregate(self, losses): + """ + Aggregate the losses. + + :param dict losses: The dictionary of losses. + :return: The aggregated losses. + :rtype: torch.Tensor + """ + return sum(losses.values()) + + +class ScalarWeighting(WeightingInterface): + """ + Weighting scheme that assigns a scalar weight to each loss term. + """ + + def __init__(self, weights): + """ + Initialization of the :class:`ScalarWeighting` class. + + :param weights: The weights to be assigned to each loss term. + If a single scalar value is provided, it is assigned to all loss + terms. If a dictionary is provided, the keys are the conditions and + the values are the weights. If a condition is not present in the + dictionary, the default value is used. + :type weights: float | int | dict + """ + super().__init__() + check_consistency([weights], (float, dict, int)) + if isinstance(weights, (float, int)): + self.default_value_weights = weights + self.weights = {} + else: + self.default_value_weights = 1 + self.weights = weights + + def aggregate(self, losses): + """ + Aggregate the losses. + + :param dict losses: The dictionary of losses. + :return: The aggregated losses. + :rtype: torch.Tensor + """ + return sum( + self.weights.get(condition, self.default_value_weights) * loss + for condition, loss in losses.items() + ) diff --git a/pina/loss/weighting_interface.py b/pina/loss/weighting_interface.py new file mode 100644 index 000000000..8b8cb2f28 --- /dev/null +++ b/pina/loss/weighting_interface.py @@ -0,0 +1,24 @@ +"""Module for the Weighting Interface.""" + +from abc import ABCMeta, abstractmethod + + +class WeightingInterface(metaclass=ABCMeta): + """ + Abstract base class for all loss weighting schemas. All weighting schemas + should inherit from this class. + """ + + def __init__(self): + """ + Initialization of the :class:`WeightingInterface` class. + """ + self.condition_names = None + + @abstractmethod + def aggregate(self, losses): + """ + Aggregate the losses. + + :param dict losses: The dictionary of losses. + """ diff --git a/pina/meta.py b/pina/meta.py deleted file mode 100644 index fa53e95e4..000000000 --- a/pina/meta.py +++ /dev/null @@ -1,22 +0,0 @@ -__all__ = [ - "__project__", - "__title__", - "__author__", - "__copyright__", - "__license__", - "__version__", - "__mail__", - "__maintainer__", - "__status__", -] - -__project__ = "PINA" -__title__ = "pina" -__author__ = "PINA Contributors" -__copyright__ = "2021-2024, PINA Contributors" -__license__ = "MIT" -__version__ = "0.1.2" -__mail__ = "demo.nicola@gmail.com, dario.coscia@sissa.it" # TODO -__maintainer__ = __author__ -__status__ = "Alpha" -__packagename__ = "pina-mathlab" diff --git a/pina/model/__init__.py b/pina/model/__init__.py index 3224d0af3..606dde7d5 100644 --- a/pina/model/__init__.py +++ b/pina/model/__init__.py @@ -1,3 +1,5 @@ +"""Module for the Neural model classes.""" + __all__ = [ "FeedForward", "ResidualFeedForward", @@ -10,13 +12,15 @@ "AveragingNeuralOperator", "LowRankNeuralOperator", "Spline", + "GraphNeuralOperator", ] from .feed_forward import FeedForward, ResidualFeedForward from .multi_feed_forward import MultiFeedForward from .deeponet import DeepONet, MIONet -from .fno import FNO, FourierIntegralKernel -from .base_no import KernelNeuralOperator -from .avno import AveragingNeuralOperator -from .lno import LowRankNeuralOperator +from .fourier_neural_operator import FNO, FourierIntegralKernel +from .kernel_neural_operator import KernelNeuralOperator +from .average_neural_operator import AveragingNeuralOperator +from .low_rank_neural_operator import LowRankNeuralOperator from .spline import Spline +from .graph_neural_operator import GraphNeuralOperator diff --git a/pina/model/average_neural_operator.py b/pina/model/average_neural_operator.py new file mode 100644 index 000000000..6019b96c6 --- /dev/null +++ b/pina/model/average_neural_operator.py @@ -0,0 +1,122 @@ +"""Module for the Averaging Neural Operator model class.""" + +import torch +from torch import nn +from .block.average_neural_operator_block import AVNOBlock +from .kernel_neural_operator import KernelNeuralOperator +from ..utils import check_consistency + + +class AveragingNeuralOperator(KernelNeuralOperator): + """ + Averaging Neural Operator model class. + + The Averaging Neural Operator is a general architecture for learning + operators, which map functions to functions. It can be trained both with + Supervised and Physics-Informed learning strategies. The Averaging Neural + Operator performs convolution by means of a field average. + + .. seealso:: + + **Original reference**: Lanthaler S., Li, Z., Stuart, A. (2020). + *The Nonlocal Neural Operator: Universal Approximation*. + DOI: `arXiv preprint arXiv:2304.13221. + `_ + """ + + def __init__( + self, + lifting_net, + projecting_net, + field_indices, + coordinates_indices, + n_layers=4, + func=nn.GELU, + ): + """ + Initialization of the :class:`AveragingNeuralOperator` class. + + :param torch.nn.Module lifting_net: The lifting neural network mapping + the input to its hidden dimension. It must take as input the input + field and the coordinates at which the input field is evaluated. + :param torch.nn.Module projecting_net: The projection neural network + mapping the hidden representation to the output function. It must + take as input the embedding dimension plus the dimension of the + coordinates. + :param list[str] field_indices: The labels of the fields in the input + tensor. + :param list[str] coordinates_indices: The labels of the coordinates in + the input tensor. + :param int n_layers: The number of hidden layers. Default is ``4``. + :param torch.nn.Module func: The activation function to use. + Default is :class:`torch.nn.GELU`. + :raises ValueError: If the input dimension does not match with the + labels of the fields and coordinates. + :raises ValueError: If the input dimension of the projecting network + does not match with the hidden dimension of the lifting network. + """ + + # check consistency + check_consistency(field_indices, str) + check_consistency(coordinates_indices, str) + check_consistency(n_layers, int) + check_consistency(func, nn.Module, subclass=True) + + # check hidden dimensions match + input_lifting_net = next(lifting_net.parameters()).size()[-1] + output_lifting_net = lifting_net( + torch.rand(size=next(lifting_net.parameters()).size()) + ).shape[-1] + projecting_net_input = next(projecting_net.parameters()).size()[-1] + + if len(field_indices) + len(coordinates_indices) != input_lifting_net: + raise ValueError( + "The lifting_net must take as input the " + "coordinates vector and the field vector." + ) + + if ( + output_lifting_net + len(coordinates_indices) + != projecting_net_input + ): + raise ValueError( + "The projecting_net input must be equal to" + "the embedding dimension (which is the output) " + "of the lifting_net plus the dimension of the " + "coordinates, i.e. len(coordinates_indices)." + ) + + # assign + self.coordinates_indices = coordinates_indices + self.field_indices = field_indices + integral_net = nn.Sequential( + *[AVNOBlock(output_lifting_net, func) for _ in range(n_layers)] + ) + super().__init__(lifting_net, integral_net, projecting_net) + + def forward(self, x): + r""" + Forward pass for the :class:`AveragingNeuralOperator` model. + + The ``lifting_net`` maps the input to the hidden dimension. + Then, several layers of + :class:`~pina.model.block.average_neural_operator_block.AVNOBlock` are + applied. Finally, the ``projection_net`` maps the hidden representation + to the output function. + + :param LabelTensor x: The input tensor for performing the computation. + It expects a tensor :math:`B \times N \times D`, where :math:`B` is + the batch_size, :math:`N` the number of points in the mesh, + :math:`D` the dimension of the problem, i.e. the sum + of ``len(coordinates_indices)`` and ``len(field_indices)``. + :return: The output tensor. + :rtype: torch.Tensor + """ + points_tmp = x.extract(self.coordinates_indices) + new_batch = x.extract(self.field_indices) + new_batch = torch.cat((new_batch, points_tmp), dim=-1) + new_batch = self._lifting_operator(new_batch) + new_batch = self._integral_kernels(new_batch) + new_batch = torch.cat((new_batch, points_tmp), dim=-1) + new_batch = self._projection_operator(new_batch) + return new_batch diff --git a/pina/model/avno.py b/pina/model/avno.py deleted file mode 100644 index 2ac3b3f7e..000000000 --- a/pina/model/avno.py +++ /dev/null @@ -1,118 +0,0 @@ -"""Module Averaging Neural Operator.""" - -import torch -from torch import nn, concatenate -from .layers import AVNOBlock -from .base_no import KernelNeuralOperator -from pina.utils import check_consistency - - -class AveragingNeuralOperator(KernelNeuralOperator): - """ - Implementation of Averaging Neural Operator. - - Averaging Neural Operator is a general architecture for - learning Operators. Unlike traditional machine learning methods - AveragingNeuralOperator is designed to map entire functions - to other functions. It can be trained with Supervised learning strategies. - AveragingNeuralOperator does convolution by performing a field average. - - .. seealso:: - - **Original reference**: Lanthaler S. Li, Z., Kovachki, - Stuart, A. (2020). *The Nonlocal Neural Operator: - Universal Approximation*. - DOI: `arXiv preprint arXiv:2304.13221. - `_ - """ - - def __init__( - self, - lifting_net, - projecting_net, - field_indices, - coordinates_indices, - n_layers=4, - func=nn.GELU, - ): - """ - :param torch.nn.Module lifting_net: The neural network for lifting - the input. It must take as input the input field and the coordinates - at which the input field is avaluated. The output of the lifting - net is chosen as embedding dimension of the problem - :param torch.nn.Module projecting_net: The neural network for - projecting the output. It must take as input the embedding dimension - (output of the ``lifting_net``) plus the dimension - of the coordinates. - :param list[str] field_indices: the label of the fields - in the input tensor. - :param list[str] coordinates_indices: the label of the - coordinates in the input tensor. - :param int n_layers: number of hidden layers. Default is 4. - :param torch.nn.Module func: the activation function to use, - default to torch.nn.GELU. - """ - - # check consistency - check_consistency(field_indices, str) - check_consistency(coordinates_indices, str) - check_consistency(n_layers, int) - check_consistency(func, nn.Module, subclass=True) - - # check hidden dimensions match - input_lifting_net = next(lifting_net.parameters()).size()[-1] - output_lifting_net = lifting_net( - torch.rand(size=next(lifting_net.parameters()).size()) - ).shape[-1] - projecting_net_input = next(projecting_net.parameters()).size()[-1] - - if len(field_indices) + len(coordinates_indices) != input_lifting_net: - raise ValueError( - "The lifting_net must take as input the " - "coordinates vector and the field vector." - ) - - if ( - output_lifting_net + len(coordinates_indices) - != projecting_net_input - ): - raise ValueError( - "The projecting_net input must be equal to" - "the embedding dimension (which is the output) " - "of the lifting_net plus the dimension of the " - "coordinates, i.e. len(coordinates_indices)." - ) - - # assign - self.coordinates_indices = coordinates_indices - self.field_indices = field_indices - integral_net = nn.Sequential( - *[AVNOBlock(output_lifting_net, func) for _ in range(n_layers)] - ) - super().__init__(lifting_net, integral_net, projecting_net) - - def forward(self, x): - r""" - Forward computation for Averaging Neural Operator. It performs a - lifting of the input by the ``lifting_net``. Then different layers - of Averaging Neural Operator Blocks are applied. - Finally the output is projected to the final dimensionality - by the ``projecting_net``. - - :param torch.Tensor x: The input tensor for fourier block, - depending on ``dimension`` in the initialization. It expects - a tensor :math:`B \times N \times D`, - where :math:`B` is the batch_size, :math:`N` the number of points - in the mesh, :math:`D` the dimension of the problem, i.e. the sum - of ``len(coordinates_indices)+len(field_indices)``. - :return: The output tensor obtained from Average Neural Operator. - :rtype: torch.Tensor - """ - points_tmp = x.extract(self.coordinates_indices) - new_batch = x.extract(self.field_indices) - new_batch = concatenate((new_batch, points_tmp), dim=-1) - new_batch = self._lifting_operator(new_batch) - new_batch = self._integral_kernels(new_batch) - new_batch = concatenate((new_batch, points_tmp), dim=-1) - new_batch = self._projection_operator(new_batch) - return new_batch diff --git a/pina/model/block/__init__.py b/pina/model/block/__init__.py new file mode 100644 index 000000000..c40135b7e --- /dev/null +++ b/pina/model/block/__init__.py @@ -0,0 +1,37 @@ +"""Module for the building blocks of the neural models.""" + +__all__ = [ + "ContinuousConvBlock", + "ResidualBlock", + "EnhancedLinear", + "SpectralConvBlock1D", + "SpectralConvBlock2D", + "SpectralConvBlock3D", + "FourierBlock1D", + "FourierBlock2D", + "FourierBlock3D", + "PODBlock", + "OrthogonalBlock", + "PeriodicBoundaryEmbedding", + "FourierFeatureEmbedding", + "AVNOBlock", + "LowRankBlock", + "RBFBlock", + "GNOBlock", +] + +from .convolution_2d import ContinuousConvBlock +from .residual import ResidualBlock, EnhancedLinear +from .spectral import ( + SpectralConvBlock1D, + SpectralConvBlock2D, + SpectralConvBlock3D, +) +from .fourier_block import FourierBlock1D, FourierBlock2D, FourierBlock3D +from .pod_block import PODBlock +from .orthogonal import OrthogonalBlock +from .embedding import PeriodicBoundaryEmbedding, FourierFeatureEmbedding +from .average_neural_operator_block import AVNOBlock +from .low_rank_block import LowRankBlock +from .rbf_block import RBFBlock +from .gno_block import GNOBlock diff --git a/pina/model/block/average_neural_operator_block.py b/pina/model/block/average_neural_operator_block.py new file mode 100644 index 000000000..91379abeb --- /dev/null +++ b/pina/model/block/average_neural_operator_block.py @@ -0,0 +1,64 @@ +"""Module for the Averaging Neural Operator Block class.""" + +import torch +from torch import nn +from ...utils import check_consistency + + +class AVNOBlock(nn.Module): + r""" + The inner block of the Averaging Neural Operator. + + The operator layer performs an affine transformation where the convolution + is approximated with a local average. Given the input function + :math:`v(x)\in\mathbb{R}^{\rm{emb}}` the layer computes the operator update + :math:`K(v)` as: + + .. math:: + K(v) = \sigma\left(Wv(x) + b + \frac{1}{|\mathcal{A}|}\int v(y)dy\right) + + where: + + * :math:`\mathbb{R}^{\rm{emb}}` is the embedding (hidden) size + corresponding to the ``hidden_size`` object + * :math:`\sigma` is a non-linear activation, corresponding to the + ``func`` object + * :math:`W\in\mathbb{R}^{\rm{emb}\times\rm{emb}}` is a tunable matrix. + * :math:`b\in\mathbb{R}^{\rm{emb}}` is a tunable bias. + + .. seealso:: + + **Original reference**: Lanthaler S., Li, Z., Stuart, A. (2020). + *The Nonlocal Neural Operator: Universal Approximation*. + DOI: `arXiv preprint arXiv:2304.13221. + `_ + """ + + def __init__(self, hidden_size=100, func=nn.GELU): + """ + Initialization of the :class:`AVNOBlock` class. + + :param int hidden_size: The size of the hidden layer. + Defaults is ``100``. + :param func: The activation function. + Default is :class:`torch.nn.GELU`. + """ + super().__init__() + + # Check type consistency + check_consistency(hidden_size, int) + check_consistency(func, nn.Module, subclass=True) + # Assignment + self._nn = nn.Linear(hidden_size, hidden_size) + self._func = func() + + def forward(self, x): + r""" + Forward pass of the block. It performs a sum of local average and an + affine transformation of the field. + + :param torch.Tensor x: The input tensor for performing the computation. + :return: The output tensor. + :rtype: torch.Tensor + """ + return self._func(self._nn(x) + torch.mean(x, dim=1, keepdim=True)) diff --git a/pina/model/block/convolution.py b/pina/model/block/convolution.py new file mode 100644 index 000000000..666f66a66 --- /dev/null +++ b/pina/model/block/convolution.py @@ -0,0 +1,234 @@ +"""Module for the Base Continuous Convolution class.""" + +from abc import ABCMeta, abstractmethod +import torch +from .stride import Stride +from .utils_convolution import optimizing + + +class BaseContinuousConv(torch.nn.Module, metaclass=ABCMeta): + r""" + Base Class for Continuous Convolution. + + The class expects the input to be in the form: + :math:`[B \times N_{in} \times N \times D]`, where :math:`B` is the + batch_size, :math:`N_{in}` is the number of input fields, :math:`N` + the number of points in the mesh, :math:`D` the dimension of the problem. + In particular: + + * :math:`D` is the number of spatial variables + 1. The last column must + contain the field value. + * :math:`N_{in}` represents the number of function components. + For instance, a vectorial function :math:`f = [f_1, f_2]` has + :math:`N_{in}=2`. + + :Note + A 2-dimensional vector-valued function defined on a 3-dimensional input + evaluated on a 100 points input mesh and batch size of 8 is represented + as a tensor of shape ``[8, 2, 100, 4]``, where the columns + ``[:, 0, :, -1]`` and ``[:, 1, :, -1]`` represent the first and second, + components of the function, respectively. + + The algorithm returns a tensor of shape: + :math:`[B \times N_{out} \times N \times D]`, where :math:`B` is the + batch_size, :math:`N_{out}` is the number of output fields, :math:`N` + the number of points in the mesh, :math:`D` the dimension of the problem. + """ + + def __init__( + self, + input_numb_field, + output_numb_field, + filter_dim, + stride, + model=None, + optimize=False, + no_overlap=False, + ): + """ + Initialization of the :class:`BaseContinuousConv` class. + + :param int input_numb_field: The number of input fields. + :param int output_numb_field: The number of input fields. + :param filter_dim: The shape of the filter. + :type filter_dim: list[int] | tuple[int] + :param dict stride: The stride of the filter. + :param torch.nn.Module model: The neural network for inner + parametrization. Default is ``None``. + :param bool optimize: If ``True``, optimization is performed on the + continuous filter. It should be used only when the training points + are fixed. If ``model`` is in ``eval`` mode, it is reset to + ``False``. Default is ``False``. + :param bool no_overlap: If ``True``, optimization is performed on the + transposed continuous filter. It should be used only when the filter + positions do not overlap for different strides. + Default is ``False``. + :raises ValueError: If ``input_numb_field`` is not an integer. + :raises ValueError: If ``output_numb_field`` is not an integer. + :raises ValueError: If ``filter_dim`` is not a list or tuple. + :raises ValueError: If ``stride`` is not a dictionary. + :raises ValueError: If ``optimize`` is not a boolean. + :raises ValueError: If ``no_overlap`` is not a boolean. + :raises NotImplementedError: If ``no_overlap`` is ``True``. + """ + super().__init__() + + if not isinstance(input_numb_field, int): + raise ValueError("input_numb_field must be int.") + self._input_numb_field = input_numb_field + + if not isinstance(output_numb_field, int): + raise ValueError("input_numb_field must be int.") + self._output_numb_field = output_numb_field + + if not isinstance(filter_dim, (tuple, list)): + raise ValueError("filter_dim must be tuple or list.") + vect = filter_dim + vect = torch.tensor(vect) + self.register_buffer("_dim", vect, persistent=False) + + if not isinstance(stride, dict): + raise ValueError("stride must be dictionary.") + self._stride = Stride(stride) + + self._net = model + + if not isinstance(optimize, bool): + raise ValueError("optimize must be bool.") + self._optimize = optimize + + # choosing how to initialize based on optimization + if self._optimize: + # optimizing decorator ensure the function is called + # just once + self._choose_initialization = optimizing( + self._initialize_convolution + ) + else: + self._choose_initialization = self._initialize_convolution + + if not isinstance(no_overlap, bool): + raise ValueError("no_overlap must be bool.") + + if no_overlap: + raise NotImplementedError + + self.transpose = self.transpose_overlap + + class DefaultKernel(torch.nn.Module): + """ + The default kernel. + """ + + def __init__(self, input_dim, output_dim): + """ + Initialization of the :class:`DefaultKernel` class. + + :param int input_dim: The input dimension. + :param int output_dim: The output dimension. + :raises ValueError: If ``input_dim`` is not an integer. + :raises ValueError: If ``output_dim`` is not an integer. + """ + super().__init__() + assert isinstance(input_dim, int) + assert isinstance(output_dim, int) + self._model = torch.nn.Sequential( + torch.nn.Linear(input_dim, 20), + torch.nn.ReLU(), + torch.nn.Linear(20, 20), + torch.nn.ReLU(), + torch.nn.Linear(20, output_dim), + ) + + def forward(self, x): + """ + Forward pass. + + :param torch.Tensor x: The input data. + :return: The output data. + :rtype: torch.Tensor + """ + return self._model(x) + + @property + def net(self): + """ + The neural network for inner parametrization. + + :return: The neural network. + :rtype: torch.nn.Module + """ + return self._net + + @property + def stride(self): + """ + The stride of the filter. + + :return: The stride of the filter. + :rtype: dict + """ + return self._stride + + @property + def filter_dim(self): + """ + The shape of the filter. + + :return: The shape of the filter. + :rtype: torch.Tensor + """ + return self._dim + + @property + def input_numb_field(self): + """ + The number of input fields. + + :return: The number of input fields. + :rtype: int + """ + return self._input_numb_field + + @property + def output_numb_field(self): + """ + The number of output fields. + + :return: The number of output fields. + :rtype: int + """ + return self._output_numb_field + + @abstractmethod + def forward(self, X): + """ + Forward pass. + + :param torch.Tensor X: The input data. + """ + + @abstractmethod + def transpose_overlap(self, X): + """ + Transpose the convolution with overlap. + + :param torch.Tensor X: The input data. + """ + + @abstractmethod + def transpose_no_overlap(self, X): + """ + Transpose the convolution without overlap. + + :param torch.Tensor X: The input data. + """ + + @abstractmethod + def _initialize_convolution(self, X, type_): + """ + Initialize the convolution. + + :param torch.Tensor X: The input data. + :param str type_: The type of initialization. + """ diff --git a/pina/model/layers/convolution_2d.py b/pina/model/block/convolution_2d.py similarity index 65% rename from pina/model/layers/convolution_2d.py rename to pina/model/block/convolution_2d.py index 665ddafab..825ae613b 100644 --- a/pina/model/layers/convolution_2d.py +++ b/pina/model/block/convolution_2d.py @@ -1,20 +1,20 @@ -"""Module for Continuous Convolution class""" +"""Module for the Continuous Convolution class.""" +import torch from .convolution import BaseContinuousConv from .utils_convolution import check_point, map_points_ from .integral import Integral -import torch class ContinuousConvBlock(BaseContinuousConv): - """ - Implementation of Continuous Convolutional operator. + r""" + Continuous Convolutional block. - The algorithm expects input to be in the form: - :math:`[B, N_{in}, N, D]` - where :math:`B` is the batch_size, :math:`N_{in}` is the number of input - fields, :math:`N` the number of points in the mesh, :math:`D` the dimension - of the problem. In particular: + The class expects the input to be in the form: + :math:`[B \times N_{in} \times N \times D]`, where :math:`B` is the + batch_size, :math:`N_{in}` is the number of input fields, :math:`N` + the number of points in the mesh, :math:`D` the dimension of the problem. + In particular: * :math:`D` is the number of spatial variables + 1. The last column must contain the field value. For example for 2D problems :math:`D=3` and @@ -26,10 +26,11 @@ class ContinuousConvBlock(BaseContinuousConv): .. seealso:: - **Original reference**: Coscia, D., Meneghetti, L., Demo, N. et al. - *A continuous convolutional trainable filter for modelling unstructured data*. - Comput Mech 72, 253–265 (2023). DOI ``_ - + **Original reference**: + Coscia, D., Meneghetti, L., Demo, N. et al. + *A continuous convolutional trainable filter for modelling unstructured + data*. Comput Mech 72, 253-265 (2023). + DOI ``_ """ def __init__( @@ -43,52 +44,48 @@ def __init__( no_overlap=False, ): """ - :param input_numb_field: Number of fields :math:`N_{in}` in the input. - :type input_numb_field: int - :param output_numb_field: Number of fields :math:`N_{out}` in the output. - :type output_numb_field: int - :param filter_dim: Dimension of the filter. - :type filter_dim: tuple(int) | list(int) - :param stride: Stride for the filter. - :type stride: dict - :param model: Neural network for inner parametrization, - defaults to ``None``. If None, a default multilayer perceptron - of width three and size twenty with ReLU activation is used. - :type model: torch.nn.Module - :param optimize: Flag for performing optimization on the continuous - filter, defaults to False. The flag `optimize=True` should be - used only when the scatter datapoints are fixed through the - training. If torch model is in ``.eval()`` mode, the flag is - automatically set to False always. - :type optimize: bool - :param no_overlap: Flag for performing optimization on the transpose - continuous filter, defaults to False. The flag set to `True` should - be used only when the filter positions do not overlap for different - strides. RuntimeError will raise in case of non-compatible strides. - :type no_overlap: bool + Initialization of the :class:`ContinuousConvBlock` class. + + :param int input_numb_field: The number of input fields. + :param int output_numb_field: The number of input fields. + :param filter_dim: The shape of the filter. + :type filter_dim: list[int] | tuple[int] + :param dict stride: The stride of the filter. + :param torch.nn.Module model: The neural network for inner + parametrization. Default is ``None``. + :param bool optimize: If ``True``, optimization is performed on the + continuous filter. It should be used only when the training points + are fixed. If ``model`` is in ``eval`` mode, it is reset to + ``False``. Default is ``False``. + :param bool no_overlap: If ``True``, optimization is performed on the + transposed continuous filter. It should be used only when the filter + positions do not overlap for different strides. + Default is ``False``. .. note:: - Using `optimize=True` the filter can be use either in `forward` - or in `transpose` mode, not both. If `optimize=False` the same - filter can be used for both `transpose` and `forward` modes. + If ``optimize=True``, the filter can be use either in ``forward`` + or in ``transpose`` mode, not both. :Example: >>> class MLP(torch.nn.Module): - def __init__(self) -> None: - super().__init__() - self. model = torch.nn.Sequential( - torch.nn.Linear(2, 8), - torch.nn.ReLU(), - torch.nn.Linear(8, 8), - torch.nn.ReLU(), - torch.nn.Linear(8, 1)) - def forward(self, x): - return self.model(x) + ... def __init__(self) -> None: + ... super().__init__() + ... self. model = torch.nn.Sequential( + ... torch.nn.Linear(2, 8), + ... torch.nn.ReLU(), + ... torch.nn.Linear(8, 8), + ... torch.nn.ReLU(), + ... torch.nn.Linear(8, 1) + ... ) + ... def forward(self, x): + ... return self.model(x) >>> dim = [3, 3] - >>> stride = {"domain": [10, 10], - "start": [0, 0], - "jumps": [3, 3], - "direction": [1, 1.]} + >>> stride = { + ... "domain": [10, 10], + ... "start": [0, 0], + ... "jumps": [3, 3], + ... "direction": [1, 1.] + ... } >>> conv = ContinuousConv2D(1, 2, dim, stride, MLP) >>> conv ContinuousConv2D( @@ -114,7 +111,6 @@ def forward(self, x): ) ) """ - super().__init__( input_numb_field=input_numb_field, output_numb_field=output_numb_field, @@ -134,15 +130,20 @@ def forward(self, x): # stride for continuous convolution overridden self._stride = self._stride._stride_discrete + # Define variables + self._index = None + self._grid = None + self._grid_transpose = None + def _spawn_networks(self, model): """ - Private method to create a collection of kernels + Create a collection of kernels - :param model: A :class:`torch.nn.Module` model in form of Object class. - :type model: torch.nn.Module - :return: List of :class:`torch.nn.Module` models. + :param torch.nn.Module model: A neural network model. + :raises ValueError: If the model is not a subclass of + ``torch.nn.Module``. + :return: A list of models. :rtype: torch.nn.ModuleList - """ nets = [] if self._net is None: @@ -152,7 +153,7 @@ def _spawn_networks(self, model): else: if not isinstance(model, object): raise ValueError( - "Expected a python class inheriting" " from torch.nn.Module" + "Expected a python class inheriting from torch.nn.Module" ) for _ in range(self._input_numb_field * self._output_numb_field): @@ -169,13 +170,11 @@ def _spawn_networks(self, model): def _extract_mapped_points(self, batch_idx, index, x): """ - Priviate method to extract mapped points in the filter + Extract mapped points in the filter. - :param x: Input tensor of shape ``[channel, N, dim]`` - :type x: torch.Tensor + :param torch.Tensor x: Input tensor of shape ``[channel, N, dim]`` :return: Mapped points and indeces for each channel, - :rtype: torch.Tensor, list - + :rtype: tuple """ mapped_points = [] indeces_channels = [] @@ -211,11 +210,9 @@ def _extract_mapped_points(self, batch_idx, index, x): def _find_index(self, X): """ - Private method to extract indeces for convolution. - - :param X: Input tensor, as in ContinuousConvBlock ``__init__``. - :type X: torch.Tensor + Extract indeces for convolution. + :param torch.Tensor X: The input tensor. """ # append the index for each stride index = [] @@ -229,11 +226,9 @@ def _find_index(self, X): def _make_grid_forward(self, X): """ - Private method to create forward convolution grid. - - :param X: Input tensor, as in ContinuousConvBlock docstring. - :type X: torch.Tensor + Create forward convolution grid. + :param torch.Tensor X: The input tensor. """ # filter dimension + number of points in output grid filter_dim = len(self._dim) @@ -257,71 +252,63 @@ def _make_grid_forward(self, X): def _make_grid_transpose(self, X): """ - Private method to create transpose convolution grid. - - :param X: Input tensor, as in ContinuousConvBlock docstring. - :type X: torch.Tensor - + Create transpose convolution grid. + :param torch.Tensor X: The input tensor. """ # initialize to all zeros - tmp = torch.zeros_like(X) + tmp = torch.zeros_like(X).as_subclass(torch.Tensor) tmp[..., :-1] = X[..., :-1] # save on tmp self._grid_transpose = tmp - def _make_grid(self, X, type): + def _make_grid(self, X, type_): """ - Private method to create convolution grid. - - :param X: Input tensor, as in ContinuousConvBlock docstring. - :type X: torch.Tensor - :param type: Type of convolution, ``['forward', 'inverse']`` the - possibilities. - :type type: str + Create convolution grid. + :param torch.Tensor X: The input tensor. + :param str type_: The type of convolution. + Available options are: ``forward`` and ``inverse``. + :raises TypeError: If the type is not in the available options. """ # choose the type of convolution - if type == "forward": - return self._make_grid_forward(X) - elif type == "inverse": + if type_ == "forward": + self._make_grid_forward(X) + return + if type_ == "inverse": self._make_grid_transpose(X) - else: - raise TypeError + return + raise TypeError - def _initialize_convolution(self, X, type="forward"): + def _initialize_convolution(self, X, type_="forward"): """ - Private method to intialize the convolution. - The convolution is initialized by setting a grid and - calculate the index for finding the points inside the - filter. - - :param X: Input tensor, as in ContinuousConvBlock docstring. - :type X: torch.Tensor - :param str type: type of convolution, ``['forward', 'inverse'] ``the - possibilities. + Initialize the convolution by setting a grid and computing the index to + find the points inside the filter. + + :param torch.Tensor X: The input tensor. + :param str type_: The type of convolution. Available options are: + ``forward`` and ``inverse``. Default is ``forward``. """ # variable for the convolution - self._make_grid(X, type) + self._make_grid(X, type_) # calculate the index self._find_index(X) def forward(self, X): """ - Forward pass in the convolutional layer. + Forward pass. - :param x: Input data for the convolution :math:`[B, N_{in}, N, D]`. - :type x: torch.Tensor - :return: Convolution output :math:`[B, N_{out}, N, D]`. + :param torch.Tensor x: The input tensor. + :return: The output tensor. :rtype: torch.Tensor """ # initialize convolution if self.training: # we choose what to do based on optimization - self._choose_initialization(X, type="forward") + self._choose_initialization(X, type_="forward") else: # we always initialize on testing self._initialize_convolution(X, "forward") @@ -373,23 +360,14 @@ def forward(self, X): def transpose_no_overlap(self, integrals, X): """ - Transpose pass in the layer for no-overlapping filters - - :param integrals: Weights for the transpose convolution. Shape - :math:`[B, N_{in}, N]` - where B is the batch_size, :math`N_{in}` is the number of input - fields, :math:`N` the number of points in the mesh, D the dimension - of the problem. - :type integral: torch.tensor - :param X: Input data. Expect tensor of shape - :math:`[B, N_{in}, M, D]` where :math:`B` is the batch_size, - :math`N_{in}`is the number of input fields, :math:`M` the number of points - in the mesh, :math:`D` the dimension of the problem. - :type X: torch.Tensor - :return: Feed forward transpose convolution. Tensor of shape - :math:`[B, N_{out}, M, D]` where :math:`B` is the batch_size, - :math`N_{out}`is the number of input fields, :math:`M` the number of points - in the mesh, :math:`D` the dimension of the problem. + Transpose pass in the layer for no-overlapping filters. + + :param torch.Tensor integrals: The weights for the transpose convolution. + Expected shape :math:`[B, N_{in}, N]`. + :param torch.Tensor X: The input data. + Expected shape :math:`[B, N_{in}, M, D]`. + :return: Feed forward transpose convolution. + Expected shape: :math:`[B, N_{out}, M, D]`. :rtype: torch.Tensor .. note:: @@ -399,7 +377,7 @@ def transpose_no_overlap(self, integrals, X): # initialize convolution if self.training: # we choose what to do based on optimization - self._choose_initialization(X, type="inverse") + self._choose_initialization(X, type_="inverse") else: # we always initialize on testing self._initialize_convolution(X, "inverse") @@ -456,23 +434,14 @@ def transpose_no_overlap(self, integrals, X): def transpose_overlap(self, integrals, X): """ - Transpose pass in the layer for overlapping filters - - :param integrals: Weights for the transpose convolution. Shape - :math:`[B, N_{in}, N]` - where B is the batch_size, :math`N_{in}` is the number of input - fields, :math:`N` the number of points in the mesh, D the dimension - of the problem. - :type integral: torch.tensor - :param X: Input data. Expect tensor of shape - :math:`[B, N_{in}, M, D]` where :math:`B` is the batch_size, - :math`N_{in}`is the number of input fields, :math:`M` the number of points - in the mesh, :math:`D` the dimension of the problem. - :type X: torch.Tensor - :return: Feed forward transpose convolution. Tensor of shape - :math:`[B, N_{out}, M, D]` where :math:`B` is the batch_size, - :math`N_{out}`is the number of input fields, :math:`M` the number of points - in the mesh, :math:`D` the dimension of the problem. + Transpose pass in the layer for overlapping filters. + + :param torch.Tensor integrals: The weights for the transpose convolution. + Expected shape :math:`[B, N_{in}, N]`. + :param torch.Tensor X: The input data. + Expected shape :math:`[B, N_{in}, M, D]`. + :return: Feed forward transpose convolution. + Expected shape: :math:`[B, N_{out}, M, D]`. :rtype: torch.Tensor .. note:: This function is automatically called when ``.transpose()`` @@ -481,7 +450,7 @@ def transpose_overlap(self, integrals, X): # initialize convolution if self.training: # we choose what to do based on optimization - self._choose_initialization(X, type="inverse") + self._choose_initialization(X, type_="inverse") else: # we always initialize on testing self._initialize_convolution(X, "inverse") @@ -491,7 +460,7 @@ def transpose_overlap(self, integrals, X): conv_transposed = self._grid_transpose.clone().detach() # list to iterate for calculating nn output - tmp = [i for i in range(self._output_numb_field)] + tmp = list(range(self._output_numb_field)) iterate_conv = [ item for item in tmp for _ in range(self._input_numb_field) ] diff --git a/pina/model/block/embedding.py b/pina/model/block/embedding.py new file mode 100644 index 000000000..1e44ec143 --- /dev/null +++ b/pina/model/block/embedding.py @@ -0,0 +1,279 @@ +"""Modules for the the Embedding blocks.""" + +import torch +from pina.utils import check_consistency + + +class PeriodicBoundaryEmbedding(torch.nn.Module): + r""" + Enforcing hard-constrained periodic boundary conditions by embedding the + input. + + A function :math:`u:\mathbb{R}^{\rm{in}} \rightarrow\mathbb{R}^{\rm{out}}` + is periodic with respect to the spatial coordinates :math:`\mathbf{x}` + with period :math:`\mathbf{L}` if: + + .. math:: + u(\mathbf{x}) = u(\mathbf{x} + n \mathbf{L})\;\; + \forall n\in\mathbb{N}. + + The :class:`PeriodicBoundaryEmbedding` augments the input as follows: + + .. math:: + \mathbf{x} \rightarrow \tilde{\mathbf{x}} = \left[1, + \cos\left(\frac{2\pi}{L_1} x_1 \right), + \sin\left(\frac{2\pi}{L_1}x_1\right), \cdots, + \cos\left(\frac{2\pi}{L_{\rm{in}}}x_{\rm{in}}\right), + \sin\left(\frac{2\pi}{L_{\rm{in}}}x_{\rm{in}}\right)\right], + + where :math:`\text{dim}(\tilde{\mathbf{x}}) = 3\text{dim}(\mathbf{x})`. + + .. seealso:: + **Original reference**: + 1. Dong, Suchuan, and Naxian Ni (2021). + *A method for representing periodic functions and enforcing + exactly periodic boundary conditions with deep neural networks*. + Journal of Computational Physics 435, 110242. + DOI: `10.1016/j.jcp.2021.110242. + `_ + 2. Wang, S., Sankaran, S., Wang, H., & Perdikaris, P. (2023). + *An expert's guide to training physics-informed neural + networks*. + DOI: `arXiv preprint arXiv:2308.0846. + `_ + + .. warning:: + The embedding is a truncated fourier expansion, and enforces periodic + boundary conditions only for the function, and not for its derivatives. + Enforcement of the approximate periodicity in the derivatives can be + performed. Extensive tests have shown (see referenced papers) that this + implementation can correctly enforce the periodic boundary conditions on + the derivatives up to the order :math:`\sim 2,3`. This is not guaranteed + for orders :math:`>3`. The PINA module is tested only for periodic + boundary conditions on the function itself. + """ + + def __init__(self, input_dimension, periods, output_dimension=None): + """ + Initialization of the :class:`PeriodicBoundaryEmbedding` block. + + :param int input_dimension: The dimension of the input tensor. + :param periods: The periodicity with respect to each dimension for the + input data. If ``float`` or ``int`` is passed, the period is assumed + to be constant over all the dimensions of the data. If a ``dict`` is + passed the `dict.values` represent periods, while the ``dict.keys`` + represent the dimension where the periodicity is enforced. + The `dict.keys` can either be `int` if working with + :class:`torch.Tensor`, or ``str`` if working with + :class:`pina.label_tensor.LabelTensor`. + :type periods: float | int | dict + :param int output_dimension: The dimension of the output after the + fourier embedding. If not ``None``, a :class:`torch.nn.Linear` layer + is applied to the fourier embedding output to match the desired + dimensionality. Default is ``None``. + :raises TypeError: If the periods dict is not consistent. + """ + super().__init__() + + # check input consistency + check_consistency(periods, (float, int, dict)) + check_consistency(input_dimension, int) + if output_dimension is not None: + check_consistency(output_dimension, int) + self._layer = torch.nn.Linear(input_dimension * 3, output_dimension) + else: + self._layer = torch.nn.Identity() + + # checks on the periods + if isinstance(periods, dict): + if not all( + isinstance(dim, (str, int)) and isinstance(period, (float, int)) + for dim, period in periods.items() + ): + raise TypeError( + "In dictionary periods, keys must be integers" + " or strings, and values must be float or int." + ) + self._period = periods + else: + self._period = {k: periods for k in range(input_dimension)} + + def forward(self, x): + """ + Forward pass. + + :param x: The input tensor. + :type x: torch.Tensor | LabelTensor + :return: Periodic embedding of the input. + :rtype: torch.Tensor + """ + omega = torch.stack( + [ + torch.pi * 2.0 / torch.tensor([val], device=x.device) + for val in self._period.values() + ], + dim=-1, + ) + x = self._get_vars(x, list(self._period.keys())) + return self._layer( + torch.cat( + [ + torch.ones_like(x), + torch.cos(omega * x), + torch.sin(omega * x), + ], + dim=-1, + ) + ) + + def _get_vars(self, x, indeces): + """ + Get the variables from input tensor ordered by specific indeces. + + :param x: The input tensor from which to extract. + :type x: torch.Tensor | LabelTensor + :param indeces: The indeces to extract. + :type indeces: list[int] | list[str] + :raises RuntimeError: If the indeces are not consistent. + :raises RuntimeError: If the extraction is not possible. + :return: The extracted tensor. + :rtype: torch.Tensor | LabelTensor + """ + if isinstance(indeces[0], str): + try: + return x.extract(indeces) + except AttributeError as e: + raise RuntimeError( + "Not possible to extract input variables from tensor." + " Ensure that the passed tensor is a LabelTensor or" + " pass list of integers to extract variables. For" + " more information refer to warning in the documentation." + ) from e + elif isinstance(indeces[0], int): + return x[..., indeces] + else: + raise RuntimeError( + "Not able to extract correct indeces for tensor." + " For more information refer to warning in the documentation." + ) + + @property + def period(self): + """ + The period of the function. + + :return: The period of the function. + :rtype: dict | float | int + """ + return self._period + + +class FourierFeatureEmbedding(torch.nn.Module): + r""" + Fourier Feature Embedding class to encode the input features using random + Fourier features. + + This class applies a Fourier transformation to the input features, which can + help in learning high-frequency variations in data. The class supports + multiscale feature embedding, creating embeddings for each scale specified + by the ``sigma`` parameter. + + The Fourier Feature Embedding augments the input features as follows + (3.10 of original paper): + + .. math:: + \mathbf{x} \rightarrow \tilde{\mathbf{x}} = \left[ + \cos\left( \mathbf{B} \mathbf{x} \right), + \sin\left( \mathbf{B} \mathbf{x} \right)\right], + + where :math:`\mathbf{B}_{ij} \sim \mathcal{N}(0, \sigma^2)`. + + If multiple ``sigma`` are passed, the resulting embeddings are concateneted: + + .. math:: + \mathbf{x} \rightarrow \tilde{\mathbf{x}} = \left[ + \cos\left( \mathbf{B}^1 \mathbf{x} \right), + \sin\left( \mathbf{B}^1 \mathbf{x} \right), + \cos\left( \mathbf{B}^2 \mathbf{x} \right), + \sin\left( \mathbf{B}^3 \mathbf{x} \right), + \dots, + \cos\left( \mathbf{B}^M \mathbf{x} \right), + \sin\left( \mathbf{B}^M \mathbf{x} \right)\right], + + where :math:`\mathbf{B}^k_{ij} \sim \mathcal{N}(0, \sigma_k^2) \quad k \in + (1, \dots, M)`. + + .. seealso:: + **Original reference**: + Wang, S., Wang, H., and Perdikaris, P. (2021). + *On the eigenvector bias of Fourier feature networks: From regression to + solving multi-scale PDEs with physics-informed neural networks.* + Computer Methods in Applied Mechanics and Engineering 384 (2021): + 113938. + DOI: `10.1016/j.cma.2021.113938. + `_ + """ + + def __init__(self, input_dimension, output_dimension, sigma): + """ + Initialization of the :class:`FourierFeatureEmbedding` block. + + :param int input_dimension: The dimension of the input tensor. + :param int output_dimension: The dimension of the output tensor. The + output is obtained as a concatenation of cosine and sine embeddings. + :param sigma: The standard deviation used for the Fourier Embedding. + This value must reflect the granularity of the scale in the + differential equation solution. + :type sigma: float | int + :raises RuntimeError: If the output dimension is not an even number. + """ + super().__init__() + + # check consistency + check_consistency(sigma, (int, float)) + check_consistency(output_dimension, int) + check_consistency(input_dimension, int) + if output_dimension % 2: + raise RuntimeError( + "Expected output_dimension to be a even number, " + f"got {output_dimension}." + ) + + # assign sigma + self._sigma = sigma + + # create non-trainable matrices + self._matrix = ( + torch.rand( + size=(input_dimension, output_dimension // 2), + requires_grad=False, + ) + * self.sigma + ) + + def forward(self, x): + """ + Forward pass. + + :param x: The input tensor. + :type x: torch.Tensor | LabelTensor + :return: Fourier embedding of the input. + :rtype: torch.Tensor + """ + # compute random matrix multiplication + out = torch.mm(x, self._matrix.to(device=x.device, dtype=x.dtype)) + # return embedding + return torch.cat( + [torch.cos(2 * torch.pi * out), torch.sin(2 * torch.pi * out)], + dim=-1, + ) + + @property + def sigma(self): + """ + The standard deviation used for the Fourier Embedding. + + :return: The standard deviation used for the Fourier Embedding. + :rtype: float | int + """ + return self._sigma diff --git a/pina/model/block/fourier_block.py b/pina/model/block/fourier_block.py new file mode 100644 index 000000000..2983c840a --- /dev/null +++ b/pina/model/block/fourier_block.py @@ -0,0 +1,204 @@ +"""Module for the Fourier Neural Operator Block class.""" + +import torch +from torch import nn +from ...utils import check_consistency + +from .spectral import ( + SpectralConvBlock1D, + SpectralConvBlock2D, + SpectralConvBlock3D, +) + + +class FourierBlock1D(nn.Module): + """ + The inner block of the Fourier Neural Operator for 1-dimensional input + tensors. + + The module computes the spectral convolution of the input with a linear + kernel in the fourier space, and then it maps the input back to the physical + space. The output is then added to a Linear tranformation of the input in + the physical space. Finally an activation function is applied to the output. + + .. seealso:: + + **Original reference**: Li, Z., Kovachki, N., Azizzadenesheli, K., + Liu, B., Bhattacharya, K., Stuart, A., & Anandkumar, A. (2020). + *Fourier neural operator for parametric partial differential equations*. + DOI: `arXiv preprint arXiv:2010.08895. + `_ + + """ + + def __init__( + self, + input_numb_fields, + output_numb_fields, + n_modes, + activation=torch.nn.Tanh, + ): + r""" + Initialization of the :class:`FourierBlock1D` class. + + :param int input_numb_fields: The number of channels for the input. + :param int output_numb_fields: The number of channels for the output. + :param n_modes: The number of modes to select for each dimension. + It must be at most equal to :math:`\floor(Nx/2)+1`. + :type n_modes: list[int] | tuple[int] + :param torch.nn.Module activation: The activation function. + Default is :class:`torch.nn.Tanh`. + """ + + super().__init__() + + # check type consistency + check_consistency(activation(), nn.Module) + + # assign variables + self._spectral_conv = SpectralConvBlock1D( + input_numb_fields=input_numb_fields, + output_numb_fields=output_numb_fields, + n_modes=n_modes, + ) + self._activation = activation() + self._linear = nn.Conv1d(input_numb_fields, output_numb_fields, 1) + + def forward(self, x): + """ + Forward pass of the block. It performs a spectral convolution and a + linear transformation of the input. Then, it sums the results. + + :param torch.Tensor x: The input tensor for performing the computation. + :return: The output tensor. + :rtype: torch.Tensor + """ + return self._activation(self._spectral_conv(x) + self._linear(x)) + + +class FourierBlock2D(nn.Module): + """ + The inner block of the Fourier Neural Operator for 2-dimensional input + tensors. + + The module computes the spectral convolution of the input with a linear + kernel in the fourier space, and then it maps the input back to the physical + space. The output is then added to a Linear tranformation of the input in + the physical space. Finally an activation function is applied to the output. + + .. seealso:: + + **Original reference**: Li, Z., Kovachki, N., Azizzadenesheli, K., + Liu, B., Bhattacharya, K., Stuart, A., & Anandkumar, A. (2020). + *Fourier neural operator for parametric partial differential equations*. + DOI: `arXiv preprint arXiv:2010.08895. + `_ + """ + + def __init__( + self, + input_numb_fields, + output_numb_fields, + n_modes, + activation=torch.nn.Tanh, + ): + r""" + Initialization of the :class:`FourierBlock2D` class. + + :param int input_numb_fields: The number of channels for the input. + :param int output_numb_fields: The number of channels for the output. + :param n_modes: The number of modes to select for each dimension. + It must be at most equal to :math:`\floor(Nx/2)+1`, + :math:`\floor(Ny/2)+1`. + :type n_modes: list[int] | tuple[int] + :param torch.nn.Module activation: The activation function. + Default is :class:`torch.nn.Tanh`. + """ + super().__init__() + + # check type consistency + check_consistency(activation(), nn.Module) + + # assign variables + self._spectral_conv = SpectralConvBlock2D( + input_numb_fields=input_numb_fields, + output_numb_fields=output_numb_fields, + n_modes=n_modes, + ) + self._activation = activation() + self._linear = nn.Conv2d(input_numb_fields, output_numb_fields, 1) + + def forward(self, x): + """ + Forward pass of the block. It performs a spectral convolution and a + linear transformation of the input. Then, it sums the results. + + :param torch.Tensor x: The input tensor for performing the computation. + :return: The output tensor. + :rtype: torch.Tensor + """ + return self._activation(self._spectral_conv(x) + self._linear(x)) + + +class FourierBlock3D(nn.Module): + """ + The inner block of the Fourier Neural Operator for 3-dimensional input + tensors. + + The module computes the spectral convolution of the input with a linear + kernel in the fourier space, and then it maps the input back to the physical + space. The output is then added to a Linear tranformation of the input in + the physical space. Finally an activation function is applied to the output. + + .. seealso:: + + **Original reference**: Li, Z., Kovachki, N., Azizzadenesheli, K., + Liu, B., Bhattacharya, K., Stuart, A., & Anandkumar, A. (2020). + *Fourier neural operator for parametric partial differential equations*. + DOI: `arXiv preprint arXiv:2010.08895. + `_ + """ + + def __init__( + self, + input_numb_fields, + output_numb_fields, + n_modes, + activation=torch.nn.Tanh, + ): + r""" + Initialization of the :class:`FourierBlock3D` class. + + :param int input_numb_fields: The number of channels for the input. + :param int output_numb_fields: The number of channels for the output. + :param n_modes: The number of modes to select for each dimension. + It must be at most equal to :math:`\floor(Nx/2)+1`, + :math:`\floor(Ny/2)+1`, :math:`\floor(Nz/2)+1`. + :type n_modes: list[int] | tuple[int] + :param torch.nn.Module activation: The activation function. + Default is :class:`torch.nn.Tanh`. + """ + super().__init__() + + # check type consistency + check_consistency(activation(), nn.Module) + + # assign variables + self._spectral_conv = SpectralConvBlock3D( + input_numb_fields=input_numb_fields, + output_numb_fields=output_numb_fields, + n_modes=n_modes, + ) + self._activation = activation() + self._linear = nn.Conv3d(input_numb_fields, output_numb_fields, 1) + + def forward(self, x): + """ + Forward pass of the block. It performs a spectral convolution and a + linear transformation of the input. Then, it sums the results. + + :param torch.Tensor x: The input tensor for performing the computation. + :return: The output tensor. + :rtype: torch.Tensor + """ + return self._activation(self._spectral_conv(x) + self._linear(x)) diff --git a/pina/model/block/gno_block.py b/pina/model/block/gno_block.py new file mode 100644 index 000000000..600803463 --- /dev/null +++ b/pina/model/block/gno_block.py @@ -0,0 +1,110 @@ +"""Module for the Graph Neural Operator Block class.""" + +import torch +from torch_geometric.nn import MessagePassing + + +class GNOBlock(MessagePassing): + """ + The inner block of the Graph Neural Operator, based on Message Passing. + """ + + def __init__( + self, + width, + edges_features, + n_layers=2, + layers=None, + inner_size=None, + internal_func=None, + external_func=None, + ): + """ + Initialization of the :class:`GNOBlock` class. + + :param int width: The width of the kernel. + :param int edge_features: The number of edge features. + :param int n_layers: The number of kernel layers. Default is ``2``. + :param layers: A list specifying the number of neurons for each layer + of the neural network. If not ``None``, it overrides the + ``inner_size`` and ``n_layers``parameters. Default is ``None``. + :type layers: list[int] | tuple[int] + :param int inner_size: The size of the inner layer. Default is ``None``. + :param torch.nn.Module internal_func: The activation function applied to + the output of each layer. If ``None``, it uses the + :class:`torch.nn.Tanh` activation. Default is ``None``. + :param torch.nn.Module external_func: The activation function applied to + the output of the block. If ``None``, it uses the + :class:`torch.nn.Tanh`. activation. Default is ``None``. + """ + + from ...model.feed_forward import FeedForward + + super().__init__(aggr="mean") # Uses PyG's default aggregation + self.width = width + + if layers is None and inner_size is None: + inner_size = width + + self.dense = FeedForward( + input_dimensions=edges_features, + output_dimensions=width**2, + n_layers=n_layers, + layers=layers, + inner_size=inner_size, + func=internal_func, + ) + + self.W = torch.nn.Linear(width, width) + self.func = external_func() + + def message_and_aggregate(self, edge_index, x, edge_attr): + """ + Combine messages and perform aggregation. + + :param torch.Tensor edge_index: The edge index. + :param torch.Tensor x: The node feature matrix. + :param torch.Tensor edge_attr: The edge features. + :return: The aggregated messages. + :rtype: torch.Tensor + """ + # Edge features are transformed into a matrix of shape + # [num_edges, width, width] + x_ = self.dense(edge_attr).view(-1, self.width, self.width) + # Messages are computed as the product of the edge features + messages = torch.einsum("bij,bj->bi", x_, x[edge_index[0]]) + # Aggregation is performed using the mean (set in the constructor) + return self.aggregate(messages, edge_index[1]) + + def edge_update(self, edge_attr): + """ + Update edge features. + + :param torch.Tensor edge_attr: The edge features. + :return: The updated edge features. + :rtype: torch.Tensor + """ + return edge_attr + + def update(self, aggr_out, x): + """ + Update node features. + + :param torch.Tensor aggr_out: The aggregated messages. + :param torch.Tensor x: The node feature matrix. + :return: The updated node features. + :rtype: torch.Tensor + """ + return aggr_out + self.W(x) + + def forward(self, x, edge_index, edge_attr): + """ + Forward pass of the block. + + :param torch.Tensor x: The node features. + :param torch.Tensor edge_index: The edge indeces. + :param torch.Tensor edge_attr: The edge features. + :return: The updated node features. + :rtype: torch.Tensor + """ + return self.func(self.propagate(edge_index, x=x, edge_attr=edge_attr)) diff --git a/pina/model/block/integral.py b/pina/model/block/integral.py new file mode 100644 index 000000000..0bab4f07a --- /dev/null +++ b/pina/model/block/integral.py @@ -0,0 +1,71 @@ +"""Module to perform integration for continuous convolution.""" + +import torch + + +class Integral: + """ + Class allowing integration for continous convolution. + """ + + def __init__(self, param): + """ + Initializzation of the :class:`Integral` class. + + :param param: The type of continuous convolution. + :type param: string + :raises TypeError: If the parameter is neither ``discrete`` + nor ``continuous``. + """ + if param == "discrete": + self.make_integral = self.integral_param_disc + elif param == "continuous": + self.make_integral = self.integral_param_cont + else: + raise TypeError + + def __call__(self, *args, **kwds): + """ + Call the integral function + + :param list args: Arguments for the integral function. + :param dict kwds: Keyword arguments for the integral function. + :return: The integral of the input. + :rtype: torch.tensor + """ + return self.make_integral(*args, **kwds) + + def _prepend_zero(self, x): + """ + Create bins to perform integration. + + :param torch.Tensor x: The input tensor. + :return: The bins for the integral. + :rtype: torch.Tensor + """ + return torch.cat((torch.zeros(1, dtype=x.dtype, device=x.device), x)) + + def integral_param_disc(self, x, y, idx): + """ + Perform discrete integration with discrete parameters. + + :param torch.Tensor x: The first input tensor. + :param torch.Tensor y: The second input tensor. + :param list[int] idx: The indices for different strides. + :return: The discrete integral. + :rtype: torch.Tensor + """ + cs_idxes = self._prepend_zero(torch.cumsum(torch.tensor(idx), 0)) + cs = self._prepend_zero(torch.cumsum(x.flatten() * y.flatten(), 0)) + return cs[cs_idxes[1:]] - cs[cs_idxes[:-1]] + + def integral_param_cont(self, x, y, idx): + """ + Perform continuous integration with continuous parameters. + + :param torch.Tensor x: The first input tensor. + :param torch.Tensor y: The second input tensor. + :param list[int] idx: The indices for different strides. + :raises NotImplementedError: The method is not implemented. + """ + raise NotImplementedError diff --git a/pina/model/block/low_rank_block.py b/pina/model/block/low_rank_block.py new file mode 100644 index 000000000..1e8925d95 --- /dev/null +++ b/pina/model/block/low_rank_block.py @@ -0,0 +1,107 @@ +"""Module for the Low Rank Neural Operator Block class.""" + +import torch + +from ...utils import check_consistency + + +class LowRankBlock(torch.nn.Module): + """ + The inner block of the Low Rank Neural Operator. + + .. seealso:: + + **Original reference**: Kovachki, N., Li, Z., Liu, B., + Azizzadenesheli, K., Bhattacharya, K., Stuart, A., & Anandkumar, A. + (2023). *Neural operator: Learning maps between function + spaces with applications to PDEs*. Journal of Machine Learning + Research, 24(89), 1-97. + """ + + def __init__( + self, + input_dimensions, + embedding_dimenion, + rank, + inner_size=20, + n_layers=2, + func=torch.nn.Tanh, + bias=True, + ): + r""" + Initialization of the :class:`LowRankBlock` class. + + :param int input_dimensions: The input dimension of the field. + :param int embedding_dimenion: The embedding dimension of the field. + :param int rank: The rank of the low rank approximation. The expected + value is :math:`2d`, where :math:`d` is the rank of each basis + function. + :param int inner_size: The number of neurons for each hidden layer in + the basis function neural network. Default is ``20``. + :param int n_layers: The number of hidden layers in the basis function + neural network. Default is ``2``. + :param func: The activation function. If a list is passed, it must have + the same length as ``n_layers``. If a single function is passed, it + is used for all layers, except for the last one. + Default is :class:`torch.nn.Tanh`. + :type func: torch.nn.Module | list[torch.nn.Module] + :param bool bias: If ``True`` bias is considered for the basis function + neural network. Default is ``True``. + """ + super().__init__() + from ..feed_forward import FeedForward + + # Assignment (check consistency inside FeedForward) + self._basis = FeedForward( + input_dimensions=input_dimensions, + output_dimensions=2 * rank * embedding_dimenion, + inner_size=inner_size, + n_layers=n_layers, + func=func, + bias=bias, + ) + self._nn = torch.nn.Linear(embedding_dimenion, embedding_dimenion) + + check_consistency(rank, int) + self._rank = rank + self._func = func() + + def forward(self, x, coords): + r""" + Forward pass of the block. It performs an affine transformation of the + field, followed by a low rank approximation. The latter is performed by + means of a dot product of the basis :math:`\psi^{(i)}` with the vector + field :math:`v` to compute coefficients used to expand + :math:`\phi^{(i)}`, evaluated in the spatial input :math:`x`. + + :param torch.Tensor x: The input tensor for performing the computation. + :param torch.Tensor coords: The coordinates for which the field is + evaluated to perform the computation. + :return: The output tensor. + :rtype: torch.Tensor + """ + # extract basis + coords = coords.as_subclass(torch.Tensor) + basis = self._basis(coords) + # reshape [B, N, D, 2*rank] + shape = list(basis.shape[:-1]) + [-1, 2 * self.rank] + basis = basis.reshape(shape) + # divide + psi = basis[..., : self.rank] + phi = basis[..., self.rank :] + # compute dot product + coeff = torch.einsum("...dr,...d->...r", psi, x) + # expand the basis + expansion = torch.einsum("...r,...dr->...d", coeff, phi) + # apply linear layer and return + return self._func(self._nn(x) + expansion) + + @property + def rank(self): + """ + The basis rank. + + :return: The basis rank. + :rtype: int + """ + return self._rank diff --git a/pina/model/layers/orthogonal.py b/pina/model/block/orthogonal.py similarity index 68% rename from pina/model/layers/orthogonal.py rename to pina/model/block/orthogonal.py index 32a060719..cd45b3c72 100644 --- a/pina/model/layers/orthogonal.py +++ b/pina/model/block/orthogonal.py @@ -1,4 +1,4 @@ -"""Module for OrthogonalBlock.""" +"""Module for the Orthogonal Block class.""" import torch from ...utils import check_consistency @@ -6,21 +6,24 @@ class OrthogonalBlock(torch.nn.Module): """ - Module to make the input orthonormal. - The module takes a tensor of size :math:`[N, M]` and returns a tensor of - size :math:`[N, M]` where the columns are orthonormal. The block performs a - Gram Schmidt orthogonalization process for the input, see + Orthogonal Block. + + This block transforms an input tensor of shape :math:`[N, M]` into a tensor + of the same shape whose columns are orthonormal. The block performs the + Gram Schmidt orthogonalization, see `here ` for details. """ def __init__(self, dim=-1, requires_grad=True): """ - Initialize the OrthogonalBlock module. + Initialization of the :class:`OrthogonalBlock` class. - :param int dim: The dimension where to orthogonalize. - :param bool requires_grad: If autograd should record operations on - the returned tensor, defaults to True. + :param int dim: The dimension on which orthogonalization is performed. + If ``-1``, the orthogonalization is performed on the last dimension. + Default is ``-1``. + :param bool requires_grad: If ``True``, the gradients are computed + during the backward pass. Default is ``True`` """ super().__init__() # store dim @@ -31,14 +34,13 @@ def __init__(self, dim=-1, requires_grad=True): def forward(self, X): """ - Forward pass of the OrthogonalBlock module using a Gram-Schmidt - algorithm. - - :raises Warning: If the dimension is greater than the other dimensions. + Forward pass. - :param torch.Tensor X: The input tensor to orthogonalize. The input must - be of dimensions :math:`[N, M]`. + :param torch.Tensor X: The input tensor to orthogonalize. + :raises Warning: If the chosen dimension is greater than the other + dimensions in the input. :return: The orthonormal tensor. + :rtype: torch.Tensor """ # check dim is less than all the other dimensions if X.shape[self.dim] > min(X.shape): @@ -65,13 +67,12 @@ def forward(self, X): def _differentiable_copy(self, result, idx, value): """ - Perform a differentiable copy operation on a tensor. + Perform a differentiable copy operation. - :param torch.Tensor result: The tensor where values will be copied to. + :param torch.Tensor result: The tensor where values are be copied to. :param int idx: The index along the specified dimension where the - value will be copied. - :param torch.Tensor value: The tensor value to copy into the - result tensor. + values are copied. + :param torch.Tensor value: The tensor value to copy into ``result``. :return: A new tensor with the copied values. :rtype: torch.Tensor """ @@ -82,7 +83,7 @@ def _differentiable_copy(self, result, idx, value): @property def dim(self): """ - Get the dimension along which operations are performed. + The dimension along which operations are performed. :return: The current dimension value. :rtype: int @@ -94,10 +95,11 @@ def dim(self, value): """ Set the dimension along which operations are performed. - :param value: The dimension to be set, which must be 0, 1, or -1. + :param value: The dimension to be set. Must be either ``0``, ``1``, or + ``-1``. :type value: int - :raises IndexError: If the provided dimension is not in the - range [-1, 1]. + :raises IndexError: If the provided dimension is not ``0``, ``1``, or + ``-1``. """ # check consistency check_consistency(value, int) @@ -115,7 +117,7 @@ def requires_grad(self): Indicates whether gradient computation is required for operations on the tensors. - :return: True if gradients are required, False otherwise. + :return: ``True`` if gradients are required, ``False`` otherwise. :rtype: bool """ return self._requires_grad diff --git a/pina/model/layers/pod.py b/pina/model/block/pod_block.py similarity index 71% rename from pina/model/layers/pod.py rename to pina/model/block/pod_block.py index e912da78c..290cb0d0f 100644 --- a/pina/model/layers/pod.py +++ b/pina/model/block/pod_block.py @@ -1,30 +1,31 @@ """Module for Base Continuous Convolution class.""" -from abc import ABCMeta, abstractmethod import torch -from .stride import Stride -from .utils_convolution import optimizing import warnings class PODBlock(torch.nn.Module): """ - POD layer: it projects the input field on the proper orthogonal - decomposition basis. It needs to be fitted to the data before being used - with the method :meth:`fit`, which invokes the singular value decomposition. - The layer is not trainable. + Proper Orthogonal Decomposition block. + + This block projects the input field on the proper orthogonal decomposition + basis. Before being used, it must be fitted to the data with the ``fit`` + method, which invokes the singular value decomposition. This block is not + trainable. .. note:: - All the POD modes are stored in memory, avoiding to recompute them when the rank changes but increasing the memory usage. + All the POD modes are stored in memory, avoiding to recompute them when + the rank changes, leading to increased memory usage. """ def __init__(self, rank, scale_coefficients=True): """ - Build the POD layer with the given rank. + Initialization of the :class:`PODBlock` class. :param int rank: The rank of the POD layer. - :param bool scale_coefficients: If True, the coefficients are scaled + :param bool scale_coefficients: If ``True``, the coefficients are scaled after the projection to have zero mean and unit variance. + Default is ``True``. """ super().__init__() self.__scale_coefficients = scale_coefficients @@ -37,12 +38,19 @@ def rank(self): """ The rank of the POD layer. + :return: The rank of the POD layer. :rtype: int """ return self._rank @rank.setter def rank(self, value): + """ + Set the rank of the POD layer. + + :param int value: The new rank of the POD layer. + :raises ValueError: If the rank is not a positive integer. + """ if value < 1 or not isinstance(value, int): raise ValueError("The rank must be positive integer") @@ -51,8 +59,10 @@ def rank(self, value): @property def basis(self): """ - The POD basis. It is a matrix whose columns are the first `self.rank` POD modes. + The POD basis. It is a matrix whose columns are the first ``rank`` POD + modes. + :return: The POD basis. :rtype: torch.Tensor """ if self._basis is None: @@ -63,13 +73,15 @@ def basis(self): @property def scaler(self): """ - The scaler. It is a dictionary with the keys `'mean'` and `'std'` that - store the mean and the standard deviation of the coefficients. + Return the scaler dictionary, having keys ``mean`` and ``std`` + corresponding to the mean and the standard deviation of the + coefficients, respectively. + :return: The scaler dictionary. :rtype: dict """ if self._scaler is None: - return + return None return { "mean": self._scaler["mean"][: self.rank], @@ -79,9 +91,9 @@ def scaler(self): @property def scale_coefficients(self): """ - If True, the coefficients are scaled after the projection to have zero - mean and unit variance. + The flag indicating if the coefficients are scaled after the projection. + :return: The flag indicating if the coefficients are scaled. :rtype: bool """ return self.__scale_coefficients @@ -89,10 +101,10 @@ def scale_coefficients(self): def fit(self, X, randomized=True): """ Set the POD basis by performing the singular value decomposition of the - given tensor. If `self.scale_coefficients` is True, the coefficients + given tensor. If ``self.scale_coefficients`` is True, the coefficients are scaled after the projection to have zero mean and unit variance. - :param torch.Tensor X: The tensor to be reduced. + :param torch.Tensor X: The input tensor to be reduced. """ self._fit_pod(X, randomized) @@ -101,10 +113,8 @@ def fit(self, X, randomized=True): def _fit_scaler(self, coeffs): """ - Private merhod that computes the mean and the standard deviation of the - given coefficients, allowing to scale them to have zero mean and unit - variance. Mean and standard deviation are stored in the private member - `_scaler`. + Compute the mean and the standard deviation of the given coefficients, + which are then stored in ``self._scaler``. :param torch.Tensor coeffs: The coefficients to be scaled. """ @@ -115,7 +125,8 @@ def _fit_scaler(self, coeffs): def _fit_pod(self, X, randomized): """ - Private method that computes the POD basis of the given tensor and stores it in the private member `_basis`. + Compute the POD basis of the given tensor, which is then stored in + ``self._basis``. :param torch.Tensor X: The tensor to be reduced. """ @@ -137,9 +148,7 @@ def _fit_pod(self, X, randomized): def forward(self, X): """ - The forward pass of the POD layer. By default it executes the - :meth:`reduce` method, reducing the input tensor to its POD - representation. The POD layer needs to be fitted before being used. + The forward pass of the POD layer. :param torch.Tensor X: The input tensor to be reduced. :return: The reduced tensor. @@ -149,10 +158,11 @@ def forward(self, X): def reduce(self, X): """ - Reduce the input tensor to its POD representation. The POD layer needs - to be fitted before being used. + Reduce the input tensor to its POD representation. The POD layer must + be fitted before being used. :param torch.Tensor X: The input tensor to be reduced. + :raises RuntimeError: If the POD layer is not fitted. :return: The reduced tensor. :rtype: torch.Tensor """ @@ -177,6 +187,7 @@ def expand(self, coeff): to be fitted before being used. :param torch.Tensor coeff: The coefficients to be expanded. + :raises RuntimeError: If the POD layer is not fitted. :return: The expanded tensor. :rtype: torch.Tensor """ diff --git a/pina/model/layers/rbf_layer.py b/pina/model/block/rbf_block.py similarity index 57% rename from pina/model/layers/rbf_layer.py rename to pina/model/block/rbf_block.py index e088d00d9..8001381bc 100644 --- a/pina/model/layers/rbf_layer.py +++ b/pina/model/block/rbf_block.py @@ -1,4 +1,4 @@ -"""Module for Radial Basis Function Interpolation layer.""" +"""Module for the Radial Basis Function Interpolation layer.""" import math import warnings @@ -10,6 +10,10 @@ def linear(r): """ Linear radial basis function. + + :param torch.Tensor r: Distance between points. + :return: The linear radial basis function. + :rtype: torch.Tensor """ return -r @@ -17,6 +21,11 @@ def linear(r): def thin_plate_spline(r, eps=1e-7): """ Thin plate spline radial basis function. + + :param torch.Tensor r: Distance between points. + :param float eps: Small value to avoid log(0). + :return: The thin plate spline radial basis function. + :rtype: torch.Tensor """ r = torch.clamp(r, min=eps) return r**2 * torch.log(r) @@ -25,6 +34,10 @@ def thin_plate_spline(r, eps=1e-7): def cubic(r): """ Cubic radial basis function. + + :param torch.Tensor r: Distance between points. + :return: The cubic radial basis function. + :rtype: torch.Tensor """ return r**3 @@ -32,6 +45,10 @@ def cubic(r): def quintic(r): """ Quintic radial basis function. + + :param torch.Tensor r: Distance between points. + :return: The quintic radial basis function. + :rtype: torch.Tensor """ return -(r**5) @@ -39,6 +56,10 @@ def quintic(r): def multiquadric(r): """ Multiquadric radial basis function. + + :param torch.Tensor r: Distance between points. + :return: The multiquadric radial basis function. + :rtype: torch.Tensor """ return -torch.sqrt(r**2 + 1) @@ -46,6 +67,10 @@ def multiquadric(r): def inverse_multiquadric(r): """ Inverse multiquadric radial basis function. + + :param torch.Tensor r: Distance between points. + :return: The inverse multiquadric radial basis function. + :rtype: torch.Tensor """ return 1 / torch.sqrt(r**2 + 1) @@ -53,6 +78,10 @@ def inverse_multiquadric(r): def inverse_quadratic(r): """ Inverse quadratic radial basis function. + + :param torch.Tensor r: Distance between points. + :return: The inverse quadratic radial basis function. + :rtype: torch.Tensor """ return 1 / (r**2 + 1) @@ -60,6 +89,10 @@ def inverse_quadratic(r): def gaussian(r): """ Gaussian radial basis function. + + :param torch.Tensor r: Distance between points. + :return: The gaussian radial basis function. + :rtype: torch.Tensor """ return torch.exp(-(r**2)) @@ -88,13 +121,14 @@ def gaussian(r): class RBFBlock(torch.nn.Module): """ - Radial Basis Function (RBF) interpolation layer. It need to be fitted with - the data with the method :meth:`fit`, before it can be used to interpolate - new points. The layer is not trainable. + Radial Basis Function (RBF) interpolation layer. + + The user needs to fit the model with the data, before using it to + interpolate new points. The layer is not trainable. .. note:: - It reproduces the implementation of ``scipy.interpolate.RBFBlock`` and - it is inspired from the implementation in `torchrbf. + It reproduces the implementation of :class:`scipy.interpolate.RBFBlock` + and it is inspired from the implementation in `torchrbf. `_ """ @@ -107,24 +141,25 @@ def __init__( degree=None, ): """ - :param int neighbors: Number of neighbors to use for the - interpolation. - If ``None``, use all data points. - :param float smoothing: Smoothing parameter for the interpolation. - if 0.0, the interpolation is exact and no smoothing is applied. - :param str kernel: Radial basis function to use. Must be one of - ``linear``, ``thin_plate_spline``, ``cubic``, ``quintic``, - ``multiquadric``, ``inverse_multiquadric``, ``inverse_quadratic``, - or ``gaussian``. - :param float epsilon: Shape parameter that scaled the input to - the RBF. This defaults to 1 for kernels in ``scale_invariant`` - dictionary, and must be specified for other kernels. - :param int degree: Degree of the added polynomial. - For some kernels, there exists a minimum degree of the polynomial - such that the RBF is well-posed. Those minimum degrees are specified - in the `min_degree_funcs` dictionary above. If `degree` is less than - the minimum degree, a warning is raised and the degree is set to the - minimum value. + Initialization of the :class:`RBFBlock` class. + + :param int neighbors: The number of neighbors used for interpolation. + If ``None``, all data are used. + :param float smoothing: The moothing parameter for the interpolation. + If ``0.0``, the interpolation is exact and no smoothing is applied. + :param str kernel: The radial basis function to use. + The available kernels are: ``linear``, ``thin_plate_spline``, + ``cubic``, ``quintic``, ``multiquadric``, ``inverse_multiquadric``, + ``inverse_quadratic``, or ``gaussian``. + :param float epsilon: The shape parameter that scales the input to the + RBF. Default is ``1`` for kernels in the ``scale_invariant`` + dictionary, while it must be specified for other kernels. + :param int degree: The degree of the polynomial. Some kernels require a + minimum degree of the polynomial to ensure that the RBF is well + defined. These minimum degrees are specified in the + ``min_degree_funcs`` dictionary. If ``degree`` is less than the + minimum degree required, a warning is raised and the degree is set + to the minimum value. """ super().__init__() @@ -151,27 +186,39 @@ def __init__( @property def smoothing(self): """ - Smoothing parameter for the interpolation. + The smoothing parameter for the interpolation. + :return: The smoothing parameter. :rtype: float """ return self._smoothing @smoothing.setter def smoothing(self, value): + """ + Set the smoothing parameter for the interpolation. + + :param float value: The smoothing parameter. + """ self._smoothing = value @property def kernel(self): """ - Radial basis function to use. + The Radial basis function. + :return: The radial basis function. :rtype: str """ return self._kernel @kernel.setter def kernel(self, value): + """ + Set the radial basis function. + + :param str value: The radial basis function. + """ if value not in radial_functions: raise ValueError(f"Unknown kernel: {value}") self._kernel = value.lower() @@ -179,14 +226,22 @@ def kernel(self, value): @property def epsilon(self): """ - Shape parameter that scaled the input to the RBF. + The shape parameter that scales the input to the RBF. + :return: The shape parameter. :rtype: float """ return self._epsilon @epsilon.setter def epsilon(self, value): + """ + Set the shape parameter. + + :param float value: The shape parameter. + :raises ValueError: If the kernel requires an epsilon and it is not + specified. + """ if value is None: if self.kernel in scale_invariant: value = 1.0 @@ -199,14 +254,23 @@ def epsilon(self, value): @property def degree(self): """ - Degree of the added polynomial. + The degree of the polynomial. + :return: The degree of the polynomial. :rtype: int """ return self._degree @degree.setter def degree(self, value): + """ + Set the degree of the polynomial. + + :param int value: The degree of the polynomial. + :raises UserWarning: If the degree is less than the minimum required + for the kernel. + :raises ValueError: If the degree is less than -1. + """ min_degree = min_degree_funcs.get(self.kernel, -1) if value is None: value = max(min_degree, 0) @@ -223,6 +287,13 @@ def degree(self, value): self._degree = value def _check_data(self, y, d): + """ + Check the data consistency. + + :param torch.Tensor y: The tensor of data points. + :param torch.Tensor d: The tensor of data values. + :raises ValueError: If the data is not consistent. + """ if y.ndim != 2: raise ValueError("y must be a 2-dimensional tensor.") @@ -241,8 +312,11 @@ def fit(self, y, d): """ Fit the RBF interpolator to the data. - :param torch.Tensor y: (n, d) tensor of data points. - :param torch.Tensor d: (n, m) tensor of data values. + :param torch.Tensor y: The tensor of data points. + :param torch.Tensor d: The tensor of data values. + :raises NotImplementedError: If the neighbors are not ``None``. + :raises ValueError: If the data is not compatible with the requested + degree. """ self._check_data(y, d) @@ -252,7 +326,7 @@ def fit(self, y, d): if self.neighbors is None: nobs = self.y.shape[0] else: - raise NotImplementedError("neighbors currently not supported") + raise NotImplementedError("Neighbors currently not supported") powers = RBFBlock.monomial_powers(self.y.shape[1], self.degree).to( y.device @@ -276,12 +350,14 @@ def fit(self, y, d): def forward(self, x): """ - Returns the interpolated data at the given points `x`. - - :param torch.Tensor x: `(n, d)` tensor of points at which - to query the interpolator - - :rtype: `(n, m)` torch.Tensor of interpolated data. + Forward pass. + + :param torch.Tensor x: The tensor of points to interpolate. + :raises ValueError: If the input is not a 2-dimensional tensor. + :raises ValueError: If the second dimension of the input is not the same + as the second dimension of the data. + :return: The interpolated data. + :rtype: torch.Tensor """ if x.ndim != 2: raise ValueError("`x` must be a 2-dimensional tensor.") @@ -309,25 +385,25 @@ def forward(self, x): @staticmethod def kernel_vector(x, y, kernel_func): """ - Evaluate radial functions with centers `y` for all points in `x`. + Evaluate for all points ``x`` the radial functions with center ``y``. - :param torch.Tensor x: `(n, d)` tensor of points. - :param torch.Tensor y: `(m, d)` tensor of centers. + :param torch.Tensor x: The tensor of points. + :param torch.Tensor y: The tensor of centers. :param str kernel_func: Radial basis function to use. - - :rtype: `(n, m)` torch.Tensor of radial function values. + :return: The radial function values. + :rtype: torch.Tensor """ return kernel_func(torch.cdist(x, y)) @staticmethod def polynomial_matrix(x, powers): """ - Evaluate monomials at `x` with given `powers`. + Evaluate monomials of power ``powers`` at points ``x``. - :param torch.Tensor x: `(n, d)` tensor of points. - :param torch.Tensor powers: `(r, d)` tensor of powers for each monomial. - - :rtype: `(n, r)` torch.Tensor of monomial values. + :param torch.Tensor x: The tensor of points. + :param torch.Tensor powers: The tensor of powers for each monomial. + :return: The monomial values. + :rtype: torch.Tensor """ x_ = torch.repeat_interleave(x, repeats=powers.shape[0], dim=0) powers_ = powers.repeat(x.shape[0], 1) @@ -336,12 +412,12 @@ def polynomial_matrix(x, powers): @staticmethod def kernel_matrix(x, kernel_func): """ - Returns radial function values for all pairs of points in `x`. - - :param torch.Tensor x: `(n, d`) tensor of points. - :param str kernel_func: Radial basis function to use. + Return the radial function values for all pairs of points in ``x``. - :rtype: `(n, n`) torch.Tensor of radial function values. + :param torch.Tensor x: The tensor of points. + :param str kernel_func: The radial basis function to use. + :return: The radial function values. + :rtype: torch.Tensor """ return kernel_func(torch.cdist(x, x)) @@ -350,12 +426,10 @@ def monomial_powers(ndim, degree): """ Return the powers for each monomial in a polynomial. - :param int ndim: Number of variables in the polynomial. - :param int degree: Degree of the polynomial. - - :rtype: `(nmonos, ndim)` torch.Tensor where each row contains the powers - for each variable in a monomial. - + :param int ndim: The number of variables in the polynomial. + :param int degree: The degree of the polynomial. + :return: The powers for each monomial. + :rtype: torch.Tensor """ nmonos = math.comb(degree + ndim, ndim) out = torch.zeros((nmonos, ndim), dtype=torch.int32) @@ -372,16 +446,16 @@ def build(y, d, smoothing, kernel, epsilon, powers): """ Build the RBF linear system. - :param torch.Tensor y: (n, d) tensor of data points. - :param torch.Tensor d: (n, m) tensor of data values. - :param torch.Tensor smoothing: (n,) tensor of smoothing parameters. - :param str kernel: Radial basis function to use. - :param float epsilon: Shape parameter that scaled the input to the RBF. - :param torch.Tensor powers: (r, d) tensor of powers for each monomial. - - :rtype: (lhs, rhs, shift, scale) where `lhs` and `rhs` are the - left-hand side and right-hand side of the linear system, and - `shift` and `scale` are the shift and scale parameters. + :param torch.Tensor y: The tensor of data points. + :param torch.Tensor d: The tensor of data values. + :param torch.Tensor smoothing: The tensor of smoothing parameters. + :param str kernel: The radial basis function to use. + :param float epsilon: The shape parameter that scales the input to the + RBF. + :param torch.Tensor powers: The tensor of powers for each monomial. + :return: The left-hand side and right-hand side of the linear system, + and the shift and scale parameters. + :rtype: tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor] """ p = d.shape[0] s = d.shape[1] @@ -413,21 +487,20 @@ def build(y, d, smoothing, kernel, epsilon, powers): @staticmethod def solve(y, d, smoothing, kernel, epsilon, powers): """ - Build then solve the RBF linear system. + Build and solve the RBF linear system. - :param torch.Tensor y: (n, d) tensor of data points. - :param torch.Tensor d: (n, m) tensor of data values. - :param torch.Tensor smoothing: (n,) tensor of smoothing parameters. - - :param str kernel: Radial basis function to use. - :param float epsilon: Shape parameter that scaled the input to the RBF. - :param torch.Tensor powers: (r, d) tensor of powers for each monomial. + :param torch.Tensor y: The tensor of data points. + :param torch.Tensor d: The tensor of data values. + :param torch.Tensor smoothing: The tensor of smoothing parameters. + :param str kernel: The radial basis function to use. + :param float epsilon: The shape parameter that scaled the input to the + RBF. + :param torch.Tensor powers: The tensor of powers for each monomial. :raises ValueError: If the linear system is singular. - - :rtype: (shift, scale, coeffs) where `shift` and `scale` are the - shift and scale parameters, and `coeffs` are the coefficients - of the interpolator + :return: The shift and scale parameters, and the coefficients of the + interpolator. + :rtype: tuple[torch.Tensor, torch.Tensor, torch.Tensor] """ lhs, rhs, shift, scale = RBFBlock.build( diff --git a/pina/model/layers/residual.py b/pina/model/block/residual.py similarity index 54% rename from pina/model/layers/residual.py rename to pina/model/block/residual.py index edd9b07c0..f109ce03d 100644 --- a/pina/model/layers/residual.py +++ b/pina/model/block/residual.py @@ -1,19 +1,21 @@ +"""Module for residual blocks and enhanced linear layers.""" + import torch -import torch.nn as nn +from torch import nn from ...utils import check_consistency class ResidualBlock(nn.Module): - """Residual block base class. Implementation of a residual block. + """ + Residual block class. .. seealso:: **Original reference**: He, Kaiming, et al. *Deep residual learning for image recognition.* - Proceedings of the IEEE conference on computer vision - and pattern recognition. 2016.. + Proceedings of the IEEE conference on computer vision and pattern + recognition. 2016. DOI: ``_. - """ def __init__( @@ -25,17 +27,15 @@ def __init__( activation=torch.nn.ReLU(), ): """ - Initializes the ResidualBlock module. - - :param int input_dim: Dimension of the input to pass to the - feedforward linear layer. - :param int output_dim: Dimension of the output from the - residual layer. - :param int hidden_dim: Hidden dimension for mapping the input - (first block). - :param bool spectral_norm: Apply spectral normalization to feedforward - layers, defaults to False. - :param torch.nn.Module activation: Cctivation function after first block. + Initialization of the :class:`ResidualBlock` class. + + :param int input_dim: The input dimension. + :param int output_dim: The output dimension. + :param int hidden_dim: The hidden dimension. + :param bool spectral_norm: If ``True``, the spectral normalization is + applied to the feedforward layers. Default is ``False``. + :param torch.nn.Module activation: The activation function. + Default is :class:`torch.nn.ReLU`. """ super().__init__() @@ -59,10 +59,11 @@ def __init__( self._l3 = self._spect_norm(nn.Linear(input_dim, output_dim)) def forward(self, x): - """Forward pass for residual block layer. + """ + Forward pass. - :param torch.Tensor x: Input tensor for the residual layer. - :return: Output tensor for the residual layer. + :param torch.Tensor x: The input tensor. + :return: The output tensor. :rtype: torch.Tensor """ y = self._activation(self._l1(x)) @@ -71,49 +72,43 @@ def forward(self, x): return y + x def _spect_norm(self, x): - """Perform spectral norm on the layers. + """ + Perform spectral normalization on the network layers. - :param x: A torch.nn.Module Linear layer - :type x: torch.nn.Module + :param torch.nn.Module x: A :class:`torch.nn.Linear` layer. :return: The spectral norm of the layer :rtype: torch.nn.Module """ return nn.utils.spectral_norm(x) if self._spectral_norm else x -import torch -import torch.nn as nn - - class EnhancedLinear(torch.nn.Module): """ - A wrapper class for enhancing a linear layer with activation and/or dropout. - - :param layer: The linear layer to be enhanced. - :type layer: torch.nn.Module - :param activation: The activation function to be applied after the linear layer. - :type activation: torch.nn.Module - :param dropout: The dropout probability to be applied after the activation (if provided). - :type dropout: float + Enhanced Linear layer class. - :Example: - - >>> linear_layer = torch.nn.Linear(10, 20) - >>> activation = torch.nn.ReLU() - >>> dropout_prob = 0.5 - >>> enhanced_linear = EnhancedLinear(linear_layer, activation, dropout_prob) + This class is a wrapper for enhancing a linear layer with activation and/or + dropout. """ def __init__(self, layer, activation=None, dropout=None): """ - Initializes the EnhancedLinear module. - - :param layer: The linear layer to be enhanced. - :type layer: torch.nn.Module - :param activation: The activation function to be applied after the linear layer. - :type activation: torch.nn.Module - :param dropout: The dropout probability to be applied after the activation (if provided). - :type dropout: float + Initialization of the :class:`EnhancedLinear` class. + + :param torch.nn.Module layer: The linear layer to be enhanced. + :param torch.nn.Module activation: The activation function. Default is + ``None``. + :param float dropout: The dropout probability. Default is ``None``. + + :Example: + + >>> linear_layer = torch.nn.Linear(10, 20) + >>> activation = torch.nn.ReLU() + >>> dropout_prob = 0.5 + >>> enhanced_linear = EnhancedLinear( + ... linear_layer, + ... activation, + ... dropout_prob + ... ) """ super().__init__() @@ -141,23 +136,19 @@ def __init__(self, layer, activation=None, dropout=None): def forward(self, x): """ - Forward pass through the enhanced linear module. - - :param x: Input tensor. - :type x: torch.Tensor + Forward pass. - :return: Output tensor after passing through the enhanced linear module. + :param torch.Tensor x: The input tensor. + :return: The output tensor. :rtype: torch.Tensor """ return self._model(x) def _drop(self, p): """ - Applies dropout with probability p. - - :param p: Dropout probability. - :type p: float + Apply dropout with probability p. + :param float p: Dropout probability. :return: Dropout layer with the specified probability. :rtype: torch.nn.Dropout """ diff --git a/pina/model/layers/spectral.py b/pina/model/block/spectral.py similarity index 68% rename from pina/model/layers/spectral.py rename to pina/model/block/spectral.py index 674f3e095..aae915a42 100644 --- a/pina/model/layers/spectral.py +++ b/pina/model/block/spectral.py @@ -1,29 +1,30 @@ +"""Module for spectral convolution blocks.""" + import torch -import torch.nn as nn +from torch import nn from ...utils import check_consistency -import warnings ######## 1D Spectral Convolution ########### class SpectralConvBlock1D(nn.Module): """ - PINA implementation of Spectral Convolution Block for one - dimensional tensors. + Spectral Convolution Block for one-dimensional tensors. + + This class computes the spectral convolution of the input with a linear + kernel in the fourier space, and then it maps the input back to the physical + space. + The block expects an input of size [``batch``, ``input_numb_fields``, ``N``] + and returns an output of size [``batch``, ``output_numb_fields``, ``N``]. """ def __init__(self, input_numb_fields, output_numb_fields, n_modes): - """ - The module computes the spectral convolution of the input with a linear kernel in the - fourier space, and then it maps the input back to the physical - space. - - The block expects an input of size ``[batch, input_numb_fields, N]`` - and returns an output of size ``[batch, output_numb_fields, N]``. + r""" + Initialization of the :class:`SpectralConvBlock1D` class. :param int input_numb_fields: The number of channels for the input. :param int output_numb_fields: The number of channels for the output. - :param int n_modes: Number of modes to select, it must be at most equal - to the ``floor(N/2)+1``. + :param int n_modes: The number of modes to select for each dimension. + It must be at most equal to :math:`\floor(Nx/2)+1`. """ super().__init__() @@ -50,30 +51,26 @@ def __init__(self, input_numb_fields, output_numb_fields, n_modes): def _compute_mult1d(self, input, weights): """ - Compute the matrix multiplication of the input - with the linear kernel weights. - - :param input: The input tensor, expect of size - ``[batch, input_numb_fields, x]``. - :type input: torch.Tensor - :param weights: The kernel weights, expect of - size ``[input_numb_fields, output_numb_fields, x]``. - :type weights: torch.Tensor - :return: The matrix multiplication of the input - with the linear kernel weights. + Compute the matrix multiplication of the input and the linear kernel + weights. + + :param torch.Tensor input: The input tensor. Expected of size + [``batch``, ``input_numb_fields``, ``N``]. + :param torch.Tensor weights: The kernel weights. Expected of size + [``input_numb_fields``, ``output_numb_fields``, ``N``]. + :return: The result of the matrix multiplication. :rtype: torch.Tensor """ return torch.einsum("bix,iox->box", input, weights) def forward(self, x): """ - Forward computation for Spectral Convolution. + Forward pass. - :param x: The input tensor, expect of size - ``[batch, input_numb_fields, x]``. - :type x: torch.Tensor - :return: The output tensor obtained from the - spectral convolution of size ``[batch, output_numb_fields, x]``. + :param torch.Tensor x: The input tensor. Expected of size + [``batch``, ``input_numb_fields``, ``N``]. + :return: The input tensor. Expected of size + [``batch``, ``output_numb_fields``, ``N``]. :rtype: torch.Tensor """ batch_size = x.shape[0] @@ -100,23 +97,29 @@ def forward(self, x): ######## 2D Spectral Convolution ########### class SpectralConvBlock2D(nn.Module): """ - PINA implementation of spectral convolution block for two - dimensional tensors. + Spectral Convolution Block for two-dimensional tensors. + + This class computes the spectral convolution of the input with a linear + kernel in the fourier space, and then it maps the input back to the physical + space. + The block expects an input of size + [``batch``, ``input_numb_fields``, ``Nx``, ``Ny``] + and returns an output of size + [``batch``, ``output_numb_fields``, ``Nx``, ``Ny``]. """ def __init__(self, input_numb_fields, output_numb_fields, n_modes): - """ - The module computes the spectral convolution of the input with a linear kernel in the - fourier space, and then it maps the input back to the physical - space. - - The block expects an input of size ``[batch, input_numb_fields, Nx, Ny]`` - and returns an output of size ``[batch, output_numb_fields, Nx, Ny]``. + r""" + Initialization of the :class:`SpectralConvBlock2D` class. :param int input_numb_fields: The number of channels for the input. :param int output_numb_fields: The number of channels for the output. - :param list | tuple n_modes: Number of modes to select for each dimension. - It must be at most equal to the ``floor(Nx/2)+1`` and ``floor(Ny/2)+1``. + :param n_modes: The number of modes to select for each dimension. + It must be at most equal to :math:`\floor(Nx/2)+1`, + :math:`\floor(Ny/2)+1`. + :type n_modes: list[int] | tuple[int] + :raises ValueError: If the number of modes is not consistent. + :raises ValueError: If the number of modes is not a list or tuple. """ super().__init__() @@ -171,30 +174,26 @@ def __init__(self, input_numb_fields, output_numb_fields, n_modes): def _compute_mult2d(self, input, weights): """ - Compute the matrix multiplication of the input - with the linear kernel weights. - - :param input: The input tensor, expect of size - ``[batch, input_numb_fields, x, y]``. - :type input: torch.Tensor - :param weights: The kernel weights, expect of - size ``[input_numb_fields, output_numb_fields, x, y]``. - :type weights: torch.Tensor - :return: The matrix multiplication of the input - with the linear kernel weights. + Compute the matrix multiplication of the input and the linear kernel + weights. + + :param torch.Tensor input: The input tensor. Expected of size + [``batch``, ``input_numb_fields``, ``Nx``, ``Ny``]. + :param torch.Tensor weights: The kernel weights. Expected of size + [``input_numb_fields``, ``output_numb_fields``, ``Nx``, ``Ny``]. + :return: The result of the matrix multiplication. :rtype: torch.Tensor """ return torch.einsum("bixy,ioxy->boxy", input, weights) def forward(self, x): """ - Forward computation for Spectral Convolution. + Forward pass. - :param x: The input tensor, expect of size - ``[batch, input_numb_fields, x, y]``. - :type x: torch.Tensor - :return: The output tensor obtained from the - spectral convolution of size ``[batch, output_numb_fields, x, y]``. + :param torch.Tensor x: The input tensor. Expected of size + [``batch``, ``input_numb_fields``, ``Nx``, ``Ny``]. + :return: The input tensor. Expected of size + [``batch``, ``output_numb_fields``, ``Nx``, ``Ny``]. :rtype: torch.Tensor """ @@ -228,24 +227,29 @@ def forward(self, x): ######## 3D Spectral Convolution ########### class SpectralConvBlock3D(nn.Module): """ - PINA implementation of spectral convolution block for three - dimensional tensors. + Spectral Convolution Block for three-dimensional tensors. + + This class computes the spectral convolution of the input with a linear + kernel in the fourier space, and then it maps the input back to the physical + space. + The block expects an input of size + [``batch``, ``input_numb_fields``, ``Nx``, ``Ny``, ``Nz``] + and returns an output of size + [``batch``, ``output_numb_fields``, ``Nx``, ``Ny``, ``Nz``]. """ def __init__(self, input_numb_fields, output_numb_fields, n_modes): - """ - The module computes the spectral convolution of the input with a linear kernel in the - fourier space, and then it maps the input back to the physical - space. - - The block expects an input of size ``[batch, input_numb_fields, Nx, Ny, Nz]`` - and returns an output of size ``[batch, output_numb_fields, Nx, Ny, Nz]``. + r""" + Initialization of the :class:`SpectralConvBlock3D` class. :param int input_numb_fields: The number of channels for the input. :param int output_numb_fields: The number of channels for the output. - :param list | tuple n_modes: Number of modes to select for each dimension. - It must be at most equal to the ``floor(Nx/2)+1``, ``floor(Ny/2)+1`` - and ``floor(Nz/2)+1``. + :param n_modes: The number of modes to select for each dimension. + It must be at most equal to :math:`\floor(Nx/2)+1`, + :math:`\floor(Ny/2)+1`, :math:`\floor(Nz/2)+1`. + :type n_modes: list[int] | tuple[int] + :raises ValueError: If the number of modes is not consistent. + :raises ValueError: If the number of modes is not a list or tuple. """ super().__init__() @@ -324,30 +328,27 @@ def __init__(self, input_numb_fields, output_numb_fields, n_modes): def _compute_mult3d(self, input, weights): """ - Compute the matrix multiplication of the input - with the linear kernel weights. - - :param input: The input tensor, expect of size - ``[batch, input_numb_fields, x, y, z]``. - :type input: torch.Tensor - :param weights: The kernel weights, expect of - size ``[input_numb_fields, output_numb_fields, x, y, z]``. - :type weights: torch.Tensor - :return: The matrix multiplication of the input - with the linear kernel weights. + Compute the matrix multiplication of the input and the linear kernel + weights. + + :param torch.Tensor input: The input tensor. Expected of size + [``batch``, ``input_numb_fields``, ``Nx``, ``Ny``, ``Nz``]. + :param torch.Tensor weights: The kernel weights. Expected of size + [``input_numb_fields``, ``output_numb_fields``, ``Nx``, ``Ny``, + ``Nz``]. + :return: The result of the matrix multiplication. :rtype: torch.Tensor """ return torch.einsum("bixyz,ioxyz->boxyz", input, weights) def forward(self, x): """ - Forward computation for Spectral Convolution. + Forward pass. - :param x: The input tensor, expect of size - ``[batch, input_numb_fields, x, y, z]``. - :type x: torch.Tensor - :return: The output tensor obtained from the - spectral convolution of size ``[batch, output_numb_fields, x, y, z]``. + :param torch.Tensor x: The input tensor. Expected of size + [``batch``, ``input_numb_fields``, ``Nx``, ``Ny``, ``Nz``]. + :return: The input tensor. Expected of size + [``batch``, ``output_numb_fields``, ``Nx``, ``Ny``, ``Nz``]. :rtype: torch.Tensor """ diff --git a/pina/model/block/stride.py b/pina/model/block/stride.py new file mode 100644 index 000000000..2a26faf07 --- /dev/null +++ b/pina/model/block/stride.py @@ -0,0 +1,90 @@ +"""Module for the Stride class.""" + +import torch + + +class Stride: + """ + Stride class for continous convolution. + """ + + def __init__(self, dict_): + """ + Initialization of the :class:`Stride` class. + + :param dict dict_: Dictionary having as keys the domain size ``domain``, + the starting position of the filter ``start``, the jump size for the + filter ``jump``, and the direction of the filter ``direction``. + """ + + self._dict_stride = dict_ + self._stride_continuous = None + self._stride_discrete = self._create_stride_discrete(dict_) + + def _create_stride_discrete(self, my_dict): + """ + Create a tensor of positions where to apply the filter. + + :param dict my_dict_: Dictionary having as keys the domain size + ``domain``, the starting position of the filter ``start``, the jump + size for the filter ``jump``, and the direction of the filter + ``direction``. + :raises IndexError: Values in the dict must have all same length. + :raises ValueError: Domain values must be greater than 0. + :raises ValueError: Direction must be either equal to ``1``, ``-1`` or + ``0``. + :raises IndexError: Direction and jumps must be zero in the same index. + :return: The positions for the filter + :rtype: torch.Tensor + + :Example: + + >>> stride_dict = { + ... "domain": [4, 4], + ... "start": [-4, 2], + ... "jump": [2, 2], + ... "direction": [1, 1], + ... } + >>> Stride(stride_dict) + """ + # we must check boundaries of the input as well + domain, start, jumps, direction = my_dict.values() + + # checking + if not all(len(s) == len(domain) for s in my_dict.values()): + raise IndexError("Values in the dict must have all same length") + + if not all(v >= 0 for v in domain): + raise ValueError("Domain values must be greater than 0") + + if not all(v in (0, -1, 1) for v in direction): + raise ValueError("Direction must be either equal to 1, -1 or 0") + + seq_jumps = [i for i, e in enumerate(jumps) if e == 0] + seq_direction = [i for i, e in enumerate(direction) if e == 0] + + if seq_direction != seq_jumps: + raise IndexError( + "Direction and jumps must have zero in the same index" + ) + + if seq_jumps: + for i in seq_jumps: + jumps[i] = domain[i] + direction[i] = 1 + + # creating the stride grid + values_mesh = [ + torch.arange(0, i, step).float() for i, step in zip(domain, jumps) + ] + + values_mesh = [ + single * dim for single, dim in zip(values_mesh, direction) + ] + + mesh = torch.meshgrid(values_mesh) + coordinates_mesh = [x.reshape(-1, 1) for x in mesh] + + stride = torch.cat(coordinates_mesh, dim=1) + torch.tensor(start) + + return stride diff --git a/pina/model/block/utils_convolution.py b/pina/model/block/utils_convolution.py new file mode 100644 index 000000000..88e0baf6c --- /dev/null +++ b/pina/model/block/utils_convolution.py @@ -0,0 +1,67 @@ +"""Module for utility functions for the convolutional layer.""" + +import torch + + +def check_point(x, current_stride, dim): + """ + Check if the point is in the current stride. + + :param torch.Tensor x: The input data. + :param int current_stride: The current stride. + :param int dim: The shape of the filter. + :return: The indeces of the points in the current stride. + :rtype: torch.Tensor + """ + max_stride = current_stride + dim + indeces = torch.logical_and( + x[..., :-1] < max_stride, x[..., :-1] >= current_stride + ).all(dim=-1) + return indeces + + +def map_points_(x, filter_position): + """ + The mapping function for n-dimensional case. + + :param torch.Tensor x: The two-dimensional input data. + :param list[int] filter_position: The position of the filter. + :return: The data mapped in-place. + :rtype: torch.tensor + """ + x.add_(-filter_position) + + return x + + +def optimizing(f): + """ + Decorator to call the function only once. + + :param f: python function + :type f: Callable + """ + + def wrapper(*args, **kwargs): + """ + Wrapper function. + + :param args: The arguments of the function. + :param kwargs: The keyword arguments of the function. + """ + if kwargs["type_"] == "forward": + if not wrapper.has_run_inverse: + wrapper.has_run_inverse = True + return f(*args, **kwargs) + + if kwargs["type_"] == "inverse": + if not wrapper.has_run: + wrapper.has_run = True + return f(*args, **kwargs) + + return f(*args, **kwargs) + + wrapper.has_run_inverse = False + wrapper.has_run = False + + return wrapper diff --git a/pina/model/deeponet.py b/pina/model/deeponet.py index eb5d618e6..6da161665 100644 --- a/pina/model/deeponet.py +++ b/pina/model/deeponet.py @@ -1,28 +1,25 @@ -"""Module for DeepONet model""" +"""Module for the DeepONet and MIONet model classes.""" +from functools import partial import torch -import torch.nn as nn +from torch import nn from ..utils import check_consistency, is_function -from functools import partial class MIONet(torch.nn.Module): """ - The PINA implementation of MIONet network. + MIONet model class. - MIONet is a general architecture for learning Operators defined - on the tensor product of Banach spaces. Unlike traditional machine - learning methods MIONet is designed to map entire functions to other functions. - It can be trained both with Physics Informed or Supervised learning strategies. + The MIONet is a general architecture for learning operators, which map + functions to functions. It can be trained with both Supervised and + Physics-Informed learning strategies. .. seealso:: - **Original reference**: Jin, Pengzhan, Shuai Meng, and Lu Lu. + **Original reference**: Jin, P., Meng, S., and Lu L. (2022). *MIONet: Learning multiple-input operators via tensor product.* SIAM Journal on Scientific Computing 44.6 (2022): A3490-A351 - DOI: `10.1137/22M1477751 - `_ - + DOI: `10.1137/22M1477751 `_ """ def __init__( @@ -34,40 +31,50 @@ def __init__( translation=True, ): """ - :param dict networks: The neural networks to use as - models. The ``dict`` takes as key a neural network, and - as value the list of indeces to extract from the input variable - in the forward pass of the neural network. If a list of ``int`` is passed, - the corresponding columns of the inner most entries are extracted. - If a list of ``str`` is passed the variables of the corresponding :py:obj:`pina.label_tensor.LabelTensor` - are extracted. The ``torch.nn.Module`` model has to take as input a - :py:obj:`pina.label_tensor.LabelTensor` or :class:`torch.Tensor`. - Default implementation consist of different branch nets and one trunk nets. - :param str or Callable aggregator: Aggregator to be used to aggregate - partial results from the modules in `nets`. Partial results are - aggregated component-wise. Available aggregators include - sum: ``+``, product: ``*``, mean: ``mean``, min: ``min``, max: ``max``. - :param str or Callable reduction: Reduction to be used to reduce - the aggregated result of the modules in `nets` to the desired output - dimension. Available reductions include - sum: ``+``, product: ``*``, mean: ``mean``, min: ``min``, max: ``max``. - :param bool or Callable scale: Scaling the final output before returning the - forward pass, default ``True``. - :param bool or Callable translation: Translating the final output before - returning the forward pass, default ``True``. + Initialization of the :class:`MIONet` class. + + :param dict networks: The neural networks to use as models. The ``dict`` + takes as key a neural network, and as value the list of indeces to + extract from the input variable in the forward pass of the neural + network. If a ``list[int]`` is passed, the corresponding columns of + the inner most entries are extracted. If a ``list[str]`` is passed + the variables of the corresponding + :class:`~pina.label_tensor.LabelTensor` are extracted. + Each :class:`torch.nn.Module` model has to take as input either a + :class:`~pina.label_tensor.LabelTensor` or a :class:`torch.Tensor`. + Default implementation consists of several branch nets and one + trunk nets. + :param aggregator: The aggregator to be used to aggregate component-wise + partial results from the modules in ``networks``. Available + aggregators include: sum: ``+``, product: ``*``, mean: ``mean``, + min: ``min``, max: ``max``. Default is ``*``. + :type aggregator: str or Callable + :param reduction: The reduction to be used to reduce the aggregated + result of the modules in ``networks`` to the desired output + dimension. Available reductions include: sum: ``+``, product: ``*``, + mean: ``mean``, min: ``min``, max: ``max``. Default is ``+``. + :type reduction: str or Callable + :param bool scale: If ``True``, the final output is scaled before being + returned in the forward pass. Default is ``True``. + :param bool translation: If ``True``, the final output is translated + before being returned in the forward pass. Default is ``True``. + :raises ValueError: If the passed networks have not the same output + dimension. .. warning:: - In the forward pass we do not check if the input is instance of - :py:obj:`pina.label_tensor.LabelTensor` or :class:`torch.Tensor`. A general rule is - that for a :py:obj:`pina.label_tensor.LabelTensor` input both list of integers and - list of strings can be passed for ``input_indeces_branch_net`` - and ``input_indeces_trunk_net``. Differently, for a :class:`torch.Tensor` - only a list of integers can be passed for ``input_indeces_branch_net`` - and ``input_indeces_trunk_net``. + No checks are performed in the forward pass to verify if the input + is instance of either :class:`~pina.label_tensor.LabelTensor` or + :class:`torch.Tensor`. In general, in case of a + :class:`~pina.label_tensor.LabelTensor`, both a ``list[int]`` or a + ``list[str]`` can be passed as ``networks`` dict values. + Differently, in case of a :class:`torch.Tensor`, only a + ``list[int]`` can be passed as ``networks`` dict values. :Example: - >>> branch_net1 = FeedForward(input_dimensons=1, output_dimensions=10) - >>> branch_net2 = FeedForward(input_dimensons=2, output_dimensions=10) + >>> branch_net1 = FeedForward(input_dimensons=1, + ... output_dimensions=10) + >>> branch_net2 = FeedForward(input_dimensons=2, + ... output_dimensions=10) >>> trunk_net = FeedForward(input_dimensons=1, output_dimensions=10) >>> networks = {branch_net1 : ['x'], branch_net2 : ['x', 'y'], @@ -125,7 +132,7 @@ def __init__( if not all(map(lambda x: x == shapes[0], shapes)): raise ValueError( - "The passed networks have not the same " "output dimension." + "The passed networks have not the same output dimension." ) # assign trunk and branch net with their input indeces @@ -153,6 +160,10 @@ def _symbol_functions(**kwargs): """ Return a dictionary of functions that can be used as aggregators or reductions. + + :param dict kwargs: Additional parameters. + :return: A dictionary of functions. + :rtype: dict """ return { "+": partial(torch.sum, **kwargs), @@ -163,7 +174,14 @@ def _symbol_functions(**kwargs): } def _init_aggregator(self, aggregator): - aggregator_funcs = DeepONet._symbol_functions(dim=2) + """ + Initialize the aggregator. + + :param aggregator: The aggregator to be used to aggregate. + :type aggregator: str or Callable + :raises ValueError: If the aggregator is not supported. + """ + aggregator_funcs = self._symbol_functions(dim=2) if aggregator in aggregator_funcs: aggregator_func = aggregator_funcs[aggregator] elif isinstance(aggregator, nn.Module) or is_function(aggregator): @@ -175,7 +193,14 @@ def _init_aggregator(self, aggregator): self._aggregator_type = aggregator def _init_reduction(self, reduction): - reduction_funcs = DeepONet._symbol_functions(dim=-1) + """ + Initialize the reduction. + + :param reduction: The reduction to be used. + :type reduction: str or Callable + :raises ValueError: If the reduction is not supported. + """ + reduction_funcs = self._symbol_functions(dim=-1) if reduction in reduction_funcs: reduction_func = reduction_funcs[reduction] elif isinstance(reduction, nn.Module) or is_function(reduction): @@ -187,16 +212,28 @@ def _init_reduction(self, reduction): self._reduction_type = reduction def _get_vars(self, x, indeces): + """ + Extract the variables from the input tensor. + + :param x: The input tensor. + :type x: LabelTensor | torch.Tensor + :param indeces: The indeces to extract. + :type indeces: list[int] | list[str] + :raises RuntimeError: If failing to extract the variables. + :raises RuntimeError: If failing to extract the right indeces. + :return: The extracted variables. + :rtype: LabelTensor | torch.Tensor + """ if isinstance(indeces[0], str): try: return x.extract(indeces) - except AttributeError: + except AttributeError as e: raise RuntimeError( "Not possible to extract input variables from tensor." " Ensure that the passed tensor is a LabelTensor or" " pass list of integers to extract variables. For" " more information refer to warning in the documentation." - ) + ) from e elif isinstance(indeces[0], int): return x[..., indeces] else: @@ -207,11 +244,12 @@ def _get_vars(self, x, indeces): def forward(self, x): """ - Defines the computation performed at every call. + Forward pass for the :class:`MIONet` model. - :param LabelTensor or torch.Tensor x: The input tensor for the forward call. - :return: The output computed by the DeepONet model. - :rtype: LabelTensor or torch.Tensor + :param x: The input tensor. + :type x: LabelTensor | torch.Tensor + :return: The output tensor. + :rtype: LabelTensor | torch.Tensor """ # forward pass @@ -225,7 +263,7 @@ def forward(self, x): # reduce output_ = self._reduction(aggregated) - if self._reduction_type in DeepONet._symbol_functions(dim=-1): + if self._reduction_type in self._symbol_functions(dim=-1): output_ = output_.reshape(-1, 1) # scale and translate @@ -238,13 +276,19 @@ def forward(self, x): def aggregator(self): """ The aggregator function. + + :return: The aggregator function. + :rtype: str or Callable """ return self._aggregator @property def reduction(self): """ - The translation factor. + The reduction function. + + :return: The reduction function. + :rtype: str or Callable """ return self._reduction @@ -252,13 +296,19 @@ def reduction(self): def scale(self): """ The scale factor. + + :return: The scale factor. + :rtype: torch.Tensor """ return self._scale @property def translation(self): """ - The translation factor for MIONet. + The translation factor. + + :return: The translation factor. + :rtype: torch.Tensor """ return self._trasl @@ -266,6 +316,9 @@ def translation(self): def indeces_variables_extracted(self): """ The input indeces for each model in form of list. + + :return: The indeces for each model. + :rtype: list """ return self._indeces @@ -273,24 +326,27 @@ def indeces_variables_extracted(self): def model(self): """ The models in form of list. + + :return: The models. + :rtype: list[torch.nn.Module] """ return self._indeces class DeepONet(MIONet): """ - The PINA implementation of DeepONet network. + DeepONet model class. - DeepONet is a general architecture for learning Operators. Unlike - traditional machine learning methods DeepONet is designed to map - entire functions to other functions. It can be trained both with - Physics Informed or Supervised learning strategies. + The MIONet is a general architecture for learning operators, which map + functions to functions. It can be trained with both Supervised and + Physics-Informed learning strategies. .. seealso:: - **Original reference**: Lu, L., Jin, P., Pang, G. et al. *Learning - nonlinear operators via DeepONet based on the universal approximation - theorem of operators*. Nat Mach Intell 3, 218–229 (2021). + **Original reference**: Lu, L., Jin, P., Pang, G. et al. + *Learning nonlinear operators via DeepONet based on the universal + approximation theorem of operator*. + Nat Mach Intell 3, 218-229 (2021). DOI: `10.1038/s42256-021-00302-5 `_ @@ -308,48 +364,67 @@ def __init__( translation=True, ): """ + Initialization of the :class:`DeepONet` class. + :param torch.nn.Module branch_net: The neural network to use as branch - model. It has to take as input a :py:obj:`pina.label_tensor.LabelTensor` - or :class:`torch.Tensor`. The number of dimensions of the output has - to be the same of the ``trunk_net``. + model. It has to take as input either a + :class:`~pina.label_tensor.LabelTensor` or a :class:`torch.Tensor`. + The output dimension has to be the same as that of ``trunk_net``. :param torch.nn.Module trunk_net: The neural network to use as trunk - model. It has to take as input a :py:obj:`pina.label_tensor.LabelTensor` - or :class:`torch.Tensor`. The number of dimensions of the output - has to be the same of the ``branch_net``. - :param list(int) or list(str) input_indeces_branch_net: List of indeces - to extract from the input variable in the forward pass for the - branch net. If a list of ``int`` is passed, the corresponding columns - of the inner most entries are extracted. If a list of ``str`` is passed - the variables of the corresponding :py:obj:`pina.label_tensor.LabelTensor` are extracted. - :param list(int) or list(str) input_indeces_trunk_net: List of indeces - to extract from the input variable in the forward pass for the - trunk net. If a list of ``int`` is passed, the corresponding columns - of the inner most entries are extracted. If a list of ``str`` is passed - the variables of the corresponding :py:obj:`pina.label_tensor.LabelTensor` are extracted. - :param str or Callable aggregator: Aggregator to be used to aggregate - partial results from the modules in `nets`. Partial results are - aggregated component-wise. Available aggregators include - sum: ``+``, product: ``*``, mean: ``mean``, min: ``min``, max: ``max``. - :param str or Callable reduction: Reduction to be used to reduce - the aggregated result of the modules in `nets` to the desired output - dimension. Available reductions include - sum: ``+``, product: ``*``, mean: ``mean``, min: ``min``, max: ``max``. - :param bool or Callable scale: Scaling the final output before returning the - forward pass, default True. - :param bool or Callable translation: Translating the final output before - returning the forward pass, default True. + model. It has to take as input either a + :class:`~pina.label_tensor.LabelTensor` or a :class:`torch.Tensor`. + The output dimension has to be the same as that of ``branch_net``. + :param input_indeces_branch_net: List of indeces to extract from the + input variable of the ``branch_net``. + If a list of ``int`` is passed, the corresponding columns of the + inner most entries are extracted. If a list of ``str`` is passed the + variables of the corresponding + :class:`~pina.label_tensor.LabelTensor` are extracted. + :type input_indeces_branch_net: list[int] | list[str] + :param input_indeces_trunk_net: List of indeces to extract from the + input variable of the ``trunk_net``. + If a list of ``int`` is passed, the corresponding columns of the + inner most entries are extracted. If a list of ``str`` is passed the + variables of the corresponding + :class:`~pina.label_tensor.LabelTensor` are extracted. + :type input_indeces_trunk_net: list[int] | list[str] + :param aggregator: The aggregator to be used to aggregate component-wise + partial results from the modules in ``networks``. Available + aggregators include: sum: ``+``, product: ``*``, mean: ``mean``, + min: ``min``, max: ``max``. Default is ``*``. + :type aggregator: str or Callable + :param reduction: The reduction to be used to reduce the aggregated + result of the modules in ``networks`` to the desired output + dimension. Available reductions include: sum: ``+``, product: ``*``, + mean: ``mean``, min: ``min``, max: ``max``. Default is ``+``. + :type reduction: str or Callable + :param bool scale: If ``True``, the final output is scaled before being + returned in the forward pass. Default is ``True``. + :param bool translation: If ``True``, the final output is translated + before being returned in the forward pass. Default is ``True``. .. warning:: In the forward pass we do not check if the input is instance of - :py:obj:`pina.label_tensor.LabelTensor` or :class:`torch.Tensor`. A general rule is - that for a :py:obj:`pina.label_tensor.LabelTensor` input both list of integers and - list of strings can be passed for ``input_indeces_branch_net`` - and ``input_indeces_trunk_net``. Differently, for a :class:`torch.Tensor` - only a list of integers can be passed for ``input_indeces_branch_net`` - and ``input_indeces_trunk_net``. + :py:obj:`pina.label_tensor.LabelTensor` or :class:`torch.Tensor`. + A general rule is that for a :py:obj:`pina.label_tensor.LabelTensor` + input both list of integers and list of strings can be passed for + ``input_indeces_branch_net`` and ``input_indeces_trunk_net``. + Differently, for a :class:`torch.Tensor` only a list of integers can + be passed for ``input_indeces_branch_net`` and + ``input_indeces_trunk_net``. + + .. warning:: + No checks are performed in the forward pass to verify if the input + is instance of either :class:`~pina.label_tensor.LabelTensor` or + :class:`torch.Tensor`. In general, in case of a + :class:`~pina.label_tensor.LabelTensor`, both a ``list[int]`` or a + ``list[str]`` can be passed as ``input_indeces_branch_net`` and + ``input_indeces_trunk_net``. Differently, in case of a + :class:`torch.Tensor`, only a ``list[int]`` can be passed. :Example: - >>> branch_net = FeedForward(input_dimensons=1, output_dimensions=10) + >>> branch_net = FeedForward(input_dimensons=1, + ... output_dimensions=10) >>> trunk_net = FeedForward(input_dimensons=1, output_dimensions=10) >>> model = DeepONet(branch_net=branch_net, ... trunk_net=trunk_net, @@ -393,24 +468,31 @@ def __init__( def forward(self, x): """ - Defines the computation performed at every call. + Forward pass for the :class:`DeepONet` model. - :param LabelTensor or torch.Tensor x: The input tensor for the forward call. - :return: The output computed by the DeepONet model. - :rtype: LabelTensor or torch.Tensor + :param x: The input tensor. + :type x: LabelTensor | torch.Tensor + :return: The output tensor. + :rtype: LabelTensor | torch.Tensor """ return super().forward(x) @property def branch_net(self): """ - The branch net for DeepONet. + The branch net of the DeepONet. + + :return: The branch net. + :rtype: torch.nn.Module """ return self.models[0] @property def trunk_net(self): """ - The trunk net for DeepONet. + The trunk net of the DeepONet. + + :return: The trunk net. + :rtype: torch.nn.Module """ return self.models[1] diff --git a/pina/model/feed_forward.py b/pina/model/feed_forward.py index 5dfd791db..a1651b38b 100644 --- a/pina/model/feed_forward.py +++ b/pina/model/feed_forward.py @@ -1,33 +1,15 @@ -"""Module for FeedForward model""" +"""Module for the Feed Forward model class.""" import torch -import torch.nn as nn +from torch import nn from ..utils import check_consistency -from .layers.residual import EnhancedLinear +from .block.residual import EnhancedLinear class FeedForward(torch.nn.Module): """ - The PINA implementation of feedforward network, also refered as multilayer - perceptron. - - :param int input_dimensions: The number of input components of the model. - Expected tensor shape of the form :math:`(*, d)`, where * - means any number of dimensions including none, and :math:`d` the ``input_dimensions``. - :param int output_dimensions: The number of output components of the model. - Expected tensor shape of the form :math:`(*, d)`, where * - means any number of dimensions including none, and :math:`d` the ``output_dimensions``. - :param int inner_size: number of neurons in the hidden layer(s). Default is - 20. - :param int n_layers: number of hidden layers. Default is 2. - :param torch.nn.Module func: the activation function to use. If a single - :class:`torch.nn.Module` is passed, this is used as activation function - after any layers, except the last one. If a list of Modules is passed, - they are used as activation functions at any layers, in order. - :param list(int) | tuple(int) layers: a list containing the number of neurons for - any hidden layers. If specified, the parameters ``n_layers`` e - ``inner_size`` are not considered. - :param bool bias: If ``True`` the MLP will consider some bias. + Feed Forward neural network model class, also known as Multi-layer + Perceptron. """ def __init__( @@ -40,7 +22,36 @@ def __init__( layers=None, bias=True, ): - """ """ + """ + Initialization of the :class:`FeedForward` class. + + :param int input_dimensions: The number of input components. + The expected tensor shape is :math:`(*, d)`, where * + represents any number of preceding dimensions (including none), and + :math:`d` corresponds to ``input_dimensions``. + :param int output_dimensions: The number of output components . + The expected tensor shape is :math:`(*, d)`, where * + represents any number of preceding dimensions (including none), and + :math:`d` corresponds to ``output_dimensions``. + :param int inner_size: The number of neurons for each hidden layer. + Default is ``20``. + :param int n_layers: The number of hidden layers. Default is ``2``. + :param func: The activation function. If a list is passed, it must have + the same length as ``n_layers``. If a single function is passed, it + is used for all layers, except for the last one. + Default is :class:`torch.nn.Tanh`. + :type func: torch.nn.Module | list[torch.nn.Module] + :param list[int] layers: The list of the dimension of inner layers. + If ``None``, ``n_layers`` of dimension ``inner_size`` are used. + Otherwise, it overrides the values passed to ``n_layers`` and + ``inner_size``. Default is ``None``. + :param bool bias: If ``True`` bias is considered for the basis function + neural network. Default is ``True``. + :raises ValueError: If the input dimension is not an integer. + :raises ValueError: If the output dimension is not an integer. + :raises RuntimeError: If the number of layers and functions are + inconsistent. + """ super().__init__() if not isinstance(input_dimensions, int): @@ -69,62 +80,44 @@ def __init__( self.functions = [func for _ in range(len(self.layers) - 1)] if len(self.layers) != len(self.functions) + 1: - raise RuntimeError("uncosistent number of layers and functions") + raise RuntimeError("Incosistent number of layers and functions") unique_list = [] - for layer, func in zip(self.layers[:-1], self.functions): + for layer, func_ in zip(self.layers[:-1], self.functions): unique_list.append(layer) - if func is not None: - unique_list.append(func()) + if func_ is not None: + unique_list.append(func_()) unique_list.append(self.layers[-1]) self.model = nn.Sequential(*unique_list) def forward(self, x): """ - Defines the computation performed at every call. + Forward pass for the :class:`FeedForward` model. - :param x: The tensor to apply the forward pass. - :type x: torch.Tensor - :return: the output computed by the model. - :rtype: torch.Tensor + :param x: The input tensor. + :type x: torch.Tensor | LabelTensor + :return: The output tensor. + :rtype: torch.Tensor | LabelTensor """ return self.model(x) class ResidualFeedForward(torch.nn.Module): """ - The PINA implementation of feedforward network, also with skipped connection - and transformer network, as presented in **Understanding and mitigating gradient - pathologies in physics-informed neural networks** + Residual Feed Forward neural network model class. + + The model is composed of a series of linear layers with a residual + connection between themm as presented in the following: .. seealso:: - **Original reference**: Wang, Sifan, Yujun Teng, and Paris Perdikaris. - *Understanding and mitigating gradient flow pathologies in physics-informed - neural networks*. SIAM Journal on Scientific Computing 43.5 (2021): A3055-A3081. + **Original reference**: Wang, S., Teng, Y., and Perdikaris, P. (2021). + *Understanding and mitigating gradient flow pathologies in + physics-informed neural networks*. + SIAM Journal on Scientific Computing 43.5 (2021): A3055-A3081. DOI: `10.1137/20M1318043 `_ - - - :param int input_dimensions: The number of input components of the model. - Expected tensor shape of the form :math:`(*, d)`, where * - means any number of dimensions including none, and :math:`d` the ``input_dimensions``. - :param int output_dimensions: The number of output components of the model. - Expected tensor shape of the form :math:`(*, d)`, where * - means any number of dimensions including none, and :math:`d` the ``output_dimensions``. - :param int inner_size: number of neurons in the hidden layer(s). Default is - 20. - :param int n_layers: number of hidden layers. Default is 2. - :param torch.nn.Module func: the activation function to use. If a single - :class:`torch.nn.Module` is passed, this is used as activation function - after any layers, except the last one. If a list of Modules is passed, - they are used as activation functions at any layers, in order. - :param bool bias: If ``True`` the MLP will consider some bias. - :param list | tuple transformer_nets: a list or tuple containing the two - torch.nn.Module which act as transformer network. The input dimension - of the network must be the same as ``input_dimensions``, and the output - dimension must be the same as ``inner_size``. """ def __init__( @@ -137,7 +130,37 @@ def __init__( bias=True, transformer_nets=None, ): - """ """ + """ + Initialization of the :class:`ResidualFeedForward` class. + + :param int input_dimensions: The number of input components. + The expected tensor shape is :math:`(*, d)`, where * + represents any number of preceding dimensions (including none), and + :math:`d` corresponds to ``input_dimensions``. + :param int output_dimensions: The number of output components . + The expected tensor shape is :math:`(*, d)`, where * + represents any number of preceding dimensions (including none), and + :math:`d` corresponds to ``output_dimensions``. + :param int inner_size: The number of neurons for each hidden layer. + Default is ``20``. + :param int n_layers: The number of hidden layers. Default is ``2``. + :param func: The activation function. If a list is passed, it must have + the same length as ``n_layers``. If a single function is passed, it + is used for all layers, except for the last one. + Default is :class:`torch.nn.Tanh`. + :type func: torch.nn.Module | list[torch.nn.Module] + :param bool bias: If ``True`` bias is considered for the basis function + neural network. Default is ``True``. + :param transformer_nets: The two :class:`torch.nn.Module` acting as + transformer network. The input dimension of both networks must be + equal to ``input_dimensions``, and the output dimension must be + equal to ``inner_size``. If ``None``, two + :class:`~pina.model.block.residual.EnhancedLinear` layers are used. + Default is ``None``. + :type transformer_nets: list[torch.nn.Module] | tuple[torch.nn.Module] + :raises RuntimeError: If the number of layers and functions are + inconsistent. + """ super().__init__() # check type consistency @@ -148,66 +171,24 @@ def __init__( check_consistency(func, torch.nn.Module, subclass=True) check_consistency(bias, bool) - # check transformer nets - if transformer_nets is None: - transformer_nets = [ - EnhancedLinear( - nn.Linear( - in_features=input_dimensions, out_features=inner_size - ), - nn.Tanh(), - ), - EnhancedLinear( - nn.Linear( - in_features=input_dimensions, out_features=inner_size - ), - nn.Tanh(), - ), - ] - elif isinstance(transformer_nets, (list, tuple)): - if len(transformer_nets) != 2: - raise ValueError( - "transformer_nets needs to be a list of len two." - ) - for net in transformer_nets: - if not isinstance(net, nn.Module): - raise ValueError( - "transformer_nets needs to be a list of torch.nn.Module." - ) - x = torch.rand(10, input_dimensions) - try: - out = net(x) - except RuntimeError: - raise ValueError( - "transformer network input incompatible with input_dimensions." - ) - if out.shape[-1] != inner_size: - raise ValueError( - "transformer network output incompatible with inner_size." - ) - else: - RuntimeError( - "Runtime error for transformer nets, check official documentation." - ) + transformer_nets = self._check_transformer_nets( + transformer_nets, input_dimensions, inner_size + ) # assign variables - self.input_dimension = input_dimensions - self.output_dimension = output_dimensions self.transformer_nets = nn.ModuleList(transformer_nets) # build layers layers = [inner_size] * n_layers - tmp_layers = layers.copy() - tmp_layers.insert(0, self.input_dimension) + layers = layers.copy() + layers.insert(0, input_dimensions) self.layers = [] - for i in range(len(tmp_layers) - 1): - self.layers.append( - nn.Linear(tmp_layers[i], tmp_layers[i + 1], bias=bias) - ) + for i in range(len(layers) - 1): + self.layers.append(nn.Linear(layers[i], layers[i + 1], bias=bias)) self.last_layer = nn.Linear( - tmp_layers[len(tmp_layers) - 1], output_dimensions, bias=bias + layers[len(layers) - 1], output_dimensions, bias=bias ) if isinstance(func, list): @@ -216,21 +197,21 @@ def __init__( self.functions = [func() for _ in range(len(self.layers))] if len(self.layers) != len(self.functions): - raise RuntimeError("uncosistent number of layers and functions") + raise RuntimeError("Incosistent number of layers and functions") unique_list = [] - for layer, func in zip(self.layers, self.functions): - unique_list.append(EnhancedLinear(layer=layer, activation=func)) + for layer, func_ in zip(self.layers, self.functions): + unique_list.append(EnhancedLinear(layer=layer, activation=func_)) self.inner_layers = torch.nn.Sequential(*unique_list) def forward(self, x): """ - Defines the computation performed at every call. + Forward pass for the :class:`ResidualFeedForward` model. - :param x: The tensor to apply the forward pass. - :type x: torch.Tensor - :return: the output computed by the model. - :rtype: torch.Tensor + :param x: The input tensor. + :type x: torch.Tensor | LabelTensor + :return: The output tensor. + :rtype: torch.Tensor | LabelTensor """ # enhance the input with transformer input_ = [] @@ -244,3 +225,72 @@ def forward(self, x): # last layer return self.last_layer(x) + + @staticmethod + def _check_transformer_nets(transformer_nets, input_dimensions, inner_size): + """ + Check the transformer networks consistency. + + :param transformer_nets: The two :class:`torch.nn.Module` acting as + transformer network. + :type transformer_nets: list[torch.nn.Module] | tuple[torch.nn.Module] + :param int input_dimensions: The number of input components. + :param int inner_size: The number of neurons for each hidden layer. + :raises ValueError: If the passed ``transformer_nets`` is not a list of + length two. + :raises ValueError: If the passed ``transformer_nets`` is not a list of + :class:`torch.nn.Module`. + :raises ValueError: If the input dimension of the transformer network + is incompatible with the input dimension of the model. + :raises ValueError: If the output dimension of the transformer network + is incompatible with the inner size of the model. + :raises RuntimeError: If unexpected error occurs. + :return: The two :class:`torch.nn.Module` acting as transformer network. + :rtype: list[torch.nn.Module] | tuple[torch.nn.Module] + """ + # check transformer nets + if transformer_nets is None: + transformer_nets = [ + EnhancedLinear( + nn.Linear( + in_features=input_dimensions, out_features=inner_size + ), + nn.Tanh(), + ), + EnhancedLinear( + nn.Linear( + in_features=input_dimensions, out_features=inner_size + ), + nn.Tanh(), + ), + ] + elif isinstance(transformer_nets, (list, tuple)): + if len(transformer_nets) != 2: + raise ValueError( + "transformer_nets needs to be a list of len two." + ) + for net in transformer_nets: + if not isinstance(net, nn.Module): + raise ValueError( + "transformer_nets needs to be a list of " + "torch.nn.Module." + ) + x = torch.rand(10, input_dimensions) + try: + out = net(x) + except RuntimeError as e: + raise ValueError( + "transformer network input incompatible with " + "input_dimensions." + ) from e + if out.shape[-1] != inner_size: + raise ValueError( + "transformer network output incompatible with " + "inner_size." + ) + else: + raise RuntimeError( + "Runtime error for transformer nets, check official " + "documentation." + ) + return transformer_nets diff --git a/pina/model/fno.py b/pina/model/fno.py deleted file mode 100644 index 910b41603..000000000 --- a/pina/model/fno.py +++ /dev/null @@ -1,272 +0,0 @@ -""" -Fourier Neural Operator Module. -""" - -import torch -import torch.nn as nn -from pina import LabelTensor -import warnings -from ..utils import check_consistency -from .layers.fourier import FourierBlock1D, FourierBlock2D, FourierBlock3D -from .base_no import KernelNeuralOperator - - -class FourierIntegralKernel(torch.nn.Module): - """ - Implementation of Fourier Integral Kernel network. - - This class implements the Fourier Integral Kernel network, which is a - PINA implementation of Fourier Neural Operator kernel network. - It performs global convolution by operating in the Fourier space. - - .. seealso:: - - **Original reference**: Li, Z., Kovachki, N., Azizzadenesheli, - K., Liu, B., Bhattacharya, K., Stuart, A., & Anandkumar, A. - (2020). *Fourier neural operator for parametric partial - differential equations*. - DOI: `arXiv preprint arXiv:2010.08895. - `_ - """ - - def __init__( - self, - input_numb_fields, - output_numb_fields, - n_modes, - dimensions=3, - padding=8, - padding_type="constant", - inner_size=20, - n_layers=2, - func=nn.Tanh, - layers=None, - ): - """ - :param int input_numb_fields: Number of input fields. - :param int output_numb_fields: Number of output fields. - :param int | list[int] n_modes: Number of modes. - :param int dimensions: Number of dimensions (1, 2, or 3). - :param int padding: Padding size, defaults to 8. - :param str padding_type: Type of padding, defaults to "constant". - :param int inner_size: Inner size, defaults to 20. - :param int n_layers: Number of layers, defaults to 2. - :param torch.nn.Module func: Activation function, defaults to nn.Tanh. - :param list[int] layers: List of layer sizes, defaults to None. - """ - super().__init__() - - # check type consistency - check_consistency(dimensions, int) - check_consistency(padding, int) - check_consistency(padding_type, str) - check_consistency(inner_size, int) - check_consistency(n_layers, int) - check_consistency(func, nn.Module, subclass=True) - - if layers is not None: - if isinstance(layers, (tuple, list)): - check_consistency(layers, int) - else: - raise ValueError("layers must be tuple or list of int.") - if not isinstance(n_modes, (list, tuple, int)): - raise ValueError( - "n_modes must be a int or list or tuple of valid modes." - " More information on the official documentation." - ) - - # assign padding - self._padding = padding - - # initialize fourier layer for each dimension - if dimensions == 1: - fourier_layer = FourierBlock1D - elif dimensions == 2: - fourier_layer = FourierBlock2D - elif dimensions == 3: - fourier_layer = FourierBlock3D - else: - raise NotImplementedError("FNO implemented only for 1D/2D/3D data.") - - # Here we build the FNO kernels by stacking Fourier Blocks - - # 1. Assign output dimensions for each FNO layer - if layers is None: - layers = [inner_size] * n_layers - - # 2. Assign activation functions for each FNO layer - if isinstance(func, list): - if len(layers) != len(func): - raise RuntimeError( - "Uncosistent number of layers and functions." - ) - _functions = func - else: - _functions = [func for _ in range(len(layers) - 1)] - _functions.append(torch.nn.Identity) - - # 3. Assign modes functions for each FNO layer - if isinstance(n_modes, list): - if all(isinstance(i, list) for i in n_modes) and len(layers) != len( - n_modes - ): - raise RuntimeError( - "Uncosistent number of layers and functions." - ) - elif all(isinstance(i, int) for i in n_modes): - n_modes = [n_modes] * len(layers) - else: - n_modes = [n_modes] * len(layers) - - # 4. Build the FNO network - _layers = [] - tmp_layers = [input_numb_fields] + layers + [output_numb_fields] - for i in range(len(layers)): - _layers.append( - fourier_layer( - input_numb_fields=tmp_layers[i], - output_numb_fields=tmp_layers[i + 1], - n_modes=n_modes[i], - activation=_functions[i], - ) - ) - self._layers = nn.Sequential(*_layers) - - # 5. Padding values for spectral conv - if isinstance(padding, int): - padding = [padding] * dimensions - self._ipad = [-pad if pad > 0 else None for pad in padding[:dimensions]] - self._padding_type = padding_type - self._pad = [ - val for pair in zip([0] * dimensions, padding) for val in pair - ] - - def forward(self, x): - """ - Forward computation for Fourier Neural Operator. It performs a - lifting of the input by the ``lifting_net``. Then different layers - of Fourier Blocks are applied. Finally the output is projected - to the final dimensionality by the ``projecting_net``. - - :param torch.Tensor x: The input tensor for fourier block, - depending on ``dimension`` in the initialization. - In particular it is expected: - - * 1D tensors: ``[batch, X, channels]`` - * 2D tensors: ``[batch, X, Y, channels]`` - * 3D tensors: ``[batch, X, Y, Z, channels]`` - :return: The output tensor obtained from the kernels convolution. - :rtype: torch.Tensor - """ - if isinstance(x, LabelTensor): # TODO remove when Network is fixed - warnings.warn( - "LabelTensor passed as input is not allowed," - " casting LabelTensor to Torch.Tensor" - ) - x = x.as_subclass(torch.Tensor) - # permuting the input [batch, channels, x, y, ...] - permutation_idx = [0, x.ndim - 1, *[i for i in range(1, x.ndim - 1)]] - x = x.permute(permutation_idx) - - # padding the input - x = torch.nn.functional.pad(x, pad=self._pad, mode=self._padding_type) - - # apply fourier layers - x = self._layers(x) - - # remove padding - idxs = [slice(None), slice(None)] + [slice(pad) for pad in self._ipad] - x = x[idxs] - - # permuting back [batch, x, y, ..., channels] - permutation_idx = [0, *[i for i in range(2, x.ndim)], 1] - x = x.permute(permutation_idx) - - return x - - -class FNO(KernelNeuralOperator): - """ - The PINA implementation of Fourier Neural Operator network. - - Fourier Neural Operator (FNO) is a general architecture for - learning Operators. Unlike traditional machine learning methods - FNO is designed to map entire functions to other functions. It - can be trained with Supervised learning strategies. FNO does global - convolution by performing the operation on the Fourier space. - - .. seealso:: - - **Original reference**: Li, Z., Kovachki, N., Azizzadenesheli, - K., Liu, B., Bhattacharya, K., Stuart, A., & Anandkumar, A. - (2020). *Fourier neural operator for parametric partial - differential equations*. - DOI: `arXiv preprint arXiv:2010.08895. - `_ - """ - - def __init__( - self, - lifting_net, - projecting_net, - n_modes, - dimensions=3, - padding=8, - padding_type="constant", - inner_size=20, - n_layers=2, - func=nn.Tanh, - layers=None, - ): - """ - :param torch.nn.Module lifting_net: The neural network for lifting - the input. - :param torch.nn.Module projecting_net: The neural network for - projecting the output. - :param int | list[int] n_modes: Number of modes. - :param int dimensions: Number of dimensions (1, 2, or 3). - :param int padding: Padding size, defaults to 8. - :param str padding_type: Type of padding, defaults to `constant`. - :param int inner_size: Inner size, defaults to 20. - :param int n_layers: Number of layers, defaults to 2. - :param torch.nn.Module func: Activation function, defaults to nn.Tanh. - :param list[int] layers: List of layer sizes, defaults to None. - """ - lifting_operator_out = lifting_net( - torch.rand(size=next(lifting_net.parameters()).size()) - ).shape[-1] - super().__init__( - lifting_operator=lifting_net, - projection_operator=projecting_net, - integral_kernels=FourierIntegralKernel( - input_numb_fields=lifting_operator_out, - output_numb_fields=next(projecting_net.parameters()).size(), - n_modes=n_modes, - dimensions=dimensions, - padding=padding, - padding_type=padding_type, - inner_size=inner_size, - n_layers=n_layers, - func=func, - layers=layers, - ), - ) - - def forward(self, x): - """ - Forward computation for Fourier Neural Operator. It performs a - lifting of the input by the ``lifting_net``. Then different layers - of Fourier Blocks are applied. Finally the output is projected - to the final dimensionality by the ``projecting_net``. - - :param torch.Tensor x: The input tensor for fourier block, - depending on ``dimension`` in the initialization. In - particular it is expected: - - * 1D tensors: ``[batch, X, channels]`` - * 2D tensors: ``[batch, X, Y, channels]`` - * 3D tensors: ``[batch, X, Y, Z, channels]`` - :return: The output tensor obtained from FNO. - :rtype: torch.Tensor - """ - return super().forward(x) diff --git a/pina/model/fourier_neural_operator.py b/pina/model/fourier_neural_operator.py new file mode 100644 index 000000000..e1336c999 --- /dev/null +++ b/pina/model/fourier_neural_operator.py @@ -0,0 +1,343 @@ +"""Module for the Fourier Neural Operator model class.""" + +import warnings +import torch +from torch import nn +from ..label_tensor import LabelTensor +from ..utils import check_consistency +from .block.fourier_block import FourierBlock1D, FourierBlock2D, FourierBlock3D +from .kernel_neural_operator import KernelNeuralOperator + + +class FourierIntegralKernel(torch.nn.Module): + """ + Fourier Integral Kernel model class. + + This class implements the Fourier Integral Kernel network, which + performs global convolution in the Fourier space. + + .. seealso:: + + **Original reference**: Li, Z., Kovachki, N., Azizzadenesheli, K., Liu, + B., Bhattacharya, K., Stuart, A., & Anandkumar, A. (2020). + *Fourier neural operator for parametric partial differential equations*. + DOI: `arXiv preprint arXiv:2010.08895. + `_ + """ + + def __init__( + self, + input_numb_fields, + output_numb_fields, + n_modes, + dimensions=3, + padding=8, + padding_type="constant", + inner_size=20, + n_layers=2, + func=nn.Tanh, + layers=None, + ): + """ + Initialization of the :class:`FourierIntegralKernel` class. + + :param int input_numb_fields: The number of input fields. + :param int output_numb_fields: The number of output fields. + :param n_modes: The number of modes. + :type n_modes: int | list[int] + :param int dimensions: The number of dimensions. It can be set to ``1``, + ``2``, or ``3``. Default is ``3``. + :param int padding: The padding size. Default is ``8``. + :param str padding_type: The padding strategy. Default is ``constant``. + :param int inner_size: The inner size. Default is ``20``. + :param int n_layers: The number of layers. Default is ``2``. + :param func: The activation function. If a list is passed, it must have + the same length as ``n_layers``. If a single function is passed, it + is used for all layers, except for the last one. + Default is :class:`torch.nn.Tanh`. + :type func: torch.nn.Module | list[torch.nn.Module] + :param list[int] layers: The list of the dimension of inner layers. + If ``None``, ``n_layers`` of dimension ``inner_size`` are used. + Otherwise, it overrides the values passed to ``n_layers`` and + ``inner_size``. Default is ``None``. + :raises RuntimeError: If the number of layers and functions are + inconsistent. + :raises RunTimeError: If the number of layers and modes are + inconsistent. + """ + super().__init__() + + # check type consistency + self._check_consistency( + dimensions, + padding, + padding_type, + inner_size, + n_layers, + func, + layers, + n_modes, + ) + + # assign padding + self._padding = padding + + # initialize fourier layer for each dimension + fourier_layer = self._get_fourier_block(dimensions) + + # Here we build the FNO kernels by stacking Fourier Blocks + + # 1. Assign output dimensions for each FNO layer + if layers is None: + layers = [inner_size] * n_layers + + # 2. Assign activation functions for each FNO layer + if isinstance(func, list): + if len(layers) != len(func): + raise RuntimeError( + "Inconsistent number of layers and functions." + ) + _functions = func + else: + _functions = [func for _ in range(len(layers) - 1)] + _functions.append(torch.nn.Identity) + + # 3. Assign modes functions for each FNO layer + if isinstance(n_modes, list): + if all(isinstance(i, list) for i in n_modes) and len(layers) != len( + n_modes + ): + raise RuntimeError("Inconsistent number of layers and modes.") + if all(isinstance(i, int) for i in n_modes): + n_modes = [n_modes] * len(layers) + else: + n_modes = [n_modes] * len(layers) + + # 4. Build the FNO network + tmp_layers = [input_numb_fields] + layers + [output_numb_fields] + self._layers = nn.Sequential( + *[ + fourier_layer( + input_numb_fields=tmp_layers[i], + output_numb_fields=tmp_layers[i + 1], + n_modes=n_modes[i], + activation=_functions[i], + ) + for i in range(len(layers)) + ] + ) + + # 5. Padding values for spectral conv + if isinstance(padding, int): + padding = [padding] * dimensions + self._ipad = [-pad if pad > 0 else None for pad in padding[:dimensions]] + self._padding_type = padding_type + self._pad = [ + val for pair in zip([0] * dimensions, padding) for val in pair + ] + + def forward(self, x): + """ + Forward pass for the :class:`FourierIntegralKernel` model. + + :param x: The input tensor for performing the computation. Depending + on the ``dimensions`` in the initialization, it expects a tensor + with the following shapes: + * 1D tensors: ``[batch, X, channels]`` + * 2D tensors: ``[batch, X, Y, channels]`` + * 3D tensors: ``[batch, X, Y, Z, channels]`` + :type x: torch.Tensor | LabelTensor + :raises Warning: If a LabelTensor is passed as input. + :return: The output tensor. + :rtype: torch.Tensor + """ + if isinstance(x, LabelTensor): + warnings.warn( + "LabelTensor passed as input is not allowed," + " casting LabelTensor to Torch.Tensor" + ) + x = x.as_subclass(torch.Tensor) + # permuting the input [batch, channels, x, y, ...] + permutation_idx = [0, x.ndim - 1, *list(range(1, x.ndim - 1))] + x = x.permute(permutation_idx) + + # padding the input + x = torch.nn.functional.pad(x, pad=self._pad, mode=self._padding_type) + + # apply fourier layers + x = self._layers(x) + + # remove padding + idxs = [slice(None), slice(None)] + [slice(pad) for pad in self._ipad] + x = x[idxs] + + # permuting back [batch, x, y, ..., channels] + permutation_idx = [0, *list(range(2, x.ndim)), 1] + x = x.permute(permutation_idx) + + return x + + @staticmethod + def _check_consistency( + dimensions, + padding, + padding_type, + inner_size, + n_layers, + func, + layers, + n_modes, + ): + """ + Check the consistency of the input parameters. + + + :param int dimensions: The number of dimensions. + :param int padding: The padding size. + :param str padding_type: The padding strategy. + :param int inner_size: The inner size. + :param int n_layers: The number of layers. + :param func: The activation function. + :type func: torch.nn.Module | list[torch.nn.Module] + :param list[int] layers: The list of the dimension of inner layers. + :param n_modes: The number of modes. + :type n_modes: int | list[int] + :raises ValueError: If the input is not consistent. + """ + check_consistency(dimensions, int) + check_consistency(padding, int) + check_consistency(padding_type, str) + check_consistency(inner_size, int) + check_consistency(n_layers, int) + check_consistency(func, nn.Module, subclass=True) + + if layers is not None: + if isinstance(layers, (tuple, list)): + check_consistency(layers, int) + else: + raise ValueError("layers must be tuple or list of int.") + if not isinstance(n_modes, (list, tuple, int)): + raise ValueError( + "n_modes must be a int or list or tuple of valid modes." + " More information on the official documentation." + ) + + @staticmethod + def _get_fourier_block(dimensions): + """ + Retrieve the Fourier Block class based on the number of dimensions. + + :param int dimensions: The number of dimensions. + :raises NotImplementedError: If the number of dimensions is not 1, 2, + or 3. + :return: The Fourier Block class. + :rtype: FourierBlock1D | FourierBlock2D | FourierBlock3D + """ + if dimensions == 1: + return FourierBlock1D + if dimensions == 2: + return FourierBlock2D + if dimensions == 3: + return FourierBlock3D + raise NotImplementedError("FNO implemented only for 1D/2D/3D data.") + + +class FNO(KernelNeuralOperator): + """ + Fourier Neural Operator model class. + + The Fourier Neural Operator (FNO) is a general architecture for learning + operators, which map functions to functions. It can be trained both with + Supervised and Physics_Informed learning strategies. The Fourier Neural + Operator performs global convolution in the Fourier space. + + .. seealso:: + + **Original reference**: Li, Z., Kovachki, N., Azizzadenesheli, K., + Liu, B., Bhattacharya, K., Stuart, A., & Anandkumar, A. (2020). + *Fourier neural operator for parametric partial differential equations*. + DOI: `arXiv preprint arXiv:2010.08895. + `_ + """ + + def __init__( + self, + lifting_net, + projecting_net, + n_modes, + dimensions=3, + padding=8, + padding_type="constant", + inner_size=20, + n_layers=2, + func=nn.Tanh, + layers=None, + ): + """ + :param torch.nn.Module lifting_net: The lifting neural network mapping + the input to its hidden dimension. + :param torch.nn.Module projecting_net: The projection neural network + mapping the hidden representation to the output function. + :param n_modes: The number of modes. + :type n_modes: int | list[int] + :param int dimensions: The number of dimensions. It can be set to ``1``, + ``2``, or ``3``. Default is ``3``. + :param int padding: The padding size. Default is ``8``. + :param str padding_type: The padding strategy. Default is ``constant``. + :param int inner_size: The inner size. Default is ``20``. + :param int n_layers: The number of layers. Default is ``2``. + :param func: The activation function. If a list is passed, it must have + the same length as ``n_layers``. If a single function is passed, it + is used for all layers, except for the last one. + Default is :class:`torch.nn.Tanh`. + :type func: torch.nn.Module | list[torch.nn.Module] + :param list[int] layers: The list of the dimension of inner layers. + If ``None``, ``n_layers`` of dimension ``inner_size`` are used. + Otherwise, it overrides the values passed to ``n_layers`` and + ``inner_size``. Default is ``None``. + """ + lifting_operator_out = lifting_net( + torch.rand(size=next(lifting_net.parameters()).size()) + ).shape[-1] + super().__init__( + lifting_operator=lifting_net, + projection_operator=projecting_net, + integral_kernels=FourierIntegralKernel( + input_numb_fields=lifting_operator_out, + output_numb_fields=next(projecting_net.parameters()).size(), + n_modes=n_modes, + dimensions=dimensions, + padding=padding, + padding_type=padding_type, + inner_size=inner_size, + n_layers=n_layers, + func=func, + layers=layers, + ), + ) + + def forward(self, x): + """ + Forward pass for the :class:`FourierNeuralOperator` model. + + The ``lifting_net`` maps the input to the hidden dimension. + Then, several layers of Fourier blocks are applied. Finally, the + ``projection_net`` maps the hidden representation to the output + function. + + :param x: The input tensor for performing the computation. Depending + on the ``dimensions`` in the initialization, it expects a tensor + with the following shapes: + + * 1D tensors: ``[batch, X, channels]`` + * 2D tensors: ``[batch, X, Y, channels]`` + * 3D tensors: ``[batch, X, Y, Z, channels]`` + + :type x: torch.Tensor | LabelTensor + :return: The output tensor. + :rtype: torch.Tensor + """ + + if isinstance(x, LabelTensor): + x = x.as_subclass(torch.Tensor) + return super().forward(x) diff --git a/pina/model/graph_neural_operator.py b/pina/model/graph_neural_operator.py new file mode 100644 index 000000000..3cb5cdd31 --- /dev/null +++ b/pina/model/graph_neural_operator.py @@ -0,0 +1,229 @@ +"""Module for the Graph Neural Operator model class.""" + +import torch +from torch.nn import Tanh +from .block.gno_block import GNOBlock +from .kernel_neural_operator import KernelNeuralOperator + + +class GraphNeuralKernel(torch.nn.Module): + """ + Graph Neural Operator kernel model class. + + This class implements the Graph Neural Operator kernel network. + + .. seealso:: + + **Original reference**: Li, Z., Kovachki, N., Azizzadenesheli, K., + Liu, B., Bhattacharya, K., Stuart, A., Anandkumar, A. (2020). + *Neural Operator: Graph Kernel Network for Partial Differential + Equations*. + DOI: `arXiv preprint arXiv:2003.03485 `_ + """ + + def __init__( + self, + width, + edge_features, + n_layers=2, + internal_n_layers=0, + internal_layers=None, + inner_size=None, + internal_func=None, + external_func=None, + shared_weights=False, + ): + """ + Initialization of the :class:`GraphNeuralKernel` class. + + :param int width: The width of the kernel. + :param int edge_features: The number of edge features. + :param int n_layers: The number of kernel layers. Default is ``2``. + :param int internal_n_layers: The number of layers of the neural network + inside each kernel layer. Default is ``0``. + :param internal_layers: The number of neurons for each layer of the + neural network inside each kernel layer. Default is ``None``. + :type internal_layers: list[int] | tuple[int] + :param torch.nn.Module internal_func: The activation function used + inside each kernel layer. If ``None``, it uses the + :class:`torch.nn.Tanh` activation. Default is ``None``. + :param torch.nn.Module external_func: The activation function applied to + the output of the each kernel layer. If ``None``, it uses the + :class:`torch.nn.Tanh` activation. Default is ``None``. + :param bool shared_weights: If ``True``, the weights of each kernel + layer are shared. Default is ``False``. + """ + super().__init__() + if external_func is None: + external_func = Tanh + if internal_func is None: + internal_func = Tanh + + if shared_weights: + self.layers = GNOBlock( + width=width, + edges_features=edge_features, + n_layers=internal_n_layers, + layers=internal_layers, + inner_size=inner_size, + internal_func=internal_func, + external_func=external_func, + ) + self.n_layers = n_layers + self._forward_func = self._forward_shared + else: + self.layers = torch.nn.ModuleList( + [ + GNOBlock( + width=width, + edges_features=edge_features, + n_layers=internal_n_layers, + layers=internal_layers, + inner_size=inner_size, + internal_func=internal_func, + external_func=external_func, + ) + for _ in range(n_layers) + ] + ) + self._forward_func = self._forward_unshared + + def _forward_unshared(self, x, edge_index, edge_attr): + """ + Forward pass for the Graph Neural Kernel with unshared weights. + + :param x: The input tensor. + :type x: torch.Tensor | LabelTensor + :param torch.Tensor edge_index: The edge index. + :param edge_attr: The edge attributes. + :type edge_attr: torch.Tensor | LabelTensor + :return: The output tensor. + :rtype: torch.Tensor + """ + for layer in self.layers: + x = layer(x, edge_index, edge_attr) + return x + + def _forward_shared(self, x, edge_index, edge_attr): + """ + Forward pass for the Graph Neural Kernel with shared weights. + + :param x: The input tensor. + :type x: torch.Tensor | LabelTensor + :param torch.Tensor edge_index: The edge index. + :param edge_attr: The edge attributes. + :type edge_attr: torch.Tensor | LabelTensor + :return: The output tensor. + :rtype: torch.Tensor + """ + for _ in range(self.n_layers): + x = self.layers(x, edge_index, edge_attr) + return x + + def forward(self, x, edge_index, edge_attr): + """ + The forward pass of the Graph Neural Kernel. + + :param x: The input tensor. + :type x: torch.Tensor | LabelTensor + :param torch.Tensor edge_index: The edge index. + :param edge_attr: The edge attributes. + :type edge_attr: torch.Tensor | LabelTensor + :return: The output tensor. + :rtype: torch.Tensor + """ + return self._forward_func(x, edge_index, edge_attr) + + +class GraphNeuralOperator(KernelNeuralOperator): + """ + Graph Neural Operator model class. + + The Graph Neural Operator is a general architecture for learning operators, + which map functions to functions. It can be trained both with Supervised + and Physics-Informed learning strategies. The Graph Neural Operator performs + graph convolution by means of a Graph Neural Kernel. + + .. seealso:: + + **Original reference**: Li, Z., Kovachki, N., Azizzadenesheli, K., + Liu, B., Bhattacharya, K., Stuart, A., Anandkumar, A. (2020). + *Neural Operator: Graph Kernel Network for Partial Differential + Equations*. + DOI: `arXiv preprint arXiv:2003.03485. + `_ + """ + + def __init__( + self, + lifting_operator, + projection_operator, + edge_features, + n_layers=10, + internal_n_layers=0, + inner_size=None, + internal_layers=None, + internal_func=None, + external_func=None, + shared_weights=True, + ): + """ + Initialization of the :class:`GraphNeuralOperator` class. + + :param torch.nn.Module lifting_operator: The lifting neural network + mapping the input to its hidden dimension. + :param torch.nn.Module projection_operator: The projection neural + network mapping the hidden representation to the output function. + :param int edge_features: The number of edge features. + :param int n_layers: The number of kernel layers. Default is ``10``. + :param int internal_n_layers: The number of layers of the neural network + inside each kernel layer. Default is ``0``. + :param int inner_size: The size of the hidden layers of the neural + network inside each kernel layer. Default is ``None``. + :param internal_layers: The number of neurons for each layer of the + neural network inside each kernel layer. Default is ``None``. + :type internal_layers: list[int] | tuple[int] + :param torch.nn.Module internal_func: The activation function used + inside each kernel layer. If ``None``, it uses the + :class:`torch.nn.Tanh`. activation. Default is ``None``. + :param torch.nn.Module external_func: The activation function applied to + the output of the each kernel layer. If ``None``, it uses the + :class:`torch.nn.Tanh`. activation. Default is ``None``. + :param bool shared_weights: If ``True``, the weights of each kernel + layer are shared. Default is ``False``. + """ + + if internal_func is None: + internal_func = Tanh + if external_func is None: + external_func = Tanh + + super().__init__( + lifting_operator=lifting_operator, + integral_kernels=GraphNeuralKernel( + width=lifting_operator.out_features, + edge_features=edge_features, + internal_n_layers=internal_n_layers, + inner_size=inner_size, + internal_layers=internal_layers, + external_func=external_func, + internal_func=internal_func, + n_layers=n_layers, + shared_weights=shared_weights, + ), + projection_operator=projection_operator, + ) + + def forward(self, x): + """ + The forward pass of the Graph Neural Operator. + + :param torch_geometric.data.Batch x: The input graph. + :return: The output tensor. + :rtype: torch.Tensor + """ + x, edge_index, edge_attr = x.x, x.edge_index, x.edge_attr + x = self.lifting_operator(x) + x = self.integral_kernels(x, edge_index, edge_attr) + x = self.projection_operator(x) + return x diff --git a/pina/model/base_no.py b/pina/model/kernel_neural_operator.py similarity index 56% rename from pina/model/base_no.py rename to pina/model/kernel_neural_operator.py index d22a18c5b..e3cb790e5 100644 --- a/pina/model/base_no.py +++ b/pina/model/kernel_neural_operator.py @@ -1,20 +1,19 @@ -""" -Kernel Neural Operator Module. -""" +"""Module for the Kernel Neural Operator model class.""" import torch -from pina.utils import check_consistency +from ..utils import check_consistency class KernelNeuralOperator(torch.nn.Module): r""" - Base class for composing Neural Operators with integral kernels. + Base class for Neural Operators with integral kernels. - This is a base class for composing neural operators with multiple - integral kernels. All neural operator models defined in PINA inherit - from this class. The structure is inspired by the work of Kovachki, N. - et al. see Figure 2 of the reference for extra details. The Neural - Operators inheriting from this class can be written as: + This class serves as a foundation for building Neural Operators that + incorporate multiple integral kernels. All Neural Operator models in + PINA inherit from this class. The design follows the framework proposed + by Kovachki et al., as illustrated in Figure 2 of their work. + + Neural Operators derived from this class can be expressed as: .. math:: G_\theta := P \circ K_m \circ \cdot \circ K_1 \circ L @@ -40,15 +39,18 @@ class KernelNeuralOperator(torch.nn.Module): **Original reference**: Kovachki, N., Li, Z., Liu, B., Azizzadenesheli, K., Bhattacharya, K., Stuart, A., & Anandkumar, A. - (2023). *Neural operator: Learning maps between function - spaces with applications to PDEs*. Journal of Machine Learning - Research, 24(89), 1-97. + (2023). + *Neural operator: Learning maps between function spaces with + applications to PDEs*. + Journal of Machine Learning Research, 24(89), 1-97. """ def __init__(self, lifting_operator, integral_kernels, projection_operator): """ - :param torch.nn.Module lifting_operator: The lifting operator - mapping the input to its hidden dimension. + Initialization of the :class:`KernelNeuralOperator` class. + + :param torch.nn.Module lifting_operator: The lifting operator mapping + the input to its hidden dimension. :param torch.nn.Module integral_kernels: List of integral kernels mapping each hidden representation to the next one. :param torch.nn.Module projection_operator: The projection operator @@ -64,16 +66,19 @@ def __init__(self, lifting_operator, integral_kernels, projection_operator): @property def lifting_operator(self): """ - The lifting operator property. + The lifting operator module. + + :return: The lifting operator module. + :rtype: torch.nn.Module """ return self._lifting_operator @lifting_operator.setter def lifting_operator(self, value): """ - The lifting operator setter + Set the lifting operator module. - :param torch.nn.Module value: The lifting operator torch module. + :param torch.nn.Module value: The lifting operator module. """ check_consistency(value, torch.nn.Module) self._lifting_operator = value @@ -81,16 +86,19 @@ def lifting_operator(self, value): @property def projection_operator(self): """ - The projection operator property. + The projection operator module. + + :return: The projection operator module. + :rtype: torch.nn.Module """ return self._projection_operator @projection_operator.setter def projection_operator(self, value): """ - The projection operator setter + Set the projection operator module. - :param torch.nn.Module value: The projection operator torch module. + :param torch.nn.Module value: The projection operator module. """ check_consistency(value, torch.nn.Module) self._projection_operator = value @@ -98,37 +106,41 @@ def projection_operator(self, value): @property def integral_kernels(self): """ - The integral kernels operator property. + The integral kernels operator module. + + :return: The integral kernels operator module. + :rtype: torch.nn.Module """ return self._integral_kernels @integral_kernels.setter def integral_kernels(self, value): """ - The integral kernels operator setter + Set the integral kernels operator module. - :param torch.nn.Module value: The integral kernels operator torch - module. + :param torch.nn.Module value: The integral kernels operator module. """ check_consistency(value, torch.nn.Module) self._integral_kernels = value def forward(self, x): r""" - Forward computation for Base Neural Operator. It performs a - lifting of the input by the ``lifting_operator``. - Then different layers integral kernels are applied using - ``integral_kernels``. Finally the output is projected - to the final dimensionality by the ``projection_operator``. - - :param torch.Tensor x: The input tensor for performing the - computation. It expects a tensor :math:`B \times N \times D`, - where :math:`B` is the batch_size, :math:`N` the number of points - in the mesh, :math:`D` the dimension of the problem. In particular - :math:`D` is the number of spatial/paramtric/temporal variables - plus the field variables. For example for 2D problems with 2 - output\ variables :math:`D=4`. - :return: The output tensor obtained from the NO. + Forward pass for the :class:`KernelNeuralOperator` model. + + The ``lifting_operator`` maps the input to the hidden dimension. + The ``integral_kernels`` apply the integral kernels to the hidden + representation. The ``projection_operator`` maps the hidden + representation to the output function. + + :param x: The input tensor for performing the computation. It expects + a tensor :math:`B \times N \times D`, where :math:`B` is the + batch_size, :math:`N` the number of points in the mesh, and + :math:`D` the dimension of the problem. In particular, :math:`D` + is the number of spatial, parametric, and/or temporal variables + plus the field variables. For instance, for 2D problems with 2 + output variables, :math:`D=4`. + :type x: torch.Tensor | LabelTensor + :return: The output tensor. :rtype: torch.Tensor """ x = self.lifting_operator(x) diff --git a/pina/model/layers/__init__.py b/pina/model/layers/__init__.py index 5108522c5..aeef265c9 100644 --- a/pina/model/layers/__init__.py +++ b/pina/model/layers/__init__.py @@ -1,33 +1,16 @@ -__all__ = [ - "ContinuousConvBlock", - "ResidualBlock", - "EnhancedLinear", - "SpectralConvBlock1D", - "SpectralConvBlock2D", - "SpectralConvBlock3D", - "FourierBlock1D", - "FourierBlock2D", - "FourierBlock3D", - "PODBlock", - "OrthogonalBlock", - "PeriodicBoundaryEmbedding", - "FourierFeatureEmbedding", - "AVNOBlock", - "LowRankBlock", - "RBFBlock", -] +"""Old layers module, deprecated in 0.2.0.""" -from .convolution_2d import ContinuousConvBlock -from .residual import ResidualBlock, EnhancedLinear -from .spectral import ( - SpectralConvBlock1D, - SpectralConvBlock2D, - SpectralConvBlock3D, +import warnings + +from ..block import * +from ...utils import custom_warning_format + +# back-compatibility 0.1 +# Set the custom format for warnings +warnings.formatwarning = custom_warning_format +warnings.filterwarnings("always", category=DeprecationWarning) +warnings.warn( + "'pina.model.layers' is deprecated and will be removed " + "in future versions. Please use 'pina.model.block' instead.", + DeprecationWarning, ) -from .fourier import FourierBlock1D, FourierBlock2D, FourierBlock3D -from .pod import PODBlock -from .orthogonal import OrthogonalBlock -from .embedding import PeriodicBoundaryEmbedding, FourierFeatureEmbedding -from .avno_layer import AVNOBlock -from .lowrank_layer import LowRankBlock -from .rbf_layer import RBFBlock diff --git a/pina/model/layers/avno_layer.py b/pina/model/layers/avno_layer.py deleted file mode 100644 index 62ed8f132..000000000 --- a/pina/model/layers/avno_layer.py +++ /dev/null @@ -1,67 +0,0 @@ -""" Module for Averaging Neural Operator Layer class. """ - -from torch import nn, mean -from pina.utils import check_consistency - - -class AVNOBlock(nn.Module): - r""" - The PINA implementation of the inner layer of the Averaging Neural Operator. - - The operator layer performs an affine transformation where the convolution - is approximated with a local average. Given the input function - :math:`v(x)\in\mathbb{R}^{\rm{emb}}` the layer computes - the operator update :math:`K(v)` as: - - .. math:: - K(v) = \sigma\left(Wv(x) + b + \frac{1}{|\mathcal{A}|}\int v(y)dy\right) - - where: - - * :math:`\mathbb{R}^{\rm{emb}}` is the embedding (hidden) size - corresponding to the ``hidden_size`` object - * :math:`\sigma` is a non-linear activation, corresponding to the - ``func`` object - * :math:`W\in\mathbb{R}^{\rm{emb}\times\rm{emb}}` is a tunable matrix. - * :math:`b\in\mathbb{R}^{\rm{emb}}` is a tunable bias. - - .. seealso:: - - **Original reference**: Lanthaler S. Li, Z., Kovachki, - Stuart, A. (2020). *The Nonlocal Neural Operator: Universal - Approximation*. - DOI: `arXiv preprint arXiv:2304.13221. - `_ - - """ - - def __init__(self, hidden_size=100, func=nn.GELU): - """ - :param int hidden_size: Size of the hidden layer, defaults to 100. - :param func: The activation function, default to nn.GELU. - """ - super().__init__() - - # Check type consistency - check_consistency(hidden_size, int) - check_consistency(func, nn.Module, subclass=True) - # Assignment - self._nn = nn.Linear(hidden_size, hidden_size) - self._func = func() - - def forward(self, x): - r""" - Forward pass of the layer, it performs a sum of local average - and an affine transformation of the field. - - :param torch.Tensor x: The input tensor for performing the - computation. It expects a tensor :math:`B \times N \times D`, - where :math:`B` is the batch_size, :math:`N` the number of points - in the mesh, :math:`D` the dimension of the problem. In particular - :math:`D` is the codomain of the function :math:`v`. For example - a scalar function has :math:`D=1`, a 4-dimensional vector function - :math:`D=4`. - :return: The output tensor obtained from Average Neural Operator Block. - :rtype: torch.Tensor - """ - return self._func(self._nn(x) + mean(x, dim=1, keepdim=True)) diff --git a/pina/model/layers/convolution.py b/pina/model/layers/convolution.py deleted file mode 100644 index c6ae4e240..000000000 --- a/pina/model/layers/convolution.py +++ /dev/null @@ -1,181 +0,0 @@ -"""Module for Base Continuous Convolution class.""" - -from abc import ABCMeta, abstractmethod -import torch -from .stride import Stride -from .utils_convolution import optimizing - - -class BaseContinuousConv(torch.nn.Module, metaclass=ABCMeta): - """ - Abstract class - """ - - def __init__( - self, - input_numb_field, - output_numb_field, - filter_dim, - stride, - model=None, - optimize=False, - no_overlap=False, - ): - """ - Base Class for Continuous Convolution. - - The algorithm expects input to be in the form: - $$[B \times N_{in} \times N \times D]$$ - where $B$ is the batch_size, $N_{in}$ is the number of input - fields, $N$ the number of points in the mesh, $D$ the dimension - of the problem. In particular: - * $D$ is the number of spatial variables + 1. The last column must - contain the field value. For example for 2D problems $D=3$ and - the tensor will be something like `[first coordinate, second - coordinate, field value]`. - * $N_{in}$ represents the number of vectorial function presented. - For example a vectorial function $f = [f_1, f_2]$ will have - $N_{in}=2$. - - :Note - A 2-dimensional vectorial function $N_{in}=2$ of 3-dimensional - input $D=3+1=4$ with 100 points input mesh and batch size of 8 - is represented as a tensor `[8, 2, 100, 4]`, where the columns - `[:, 0, :, -1]` and `[:, 1, :, -1]` represent the first and - second filed value respectively - - The algorithm returns a tensor of shape: - $$[B \times N_{out} \times N' \times D]$$ - where $B$ is the batch_size, $N_{out}$ is the number of output - fields, $N'$ the number of points in the mesh, $D$ the dimension - of the problem. - - :param input_numb_field: number of fields in the input - :type input_numb_field: int - :param output_numb_field: number of fields in the output - :type output_numb_field: int - :param filter_dim: dimension of the filter - :type filter_dim: tuple/ list - :param stride: stride for the filter - :type stride: dict - :param model: neural network for inner parametrization, - defaults to None. - :type model: torch.nn.Module, optional - :param optimize: flag for performing optimization on the continuous - filter, defaults to False. The flag `optimize=True` should be - used only when the scatter datapoints are fixed through the - training. If torch model is in `.eval()` mode, the flag is - automatically set to False always. - :type optimize: bool, optional - :param no_overlap: flag for performing optimization on the transpose - continuous filter, defaults to False. The flag set to `True` should - be used only when the filter positions do not overlap for different - strides. RuntimeError will raise in case of non-compatible strides. - :type no_overlap: bool, optional - """ - super().__init__() - - if isinstance(input_numb_field, int): - self._input_numb_field = input_numb_field - else: - raise ValueError("input_numb_field must be int.") - - if isinstance(output_numb_field, int): - self._output_numb_field = output_numb_field - else: - raise ValueError("input_numb_field must be int.") - - if isinstance(filter_dim, (tuple, list)): - vect = filter_dim - else: - raise ValueError("filter_dim must be tuple or list.") - vect = torch.tensor(vect) - self.register_buffer("_dim", vect, persistent=False) - - if isinstance(stride, dict): - self._stride = Stride(stride) - else: - raise ValueError("stride must be dictionary.") - - self._net = model - - if isinstance(optimize, bool): - self._optimize = optimize - else: - raise ValueError("optimize must be bool.") - - # choosing how to initialize based on optimization - if self._optimize: - # optimizing decorator ensure the function is called - # just once - self._choose_initialization = optimizing( - self._initialize_convolution - ) - else: - self._choose_initialization = self._initialize_convolution - - if not isinstance(no_overlap, bool): - raise ValueError("no_overlap must be bool.") - - if no_overlap: - raise NotImplementedError - self.transpose = self.transpose_no_overlap - else: - self.transpose = self.transpose_overlap - - class DefaultKernel(torch.nn.Module): - - def __init__(self, input_dim, output_dim): - super().__init__() - assert isinstance(input_dim, int) - assert isinstance(output_dim, int) - self._model = torch.nn.Sequential( - torch.nn.Linear(input_dim, 20), - torch.nn.ReLU(), - torch.nn.Linear(20, 20), - torch.nn.ReLU(), - torch.nn.Linear(20, output_dim), - ) - - def forward(self, x): - return self._model(x) - - @property - def net(self): - return self._net - - @property - def stride(self): - return self._stride - - @property - def filter_dim(self): - return self._dim - - @property - def input_numb_field(self): - return self._input_numb_field - - @property - def output_numb_field(self): - return self._output_numb_field - - @property - @abstractmethod - def forward(self, X): - pass - - @property - @abstractmethod - def transpose_overlap(self, X): - pass - - @property - @abstractmethod - def transpose_no_overlap(self, X): - pass - - @property - @abstractmethod - def _initialize_convolution(self, X, type): - pass diff --git a/pina/model/layers/embedding.py b/pina/model/layers/embedding.py deleted file mode 100644 index 42481366b..000000000 --- a/pina/model/layers/embedding.py +++ /dev/null @@ -1,261 +0,0 @@ -""" Embedding modulus. """ - -import torch -from pina.utils import check_consistency -from typing import Union, Sequence - - -class PeriodicBoundaryEmbedding(torch.nn.Module): - r""" - Imposing hard constraint periodic boundary conditions by embedding the - input. - - A periodic function :math:`u:\mathbb{R}^{\rm{in}} - \rightarrow\mathbb{R}^{\rm{out}}` periodic in the spatial - coordinates :math:`\mathbf{x}` with periods :math:`\mathbf{L}` is such that: - - .. math:: - u(\mathbf{x}) = u(\mathbf{x} + n \mathbf{L})\;\; - \forall n\in\mathbb{N}. - - The :meth:`PeriodicBoundaryEmbedding` augments the input such that the periodic conditons - is guarantee. The input is augmented by the following formula: - - .. math:: - \mathbf{x} \rightarrow \tilde{\mathbf{x}} = \left[1, - \cos\left(\frac{2\pi}{L_1} x_1 \right), - \sin\left(\frac{2\pi}{L_1}x_1\right), \cdots, - \cos\left(\frac{2\pi}{L_{\rm{in}}}x_{\rm{in}}\right), - \sin\left(\frac{2\pi}{L_{\rm{in}}}x_{\rm{in}}\right)\right], - - where :math:`\text{dim}(\tilde{\mathbf{x}}) = 3\text{dim}(\mathbf{x})`. - - .. seealso:: - **Original reference**: - 1. Dong, Suchuan, and Naxian Ni (2021). *A method for representing - periodic functions and enforcing exactly periodic boundary - conditions with deep neural networks*. Journal of Computational - Physics 435, 110242. - DOI: `10.1016/j.jcp.2021.110242. - `_ - 2. Wang, S., Sankaran, S., Wang, H., & Perdikaris, P. (2023). *An - expert's guide to training physics-informed neural networks*. - DOI: `arXiv preprint arXiv:2308.0846. - `_ - .. warning:: - The embedding is a truncated fourier expansion, and only ensures - function PBC and not for its derivatives. Ensuring approximate - periodicity in - the derivatives of :math:`u` can be done, and extensive - tests have shown (also in the reference papers) that this implementation - can correctly compute the PBC on the derivatives up to the order - :math:`\sim 2,3`, while it is not guarantee the periodicity for - :math:`>3`. The PINA code is tested only for function PBC and not for - its derivatives. - """ - - def __init__(self, input_dimension, periods, output_dimension=None): - """ - :param int input_dimension: The dimension of the input tensor, it can - be checked with `tensor.ndim` method. - :param float | int | dict periods: The periodicity in each dimension for - the input data. If ``float`` or ``int`` is passed, - the period is assumed constant for all the dimensions of the data. - If a ``dict`` is passed the `dict.values` represent periods, - while the ``dict.keys`` represent the dimension where the - periodicity is applied. The `dict.keys` can either be `int` - if working with ``torch.Tensor`` or ``str`` if - working with ``LabelTensor``. - :param int output_dimension: The dimension of the output after the - fourier embedding. If not ``None`` a ``torch.nn.Linear`` layer - is applied to the fourier embedding output to match the desired - dimensionality, default ``None``. - """ - super().__init__() - - # check input consistency - check_consistency(periods, (float, int, dict)) - check_consistency(input_dimension, int) - if output_dimension is not None: - check_consistency(output_dimension, int) - self._layer = torch.nn.Linear(input_dimension * 3, output_dimension) - else: - self._layer = torch.nn.Identity() - - # checks on the periods - if isinstance(periods, dict): - if not all( - isinstance(dim, (str, int)) and isinstance(period, (float, int)) - for dim, period in periods.items() - ): - raise TypeError( - "In dictionary periods, keys must be integers" - " or strings, and values must be float or int." - ) - self._period = periods - else: - self._period = {k: periods for k in range(input_dimension)} - - def forward(self, x): - """ - Forward pass to compute the periodic boundary conditions embedding. - - :param torch.Tensor x: Input tensor. - :return: Periodic embedding of the input. - :rtype: torch.Tensor - """ - omega = torch.stack( - [ - torch.pi * 2.0 / torch.tensor([val], device=x.device) - for val in self._period.values() - ], - dim=-1, - ) - x = self._get_vars(x, list(self._period.keys())) - return self._layer( - torch.cat( - [ - torch.ones_like(x), - torch.cos(omega * x), - torch.sin(omega * x), - ], - dim=-1, - ) - ) - - def _get_vars(self, x, indeces): - """ - Get variables from input tensor ordered by specific indeces. - - :param torch.Tensor x: The input tensor to extract. - :param list[int] | list[str] indeces: List of indeces to extract. - :return: The extracted tensor given the indeces. - :rtype: torch.Tensor - """ - if isinstance(indeces[0], str): - try: - return x.extract(indeces) - except AttributeError: - raise RuntimeError( - "Not possible to extract input variables from tensor." - " Ensure that the passed tensor is a LabelTensor or" - " pass list of integers to extract variables. For" - " more information refer to warning in the documentation." - ) - elif isinstance(indeces[0], int): - return x[..., indeces] - else: - raise RuntimeError( - "Not able to extract right indeces for tensor." - " For more information refer to warning in the documentation." - ) - - @property - def period(self): - """ - The period of the periodic function to approximate. - """ - return self._period - - -class FourierFeatureEmbedding(torch.nn.Module): - def __init__(self, input_dimension, output_dimension, sigma): - r""" - Fourier Feature Embedding class for encoding input features - using random Fourier features.This class applies a Fourier - transformation to the input features, - which can help in learning high-frequency variations in data. - If multiple sigma are provided, the class - supports multiscale feature embedding, creating embeddings for - each scale specified by the sigma. - - The :obj:`FourierFeatureEmbedding` augments the input - by the following formula (3.10 of original paper): - - .. math:: - \mathbf{x} \rightarrow \tilde{\mathbf{x}} = \left[ - \cos\left( \mathbf{B} \mathbf{x} \right), - \sin\left( \mathbf{B} \mathbf{x} \right)\right], - - where :math:`\mathbf{B}_{ij} \sim \mathcal{N}(0, \sigma^2)`. - - In case multiple ``sigma`` are passed, the resulting embeddings - are concateneted: - - .. math:: - \mathbf{x} \rightarrow \tilde{\mathbf{x}} = \left[ - \cos\left( \mathbf{B}^1 \mathbf{x} \right), - \sin\left( \mathbf{B}^1 \mathbf{x} \right), - \cos\left( \mathbf{B}^2 \mathbf{x} \right), - \sin\left( \mathbf{B}^3 \mathbf{x} \right), - \dots, - \cos\left( \mathbf{B}^M \mathbf{x} \right), - \sin\left( \mathbf{B}^M \mathbf{x} \right)\right], - - where :math:`\mathbf{B}^k_{ij} \sim \mathcal{N}(0, \sigma_k^2) \quad - k \in (1, \dots, M)`. - - .. seealso:: - **Original reference**: - Wang, Sifan, Hanwen Wang, and Paris Perdikaris. *On the eigenvector - bias of Fourier feature networks: From regression to solving - multi-scale PDEs with physics-informed neural networks.* - Computer Methods in Applied Mechanics and - Engineering 384 (2021): 113938. - DOI: `10.1016/j.cma.2021.113938. - `_ - - :param int input_dimension: The input vector dimension of the layer. - :param int output_dimension: The output dimension of the layer. The - output is obtained as a concatenation of the cosine and sine - embedding, hence it must be a multiple of two (even number). - :param int | float sigma: The standard deviation used for the - Fourier Embedding. This value must reflect the granularity of the - scale in the differential equation solution. - """ - super().__init__() - - # check consistency - check_consistency(sigma, (int, float)) - check_consistency(output_dimension, int) - check_consistency(input_dimension, int) - if output_dimension % 2: - raise RuntimeError( - "Expected output_dimension to be a even number, " - f"got {output_dimension}." - ) - - # assign sigma - self._sigma = sigma - - # create non-trainable matrices - self._matrix = ( - torch.rand( - size=(input_dimension, output_dimension // 2), - requires_grad=False, - ) - * self.sigma - ) - - def forward(self, x): - """ - Forward pass to compute the fourier embedding. - - :param torch.Tensor x: Input tensor. - :return: Fourier embeddings of the input. - :rtype: torch.Tensor - """ - # compute random matrix multiplication - out = torch.mm(x, self._matrix.to(device=x.device, dtype=x.dtype)) - # return embedding - return torch.cat( - [torch.cos(2 * torch.pi * out), torch.sin(2 * torch.pi * out)], - dim=-1, - ) - - @property - def sigma(self): - """ - Returning the variance of the sampled matrix for Fourier Embedding. - """ - return self._sigma diff --git a/pina/model/layers/fourier.py b/pina/model/layers/fourier.py deleted file mode 100644 index 3b6078e0b..000000000 --- a/pina/model/layers/fourier.py +++ /dev/null @@ -1,219 +0,0 @@ -import torch -import torch.nn as nn -from ...utils import check_consistency - -from pina.model.layers import ( - SpectralConvBlock1D, - SpectralConvBlock2D, - SpectralConvBlock3D, -) - - -class FourierBlock1D(nn.Module): - """ - Fourier block implementation for three dimensional - input tensor. The combination of Fourier blocks - make up the Fourier Neural Operator - - .. seealso:: - - **Original reference**: Li, Z., Kovachki, N., Azizzadenesheli, K., Liu, B., - Bhattacharya, K., Stuart, A., & Anandkumar, A. (2020). *Fourier neural operator for - parametric partial differential equations*. - DOI: `arXiv preprint arXiv:2010.08895. - `_ - - """ - - def __init__( - self, - input_numb_fields, - output_numb_fields, - n_modes, - activation=torch.nn.Tanh, - ): - super().__init__() - """ - PINA implementation of Fourier block one dimension. The module computes - the spectral convolution of the input with a linear kernel in the - fourier space, and then it maps the input back to the physical - space. The output is then added to a Linear tranformation of the - input in the physical space. Finally an activation function is - applied to the output. - - The block expects an input of size ``[batch, input_numb_fields, N]`` - and returns an output of size ``[batch, output_numb_fields, N]``. - - :param int input_numb_fields: The number of channels for the input. - :param int output_numb_fields: The number of channels for the output. - :param list | tuple n_modes: Number of modes to select for each dimension. - It must be at most equal to the ``floor(N/2)+1``. - :param torch.nn.Module activation: The activation function. - """ - # check type consistency - check_consistency(activation(), nn.Module) - - # assign variables - self._spectral_conv = SpectralConvBlock1D( - input_numb_fields=input_numb_fields, - output_numb_fields=output_numb_fields, - n_modes=n_modes, - ) - self._activation = activation() - self._linear = nn.Conv1d(input_numb_fields, output_numb_fields, 1) - - def forward(self, x): - """ - Forward computation for Fourier Block. It performs a spectral - convolution and a linear transformation of the input and sum the - results. - - :param x: The input tensor for fourier block, expect of size - ``[batch, input_numb_fields, x]``. - :type x: torch.Tensor - :return: The output tensor obtained from the - fourier block of size ``[batch, output_numb_fields, x]``. - :rtype: torch.Tensor - """ - return self._activation(self._spectral_conv(x) + self._linear(x)) - - -class FourierBlock2D(nn.Module): - """ - Fourier block implementation for two dimensional - input tensor. The combination of Fourier blocks - make up the Fourier Neural Operator - - .. seealso:: - - **Original reference**: Li, Zongyi, et al. - *Fourier neural operator for parametric partial - differential equations*. arXiv preprint - arXiv:2010.08895 (2020) - `_. - - """ - - def __init__( - self, - input_numb_fields, - output_numb_fields, - n_modes, - activation=torch.nn.Tanh, - ): - """ - PINA implementation of Fourier block two dimensions. The module computes - the spectral convolution of the input with a linear kernel in the - fourier space, and then it maps the input back to the physical - space. The output is then added to a Linear tranformation of the - input in the physical space. Finally an activation function is - applied to the output. - - The block expects an input of size ``[batch, input_numb_fields, Nx, Ny]`` - and returns an output of size ``[batch, output_numb_fields, Nx, Ny]``. - - :param int input_numb_fields: The number of channels for the input. - :param int output_numb_fields: The number of channels for the output. - :param list | tuple n_modes: Number of modes to select for each dimension. - It must be at most equal to the ``floor(Nx/2)+1`` and ``floor(Ny/2)+1``. - :param torch.nn.Module activation: The activation function. - """ - super().__init__() - - # check type consistency - check_consistency(activation(), nn.Module) - - # assign variables - self._spectral_conv = SpectralConvBlock2D( - input_numb_fields=input_numb_fields, - output_numb_fields=output_numb_fields, - n_modes=n_modes, - ) - self._activation = activation() - self._linear = nn.Conv2d(input_numb_fields, output_numb_fields, 1) - - def forward(self, x): - """ - Forward computation for Fourier Block. It performs a spectral - convolution and a linear transformation of the input and sum the - results. - - :param x: The input tensor for fourier block, expect of size - ``[batch, input_numb_fields, x, y]``. - :type x: torch.Tensor - :return: The output tensor obtained from the - fourier block of size ``[batch, output_numb_fields, x, y, z]``. - :rtype: torch.Tensor - """ - return self._activation(self._spectral_conv(x) + self._linear(x)) - - -class FourierBlock3D(nn.Module): - """ - Fourier block implementation for three dimensional - input tensor. The combination of Fourier blocks - make up the Fourier Neural Operator - - .. seealso:: - - **Original reference**: Li, Zongyi, et al. - *Fourier neural operator for parametric partial - differential equations*. arXiv preprint - arXiv:2010.08895 (2020) - `_. - - """ - - def __init__( - self, - input_numb_fields, - output_numb_fields, - n_modes, - activation=torch.nn.Tanh, - ): - """ - PINA implementation of Fourier block three dimensions. The module computes - the spectral convolution of the input with a linear kernel in the - fourier space, and then it maps the input back to the physical - space. The output is then added to a Linear tranformation of the - input in the physical space. Finally an activation function is - applied to the output. - - The block expects an input of size ``[batch, input_numb_fields, Nx, Ny, Nz]`` - and returns an output of size ``[batch, output_numb_fields, Nx, Ny, Nz]``. - - :param int input_numb_fields: The number of channels for the input. - :param int output_numb_fields: The number of channels for the output. - :param list | tuple n_modes: Number of modes to select for each dimension. - It must be at most equal to the ``floor(Nx/2)+1``, ``floor(Ny/2)+1`` - and ``floor(Nz/2)+1``. - :param torch.nn.Module activation: The activation function. - """ - super().__init__() - - # check type consistency - check_consistency(activation(), nn.Module) - - # assign variables - self._spectral_conv = SpectralConvBlock3D( - input_numb_fields=input_numb_fields, - output_numb_fields=output_numb_fields, - n_modes=n_modes, - ) - self._activation = activation() - self._linear = nn.Conv3d(input_numb_fields, output_numb_fields, 1) - - def forward(self, x): - """ - Forward computation for Fourier Block. It performs a spectral - convolution and a linear transformation of the input and sum the - results. - - :param x: The input tensor for fourier block, expect of size - ``[batch, input_numb_fields, x, y, z]``. - :type x: torch.Tensor - :return: The output tensor obtained from the - fourier block of size ``[batch, output_numb_fields, x, y, z]``. - :rtype: torch.Tensor - """ - return self._activation(self._spectral_conv(x) + self._linear(x)) diff --git a/pina/model/layers/integral.py b/pina/model/layers/integral.py deleted file mode 100644 index 565aec3cf..000000000 --- a/pina/model/layers/integral.py +++ /dev/null @@ -1,63 +0,0 @@ -import torch - - -class Integral(object): - - def __init__(self, param): - """Integral class for continous convolution - - :param param: type of continuous convolution - :type param: string - """ - - if param == "discrete": - self.make_integral = self.integral_param_disc - elif param == "continuous": - self.make_integral = self.integral_param_cont - else: - raise TypeError - - def __call__(self, *args, **kwds): - return self.make_integral(*args, **kwds) - - def _prepend_zero(self, x): - """Create bins for performing integral - - :param x: input tensor - :type x: torch.tensor - :return: bins for integrals - :rtype: torch.tensor - """ - return torch.cat((torch.zeros(1, dtype=x.dtype, device=x.device), x)) - - def integral_param_disc(self, x, y, idx): - """Perform discretize integral - with discrete parameters - - :param x: input vector - :type x: torch.tensor - :param y: input vector - :type y: torch.tensor - :param idx: indeces for different strides - :type idx: list - :return: integral - :rtype: torch.tensor - """ - cs_idxes = self._prepend_zero(torch.cumsum(torch.tensor(idx), 0)) - cs = self._prepend_zero(torch.cumsum(x.flatten() * y.flatten(), 0)) - return cs[cs_idxes[1:]] - cs[cs_idxes[:-1]] - - def integral_param_cont(self, x, y, idx): - """Perform discretize integral for continuous convolution - with continuous parameters - - :param x: input vector - :type x: torch.tensor - :param y: input vector - :type y: torch.tensor - :param idx: indeces for different strides - :type idx: list - :return: integral - :rtype: torch.tensor - """ - raise NotImplementedError diff --git a/pina/model/layers/lowrank_layer.py b/pina/model/layers/lowrank_layer.py deleted file mode 100644 index 80fb43e4e..000000000 --- a/pina/model/layers/lowrank_layer.py +++ /dev/null @@ -1,141 +0,0 @@ -""" Module for Averaging Neural Operator Layer class. """ - -import torch - -from pina.utils import check_consistency -import pina.model as pm # avoid circular import - - -class LowRankBlock(torch.nn.Module): - r""" - The PINA implementation of the inner layer of the Averaging Neural Operator. - - The operator layer performs an affine transformation where the convolution - is approximated with a local average. Given the input function - :math:`v(x)\in\mathbb{R}^{\rm{emb}}` the layer computes - the operator update :math:`K(v)` as: - - .. math:: - K(v) = \sigma\left(Wv(x) + b + \sum_{i=1}^r \langle - \psi^{(i)} , v(x) \rangle \phi^{(i)} \right) - - where: - - * :math:`\mathbb{R}^{\rm{emb}}` is the embedding (hidden) size - corresponding to the ``hidden_size`` object - * :math:`\sigma` is a non-linear activation, corresponding to the - ``func`` object - * :math:`W\in\mathbb{R}^{\rm{emb}\times\rm{emb}}` is a tunable matrix. - * :math:`b\in\mathbb{R}^{\rm{emb}}` is a tunable bias. - * :math:`\psi^{(i)}\in\mathbb{R}^{\rm{emb}}` and - :math:`\phi^{(i)}\in\mathbb{R}^{\rm{emb}}` are :math:`r` a low rank - basis functions mapping. - * :math:`b\in\mathbb{R}^{\rm{emb}}` is a tunable bias. - - .. seealso:: - - **Original reference**: Kovachki, N., Li, Z., Liu, B., - Azizzadenesheli, K., Bhattacharya, K., Stuart, A., & Anandkumar, A. - (2023). *Neural operator: Learning maps between function - spaces with applications to PDEs*. Journal of Machine Learning - Research, 24(89), 1-97. - - """ - - def __init__( - self, - input_dimensions, - embedding_dimenion, - rank, - inner_size=20, - n_layers=2, - func=torch.nn.Tanh, - bias=True, - ): - """ - :param int input_dimensions: The number of input components of the - model. - Expected tensor shape of the form :math:`(*, d)`, where * - means any number of dimensions including none, - and :math:`d` the ``input_dimensions``. - :param int embedding_dimenion: Size of the embedding dimension of the - field. - :param int rank: The rank number of the basis approximation components - of the model. Expected tensor shape of the form :math:`(*, 2d)`, - where * means any number of dimensions including none, - and :math:`2d` the ``rank`` for both basis functions. - :param int inner_size: Number of neurons in the hidden layer(s) for the - basis function network. Default is 20. - :param int n_layers: Number of hidden layers. for the - basis function network. Default is 2. - :param func: The activation function to use for the - basis function network. If a single - :class:`torch.nn.Module` is passed, this is used as - activation function after any layers, except the last one. - If a list of Modules is passed, - they are used as activation functions at any layers, in order. - :param bool bias: If ``True`` the MLP will consider some bias for the - basis function network. - """ - super().__init__() - - # Assignment (check consistency inside FeedForward) - self._basis = pm.FeedForward( - input_dimensions=input_dimensions, - output_dimensions=2 * rank * embedding_dimenion, - inner_size=inner_size, - n_layers=n_layers, - func=func, - bias=bias, - ) - self._nn = torch.nn.Linear(embedding_dimenion, embedding_dimenion) - - check_consistency(rank, int) - self._rank = rank - self._func = func() - - def forward(self, x, coords): - r""" - Forward pass of the layer, it performs an affine transformation of - the field, and a low rank approximation by - doing a dot product of the basis - :math:`\psi^{(i)}` with the filed vector :math:`v`, and use this - coefficients to expand :math:`\phi^{(i)}` evaluated in the - spatial input :math:`x`. - - :param torch.Tensor x: The input tensor for performing the - computation. It expects a tensor :math:`B \times N \times D`, - where :math:`B` is the batch_size, :math:`N` the number of points - in the mesh, :math:`D` the dimension of the problem. In particular - :math:`D` is the codomain of the function :math:`v`. For example - a scalar function has :math:`D=1`, a 4-dimensional vector function - :math:`D=4`. - :param torch.Tensor coords: The coordinates in which the field is - evaluated for performing the computation. It expects a - tensor :math:`B \times N \times d`, - where :math:`B` is the batch_size, :math:`N` the number of points - in the mesh, :math:`D` the dimension of the domain. - :return: The output tensor obtained from Average Neural Operator Block. - :rtype: torch.Tensor - """ - # extract basis - basis = self._basis(coords) - # reshape [B, N, D, 2*rank] - shape = list(basis.shape[:-1]) + [-1, 2 * self.rank] - basis = basis.reshape(shape) - # divide - psi = basis[..., : self.rank] - phi = basis[..., self.rank :] - # compute dot product - coeff = torch.einsum("...dr,...d->...r", psi, x) - # expand the basis - expansion = torch.einsum("...r,...dr->...d", coeff, phi) - # apply linear layer and return - return self._func(self._nn(x) + expansion) - - @property - def rank(self): - """ - The basis rank. - """ - return self._rank diff --git a/pina/model/layers/stride.py b/pina/model/layers/stride.py deleted file mode 100644 index 7832ac4e1..000000000 --- a/pina/model/layers/stride.py +++ /dev/null @@ -1,85 +0,0 @@ -import torch - - -class Stride(object): - - def __init__(self, dict): - """Stride class for continous convolution - - :param param: type of continuous convolution - :type param: string - """ - - self._dict_stride = dict - self._stride_continuous = None - self._stride_discrete = self._create_stride_discrete(dict) - - def _create_stride_discrete(self, my_dict): - """Creating the list for applying the filter - - :param my_dict: Dictionary with the following arguments: - domain size, starting position of the filter, jump size - for the filter and direction of the filter - :type my_dict: dict - :raises IndexError: Values in the dict must have all same length - :raises ValueError: Domain values must be greater than 0 - :raises ValueError: Direction must be either equal to 1, -1 or 0 - :raises IndexError: Direction and jumps must have zero in the same - index - :return: list of positions for the filter - :rtype: list - :Example: - - - >>> stride = {"domain": [4, 4], - "start": [-4, 2], - "jump": [2, 2], - "direction": [1, 1], - } - >>> create_stride(stride) - [[-4.0, 2.0], [-4.0, 4.0], [-2.0, 2.0], [-2.0, 4.0]] - """ - - # we must check boundaries of the input as well - - domain, start, jumps, direction = my_dict.values() - - # checking - - if not all([len(s) == len(domain) for s in my_dict.values()]): - raise IndexError("values in the dict must have all same length") - - if not all(v >= 0 for v in domain): - raise ValueError("domain values must be greater than 0") - - if not all(v == 1 or v == -1 or v == 0 for v in direction): - raise ValueError("direction must be either equal to 1, -1 or 0") - - seq_jumps = [i for i, e in enumerate(jumps) if e == 0] - seq_direction = [i for i, e in enumerate(direction) if e == 0] - - if seq_direction != seq_jumps: - raise IndexError( - "direction and jumps must have zero in the same index" - ) - - if seq_jumps: - for i in seq_jumps: - jumps[i] = domain[i] - direction[i] = 1 - - # creating the stride grid - values_mesh = [ - torch.arange(0, i, step).float() for i, step in zip(domain, jumps) - ] - - values_mesh = [ - single * dim for single, dim in zip(values_mesh, direction) - ] - - mesh = torch.meshgrid(values_mesh) - coordinates_mesh = [x.reshape(-1, 1) for x in mesh] - - stride = torch.cat(coordinates_mesh, dim=1) + torch.tensor(start) - - return stride diff --git a/pina/model/layers/utils_convolution.py b/pina/model/layers/utils_convolution.py deleted file mode 100644 index 5442ff48d..000000000 --- a/pina/model/layers/utils_convolution.py +++ /dev/null @@ -1,49 +0,0 @@ -import torch - - -def check_point(x, current_stride, dim): - max_stride = current_stride + dim - indeces = torch.logical_and( - x[..., :-1] < max_stride, x[..., :-1] >= current_stride - ).all(dim=-1) - return indeces - - -def map_points_(x, filter_position): - """Mapping function n dimensional case - - :param x: input data of two dimension - :type x: torch.tensor - :param filter_position: position of the filter - :type dim: list[numeric] - :return: data mapped inplace - :rtype: torch.tensor - """ - x.add_(-filter_position) - - return x - - -def optimizing(f): - """Decorator for calling a function just once - - :param f: python function - :type f: function - """ - - def wrapper(*args, **kwargs): - - if kwargs["type"] == "forward": - if not wrapper.has_run_inverse: - wrapper.has_run_inverse = True - return f(*args, **kwargs) - - if kwargs["type"] == "inverse": - if not wrapper.has_run: - wrapper.has_run = True - return f(*args, **kwargs) - - wrapper.has_run_inverse = False - wrapper.has_run = False - - return wrapper diff --git a/pina/model/lno.py b/pina/model/lno.py deleted file mode 100644 index 077a6b929..000000000 --- a/pina/model/lno.py +++ /dev/null @@ -1,148 +0,0 @@ -"""Module LowRank Neural Operator.""" - -import torch -from torch import nn, concatenate - -from pina.utils import check_consistency - -from .base_no import KernelNeuralOperator -from .layers.lowrank_layer import LowRankBlock - - -class LowRankNeuralOperator(KernelNeuralOperator): - """ - Implementation of LowRank Neural Operator. - - LowRank Neural Operator is a general architecture for - learning Operators. Unlike traditional machine learning methods - LowRankNeuralOperator is designed to map entire functions - to other functions. It can be trained with Supervised or PINN based - learning strategies. - LowRankNeuralOperator does convolution by performing a low rank - approximation, see :class:`~pina.model.layers.lowrank_layer.LowRankBlock`. - - .. seealso:: - - **Original reference**: Kovachki, N., Li, Z., Liu, B., - Azizzadenesheli, K., Bhattacharya, K., Stuart, A., & Anandkumar, A. - (2023). *Neural operator: Learning maps between function - spaces with applications to PDEs*. Journal of Machine Learning - Research, 24(89), 1-97. - """ - - def __init__( - self, - lifting_net, - projecting_net, - field_indices, - coordinates_indices, - n_kernel_layers, - rank, - inner_size=20, - n_layers=2, - func=torch.nn.Tanh, - bias=True, - ): - """ - :param torch.nn.Module lifting_net: The neural network for lifting - the input. It must take as input the input field and the coordinates - at which the input field is avaluated. The output of the lifting - net is chosen as embedding dimension of the problem - :param torch.nn.Module projecting_net: The neural network for - projecting the output. It must take as input the embedding dimension - (output of the ``lifting_net``) plus the dimension - of the coordinates. - :param list[str] field_indices: the label of the fields - in the input tensor. - :param list[str] coordinates_indices: the label of the - coordinates in the input tensor. - :param int n_kernel_layers: number of hidden kernel layers. - Default is 4. - :param int inner_size: Number of neurons in the hidden layer(s) for the - basis function network. Default is 20. - :param int n_layers: Number of hidden layers. for the - basis function network. Default is 2. - :param func: The activation function to use for the - basis function network. If a single - :class:`torch.nn.Module` is passed, this is used as - activation function after any layers, except the last one. - If a list of Modules is passed, - they are used as activation functions at any layers, in order. - :param bool bias: If ``True`` the MLP will consider some bias for the - basis function network. - """ - - # check consistency - check_consistency(field_indices, str) - check_consistency(coordinates_indices, str) - check_consistency(n_kernel_layers, int) - - # check hidden dimensions match - input_lifting_net = next(lifting_net.parameters()).size()[-1] - output_lifting_net = lifting_net( - torch.rand(size=next(lifting_net.parameters()).size()) - ).shape[-1] - projecting_net_input = next(projecting_net.parameters()).size()[-1] - - if len(field_indices) + len(coordinates_indices) != input_lifting_net: - raise ValueError( - "The lifting_net must take as input the " - "coordinates vector and the field vector." - ) - - if ( - output_lifting_net + len(coordinates_indices) - != projecting_net_input - ): - raise ValueError( - "The projecting_net input must be equal to " - "the embedding dimension (which is the output) " - "of the lifting_net plus the dimension of the " - "coordinates, i.e. len(coordinates_indices)." - ) - - # assign - self.coordinates_indices = coordinates_indices - self.field_indices = field_indices - integral_net = nn.Sequential( - *[ - LowRankBlock( - input_dimensions=len(coordinates_indices), - embedding_dimenion=output_lifting_net, - rank=rank, - inner_size=inner_size, - n_layers=n_layers, - func=func, - bias=bias, - ) - for _ in range(n_kernel_layers) - ] - ) - super().__init__(lifting_net, integral_net, projecting_net) - - def forward(self, x): - r""" - Forward computation for LowRank Neural Operator. It performs a - lifting of the input by the ``lifting_net``. Then different layers - of LowRank Neural Operator Blocks are applied. - Finally the output is projected to the final dimensionality - by the ``projecting_net``. - - :param torch.Tensor x: The input tensor for fourier block, - depending on ``dimension`` in the initialization. It expects - a tensor :math:`B \times N \times D`, - where :math:`B` is the batch_size, :math:`N` the number of points - in the mesh, :math:`D` the dimension of the problem, i.e. the sum - of ``len(coordinates_indices)+len(field_indices)``. - :return: The output tensor obtained from Average Neural Operator. - :rtype: torch.Tensor - """ - # extract points - coords = x.extract(self.coordinates_indices) - # lifting - x = self._lifting_operator(x) - # kernel - for module in self._integral_kernels: - x = module(x, coords) - # projecting - return self._projection_operator(concatenate((x, coords), dim=-1)) diff --git a/pina/model/low_rank_neural_operator.py b/pina/model/low_rank_neural_operator.py new file mode 100644 index 000000000..1a7082dff --- /dev/null +++ b/pina/model/low_rank_neural_operator.py @@ -0,0 +1,150 @@ +"""Module for the Low Rank Neural Operator model class.""" + +import torch +from torch import nn + +from ..utils import check_consistency + +from .kernel_neural_operator import KernelNeuralOperator +from .block.low_rank_block import LowRankBlock + + +class LowRankNeuralOperator(KernelNeuralOperator): + """ + Low Rank Neural Operator model class. + + The Low Rank Neural Operator is a general architecture for learning + operators, which map functions to functions. It can be trained both with + Supervised and Physics-Informed learning strategies. The Low Rank Neural + Operator performs convolution by means of a low rank approximation. + + .. seealso:: + + **Original reference**: Kovachki, N., Li, Z., Liu, B., Azizzadenesheli, + K., Bhattacharya, K., Stuart, A., & Anandkumar, A. (2023). + *Neural operator: Learning maps between function spaces with + applications to PDEs*. + Journal of Machine Learning Research, 24(89), 1-97. + """ + + def __init__( + self, + lifting_net, + projecting_net, + field_indices, + coordinates_indices, + n_kernel_layers, + rank, + inner_size=20, + n_layers=2, + func=torch.nn.Tanh, + bias=True, + ): + """ + Initialization of the :class:`LowRankNeuralOperator` class. + + :param torch.nn.Module lifting_net: The lifting neural network mapping + the input to its hidden dimension. It must take as input the input + field and the coordinates at which the input field is evaluated. + :param torch.nn.Module projecting_net: The projection neural network + mapping the hidden representation to the output function. It must + take as input the embedding dimension plus the dimension of the + coordinates. + :param list[str] field_indices: The labels of the fields in the input + tensor. + :param list[str] coordinates_indices: The labels of the coordinates in + the input tensor. + :param int n_kernel_layers: The number of hidden kernel layers. + :param int rank: The rank of the low rank approximation. + :param int inner_size: The number of neurons for each hidden layer in + the basis function neural network. Default is ``20``. + :param int n_layers: The number of hidden layers in the basis function + neural network. Default is ``2``. + :param func: The activation function. If a list is passed, it must have + the same length as ``n_layers``. If a single function is passed, it + is used for all layers, except for the last one. + Default is :class:`torch.nn.Tanh`. + :type func: torch.nn.Module | list[torch.nn.Module] + :param bool bias: If ``True`` bias is considered for the basis function + neural network. Default is ``True``. + :raises ValueError: If the input dimension does not match with the + labels of the fields and coordinates. + :raises ValueError: If the input dimension of the projecting network + does not match with the hidden dimension of the lifting network. + """ + + # check consistency + check_consistency(field_indices, str) + check_consistency(coordinates_indices, str) + check_consistency(n_kernel_layers, int) + + # check hidden dimensions match + input_lifting_net = next(lifting_net.parameters()).size()[-1] + output_lifting_net = lifting_net( + torch.rand(size=next(lifting_net.parameters()).size()) + ).shape[-1] + projecting_net_input = next(projecting_net.parameters()).size()[-1] + + if len(field_indices) + len(coordinates_indices) != input_lifting_net: + raise ValueError( + "The lifting_net must take as input the " + "coordinates vector and the field vector." + ) + + if ( + output_lifting_net + len(coordinates_indices) + != projecting_net_input + ): + raise ValueError( + "The projecting_net input must be equal to " + "the embedding dimension (which is the output) " + "of the lifting_net plus the dimension of the " + "coordinates, i.e. len(coordinates_indices)." + ) + + # assign + self.coordinates_indices = coordinates_indices + self.field_indices = field_indices + integral_net = nn.Sequential( + *[ + LowRankBlock( + input_dimensions=len(coordinates_indices), + embedding_dimenion=output_lifting_net, + rank=rank, + inner_size=inner_size, + n_layers=n_layers, + func=func, + bias=bias, + ) + for _ in range(n_kernel_layers) + ] + ) + super().__init__(lifting_net, integral_net, projecting_net) + + def forward(self, x): + r""" + Forward pass for the :class:`LowRankNeuralOperator` model. + + The ``lifting_net`` maps the input to the hidden dimension. + Then, several layers of + :class:`~pina.model.block.low_rank_block.LowRankBlock` are + applied. Finally, the ``projecting_net`` maps the hidden representation + to the output function. + + :param LabelTensor x: The input tensor for performing the computation. + It expects a tensor :math:`B \times N \times D`, where :math:`B` is + the batch_size, :math:`N` the number of points in the mesh, + :math:`D` the dimension of the problem, i.e. the sum + of ``len(coordinates_indices)`` and ``len(field_indices)``. + :return: The output tensor. + :rtype: torch.Tensor + """ + # extract points + coords = x.extract(self.coordinates_indices) + # lifting + x = self._lifting_operator(x) + # kernel + for module in self._integral_kernels: + x = module(x, coords) + # projecting + return self._projection_operator(torch.cat((x, coords), dim=-1)) diff --git a/pina/model/multi_feed_forward.py b/pina/model/multi_feed_forward.py index b04708db2..f2f149ca6 100644 --- a/pina/model/multi_feed_forward.py +++ b/pina/model/multi_feed_forward.py @@ -1,22 +1,27 @@ -"""Module for Multi FeedForward model""" +"""Module for the Multi Feed Forward model class.""" +from abc import ABC, abstractmethod import torch - from .feed_forward import FeedForward -class MultiFeedForward(torch.nn.Module): +class MultiFeedForward(torch.nn.Module, ABC): """ - The PINA implementation of MultiFeedForward network. - - This model allows to create a network with multiple FeedForward combined - together. The user has to define the `forward` method choosing how to - combine the different FeedForward networks. + Multi Feed Forward neural network model class. - :param dict ffn_dict: dictionary of FeedForward networks. + This model allows to create a network with multiple Feed Forward neural + networks combined together. The user is required to define the ``forward`` + method to choose how to combine the networks. """ def __init__(self, ffn_dict): + """ + Initialization of the :class:`MultiFeedForward` class. + + :param dict ffn_dict: A dictionary containing the Feed Forward neural + networks to be combined. + :raises TypeError: If the input is not a dictionary. + """ super().__init__() if not isinstance(ffn_dict, dict): @@ -24,3 +29,12 @@ def __init__(self, ffn_dict): for name, constructor_args in ffn_dict.items(): setattr(self, name, FeedForward(**constructor_args)) + + @abstractmethod + def forward(self, *args, **kwargs): + """ + Forward pass for the :class:`MultiFeedForward` model. + + The user is required to define this method to choose how to combine the + networks. + """ diff --git a/pina/model/network.py b/pina/model/network.py deleted file mode 100644 index 6fde8039c..000000000 --- a/pina/model/network.py +++ /dev/null @@ -1,117 +0,0 @@ -import torch -import torch.nn as nn -from ..utils import check_consistency -from ..label_tensor import LabelTensor - - -class Network(torch.nn.Module): - - def __init__( - self, model, input_variables, output_variables, extra_features=None - ): - """ - Network class with standard forward method - and possibility to pass extra features. This - class is used internally in PINA to convert - any :class:`torch.nn.Module` s in a PINA module. - - :param model: The torch model to convert in a PINA model. - :type model: torch.nn.Module - :param list(str) input_variables: The input variables of the :class:`AbstractProblem`, whose type depends on the - type of domain (spatial, temporal, and parameter). - :param list(str) output_variables: The output variables of the :class:`AbstractProblem`, whose type depends on the - problem setting. - :param extra_features: List of torch models to augment the input, defaults to None. - :type extra_features: list(torch.nn.Module) - """ - super().__init__() - - # check model consistency - check_consistency(model, nn.Module) - check_consistency(input_variables, str) - check_consistency(output_variables, str) - - self._model = model - self._input_variables = input_variables - self._output_variables = output_variables - - # check consistency and assign extra fatures - if extra_features is None: - self._extra_features = [] - else: - for feat in extra_features: - check_consistency(feat, nn.Module) - self._extra_features = nn.Sequential(*extra_features) - - # check model works with inputs - # TODO - - def forward(self, x): - """ - Forward method for Network class. This class - implements the standard forward method, and - it adds the possibility to pass extra features. - All the PINA models ``forward`` s are overriden - by this class, to enable :class:`pina.label_tensor.LabelTensor` labels - extraction. - - :param torch.Tensor x: Input of the network. - :return torch.Tensor: Output of the network. - """ - # only labeltensors as input - assert isinstance( - x, LabelTensor - ), "Expected LabelTensor as input to the model." - - # extract torch.Tensor from corresponding label - # in case `input_variables = []` all points are used - if self._input_variables: - x = x.extract(self._input_variables) - - # extract features and append - for feature in self._extra_features: - x = x.append(feature(x)) - - # perform forward pass + converting to LabelTensor - output = self._model(x).as_subclass(LabelTensor) - - # set the labels for LabelTensor - output.labels = self._output_variables - - return output - - # TODO to remove in next releases (only used in GAROM solver) - def forward_map(self, x): - """ - Forward method for Network class when the input is - a tuple. This class is simply a forward with the input casted as a - tuple or list :class`torch.Tensor`. - All the PINA models ``forward`` s are overriden - by this class, to enable :class:`pina.label_tensor.LabelTensor` labels - extraction. - - :param list (torch.Tensor) | tuple(torch.Tensor) x: Input of the network. - :return torch.Tensor: Output of the network. - - .. note:: - This function does not extract the input variables, all the variables - are used for both tensors. Output variables are correctly applied. - """ - # convert LabelTensor s to torch.Tensor s - x = list(map(lambda x: x.as_subclass(torch.Tensor), x)) - - # perform forward pass (using torch.Tensor) + converting to LabelTensor - output = self._model(x).as_subclass(LabelTensor) - - # set the labels for LabelTensor - output.labels = self._output_variables - - return output - - @property - def torchmodel(self): - return self._model - - @property - def extra_features(self): - return self._extra_features diff --git a/pina/model/spline.py b/pina/model/spline.py index 2328986aa..c22c7937c 100644 --- a/pina/model/spline.py +++ b/pina/model/spline.py @@ -1,19 +1,26 @@ -"""Module for Spline model""" +"""Module for the Spline model class.""" import torch -import torch.nn as nn from ..utils import check_consistency class Spline(torch.nn.Module): + """ + Spline model class. + """ def __init__(self, order=4, knots=None, control_points=None) -> None: """ - Spline model. - - :param int order: the order of the spline. - :param torch.Tensor knots: the knot vector. - :param torch.Tensor control_points: the control points. + Initialization of the :class:`Spline` class. + + :param int order: The order of the spline. Default is ``4``. + :param torch.Tensor knots: The tensor representing knots. If ``None``, + the knots will be initialized automatically. Default is ``None``. + :param torch.Tensor control_points: The control points. Default is + ``None``. + :raises ValueError: If the order is negative. + :raises ValueError: If both knots and control points are ``None``. + :raises ValueError: If the knot tensor is not one-dimensional. """ super().__init__() @@ -63,13 +70,13 @@ def __init__(self, order=4, knots=None, control_points=None) -> None: def basis(self, x, k, i, t): """ - Recursive function to compute the basis functions of the spline. + Recursive method to compute the basis functions of the spline. - :param torch.Tensor x: points to be evaluated. - :param int k: spline degree - :param int i: the index of the interval - :param torch.Tensor t: vector of knots - :return: the basis functions evaluated at x + :param torch.Tensor x: The points to be evaluated. + :param int k: The spline degree. + :param int i: The index of the interval. + :param torch.Tensor t: The tensor of knots. + :return: The basis functions evaluated at x :rtype: torch.Tensor """ @@ -100,10 +107,23 @@ def basis(self, x, k, i, t): @property def control_points(self): + """ + The control points of the spline. + + :return: The control points. + :rtype: torch.Tensor + """ return self._control_points @control_points.setter def control_points(self, value): + """ + Set the control points of the spline. + + :param value: The control points. + :type value: torch.Tensor | dict + :raises ValueError: If invalid value is passed. + """ if isinstance(value, dict): if "n" not in value: raise ValueError("Invalid value for control_points") @@ -117,10 +137,23 @@ def control_points(self, value): @property def knots(self): + """ + The knots of the spline. + + :return: The knots. + :rtype: torch.Tensor + """ return self._knots @knots.setter def knots(self, value): + """ + Set the knots of the spline. + + :param value: The knots. + :type value: torch.Tensor | dict + :raises ValueError: If invalid value is passed. + """ if isinstance(value, dict): type_ = value.get("type", "auto") @@ -150,10 +183,10 @@ def knots(self, value): def forward(self, x): """ - Forward pass of the spline model. + Forward pass for the :class:`Spline` model. - :param torch.Tensor x: points to be evaluated. - :return: the spline evaluated at x + :param torch.Tensor x: The input tensor. + :return: The output tensor. :rtype: torch.Tensor """ t = self.knots diff --git a/pina/operator.py b/pina/operator.py new file mode 100644 index 000000000..68e2cb409 --- /dev/null +++ b/pina/operator.py @@ -0,0 +1,276 @@ +""" +Module for vectorized differential operators implementation. + +Differential operators are used to define differential problems and are +implemented to run efficiently on various accelerators, including CPU, GPU, TPU, +and MPS. + +Each differential operator takes the following inputs: +- A tensor on which the operator is applied. +- A tensor with respect to which the operator is computed. +- The names of the output variables for which the operator is evaluated. +- The names of the variables with respect to which the operator is computed. +""" + +import torch +from pina.label_tensor import LabelTensor + + +def grad(output_, input_, components=None, d=None): + """ + Compute the gradient of the ``output_`` with respect to the ``input``. + + This operator supports both vector-valued and scalar-valued functions with + one or multiple input coordinates. + + :param LabelTensor output_: The output tensor on which the gradient is + computed. + :param LabelTensor input_: The input tensor with respect to which the + gradient is computed. + :param list[str] components: The names of the output variables for which to + compute the gradient. It must be a subset of the output labels. + If ``None``, all output variables are considered. Default is ``None``. + :param list[str] d: The names of the input variables with respect to which + the gradient is computed. It must be a subset of the input labels. + If ``None``, all input variables are considered. Default is ``None``. + :raises TypeError: If the input tensor is not a LabelTensor. + :raises RuntimeError: If the output is a scalar field and the components + are not equal to the output labels. + :raises NotImplementedError: If the output is neither a vector field nor a + scalar field. + :return: The computed gradient tensor. + :rtype: LabelTensor + """ + + def grad_scalar_output(output_, input_, d): + """ + Compute the gradient of a scalar-valued ``output_``. + + :param LabelTensor output_: The output tensor on which the gradient is + computed. It must be a column tensor. + :param LabelTensor input_: The input tensor with respect to which the + gradient is computed. + :param list[str] d: The names of the input variables with respect to + which the gradient is computed. It must be a subset of the input + labels. If ``None``, all input variables are considered. + :raises RuntimeError: If a vectorial function is passed. + :raises RuntimeError: If missing derivative labels. + :return: The computed gradient tensor. + :rtype: LabelTensor + """ + + if len(output_.labels) != 1: + raise RuntimeError("only scalar function can be differentiated") + if not all(di in input_.labels for di in d): + raise RuntimeError("derivative labels missing from input tensor") + + output_fieldname = output_.labels[0] + gradients = torch.autograd.grad( + output_, + input_, + grad_outputs=torch.ones( + output_.size(), dtype=output_.dtype, device=output_.device + ), + create_graph=True, + retain_graph=True, + allow_unused=True, + )[0] + gradients.labels = input_.stored_labels + gradients = gradients[..., [input_.labels.index(i) for i in d]] + gradients.labels = [f"d{output_fieldname}d{i}" for i in d] + return gradients + + if not isinstance(input_, LabelTensor): + raise TypeError + + if d is None: + d = input_.labels + + if components is None: + components = output_.labels + + if output_.shape[1] == 1: # scalar output ################################ + + if components != output_.labels: + raise RuntimeError + gradients = grad_scalar_output(output_, input_, d) + + elif ( + output_.shape[output_.ndim - 1] >= 2 + ): # vector output ############################## + tensor_to_cat = [] + for i, c in enumerate(components): + c_output = output_.extract([c]) + tensor_to_cat.append(grad_scalar_output(c_output, input_, d)) + gradients = LabelTensor.cat(tensor_to_cat, dim=output_.tensor.ndim - 1) + else: + raise NotImplementedError + + return gradients + + +def div(output_, input_, components=None, d=None): + """ + Compute the divergence of the ``output_`` with respect to ``input``. + + This operator supports vector-valued functions with multiple input + coordinates. + + :param LabelTensor output_: The output tensor on which the divergence is + computed. + :param LabelTensor input_: The input tensor with respect to which the + divergence is computed. + :param list[str] components: The names of the output variables for which to + compute the divergence. It must be a subset of the output labels. + If ``None``, all output variables are considered. Default is ``None``. + :param list[str] d: The names of the input variables with respect to which + the divergence is computed. It must be a subset of the input labels. + If ``None``, all input variables are considered. Default is ``None``. + :raises TypeError: If the input tensor is not a LabelTensor. + :raises ValueError: If the output is a scalar field. + :raises ValueError: If the number of components is not equal to the number + of input variables. + :return: The computed divergence tensor. + :rtype: LabelTensor + """ + if not isinstance(input_, LabelTensor): + raise TypeError + + if d is None: + d = input_.labels + + if components is None: + components = output_.labels + + if output_.shape[1] < 2 or len(components) < 2: + raise ValueError("div supported only for vector fields") + + if len(components) != len(d): + raise ValueError + + grad_output = grad(output_, input_, components, d) + labels = [None] * len(components) + tensors_to_sum = [] + for i, (c, d_) in enumerate(zip(components, d)): + c_fields = f"d{c}d{d_}" + tensors_to_sum.append(grad_output.extract(c_fields)) + labels[i] = c_fields + div_result = LabelTensor.summation(tensors_to_sum) + return div_result + + +def laplacian(output_, input_, components=None, d=None, method="std"): + """ + Compute the laplacian of the ``output_`` with respect to ``input``. + + This operator supports both vector-valued and scalar-valued functions with + one or multiple input coordinates. + + :param LabelTensor output_: The output tensor on which the laplacian is + computed. + :param LabelTensor input_: The input tensor with respect to which the + laplacian is computed. + :param list[str] components: The names of the output variables for which to + compute the laplacian. It must be a subset of the output labels. + If ``None``, all output variables are considered. Default is ``None``. + :param list[str] d: The names of the input variables with respect to which + the laplacian is computed. It must be a subset of the input labels. + If ``None``, all input variables are considered. Default is ``None``. + :param str method: The method used to compute the Laplacian. Default is + ``std``. + :raises NotImplementedError: If ``std=divgrad``. + :return: The computed laplacian tensor. + :rtype: LabelTensor + """ + + def scalar_laplace(output_, input_, components, d): + """ + Compute the laplacian of a scalar-valued ``output_``. + + :param LabelTensor output_: The output tensor on which the laplacian is + computed. It must be a column tensor. + :param LabelTensor input_: The input tensor with respect to which the + laplacian is computed. + :param list[str] components: The names of the output variables for which + to compute the laplacian. It must be a subset of the output labels. + If ``None``, all output variables are considered. + :param list[str] d: The names of the input variables with respect to + which the laplacian is computed. It must be a subset of the input + labels. If ``None``, all input variables are considered. + :return: The computed laplacian tensor. + :rtype: LabelTensor + """ + + grad_output = grad(output_, input_, components=components, d=d) + result = torch.zeros(output_.shape[0], 1, device=output_.device) + + for i, label in enumerate(grad_output.labels): + gg = grad(grad_output, input_, d=d, components=[label]) + result[:, 0] += super(torch.Tensor, gg.T).__getitem__(i) + + return result + + if d is None: + d = input_.labels + + if components is None: + components = output_.labels + + if method == "divgrad": + raise NotImplementedError("divgrad not implemented as method") + + if method == "std": + if len(components) == 1: + result = scalar_laplace(output_, input_, components, d) + labels = [f"dd{components[0]}"] + + else: + result = torch.empty( + input_.shape[0], len(components), device=output_.device + ) + labels = [None] * len(components) + for idx, c in enumerate(components): + result[:, idx] = scalar_laplace(output_, input_, c, d).flatten() + labels[idx] = f"dd{c}" + + result = result.as_subclass(LabelTensor) + result.labels = labels + + return result + + +def advection(output_, input_, velocity_field, components=None, d=None): + """ + Perform the advection operation on the ``output_`` with respect to the + ``input``. This operator support vector-valued functions with multiple input + coordinates. + + :param LabelTensor output_: The output tensor on which the advection is + computed. + :param LabelTensor input_: the input tensor with respect to which advection + is computed. + :param str velocity_field: The name of the output variable used as velocity + field. It must be chosen among the output labels. + :param list[str] components: The names of the output variables for which + to compute the advection. It must be a subset of the output labels. + If ``None``, all output variables are considered. Default is ``None``. + :param list[str] d: The names of the input variables with respect to which + the advection is computed. It must be a subset of the input labels. + If ``None``, all input variables are considered. Default is ``None``. + :return: The computed advection tensor. + :rtype: LabelTensor + """ + if d is None: + d = input_.labels + + if components is None: + components = output_.labels + + tmp = ( + grad(output_, input_, components, d) + .reshape(-1, len(components), len(d)) + .transpose(0, 1) + ) + + tmp *= output_.extract(velocity_field) + return tmp.sum(dim=2).T diff --git a/pina/operators.py b/pina/operators.py index e523ed922..cb2fb5e00 100644 --- a/pina/operators.py +++ b/pina/operators.py @@ -1,273 +1,16 @@ -""" -Module for operators vectorize implementation. Differential operators are used to write any differential problem. -These operators are implemented to work on different accellerators: CPU, GPU, TPU or MPS. -All operators take as input a tensor onto which computing the operator, a tensor with respect -to which computing the operator, the name of the output variables to calculate the operator -for (in case of multidimensional functions), and the variables name on which the operator is calculated. -""" - -import torch - -from pina.label_tensor import LabelTensor - - -def grad(output_, input_, components=None, d=None): - """ - Perform gradient operation. The operator works for vectorial and scalar - functions, with multiple input coordinates. - - :param LabelTensor output_: the output tensor onto which computing the - gradient. - :param LabelTensor input_: the input tensor with respect to which computing - the gradient. - :param list(str) components: the name of the output variables to calculate - the gradient for. It should be a subset of the output labels. If None, - all the output variables are considered. Default is None. - :param list(str) d: the name of the input variables on which the gradient is - calculated. d should be a subset of the input labels. If None, all the - input variables are considered. Default is None. - - :return: the gradient tensor. - :rtype: LabelTensor - """ - - def grad_scalar_output(output_, input_, d): - """ - Perform gradient operation for a scalar output. - - :param LabelTensor output_: the output tensor onto which computing the - gradient. It has to be a column tensor. - :param LabelTensor input_: the input tensor with respect to which - computing the gradient. - :param list(str) d: the name of the input variables on which the - gradient is calculated. d should be a subset of the input labels. If - None, all the input variables are considered. Default is None. - - :raises RuntimeError: a vectorial function is passed. - :raises RuntimeError: missing derivative labels. - :return: the gradient tensor. - :rtype: LabelTensor - """ - - if len(output_.labels) != 1: - raise RuntimeError("only scalar function can be differentiated") - if not all([di in input_.labels for di in d]): - raise RuntimeError("derivative labels missing from input tensor") - - output_fieldname = output_.labels[0] - gradients = torch.autograd.grad( - output_, - input_, - grad_outputs=torch.ones( - output_.size(), dtype=output_.dtype, device=output_.device - ), - create_graph=True, - retain_graph=True, - allow_unused=True, - )[0] - - gradients.labels = input_.labels - gradients = gradients.extract(d) - gradients.labels = [f"d{output_fieldname}d{i}" for i in d] - - return gradients - - if not isinstance(input_, LabelTensor): - raise TypeError - - if d is None: - d = input_.labels - - if components is None: - components = output_.labels - - if output_.shape[1] == 1: # scalar output ################################ - - if components != output_.labels: - raise RuntimeError - gradients = grad_scalar_output(output_, input_, d) - - elif output_.shape[1] >= 2: # vector output ############################## - - for i, c in enumerate(components): - c_output = output_.extract([c]) - if i == 0: - gradients = grad_scalar_output(c_output, input_, d) - else: - gradients = gradients.append( - grad_scalar_output(c_output, input_, d) - ) - else: - raise NotImplementedError - - return gradients - - -def div(output_, input_, components=None, d=None): - """ - Perform divergence operation. The operator works for vectorial functions, - with multiple input coordinates. - - :param LabelTensor output_: the output tensor onto which computing the - divergence. - :param LabelTensor input_: the input tensor with respect to which computing - the divergence. - :param list(str) components: the name of the output variables to calculate - the divergence for. It should be a subset of the output labels. If None, - all the output variables are considered. Default is None. - :param list(str) d: the name of the input variables on which the divergence - is calculated. d should be a subset of the input labels. If None, all - the input variables are considered. Default is None. - - :raises TypeError: div operator works only for LabelTensor. - :raises ValueError: div operator works only for vector fields. - :raises ValueError: div operator must derive all components with - respect to all coordinates. - :return: the divergenge tensor. - :rtype: LabelTensor - """ - if not isinstance(input_, LabelTensor): - raise TypeError - - if d is None: - d = input_.labels - - if components is None: - components = output_.labels - - if output_.shape[1] < 2 or len(components) < 2: - raise ValueError("div supported only for vector fields") - - if len(components) != len(d): - raise ValueError - - grad_output = grad(output_, input_, components, d) - div = torch.zeros(input_.shape[0], 1, device=output_.device) - labels = [None] * len(components) - for i, (c, d) in enumerate(zip(components, d)): - c_fields = f"d{c}d{d}" - div[:, 0] += grad_output.extract(c_fields).sum(axis=1) - labels[i] = c_fields - - div = div.as_subclass(LabelTensor) - div.labels = ["+".join(labels)] - return div - - -def laplacian(output_, input_, components=None, d=None, method="std"): - """ - Compute Laplace operator. The operator works for vectorial and - scalar functions, with multiple input coordinates. - - :param LabelTensor output_: the output tensor onto which computing the - Laplacian. - :param LabelTensor input_: the input tensor with respect to which computing - the Laplacian. - :param list(str) components: the name of the output variables to calculate - the Laplacian for. It should be a subset of the output labels. If None, - all the output variables are considered. Default is None. - :param list(str) d: the name of the input variables on which the Laplacian - is calculated. d should be a subset of the input labels. If None, all - the input variables are considered. Default is None. - :param str method: used method to calculate Laplacian, defaults to 'std'. - - :raises NotImplementedError: 'divgrad' not implemented as method. - :return: The tensor containing the result of the Laplacian operator. - :rtype: LabelTensor - """ - - def scalar_laplace(output_, input_, components, d): - """ - Compute Laplace operator for a scalar output. - - :param LabelTensor output_: the output tensor onto which computing the - Laplacian. It has to be a column tensor. - :param LabelTensor input_: the input tensor with respect to which - computing the Laplacian. - :param list(str) components: the name of the output variables to - calculate the Laplacian for. It should be a subset of the output - labels. If None, all the output variables are considered. - :param list(str) d: the name of the input variables on which the - Laplacian is computed. d should be a subset of the input labels. - If None, all the input variables are considered. Default is None. - - :return: The tensor containing the result of the Laplacian operator. - :rtype: LabelTensor - """ - - grad_output = grad(output_, input_, components=components, d=d) - result = torch.zeros(output_.shape[0], 1, device=output_.device) - - for i, label in enumerate(grad_output.labels): - gg = grad(grad_output, input_, d=d, components=[label]) - result[:, 0] += super(torch.Tensor, gg.T).__getitem__(i) - - return result - - if d is None: - d = input_.labels - - if components is None: - components = output_.labels - - if method == "divgrad": - raise NotImplementedError("divgrad not implemented as method") - # TODO fix - # grad_output = grad(output_, input_, components, d) - # result = div(grad_output, input_, d=d) - - elif method == "std": - if len(components) == 1: - result = scalar_laplace(output_, input_, components, d) - labels = [f"dd{components[0]}"] - - else: - result = torch.empty( - size=(input_.shape[0], len(components)), - dtype=output_.dtype, - device=output_.device, - ) - labels = [None] * len(components) - for idx, c in enumerate(components): - result[:, idx] = scalar_laplace(output_, input_, c, d).flatten() - labels[idx] = f"dd{c}" - - result = result.as_subclass(LabelTensor) - result.labels = labels - return result - - -def advection(output_, input_, velocity_field, components=None, d=None): - """ - Perform advection operation. The operator works for vectorial functions, - with multiple input coordinates. - - :param LabelTensor output_: the output tensor onto which computing the - advection. - :param LabelTensor input_: the input tensor with respect to which computing - the advection. - :param str velocity_field: the name of the output variables which is used - as velocity field. It should be a subset of the output labels. - :param list(str) components: the name of the output variables to calculate - the Laplacian for. It should be a subset of the output labels. If None, - all the output variables are considered. Default is None. - :param list(str) d: the name of the input variables on which the advection - is calculated. d should be a subset of the input labels. If None, all - the input variables are considered. Default is None. - :return: the tensor containing the result of the advection operator. - :rtype: LabelTensor - """ - if d is None: - d = input_.labels - - if components is None: - components = output_.labels - - tmp = ( - grad(output_, input_, components, d) - .reshape(-1, len(components), len(d)) - .transpose(0, 1) - ) - - tmp *= output_.extract(velocity_field) - return tmp.sum(dim=2).T +"""Old module for operators. Deprecated in 0.2.0.""" + +import warnings + +from .operator import * +from .utils import custom_warning_format + +# back-compatibility 0.1 +# Set the custom format for warnings +warnings.formatwarning = custom_warning_format +warnings.filterwarnings("always", category=DeprecationWarning) +warnings.warn( + "'pina.operators' is deprecated and will be removed " + "in future versions. Please use 'pina.operator' instead.", + DeprecationWarning, +) diff --git a/pina/optim/__init__.py b/pina/optim/__init__.py new file mode 100644 index 000000000..8266c8ca1 --- /dev/null +++ b/pina/optim/__init__.py @@ -0,0 +1,13 @@ +"""Module for the Optimizers and Schedulers.""" + +__all__ = [ + "Optimizer", + "TorchOptimizer", + "Scheduler", + "TorchScheduler", +] + +from .optimizer_interface import Optimizer +from .torch_optimizer import TorchOptimizer +from .scheduler_interface import Scheduler +from .torch_scheduler import TorchScheduler diff --git a/pina/optim/optimizer_interface.py b/pina/optim/optimizer_interface.py new file mode 100644 index 000000000..5f2fbe66a --- /dev/null +++ b/pina/optim/optimizer_interface.py @@ -0,0 +1,23 @@ +"""Module for the PINA Optimizer.""" + +from abc import ABCMeta, abstractmethod + + +class Optimizer(metaclass=ABCMeta): + """ + Abstract base class for defining an optimizer. All specific optimizers + should inherit form this class and implement the required methods. + """ + + @property + @abstractmethod + def instance(self): + """ + Abstract property to retrieve the optimizer instance. + """ + + @abstractmethod + def hook(self): + """ + Abstract method to define the hook logic for the optimizer. + """ diff --git a/pina/optim/scheduler_interface.py b/pina/optim/scheduler_interface.py new file mode 100644 index 000000000..5ae5d8b99 --- /dev/null +++ b/pina/optim/scheduler_interface.py @@ -0,0 +1,23 @@ +"""Module for the PINA Scheduler.""" + +from abc import ABCMeta, abstractmethod + + +class Scheduler(metaclass=ABCMeta): + """ + Abstract base class for defining a scheduler. All specific schedulers should + inherit form this class and implement the required methods. + """ + + @property + @abstractmethod + def instance(self): + """ + Abstract property to retrieve the scheduler instance. + """ + + @abstractmethod + def hook(self): + """ + Abstract method to define the hook logic for the scheduler. + """ diff --git a/pina/optim/torch_optimizer.py b/pina/optim/torch_optimizer.py new file mode 100644 index 000000000..7163c295e --- /dev/null +++ b/pina/optim/torch_optimizer.py @@ -0,0 +1,48 @@ +"""Module for the PINA Torch Optimizer""" + +import torch + +from ..utils import check_consistency +from .optimizer_interface import Optimizer + + +class TorchOptimizer(Optimizer): + """ + A wrapper class for using PyTorch optimizers. + """ + + def __init__(self, optimizer_class, **kwargs): + """ + Initialization of the :class:`TorchOptimizer` class. + + :param torch.optim.Optimizer optimizer_class: A + :class:`torch.optim.Optimizer` class. + :param dict kwargs: Additional parameters passed to ``optimizer_class``, + see more + `here `_. + """ + check_consistency(optimizer_class, torch.optim.Optimizer, subclass=True) + + self.optimizer_class = optimizer_class + self.kwargs = kwargs + self._optimizer_instance = None + + def hook(self, parameters): + """ + Initialize the optimizer instance with the given parameters. + + :param dict parameters: The parameters of the model to be optimized. + """ + self._optimizer_instance = self.optimizer_class( + parameters, **self.kwargs + ) + + @property + def instance(self): + """ + Get the optimizer instance. + + :return: The optimizer instance. + :rtype: torch.optim.Optimizer + """ + return self._optimizer_instance diff --git a/pina/optim/torch_scheduler.py b/pina/optim/torch_scheduler.py new file mode 100644 index 000000000..ff12300a1 --- /dev/null +++ b/pina/optim/torch_scheduler.py @@ -0,0 +1,55 @@ +"""Module for the PINA Torch Optimizer""" + +try: + from torch.optim.lr_scheduler import LRScheduler # torch >= 2.0 +except ImportError: + from torch.optim.lr_scheduler import ( + _LRScheduler as LRScheduler, + ) # torch < 2.0 + +from ..utils import check_consistency +from .optimizer_interface import Optimizer +from .scheduler_interface import Scheduler + + +class TorchScheduler(Scheduler): + """ + A wrapper class for using PyTorch schedulers. + """ + + def __init__(self, scheduler_class, **kwargs): + """ + Initialization of the :class:`TorchScheduler` class. + + :param torch.optim.LRScheduler scheduler_class: A + :class:`torch.optim.LRScheduler` class. + :param dict kwargs: Additional parameters passed to ``scheduler_class``, + see more + `here _`. + """ + check_consistency(scheduler_class, LRScheduler, subclass=True) + + self.scheduler_class = scheduler_class + self.kwargs = kwargs + self._scheduler_instance = None + + def hook(self, optimizer): + """ + Initialize the scheduler instance with the given parameters. + + :param dict parameters: The parameters of the optimizer. + """ + check_consistency(optimizer, Optimizer) + self._scheduler_instance = self.scheduler_class( + optimizer.instance, **self.kwargs + ) + + @property + def instance(self): + """ + Get the scheduler instance. + + :return: The scheduelr instance. + :rtype: torch.optim.LRScheduler + """ + return self._scheduler_instance diff --git a/pina/plotter.py b/pina/plotter.py index 041ef0575..fcd4dedba 100644 --- a/pina/plotter.py +++ b/pina/plotter.py @@ -1,323 +1,3 @@ -""" Module for plotting. """ +"""Module for Plotter""" -import matplotlib.pyplot as plt -import torch -from pina.callbacks import MetricTracker -from pina import LabelTensor - - -class Plotter: - """ - Implementation of a plotter class, for easy visualizations. - """ - - def plot_samples(self, problem, variables=None, filename=None, **kwargs): - """ - Plot the training grid samples. - - :param AbstractProblem problem: The PINA problem from where to plot - the domain. - :param list(str) variables: Variables to plot. If None, all variables - are plotted. If 'spatial', only spatial variables are plotted. If - 'temporal', only temporal variables are plotted. Defaults to None. - :param str filename: The file name to save the plot. If None, the plot - is shown using the setted matplotlib frontend. Default is None. - - .. todo:: - - Add support for 3D plots. - - Fix support for more complex problems. - - :Example: - >>> plotter = Plotter() - >>> plotter.plot_samples(problem=problem, variables='spatial') - """ - - if variables is None: - variables = problem.domain.variables - elif variables == "spatial": - variables = problem.spatial_domain.variables - elif variables == "temporal": - variables = problem.temporal_domain.variables - - if len(variables) not in [1, 2, 3]: - raise ValueError( - "Samples can be plotted only in " "dimensions 1, 2 and 3." - ) - - fig = plt.figure() - proj = "3d" if len(variables) == 3 else None - ax = fig.add_subplot(projection=proj) - for location in problem.input_pts: - coords = problem.input_pts[location].extract(variables).T.detach() - if len(variables) == 1: # 1D samples - ax.plot( - coords.flatten(), - torch.zeros(coords.flatten().shape), - ".", - label=location, - **kwargs, - ) - elif len(variables) == 2: - ax.plot(*coords, ".", label=location, **kwargs) - elif len(variables) == 3: - ax.scatter(*coords, ".", label=location, **kwargs) - - ax.set_xlabel(variables[0]) - try: - ax.set_ylabel(variables[1]) - except (IndexError, AttributeError): - pass - - try: - ax.set_zlabel(variables[2]) - except (IndexError, AttributeError): - pass - - plt.legend() - if filename: - plt.savefig(filename) - plt.close() - else: - plt.show() - - def _1d_plot(self, pts, pred, v, method, truth_solution=None, **kwargs): - """Plot solution for one dimensional function - - :param pts: Points to plot the solution. - :type pts: torch.Tensor - :param pred: SolverInterface solution evaluated at 'pts'. - :type pred: torch.Tensor - :param v: Fixed variables when plotting the solution. - :type v: torch.Tensor - :param method: Not used, kept for code compatibility - :type method: None - :param truth_solution: Real solution evaluated at 'pts', - defaults to None. - :type truth_solution: torch.Tensor, optional - """ - fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8, 8)) - - ax.plot(pts.extract(v), pred, label="Neural Network solution", **kwargs) - - if truth_solution: - truth_output = truth_solution(pts).detach() - ax.plot( - pts.extract(v), truth_output, label="True solution", **kwargs - ) - - # TODO: pred is a torch.Tensor, so no labels is available - # extra variable for labels should be - # passed in the function arguments. - # plt.ylabel(pred.labels[0]) - plt.legend() - - def _2d_plot( - self, pts, pred, v, res, method, truth_solution=None, **kwargs - ): - """Plot solution for two dimensional function - - :param pts: Points to plot the solution. - :type pts: torch.Tensor - :param pred: ``SolverInterface`` solution evaluated at 'pts'. - :type pred: torch.Tensor - :param v: Fixed variables when plotting the solution. - :type v: torch.Tensor - :param method: Matplotlib method to plot 2-dimensional data, - see https://matplotlib.org/stable/api/axes_api.html for - reference. - :type method: str - :param truth_solution: Real solution evaluated at 'pts', - defaults to None. - :type truth_solution: torch.Tensor, optional - """ - - grids = [p_.reshape(res, res) for p_ in pts.extract(v).T] - - pred_output = pred.reshape(res, res) - if truth_solution: - truth_output = ( - truth_solution(pts) - .float() - .reshape(res, res) - .as_subclass(torch.Tensor) - ) - fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(16, 6)) - - cb = getattr(ax[0], method)(*grids, pred_output, **kwargs) - fig.colorbar(cb, ax=ax[0]) - ax[0].title.set_text("Neural Network prediction") - cb = getattr(ax[1], method)(*grids, truth_output, **kwargs) - fig.colorbar(cb, ax=ax[1]) - ax[1].title.set_text("True solution") - cb = getattr(ax[2], method)( - *grids, (truth_output - pred_output), **kwargs - ) - fig.colorbar(cb, ax=ax[2]) - ax[2].title.set_text("Residual") - else: - fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8, 6)) - cb = getattr(ax, method)(*grids, pred_output, **kwargs) - fig.colorbar(cb, ax=ax) - ax.title.set_text("Neural Network prediction") - - def plot( - self, - solver, - components=None, - fixed_variables={}, - method="contourf", - res=256, - filename=None, - title=None, - **kwargs, - ): - """ - Plot sample of SolverInterface output. - - :param SolverInterface solver: The ``SolverInterface`` object instance. - :param str | list(str) components: The output variable(s) to plot. - If None, all the output variables of the problem are selected. - Default value is None. - :param dict fixed_variables: A dictionary with all the variables that - should be kept fixed during the plot. The keys of the dictionary - are the variables name whereas the values are the corresponding - values of the variables. Defaults is `dict()`. - :param str method: The matplotlib method to use for - plotting the solution. Available methods are {'contourf', 'pcolor'}. - Default is 'contourf'. - :param int res: The resolution, aka the number of points used for - plotting in each axis. Default is 256. - :param str title: The title for the plot. If None, the plot - is shown without a title. Default is None. - :param str filename: The file name to save the plot. If None, the plot - is shown using the setted matplotlib frontend. Default is None. - """ - - if components is None: - components = solver.problem.output_variables - - if isinstance(components, str): - components = [components] - - if not isinstance(components, list): - raise NotImplementedError( - "Output variables must be passed" - "as a string or a list of strings." - ) - - if len(components) > 1: - raise NotImplementedError( - "Multidimensional plots are not implemented, " - "set components to an available components of" - " the problem." - ) - v = [ - var - for var in solver.problem.input_variables - if var not in fixed_variables.keys() - ] - pts = solver.problem.domain.sample(res, "grid", variables=v) - - fixed_pts = torch.ones(pts.shape[0], len(fixed_variables)) - fixed_pts *= torch.tensor(list(fixed_variables.values())) - fixed_pts = fixed_pts.as_subclass(LabelTensor) - fixed_pts.labels = list(fixed_variables.keys()) - - pts = pts.append(fixed_pts) - pts = pts.to(device=solver.device) - - # computing soluting and sending to cpu - predicted_output = solver.forward(pts).extract(components) - predicted_output = ( - predicted_output.as_subclass(torch.Tensor).cpu().detach() - ) - pts = pts.cpu() - truth_solution = getattr(solver.problem, "truth_solution", None) - - if len(v) == 1: - self._1d_plot( - pts, predicted_output, v, method, truth_solution, **kwargs - ) - elif len(v) == 2: - self._2d_plot( - pts, predicted_output, v, res, method, truth_solution, **kwargs - ) - - plt.tight_layout() - if title is not None: - plt.title(title) - - if filename: - plt.savefig(filename) - plt.close() - else: - plt.show() - - def plot_loss( - self, - trainer, - metrics=None, - logy=False, - logx=False, - filename=None, - **kwargs, - ): - """ - Plot the loss function values during traininig. - - :param trainer: the PINA Trainer object instance. - :type trainer: Trainer - :param str | list(str) metric: The metrics to use in the y axis. If None, the mean loss - is plotted. - :param bool logy: If True, the y axis is in log scale. Default is - True. - :param bool logx: If True, the x axis is in log scale. Default is - True. - :param str filename: The file name to save the plot. If None, the plot - is shown using the setted matplotlib frontend. Default is None. - """ - - # check that MetricTracker has been used - list_ = [ - idx - for idx, s in enumerate(trainer.callbacks) - if isinstance(s, MetricTracker) - ] - if not bool(list_): - raise FileNotFoundError( - "MetricTracker should be used as a callback during training to" - " use this method." - ) - - # extract trainer metrics - trainer_metrics = trainer.callbacks[list_[0]].metrics - if metrics is None: - metrics = ["mean_loss"] - elif not isinstance(metrics, list): - raise ValueError("metrics must be class list.") - - # loop over metrics to plot - for metric in metrics: - if metric not in trainer_metrics: - raise ValueError( - f"{metric} not a valid metric. Available metrics are {list(trainer_metrics.keys())}." - ) - loss = trainer_metrics[metric] - epochs = range(len(loss)) - plt.plot(epochs, loss.cpu(), **kwargs) - - # plotting - plt.xlabel("epoch") - plt.ylabel("loss") - plt.legend() - - # log axis - if logy: - plt.yscale("log") - if logx: - plt.xscale("log") - - # saving in file - if filename: - plt.savefig(filename) - plt.close() +raise ImportError("'pina.plotter' is deprecated and cannot be imported.") diff --git a/pina/problem/__init__.py b/pina/problem/__init__.py index 35251aaf9..e95f99703 100644 --- a/pina/problem/__init__.py +++ b/pina/problem/__init__.py @@ -1,3 +1,5 @@ +"""Module for the Problems.""" + __all__ = [ "AbstractProblem", "SpatialProblem", @@ -8,6 +10,6 @@ from .abstract_problem import AbstractProblem from .spatial_problem import SpatialProblem -from .timedep_problem import TimeDependentProblem +from .time_dependent_problem import TimeDependentProblem from .parametric_problem import ParametricProblem from .inverse_problem import InverseProblem diff --git a/pina/problem/abstract_problem.py b/pina/problem/abstract_problem.py index 6e5e31789..5f601acff 100644 --- a/pina/problem/abstract_problem.py +++ b/pina/problem/abstract_problem.py @@ -1,43 +1,101 @@ -""" Module for AbstractProblem class """ +"""Module for the AbstractProblem class.""" from abc import ABCMeta, abstractmethod -from ..utils import merge_tensors, check_consistency from copy import deepcopy -import torch +from ..utils import check_consistency +from ..domain import DomainInterface, CartesianDomain +from ..condition.domain_equation_condition import DomainEquationCondition +from ..label_tensor import LabelTensor +from ..utils import merge_tensors class AbstractProblem(metaclass=ABCMeta): """ - The abstract `AbstractProblem` class. All the class defining a PINA Problem - should be inheritied from this class. + Abstract base class for PINA problems. All specific problem types should + inherit from this class. - In the definition of a PINA problem, the fundamental elements are: - the output variables, the condition(s), and the domain(s) where the - conditions are applied. + A PINA problem is defined by key components, which typically include output + variables, conditions, and domains over which the conditions are applied. """ def __init__(self): + """ + Initialization of the :class:`AbstractProblem` class. + """ + self._discretised_domains = {} + # create collector to manage problem data - # variable storing all points - self.input_pts = {} - - # varible to check if sampling is done. If no location - # element is presented in Condition this variable is set to true - self._have_sampled_points = {} + # create hook conditions <-> problems for condition_name in self.conditions: - self._have_sampled_points[condition_name] = False + self.conditions[condition_name].problem = self + + self._batching_dimension = 0 - # put in self.input_pts all the points that we don't need to sample - self._span_condition_points() + # Store in domains dict all the domains object directly passed to + # ConditionInterface. Done for back compatibility with PINA <0.2 + if not hasattr(self, "domains"): + self.domains = {} + for cond_name, cond in self.conditions.items(): + if isinstance(cond, DomainEquationCondition): + if isinstance(cond.domain, DomainInterface): + self.domains[cond_name] = cond.domain + cond.domain = cond_name + + @property + def batching_dimension(self): + """ + Get batching dimension. + + :return: The batching dimension. + :rtype: int + """ + return self._batching_dimension + + @batching_dimension.setter + def batching_dimension(self, value): + """ + Set the batching dimension. + + :param int value: The batching dimension. + """ + self._batching_dimension = value + + # back compatibility 0.1 + @property + def input_pts(self): + """ + Return a dictionary mapping condition names to their corresponding + input points. + + :return: The input points of the problem. + :rtype: dict + """ + to_return = {} + for cond_name, cond in self.conditions.items(): + if hasattr(cond, "input"): + to_return[cond_name] = cond.input + elif hasattr(cond, "domain"): + to_return[cond_name] = self._discretised_domains[cond.domain] + return to_return + + @property + def discretised_domains(self): + """ + Return a dictionary mapping domains to their corresponding sampled + points. + + :return: The discretised domains. + :rtype: dict + """ + return self._discretised_domains def __deepcopy__(self, memo): """ - Implements deepcopy for the - :class:`~pina.problem.abstract_problem.AbstractProblem` class. + Perform a deep copy of the :class:`AbstractProblem` instance. - :param dict memo: Memory dictionary, to avoid excess copy - :return: The deep copy of the - :class:`~pina.problem.abstract_problem.AbstractProblem` class + :param dict memo: A dictionary used to track objects already copied + during the deep copy process to prevent redundant copies. + :return: A deep copy of the :class:`AbstractProblem` instance. :rtype: AbstractProblem """ cls = self.__class__ @@ -48,14 +106,24 @@ def __deepcopy__(self, memo): return result @property - def input_variables(self): + def are_all_domains_discretised(self): + """ + Check if all the domains are discretised. + + :return: ``True`` if all domains are discretised, ``False`` otherwise. + :rtype: bool """ - The input variables of the AbstractProblem, whose type depends on the - type of domain (spatial, temporal, and parameter). + return all( + domain in self.discretised_domains for domain in self.domains + ) - :return: the input variables of self - :rtype: list + @property + def input_variables(self): + """ + Get the input variables of the problem. + :return: The input variables of the problem. + :rtype: list[str] """ variables = [] @@ -65,244 +133,170 @@ def input_variables(self): variables += self.temporal_variable if hasattr(self, "parameters"): variables += self.parameters - if hasattr(self, "custom_variables"): - variables += self.custom_variables return variables - @property - def domain(self): + @input_variables.setter + def input_variables(self, variables): """ - The domain(s) where the conditions of the AbstractProblem are valid. - If more than one domain type is passed, a list of Location is - retured. + Set the input variables of the AbstractProblem. - :return: the domain(s) of ``self`` - :rtype: list[Location] + :param list[str] variables: The input variables of the problem. + :raises RuntimeError: Not implemented. """ - domains = [ - getattr(self, f"{t}_domain") - for t in ["spatial", "temporal", "parameter"] - if hasattr(self, f"{t}_domain") - ] - - if len(domains) == 1: - return domains[0] - elif len(domains) == 0: - raise RuntimeError - - if len(set(map(type, domains))) == 1: - domain = domains[0].__class__({}) - [domain.update(d) for d in domains] - return domain - else: - raise RuntimeError("different domains") - - @input_variables.setter - def input_variables(self, variables): raise RuntimeError @property @abstractmethod def output_variables(self): """ - The output variables of the problem. + Get the output variables of the problem. """ - pass @property @abstractmethod def conditions(self): """ - The conditions of the problem. - """ - pass + Get the conditions of the problem. - def _span_condition_points(self): - """ - Simple function to get the condition points + :return: The conditions of the problem. + :rtype: dict """ - for condition_name in self.conditions: - condition = self.conditions[condition_name] - if hasattr(condition, "input_points"): - samples = condition.input_points - self.input_pts[condition_name] = samples - self._have_sampled_points[condition_name] = True - if hasattr(self, "unknown_parameter_domain"): - # initialize the unknown parameters of the inverse problem given - # the domain the user gives - self.unknown_parameters = {} - for i, var in enumerate(self.unknown_variables): - range_var = self.unknown_parameter_domain.range_[var] - tensor_var = ( - torch.rand(1, requires_grad=True) * range_var[1] - + range_var[0] - ) - self.unknown_parameters[var] = torch.nn.Parameter( - tensor_var - ) + return self.conditions def discretise_domain( - self, n, mode="random", variables="all", locations="all" + self, n=None, mode="random", domains="all", sample_rules=None ): """ - Generate a set of points to span the `Location` of all the conditions of - the problem. + Discretize the problem's domains by sampling a specified number of + points according to the selected sampling mode. - :param n: Number of points to sample, see Note below - for reference. - :type n: int - :param mode: Mode for sampling, defaults to ``random``. + :param int n: The number of points to sample. + :param mode: The sampling method. Default is ``random``. Available modes include: random sampling, ``random``; latin hypercube sampling, ``latin`` or ``lh``; chebyshev sampling, ``chebyshev``; grid sampling ``grid``. - :param variables: problem's variables to be sampled, defaults to 'all'. - :type variables: str | list[str] - :param locations: problem's locations from where to sample, defaults to 'all'. - :type locations: str + :param domains: The domains from which to sample. Default is ``all``. + :type domains: str | list[str] + :param dict sample_rules: A dictionary defining custom sampling rules + for input variables. If provided, it must contain a dictionary + specifying the sampling rule for each variable, overriding the + ``n`` and ``mode`` arguments. Each key must correspond to the + input variables from + :meth:~pina.problem.AbstractProblem.input_variables, and its value + should be another dictionary with + two keys: ``n`` (number of points to sample) and ``mode`` + (sampling method). Defaults to None. + :raises RuntimeError: If both ``n`` and ``sample_rules`` are specified. + :raises RuntimeError: If neither ``n`` nor ``sample_rules`` are set. :Example: - >>> pinn.discretise_domain(n=10, mode='grid') - >>> pinn.discretise_domain(n=10, mode='grid', location=['bound1']) - >>> pinn.discretise_domain(n=10, mode='grid', variables=['x']) + >>> problem.discretise_domain(n=10, mode='grid') + >>> problem.discretise_domain(n=10, mode='grid', domains=['gamma1']) + >>> problem.discretise_domain( + ... sample_rules={ + ... 'x': {'n': 10, 'mode': 'grid'}, + ... 'y': {'n': 100, 'mode': 'grid'} + ... }, + ... domains=['D'] + ... ) .. warning:: - ``random`` is currently the only implemented ``mode`` for all geometries, i.e. - ``EllipsoidDomain``, ``CartesianDomain``, ``SimplexDomain`` and the geometries - compositions ``Union``, ``Difference``, ``Exclusion``, ``Intersection``. The - modes ``latin`` or ``lh``, ``chebyshev``, ``grid`` are only implemented for - ``CartesianDomain``. - """ - - # check consistecy n - check_consistency(n, int) - - # check consistency mode - check_consistency(mode, str) - if mode not in ["random", "grid", "lh", "chebyshev", "latin"]: - raise TypeError(f"mode {mode} not valid.") + ``random`` is currently the only implemented ``mode`` for all + geometries, i.e. :class:`~pina.domain.ellipsoid.EllipsoidDomain`, + :class:`~pina.domain.cartesian.CartesianDomain`, + :class:`~pina.domain.simplex.SimplexDomain`, and geometry + compositions :class:`~pina.domain.union_domain.Union`, + :class:`~pina.domain.difference_domain.Difference`, + :class:`~pina.domain.exclusion_domain.Exclusion`, and + :class:`~pina.domain.intersection_domain.Intersection`. + The modes ``latin`` or ``lh``, ``chebyshev``, ``grid`` are only + implemented for :class:`~pina.domain.cartesian.CartesianDomain`. - # check consistency variables - if variables == "all": - variables = self.input_variables - else: - check_consistency(variables, str) - - if sorted(variables) != sorted(self.input_variables): - TypeError( - f"Wrong variables for sampling. Variables ", - f"should be in {self.input_variables}.", - ) + .. warning:: + If custom discretisation is applied by setting ``sample_rules`` not + to ``None``, then the discretised domain must be of class + :class:`~pina.domain.cartesian.CartesianDomain` + """ - # check consistency location - locations_to_sample = [ - condition - for condition in self.conditions - if hasattr(self.conditions[condition], "location") - ] - if locations == "all": - # only locations that can be sampled - locations = locations_to_sample - else: - check_consistency(locations, str) - - if sorted(locations) != sorted(locations_to_sample): - TypeError( - f"Wrong locations for sampling. Location ", - f"should be in {locations_to_sample}.", + # check consistecy n, mode, variables, locations + if sample_rules is not None: + check_consistency(sample_rules, dict) + if mode is not None: + check_consistency(mode, str) + check_consistency(domains, (list, str)) + + # check correct location + if domains == "all": + domains = self.domains.keys() + elif not isinstance(domains, (list)): + domains = [domains] + if n is not None and sample_rules is None: + self._apply_default_discretization(n, mode, domains) + if n is None and sample_rules is not None: + self._apply_custom_discretization(sample_rules, domains) + elif n is not None and sample_rules is not None: + raise RuntimeError( + "You can't specify both n and sample_rules at the same time." ) + elif n is None and sample_rules is None: + raise RuntimeError("You have to specify either n or sample_rules.") - # sampling - for location in locations: - condition = self.conditions[location] - - # we try to check if we have already sampled - try: - already_sampled = [self.input_pts[location]] - # if we have not sampled, a key error is thrown - except KeyError: - already_sampled = [] - - # if we have already sampled fully the condition - # but we want to sample again we set already_sampled - # to an empty list since we need to sample again, and - # self._have_sampled_points to False. - if self._have_sampled_points[location]: - already_sampled = [] - self._have_sampled_points[location] = False - - # build samples - samples = [ - condition.location.sample(n=n, mode=mode, variables=variables) - ] + already_sampled - pts = merge_tensors(samples) - self.input_pts[location] = pts - - # the condition is sampled if input_pts contains all labels - if sorted(self.input_pts[location].labels) == sorted( - self.input_variables - ): - self._have_sampled_points[location] = True - self.input_pts[location] = self.input_pts[location].extract( - sorted(self.input_variables) - ) - - def add_points(self, new_points): + def _apply_default_discretization(self, n, mode, domains): """ - Adding points to the already sampled points. + Apply default discretization to the problem's domains. - :param dict new_points: a dictionary with key the location to add the points - and values the torch.Tensor points. + :param int n: The number of points to sample. + :param mode: The sampling method. + :param domains: The domains from which to sample. + :type domains: str | list[str] """ - - if sorted(new_points.keys()) != sorted(self.conditions): - TypeError( - f"Wrong locations for new points. Location ", - f"should be in {self.conditions}.", + for domain in domains: + self.discretised_domains[domain] = ( + self.domains[domain].sample(n, mode).sort_labels() ) - for location in new_points.keys(): - # extract old and new points - old_pts = self.input_pts[location] - new_pts = new_points[location] - - # if they don't have the same variables error - if sorted(old_pts.labels) != sorted(new_pts.labels): - TypeError( - f"Not matching variables for old and new points " - f"in condition {location}." - ) - if old_pts.labels != new_pts.labels: - new_pts = torch.hstack( - [new_pts.extract([i]) for i in old_pts.labels] + def _apply_custom_discretization(self, sample_rules, domains): + """ + Apply custom discretization to the problem's domains. + + :param dict sample_rules: A dictionary of custom sampling rules. + :param domains: The domains from which to sample. + :type domains: str | list[str] + :raises RuntimeError: If the keys of the sample_rules dictionary are not + the same as the input variables. + :raises RuntimeError: If custom discretisation is applied on a domain + that is not a CartesianDomain. + """ + if sorted(list(sample_rules.keys())) != sorted(self.input_variables): + raise RuntimeError( + "The keys of the sample_rules dictionary must be the same as " + "the input variables." + ) + for domain in domains: + if not isinstance(self.domains[domain], CartesianDomain): + raise RuntimeError( + "Custom discretisation can be applied only on Cartesian " + "domains" ) - new_pts.labels = old_pts.labels + discretised_tensor = [] + for var, rules in sample_rules.items(): + n, mode = rules["n"], rules["mode"] + points = self.domains[domain].sample(n, mode, var) + discretised_tensor.append(points) - # merging - merged_pts = torch.vstack([old_pts, new_pts]) - merged_pts.labels = old_pts.labels - self.input_pts[location] = merged_pts + self.discretised_domains[domain] = merge_tensors( + discretised_tensor + ).sort_labels() - @property - def have_sampled_points(self): - """ - Check if all points for - ``Location`` are sampled. + def add_points(self, new_points_dict): """ - return all(self._have_sampled_points.values()) + Add new points to an already sampled domain. - @property - def not_sampled_points(self): - """ - Check which points are - not sampled. + :param dict new_points_dict: The dictionary mapping new points to their + corresponding domain. """ - # variables which are not sampled - not_sampled = None - if self.have_sampled_points is False: - # check which one are not sampled: - not_sampled = [] - for condition_name, is_sample in self._have_sampled_points.items(): - if not is_sample: - not_sampled.append(condition_name) - return not_sampled + for k, v in new_points_dict.items(): + self.discretised_domains[k] = LabelTensor.vstack( + [self.discretised_domains[k], v] + ) diff --git a/pina/problem/inverse_problem.py b/pina/problem/inverse_problem.py index 5a83566ae..231d01441 100644 --- a/pina/problem/inverse_problem.py +++ b/pina/problem/inverse_problem.py @@ -1,71 +1,61 @@ -"""Module for the ParametricProblem class""" +"""Module for the InverseProblem class.""" from abc import abstractmethod - +import torch from .abstract_problem import AbstractProblem class InverseProblem(AbstractProblem): """ - The class for the definition of inverse problems, i.e., problems - with unknown parameters that have to be learned during the training process - from given data. - - Here's an example of a spatial inverse ODE problem, i.e., a spatial - ODE problem with an unknown parameter `alpha` as coefficient of the - derivative term. - - :Example: - >>> from pina.problem import SpatialProblem, InverseProblem - >>> from pina.operators import grad - >>> from pina.equation import ParametricEquation, FixedValue - >>> from pina import Condition - >>> from pina.geometry import CartesianDomain - >>> import torch - >>> - >>> class InverseODE(SpatialProblem, InverseProblem): - >>> - >>> output_variables = ['u'] - >>> spatial_domain = CartesianDomain({'x': [0, 1]}) - >>> unknown_parameter_domain = CartesianDomain({'alpha': [1, 10]}) - >>> - >>> def ode_equation(input_, output_, params_): - >>> u_x = grad(output_, input_, components=['u'], d=['x']) - >>> u = output_.extract(['u']) - >>> return params_.extract(['alpha']) * u_x - u - >>> - >>> def solution_data(input_, output_): - >>> x = input_.extract(['x']) - >>> solution = torch.exp(x) - >>> return output_ - solution - >>> - >>> conditions = { - >>> 'x0': Condition(CartesianDomain({'x': 0}), FixedValue(1.0)), - >>> 'D': Condition(CartesianDomain({'x': [0, 1]}), ParametricEquation(ode_equation)), - >>> 'data': Condition(CartesianDomain({'x': [0, 1]}), Equation(solution_data)) + Class for defining inverse problems, where the objective is to determine + unknown parameters through training, based on given data. """ + def __init__(self): + """ + Initialization of the :class:`InverseProblem` class. + """ + super().__init__() + # storing unknown_parameters for optimization + self.unknown_parameters = {} + for var in self.unknown_variables: + range_var = self.unknown_parameter_domain.range_[var] + tensor_var = ( + torch.rand(1, requires_grad=True) * range_var[1] + range_var[0] + ) + self.unknown_parameters[var] = torch.nn.Parameter(tensor_var) + @abstractmethod def unknown_parameter_domain(self): """ - The parameters' domain of the problem. + The domain of the unknown parameters of the problem. """ - pass @property def unknown_variables(self): """ - The parameters of the problem. + Get the unknown variables of the problem. + + :return: The unknown variables of the problem. + :rtype: list[str] """ return self.unknown_parameter_domain.variables @property def unknown_parameters(self): """ - The parameters of the problem. + Get the unknown parameters of the problem. + + :return: The unknown parameters of the problem. + :rtype: torch.nn.Parameter """ return self.__unknown_parameters @unknown_parameters.setter def unknown_parameters(self, value): + """ + Set the unknown parameters of the problem. + + :param torch.nn.Parameter value: The unknown parameters of the problem. + """ self.__unknown_parameters = value diff --git a/pina/problem/parametric_problem.py b/pina/problem/parametric_problem.py index 600eab062..e361074b3 100644 --- a/pina/problem/parametric_problem.py +++ b/pina/problem/parametric_problem.py @@ -1,4 +1,4 @@ -"""Module for the ParametricProblem class""" +"""Module for the ParametricProblem class.""" from abc import abstractmethod @@ -7,49 +7,23 @@ class ParametricProblem(AbstractProblem): """ - The class for the definition of parametric problems, i.e., problems - with parameters among the input variables. - - Here's an example of a spatial parametric ODE problem, i.e., a spatial - ODE problem with an additional parameter `alpha` as coefficient of the - derivative term. - - :Example: - >>> from pina.problem import SpatialProblem, ParametricProblem - >>> from pina.operators import grad - >>> from pina.equations import Equation, FixedValue - >>> from pina import Condition - >>> from pina.geometry import CartesianDomain - >>> import torch - >>> - >>> - >>> class ParametricODE(SpatialProblem, ParametricProblem): - >>> - >>> output_variables = ['u'] - >>> spatial_domain = CartesianDomain({'x': [0, 1]}) - >>> parameter_domain = CartesianDomain({'alpha': [1, 10]}) - >>> - >>> def ode_equation(input_, output_): - >>> u_x = grad(output_, input_, components=['u'], d=['x']) - >>> u = output_.extract(['u']) - >>> alpha = input_.extract(['alpha']) - >>> return alpha * u_x - u - >>> - >>> conditions = { - >>> 'x0': Condition(CartesianDomain({'x': 0, 'alpha':[1, 10]}), FixedValue(1.)), - >>> 'D': Condition(CartesianDomain({'x': [0, 1], 'alpha':[1, 10]}), Equation(ode_equation))} + Class for defining parametric problems, where certain input variables are + treated as parameters that can vary, allowing the model to adapt to + different scenarios based on the chosen parameters. """ @abstractmethod def parameter_domain(self): """ - The parameters' domain of the problem. + The domain of the parameters of the problem. """ - pass @property def parameters(self): """ - The parameters' variables of the problem. + Get the parameters of the problem. + + :return: The parameters of the problem. + :rtype: list[str] """ return self.parameter_domain.variables diff --git a/pina/problem/spatial_problem.py b/pina/problem/spatial_problem.py index e34414278..608e31691 100644 --- a/pina/problem/spatial_problem.py +++ b/pina/problem/spatial_problem.py @@ -1,4 +1,4 @@ -"""Module for the SpatialProblem class""" +"""Module for the SpatialProblem class.""" from abc import abstractmethod @@ -7,33 +7,8 @@ class SpatialProblem(AbstractProblem): """ - The class for the definition of spatial problems, i.e., for problems - with spatial input variables. - - Here's an example of a spatial 1-dimensional ODE problem. - - :Example: - >>> from pina.problem import SpatialProblem - >>> from pina.operators import grad - >>> from pina.equation import Equation, FixedValue - >>> from pina import Condition - >>> from pina.geometry import CartesianDomain - >>> import torch - >>> - >>> - >>> class SpatialODE(SpatialProblem: - >>> - >>> output_variables = ['u'] - >>> spatial_domain = CartesianDomain({'x': [0, 1]}) - >>> - >>> def ode_equation(input_, output_): - >>> u_x = grad(output_, input_, components=['u'], d=['x']) - >>> u = output_.extract(['u']) - >>> return u_x - u - >>> - >>> conditions = { - >>> 'x0': Condition(CartesianDomain({'x': 0, 'alpha':[1, 10]}), FixedValue(1.)), - >>> 'D': Condition(CartesianDomain({'x': [0, 1], 'alpha':[1, 10]}), Equation(ode_equation))} + Class for defining spatial problems, where the problem domain is defined in + terms of spatial variables. """ @abstractmethod @@ -41,11 +16,13 @@ def spatial_domain(self): """ The spatial domain of the problem. """ - pass @property def spatial_variables(self): """ - The spatial input variables of the problem. + Get the spatial input variables of the problem. + + :return: The spatial input variables of the problem. + :rtype: list[str] """ return self.spatial_domain.variables diff --git a/pina/problem/time_dependent_problem.py b/pina/problem/time_dependent_problem.py new file mode 100644 index 000000000..ea2ad7d54 --- /dev/null +++ b/pina/problem/time_dependent_problem.py @@ -0,0 +1,28 @@ +"""Module for the TimeDependentProblem class.""" + +from abc import abstractmethod + +from .abstract_problem import AbstractProblem + + +class TimeDependentProblem(AbstractProblem): + """ + Class for defining time-dependent problems, where the system's behavior + changes with respect to time. + """ + + @abstractmethod + def temporal_domain(self): + """ + The temporal domain of the problem. + """ + + @property + def temporal_variable(self): + """ + Get the time variable of the problem. + + :return: The time variable of the problem. + :rtype: list[str] + """ + return self.temporal_domain.variables diff --git a/pina/problem/timedep_problem.py b/pina/problem/timedep_problem.py deleted file mode 100644 index cefdb54b1..000000000 --- a/pina/problem/timedep_problem.py +++ /dev/null @@ -1,61 +0,0 @@ -"""Module for the TimeDependentProblem class""" - -from abc import abstractmethod - -from .abstract_problem import AbstractProblem - - -class TimeDependentProblem(AbstractProblem): - """ - The class for the definition of time-dependent problems, i.e., for problems - depending on time. - - Here's an example of a 1D wave problem. - - :Example: - >>> from pina.problem import SpatialProblem, TimeDependentProblem - >>> from pina.operators import grad, laplacian - >>> from pina.equation import Equation, FixedValue - >>> from pina import Condition - >>> from pina.geometry import CartesianDomain - >>> import torch - >>> - >>> - >>> class Wave(TimeDependentSpatialProblem): - >>> - >>> output_variables = ['u'] - >>> spatial_domain = CartesianDomain({'x': [0, 3]}) - >>> temporal_domain = CartesianDomain({'t': [0, 1]}) - >>> - >>> def wave_equation(input_, output_): - >>> u_t = grad(output_, input_, components=['u'], d=['t']) - >>> u_tt = grad(u_t, input_, components=['dudt'], d=['t']) - >>> delta_u = laplacian(output_, input_, components=['u'], d=['x']) - >>> return delta_u - u_tt - >>> - >>> def initial_condition(input_, output_): - >>> u_expected = (-3*torch.sin(2*torch.pi*input_.extract(['x'])) - >>> + 5*torch.sin(8/3*torch.pi*input_.extract(['x']))) - >>> u = output_.extract(['u']) - >>> return u - u_expected - >>> - >>> conditions = { - >>> 't0': Condition(CartesianDomain({'x': [0, 3], 't':0}), Equation(initial_condition)), - >>> 'gamma1': Condition(CartesianDomain({'x':0, 't':[0, 1]}), FixedValue(0.)), - >>> 'gamma2': Condition(CartesianDomain({'x':3, 't':[0, 1]}), FixedValue(0.)), - >>> 'D': Condition(CartesianDomain({'x': [0, 3], 't':[0, 1]}), Equation(wave_equation))} - """ - - @abstractmethod - def temporal_domain(self): - """ - The temporal domain of the problem. - """ - pass - - @property - def temporal_variable(self): - """ - The time variable of the problem. - """ - return self.temporal_domain.variables diff --git a/pina/problem/zoo/__init__.py b/pina/problem/zoo/__init__.py new file mode 100644 index 000000000..e129c2cb3 --- /dev/null +++ b/pina/problem/zoo/__init__.py @@ -0,0 +1,19 @@ +"""Module for implemented problems.""" + +__all__ = [ + "SupervisedProblem", + "HelmholtzProblem", + "AllenCahnProblem", + "AdvectionProblem", + "Poisson2DSquareProblem", + "DiffusionReactionProblem", + "InversePoisson2DSquareProblem", +] + +from .supervised_problem import SupervisedProblem +from .helmholtz import HelmholtzProblem +from .allen_cahn import AllenCahnProblem +from .advection import AdvectionProblem +from .poisson_2d_square import Poisson2DSquareProblem +from .diffusion_reaction import DiffusionReactionProblem +from .inverse_poisson_2d_square import InversePoisson2DSquareProblem diff --git a/pina/problem/zoo/advection.py b/pina/problem/zoo/advection.py new file mode 100644 index 000000000..a2e801562 --- /dev/null +++ b/pina/problem/zoo/advection.py @@ -0,0 +1,110 @@ +"""Formulation of the advection problem.""" + +import torch +from ... import Condition +from ...operator import grad +from ...equation import Equation +from ...domain import CartesianDomain +from ...utils import check_consistency +from ...problem import SpatialProblem, TimeDependentProblem + + +class AdvectionEquation(Equation): + """ + Implementation of the advection equation. + """ + + def __init__(self, c): + """ + Initialization of the :class:`AdvectionEquation`. + + :param c: The advection velocity parameter. + :type c: float | int + """ + self.c = c + check_consistency(self.c, (float, int)) + + def equation(input_, output_): + """ + Implementation of the advection equation. + + :param LabelTensor input_: Input data of the problem. + :param LabelTensor output_: Output data of the problem. + :return: The residual of the advection equation. + :rtype: LabelTensor + """ + u_x = grad(output_, input_, components=["u"], d=["x"]) + u_t = grad(output_, input_, components=["u"], d=["t"]) + return u_t + self.c * u_x + + super().__init__(equation) + + +def initial_condition(input_, output_): + """ + Implementation of the initial condition. + + :param LabelTensor input_: Input data of the problem. + :param LabelTensor output_: Output data of the problem. + :return: The residual of the initial condition. + :rtype: LabelTensor + """ + return output_ - torch.sin(input_.extract("x")) + + +class AdvectionProblem(SpatialProblem, TimeDependentProblem): + r""" + Implementation of the advection problem in the spatial interval + :math:`[0, 2 \pi]` and temporal interval :math:`[0, 1]`. + + .. seealso:: + + **Original reference**: Wang, Sifan, et al. *An expert's guide to + training physics-informed neural networks*. + arXiv preprint arXiv:2308.08468 (2023). + DOI: `arXiv:2308.08468 `_. + + :Example: + >>> problem = AdvectionProblem(c=1.0) + """ + + output_variables = ["u"] + spatial_domain = CartesianDomain({"x": [0, 2 * torch.pi]}) + temporal_domain = CartesianDomain({"t": [0, 1]}) + + domains = { + "D": CartesianDomain({"x": [0, 2 * torch.pi], "t": [0, 1]}), + "t0": CartesianDomain({"x": [0, 2 * torch.pi], "t": 0.0}), + } + + conditions = { + "t0": Condition(domain="t0", equation=Equation(initial_condition)), + } + + def __init__(self, c=1.0): + """ + Initialization of the :class:`AdvectionProblem`. + + :param c: The advection velocity parameter. + :type c: float | int + """ + super().__init__() + + self.c = c + check_consistency(self.c, (float, int)) + + self.conditions["D"] = Condition( + domain="D", equation=AdvectionEquation(self.c) + ) + + def solution(self, pts): + """ + Implementation of the analytical solution of the advection problem. + + :param LabelTensor pts: Points where the solution is evaluated. + :return: The analytical solution of the advection problem. + :rtype: LabelTensor + """ + sol = torch.sin(pts.extract("x") - self.c * pts.extract("t")) + sol.labels = self.output_variables + return sol diff --git a/pina/problem/zoo/allen_cahn.py b/pina/problem/zoo/allen_cahn.py new file mode 100644 index 000000000..4e05eaf68 --- /dev/null +++ b/pina/problem/zoo/allen_cahn.py @@ -0,0 +1,69 @@ +"""Formulation of the Allen Cahn problem.""" + +import torch +from ... import Condition +from ...equation import Equation +from ...domain import CartesianDomain +from ...operator import grad, laplacian +from ...problem import SpatialProblem, TimeDependentProblem + + +def allen_cahn_equation(input_, output_): + """ + Implementation of the Allen Cahn equation. + + :param LabelTensor input_: Input data of the problem. + :param LabelTensor output_: Output data of the problem. + :return: The residual of the Allen Cahn equation. + :rtype: LabelTensor + """ + u_t = grad(output_, input_, components=["u"], d=["t"]) + u_xx = laplacian(output_, input_, components=["u"], d=["x"]) + return u_t - 0.0001 * u_xx + 5 * output_**3 - 5 * output_ + + +def initial_condition(input_, output_): + """ + Definition of the initial condition of the Allen Cahn problem. + + :param LabelTensor input_: Input data of the problem. + :param LabelTensor output_: Output data of the problem. + :return: The residual of the initial condition. + :rtype: LabelTensor + """ + x = input_.extract("x") + u_0 = x**2 * torch.cos(torch.pi * x) + return output_ - u_0 + + +class AllenCahnProblem(TimeDependentProblem, SpatialProblem): + r""" + Implementation of the Allen Cahn problem in the spatial interval + :math:`[-1, 1]` and temporal interval :math:`[0, 1]`. + + .. seealso:: + **Original reference**: Sokratis J. Anagnostopoulos, Juan D. Toscano, + Nikolaos Stergiopulos, and George E. Karniadakis. + *Residual-based attention and connection to information + bottleneck theory in PINNs*. + Computer Methods in Applied Mechanics and Engineering 421 (2024): 116805 + DOI: `10.1016/ + j.cma.2024.116805 `_. + + :Example: + >>> problem = AllenCahnProblem() + """ + + output_variables = ["u"] + spatial_domain = CartesianDomain({"x": [-1, 1]}) + temporal_domain = CartesianDomain({"t": [0, 1]}) + + domains = { + "D": CartesianDomain({"x": [-1, 1], "t": [0, 1]}), + "t0": CartesianDomain({"x": [-1, 1], "t": 0.0}), + } + + conditions = { + "D": Condition(domain="D", equation=Equation(allen_cahn_equation)), + "t0": Condition(domain="t0", equation=Equation(initial_condition)), + } diff --git a/pina/problem/zoo/diffusion_reaction.py b/pina/problem/zoo/diffusion_reaction.py new file mode 100644 index 000000000..d7a26c59a --- /dev/null +++ b/pina/problem/zoo/diffusion_reaction.py @@ -0,0 +1,104 @@ +"""Formulation of the diffusion-reaction problem.""" + +import torch +from ... import Condition +from ...domain import CartesianDomain +from ...operator import grad, laplacian +from ...equation import Equation, FixedValue +from ...problem import SpatialProblem, TimeDependentProblem + + +def diffusion_reaction(input_, output_): + """ + Implementation of the diffusion-reaction equation. + + :param LabelTensor input_: Input data of the problem. + :param LabelTensor output_: Output data of the problem. + :return: The residual of the diffusion-reaction equation. + :rtype: LabelTensor + """ + x = input_.extract("x") + t = input_.extract("t") + u_t = grad(output_, input_, components=["u"], d=["t"]) + u_xx = laplacian(output_, input_, components=["u"], d=["x"]) + r = torch.exp(-t) * ( + 1.5 * torch.sin(2 * x) + + (8 / 3) * torch.sin(3 * x) + + (15 / 4) * torch.sin(4 * x) + + (63 / 8) * torch.sin(8 * x) + ) + return u_t - u_xx - r + + +def initial_condition(input_, output_): + """ + Definition of the initial condition of the diffusion-reaction problem. + + :param LabelTensor input_: Input data of the problem. + :param LabelTensor output_: Output data of the problem. + :return: The residual of the initial condition. + :rtype: LabelTensor + """ + x = input_.extract("x") + u_0 = ( + torch.sin(x) + + (1 / 2) * torch.sin(2 * x) + + (1 / 3) * torch.sin(3 * x) + + (1 / 4) * torch.sin(4 * x) + + (1 / 8) * torch.sin(8 * x) + ) + return output_ - u_0 + + +class DiffusionReactionProblem(TimeDependentProblem, SpatialProblem): + r""" + Implementation of the diffusion-reaction problem in the spatial interval + :math:`[-\pi, \pi]` and temporal interval :math:`[0, 1]`. + + .. seealso:: + **Original reference**: Si, Chenhao, et al. *Complex Physics-Informed + Neural Network.* arXiv preprint arXiv:2502.04917 (2025). + DOI: `arXiv:2502.04917 `_. + + :Example: + >>> problem = DiffusionReactionProblem() + """ + + output_variables = ["u"] + spatial_domain = CartesianDomain({"x": [-torch.pi, torch.pi]}) + temporal_domain = CartesianDomain({"t": [0, 1]}) + + domains = { + "D": CartesianDomain({"x": [-torch.pi, torch.pi], "t": [0, 1]}), + "g1": CartesianDomain({"x": -torch.pi, "t": [0, 1]}), + "g2": CartesianDomain({"x": torch.pi, "t": [0, 1]}), + "t0": CartesianDomain({"x": [-torch.pi, torch.pi], "t": 0.0}), + } + + conditions = { + "D": Condition(domain="D", equation=Equation(diffusion_reaction)), + "g1": Condition(domain="g1", equation=FixedValue(0.0)), + "g2": Condition(domain="g2", equation=FixedValue(0.0)), + "t0": Condition(domain="t0", equation=Equation(initial_condition)), + } + + def solution(self, pts): + """ + Implementation of the analytical solution of the diffusion-reaction + problem. + + :param LabelTensor pts: Points where the solution is evaluated. + :return: The analytical solution of the diffusion-reaction problem. + :rtype: LabelTensor + """ + t = pts.extract("t") + x = pts.extract("x") + sol = torch.exp(-t) * ( + torch.sin(x) + + (1 / 2) * torch.sin(2 * x) + + (1 / 3) * torch.sin(3 * x) + + (1 / 4) * torch.sin(4 * x) + + (1 / 8) * torch.sin(8 * x) + ) + sol.labels = self.output_variables + return sol diff --git a/pina/problem/zoo/helmholtz.py b/pina/problem/zoo/helmholtz.py new file mode 100644 index 000000000..34d389319 --- /dev/null +++ b/pina/problem/zoo/helmholtz.py @@ -0,0 +1,107 @@ +"""Formulation of the Helmholtz problem.""" + +import torch +from ... import Condition +from ...operator import laplacian +from ...domain import CartesianDomain +from ...problem import SpatialProblem +from ...utils import check_consistency +from ...equation import Equation, FixedValue + + +class HelmholtzEquation(Equation): + """ + Implementation of the Helmholtz equation. + """ + + def __init__(self, alpha): + """ + Initialization of the :class:`HelmholtzEquation` class. + + :param alpha: Parameter of the forcing term. + :type alpha: float | int + """ + self.alpha = alpha + check_consistency(alpha, (int, float)) + + def equation(input_, output_): + """ + Implementation of the Helmholtz equation. + + :param LabelTensor input_: Input data of the problem. + :param LabelTensor output_: Output data of the problem. + :return: The residual of the Helmholtz equation. + :rtype: LabelTensor + """ + lap = laplacian(output_, input_, components=["u"], d=["x", "y"]) + q = ( + (1 - 2 * (self.alpha * torch.pi) ** 2) + * torch.sin(self.alpha * torch.pi * input_.extract("x")) + * torch.sin(self.alpha * torch.pi * input_.extract("y")) + ) + return lap + output_ - q + + super().__init__(equation) + + +class HelmholtzProblem(SpatialProblem): + r""" + Implementation of the Helmholtz problem in the square domain + :math:`[-1, 1] \times [-1, 1]`. + + .. seealso:: + **Original reference**: Si, Chenhao, et al. *Complex Physics-Informed + Neural Network.* arXiv preprint arXiv:2502.04917 (2025). + DOI: `arXiv:2502.04917 `_. + + :Example: + >>> problem = HelmholtzProblem() + """ + + output_variables = ["u"] + spatial_domain = CartesianDomain({"x": [-1, 1], "y": [-1, 1]}) + + domains = { + "D": CartesianDomain({"x": [-1, 1], "y": [-1, 1]}), + "g1": CartesianDomain({"x": [-1, 1], "y": 1.0}), + "g2": CartesianDomain({"x": [-1, 1], "y": -1.0}), + "g3": CartesianDomain({"x": 1.0, "y": [-1, 1]}), + "g4": CartesianDomain({"x": -1.0, "y": [-1, 1]}), + } + + conditions = { + "g1": Condition(domain="g1", equation=FixedValue(0.0)), + "g2": Condition(domain="g2", equation=FixedValue(0.0)), + "g3": Condition(domain="g3", equation=FixedValue(0.0)), + "g4": Condition(domain="g4", equation=FixedValue(0.0)), + } + + def __init__(self, alpha=3.0): + """ + Initialization of the :class:`HelmholtzProblem` class. + + :param alpha: Parameter of the forcing term. + :type alpha: float | int + """ + super().__init__() + + self.alpha = alpha + check_consistency(alpha, (int, float)) + + self.conditions["D"] = Condition( + domain="D", equation=HelmholtzEquation(self.alpha) + ) + + def solution(self, pts): + """ + Implementation of the analytical solution of the Helmholtz problem. + + :param LabelTensor pts: Points where the solution is evaluated. + :return: The analytical solution of the Poisson problem. + :rtype: LabelTensor + """ + sol = torch.sin(self.alpha * torch.pi * pts.extract("x")) * torch.sin( + self.alpha * torch.pi * pts.extract("y") + ) + sol.labels = self.output_variables + return sol diff --git a/pina/problem/zoo/inverse_poisson_2d_square.py b/pina/problem/zoo/inverse_poisson_2d_square.py new file mode 100644 index 000000000..f112ebfc0 --- /dev/null +++ b/pina/problem/zoo/inverse_poisson_2d_square.py @@ -0,0 +1,87 @@ +"""Formulation of the inverse Poisson problem in a square domain.""" + +import requests +import torch +from io import BytesIO +from ... import Condition +from ... import LabelTensor +from ...operator import laplacian +from ...domain import CartesianDomain +from ...equation import Equation, FixedValue +from ...problem import SpatialProblem, InverseProblem + + +def laplace_equation(input_, output_, params_): + """ + Implementation of the laplace equation. + + :param LabelTensor input_: Input data of the problem. + :param LabelTensor output_: Output data of the problem. + :param dict params_: Parameters of the problem. + :return: The residual of the laplace equation. + :rtype: LabelTensor + """ + force_term = torch.exp( + -2 * (input_.extract(["x"]) - params_["mu1"]) ** 2 + - 2 * (input_.extract(["y"]) - params_["mu2"]) ** 2 + ) + delta_u = laplacian(output_, input_, components=["u"], d=["x", "y"]) + return delta_u - force_term + + +# URL of the file +url = "https://github.com/mathLab/PINA/raw/refs/heads/master/tutorials/tutorial7/data/pts_0.5_0.5" +# Download the file +response = requests.get(url) +response.raise_for_status() +file_like_object = BytesIO(response.content) +# Set the data +input_data = LabelTensor( + torch.load(file_like_object, weights_only=False).tensor.detach(), + ["x", "y", "mu1", "mu2"], +) + +# URL of the file +url = "https://github.com/mathLab/PINA/raw/refs/heads/master/tutorials/tutorial7/data/pinn_solution_0.5_0.5" +# Download the file +response = requests.get(url) +response.raise_for_status() +file_like_object = BytesIO(response.content) +# Set the data +output_data = LabelTensor( + torch.load(file_like_object, weights_only=False).tensor.detach(), ["u"] +) + + +class InversePoisson2DSquareProblem(SpatialProblem, InverseProblem): + r""" + Implementation of the inverse 2-dimensional Poisson problem in the square + domain :math:`[0, 1] \times [0, 1]`, + with unknown parameter domain :math:`[-1, 1] \times [-1, 1]`. + + :Example: + >>> problem = InversePoisson2DSquareProblem() + """ + + output_variables = ["u"] + x_min, x_max = -2, 2 + y_min, y_max = -2, 2 + spatial_domain = CartesianDomain({"x": [x_min, x_max], "y": [y_min, y_max]}) + unknown_parameter_domain = CartesianDomain({"mu1": [-1, 1], "mu2": [-1, 1]}) + + domains = { + "g1": CartesianDomain({"x": [x_min, x_max], "y": y_max}), + "g2": CartesianDomain({"x": [x_min, x_max], "y": y_min}), + "g3": CartesianDomain({"x": x_max, "y": [y_min, y_max]}), + "g4": CartesianDomain({"x": x_min, "y": [y_min, y_max]}), + "D": CartesianDomain({"x": [x_min, x_max], "y": [y_min, y_max]}), + } + + conditions = { + "g1": Condition(domain="g1", equation=FixedValue(0.0)), + "g2": Condition(domain="g2", equation=FixedValue(0.0)), + "g3": Condition(domain="g3", equation=FixedValue(0.0)), + "g4": Condition(domain="g4", equation=FixedValue(0.0)), + "D": Condition(domain="D", equation=Equation(laplace_equation)), + "data": Condition(input=input_data, target=output_data), + } diff --git a/pina/problem/zoo/poisson_2d_square.py b/pina/problem/zoo/poisson_2d_square.py new file mode 100644 index 000000000..c6644c462 --- /dev/null +++ b/pina/problem/zoo/poisson_2d_square.py @@ -0,0 +1,70 @@ +"""Formulation of the Poisson problem in a square domain.""" + +import torch +from ... import Condition +from ...operator import laplacian +from ...problem import SpatialProblem +from ...domain import CartesianDomain +from ...equation import Equation, FixedValue + + +def laplace_equation(input_, output_): + """ + Implementation of the laplace equation. + + :param LabelTensor input_: Input data of the problem. + :param LabelTensor output_: Output data of the problem. + :return: The residual of the laplace equation. + :rtype: LabelTensor + """ + force_term = ( + torch.sin(input_.extract(["x"]) * torch.pi) + * torch.sin(input_.extract(["y"]) * torch.pi) + * (2 * torch.pi**2) + ) + delta_u = laplacian(output_, input_, components=["u"], d=["x", "y"]) + return delta_u - force_term + + +class Poisson2DSquareProblem(SpatialProblem): + r""" + Implementation of the 2-dimensional Poisson problem in the square domain + :math:`[0, 1] \times [0, 1]`. + + :Example: + >>> problem = Poisson2DSquareProblem() + """ + + output_variables = ["u"] + spatial_domain = CartesianDomain({"x": [0, 1], "y": [0, 1]}) + + domains = { + "D": CartesianDomain({"x": [0, 1], "y": [0, 1]}), + "g1": CartesianDomain({"x": [0, 1], "y": 1.0}), + "g2": CartesianDomain({"x": [0, 1], "y": 0.0}), + "g3": CartesianDomain({"x": 1.0, "y": [0, 1]}), + "g4": CartesianDomain({"x": 0.0, "y": [0, 1]}), + } + + conditions = { + "g1": Condition(domain="g1", equation=FixedValue(0.0)), + "g2": Condition(domain="g2", equation=FixedValue(0.0)), + "g3": Condition(domain="g3", equation=FixedValue(0.0)), + "g4": Condition(domain="g4", equation=FixedValue(0.0)), + "D": Condition(domain="D", equation=Equation(laplace_equation)), + } + + def solution(self, pts): + """ + Implementation of the analytical solution of the Poisson problem. + + :param LabelTensor pts: Points where the solution is evaluated. + :return: The analytical solution of the Poisson problem. + :rtype: LabelTensor + """ + sol = -( + torch.sin(pts.extract(["x"]) * torch.pi) + * torch.sin(pts.extract(["y"]) * torch.pi) + ) + sol.labels = self.output_variables + return sol diff --git a/pina/problem/zoo/supervised_problem.py b/pina/problem/zoo/supervised_problem.py new file mode 100644 index 000000000..3fe683f13 --- /dev/null +++ b/pina/problem/zoo/supervised_problem.py @@ -0,0 +1,42 @@ +"""Formulation of a Supervised Problem in PINA.""" + +from ..abstract_problem import AbstractProblem +from ... import Condition + + +class SupervisedProblem(AbstractProblem): + """ + Definition of a supervised-learning problem. + + This class provides a simple way to define a supervised problem + using a single condition of type + :class:`~pina.condition.input_target_condition.InputTargetCondition`. + + :Example: + >>> import torch + >>> input_data = torch.rand((100, 10)) + >>> output_data = torch.rand((100, 10)) + >>> problem = SupervisedProblem(input_data, output_data) + """ + + conditions = {} + output_variables = None + input_variables = None + + def __init__( + self, input_, output_, input_variables=None, output_variables=None + ): + """ + Initialization of the :class:`SupervisedProblem` class. + + :param input_: Input data of the problem. + :type input_: torch.Tensor | LabelTensor | Graph | Data + :param output_: Output data of the problem. + :type output_: torch.Tensor | LabelTensor | Graph | Data + """ + # Set input and output variables + self.input_variables = input_variables + self.output_variables = output_variables + # Set the condition + self.conditions["data"] = Condition(input=input_, target=output_) + super().__init__() diff --git a/pina/solver/__init__.py b/pina/solver/__init__.py new file mode 100644 index 000000000..c89c62648 --- /dev/null +++ b/pina/solver/__init__.py @@ -0,0 +1,23 @@ +"""Module for the solver classes.""" + +__all__ = [ + "SolverInterface", + "SingleSolverInterface", + "MultiSolverInterface", + "PINNInterface", + "PINN", + "GradientPINN", + "CausalPINN", + "CompetitivePINN", + "SelfAdaptivePINN", + "RBAPINN", + "SupervisedSolver", + "ReducedOrderModelSolver", + "GAROM", +] + +from .solver import SolverInterface, SingleSolverInterface, MultiSolverInterface +from .physics_informed_solver import * +from .supervised import SupervisedSolver +from .reduced_order_model import ReducedOrderModelSolver +from .garom import GAROM diff --git a/pina/solver/garom.py b/pina/solver/garom.py new file mode 100644 index 000000000..2f763a700 --- /dev/null +++ b/pina/solver/garom.py @@ -0,0 +1,378 @@ +"""Module for the GAROM solver.""" + +import torch +from torch.nn.modules.loss import _Loss +from .solver import MultiSolverInterface +from ..condition import InputTargetCondition +from ..utils import check_consistency +from ..loss import LossInterface, PowerLoss + + +class GAROM(MultiSolverInterface): + """ + GAROM solver class. This class implements Generative Adversarial Reduced + Order Model solver, using user specified ``models`` to solve a specific + order reduction ``problem``. + + .. seealso:: + + **Original reference**: Coscia, D., Demo, N., & Rozza, G. (2023). + *Generative Adversarial Reduced Order Modelling*. + DOI: `arXiv preprint arXiv:2305.15881. + `_. + """ + + accepted_conditions_types = InputTargetCondition + + def __init__( + self, + problem, + generator, + discriminator, + loss=None, + optimizer_generator=None, + optimizer_discriminator=None, + scheduler_generator=None, + scheduler_discriminator=None, + gamma=0.3, + lambda_k=0.001, + regularizer=False, + ): + """ + Initialization of the :class:`GAROM` class. + + :param AbstractProblem problem: The formulation of the problem. + :param torch.nn.Module generator: The generator model. + :param torch.nn.Module discriminator: The discriminator model. + :param torch.nn.Module loss: The loss function to be minimized. + If ``None``, :class:`~pina.loss.power_loss.PowerLoss` with ``p=1`` + is used. Default is ``None``. + :param Optimizer optimizer_generator: The optimizer for the generator. + If `None`, the :class:`torch.optim.Adam` optimizer is used. + Default is ``None``. + :param Optimizer optimizer_discriminator: The optimizer for the + discriminator. If `None`, the :class:`torch.optim.Adam` optimizer is + used. Default is ``None``. + :param Scheduler scheduler_generator: The learning rate scheduler for + the generator. + If `None`, the :class:`torch.optim.lr_scheduler.ConstantLR` + scheduler is used. Default is ``None``. + :param Scheduler scheduler_discriminator: The learning rate scheduler + for the discriminator. + If `None`, the :class:`torch.optim.lr_scheduler.ConstantLR` + scheduler is used. Default is ``None``. + :param float gamma: Ratio of expected loss for generator and + discriminator. Default is ``0.3``. + :param float lambda_k: Learning rate for control theory optimization. + Default is ``0.001``. + :param bool regularizer: If ``True``, uses a regularization term in the + GAROM loss. Default is ``False``. + """ + + # set loss + if loss is None: + loss = PowerLoss(p=1) + + super().__init__( + models=[generator, discriminator], + problem=problem, + optimizers=[optimizer_generator, optimizer_discriminator], + schedulers=[ + scheduler_generator, + scheduler_discriminator, + ], + use_lt=False, + ) + + # check consistency + check_consistency( + loss, (LossInterface, _Loss, torch.nn.Module), subclass=False + ) + self._loss = loss + + # set automatic optimization for GANs + self.automatic_optimization = False + + # check consistency + check_consistency(gamma, float) + check_consistency(lambda_k, float) + check_consistency(regularizer, bool) + + # began hyperparameters + self.k = 0 + self.gamma = gamma + self.lambda_k = lambda_k + self.regularizer = float(regularizer) + + def forward(self, x, mc_steps=20, variance=False): + """ + Forward pass implementation. + + :param torch.Tensor x: The input tensor. + :param int mc_steps: Number of Montecarlo samples to approximate the + expected value. Default is ``20``. + :param bool variance: If ``True``, the method returns also the variance + of the solution. Default is ``False``. + :return: The expected value of the generator distribution. If + ``variance=True``, the method returns also the variance. + :rtype: torch.Tensor | tuple[torch.Tensor, torch.Tensor] + """ + + # sampling + field_sample = [self.sample(x) for _ in range(mc_steps)] + field_sample = torch.stack(field_sample) + + # extract mean + mean = field_sample.mean(dim=0) + + if variance: + var = field_sample.var(dim=0) + return mean, var + + return mean + + def sample(self, x): + """ + Sample from the generator distribution. + + :param torch.Tensor x: The input tensor. + :return: The generated sample. + :rtype: torch.Tensor + """ + # sampling + return self.generator(x) + + def _train_generator(self, parameters, snapshots): + """ + Train the generator model. + + :param torch.Tensor parameters: The input tensor. + :param torch.Tensor snapshots: The target tensor. + :return: The residual loss and the generator loss. + :rtype: tuple[torch.Tensor, torch.Tensor] + """ + optimizer = self.optimizer_generator + optimizer.zero_grad() + + generated_snapshots = self.sample(parameters) + + # generator loss + r_loss = self._loss(snapshots, generated_snapshots) + d_fake = self.discriminator([generated_snapshots, parameters]) + g_loss = ( + self._loss(d_fake, generated_snapshots) + self.regularizer * r_loss + ) + + # backward step + g_loss.backward() + optimizer.step() + + return r_loss, g_loss + + def on_train_batch_end(self, outputs, batch, batch_idx): + """ + This method is called at the end of each training batch and overrides + the PyTorch Lightning implementation to log checkpoints. + + :param torch.Tensor outputs: The ``model``'s output for the current + batch. + :param list[tuple[str, dict]] batch: A batch of data. Each element is a + tuple containing a condition name and a dictionary of points. + :param int batch_idx: The index of the current batch. + """ + # increase by one the counter of optimization to save loggers + ( + self.trainer.fit_loop.epoch_loop.manual_optimization.optim_step_progress.total.completed + ) += 1 + + return super().on_train_batch_end(outputs, batch, batch_idx) + + def _train_discriminator(self, parameters, snapshots): + """ + Train the discriminator model. + + :param torch.Tensor parameters: The input tensor. + :param torch.Tensor snapshots: The target tensor. + :return: The residual loss and the generator loss. + :rtype: tuple[torch.Tensor, torch.Tensor] + """ + optimizer = self.optimizer_discriminator + optimizer.zero_grad() + + # Generate a batch of images + generated_snapshots = self.sample(parameters) + + # Discriminator pass + d_real = self.discriminator([snapshots, parameters]) + d_fake = self.discriminator([generated_snapshots, parameters]) + + # evaluate loss + d_loss_real = self._loss(d_real, snapshots) + d_loss_fake = self._loss(d_fake, generated_snapshots.detach()) + d_loss = d_loss_real - self.k * d_loss_fake + + # backward step + d_loss.backward() + optimizer.step() + + return d_loss_real, d_loss_fake, d_loss + + def _update_weights(self, d_loss_real, d_loss_fake): + """ + Update the weights of the generator and discriminator models. + + :param torch.Tensor d_loss_real: The discriminator loss computed on + dataset samples. + :param torch.Tensor d_loss_fake: The discriminator loss computed on + generated samples. + :return: The difference between the loss computed on the dataset samples + and the loss computed on the generated samples. + :rtype: torch.Tensor + """ + + diff = torch.mean(self.gamma * d_loss_real - d_loss_fake) + + # Update weight term for fake samples + self.k += self.lambda_k * diff.item() + self.k = min(max(self.k, 0), 1) # Constraint to interval [0, 1] + return diff + + def optimization_cycle(self, batch): + """ + The optimization cycle for the GAROM solver. + + :param list[tuple[str, dict]] batch: A batch of data. Each element is a + tuple containing a condition name and a dictionary of points. + :return: The losses computed for all conditions in the batch, casted + to a subclass of :class:`torch.Tensor`. It should return a dict + containing the condition name and the associated scalar loss. + :rtype: dict + """ + condition_loss = {} + for condition_name, points in batch: + parameters, snapshots = ( + points["input"], + points["target"], + ) + d_loss_real, d_loss_fake, d_loss = self._train_discriminator( + parameters, snapshots + ) + r_loss, g_loss = self._train_generator(parameters, snapshots) + diff = self._update_weights(d_loss_real, d_loss_fake) + condition_loss[condition_name] = r_loss + + # some extra logging + self.store_log("d_loss", float(d_loss), self.get_batch_size(batch)) + self.store_log("g_loss", float(g_loss), self.get_batch_size(batch)) + self.store_log( + "stability_metric", + float(d_loss_real + torch.abs(diff)), + self.get_batch_size(batch), + ) + return condition_loss + + def validation_step(self, batch): + """ + The validation step for the PINN solver. + + :param list[tuple[str, dict]] batch: A batch of data. Each element is a + tuple containing a condition name and a dictionary of points. + :return: The loss of the validation step. + :rtype: torch.Tensor + """ + condition_loss = {} + for condition_name, points in batch: + parameters, snapshots = ( + points["input"], + points["target"], + ) + snapshots_gen = self.generator(parameters) + condition_loss[condition_name] = self._loss( + snapshots, snapshots_gen + ) + loss = self.weighting.aggregate(condition_loss) + self.store_log("val_loss", loss, self.get_batch_size(batch)) + return loss + + def test_step(self, batch): + """ + The test step for the PINN solver. + + :param list[tuple[str, dict]] batch: A batch of data. Each element is a + tuple containing a condition name and a dictionary of points. + :return: The loss of the test step. + :rtype: torch.Tensor + """ + condition_loss = {} + for condition_name, points in batch: + parameters, snapshots = ( + points["input"], + points["target"], + ) + snapshots_gen = self.generator(parameters) + condition_loss[condition_name] = self._loss( + snapshots, snapshots_gen + ) + loss = self.weighting.aggregate(condition_loss) + self.store_log("test_loss", loss, self.get_batch_size(batch)) + return loss + + @property + def generator(self): + """ + The generator model. + + :return: The generator model. + :rtype: torch.nn.Module + """ + return self.models[0] + + @property + def discriminator(self): + """ + The discriminator model. + + :return: The discriminator model. + :rtype: torch.nn.Module + """ + return self.models[1] + + @property + def optimizer_generator(self): + """ + The optimizer for the generator. + + :return: The optimizer for the generator. + :rtype: Optimizer + """ + return self.optimizers[0].instance + + @property + def optimizer_discriminator(self): + """ + The optimizer for the discriminator. + + :return: The optimizer for the discriminator. + :rtype: Optimizer + """ + return self.optimizers[1].instance + + @property + def scheduler_generator(self): + """ + The scheduler for the generator. + + :return: The scheduler for the generator. + :rtype: Scheduler + """ + return self.schedulers[0].instance + + @property + def scheduler_discriminator(self): + """ + The scheduler for the discriminator. + + :return: The scheduler for the discriminator. + :rtype: Scheduler + """ + return self.schedulers[1].instance diff --git a/pina/solver/physics_informed_solver/__init__.py b/pina/solver/physics_informed_solver/__init__.py new file mode 100644 index 000000000..f0fb8ebcd --- /dev/null +++ b/pina/solver/physics_informed_solver/__init__.py @@ -0,0 +1,19 @@ +"""Module for the Physics-Informed solvers.""" + +__all__ = [ + "PINNInterface", + "PINN", + "GradientPINN", + "CausalPINN", + "CompetitivePINN", + "SelfAdaptivePINN", + "RBAPINN", +] + +from .pinn_interface import PINNInterface +from .pinn import PINN +from .rba_pinn import RBAPINN +from .causal_pinn import CausalPINN +from .gradient_pinn import GradientPINN +from .competitive_pinn import CompetitivePINN +from .self_adaptive_pinn import SelfAdaptivePINN diff --git a/pina/solvers/pinns/causalpinn.py b/pina/solver/physics_informed_solver/causal_pinn.py similarity index 51% rename from pina/solvers/pinns/causalpinn.py rename to pina/solver/physics_informed_solver/causal_pinn.py index 476e4c55c..1fb102a05 100644 --- a/pina/solvers/pinns/causalpinn.py +++ b/pina/solver/physics_informed_solver/causal_pinn.py @@ -1,25 +1,21 @@ -""" Module for CausalPINN """ +"""Module for the Causal PINN solver.""" import torch - -from torch.optim.lr_scheduler import ConstantLR - +from ...problem import TimeDependentProblem from .pinn import PINN -from pina.problem import TimeDependentProblem -from pina.utils import check_consistency +from ...utils import check_consistency class CausalPINN(PINN): r""" - Causal Physics Informed Neural Network (PINN) solver class. - This class implements Causal Physics Informed Neural - Network solvers, using a user specified ``model`` to solve a specific - ``problem``. It can be used for solving both forward and inverse problems. + Causal Physics-Informed Neural Network (CausalPINN) solver class. + This class implements the Causal Physics-Informed Neural Network solver, + using a user specified ``model`` to solve a specific ``problem``. + It can be used to solve both forward and inverse problems. - The Causal Physics Informed Network aims to find - the solution :math:`\mathbf{u}:\Omega\rightarrow\mathbb{R}^m` - of the differential problem: + The Causal Physics-Informed Neural Network solver aims to find the solution + :math:`\mathbf{u}:\Omega\rightarrow\mathbb{R}^m` of a differential problem: .. math:: @@ -29,7 +25,7 @@ class CausalPINN(PINN): \mathbf{x}\in\partial\Omega \end{cases} - minimizing the loss function + minimizing the loss function: .. math:: \mathcal{L}_{\rm{problem}} = \frac{1}{N_t}\sum_{i=1}^{N_t} @@ -48,66 +44,65 @@ class CausalPINN(PINN): .. math:: \omega_i = \exp\left(\epsilon \sum_{k=1}^{i-1}\mathcal{L}_r(t_k)\right). - :math:`\epsilon` is an hyperparameter, default set to :math:`100`, while - :math:`\mathcal{L}` is a specific loss function, - default Mean Square Error: + :math:`\epsilon` is an hyperparameter, set by default to :math:`100`, while + :math:`\mathcal{L}` is a specific loss function, typically the MSE: .. math:: \mathcal{L}(v) = \| v \|^2_2. - .. seealso:: **Original reference**: Wang, Sifan, Shyam Sankaran, and Paris - Perdikaris. "Respecting causality for training physics-informed - neural networks." Computer Methods in Applied Mechanics - and Engineering 421 (2024): 116813. - DOI `10.1016 `_. + Perdikaris. + *Respecting causality for training physics-informed + neural networks.* + Computer Methods in Applied Mechanics and Engineering 421 (2024):116813. + DOI: `10.1016 `_. .. note:: - This class can only work for problems inheriting - from at least - :class:`~pina.problem.timedep_problem.TimeDependentProblem` class. + This class is only compatible with problems that inherit from the + :class:`~pina.problem.time_dependent_problem.TimeDependentProblem` + class. """ def __init__( self, problem, model, - extra_features=None, - loss=torch.nn.MSELoss(), - optimizer=torch.optim.Adam, - optimizer_kwargs={"lr": 0.001}, - scheduler=ConstantLR, - scheduler_kwargs={"factor": 1, "total_iters": 0}, + optimizer=None, + scheduler=None, + weighting=None, + loss=None, eps=100, ): """ - :param AbstractProblem problem: The formulation of the problem. - :param torch.nn.Module model: The neural network model to use. - :param torch.nn.Module loss: The loss function used as minimizer, - default :class:`torch.nn.MSELoss`. - :param torch.nn.Module extra_features: The additional input - features to use as augmented input. - :param torch.optim.Optimizer optimizer: The neural network optimizer to - use; default is :class:`torch.optim.Adam`. - :param dict optimizer_kwargs: Optimizer constructor keyword args. - :param torch.optim.LRScheduler scheduler: Learning - rate scheduler. - :param dict scheduler_kwargs: LR scheduler constructor keyword args. - :param int | float eps: The exponential decay parameter. Note that this - value is kept fixed during the training, but can be changed by means - of a callback, e.g. for annealing. + Initialization of the :class:`CausalPINN` class. + + :param AbstractProblem problem: The problem to be solved. It must + inherit from at least + :class:`~pina.problem.time_dependent_problem.TimeDependentProblem`. + :param torch.nn.Module model: The neural network model to be used. + :param Optimizer optimizer: The optimizer to be used. + If `None`, the :class:`torch.optim.Adam` optimizer is used. + Default is ``None``. + :param torch.optim.LRScheduler scheduler: Learning rate scheduler. + If `None`, the :class:`torch.optim.lr_scheduler.ConstantLR` + scheduler is used. Default is ``None``. + :param WeightingInterface weighting: The weighting schema to be used. + If `None`, no weighting schema is used. Default is ``None``. + :param torch.nn.Module loss: The loss function to be minimized. + If `None`, the :class:`torch.nn.MSELoss` loss is used. + Default is `None`. + :param float eps: The exponential decay parameter. Default is ``100``. + :raises ValueError: If the problem is not a TimeDependentProblem. """ super().__init__( - problem=problem, model=model, - extra_features=extra_features, - loss=loss, + problem=problem, optimizer=optimizer, - optimizer_kwargs=optimizer_kwargs, scheduler=scheduler, - scheduler_kwargs=scheduler_kwargs, + weighting=weighting, + loss=loss, ) # checking consistency @@ -116,26 +111,24 @@ def __init__( if not isinstance(self.problem, TimeDependentProblem): raise ValueError( "Casual PINN works only for problems" - "inheritig from TimeDependentProblem." + "inheriting from TimeDependentProblem." ) def loss_phys(self, samples, equation): """ - Computes the physics loss for the Causal PINN solver based on given - samples and equation. + Computes the physics loss for the physics-informed solver based on the + provided samples and equation. :param LabelTensor samples: The samples to evaluate the physics loss. - :param EquationInterface equation: The governing equation - representing the physics. - :return: The physics loss calculated based on given - samples and equation. + :param EquationInterface equation: The governing equation. + :return: The computed physics loss. :rtype: LabelTensor """ # split sequentially ordered time tensors into chunks chunks, labels = self._split_tensor_into_chunks(samples) # compute residuals - this correspond to ordered loss functions - # values for each time step. We apply `flatten` such that after - # concataning the residuals we obtain a tensor of shape #chunks + # values for each time step. Apply `flatten` to ensure obtaining + # a tensor of shape #chunks after concatenating the residuals time_loss = [] for chunk in chunks: chunk.labels = labels @@ -145,11 +138,10 @@ def loss_phys(self, samples, equation): torch.zeros_like(residual, requires_grad=True), residual ) time_loss.append(loss_val) - # store results - self.store_log(loss_value=float(sum(time_loss) / len(time_loss))) + # concatenate residuals time_loss = torch.stack(time_loss) - # compute weights (without the gradient storing) + # compute weights without storing the gradient with torch.no_grad(): weights = self._compute_weights(time_loss) return (weights * time_loss).mean() @@ -158,13 +150,16 @@ def loss_phys(self, samples, equation): def eps(self): """ The exponential decay parameter. + + :return: The exponential decay parameter. + :rtype: float """ return self._eps @eps.setter def eps(self, value): """ - Setter method for the eps parameter. + Set the exponential decay parameter. :param float value: The exponential decay parameter. """ @@ -173,10 +168,10 @@ def eps(self, value): def _sort_label_tensor(self, tensor): """ - Sorts the label tensor based on time variables. + Sort the tensor with respect to the temporal variables. - :param LabelTensor tensor: The label tensor to be sorted. - :return: The sorted label tensor based on time variables. + :param LabelTensor tensor: The tensor to be sorted. + :return: The tensor sorted with respect to the temporal variables. :rtype: LabelTensor """ # labels input tensors @@ -191,33 +186,34 @@ def _sort_label_tensor(self, tensor): def _split_tensor_into_chunks(self, tensor): """ - Splits the label tensor into chunks based on time. + Split the tensor into chunks based on time. - :param LabelTensor tensor: The label tensor to be split. - :return: Tuple containing the chunks and the original labels. - :rtype: Tuple[List[LabelTensor], List] + :param LabelTensor tensor: The tensor to be split. + :return: A tuple containing the list of tensor chunks and the + corresponding labels. + :rtype: tuple[list[LabelTensor], list[str]] """ - # labels input tensors + # extract labels labels = tensor.labels - # labels input tensors + # sort input tensor based on time tensor = self._sort_label_tensor(tensor) # extract time tensor time_tensor = tensor.extract(self.problem.temporal_domain.variables) # count unique tensors in time _, idx_split = time_tensor.unique(return_counts=True) - # splitting + # split the tensor based on time chunks = torch.split(tensor, tuple(idx_split)) - return chunks, labels # return chunks + return chunks, labels def _compute_weights(self, loss): """ - Computes the weights for the physics loss based on the cumulative loss. + Compute the weights for the physics loss based on the cumulative loss. :param LabelTensor loss: The physics loss values. :return: The computed weights for the physics loss. :rtype: LabelTensor """ - # compute comulative loss and multiply by epsilos + # compute comulative loss and multiply by epsilon cumulative_loss = self._eps * torch.cumsum(loss, dim=0) - # return the exponential of the weghited negative cumulative sum + # return the exponential of the negative weighted cumulative sum return torch.exp(-cumulative_loss) diff --git a/pina/solver/physics_informed_solver/competitive_pinn.py b/pina/solver/physics_informed_solver/competitive_pinn.py new file mode 100644 index 000000000..058c53f40 --- /dev/null +++ b/pina/solver/physics_informed_solver/competitive_pinn.py @@ -0,0 +1,273 @@ +"""Module for the Competitive PINN solver.""" + +import copy +import torch + +from ...problem import InverseProblem +from .pinn_interface import PINNInterface +from ..solver import MultiSolverInterface + + +class CompetitivePINN(PINNInterface, MultiSolverInterface): + r""" + Competitive Physics-Informed Neural Network (CompetitivePINN) solver class. + This class implements the Competitive Physics-Informed Neural Network + solver, using a user specified ``model`` to solve a specific ``problem``. + It can be used to solve both forward and inverse problems. + + The Competitive Physics-Informed Neural Network solver aims to find the + solution :math:`\mathbf{u}:\Omega\rightarrow\mathbb{R}^m` of a differential + problem: + + .. math:: + + \begin{cases} + \mathcal{A}[\mathbf{u}](\mathbf{x})=0\quad,\mathbf{x}\in\Omega\\ + \mathcal{B}[\mathbf{u}](\mathbf{x})=0\quad, + \mathbf{x}\in\partial\Omega + \end{cases} + + minimizing the loss function with respect to the model parameters, while + maximizing it with respect to the discriminator parameters: + + .. math:: + \mathcal{L}_{\rm{problem}} = \frac{1}{N}\sum_{i=1}^N + \mathcal{L}(D(\mathbf{x}_i)\mathcal{A}[\mathbf{u}](\mathbf{x}_i))+ + \frac{1}{N}\sum_{i=1}^N + \mathcal{L}(D(\mathbf{x}_i)\mathcal{B}[\mathbf{u}](\mathbf{x}_i)), + + where :math:D is the discriminator network, which identifies the points + where the model performs worst, and :math:\mathcal{L} is a specific loss + function, typically the MSE: + + .. math:: + \mathcal{L}(v) = \| v \|^2_2. + + .. seealso:: + + **Original reference**: Zeng, Qi, et al. + *Competitive physics informed networks.* + International Conference on Learning Representations, ICLR 2022 + `OpenReview Preprint `_. + """ + + def __init__( + self, + problem, + model, + discriminator=None, + optimizer_model=None, + optimizer_discriminator=None, + scheduler_model=None, + scheduler_discriminator=None, + weighting=None, + loss=None, + ): + """ + Initialization of the :class:`CompetitivePINN` class. + + :param AbstractProblem problem: The problem to be solved. + :param torch.nn.Module model: The neural network model to be used. + :param torch.nn.Module discriminator: The discriminator to be used. + If `None`, the discriminator is a deepcopy of the ``model``. + Default is ``None``. + :param torch.optim.Optimizer optimizer_model: The optimizer of the + ``model``. If `None`, the :class:`torch.optim.Adam` optimizer is + used. Default is ``None``. + :param torch.optim.Optimizer optimizer_discriminator: The optimizer of + the ``discriminator``. If `None`, the :class:`torch.optim.Adam` + optimizer is used. Default is ``None``. + :param Scheduler scheduler_model: Learning rate scheduler for the + ``model``. + If `None`, the :class:`torch.optim.lr_scheduler.ConstantLR` + scheduler is used. Default is ``None``. + :param Scheduler scheduler_discriminator: Learning rate scheduler for + the ``discriminator``. + If `None`, the :class:`torch.optim.lr_scheduler.ConstantLR` + scheduler is used. Default is ``None``. + :param WeightingInterface weighting: The weighting schema to be used. + If `None`, no weighting schema is used. Default is ``None``. + :param torch.nn.Module loss: The loss function to be minimized. + If `None`, the :class:`torch.nn.MSELoss` loss is used. + Default is `None`. + """ + if discriminator is None: + discriminator = copy.deepcopy(model) + + super().__init__( + models=[model, discriminator], + problem=problem, + optimizers=[optimizer_model, optimizer_discriminator], + schedulers=[scheduler_model, scheduler_discriminator], + weighting=weighting, + loss=loss, + ) + + # Set automatic optimization to False + self.automatic_optimization = False + + def forward(self, x): + """ + Forward pass. + + :param LabelTensor x: Input tensor. + :return: The output of the neural network. + :rtype: LabelTensor + """ + return self.neural_net(x) + + def training_step(self, batch): + """ + Solver training step, overridden to perform manual optimization. + + :param list[tuple[str, dict]] batch: A batch of data. Each element is a + tuple containing a condition name and a dictionary of points. + :return: The aggregated loss. + :rtype: LabelTensor + """ + # train model + self.optimizer_model.instance.zero_grad() + loss = super().training_step(batch) + self.manual_backward(loss) + self.optimizer_model.instance.step() + # train discriminator + self.optimizer_discriminator.instance.zero_grad() + loss = super().training_step(batch) + self.manual_backward(-loss) + self.optimizer_discriminator.instance.step() + return loss + + def loss_phys(self, samples, equation): + """ + Computes the physics loss for the physics-informed solver based on the + provided samples and equation. + + :param LabelTensor samples: The samples to evaluate the physics loss. + :param EquationInterface equation: The governing equation. + :return: The computed physics loss. + :rtype: LabelTensor + """ + # Compute discriminator bets + discriminator_bets = self.discriminator(samples) + + # Compute residual and multiply discriminator_bets + residual = self.compute_residual(samples=samples, equation=equation) + residual = residual * discriminator_bets + + # Compute competitive residual. + loss_val = self.loss( + torch.zeros_like(residual, requires_grad=True), + residual, + ) + return loss_val + + def configure_optimizers(self): + """ + Optimizer configuration. + + :return: The optimizers and the schedulers + :rtype: tuple[list[Optimizer], list[Scheduler]] + """ + # If the problem is an InverseProblem, add the unknown parameters + # to the parameters to be optimized + self.optimizer_model.hook(self.neural_net.parameters()) + self.optimizer_discriminator.hook(self.discriminator.parameters()) + if isinstance(self.problem, InverseProblem): + self.optimizer_model.instance.add_param_group( + { + "params": [ + self._params[var] + for var in self.problem.unknown_variables + ] + } + ) + self.scheduler_model.hook(self.optimizer_model) + self.scheduler_discriminator.hook(self.optimizer_discriminator) + return ( + [ + self.optimizer_model.instance, + self.optimizer_discriminator.instance, + ], + [ + self.scheduler_model.instance, + self.scheduler_discriminator.instance, + ], + ) + + def on_train_batch_end(self, outputs, batch, batch_idx): + """ + This method is called at the end of each training batch and overrides + the PyTorch Lightning implementation to log checkpoints. + + :param torch.Tensor outputs: The ``model``'s output for the current + batch. + :param list[tuple[str, dict]] batch: A batch of data. Each element is a + tuple containing a condition name and a dictionary of points. + :param int batch_idx: The index of the current batch. + """ + # increase by one the counter of optimization to save loggers + ( + self.trainer.fit_loop.epoch_loop.manual_optimization.optim_step_progress.total.completed + ) += 1 + + return super().on_train_batch_end(outputs, batch, batch_idx) + + @property + def neural_net(self): + """ + The model. + + :return: The model. + :rtype: torch.nn.Module + """ + return self.models[0] + + @property + def discriminator(self): + """ + The discriminator. + + :return: The discriminator. + :rtype: torch.nn.Module + """ + return self.models[1] + + @property + def optimizer_model(self): + """ + The optimizer associated to the model. + + :return: The optimizer for the model. + :rtype: Optimizer + """ + return self.optimizers[0] + + @property + def optimizer_discriminator(self): + """ + The optimizer associated to the discriminator. + + :return: The optimizer for the discriminator. + :rtype: Optimizer + """ + return self.optimizers[1] + + @property + def scheduler_model(self): + """ + The scheduler associated to the model. + + :return: The scheduler for the model. + :rtype: Scheduler + """ + return self.schedulers[0] + + @property + def scheduler_discriminator(self): + """ + The scheduler associated to the discriminator. + + :return: The scheduler for the discriminator. + :rtype: Scheduler + """ + return self.schedulers[1] diff --git a/pina/solver/physics_informed_solver/gradient_pinn.py b/pina/solver/physics_informed_solver/gradient_pinn.py new file mode 100644 index 000000000..4ac2b4c69 --- /dev/null +++ b/pina/solver/physics_informed_solver/gradient_pinn.py @@ -0,0 +1,130 @@ +"""Module for the Gradient PINN solver.""" + +import torch + +from .pinn import PINN +from ...operator import grad +from ...problem import SpatialProblem + + +class GradientPINN(PINN): + r""" + Gradient Physics-Informed Neural Network (GradientPINN) solver class. + This class implements the Gradient Physics-Informed Neural Network solver, + using a user specified ``model`` to solve a specific ``problem``. + It can be used to solve both forward and inverse problems. + + The Gradient Physics-Informed Neural Network solver aims to find the + solution :math:`\mathbf{u}:\Omega\rightarrow\mathbb{R}^m` of a differential + problem: + + .. math:: + + \begin{cases} + \mathcal{A}[\mathbf{u}](\mathbf{x})=0\quad,\mathbf{x}\in\Omega\\ + \mathcal{B}[\mathbf{u}](\mathbf{x})=0\quad, + \mathbf{x}\in\partial\Omega + \end{cases} + + minimizing the loss function; + + .. math:: + \mathcal{L}_{\rm{problem}} =& \frac{1}{N}\sum_{i=1}^N + \mathcal{L}(\mathcal{A}[\mathbf{u}](\mathbf{x}_i)) + + \frac{1}{N}\sum_{i=1}^N + \mathcal{L}(\mathcal{B}[\mathbf{u}](\mathbf{x}_i)) + + &\frac{1}{N}\sum_{i=1}^N + \nabla_{\mathbf{x}}\mathcal{L}(\mathcal{A}[\mathbf{u}](\mathbf{x}_i)) + + \frac{1}{N}\sum_{i=1}^N + \nabla_{\mathbf{x}}\mathcal{L}(\mathcal{B}[\mathbf{u}](\mathbf{x}_i)) + + + where :math:`\mathcal{L}` is a specific loss function, typically the MSE: + + .. math:: + \mathcal{L}(v) = \| v \|^2_2. + + .. seealso:: + + **Original reference**: Yu, Jeremy, et al. + *Gradient-enhanced physics-informed neural networks for forward and + inverse PDE problems.* + Computer Methods in Applied Mechanics and Engineering 393 (2022):114823. + DOI: `10.1016 `_. + + .. note:: + This class is only compatible with problems that inherit from the + :class:`~pina.problem.spatial_problem.SpatialProblem` class. + """ + + def __init__( + self, + problem, + model, + optimizer=None, + scheduler=None, + weighting=None, + loss=None, + ): + """ + Initialization of the :class:`GradientPINN` class. + + :param AbstractProblem problem: The problem to be solved. + It must inherit from at least + :class:`~pina.problem.spatial_problem.SpatialProblem` to compute the + gradient of the loss. + :param torch.nn.Module model: The neural network model to be used. + :param Optimizer optimizer: The optimizer to be used. + If `None`, the :class:`torch.optim.Adam` optimizer is used. + Default is ``None``. + :param Scheduler scheduler: Learning rate scheduler. + If `None`, the :class:`torch.optim.lr_scheduler.ConstantLR` + scheduler is used. Default is ``None``. + :param WeightingInterface weighting: The weighting schema to be used. + If `None`, no weighting schema is used. Default is ``None``. + :param torch.nn.Module loss: The loss function to be minimized. + If `None`, the :class:`torch.nn.MSELoss` loss is used. + Default is `None`. + :raises ValueError: If the problem is not a SpatialProblem. + """ + super().__init__( + model=model, + problem=problem, + optimizer=optimizer, + scheduler=scheduler, + weighting=weighting, + loss=loss, + ) + + if not isinstance(self.problem, SpatialProblem): + raise ValueError( + "Gradient PINN computes the gradient of the " + "PINN loss with respect to the spatial " + "coordinates, thus the PINA problem must be " + "a SpatialProblem." + ) + + def loss_phys(self, samples, equation): + """ + Computes the physics loss for the physics-informed solver based on the + provided samples and equation. + + :param LabelTensor samples: The samples to evaluate the physics loss. + :param EquationInterface equation: The governing equation. + :return: The computed physics loss. + :rtype: LabelTensor + """ + # classical PINN loss + residual = self.compute_residual(samples=samples, equation=equation) + loss_value = self.loss( + torch.zeros_like(residual, requires_grad=True), residual + ) + + # gradient PINN loss + loss_value = loss_value.reshape(-1, 1) + loss_value.labels = ["__loss"] + loss_grad = grad(loss_value, samples, d=self.problem.spatial_variables) + g_loss_phys = self.loss( + torch.zeros_like(loss_grad, requires_grad=True), loss_grad + ) + return loss_value + g_loss_phys diff --git a/pina/solver/physics_informed_solver/pinn.py b/pina/solver/physics_informed_solver/pinn.py new file mode 100644 index 000000000..6d92d9c36 --- /dev/null +++ b/pina/solver/physics_informed_solver/pinn.py @@ -0,0 +1,121 @@ +"""Module for the Physics-Informed Neural Network solver.""" + +import torch + +from .pinn_interface import PINNInterface +from ..solver import SingleSolverInterface +from ...problem import InverseProblem + + +class PINN(PINNInterface, SingleSolverInterface): + r""" + Physics-Informed Neural Network (PINN) solver class. + This class implements Physics-Informed Neural Network solver, using a user + specified ``model`` to solve a specific ``problem``. + It can be used to solve both forward and inverse problems. + + The Physics Informed Neural Network solver aims to find the solution + :math:`\mathbf{u}:\Omega\rightarrow\mathbb{R}^m` of a differential problem: + + .. math:: + + \begin{cases} + \mathcal{A}[\mathbf{u}](\mathbf{x})=0\quad,\mathbf{x}\in\Omega\\ + \mathcal{B}[\mathbf{u}](\mathbf{x})=0\quad, + \mathbf{x}\in\partial\Omega + \end{cases} + + minimizing the loss function: + + .. math:: + \mathcal{L}_{\rm{problem}} = \frac{1}{N}\sum_{i=1}^N + \mathcal{L}(\mathcal{A}[\mathbf{u}](\mathbf{x}_i)) + + \frac{1}{N}\sum_{i=1}^N + \mathcal{L}(\mathcal{B}[\mathbf{u}](\mathbf{x}_i)), + + where :math:`\mathcal{L}` is a specific loss function, typically the MSE: + + .. math:: + \mathcal{L}(v) = \| v \|^2_2. + + .. seealso:: + + **Original reference**: Karniadakis, G. E., Kevrekidis, I. G., Lu, L., + Perdikaris, P., Wang, S., & Yang, L. (2021). + *Physics-informed machine learning.* + Nature Reviews Physics, 3, 422-440. + DOI: `10.1038 `_. + """ + + def __init__( + self, + problem, + model, + optimizer=None, + scheduler=None, + weighting=None, + loss=None, + ): + """ + Initialization of the :class:`PINN` class. + + :param AbstractProblem problem: The problem to be solved. + :param torch.nn.Module model: The neural network model to be used. + :param Optimizer optimizer: The optimizer to be used. + If `None`, the :class:`torch.optim.Adam` optimizer is used. + Default is ``None``. + :param Scheduler scheduler: Learning rate scheduler. + If `None`, the :class:`torch.optim.lr_scheduler.ConstantLR` + scheduler is used. Default is ``None``. + :param WeightingInterface weighting: The weighting schema to be used. + If `None`, no weighting schema is used. Default is ``None``. + :param torch.nn.Module loss: The loss function to be minimized. + If `None`, the :class:`torch.nn.MSELoss` loss is used. + Default is `None`. + """ + super().__init__( + model=model, + problem=problem, + optimizer=optimizer, + scheduler=scheduler, + weighting=weighting, + loss=loss, + ) + + def loss_phys(self, samples, equation): + """ + Computes the physics loss for the physics-informed solver based on the + provided samples and equation. + + :param LabelTensor samples: The samples to evaluate the physics loss. + :param EquationInterface equation: The governing equation. + :return: The computed physics loss. + :rtype: LabelTensor + """ + residual = self.compute_residual(samples=samples, equation=equation) + loss_value = self.loss( + torch.zeros_like(residual, requires_grad=True), residual + ) + return loss_value + + def configure_optimizers(self): + """ + Optimizer configuration for the PINN solver. + + :return: The optimizers and the schedulers + :rtype: tuple[list[Optimizer], list[Scheduler]] + """ + # If the problem is an InverseProblem, add the unknown parameters + # to the parameters to be optimized. + self.optimizer.hook(self.model.parameters()) + if isinstance(self.problem, InverseProblem): + self.optimizer.instance.add_param_group( + { + "params": [ + self._params[var] + for var in self.problem.unknown_variables + ] + } + ) + self.scheduler.hook(self.optimizer) + return ([self.optimizer.instance], [self.scheduler.instance]) diff --git a/pina/solver/physics_informed_solver/pinn_interface.py b/pina/solver/physics_informed_solver/pinn_interface.py new file mode 100644 index 000000000..09e152feb --- /dev/null +++ b/pina/solver/physics_informed_solver/pinn_interface.py @@ -0,0 +1,236 @@ +"""Module for the Physics-Informed Neural Network Interface.""" + +from abc import ABCMeta, abstractmethod +import torch +from torch.nn.modules.loss import _Loss + +from ..solver import SolverInterface +from ...utils import check_consistency +from ...loss.loss_interface import LossInterface +from ...problem import InverseProblem +from ...condition import ( + InputTargetCondition, + InputEquationCondition, + DomainEquationCondition, +) + + +class PINNInterface(SolverInterface, metaclass=ABCMeta): + """ + Base class for Physics-Informed Neural Network (PINN) solvers, implementing + the :class:`~pina.solver.solver.SolverInterface` class. + + The `PINNInterface` class can be used to define PINNs that work with one or + multiple optimizers and/or models. By default, it is compatible with + problems defined by :class:`~pina.problem.abstract_problem.AbstractProblem`, + and users can choose the problem type the solver is meant to address. + """ + + accepted_conditions_types = ( + InputTargetCondition, + InputEquationCondition, + DomainEquationCondition, + ) + + def __init__(self, problem, loss=None, **kwargs): + """ + Initialization of the :class:`PINNInterface` class. + + :param AbstractProblem problem: The problem to be solved. + :param torch.nn.Module loss: The loss function to be minimized. + If `None`, the :class:`torch.nn.MSELoss` loss is used. + Default is `None`. + :param kwargs: Additional keyword arguments to be passed to the + :class:`~pina.solver.solver.SolverInterface` class. + """ + + if loss is None: + loss = torch.nn.MSELoss() + + super().__init__(problem=problem, use_lt=True, **kwargs) + + # check consistency + check_consistency(loss, (LossInterface, _Loss), subclass=False) + + # assign variables + self._loss = loss + + # inverse problem handling + if isinstance(self.problem, InverseProblem): + self._params = self.problem.unknown_parameters + self._clamp_params = self._clamp_inverse_problem_params + else: + self._params = None + self._clamp_params = lambda: None + + self.__metric = None + + def optimization_cycle(self, batch): + """ + The optimization cycle for the PINN solver. + + This method allows to call `_run_optimization_cycle` with the physics + loss as argument, thus distinguishing the training step from the + validation and test steps. + + :param list[tuple[str, dict]] batch: A batch of data. Each element is a + tuple containing a condition name and a dictionary of points. + :return: The losses computed for all conditions in the batch, casted + to a subclass of :class:`torch.Tensor`. It should return a dict + containing the condition name and the associated scalar loss. + :rtype: dict + """ + return self._run_optimization_cycle(batch, self.loss_phys) + + @torch.set_grad_enabled(True) + def validation_step(self, batch): + """ + The validation step for the PINN solver. + + :param list[tuple[str, dict]] batch: A batch of data. Each element is a + tuple containing a condition name and a dictionary of points. + :return: The loss of the validation step. + :rtype: torch.Tensor + """ + losses = self._run_optimization_cycle(batch, self._residual_loss) + loss = self.weighting.aggregate(losses).as_subclass(torch.Tensor) + self.store_log("val_loss", loss, self.get_batch_size(batch)) + return loss + + @torch.set_grad_enabled(True) + def test_step(self, batch): + """ + The test step for the PINN solver. + + :param list[tuple[str, dict]] batch: A batch of data. Each element is a + tuple containing a condition name and a dictionary of points. + :return: The loss of the test step. + :rtype: torch.Tensor + """ + losses = self._run_optimization_cycle(batch, self._residual_loss) + loss = self.weighting.aggregate(losses).as_subclass(torch.Tensor) + self.store_log("test_loss", loss, self.get_batch_size(batch)) + return loss + + def loss_data(self, input_pts, output_pts): + """ + Compute the data loss for the PINN solver by evaluating the loss + between the network's output and the true solution. This method should + not be overridden, if not intentionally. + + :param LabelTensor input_pts: The input points to the neural network. + :param LabelTensor output_pts: The true solution to compare with the + network's output. + :return: The supervised loss, averaged over the number of observations. + :rtype: torch.Tensor + """ + return self._loss(self.forward(input_pts), output_pts) + + @abstractmethod + def loss_phys(self, samples, equation): + """ + Computes the physics loss for the physics-informed solver based on the + provided samples and equation. This method must be overridden in + subclasses. It distinguishes different types of PINN solvers. + + :param LabelTensor samples: The samples to evaluate the physics loss. + :param EquationInterface equation: The governing equation. + :return: The computed physics loss. + :rtype: LabelTensor + """ + + def compute_residual(self, samples, equation): + """ + Compute the residuals of the equation. + + :param LabelTensor samples: The samples to evaluate the loss. + :param EquationInterface equation: The governing equation. + :return: The residual of the solution of the model. + :rtype: LabelTensor + """ + try: + residual = equation.residual(samples, self.forward(samples)) + except TypeError: + # this occurs when the function has three inputs (inverse problem) + residual = equation.residual( + samples, self.forward(samples), self._params + ) + return residual + + def _residual_loss(self, samples, equation): + """ + Compute the residual loss. + + :param LabelTensor samples: The samples to evaluate the loss. + :param EquationInterface equation: The governing equation. + :return: The residual loss. + :rtype: torch.Tensor + """ + residuals = self.compute_residual(samples, equation) + return self.loss(residuals, torch.zeros_like(residuals)) + + def _run_optimization_cycle(self, batch, loss_residuals): + """ + Compute, given a batch, the loss for each condition and return a + dictionary with the condition name as key and the loss as value. + + :param list[tuple[str, dict]] batch: A batch of data. Each element is a + tuple containing a condition name and a dictionary of points. + :param function loss_residuals: The loss function to be minimized. + :return: The losses computed for all conditions in the batch, casted + to a subclass of :class:`torch.Tensor`. It should return a dict + containing the condition name and the associated scalar loss. + :rtype: dict + """ + condition_loss = {} + for condition_name, points in batch: + self.__metric = condition_name + # if equations are passed + if "target" not in points: + input_pts = points["input"] + condition = self.problem.conditions[condition_name] + loss = loss_residuals( + input_pts.requires_grad_(), condition.equation + ) + # if data are passed + else: + input_pts = points["input"] + output_pts = points["target"] + loss = self.loss_data( + input_pts=input_pts.requires_grad_(), output_pts=output_pts + ) + # append loss + condition_loss[condition_name] = loss + # clamp unknown parameters in InverseProblem (if needed) + self._clamp_params() + return condition_loss + + def _clamp_inverse_problem_params(self): + """ + Clamps the parameters of the inverse problem solver to specified ranges. + """ + for v in self._params: + self._params[v].data.clamp_( + self.problem.unknown_parameter_domain.range_[v][0], + self.problem.unknown_parameter_domain.range_[v][1], + ) + + @property + def loss(self): + """ + The loss used for training. + + :return: The loss function used for training. + :rtype: torch.nn.Module + """ + return self._loss + + @property + def current_condition_name(self): + """ + The current condition name. + + :return: The current condition name. + :rtype: str + """ + return self.__metric diff --git a/pina/solver/physics_informed_solver/rba_pinn.py b/pina/solver/physics_informed_solver/rba_pinn.py new file mode 100644 index 000000000..feeb5c817 --- /dev/null +++ b/pina/solver/physics_informed_solver/rba_pinn.py @@ -0,0 +1,188 @@ +"""Module for the Residual-Based Attention PINN solver.""" + +from copy import deepcopy +import torch + +from .pinn import PINN +from ...utils import check_consistency + + +class RBAPINN(PINN): + r""" + Residual-based Attention Physics-Informed Neural Network (RBAPINN) solver + class. This class implements the Residual-based Attention Physics-Informed + Neural Network solver, using a user specified ``model`` to solve a specific + ``problem``. It can be used to solve both forward and inverse problems. + + The Residual-based Attention Physics-Informed Neural Network solver aims to + find the solution :math:`\mathbf{u}:\Omega\rightarrow\mathbb{R}^m` of a + differential problem: + + .. math:: + + \begin{cases} + \mathcal{A}[\mathbf{u}](\mathbf{x})=0\quad,\mathbf{x}\in\Omega\\ + \mathcal{B}[\mathbf{u}](\mathbf{x})=0\quad, + \mathbf{x}\in\partial\Omega + \end{cases} + + minimizing the loss function: + + .. math:: + + \mathcal{L}_{\rm{problem}} = \frac{1}{N} \sum_{i=1}^{N_\Omega} + \lambda_{\Omega}^{i} \mathcal{L} \left( \mathcal{A} + [\mathbf{u}](\mathbf{x}) \right) + \frac{1}{N} + \sum_{i=1}^{N_{\partial\Omega}} + \lambda_{\partial\Omega}^{i} \mathcal{L} + \left( \mathcal{B}[\mathbf{u}](\mathbf{x}) + \right), + + denoting the weights as: + :math:`\lambda_{\Omega}^1, \dots, \lambda_{\Omega}^{N_\Omega}` and + :math:`\lambda_{\partial \Omega}^1, \dots, + \lambda_{\Omega}^{N_\partial \Omega}` + for :math:`\Omega` and :math:`\partial \Omega`, respectively. + + Residual-based Attention Physics-Informed Neural Network updates the weights + of the residuals at every epoch as follows: + + .. math:: + + \lambda_i^{k+1} \leftarrow \gamma\lambda_i^{k} + + \eta\frac{\lvert r_i\rvert}{\max_j \lvert r_j\rvert}, + + where :math:`r_i` denotes the residual at point :math:`i`, :math:`\gamma` + denotes the decay rate, and :math:`\eta` is the learning rate for the + weights' update. + + .. seealso:: + **Original reference**: Sokratis J. Anagnostopoulos, Juan D. Toscano, + Nikolaos Stergiopulos, and George E. Karniadakis. + *Residual-based attention and connection to information + bottleneck theory in PINNs.* + Computer Methods in Applied Mechanics and Engineering 421 (2024): 116805 + DOI: `10.1016/j.cma.2024.116805 + `_. + """ + + def __init__( + self, + problem, + model, + optimizer=None, + scheduler=None, + weighting=None, + loss=None, + eta=0.001, + gamma=0.999, + ): + """ + Initialization of the :class:`RBAPINN` class. + + :param AbstractProblem problem: The problem to be solved. + :param torch.nn.Module model: The neural network model to be used. + :param Optimizer optimizer: The optimizer to be used. + If `None`, the :class:`torch.optim.Adam` optimizer is used. + Default is ``None``. + :param Scheduler scheduler: Learning rate scheduler. + If `None`, the :class:`torch.optim.lr_scheduler.ConstantLR` + scheduler is used. Default is ``None``. + :param WeightingInterface weighting: The weighting schema to be used. + If `None`, no weighting schema is used. Default is ``None``. + :param torch.nn.Module loss: The loss function to be minimized. + If `None`, the :class:`torch.nn.MSELoss` loss is used. + Default is `None`. + :param float | int eta: The learning rate for the weights of the + residuals. Default is ``0.001``. + :param float gamma: The decay parameter in the update of the weights + of the residuals. Must be between ``0`` and ``1``. + Default is ``0.999``. + """ + super().__init__( + model=model, + problem=problem, + optimizer=optimizer, + scheduler=scheduler, + weighting=weighting, + loss=loss, + ) + + # check consistency + check_consistency(eta, (float, int)) + check_consistency(gamma, float) + assert ( + 0 < gamma < 1 + ), f"Invalid range: expected 0 < gamma < 1, got {gamma=}" + self.eta = eta + self.gamma = gamma + + # initialize weights + self.weights = {} + for condition_name in problem.conditions: + self.weights[condition_name] = 0 + + # define vectorial loss + self._vectorial_loss = deepcopy(self.loss) + self._vectorial_loss.reduction = "none" + + # for now RBAPINN is implemented only for batch_size = None + def on_train_start(self): + """ + Hook method called at the beginning of training. + + :raises NotImplementedError: If the batch size is not ``None``. + """ + if self.trainer.batch_size is not None: + raise NotImplementedError( + "RBAPINN only works with full batch " + "size, set batch_size=None inside the " + "Trainer to use the solver." + ) + return super().on_train_start() + + def _vect_to_scalar(self, loss_value): + """ + Computation of the scalar loss. + + :param LabelTensor loss_value: the tensor of pointwise losses. + :raises RuntimeError: If the loss reduction is not ``mean`` or ``sum``. + :return: The computed scalar loss. + :rtype: LabelTensor + """ + if self.loss.reduction == "mean": + ret = torch.mean(loss_value) + elif self.loss.reduction == "sum": + ret = torch.sum(loss_value) + else: + raise RuntimeError( + f"Invalid reduction, got {self.loss.reduction} " + "but expected mean or sum." + ) + return ret + + def loss_phys(self, samples, equation): + """ + Computes the physics loss for the physics-informed solver based on the + provided samples and equation. + + :param LabelTensor samples: The samples to evaluate the physics loss. + :param EquationInterface equation: The governing equation. + :return: The computed physics loss. + :rtype: LabelTensor + """ + residual = self.compute_residual(samples=samples, equation=equation) + cond = self.current_condition_name + + r_norm = ( + self.eta + * torch.abs(residual) + / (torch.max(torch.abs(residual)) + 1e-12) + ) + self.weights[cond] = (self.gamma * self.weights[cond] + r_norm).detach() + + loss_value = self._vectorial_loss( + torch.zeros_like(residual, requires_grad=True), residual + ) + + return self._vect_to_scalar(self.weights[cond] ** 2 * loss_value) diff --git a/pina/solver/physics_informed_solver/self_adaptive_pinn.py b/pina/solver/physics_informed_solver/self_adaptive_pinn.py new file mode 100644 index 000000000..a6310d515 --- /dev/null +++ b/pina/solver/physics_informed_solver/self_adaptive_pinn.py @@ -0,0 +1,386 @@ +"""Module for the Self-Adaptive PINN solver.""" + +from copy import deepcopy +import torch + +from ...utils import check_consistency +from ...problem import InverseProblem +from ..solver import MultiSolverInterface +from .pinn_interface import PINNInterface + + +class Weights(torch.nn.Module): + """ + Implementation of the mask model for the self-adaptive weights of the + :class:`SelfAdaptivePINN` solver. + """ + + def __init__(self, func): + """ + Initialization of the :class:`Weights` class. + + :param torch.nn.Module func: the mask model. + """ + super().__init__() + check_consistency(func, torch.nn.Module) + self.sa_weights = torch.nn.Parameter(torch.Tensor()) + self.func = func + + def forward(self): + """ + Forward pass implementation for the mask module. + + :return: evaluation of self adaptive weights through the mask. + :rtype: torch.Tensor + """ + return self.func(self.sa_weights) + + +class SelfAdaptivePINN(PINNInterface, MultiSolverInterface): + r""" + Self-Adaptive Physics-Informed Neural Network (SelfAdaptivePINN) solver + class. This class implements the Self-Adaptive Physics-Informed Neural + Network solver, using a user specified ``model`` to solve a specific + ``problem``. It can be used to solve both forward and inverse problems. + + The Self-Adapive Physics-Informed Neural Network solver aims to find the + solution :math:`\mathbf{u}:\Omega\rightarrow\mathbb{R}^m` of a differential + problem: + + .. math:: + + \begin{cases} + \mathcal{A}[\mathbf{u}](\mathbf{x})=0\quad,\mathbf{x}\in\Omega\\ + \mathcal{B}[\mathbf{u}](\mathbf{x})=0\quad, + \mathbf{x}\in\partial\Omega + \end{cases} + + integrating pointwise loss evaluation using a mask :math:m and self-adaptive + weights, which allow the model to focus on regions of the domain where the + residual is higher. + + The loss function to solve the problem is + + .. math:: + + \mathcal{L}_{\rm{problem}} = \frac{1}{N} \sum_{i=1}^{N_\Omega} m + \left( \lambda_{\Omega}^{i} \right) \mathcal{L} \left( \mathcal{A} + [\mathbf{u}](\mathbf{x}) \right) + \frac{1}{N} + \sum_{i=1}^{N_{\partial\Omega}} + m \left( \lambda_{\partial\Omega}^{i} \right) \mathcal{L} + \left( \mathcal{B}[\mathbf{u}](\mathbf{x}) + \right), + + denoting the self adaptive weights as + :math:`\lambda_{\Omega}^1, \dots, \lambda_{\Omega}^{N_\Omega}` and + :math:`\lambda_{\partial \Omega}^1, \dots, + \lambda_{\Omega}^{N_\partial \Omega}` + for :math:`\Omega` and :math:`\partial \Omega`, respectively. + + The Self-Adaptive Physics-Informed Neural Network solver identifies the + solution and appropriate self adaptive weights by solving the following + optimization problem: + + .. math:: + + \min_{w} \max_{\lambda_{\Omega}^k, \lambda_{\partial \Omega}^s} + \mathcal{L} , + + where :math:`w` denotes the network parameters, and :math:`\mathcal{L}` is a + specific loss function, , typically the MSE: + + .. math:: + \mathcal{L}(v) = \| v \|^2_2. + + .. seealso:: + **Original reference**: McClenny, Levi D., and Ulisses M. Braga-Neto. + *Self-adaptive physics-informed neural networks.* + Journal of Computational Physics 474 (2023): 111722. + DOI: `10.1016/j.jcp.2022.111722 + `_. + """ + + def __init__( + self, + problem, + model, + weight_function=torch.nn.Sigmoid(), + optimizer_model=None, + optimizer_weights=None, + scheduler_model=None, + scheduler_weights=None, + weighting=None, + loss=None, + ): + """ + Initialization of the :class:`SelfAdaptivePINN` class. + + :param AbstractProblem problem: The problem to be solved. + :param torch.nn.Module model: The model to be used. + :param torch.nn.Module weight_function: The Self-Adaptive mask model. + Default is ``torch.nn.Sigmoid()``. + :param Optimizer optimizer_model: The optimizer of the ``model``. + If `None`, the :class:`torch.optim.Adam` optimizer is used. + Default is ``None``. + :param Optimizer optimizer_weights: The optimizer of the + ``weight_function``. + If `None`, the :class:`torch.optim.Adam` optimizer is used. + Default is ``None``. + :param Scheduler scheduler_model: Learning rate scheduler for the + ``model``. + If `None`, the :class:`torch.optim.lr_scheduler.ConstantLR` + scheduler is used. Default is ``None``. + :param Scheduler scheduler_weights: Learning rate scheduler for the + ``weight_function``. + If `None`, the :class:`torch.optim.lr_scheduler.ConstantLR` + scheduler is used. Default is ``None``. + :param WeightingInterface weighting: The weighting schema to be used. + If `None`, no weighting schema is used. Default is ``None``. + :param torch.nn.Module loss: The loss function to be minimized. + If `None`, the :class:`torch.nn.MSELoss` loss is used. + Default is `None`. + """ + # check consistency weitghs_function + check_consistency(weight_function, torch.nn.Module) + + # create models for weights + weights_dict = {} + for condition_name in problem.conditions: + weights_dict[condition_name] = Weights(weight_function) + weights_dict = torch.nn.ModuleDict(weights_dict) + + super().__init__( + models=[model, weights_dict], + problem=problem, + optimizers=[optimizer_model, optimizer_weights], + schedulers=[scheduler_model, scheduler_weights], + weighting=weighting, + loss=loss, + ) + + # Set automatic optimization to False + self.automatic_optimization = False + + self._vectorial_loss = deepcopy(self.loss) + self._vectorial_loss.reduction = "none" + + def forward(self, x): + """ + Forward pass. + + :param LabelTensor x: Input tensor. + :return: The output of the neural network. + :rtype: LabelTensor + """ + return self.model(x) + + def training_step(self, batch): + """ + Solver training step, overridden to perform manual optimization. + + :param list[tuple[str, dict]] batch: A batch of data. Each element is a + tuple containing a condition name and a dictionary of points. + :return: The aggregated loss. + :rtype: LabelTensor + """ + # Weights optimization + self.optimizer_weights.instance.zero_grad() + loss = super().training_step(batch) + self.manual_backward(-loss) + self.optimizer_weights.instance.step() + + # Model optimization + self.optimizer_model.instance.zero_grad() + loss = super().training_step(batch) + self.manual_backward(loss) + self.optimizer_model.instance.step() + + return loss + + def configure_optimizers(self): + """ + Optimizer configuration. + + :return: The optimizers and the schedulers + :rtype: tuple[list[Optimizer], list[Scheduler]] + """ + # If the problem is an InverseProblem, add the unknown parameters + # to the parameters to be optimized + self.optimizer_model.hook(self.model.parameters()) + self.optimizer_weights.hook(self.weights_dict.parameters()) + if isinstance(self.problem, InverseProblem): + self.optimizer_model.instance.add_param_group( + { + "params": [ + self._params[var] + for var in self.problem.unknown_variables + ] + } + ) + self.scheduler_model.hook(self.optimizer_model) + self.scheduler_weights.hook(self.optimizer_weights) + return ( + [self.optimizer_model.instance, self.optimizer_weights.instance], + [self.scheduler_model.instance, self.scheduler_weights.instance], + ) + + def on_train_batch_end(self, outputs, batch, batch_idx): + """ + This method is called at the end of each training batch and overrides + the PyTorch Lightning implementation to log checkpoints. + + :param torch.Tensor outputs: The ``model``'s output for the current + batch. + :param list[tuple[str, dict]] batch: A batch of data. Each element is a + tuple containing a condition name and a dictionary of points. + :param int batch_idx: The index of the current batch. + """ + # increase by one the counter of optimization to save loggers + ( + self.trainer.fit_loop.epoch_loop.manual_optimization.optim_step_progress.total.completed + ) += 1 + + return super().on_train_batch_end(outputs, batch, batch_idx) + + def on_train_start(self): + """ + This method is called at the start of the training process to set the + self-adaptive weights as parameters of the mask model. + + :raises NotImplementedError: If the batch size is not ``None``. + """ + if self.trainer.batch_size is not None: + raise NotImplementedError( + "SelfAdaptivePINN only works with full " + "batch size, set batch_size=None inside " + "the Trainer to use the solver." + ) + device = torch.device( + self.trainer._accelerator_connector._accelerator_flag + ) + + # Initialize the self adaptive weights only for training points + for ( + condition_name, + tensor, + ) in self.trainer.data_module.train_dataset.input.items(): + self.weights_dict[condition_name].sa_weights.data = torch.rand( + (tensor.shape[0], 1), device=device + ) + return super().on_train_start() + + def on_load_checkpoint(self, checkpoint): + """ + Override of the Pytorch Lightning ``on_load_checkpoint`` method to + handle checkpoints for Self-Adaptive Weights. This method should not be + overridden, if not intentionally. + + :param dict checkpoint: Pytorch Lightning checkpoint dict. + """ + # First initialize self-adaptive weights with correct shape, + # then load the values from the checkpoint. + for condition_name, _ in self.problem.input_pts.items(): + shape = checkpoint["state_dict"][ + f"_pina_models.1.{condition_name}.sa_weights" + ].shape + self.weights_dict[condition_name].sa_weights.data = torch.rand( + shape + ) + return super().on_load_checkpoint(checkpoint) + + def loss_phys(self, samples, equation): + """ + Computes the physics loss for the physics-informed solver based on the + provided samples and equation. + + :param LabelTensor samples: The samples to evaluate the physics loss. + :param EquationInterface equation: The governing equation. + :return: The computed physics loss. + :rtype: LabelTensor + """ + residual = self.compute_residual(samples, equation) + weights = self.weights_dict[self.current_condition_name].forward() + loss_value = self._vectorial_loss( + torch.zeros_like(residual, requires_grad=True), residual + ) + return self._vect_to_scalar(weights * loss_value) + + def _vect_to_scalar(self, loss_value): + """ + Computation of the scalar loss. + + :param LabelTensor loss_value: the tensor of pointwise losses. + :raises RuntimeError: If the loss reduction is not ``mean`` or ``sum``. + :return: The computed scalar loss. + :rtype: LabelTensor + """ + if self.loss.reduction == "mean": + ret = torch.mean(loss_value) + elif self.loss.reduction == "sum": + ret = torch.sum(loss_value) + else: + raise RuntimeError( + f"Invalid reduction, got {self.loss.reduction} " + "but expected mean or sum." + ) + return ret + + @property + def model(self): + """ + The model. + + :return: The model. + :rtype: torch.nn.Module + """ + return self.models[0] + + @property + def weights_dict(self): + """ + The self-adaptive weights. + + :return: The self-adaptive weights. + :rtype: torch.nn.Module + """ + return self.models[1] + + @property + def scheduler_model(self): + """ + The scheduler associated to the model. + + :return: The scheduler for the model. + :rtype: Scheduler + """ + return self.schedulers[0] + + @property + def scheduler_weights(self): + """ + The scheduler associated to the mask model. + + :return: The scheduler for the mask model. + :rtype: Scheduler + """ + return self.schedulers[1] + + @property + def optimizer_model(self): + """ + Returns the optimizer associated to the model. + + :return: The optimizer for the model. + :rtype: Optimizer + """ + return self.optimizers[0] + + @property + def optimizer_weights(self): + """ + The optimizer associated to the mask model. + + :return: The optimizer for the mask model. + :rtype: Optimizer + """ + return self.optimizers[1] diff --git a/pina/solver/reduced_order_model.py b/pina/solver/reduced_order_model.py new file mode 100644 index 000000000..949cb0111 --- /dev/null +++ b/pina/solver/reduced_order_model.py @@ -0,0 +1,191 @@ +"""Module for the Reduced Order Model solver""" + +import torch +from .supervised import SupervisedSolver + + +class ReducedOrderModelSolver(SupervisedSolver): + r""" + Reduced Order Model solver class. This class implements the Reduced Order + Model solver, using user specified ``reduction_network`` and + ``interpolation_network`` to solve a specific ``problem``. + + The Reduced Order Model solver aims to find the solution + :math:`\mathbf{u}:\Omega\rightarrow\mathbb{R}^m` of a differential problem: + + .. math:: + + \begin{cases} + \mathcal{A}[\mathbf{u}(\mu)](\mathbf{x})=0\quad,\mathbf{x}\in\Omega\\ + \mathcal{B}[\mathbf{u}(\mu)](\mathbf{x})=0\quad, + \mathbf{x}\in\partial\Omega + \end{cases} + + This is done by means of two neural networks: the ``reduction_network``, + which defines an encoder :math:`\mathcal{E}_{\rm{net}}`, and a decoder + :math:`\mathcal{D}_{\rm{net}}`; and the ``interpolation_network`` + :math:`\mathcal{I}_{\rm{net}}`. The input is assumed to be discretised in + the spatial dimensions. + + The following loss function is minimized during training: + + .. math:: + \mathcal{L}_{\rm{problem}} = \frac{1}{N}\sum_{i=1}^N + \mathcal{L}(\mathcal{E}_{\rm{net}}[\mathbf{u}(\mu_i)] - + \mathcal{I}_{\rm{net}}[\mu_i]) + + \mathcal{L}( + \mathcal{D}_{\rm{net}}[\mathcal{E}_{\rm{net}}[\mathbf{u}(\mu_i)]] - + \mathbf{u}(\mu_i)) + + where :math:`\mathcal{L}` is a specific loss function, typically the MSE: + + .. math:: + \mathcal{L}(v) = \| v \|^2_2. + + .. seealso:: + + **Original reference**: Hesthaven, Jan S., and Stefano Ubbiali. + *Non-intrusive reduced order modeling of nonlinear problems using + neural networks.* + Journal of Computational Physics 363 (2018): 55-78. + DOI `10.1016/j.jcp.2018.02.037 + `_. + + .. note:: + The specified ``reduction_network`` must contain two methods, namely + ``encode`` for input encoding, and ``decode`` for decoding the former + result. The ``interpolation_network`` network ``forward`` output + represents the interpolation of the latent space obtained with + ``reduction_network.encode``. + + .. note:: + This solver uses the end-to-end training strategy, i.e. the + ``reduction_network`` and ``interpolation_network`` are trained + simultaneously. For reference on this trainig strategy look at the + following: + + ..seealso:: + **Original reference**: Pichi, Federico, Beatriz Moya, and Jan S. + Hesthaven. + *A graph convolutional autoencoder approach to model order reduction + for parametrized PDEs.* + Journal of Computational Physics 501 (2024): 112762. + DOI `10.1016/j.jcp.2024.112762 + `_. + + .. warning:: + This solver works only for data-driven model. Hence in the ``problem`` + definition the codition must only contain ``input`` + (e.g. coefficient parameters, time parameters), and ``target``. + """ + + def __init__( + self, + problem, + reduction_network, + interpolation_network, + loss=None, + optimizer=None, + scheduler=None, + weighting=None, + use_lt=True, + ): + """ + Initialization of the :class:`ReducedOrderModelSolver` class. + + :param AbstractProblem problem: The formualation of the problem. + :param torch.nn.Module reduction_network: The reduction network used + for reducing the input space. It must contain two methods, namely + ``encode`` for input encoding, and ``decode`` for decoding the + former result. + :param torch.nn.Module interpolation_network: The interpolation network + for interpolating the control parameters to latent space obtained by + the ``reduction_network`` encoding. + :param torch.nn.Module loss: The loss function to be minimized. + If `None`, the :class:`torch.nn.MSELoss` loss is used. + Default is `None`. + :param Optimizer optimizer: The optimizer to be used. + If `None`, the :class:`torch.optim.Adam` optimizer is used. + Default is ``None``. + :param Scheduler scheduler: Learning rate scheduler. + If `None`, the :class:`torch.optim.lr_scheduler.ConstantLR` + scheduler is used. Default is ``None``. + :param WeightingInterface weighting: The weighting schema to be used. + If `None`, no weighting schema is used. Default is ``None``. + :param bool use_lt: If ``True``, the solver uses LabelTensors as input. + Default is ``True``. + """ + model = torch.nn.ModuleDict( + { + "reduction_network": reduction_network, + "interpolation_network": interpolation_network, + } + ) + + super().__init__( + model=model, + problem=problem, + loss=loss, + optimizer=optimizer, + scheduler=scheduler, + weighting=weighting, + use_lt=use_lt, + ) + + # assert reduction object contains encode/ decode + if not hasattr(self.model["reduction_network"], "encode"): + raise SyntaxError( + "reduction_network must have encode method. " + "The encode method should return a lower " + "dimensional representation of the input." + ) + if not hasattr(self.model["reduction_network"], "decode"): + raise SyntaxError( + "reduction_network must have decode method. " + "The decode method should return a high " + "dimensional representation of the encoding." + ) + + def forward(self, x): + """ + Forward pass implementation. + It computes the encoder representation by calling the forward method + of the ``interpolation_network`` on the input, and maps it to output + space by calling the decode methode of the ``reduction_network``. + + :param x: Input tensor. + :type x: torch.Tensor | LabelTensor + :return: Solver solution. + :rtype: torch.Tensor | LabelTensor + """ + reduction_network = self.model["reduction_network"] + interpolation_network = self.model["interpolation_network"] + return reduction_network.decode(interpolation_network(x)) + + def loss_data(self, input_pts, output_pts): + """ + Compute the data loss by evaluating the loss between the network's + output and the true solution. This method should not be overridden, if + not intentionally. + + :param LabelTensor input_pts: The input points to the neural network. + :param LabelTensor output_pts: The true solution to compare with the + network's output. + :return: The supervised loss, averaged over the number of observations. + :rtype: torch.Tensor + """ + # extract networks + reduction_network = self.model["reduction_network"] + interpolation_network = self.model["interpolation_network"] + # encoded representations loss + encode_repr_inter_net = interpolation_network(input_pts) + encode_repr_reduction_network = reduction_network.encode(output_pts) + loss_encode = self.loss( + encode_repr_inter_net, encode_repr_reduction_network + ) + # reconstruction loss + loss_reconstruction = self.loss( + reduction_network.decode(encode_repr_reduction_network), output_pts + ) + + return loss_encode + loss_reconstruction diff --git a/pina/solver/solver.py b/pina/solver/solver.py new file mode 100644 index 000000000..2a173b33d --- /dev/null +++ b/pina/solver/solver.py @@ -0,0 +1,543 @@ +"""Solver module.""" + +from abc import ABCMeta, abstractmethod +import lightning +import torch + +from torch._dynamo.eval_frame import OptimizedModule +from ..problem import AbstractProblem +from ..optim import Optimizer, Scheduler, TorchOptimizer, TorchScheduler +from ..loss import WeightingInterface +from ..loss.scalar_weighting import _NoWeighting +from ..utils import check_consistency, labelize_forward + + +class SolverInterface(lightning.pytorch.LightningModule, metaclass=ABCMeta): + """ + Abstract base class for PINA solvers. All specific solvers should inherit + from this interface. This class is a wrapper of + :class:`~lightning.pytorch.LightningModule`. + """ + + def __init__(self, problem, weighting, use_lt): + """ + Initialization of the :class:`SolverInterface` class. + + :param AbstractProblem problem: The problem to be solved. + :param WeightingInterface weighting: The weighting schema to be used. + If `None`, no weighting schema is used. Default is ``None``. + :param bool use_lt: If ``True``, the solver uses LabelTensors as input. + """ + super().__init__() + + # check consistency of the problem + check_consistency(problem, AbstractProblem) + self._check_solver_consistency(problem) + self._pina_problem = problem + + # check consistency of the weighting and hook the condition names + if weighting is None: + weighting = _NoWeighting() + check_consistency(weighting, WeightingInterface) + self._pina_weighting = weighting + weighting.condition_names = list(self._pina_problem.conditions.keys()) + + # check consistency use_lt + check_consistency(use_lt, bool) + self._use_lt = use_lt + + # if use_lt is true add extract operation in input + if use_lt is True: + self.forward = labelize_forward( + forward=self.forward, + input_variables=problem.input_variables, + output_variables=problem.output_variables, + ) + + # PINA private attributes (some are overridden by derived classes) + self._pina_problem = problem + self._pina_models = None + self._pina_optimizers = None + self._pina_schedulers = None + + def _check_solver_consistency(self, problem): + """ + Check the consistency of the solver with the problem formulation. + + :param AbstractProblem problem: The problem to be solved. + """ + for condition in problem.conditions.values(): + check_consistency(condition, self.accepted_conditions_types) + + def _optimization_cycle(self, batch): + """ + Aggregate the loss for each condition in the batch. + + :param list[tuple[str, dict]] batch: A batch of data. Each element is a + tuple containing a condition name and a dictionary of points. + :return: The losses computed for all conditions in the batch, casted + to a subclass of :class:`torch.Tensor`. It should return a dict + containing the condition name and the associated scalar loss. + :rtype: dict + """ + losses = self.optimization_cycle(batch) + for name, value in losses.items(): + self.store_log( + f"{name}_loss", value.item(), self.get_batch_size(batch) + ) + loss = self.weighting.aggregate(losses).as_subclass(torch.Tensor) + return loss + + def training_step(self, batch): + """ + Solver training step. + + :param list[tuple[str, dict]] batch: A batch of data. Each element is a + tuple containing a condition name and a dictionary of points. + :return: The loss of the training step. + :rtype: LabelTensor + """ + loss = self._optimization_cycle(batch=batch) + self.store_log("train_loss", loss, self.get_batch_size(batch)) + return loss + + def validation_step(self, batch): + """ + Solver validation step. + + :param list[tuple[str, dict]] batch: A batch of data. Each element is a + tuple containing a condition name and a dictionary of points. + """ + loss = self._optimization_cycle(batch=batch) + self.store_log("val_loss", loss, self.get_batch_size(batch)) + + def test_step(self, batch): + """ + Solver test step. + + :param list[tuple[str, dict]] batch: A batch of data. Each element is a + tuple containing a condition name and a dictionary of points. + """ + loss = self._optimization_cycle(batch=batch) + self.store_log("test_loss", loss, self.get_batch_size(batch)) + + def store_log(self, name, value, batch_size): + """ + Store the log of the solver. + + :param str name: The name of the log. + :param torch.Tensor value: The value of the log. + :param int batch_size: The size of the batch. + """ + + self.log( + name=name, + value=value, + batch_size=batch_size, + **self.trainer.logging_kwargs, + ) + + @abstractmethod + def forward(self, *args, **kwargs): + """ + Abstract method for the forward pass implementation. + + :param args: The input tensor. + :type args: torch.Tensor | LabelTensor + :param dict kwargs: Additional keyword arguments. + """ + + @abstractmethod + def optimization_cycle(self, batch): + """ + The optimization cycle for the solvers. + + :param list[tuple[str, dict]] batch: A batch of data. Each element is a + tuple containing a condition name and a dictionary of points. + :return: The losses computed for all conditions in the batch, casted + to a subclass of :class:`torch.Tensor`. It should return a dict + containing the condition name and the associated scalar loss. + :rtype: dict + """ + + @property + def problem(self): + """ + The problem instance. + + :return: The problem instance. + :rtype: :class:`~pina.problem.abstract_problem.AbstractProblem` + """ + return self._pina_problem + + @property + def use_lt(self): + """ + Using LabelTensors as input during training. + + :return: The use_lt attribute. + :rtype: bool + """ + return self._use_lt + + @property + def weighting(self): + """ + The weighting schema. + + :return: The weighting schema. + :rtype: :class:`~pina.loss.weighting_interface.WeightingInterface` + """ + return self._pina_weighting + + @staticmethod + def get_batch_size(batch): + """ + Get the batch size. + + :param list[tuple[str, dict]] batch: A batch of data. Each element is a + tuple containing a condition name and a dictionary of points. + :return: The size of the batch. + :rtype: int + """ + + batch_size = 0 + for data in batch: + batch_size += len(data[1]["input"]) + return batch_size + + @staticmethod + def default_torch_optimizer(): + """ + Set the default optimizer to :class:`torch.optim.Adam`. + + :return: The default optimizer. + :rtype: Optimizer + """ + return TorchOptimizer(torch.optim.Adam, lr=0.001) + + @staticmethod + def default_torch_scheduler(): + """ + Set the default scheduler to + :class:`torch.optim.lr_scheduler.ConstantLR`. + + :return: The default scheduler. + :rtype: Scheduler + """ + + return TorchScheduler(torch.optim.lr_scheduler.ConstantLR) + + def on_train_start(self): + """ + This method is called at the start of the training process to compile + the model if the :class:`~pina.trainer.Trainer` ``compile`` is ``True``. + """ + super().on_train_start() + if self.trainer.compile: + self._compile_model() + + def on_test_start(self): + """ + This method is called at the start of the test process to compile + the model if the :class:`~pina.trainer.Trainer` ``compile`` is ``True``. + """ + super().on_train_start() + if self.trainer.compile and not self._check_already_compiled(): + self._compile_model() + + def _check_already_compiled(self): + """ + Check if the model is already compiled. + + :return: ``True`` if the model is already compiled, ``False`` otherwise. + :rtype: bool + """ + + models = self._pina_models + if len(models) == 1 and isinstance( + self._pina_models[0], torch.nn.ModuleDict + ): + models = list(self._pina_models.values()) + for model in models: + if not isinstance(model, (OptimizedModule, torch.nn.ModuleDict)): + return False + return True + + @staticmethod + def _perform_compilation(model): + """ + Perform the compilation of the model. + + :param torch.nn.Module model: The model to compile. + :raises Exception: If the compilation fails. + :return: The compiled model. + :rtype: torch.nn.Module + """ + + model_device = next(model.parameters()).device + try: + if model_device == torch.device("mps:0"): + model = torch.compile(model, backend="eager") + else: + model = torch.compile(model, backend="inductor") + except Exception as e: + print("Compilation failed, running in normal mode.:\n", e) + return model + + +class SingleSolverInterface(SolverInterface, metaclass=ABCMeta): + """ + Base class for PINA solvers using a single :class:`torch.nn.Module`. + """ + + def __init__( + self, + problem, + model, + optimizer=None, + scheduler=None, + weighting=None, + use_lt=True, + ): + """ + Initialization of the :class:`SingleSolverInterface` class. + + :param AbstractProblem problem: The problem to be solved. + :param torch.nn.Module model: The neural network model to be used. + :param Optimizer optimizer: The optimizer to be used. + If `None`, the :class:`torch.optim.Adam` optimizer is + used. Default is ``None``. + :param Scheduler scheduler: The scheduler to be used. + If `None`, the :class:`torch.optim.lr_scheduler.ConstantLR` + scheduler is used. Default is ``None``. + :param WeightingInterface weighting: The weighting schema to be used. + If `None`, no weighting schema is used. Default is ``None``. + :param bool use_lt: If ``True``, the solver uses LabelTensors as input. + """ + if optimizer is None: + optimizer = self.default_torch_optimizer() + + if scheduler is None: + scheduler = self.default_torch_scheduler() + + super().__init__(problem=problem, use_lt=use_lt, weighting=weighting) + + # check consistency of models argument and encapsulate in list + check_consistency(model, torch.nn.Module) + # check scheduler consistency and encapsulate in list + check_consistency(scheduler, Scheduler) + # check optimizer consistency and encapsulate in list + check_consistency(optimizer, Optimizer) + + # initialize the model (needed by Lightining to go to different devices) + self._pina_models = torch.nn.ModuleList([model]) + self._pina_optimizers = [optimizer] + self._pina_schedulers = [scheduler] + + def forward(self, x): + """ + Forward pass implementation. + + :param x: Input tensor. + :type x: torch.Tensor | LabelTensor + :return: Solver solution. + :rtype: torch.Tensor | LabelTensor + """ + x = self.model(x) + return x + + def configure_optimizers(self): + """ + Optimizer configuration for the solver. + + :return: The optimizer and the scheduler + :rtype: tuple[list[Optimizer], list[Scheduler]] + """ + self.optimizer.hook(self.model.parameters()) + self.scheduler.hook(self.optimizer) + return ([self.optimizer.instance], [self.scheduler.instance]) + + def _compile_model(self): + """ + Compile the model. + """ + if isinstance(self._pina_models[0], torch.nn.ModuleDict): + self._compile_module_dict() + else: + self._compile_single_model() + + def _compile_module_dict(self): + """ + Compile the model if it is a :class:`torch.nn.ModuleDict`. + """ + for name, model in self._pina_models[0].items(): + self._pina_models[0][name] = self._perform_compilation(model) + + def _compile_single_model(self): + """ + Compile the model if it is a single :class:`torch.nn.Module`. + """ + self._pina_models[0] = self._perform_compilation(self._pina_models[0]) + + @property + def model(self): + """ + The model used for training. + + :return: The model used for training. + :rtype: torch.nn.Module + """ + return self._pina_models[0] + + @property + def scheduler(self): + """ + The scheduler used for training. + + :return: The scheduler used for training. + :rtype: Scheduler + """ + return self._pina_schedulers[0] + + @property + def optimizer(self): + """ + The optimizer used for training. + + :return: The optimizer used for training. + :rtype: Optimizer + """ + return self._pina_optimizers[0] + + +class MultiSolverInterface(SolverInterface, metaclass=ABCMeta): + """ + Base class for PINA solvers using multiple :class:`torch.nn.Module`. + """ + + def __init__( + self, + problem, + models, + optimizers=None, + schedulers=None, + weighting=None, + use_lt=True, + ): + """ + Initialization of the :class:`MultiSolverInterface` class. + + :param AbstractProblem problem: The problem to be solved. + :param models: The neural network models to be used. + :type model: list[torch.nn.Module] | tuple[torch.nn.Module] + :param list[Optimizer] optimizers: The optimizers to be used. + If `None`, the :class:`torch.optim.Adam` optimizer is used for all + models. Default is ``None``. + :param list[Scheduler] schedulers: The schedulers to be used. + If `None`, the :class:`torch.optim.lr_scheduler.ConstantLR` + scheduler is used for all the models. Default is ``None``. + :param WeightingInterface weighting: The weighting schema to be used. + If `None`, no weighting schema is used. Default is ``None``. + :param bool use_lt: If ``True``, the solver uses LabelTensors as input. + :raises ValueError: If the models are not a list or tuple with length + greater than one. + """ + if not isinstance(models, (list, tuple)) or len(models) < 2: + raise ValueError( + "models should be list[torch.nn.Module] or " + "tuple[torch.nn.Module] with len greater than " + "one." + ) + + if any(opt is None for opt in optimizers): + optimizers = [ + self.default_torch_optimizer() if opt is None else opt + for opt in optimizers + ] + + if any(sched is None for sched in schedulers): + schedulers = [ + self.default_torch_scheduler() if sched is None else sched + for sched in schedulers + ] + + super().__init__(problem=problem, use_lt=use_lt, weighting=weighting) + + # check consistency of models argument and encapsulate in list + check_consistency(models, torch.nn.Module) + + # check scheduler consistency and encapsulate in list + check_consistency(schedulers, Scheduler) + + # check optimizer consistency and encapsulate in list + check_consistency(optimizers, Optimizer) + + # check length consistency optimizers + if len(models) != len(optimizers): + raise ValueError( + "You must define one optimizer for each model." + f"Got {len(models)} models, and {len(optimizers)}" + " optimizers." + ) + + # initialize the model + self._pina_models = torch.nn.ModuleList(models) + self._pina_optimizers = optimizers + self._pina_schedulers = schedulers + + def configure_optimizers(self): + """ + Optimizer configuration for the solver. + + :return: The optimizer and the scheduler + :rtype: tuple[list[Optimizer], list[Scheduler]] + """ + for optimizer, scheduler, model in zip( + self.optimizers, self.schedulers, self.models + ): + optimizer.hook(model.parameters()) + scheduler.hook(optimizer) + + return ( + [optimizer.instance for optimizer in self.optimizers], + [scheduler.instance for scheduler in self.schedulers], + ) + + def _compile_model(self): + """ + Compile the model. + """ + for i, model in enumerate(self._pina_models): + if not isinstance(model, torch.nn.ModuleDict): + self._pina_models[i] = self._perform_compilation(model) + + @property + def models(self): + """ + The models used for training. + + :return: The models used for training. + :rtype: torch.nn.ModuleList + """ + return self._pina_models + + @property + def optimizers(self): + """ + The optimizers used for training. + + :return: The optimizers used for training. + :rtype: list[Optimizer] + """ + return self._pina_optimizers + + @property + def schedulers(self): + """ + The schedulers used for training. + + :return: The schedulers used for training. + :rtype: list[Scheduler] + """ + return self._pina_schedulers diff --git a/pina/solver/supervised.py b/pina/solver/supervised.py new file mode 100644 index 000000000..9a5a5f4f8 --- /dev/null +++ b/pina/solver/supervised.py @@ -0,0 +1,132 @@ +"""Module for the Supervised solver.""" + +import torch +from torch.nn.modules.loss import _Loss +from .solver import SingleSolverInterface +from ..utils import check_consistency +from ..loss.loss_interface import LossInterface +from ..condition import InputTargetCondition + + +class SupervisedSolver(SingleSolverInterface): + r""" + Supervised Solver solver class. This class implements a Supervised Solver, + using a user specified ``model`` to solve a specific ``problem``. + + The Supervised Solver class aims to find a map between the input + :math:`\mathbf{s}:\Omega\rightarrow\mathbb{R}^m` and the output + :math:`\mathbf{u}:\Omega\rightarrow\mathbb{R}^m`. + + Given a model :math:`\mathcal{M}`, the following loss function is + minimized during training: + + .. math:: + \mathcal{L}_{\rm{problem}} = \frac{1}{N}\sum_{i=1}^N + \mathcal{L}(\mathbf{u}_i - \mathcal{M}(\mathbf{v}_i)), + + where :math:`\mathcal{L}` is a specific loss function, typically the MSE: + + .. math:: + \mathcal{L}(v) = \| v \|^2_2. + + In this context, :math:`\mathbf{u}_i` and :math:`\mathbf{v}_i` indicates + the will to approximate multiple (discretised) functions given multiple + (discretised) input functions. + """ + + accepted_conditions_types = InputTargetCondition + + def __init__( + self, + problem, + model, + loss=None, + optimizer=None, + scheduler=None, + weighting=None, + use_lt=True, + ): + """ + Initialization of the :class:`SupervisedSolver` class. + + :param AbstractProblem problem: The problem to be solved. + :param torch.nn.Module model: The neural network model to be used. + :param torch.nn.Module loss: The loss function to be minimized. + If `None`, the :class:`torch.nn.MSELoss` loss is used. + Default is `None`. + :param Optimizer optimizer: The optimizer to be used. + If `None`, the :class:`torch.optim.Adam` optimizer is used. + Default is ``None``. + :param Scheduler scheduler: Learning rate scheduler. + If `None`, the :class:`torch.optim.lr_scheduler.ConstantLR` + scheduler is used. Default is ``None``. + :param WeightingInterface weighting: The weighting schema to be used. + If `None`, no weighting schema is used. Default is ``None``. + :param bool use_lt: If ``True``, the solver uses LabelTensors as input. + Default is ``True``. + """ + if loss is None: + loss = torch.nn.MSELoss() + + super().__init__( + model=model, + problem=problem, + optimizer=optimizer, + scheduler=scheduler, + weighting=weighting, + use_lt=use_lt, + ) + + # check consistency + check_consistency( + loss, (LossInterface, _Loss, torch.nn.Module), subclass=False + ) + self._loss = loss + + def optimization_cycle(self, batch): + """ + The optimization cycle for the solvers. + + :param list[tuple[str, dict]] batch: A batch of data. Each element is a + tuple containing a condition name and a dictionary of points. + :return: The losses computed for all conditions in the batch, casted + to a subclass of :class:`torch.Tensor`. It should return a dict + containing the condition name and the associated scalar loss. + :rtype: dict + """ + condition_loss = {} + for condition_name, points in batch: + input_pts, output_pts = ( + points["input"], + points["target"], + ) + condition_loss[condition_name] = self.loss_data( + input_pts=input_pts, output_pts=output_pts + ) + return condition_loss + + def loss_data(self, input_pts, output_pts): + """ + Compute the data loss for the Supervised solver by evaluating the loss + between the network's output and the true solution. This method should + not be overridden, if not intentionally. + + :param input_pts: The input points to the neural network. + :type input_pts: LabelTensor | torch.Tensor + :param output_pts: The true solution to compare with the network's + output. + :type output_pts: LabelTensor | torch.Tensor + :return: The supervised loss, averaged over the number of observations. + :rtype: torch.Tensor + """ + return self._loss(self.forward(input_pts), output_pts) + + @property + def loss(self): + """ + The loss function to be minimized. + + :return: The loss function to be minimized. + :rtype: torch.nn.Module + """ + return self._loss diff --git a/pina/solvers/__init__.py b/pina/solvers/__init__.py index 7bb988d56..366b1b7b9 100644 --- a/pina/solvers/__init__.py +++ b/pina/solvers/__init__.py @@ -1,19 +1,16 @@ -__all__ = [ - "SolverInterface", - "PINNInterface", - "PINN", - "GPINN", - "CausalPINN", - "CompetitivePINN", - "SAPINN", - "RBAPINN", - "SupervisedSolver", - "ReducedOrderModelSolver", - "GAROM", -] +"""Old module for solvers. Deprecated in 0.2.0 .""" -from .solver import SolverInterface -from .pinns import * -from .supervised import SupervisedSolver -from .rom import ReducedOrderModelSolver -from .garom import GAROM +import warnings + +from ..solver import * +from ..utils import custom_warning_format + +# back-compatibility 0.1 +# Set the custom format for warnings +warnings.formatwarning = custom_warning_format +warnings.filterwarnings("always", category=DeprecationWarning) +warnings.warn( + "'pina.solvers' is deprecated and will be removed " + "in future versions. Please use 'pina.solver' instead.", + DeprecationWarning, +) diff --git a/pina/solvers/garom.py b/pina/solvers/garom.py deleted file mode 100644 index d6cd6246e..000000000 --- a/pina/solvers/garom.py +++ /dev/null @@ -1,344 +0,0 @@ -""" Module for GAROM """ - -import torch -import sys - -try: - from torch.optim.lr_scheduler import LRScheduler # torch >= 2.0 -except ImportError: - from torch.optim.lr_scheduler import ( - _LRScheduler as LRScheduler, - ) # torch < 2.0 - -from torch.optim.lr_scheduler import ConstantLR -from .solver import SolverInterface -from ..utils import check_consistency -from ..loss import LossInterface, PowerLoss -from torch.nn.modules.loss import _Loss - - -class GAROM(SolverInterface): - """ - GAROM solver class. This class implements Generative Adversarial - Reduced Order Model solver, using user specified ``models`` to solve - a specific order reduction``problem``. - - .. seealso:: - - **Original reference**: Coscia, D., Demo, N., & Rozza, G. (2023). - *Generative Adversarial Reduced Order Modelling*. - DOI: `arXiv preprint arXiv:2305.15881. - `_. - """ - - def __init__( - self, - problem, - generator, - discriminator, - loss=None, - optimizer_generator=torch.optim.Adam, - optimizer_generator_kwargs={"lr": 0.001}, - optimizer_discriminator=torch.optim.Adam, - optimizer_discriminator_kwargs={"lr": 0.001}, - scheduler_generator=ConstantLR, - scheduler_generator_kwargs={"factor": 1, "total_iters": 0}, - scheduler_discriminator=ConstantLR, - scheduler_discriminator_kwargs={"factor": 1, "total_iters": 0}, - gamma=0.3, - lambda_k=0.001, - regularizer=False, - ): - """ - :param AbstractProblem problem: The formualation of the problem. - :param torch.nn.Module generator: The neural network model to use - for the generator. - :param torch.nn.Module discriminator: The neural network model to use - for the discriminator. - :param torch.nn.Module loss: The loss function used as minimizer, - default ``None``. If ``loss`` is ``None`` the defualt - ``PowerLoss(p=1)`` is used, as in the original paper. - :param torch.optim.Optimizer optimizer_generator: The neural - network optimizer to use for the generator network - , default is `torch.optim.Adam`. - :param dict optimizer_generator_kwargs: Optimizer constructor keyword - args. for the generator. - :param torch.optim.Optimizer optimizer_discriminator: The neural - network optimizer to use for the discriminator network - , default is `torch.optim.Adam`. - :param dict optimizer_discriminator_kwargs: Optimizer constructor keyword - args. for the discriminator. - :param torch.optim.LRScheduler scheduler_generator: Learning - rate scheduler for the generator. - :param dict scheduler_generator_kwargs: LR scheduler constructor keyword args. - :param torch.optim.LRScheduler scheduler_discriminator: Learning - rate scheduler for the discriminator. - :param dict scheduler_discriminator_kwargs: LR scheduler constructor keyword args. - :param gamma: Ratio of expected loss for generator and discriminator, defaults to 0.3. - :type gamma: float - :param lambda_k: Learning rate for control theory optimization, defaults to 0.001. - :type lambda_k: float - :param regularizer: Regularization term in the GAROM loss, defaults to False. - :type regularizer: bool - - .. warning:: - The algorithm works only for data-driven model. Hence in the ``problem`` definition - the codition must only contain ``input_points`` (e.g. coefficient parameters, time - parameters), and ``output_points``. - """ - - super().__init__( - models=[generator, discriminator], - problem=problem, - optimizers=[optimizer_generator, optimizer_discriminator], - optimizers_kwargs=[ - optimizer_generator_kwargs, - optimizer_discriminator_kwargs, - ], - ) - - # set automatic optimization for GANs - self.automatic_optimization = False - - # set loss - if loss is None: - loss = PowerLoss(p=1) - - # check consistency - check_consistency(scheduler_generator, LRScheduler, subclass=True) - check_consistency(scheduler_generator_kwargs, dict) - check_consistency(scheduler_discriminator, LRScheduler, subclass=True) - check_consistency(scheduler_discriminator_kwargs, dict) - check_consistency(loss, (LossInterface, _Loss)) - check_consistency(gamma, float) - check_consistency(lambda_k, float) - check_consistency(regularizer, bool) - - # assign schedulers - self._schedulers = [ - scheduler_generator( - self.optimizers[0], **scheduler_generator_kwargs - ), - scheduler_discriminator( - self.optimizers[1], **scheduler_discriminator_kwargs - ), - ] - - # loss and writer - self._loss = loss - - # began hyperparameters - self.k = 0 - self.gamma = gamma - self.lambda_k = lambda_k - self.regularizer = float(regularizer) - self._generator = self.models[0] - self._discriminator = self.models[1] - - def forward(self, x, mc_steps=20, variance=False): - """ - Forward step for GAROM solver - - :param x: The input tensor. - :type x: torch.Tensor - :param mc_steps: Number of montecarlo samples to approximate the - expected value, defaults to 20. - :type mc_steps: int - :param variance: Returining also the sample variance of the solution, defaults to False. - :type variance: bool - :return: The expected value of the generator distribution. If ``variance=True`` also the - sample variance is returned. - :rtype: torch.Tensor | tuple(torch.Tensor, torch.Tensor) - """ - - # sampling - field_sample = [self.sample(x) for _ in range(mc_steps)] - field_sample = torch.stack(field_sample) - - # extract mean - mean = field_sample.mean(dim=0) - - if variance: - var = field_sample.var(dim=0) - return mean, var - - return mean - - def configure_optimizers(self): - """ - Optimizer configuration for the GAROM - solver. - - :return: The optimizers and the schedulers - :rtype: tuple(list, list) - """ - return self.optimizers, self._schedulers - - def sample(self, x): - # sampling - return self.generator(x) - - def _train_generator(self, parameters, snapshots): - """ - Private method to train the generator network. - """ - optimizer = self.optimizer_generator - optimizer.zero_grad() - - generated_snapshots = self.generator(parameters) - - # generator loss - r_loss = self._loss(snapshots, generated_snapshots) - d_fake = self.discriminator.forward_map( - [generated_snapshots, parameters] - ) - g_loss = ( - self._loss(d_fake, generated_snapshots) + self.regularizer * r_loss - ) - - # backward step - g_loss.backward() - optimizer.step() - - return r_loss, g_loss - - def _train_discriminator(self, parameters, snapshots): - """ - Private method to train the discriminator network. - """ - optimizer = self.optimizer_discriminator - optimizer.zero_grad() - - # Generate a batch of images - generated_snapshots = self.generator(parameters) - - # Discriminator pass - d_real = self.discriminator.forward_map([snapshots, parameters]) - d_fake = self.discriminator.forward_map( - [generated_snapshots, parameters] - ) - - # evaluate loss - d_loss_real = self._loss(d_real, snapshots) - d_loss_fake = self._loss(d_fake, generated_snapshots.detach()) - d_loss = d_loss_real - self.k * d_loss_fake - - # backward step - d_loss.backward() - optimizer.step() - - return d_loss_real, d_loss_fake, d_loss - - def _update_weights(self, d_loss_real, d_loss_fake): - """ - Private method to Update the weights of the generator and discriminator - networks. - """ - - diff = torch.mean(self.gamma * d_loss_real - d_loss_fake) - - # Update weight term for fake samples - self.k += self.lambda_k * diff.item() - self.k = min(max(self.k, 0), 1) # Constraint to interval [0, 1] - return diff - - def training_step(self, batch, batch_idx): - """GAROM solver training step. - - :param batch: The batch element in the dataloader. - :type batch: tuple - :param batch_idx: The batch index. - :type batch_idx: int - :return: The sum of the loss functions. - :rtype: LabelTensor - """ - - condition_idx = batch["condition"] - - for condition_id in range(condition_idx.min(), condition_idx.max() + 1): - - condition_name = self._dataloader.condition_names[condition_id] - condition = self.problem.conditions[condition_name] - pts = batch["pts"].detach() - out = batch["output"] - - if condition_name not in self.problem.conditions: - raise RuntimeError("Something wrong happened.") - - # for data driven mode - if not hasattr(condition, "output_points"): - raise NotImplementedError( - "GAROM works only in data-driven mode." - ) - - # get data - snapshots = out[condition_idx == condition_id] - parameters = pts[condition_idx == condition_id] - - d_loss_real, d_loss_fake, d_loss = self._train_discriminator( - parameters, snapshots - ) - - r_loss, g_loss = self._train_generator(parameters, snapshots) - - diff = self._update_weights(d_loss_real, d_loss_fake) - - # logging - self.log( - "mean_loss", - float(r_loss), - prog_bar=True, - logger=True, - on_epoch=True, - on_step=False, - ) - self.log( - "d_loss", - float(d_loss), - prog_bar=True, - logger=True, - on_epoch=True, - on_step=False, - ) - self.log( - "g_loss", - float(g_loss), - prog_bar=True, - logger=True, - on_epoch=True, - on_step=False, - ) - self.log( - "stability_metric", - float(d_loss_real + torch.abs(diff)), - prog_bar=True, - logger=True, - on_epoch=True, - on_step=False, - ) - - return - - @property - def generator(self): - return self._generator - - @property - def discriminator(self): - return self._discriminator - - @property - def optimizer_generator(self): - return self.optimizers[0] - - @property - def optimizer_discriminator(self): - return self.optimizers[1] - - @property - def scheduler_generator(self): - return self._schedulers[0] - - @property - def scheduler_discriminator(self): - return self._schedulers[1] diff --git a/pina/solvers/pinns/__init__.py b/pina/solvers/pinns/__init__.py index 8c779665a..4ae88449a 100644 --- a/pina/solvers/pinns/__init__.py +++ b/pina/solvers/pinns/__init__.py @@ -1,17 +1,17 @@ -__all__ = [ - "PINNInterface", - "PINN", - "GPINN", - "CausalPINN", - "CompetitivePINN", - "SAPINN", - "RBAPINN", -] +"""Old module for the PINNs solver. Deprecated in 0.2.0.""" -from .basepinn import PINNInterface -from .pinn import PINN -from .gpinn import GPINN -from .causalpinn import CausalPINN -from .competitive_pinn import CompetitivePINN -from .sapinn import SAPINN -from .rbapinn import RBAPINN +import warnings + +from ...solver.physics_informed_solver import * +from ...utils import custom_warning_format + +# back-compatibility 0.1 +# Set the custom format for warnings +warnings.formatwarning = custom_warning_format +warnings.filterwarnings("always", category=DeprecationWarning) +warnings.warn( + "'pina.solvers.pinns' is deprecated and will be removed " + "in future versions. Please use " + "'pina.solver.physics_informed_solver' instead.", + DeprecationWarning, +) diff --git a/pina/solvers/pinns/basepinn.py b/pina/solvers/pinns/basepinn.py deleted file mode 100644 index 0f82056a4..000000000 --- a/pina/solvers/pinns/basepinn.py +++ /dev/null @@ -1,248 +0,0 @@ -""" Module for PINN """ - -import sys -from abc import ABCMeta, abstractmethod -import torch - -from ...solvers.solver import SolverInterface -from pina.utils import check_consistency -from pina.loss import LossInterface -from pina.problem import InverseProblem -from torch.nn.modules.loss import _Loss - -torch.pi = torch.acos(torch.zeros(1)).item() * 2 # which is 3.1415927410125732 - - -class PINNInterface(SolverInterface, metaclass=ABCMeta): - """ - Base PINN solver class. This class implements the Solver Interface - for Physics Informed Neural Network solvers. - - This class can be used to - define PINNs with multiple ``optimizers``, and/or ``models``. - By default it takes - an :class:`~pina.problem.abstract_problem.AbstractProblem`, so it is up - to the user to choose which problem the implemented solver inheriting from - this class is suitable for. - """ - - def __init__( - self, - models, - problem, - optimizers, - optimizers_kwargs, - extra_features, - loss, - ): - """ - :param models: Multiple torch neural network models instances. - :type models: list(torch.nn.Module) - :param problem: A problem definition instance. - :type problem: AbstractProblem - :param list(torch.optim.Optimizer) optimizer: A list of neural network - optimizers to use. - :param list(dict) optimizer_kwargs: A list of optimizer constructor - keyword args. - :param list(torch.nn.Module) extra_features: The additional input - features to use as augmented input. If ``None`` no extra features - are passed. If it is a list of :class:`torch.nn.Module`, - the extra feature list is passed to all models. If it is a list - of extra features' lists, each single list of extra feature - is passed to a model. - :param torch.nn.Module loss: The loss function used as minimizer, - default :class:`torch.nn.MSELoss`. - """ - super().__init__( - models=models, - problem=problem, - optimizers=optimizers, - optimizers_kwargs=optimizers_kwargs, - extra_features=extra_features, - ) - - # check consistency - check_consistency(loss, (LossInterface, _Loss), subclass=False) - - # assign variables - self._loss = loss - - # inverse problem handling - if isinstance(self.problem, InverseProblem): - self._params = self.problem.unknown_parameters - self._clamp_params = self._clamp_inverse_problem_params - else: - self._params = None - self._clamp_params = lambda: None - - # variable used internally to store residual losses at each epoch - # this variable save the residual at each iteration (not weighted) - self.__logged_res_losses = [] - - # variable used internally in pina for logging. This variable points to - # the current condition during the training step and returns the - # condition name. Whenever :meth:`store_log` is called the logged - # variable will be stored with name = self.__logged_metric - self.__logged_metric = None - - def training_step(self, batch, _): - """ - The Physics Informed Solver Training Step. This function takes care - of the physics informed training step, and it must not be override - if not intentionally. It handles the batching mechanism, the workload - division for the various conditions, the inverse problem clamping, - and loggers. - - :param tuple batch: The batch element in the dataloader. - :param int batch_idx: The batch index. - :return: The sum of the loss functions. - :rtype: LabelTensor - """ - - condition_losses = [] - condition_idx = batch["condition"] - - for condition_id in range(condition_idx.min(), condition_idx.max() + 1): - - condition_name = self._dataloader.condition_names[condition_id] - condition = self.problem.conditions[condition_name] - pts = batch["pts"] - # condition name is logged (if logs enabled) - self.__logged_metric = condition_name - - if len(batch) == 2: - samples = pts[condition_idx == condition_id] - loss = self.loss_phys(samples, condition.equation) - elif len(batch) == 3: - samples = pts[condition_idx == condition_id] - ground_truth = batch["output"][condition_idx == condition_id] - loss = self.loss_data(samples, ground_truth) - else: - raise ValueError("Batch size not supported") - - # add condition losses for each epoch - condition_losses.append(loss * condition.data_weight) - - # clamp unknown parameters in InverseProblem (if needed) - self._clamp_params() - - # total loss (must be a torch.Tensor), and logs - total_loss = sum(condition_losses) - self.save_logs_and_release() - return total_loss.as_subclass(torch.Tensor) - - def loss_data(self, input_tensor, output_tensor): - """ - The data loss for the PINN solver. It computes the loss between - the network output against the true solution. This function - should not be override if not intentionally. - - :param LabelTensor input_tensor: The input to the neural networks. - :param LabelTensor output_tensor: The true solution to compare the - network solution. - :return: The residual loss averaged on the input coordinates - :rtype: torch.Tensor - """ - loss_value = self.loss(self.forward(input_tensor), output_tensor) - self.store_log(loss_value=float(loss_value)) - return self.loss(self.forward(input_tensor), output_tensor) - - @abstractmethod - def loss_phys(self, samples, equation): - """ - Computes the physics loss for the physics informed solver based on given - samples and equation. This method must be override by all inherited - classes and it is the core to define a new physics informed solver. - - :param LabelTensor samples: The samples to evaluate the physics loss. - :param EquationInterface equation: The governing equation - representing the physics. - :return: The physics loss calculated based on given - samples and equation. - :rtype: LabelTensor - """ - pass - - def compute_residual(self, samples, equation): - """ - Compute the residual for Physics Informed learning. This function - returns the :obj:`~pina.equation.equation.Equation` specified in the - :obj:`~pina.condition.Condition` evaluated at the ``samples`` points. - - :param LabelTensor samples: The samples to evaluate the physics loss. - :param EquationInterface equation: The governing equation - representing the physics. - :return: The residual of the neural network solution. - :rtype: LabelTensor - """ - try: - residual = equation.residual(samples, self.forward(samples)) - except ( - TypeError - ): # this occurs when the function has three inputs, i.e. inverse problem - residual = equation.residual( - samples, self.forward(samples), self._params - ) - return residual - - def store_log(self, loss_value): - """ - Stores the loss value in the logger. This function should be - called for all conditions. It automatically handles the storing - conditions names. It must be used - anytime a specific variable wants to be stored for a specific condition. - A simple example is to use the variable to store the residual. - - :param str name: The name of the loss. - :param torch.Tensor loss_value: The value of the loss. - """ - self.log( - self.__logged_metric + "_loss", - loss_value, - prog_bar=True, - logger=True, - on_epoch=True, - on_step=False, - ) - self.__logged_res_losses.append(loss_value) - - def save_logs_and_release(self): - """ - At the end of each epoch we free the stored losses. This function - should not be override if not intentionally. - """ - if self.__logged_res_losses: - # storing mean loss - self.__logged_metric = "mean" - self.store_log( - sum(self.__logged_res_losses) / len(self.__logged_res_losses) - ) - # free the logged losses - self.__logged_res_losses = [] - - def _clamp_inverse_problem_params(self): - """ - Clamps the parameters of the inverse problem - solver to the specified ranges. - """ - for v in self._params: - self._params[v].data.clamp_( - self.problem.unknown_parameter_domain.range_[v][0], - self.problem.unknown_parameter_domain.range_[v][1], - ) - - @property - def loss(self): - """ - Loss used for training. - """ - return self._loss - - @property - def current_condition_name(self): - """ - Returns the condition name. This function can be used inside the - :meth:`loss_phys` to extract the condition at which the loss is - computed. - """ - return self.__logged_metric diff --git a/pina/solvers/pinns/competitive_pinn.py b/pina/solvers/pinns/competitive_pinn.py deleted file mode 100644 index 5e011a473..000000000 --- a/pina/solvers/pinns/competitive_pinn.py +++ /dev/null @@ -1,361 +0,0 @@ -""" Module for CompetitivePINN """ - -import torch -import copy - -try: - from torch.optim.lr_scheduler import LRScheduler # torch >= 2.0 -except ImportError: - from torch.optim.lr_scheduler import ( - _LRScheduler as LRScheduler, - ) # torch < 2.0 - -from torch.optim.lr_scheduler import ConstantLR - -from .basepinn import PINNInterface -from pina.utils import check_consistency -from pina.problem import InverseProblem - - -class CompetitivePINN(PINNInterface): - r""" - Competitive Physics Informed Neural Network (PINN) solver class. - This class implements Competitive Physics Informed Neural - Network solvers, using a user specified ``model`` to solve a specific - ``problem``. It can be used for solving both forward and inverse problems. - - The Competitive Physics Informed Network aims to find - the solution :math:`\mathbf{u}:\Omega\rightarrow\mathbb{R}^m` - of the differential problem: - - .. math:: - - \begin{cases} - \mathcal{A}[\mathbf{u}](\mathbf{x})=0\quad,\mathbf{x}\in\Omega\\ - \mathcal{B}[\mathbf{u}](\mathbf{x})=0\quad, - \mathbf{x}\in\partial\Omega - \end{cases} - - with a minimization (on ``model`` parameters) maximation ( - on ``discriminator`` parameters) of the loss function - - .. math:: - \mathcal{L}_{\rm{problem}} = \frac{1}{N}\sum_{i=1}^N - \mathcal{L}(D(\mathbf{x}_i)\mathcal{A}[\mathbf{u}](\mathbf{x}_i))+ - \frac{1}{N}\sum_{i=1}^N - \mathcal{L}(D(\mathbf{x}_i)\mathcal{B}[\mathbf{u}](\mathbf{x}_i)) - - where :math:`D` is the discriminator network, which tries to find the points - where the network performs worst, and :math:`\mathcal{L}` is a specific loss - function, default Mean Square Error: - - .. math:: - \mathcal{L}(v) = \| v \|^2_2. - - .. seealso:: - - **Original reference**: Zeng, Qi, et al. - "Competitive physics informed networks." International Conference on - Learning Representations, ICLR 2022 - `OpenReview Preprint `_. - - .. warning:: - This solver does not currently support the possibility to pass - ``extra_feature``. - """ - - def __init__( - self, - problem, - model, - discriminator=None, - loss=torch.nn.MSELoss(), - optimizer_model=torch.optim.Adam, - optimizer_model_kwargs={"lr": 0.001}, - optimizer_discriminator=torch.optim.Adam, - optimizer_discriminator_kwargs={"lr": 0.001}, - scheduler_model=ConstantLR, - scheduler_model_kwargs={"factor": 1, "total_iters": 0}, - scheduler_discriminator=ConstantLR, - scheduler_discriminator_kwargs={"factor": 1, "total_iters": 0}, - ): - """ - :param AbstractProblem problem: The formualation of the problem. - :param torch.nn.Module model: The neural network model to use - for the model. - :param torch.nn.Module discriminator: The neural network model to use - for the discriminator. If ``None``, the discriminator network will - have the same architecture as the model network. - :param torch.nn.Module loss: The loss function used as minimizer, - default :class:`torch.nn.MSELoss`. - :param torch.optim.Optimizer optimizer_model: The neural - network optimizer to use for the model network - , default is `torch.optim.Adam`. - :param dict optimizer_model_kwargs: Optimizer constructor keyword - args. for the model. - :param torch.optim.Optimizer optimizer_discriminator: The neural - network optimizer to use for the discriminator network - , default is `torch.optim.Adam`. - :param dict optimizer_discriminator_kwargs: Optimizer constructor - keyword args. for the discriminator. - :param torch.optim.LRScheduler scheduler_model: Learning - rate scheduler for the model. - :param dict scheduler_model_kwargs: LR scheduler constructor - keyword args. - :param torch.optim.LRScheduler scheduler_discriminator: Learning - rate scheduler for the discriminator. - """ - if discriminator is None: - discriminator = copy.deepcopy(model) - - super().__init__( - models=[model, discriminator], - problem=problem, - optimizers=[optimizer_model, optimizer_discriminator], - optimizers_kwargs=[ - optimizer_model_kwargs, - optimizer_discriminator_kwargs, - ], - extra_features=None, # CompetitivePINN doesn't take extra features - loss=loss, - ) - - # set automatic optimization for GANs - self.automatic_optimization = False - - # check consistency - check_consistency(scheduler_model, LRScheduler, subclass=True) - check_consistency(scheduler_model_kwargs, dict) - check_consistency(scheduler_discriminator, LRScheduler, subclass=True) - check_consistency(scheduler_discriminator_kwargs, dict) - - # assign schedulers - self._schedulers = [ - scheduler_model(self.optimizers[0], **scheduler_model_kwargs), - scheduler_discriminator( - self.optimizers[1], **scheduler_discriminator_kwargs - ), - ] - - self._model = self.models[0] - self._discriminator = self.models[1] - - def forward(self, x): - r""" - Forward pass implementation for the PINN solver. It returns the function - evaluation :math:`\mathbf{u}(\mathbf{x})` at the control points - :math:`\mathbf{x}`. - - :param LabelTensor x: Input tensor for the PINN solver. It expects - a tensor :math:`N \times D`, where :math:`N` the number of points - in the mesh, :math:`D` the dimension of the problem, - :return: PINN solution evaluated at contro points. - :rtype: LabelTensor - """ - return self.neural_net(x) - - def loss_phys(self, samples, equation): - """ - Computes the physics loss for the Competitive PINN solver based on given - samples and equation. - - :param LabelTensor samples: The samples to evaluate the physics loss. - :param EquationInterface equation: The governing equation - representing the physics. - :return: The physics loss calculated based on given - samples and equation. - :rtype: LabelTensor - """ - # train one step of the model - with torch.no_grad(): - discriminator_bets = self.discriminator(samples) - loss_val = self._train_model(samples, equation, discriminator_bets) - self.store_log(loss_value=float(loss_val)) - # detaching samples from the computational graph to erase it and setting - # the gradient to true to create a new computational graph. - # In alternative set `retain_graph=True`. - samples = samples.detach() - samples.requires_grad = True - # train one step of discriminator - discriminator_bets = self.discriminator(samples) - self._train_discriminator(samples, equation, discriminator_bets) - return loss_val - - def loss_data(self, input_tensor, output_tensor): - """ - The data loss for the PINN solver. It computes the loss between the - network output against the true solution. - - :param LabelTensor input_tensor: The input to the neural networks. - :param LabelTensor output_tensor: The true solution to compare the - network solution. - :return: The computed data loss. - :rtype: torch.Tensor - """ - self.optimizer_model.zero_grad() - loss_val = ( - super() - .loss_data(input_tensor, output_tensor) - .as_subclass(torch.Tensor) - ) - loss_val.backward() - self.optimizer_model.step() - return loss_val - - def configure_optimizers(self): - """ - Optimizer configuration for the Competitive PINN solver. - - :return: The optimizers and the schedulers - :rtype: tuple(list, list) - """ - # if the problem is an InverseProblem, add the unknown parameters - # to the parameters that the optimizer needs to optimize - if isinstance(self.problem, InverseProblem): - self.optimizer_model.add_param_group( - { - "params": [ - self._params[var] - for var in self.problem.unknown_variables - ] - } - ) - return self.optimizers, self._schedulers - - def on_train_batch_end(self, outputs, batch, batch_idx): - """ - This method is called at the end of each training batch, and ovverides - the PytorchLightining implementation for logging the checkpoints. - - :param torch.Tensor outputs: The output from the model for the - current batch. - :param tuple batch: The current batch of data. - :param int batch_idx: The index of the current batch. - :return: Whatever is returned by the parent - method ``on_train_batch_end``. - :rtype: Any - """ - # increase by one the counter of optimization to save loggers - self.trainer.fit_loop.epoch_loop.manual_optimization.optim_step_progress.total.completed += ( - 1 - ) - return super().on_train_batch_end(outputs, batch, batch_idx) - - def _train_discriminator(self, samples, equation, discriminator_bets): - """ - Trains the discriminator network of the Competitive PINN. - - :param LabelTensor samples: Input samples to evaluate the physics loss. - :param EquationInterface equation: The governing equation representing - the physics. - :param Tensor discriminator_bets: Predictions made by the discriminator - network. - """ - # manual optimization - self.optimizer_discriminator.zero_grad() - # compute residual, we detach because the weights of the generator - # model are fixed - residual = self.compute_residual( - samples=samples, equation=equation - ).detach() - # compute competitive residual, the minus is because we maximise - competitive_residual = residual * discriminator_bets - loss_val = -self.loss( - torch.zeros_like(competitive_residual, requires_grad=True), - competitive_residual, - ).as_subclass(torch.Tensor) - # backprop - self.manual_backward(loss_val) - self.optimizer_discriminator.step() - return - - def _train_model(self, samples, equation, discriminator_bets): - """ - Trains the model network of the Competitive PINN. - - :param LabelTensor samples: Input samples to evaluate the physics loss. - :param EquationInterface equation: The governing equation representing - the physics. - :param Tensor discriminator_bets: Predictions made by the discriminator. - network. - :return: The computed data loss. - :rtype: torch.Tensor - """ - # manual optimization - self.optimizer_model.zero_grad() - # compute residual (detached for discriminator) and log - residual = self.compute_residual(samples=samples, equation=equation) - # store logging - with torch.no_grad(): - loss_residual = self.loss(torch.zeros_like(residual), residual) - # compute competitive residual, discriminator_bets are detached becase - # we optimize only the generator model - competitive_residual = residual * discriminator_bets.detach() - loss_val = self.loss( - torch.zeros_like(competitive_residual, requires_grad=True), - competitive_residual, - ).as_subclass(torch.Tensor) - # backprop - self.manual_backward(loss_val) - self.optimizer_model.step() - return loss_residual - - @property - def neural_net(self): - """ - Returns the neural network model. - - :return: The neural network model. - :rtype: torch.nn.Module - """ - return self._model - - @property - def discriminator(self): - """ - Returns the discriminator model (if applicable). - - :return: The discriminator model. - :rtype: torch.nn.Module - """ - return self._discriminator - - @property - def optimizer_model(self): - """ - Returns the optimizer associated with the neural network model. - - :return: The optimizer for the neural network model. - :rtype: torch.optim.Optimizer - """ - return self.optimizers[0] - - @property - def optimizer_discriminator(self): - """ - Returns the optimizer associated with the discriminator (if applicable). - - :return: The optimizer for the discriminator. - :rtype: torch.optim.Optimizer - """ - return self.optimizers[1] - - @property - def scheduler_model(self): - """ - Returns the scheduler associated with the neural network model. - - :return: The scheduler for the neural network model. - :rtype: torch.optim.lr_scheduler._LRScheduler - """ - return self._schedulers[0] - - @property - def scheduler_discriminator(self): - """ - Returns the scheduler associated with the discriminator (if applicable). - - :return: The scheduler for the discriminator. - :rtype: torch.optim.lr_scheduler._LRScheduler - """ - return self._schedulers[1] diff --git a/pina/solvers/pinns/gpinn.py b/pina/solvers/pinns/gpinn.py deleted file mode 100644 index 5f259ca21..000000000 --- a/pina/solvers/pinns/gpinn.py +++ /dev/null @@ -1,135 +0,0 @@ -""" Module for GPINN """ - -import torch - - -from torch.optim.lr_scheduler import ConstantLR - -from .pinn import PINN -from pina.operators import grad -from pina.problem import SpatialProblem - - -class GPINN(PINN): - r""" - Gradient Physics Informed Neural Network (GPINN) solver class. - This class implements Gradient Physics Informed Neural - Network solvers, using a user specified ``model`` to solve a specific - ``problem``. It can be used for solving both forward and inverse problems. - - The Gradient Physics Informed Network aims to find - the solution :math:`\mathbf{u}:\Omega\rightarrow\mathbb{R}^m` - of the differential problem: - - .. math:: - - \begin{cases} - \mathcal{A}[\mathbf{u}](\mathbf{x})=0\quad,\mathbf{x}\in\Omega\\ - \mathcal{B}[\mathbf{u}](\mathbf{x})=0\quad, - \mathbf{x}\in\partial\Omega - \end{cases} - - minimizing the loss function - - .. math:: - \mathcal{L}_{\rm{problem}} =& \frac{1}{N}\sum_{i=1}^N - \mathcal{L}(\mathcal{A}[\mathbf{u}](\mathbf{x}_i)) + - \frac{1}{N}\sum_{i=1}^N - \mathcal{L}(\mathcal{B}[\mathbf{u}](\mathbf{x}_i)) + \\ - &\frac{1}{N}\sum_{i=1}^N - \nabla_{\mathbf{x}}\mathcal{L}(\mathcal{A}[\mathbf{u}](\mathbf{x}_i)) + - \frac{1}{N}\sum_{i=1}^N - \nabla_{\mathbf{x}}\mathcal{L}(\mathcal{B}[\mathbf{u}](\mathbf{x}_i)) - - - where :math:`\mathcal{L}` is a specific loss function, default Mean Square Error: - - .. math:: - \mathcal{L}(v) = \| v \|^2_2. - - .. seealso:: - - **Original reference**: Yu, Jeremy, et al. "Gradient-enhanced - physics-informed neural networks for forward and inverse - PDE problems." Computer Methods in Applied Mechanics - and Engineering 393 (2022): 114823. - DOI: `10.1016 `_. - - .. note:: - This class can only work for problems inheriting - from at least :class:`~pina.problem.spatial_problem.SpatialProblem` - class. - """ - - def __init__( - self, - problem, - model, - extra_features=None, - loss=torch.nn.MSELoss(), - optimizer=torch.optim.Adam, - optimizer_kwargs={"lr": 0.001}, - scheduler=ConstantLR, - scheduler_kwargs={"factor": 1, "total_iters": 0}, - ): - """ - :param AbstractProblem problem: The formulation of the problem. It must - inherit from at least - :class:`~pina.problem.spatial_problem.SpatialProblem` in order to - compute the gradient of the loss. - :param torch.nn.Module model: The neural network model to use. - :param torch.nn.Module loss: The loss function used as minimizer, - default :class:`torch.nn.MSELoss`. - :param torch.nn.Module extra_features: The additional input - features to use as augmented input. - :param torch.optim.Optimizer optimizer: The neural network optimizer to - use; default is :class:`torch.optim.Adam`. - :param dict optimizer_kwargs: Optimizer constructor keyword args. - :param torch.optim.LRScheduler scheduler: Learning - rate scheduler. - :param dict scheduler_kwargs: LR scheduler constructor keyword args. - """ - super().__init__( - problem=problem, - model=model, - extra_features=extra_features, - loss=loss, - optimizer=optimizer, - optimizer_kwargs=optimizer_kwargs, - scheduler=scheduler, - scheduler_kwargs=scheduler_kwargs, - ) - if not isinstance(self.problem, SpatialProblem): - raise ValueError( - "Gradient PINN computes the gradient of the " - "PINN loss with respect to the spatial " - "coordinates, thus the PINA problem must be " - "a SpatialProblem." - ) - - def loss_phys(self, samples, equation): - """ - Computes the physics loss for the GPINN solver based on given - samples and equation. - - :param LabelTensor samples: The samples to evaluate the physics loss. - :param EquationInterface equation: The governing equation - representing the physics. - :return: The physics loss calculated based on given - samples and equation. - :rtype: LabelTensor - """ - # classical PINN loss - residual = self.compute_residual(samples=samples, equation=equation) - loss_value = self.loss( - torch.zeros_like(residual, requires_grad=True), residual - ) - self.store_log(loss_value=float(loss_value)) - # gradient PINN loss - loss_value = loss_value.reshape(-1, 1) - loss_value.labels = ["__LOSS"] - loss_grad = grad(loss_value, samples, d=self.problem.spatial_variables) - g_loss_phys = self.loss( - torch.zeros_like(loss_grad, requires_grad=True), loss_grad - ) - return loss_value + g_loss_phys diff --git a/pina/solvers/pinns/pinn.py b/pina/solvers/pinns/pinn.py deleted file mode 100644 index 15f908182..000000000 --- a/pina/solvers/pinns/pinn.py +++ /dev/null @@ -1,167 +0,0 @@ -""" Module for Physics Informed Neural Network. """ - -import torch - -try: - from torch.optim.lr_scheduler import LRScheduler # torch >= 2.0 -except ImportError: - from torch.optim.lr_scheduler import ( - _LRScheduler as LRScheduler, - ) # torch < 2.0 - -from torch.optim.lr_scheduler import ConstantLR - -from .basepinn import PINNInterface -from pina.utils import check_consistency -from pina.problem import InverseProblem - - -class PINN(PINNInterface): - r""" - Physics Informed Neural Network (PINN) solver class. - This class implements Physics Informed Neural - Network solvers, using a user specified ``model`` to solve a specific - ``problem``. It can be used for solving both forward and inverse problems. - - The Physics Informed Network aims to find - the solution :math:`\mathbf{u}:\Omega\rightarrow\mathbb{R}^m` - of the differential problem: - - .. math:: - - \begin{cases} - \mathcal{A}[\mathbf{u}](\mathbf{x})=0\quad,\mathbf{x}\in\Omega\\ - \mathcal{B}[\mathbf{u}](\mathbf{x})=0\quad, - \mathbf{x}\in\partial\Omega - \end{cases} - - minimizing the loss function - - .. math:: - \mathcal{L}_{\rm{problem}} = \frac{1}{N}\sum_{i=1}^N - \mathcal{L}(\mathcal{A}[\mathbf{u}](\mathbf{x}_i)) + - \frac{1}{N}\sum_{i=1}^N - \mathcal{L}(\mathcal{B}[\mathbf{u}](\mathbf{x}_i)) - - where :math:`\mathcal{L}` is a specific loss function, default Mean Square Error: - - .. math:: - \mathcal{L}(v) = \| v \|^2_2. - - .. seealso:: - - **Original reference**: Karniadakis, G. E., Kevrekidis, I. G., Lu, L., - Perdikaris, P., Wang, S., & Yang, L. (2021). - Physics-informed machine learning. Nature Reviews Physics, 3, 422-440. - DOI: `10.1038 `_. - """ - - def __init__( - self, - problem, - model, - extra_features=None, - loss=torch.nn.MSELoss(), - optimizer=torch.optim.Adam, - optimizer_kwargs={"lr": 0.001}, - scheduler=ConstantLR, - scheduler_kwargs={"factor": 1, "total_iters": 0}, - ): - """ - :param AbstractProblem problem: The formulation of the problem. - :param torch.nn.Module model: The neural network model to use. - :param torch.nn.Module loss: The loss function used as minimizer, - default :class:`torch.nn.MSELoss`. - :param torch.nn.Module extra_features: The additional input - features to use as augmented input. - :param torch.optim.Optimizer optimizer: The neural network optimizer to - use; default is :class:`torch.optim.Adam`. - :param dict optimizer_kwargs: Optimizer constructor keyword args. - :param torch.optim.LRScheduler scheduler: Learning - rate scheduler. - :param dict scheduler_kwargs: LR scheduler constructor keyword args. - """ - super().__init__( - models=[model], - problem=problem, - optimizers=[optimizer], - optimizers_kwargs=[optimizer_kwargs], - extra_features=extra_features, - loss=loss, - ) - - # check consistency - check_consistency(scheduler, LRScheduler, subclass=True) - check_consistency(scheduler_kwargs, dict) - - # assign variables - self._scheduler = scheduler(self.optimizers[0], **scheduler_kwargs) - self._neural_net = self.models[0] - - def forward(self, x): - r""" - Forward pass implementation for the PINN solver. It returns the function - evaluation :math:`\mathbf{u}(\mathbf{x})` at the control points - :math:`\mathbf{x}`. - - :param LabelTensor x: Input tensor for the PINN solver. It expects - a tensor :math:`N \times D`, where :math:`N` the number of points - in the mesh, :math:`D` the dimension of the problem, - :return: PINN solution evaluated at contro points. - :rtype: LabelTensor - """ - return self.neural_net(x) - - def loss_phys(self, samples, equation): - """ - Computes the physics loss for the PINN solver based on given - samples and equation. - - :param LabelTensor samples: The samples to evaluate the physics loss. - :param EquationInterface equation: The governing equation - representing the physics. - :return: The physics loss calculated based on given - samples and equation. - :rtype: LabelTensor - """ - residual = self.compute_residual(samples=samples, equation=equation) - loss_value = self.loss( - torch.zeros_like(residual, requires_grad=True), residual - ) - self.store_log(loss_value=float(loss_value)) - return loss_value - - def configure_optimizers(self): - """ - Optimizer configuration for the PINN - solver. - - :return: The optimizers and the schedulers - :rtype: tuple(list, list) - """ - # if the problem is an InverseProblem, add the unknown parameters - # to the parameters that the optimizer needs to optimize - if isinstance(self.problem, InverseProblem): - self.optimizers[0].add_param_group( - { - "params": [ - self._params[var] - for var in self.problem.unknown_variables - ] - } - ) - return self.optimizers, [self.scheduler] - - @property - def scheduler(self): - """ - Scheduler for the PINN training. - """ - return self._scheduler - - @property - def neural_net(self): - """ - Neural network for the PINN training. - """ - return self._neural_net diff --git a/pina/solvers/pinns/rbapinn.py b/pina/solvers/pinns/rbapinn.py deleted file mode 100644 index fd551ac5e..000000000 --- a/pina/solvers/pinns/rbapinn.py +++ /dev/null @@ -1,174 +0,0 @@ -""" Module for RBAPINN. """ - -from copy import deepcopy -import torch -from torch.optim.lr_scheduler import ConstantLR -from .pinn import PINN -from ...utils import check_consistency - - -class RBAPINN(PINN): - r""" - Residual-based Attention PINN (RBAPINN) solver class. - This class implements Residual-based Attention Physics Informed Neural - Network solvers, using a user specified ``model`` to solve a specific - ``problem``. It can be used for solving both forward and inverse problems. - - The Residual-based Attention Physics Informed Neural Network aims to find - the solution :math:`\mathbf{u}:\Omega\rightarrow\mathbb{R}^m` - of the differential problem: - - .. math:: - - \begin{cases} - \mathcal{A}[\mathbf{u}](\mathbf{x})=0\quad,\mathbf{x}\in\Omega\\ - \mathcal{B}[\mathbf{u}](\mathbf{x})=0\quad, - \mathbf{x}\in\partial\Omega - \end{cases} - - minimizing the loss function - - .. math:: - - \mathcal{L}_{\rm{problem}} = \frac{1}{N} \sum_{i=1}^{N_\Omega} - \lambda_{\Omega}^{i} \mathcal{L} \left( \mathcal{A} - [\mathbf{u}](\mathbf{x}) \right) + \frac{1}{N} - \sum_{i=1}^{N_{\partial\Omega}} - \lambda_{\partial\Omega}^{i} \mathcal{L} - \left( \mathcal{B}[\mathbf{u}](\mathbf{x}) - \right), - - denoting the weights as - :math:`\lambda_{\Omega}^1, \dots, \lambda_{\Omega}^{N_\Omega}` and - :math:`\lambda_{\partial \Omega}^1, \dots, - \lambda_{\Omega}^{N_\partial \Omega}` - for :math:`\Omega` and :math:`\partial \Omega`, respectively. - - Residual-based Attention Physics Informed Neural Network computes - the weights by updating them at every epoch as follows - - .. math:: - - \lambda_i^{k+1} \leftarrow \gamma\lambda_i^{k} + - \eta\frac{\lvert r_i\rvert}{\max_j \lvert r_j\rvert}, - - where :math:`r_i` denotes the residual at point :math:`i`, - :math:`\gamma` denotes the decay rate, and :math:`\eta` is - the learning rate for the weights' update. - - .. seealso:: - **Original reference**: Sokratis J. Anagnostopoulos, Juan D. Toscano, - Nikolaos Stergiopulos, and George E. Karniadakis. - "Residual-based attention and connection to information - bottleneck theory in PINNs". - Computer Methods in Applied Mechanics and Engineering 421 (2024): 116805 - DOI: `10.1016/ - j.cma.2024.116805 `_. - """ - - def __init__( - self, - problem, - model, - extra_features=None, - loss=torch.nn.MSELoss(), - optimizer=torch.optim.Adam, - optimizer_kwargs={"lr": 0.001}, - scheduler=ConstantLR, - scheduler_kwargs={"factor": 1, "total_iters": 0}, - eta=0.001, - gamma=0.999, - ): - """ - :param AbstractProblem problem: The formulation of the problem. - :param torch.nn.Module model: The neural network model to use. - :param torch.nn.Module extra_features: The additional input - features to use as augmented input. - :param torch.nn.Module loss: The loss function used as minimizer, - default :class:`torch.nn.MSELoss`. - :param torch.optim.Optimizer optimizer: The neural network optimizer to - use; default is :class:`torch.optim.Adam`. - :param dict optimizer_kwargs: Optimizer constructor keyword args. - :param torch.optim.LRScheduler scheduler: Learning - rate scheduler. - :param dict scheduler_kwargs: LR scheduler constructor keyword args. - :param float | int eta: The learning rate for the - weights of the residual. - :param float gamma: The decay parameter in the update of the weights - of the residual. - """ - super().__init__( - problem=problem, - model=model, - extra_features=extra_features, - loss=loss, - optimizer=optimizer, - optimizer_kwargs=optimizer_kwargs, - scheduler=scheduler, - scheduler_kwargs=scheduler_kwargs, - ) - - # check consistency - check_consistency(eta, (float, int)) - check_consistency(gamma, float) - self.eta = eta - self.gamma = gamma - - # initialize weights - self.weights = {} - for condition_name in problem.conditions: - self.weights[condition_name] = 0 - - # define vectorial loss - self._vectorial_loss = deepcopy(loss) - self._vectorial_loss.reduction = "none" - - def _vect_to_scalar(self, loss_value): - """ - Elaboration of the pointwise loss. - - :param LabelTensor loss_value: the matrix of pointwise loss. - - :return: the scalar loss. - :rtype LabelTensor - """ - if self.loss.reduction == "mean": - ret = torch.mean(loss_value) - elif self.loss.reduction == "sum": - ret = torch.sum(loss_value) - else: - raise RuntimeError( - f"Invalid reduction, got {self.loss.reduction} " - "but expected mean or sum." - ) - return ret - - def loss_phys(self, samples, equation): - """ - Computes the physics loss for the residual-based attention PINN - solver based on given samples and equation. - - :param LabelTensor samples: The samples to evaluate the physics loss. - :param EquationInterface equation: The governing equation - representing the physics. - :return: The physics loss calculated based on given - samples and equation. - :rtype: LabelTensor - """ - residual = self.compute_residual(samples=samples, equation=equation) - cond = self.current_condition_name - - r_norm = ( - self.eta - * torch.abs(residual) - / (torch.max(torch.abs(residual)) + 1e-12) - ) - self.weights[cond] = (self.gamma * self.weights[cond] + r_norm).detach() - - loss_value = self._vectorial_loss( - torch.zeros_like(residual, requires_grad=True), residual - ) - - self.store_log(loss_value=float(self._vect_to_scalar(loss_value))) - - return self._vect_to_scalar(self.weights[cond] ** 2 * loss_value) diff --git a/pina/solvers/pinns/sapinn.py b/pina/solvers/pinns/sapinn.py deleted file mode 100644 index 751e21eff..000000000 --- a/pina/solvers/pinns/sapinn.py +++ /dev/null @@ -1,493 +0,0 @@ -import torch -from copy import deepcopy - -try: - from torch.optim.lr_scheduler import LRScheduler # torch >= 2.0 -except ImportError: - from torch.optim.lr_scheduler import ( - _LRScheduler as LRScheduler, - ) # torch < 2.0 - -from .basepinn import PINNInterface -from pina.utils import check_consistency -from pina.problem import InverseProblem - -from torch.optim.lr_scheduler import ConstantLR - - -class Weights(torch.nn.Module): - """ - This class aims to implements the mask model for - self adaptive weights of the Self-Adaptive - PINN solver. - """ - - def __init__(self, func): - """ - :param torch.nn.Module func: the mask module of SAPINN - """ - super().__init__() - check_consistency(func, torch.nn.Module) - self.sa_weights = torch.nn.Parameter(torch.Tensor()) - self.func = func - - def forward(self): - """ - Forward pass implementation for the mask module. - It returns the function on the weights - evaluation. - - :return: evaluation of self adaptive weights through the mask. - :rtype: torch.Tensor - """ - return self.func(self.sa_weights) - - -class SAPINN(PINNInterface): - r""" - Self Adaptive Physics Informed Neural Network (SAPINN) solver class. - This class implements Self-Adaptive Physics Informed Neural - Network solvers, using a user specified ``model`` to solve a specific - ``problem``. It can be used for solving both forward and inverse problems. - - The Self Adapive Physics Informed Neural Network aims to find - the solution :math:`\mathbf{u}:\Omega\rightarrow\mathbb{R}^m` - of the differential problem: - - .. math:: - - \begin{cases} - \mathcal{A}[\mathbf{u}](\mathbf{x})=0\quad,\mathbf{x}\in\Omega\\ - \mathcal{B}[\mathbf{u}](\mathbf{x})=0\quad, - \mathbf{x}\in\partial\Omega - \end{cases} - - integrating the pointwise loss evaluation through a mask :math:`m` and - self adaptive weights that permit to focus the loss function on - specific training samples. - The loss function to solve the problem is - - .. math:: - - \mathcal{L}_{\rm{problem}} = \frac{1}{N} \sum_{i=1}^{N_\Omega} m - \left( \lambda_{\Omega}^{i} \right) \mathcal{L} \left( \mathcal{A} - [\mathbf{u}](\mathbf{x}) \right) + \frac{1}{N} - \sum_{i=1}^{N_{\partial\Omega}} - m \left( \lambda_{\partial\Omega}^{i} \right) \mathcal{L} - \left( \mathcal{B}[\mathbf{u}](\mathbf{x}) - \right), - - - denoting the self adaptive weights as - :math:`\lambda_{\Omega}^1, \dots, \lambda_{\Omega}^{N_\Omega}` and - :math:`\lambda_{\partial \Omega}^1, \dots, - \lambda_{\Omega}^{N_\partial \Omega}` - for :math:`\Omega` and :math:`\partial \Omega`, respectively. - - Self Adaptive Physics Informed Neural Network identifies the solution - and appropriate self adaptive weights by solving the following problem - - .. math:: - - \min_{w} \max_{\lambda_{\Omega}^k, \lambda_{\partial \Omega}^s} - \mathcal{L} , - - where :math:`w` denotes the network parameters, and - :math:`\mathcal{L}` is a specific loss - function, default Mean Square Error: - - .. math:: - \mathcal{L}(v) = \| v \|^2_2. - - .. seealso:: - **Original reference**: McClenny, Levi D., and Ulisses M. Braga-Neto. - "Self-adaptive physics-informed neural networks." - Journal of Computational Physics 474 (2023): 111722. - DOI: `10.1016/ - j.jcp.2022.111722 `_. - """ - - def __init__( - self, - problem, - model, - weights_function=torch.nn.Sigmoid(), - extra_features=None, - loss=torch.nn.MSELoss(), - optimizer_model=torch.optim.Adam, - optimizer_model_kwargs={"lr": 0.001}, - optimizer_weights=torch.optim.Adam, - optimizer_weights_kwargs={"lr": 0.001}, - scheduler_model=ConstantLR, - scheduler_model_kwargs={"factor": 1, "total_iters": 0}, - scheduler_weights=ConstantLR, - scheduler_weights_kwargs={"factor": 1, "total_iters": 0}, - ): - """ - :param AbstractProblem problem: The formualation of the problem. - :param torch.nn.Module model: The neural network model to use - for the model. - :param torch.nn.Module weights_function: The neural network model - related to the mask of SAPINN. - default :obj:`~torch.nn.Sigmoid`. - :param list(torch.nn.Module) extra_features: The additional input - features to use as augmented input. If ``None`` no extra features - are passed. If it is a list of :class:`torch.nn.Module`, - the extra feature list is passed to all models. If it is a list - of extra features' lists, each single list of extra feature - is passed to a model. - :param torch.nn.Module loss: The loss function used as minimizer, - default :class:`torch.nn.MSELoss`. - :param torch.optim.Optimizer optimizer_model: The neural - network optimizer to use for the model network - , default is `torch.optim.Adam`. - :param dict optimizer_model_kwargs: Optimizer constructor keyword - args. for the model. - :param torch.optim.Optimizer optimizer_weights: The neural - network optimizer to use for mask model model, - default is `torch.optim.Adam`. - :param dict optimizer_weights_kwargs: Optimizer constructor - keyword args. for the mask module. - :param torch.optim.LRScheduler scheduler_model: Learning - rate scheduler for the model. - :param dict scheduler_model_kwargs: LR scheduler constructor - keyword args. - :param torch.optim.LRScheduler scheduler_weights: Learning - rate scheduler for the mask model. - :param dict scheduler_model_kwargs: LR scheduler constructor - keyword args. - """ - - # check consistency weitghs_function - check_consistency(weights_function, torch.nn.Module) - - # create models for weights - weights_dict = {} - for condition_name in problem.conditions: - weights_dict[condition_name] = Weights(weights_function) - weights_dict = torch.nn.ModuleDict(weights_dict) - - super().__init__( - models=[model, weights_dict], - problem=problem, - optimizers=[optimizer_model, optimizer_weights], - optimizers_kwargs=[ - optimizer_model_kwargs, - optimizer_weights_kwargs, - ], - extra_features=extra_features, - loss=loss, - ) - - # set automatic optimization - self.automatic_optimization = False - - # check consistency - check_consistency(scheduler_model, LRScheduler, subclass=True) - check_consistency(scheduler_model_kwargs, dict) - check_consistency(scheduler_weights, LRScheduler, subclass=True) - check_consistency(scheduler_weights_kwargs, dict) - - # assign schedulers - self._schedulers = [ - scheduler_model(self.optimizers[0], **scheduler_model_kwargs), - scheduler_weights(self.optimizers[1], **scheduler_weights_kwargs), - ] - - self._model = self.models[0] - self._weights = self.models[1] - - self._vectorial_loss = deepcopy(loss) - self._vectorial_loss.reduction = "none" - - def forward(self, x): - """ - Forward pass implementation for the PINN - solver. It returns the function - evaluation :math:`\mathbf{u}(\mathbf{x})` at the control points - :math:`\mathbf{x}`. - - :param LabelTensor x: Input tensor for the SAPINN solver. It expects - a tensor :math:`N \\times D`, where :math:`N` the number of points - in the mesh, :math:`D` the dimension of the problem, - :return: PINN solution. - :rtype: LabelTensor - """ - return self.neural_net(x) - - def loss_phys(self, samples, equation): - """ - Computes the physics loss for the SAPINN solver based on given - samples and equation. - - :param LabelTensor samples: The samples to evaluate the physics loss. - :param EquationInterface equation: The governing equation - representing the physics. - :return: The physics loss calculated based on given - samples and equation. - :rtype: torch.Tensor - """ - # train weights - self.optimizer_weights.zero_grad() - weighted_loss, _ = self._loss_phys(samples, equation) - loss_value = -weighted_loss.as_subclass(torch.Tensor) - self.manual_backward(loss_value) - self.optimizer_weights.step() - - # detaching samples from the computational graph to erase it and setting - # the gradient to true to create a new computational graph. - # In alternative set `retain_graph=True`. - samples = samples.detach() - samples.requires_grad = True - - # train model - self.optimizer_model.zero_grad() - weighted_loss, loss = self._loss_phys(samples, equation) - loss_value = weighted_loss.as_subclass(torch.Tensor) - self.manual_backward(loss_value) - self.optimizer_model.step() - - # store loss without weights - self.store_log(loss_value=float(loss)) - return loss_value - - def loss_data(self, input_tensor, output_tensor): - """ - Computes the data loss for the SAPINN solver based on input and - output. It computes the loss between the - network output against the true solution. - - :param LabelTensor input_tensor: The input to the neural networks. - :param LabelTensor output_tensor: The true solution to compare the - network solution. - :return: The computed data loss. - :rtype: torch.Tensor - """ - # train weights - self.optimizer_weights.zero_grad() - weighted_loss, _ = self._loss_data(input_tensor, output_tensor) - loss_value = -weighted_loss.as_subclass(torch.Tensor) - self.manual_backward(loss_value) - self.optimizer_weights.step() - - # detaching samples from the computational graph to erase it and setting - # the gradient to true to create a new computational graph. - # In alternative set `retain_graph=True`. - input_tensor = input_tensor.detach() - input_tensor.requires_grad = True - - # train model - self.optimizer_model.zero_grad() - weighted_loss, loss = self._loss_data(input_tensor, output_tensor) - loss_value = weighted_loss.as_subclass(torch.Tensor) - self.manual_backward(loss_value) - self.optimizer_model.step() - - # store loss without weights - self.store_log(loss_value=float(loss)) - return loss_value - - def configure_optimizers(self): - """ - Optimizer configuration for the SAPINN - solver. - - :return: The optimizers and the schedulers - :rtype: tuple(list, list) - """ - # if the problem is an InverseProblem, add the unknown parameters - # to the parameters that the optimizer needs to optimize - if isinstance(self.problem, InverseProblem): - self.optimizers[0].add_param_group( - { - "params": [ - self._params[var] - for var in self.problem.unknown_variables - ] - } - ) - return self.optimizers, self._schedulers - - def on_train_batch_end(self, outputs, batch, batch_idx): - """ - This method is called at the end of each training batch, and ovverides - the PytorchLightining implementation for logging the checkpoints. - - :param torch.Tensor outputs: The output from the model for the - current batch. - :param tuple batch: The current batch of data. - :param int batch_idx: The index of the current batch. - :return: Whatever is returned by the parent - method ``on_train_batch_end``. - :rtype: Any - """ - # increase by one the counter of optimization to save loggers - self.trainer.fit_loop.epoch_loop.manual_optimization.optim_step_progress.total.completed += ( - 1 - ) - return super().on_train_batch_end(outputs, batch, batch_idx) - - def on_train_start(self): - """ - This method is called at the start of the training for setting - the self adaptive weights as parameters of the mask model. - - :return: Whatever is returned by the parent - method ``on_train_start``. - :rtype: Any - """ - device = torch.device( - self.trainer._accelerator_connector._accelerator_flag - ) - for condition_name, tensor in self.problem.input_pts.items(): - self.weights_dict.torchmodel[condition_name].sa_weights.data = ( - torch.rand((tensor.shape[0], 1), device=device) - ) - return super().on_train_start() - - def on_load_checkpoint(self, checkpoint): - """ - Overriding the Pytorch Lightning ``on_load_checkpoint`` to handle - checkpoints for Self Adaptive Weights. This method should not be - overridden if not intentionally. - - :param dict checkpoint: Pytorch Lightning checkpoint dict. - """ - for condition_name, tensor in self.problem.input_pts.items(): - self.weights_dict.torchmodel[condition_name].sa_weights.data = ( - torch.rand((tensor.shape[0], 1)) - ) - return super().on_load_checkpoint(checkpoint) - - def _loss_phys(self, samples, equation): - """ - Elaboration of the physical loss for the SAPINN solver. - - :param LabelTensor samples: Input samples to evaluate the physics loss. - :param EquationInterface equation: the governing equation representing - the physics. - - :return: tuple with weighted and not weighted scalar loss - :rtype: List[LabelTensor, LabelTensor] - """ - residual = self.compute_residual(samples, equation) - return self._compute_loss(residual) - - def _loss_data(self, input_tensor, output_tensor): - """ - Elaboration of the loss related to data for the SAPINN solver. - - :param LabelTensor input_tensor: The input to the neural networks. - :param LabelTensor output_tensor: The true solution to compare the - network solution. - - :return: tuple with weighted and not weighted scalar loss - :rtype: List[LabelTensor, LabelTensor] - """ - residual = self.forward(input_tensor) - output_tensor - return self._compute_loss(residual) - - def _compute_loss(self, residual): - """ - Elaboration of the pointwise loss through the mask model and the - self adaptive weights - - :param LabelTensor residual: the matrix of residuals that have to - be weighted - - :return: tuple with weighted and not weighted loss - :rtype List[LabelTensor, LabelTensor] - """ - weights = self.weights_dict.torchmodel[ - self.current_condition_name - ].forward() - loss_value = self._vectorial_loss( - torch.zeros_like(residual, requires_grad=True), residual - ) - return ( - self._vect_to_scalar(weights * loss_value), - self._vect_to_scalar(loss_value), - ) - - def _vect_to_scalar(self, loss_value): - """ - Elaboration of the pointwise loss through the mask model and the - self adaptive weights - - :param LabelTensor loss_value: the matrix of pointwise loss - - :return: the scalar loss - :rtype LabelTensor - """ - if self.loss.reduction == "mean": - ret = torch.mean(loss_value) - elif self.loss.reduction == "sum": - ret = torch.sum(loss_value) - else: - raise RuntimeError( - f"Invalid reduction, got {self.loss.reduction} " - "but expected mean or sum." - ) - return ret - - @property - def neural_net(self): - """ - Returns the neural network model. - - :return: The neural network model. - :rtype: torch.nn.Module - """ - return self.models[0] - - @property - def weights_dict(self): - """ - Return the mask models associate to the application of - the mask to the self adaptive weights for each loss that - compones the global loss of the problem. - - :return: The ModuleDict for mask models. - :rtype: torch.nn.ModuleDict - """ - return self.models[1] - - @property - def scheduler_model(self): - """ - Returns the scheduler associated with the neural network model. - - :return: The scheduler for the neural network model. - :rtype: torch.optim.lr_scheduler._LRScheduler - """ - return self._scheduler[0] - - @property - def scheduler_weights(self): - """ - Returns the scheduler associated with the mask model (if applicable). - - :return: The scheduler for the mask model. - :rtype: torch.optim.lr_scheduler._LRScheduler - """ - return self._scheduler[1] - - @property - def optimizer_model(self): - """ - Returns the optimizer associated with the neural network model. - - :return: The optimizer for the neural network model. - :rtype: torch.optim.Optimizer - """ - return self.optimizers[0] - - @property - def optimizer_weights(self): - """ - Returns the optimizer associated with the mask model (if applicable). - - :return: The optimizer for the mask model. - :rtype: torch.optim.Optimizer - """ - return self.optimizers[1] diff --git a/pina/solvers/rom.py b/pina/solvers/rom.py deleted file mode 100644 index ee4bcff43..000000000 --- a/pina/solvers/rom.py +++ /dev/null @@ -1,199 +0,0 @@ -""" Module for ReducedOrderModelSolver """ - -import torch - -from pina.solvers import SupervisedSolver - - -class ReducedOrderModelSolver(SupervisedSolver): - r""" - ReducedOrderModelSolver solver class. This class implements a - Reduced Order Model solver, using user specified ``reduction_network`` and - ``interpolation_network`` to solve a specific ``problem``. - - The Reduced Order Model approach aims to find - the solution :math:`\mathbf{u}:\Omega\rightarrow\mathbb{R}^m` - of the differential problem: - - .. math:: - - \begin{cases} - \mathcal{A}[\mathbf{u}(\mu)](\mathbf{x})=0\quad,\mathbf{x}\in\Omega\\ - \mathcal{B}[\mathbf{u}(\mu)](\mathbf{x})=0\quad, - \mathbf{x}\in\partial\Omega - \end{cases} - - This is done by using two neural networks. The ``reduction_network``, which - contains an encoder :math:`\mathcal{E}_{\rm{net}}`, a decoder - :math:`\mathcal{D}_{\rm{net}}`; and an ``interpolation_network`` - :math:`\mathcal{I}_{\rm{net}}`. The input is assumed to be discretised in - the spatial dimensions. - - The following loss function is minimized during training - - .. math:: - \mathcal{L}_{\rm{problem}} = \frac{1}{N}\sum_{i=1}^N - \mathcal{L}(\mathcal{E}_{\rm{net}}[\mathbf{u}(\mu_i)] - - \mathcal{I}_{\rm{net}}[\mu_i]) + - \mathcal{L}( - \mathcal{D}_{\rm{net}}[\mathcal{E}_{\rm{net}}[\mathbf{u}(\mu_i)]] - - \mathbf{u}(\mu_i)) - - where :math:`\mathcal{L}` is a specific loss function, default Mean Square Error: - - .. math:: - \mathcal{L}(v) = \| v \|^2_2. - - - .. seealso:: - - **Original reference**: Hesthaven, Jan S., and Stefano Ubbiali. - "Non-intrusive reduced order modeling of nonlinear problems - using neural networks." Journal of Computational - Physics 363 (2018): 55-78. - DOI `10.1016/j.jcp.2018.02.037 - `_. - - .. note:: - The specified ``reduction_network`` must contain two methods, - namely ``encode`` for input encoding and ``decode`` for decoding the - former result. The ``interpolation_network`` network ``forward`` output - represents the interpolation of the latent space obtain with - ``reduction_network.encode``. - - .. note:: - This solver uses the end-to-end training strategy, i.e. the - ``reduction_network`` and ``interpolation_network`` are trained - simultaneously. For reference on this trainig strategy look at: - Pichi, Federico, Beatriz Moya, and Jan S. Hesthaven. - "A graph convolutional autoencoder approach to model order reduction - for parametrized PDEs." Journal of - Computational Physics 501 (2024): 112762. - DOI - `10.1016/j.jcp.2024.112762 `_. - - .. warning:: - This solver works only for data-driven model. Hence in the ``problem`` - definition the codition must only contain ``input_points`` - (e.g. coefficient parameters, time parameters), and ``output_points``. - - .. warning:: - This solver does not currently support the possibility to pass - ``extra_feature``. - """ - - def __init__( - self, - problem, - reduction_network, - interpolation_network, - loss=torch.nn.MSELoss(), - optimizer=torch.optim.Adam, - optimizer_kwargs={"lr": 0.001}, - scheduler=torch.optim.lr_scheduler.ConstantLR, - scheduler_kwargs={"factor": 1, "total_iters": 0}, - ): - """ - :param AbstractProblem problem: The formualation of the problem. - :param torch.nn.Module reduction_network: The reduction network used - for reducing the input space. It must contain two methods, - namely ``encode`` for input encoding and ``decode`` for decoding the - former result. - :param torch.nn.Module interpolation_network: The interpolation network - for interpolating the control parameters to latent space obtain by - the ``reduction_network`` encoding. - :param torch.nn.Module loss: The loss function used as minimizer, - default :class:`torch.nn.MSELoss`. - :param torch.nn.Module extra_features: The additional input - features to use as augmented input. - :param torch.optim.Optimizer optimizer: The neural network optimizer to - use; default is :class:`torch.optim.Adam`. - :param dict optimizer_kwargs: Optimizer constructor keyword args. - :param float lr: The learning rate; default is 0.001. - :param torch.optim.LRScheduler scheduler: Learning - rate scheduler. - :param dict scheduler_kwargs: LR scheduler constructor keyword args. - """ - model = torch.nn.ModuleDict( - { - "reduction_network": reduction_network, - "interpolation_network": interpolation_network, - } - ) - - super().__init__( - model=model, - problem=problem, - loss=loss, - optimizer=optimizer, - optimizer_kwargs=optimizer_kwargs, - scheduler=scheduler, - scheduler_kwargs=scheduler_kwargs, - ) - - # assert reduction object contains encode/ decode - if not hasattr(self.neural_net["reduction_network"], "encode"): - raise SyntaxError( - "reduction_network must have encode method. " - "The encode method should return a lower " - "dimensional representation of the input." - ) - if not hasattr(self.neural_net["reduction_network"], "decode"): - raise SyntaxError( - "reduction_network must have decode method. " - "The decode method should return a high " - "dimensional representation of the encoding." - ) - - def forward(self, x): - """ - Forward pass implementation for the solver. It finds the encoder - representation by calling ``interpolation_network.forward`` on the - input, and maps this representation to output space by calling - ``reduction_network.decode``. - - :param torch.Tensor x: Input tensor. - :return: Solver solution. - :rtype: torch.Tensor - """ - reduction_network = self.neural_net["reduction_network"] - interpolation_network = self.neural_net["interpolation_network"] - return reduction_network.decode(interpolation_network(x)) - - def loss_data(self, input_pts, output_pts): - """ - The data loss for the ReducedOrderModelSolver solver. - It computes the loss between - the network output against the true solution. This function - should not be override if not intentionally. - - :param LabelTensor input_tensor: The input to the neural networks. - :param LabelTensor output_tensor: The true solution to compare the - network solution. - :return: The residual loss averaged on the input coordinates - :rtype: torch.Tensor - """ - # extract networks - reduction_network = self.neural_net["reduction_network"] - interpolation_network = self.neural_net["interpolation_network"] - # encoded representations loss - encode_repr_inter_net = interpolation_network(input_pts) - encode_repr_reduction_network = reduction_network.encode(output_pts) - loss_encode = self.loss( - encode_repr_inter_net, encode_repr_reduction_network - ) - # reconstruction loss - loss_reconstruction = self.loss( - reduction_network.decode(encode_repr_reduction_network), output_pts - ) - - return loss_encode + loss_reconstruction - - @property - def neural_net(self): - """ - Neural network for training. It returns a :obj:`~torch.nn.ModuleDict` - containing the ``reduction_network`` and ``interpolation_network``. - """ - return self._neural_net.torchmodel diff --git a/pina/solvers/solver.py b/pina/solvers/solver.py deleted file mode 100644 index ec2f40c8d..000000000 --- a/pina/solvers/solver.py +++ /dev/null @@ -1,172 +0,0 @@ -""" Solver module. """ - -from abc import ABCMeta, abstractmethod -from ..model.network import Network -import pytorch_lightning -from ..utils import check_consistency -from ..problem import AbstractProblem -import torch -import sys - - -class SolverInterface(pytorch_lightning.LightningModule, metaclass=ABCMeta): - """ - Solver base class. This class inherits is a wrapper of - LightningModule class, inheriting all the - LightningModule methods. - """ - - def __init__( - self, - models, - problem, - optimizers, - optimizers_kwargs, - extra_features=None, - ): - """ - :param models: A torch neural network model instance. - :type models: torch.nn.Module - :param problem: A problem definition instance. - :type problem: AbstractProblem - :param list(torch.optim.Optimizer) optimizer: A list of neural network optimizers to - use. - :param list(dict) optimizer_kwargs: A list of optimizer constructor keyword args. - :param list(torch.nn.Module) extra_features: The additional input - features to use as augmented input. If ``None`` no extra features - are passed. If it is a list of :class:`torch.nn.Module`, the extra feature - list is passed to all models. If it is a list of extra features' lists, - each single list of extra feature is passed to a model. - """ - super().__init__() - - # check consistency of the inputs - check_consistency(models, torch.nn.Module) - check_consistency(problem, AbstractProblem) - check_consistency(optimizers, torch.optim.Optimizer, subclass=True) - check_consistency(optimizers_kwargs, dict) - - # put everything in a list if only one input - if not isinstance(models, list): - models = [models] - if not isinstance(optimizers, list): - optimizers = [optimizers] - optimizers_kwargs = [optimizers_kwargs] - - # number of models and optimizers - len_model = len(models) - len_optimizer = len(optimizers) - len_optimizer_kwargs = len(optimizers_kwargs) - - # check length consistency optimizers - if len_model != len_optimizer: - raise ValueError( - "You must define one optimizer for each model." - f"Got {len_model} models, and {len_optimizer}" - " optimizers." - ) - - # check length consistency optimizers kwargs - if len_optimizer_kwargs != len_optimizer: - raise ValueError( - "You must define one dictionary of keyword" - " arguments for each optimizers." - f"Got {len_optimizer} optimizers, and" - f" {len_optimizer_kwargs} dicitionaries" - ) - - # extra features handling - if (extra_features is None) or (len(extra_features) == 0): - extra_features = [None] * len_model - else: - # if we only have a list of extra features - if not isinstance(extra_features[0], (tuple, list)): - extra_features = [extra_features] * len_model - else: # if we have a list of list extra features - if len(extra_features) != len_model: - raise ValueError( - "You passed a list of extrafeatures list with len" - f"different of models len. Expected {len_model} " - f"got {len(extra_features)}. If you want to use " - "the same list of extra features for all models, " - "just pass a list of extrafeatures and not a list " - "of list of extra features." - ) - - # assigning model and optimizers - self._pina_models = [] - self._pina_optimizers = [] - - for idx in range(len_model): - model_ = Network( - model=models[idx], - input_variables=problem.input_variables, - output_variables=problem.output_variables, - extra_features=extra_features[idx], - ) - optim_ = optimizers[idx]( - model_.parameters(), **optimizers_kwargs[idx] - ) - self._pina_models.append(model_) - self._pina_optimizers.append(optim_) - - # assigning problem - self._pina_problem = problem - - @abstractmethod - def forward(self, *args, **kwargs): - pass - - @abstractmethod - def training_step(self): - pass - - @abstractmethod - def configure_optimizers(self): - pass - - @property - def models(self): - """ - The torch model.""" - return self._pina_models - - @property - def optimizers(self): - """ - The torch model.""" - return self._pina_optimizers - - @property - def problem(self): - """ - The problem formulation.""" - return self._pina_problem - - def on_train_start(self): - """ - On training epoch start this function is call to do global checks for - the different solvers. - """ - - # 1. Check the verison for dataloader - dataloader = self.trainer.train_dataloader - if sys.version_info < (3, 8): - dataloader = dataloader.loaders - self._dataloader = dataloader - - return super().on_train_start() - - # @model.setter - # def model(self, new_model): - # """ - # Set the torch.""" - # check_consistency(new_model, nn.Module, 'torch model') - # self._model= new_model - - # @problem.setter - # def problem(self, problem): - # """ - # Set the problem formulation.""" - # check_consistency(problem, AbstractProblem, 'pina problem') - # self._problem = problem diff --git a/pina/solvers/supervised.py b/pina/solvers/supervised.py deleted file mode 100644 index 425364614..000000000 --- a/pina/solvers/supervised.py +++ /dev/null @@ -1,185 +0,0 @@ -""" Module for SupervisedSolver """ - -import torch - -try: - from torch.optim.lr_scheduler import LRScheduler # torch >= 2.0 -except ImportError: - from torch.optim.lr_scheduler import ( - _LRScheduler as LRScheduler, - ) # torch < 2.0 - -from torch.optim.lr_scheduler import ConstantLR - -from .solver import SolverInterface -from ..label_tensor import LabelTensor -from ..utils import check_consistency -from ..loss import LossInterface -from torch.nn.modules.loss import _Loss - - -class SupervisedSolver(SolverInterface): - r""" - SupervisedSolver solver class. This class implements a SupervisedSolver, - using a user specified ``model`` to solve a specific ``problem``. - - The Supervised Solver class aims to find - a map between the input :math:`\mathbf{s}:\Omega\rightarrow\mathbb{R}^m` - and the output :math:`\mathbf{u}:\Omega\rightarrow\mathbb{R}^m`. The input - can be discretised in space (as in :obj:`~pina.solvers.rom.ROMe2eSolver`), - or not (e.g. when training Neural Operators). - - Given a model :math:`\mathcal{M}`, the following loss function is - minimized during training: - - .. math:: - \mathcal{L}_{\rm{problem}} = \frac{1}{N}\sum_{i=1}^N - \mathcal{L}(\mathbf{u}_i - \mathcal{M}(\mathbf{v}_i)) - - where :math:`\mathcal{L}` is a specific loss function, - default Mean Square Error: - - .. math:: - \mathcal{L}(v) = \| v \|^2_2. - - In this context :math:`\mathbf{u}_i` and :math:`\mathbf{v}_i` means that - we are seeking to approximate multiple (discretised) functions given - multiple (discretised) input functions. - """ - - def __init__( - self, - problem, - model, - extra_features=None, - loss=torch.nn.MSELoss(), - optimizer=torch.optim.Adam, - optimizer_kwargs={"lr": 0.001}, - scheduler=ConstantLR, - scheduler_kwargs={"factor": 1, "total_iters": 0}, - ): - """ - :param AbstractProblem problem: The formualation of the problem. - :param torch.nn.Module model: The neural network model to use. - :param torch.nn.Module loss: The loss function used as minimizer, - default :class:`torch.nn.MSELoss`. - :param torch.nn.Module extra_features: The additional input - features to use as augmented input. - :param torch.optim.Optimizer optimizer: The neural network optimizer to - use; default is :class:`torch.optim.Adam`. - :param dict optimizer_kwargs: Optimizer constructor keyword args. - :param float lr: The learning rate; default is 0.001. - :param torch.optim.LRScheduler scheduler: Learning - rate scheduler. - :param dict scheduler_kwargs: LR scheduler constructor keyword args. - """ - super().__init__( - models=[model], - problem=problem, - optimizers=[optimizer], - optimizers_kwargs=[optimizer_kwargs], - extra_features=extra_features, - ) - - # check consistency - check_consistency(scheduler, LRScheduler, subclass=True) - check_consistency(scheduler_kwargs, dict) - check_consistency(loss, (LossInterface, _Loss), subclass=False) - - # assign variables - self._scheduler = scheduler(self.optimizers[0], **scheduler_kwargs) - self._loss = loss - self._neural_net = self.models[0] - - def forward(self, x): - """Forward pass implementation for the solver. - - :param torch.Tensor x: Input tensor. - :return: Solver solution. - :rtype: torch.Tensor - """ - return self.neural_net(x) - - def configure_optimizers(self): - """Optimizer configuration for the solver. - - :return: The optimizers and the schedulers - :rtype: tuple(list, list) - """ - return self.optimizers, [self.scheduler] - - def training_step(self, batch, batch_idx): - """Solver training step. - - :param batch: The batch element in the dataloader. - :type batch: tuple - :param batch_idx: The batch index. - :type batch_idx: int - :return: The sum of the loss functions. - :rtype: LabelTensor - """ - - condition_idx = batch["condition"] - - for condition_id in range(condition_idx.min(), condition_idx.max() + 1): - - condition_name = self._dataloader.condition_names[condition_id] - condition = self.problem.conditions[condition_name] - pts = batch["pts"] - out = batch["output"] - - if condition_name not in self.problem.conditions: - raise RuntimeError("Something wrong happened.") - - # for data driven mode - if not hasattr(condition, "output_points"): - raise NotImplementedError( - f"{type(self).__name__} works only in data-driven mode." - ) - - output_pts = out[condition_idx == condition_id] - input_pts = pts[condition_idx == condition_id] - - loss = ( - self.loss_data(input_pts=input_pts, output_pts=output_pts) - * condition.data_weight - ) - loss = loss.as_subclass(torch.Tensor) - - self.log("mean_loss", float(loss), prog_bar=True, logger=True) - return loss - - def loss_data(self, input_pts, output_pts): - """ - The data loss for the Supervised solver. It computes the loss between - the network output against the true solution. This function - should not be override if not intentionally. - - :param LabelTensor input_tensor: The input to the neural networks. - :param LabelTensor output_tensor: The true solution to compare the - network solution. - :return: The residual loss averaged on the input coordinates - :rtype: torch.Tensor - """ - return self.loss(self.forward(input_pts), output_pts) - - @property - def scheduler(self): - """ - Scheduler for training. - """ - return self._scheduler - - @property - def neural_net(self): - """ - Neural network for training. - """ - return self._neural_net - - @property - def loss(self): - """ - Loss for training. - """ - return self._loss diff --git a/pina/trainer.py b/pina/trainer.py index 40f4eb691..78dd77adf 100644 --- a/pina/trainer.py +++ b/pina/trainer.py @@ -1,87 +1,327 @@ -""" Trainer module. """ +"""Module for the Trainer.""" +import sys import torch -import pytorch_lightning +import lightning from .utils import check_consistency -from .dataset import SamplePointDataset, SamplePointLoader, DataPointDataset -from .solvers.solver import SolverInterface +from .data import PinaDataModule +from .solver import SolverInterface, PINNInterface -class Trainer(pytorch_lightning.Trainer): +class Trainer(lightning.pytorch.Trainer): + """ + PINA custom Trainer class to extend the standard Lightning functionality. - def __init__(self, solver, batch_size=None, **kwargs): - """ - PINA Trainer class for costumizing every aspect of training via flags. + This class enables specific features or behaviors required by the PINA + framework. It modifies the standard + :class:`lightning.pytorch.Trainer ` + class to better support the training process in PINA. + """ - :param solver: A pina:class:`SolverInterface` solver for the differential problem. - :type solver: SolverInterface - :param batch_size: How many samples per batch to load. If ``batch_size=None`` all - samples are loaded and data are not batched, defaults to None. - :type batch_size: int | None + def __init__( + self, + solver, + batch_size=None, + train_size=1.0, + test_size=0.0, + val_size=0.0, + compile=None, + repeat=None, + automatic_batching=None, + num_workers=None, + pin_memory=None, + shuffle=None, + **kwargs, + ): + """ + Initialization of the :class:`Trainer` class. - :Keyword Arguments: - The additional keyword arguments specify the training setup - and can be choosen from the `pytorch-lightning - Trainer API `_ + :param SolverInterface solver: A + :class:`~pina.solver.solver.SolverInterface` solver used to solve a + :class:`~pina.problem.abstract_problem.AbstractProblem`. + :param int batch_size: The number of samples per batch to load. + If ``None``, all samples are loaded and data is not batched. + Default is ``None``. + :param float train_size: The percentage of elements to include in the + training dataset. Default is ``1.0``. + :param float test_size: The percentage of elements to include in the + test dataset. Default is ``0.0``. + :param float val_size: The percentage of elements to include in the + validation dataset. Default is ``0.0``. + :param bool compile: If ``True``, the model is compiled before training. + Default is ``False``. For Windows users, it is always disabled. + :param bool repeat: Whether to repeat the dataset data in each + condition during training. For further details, see the + :class:`~pina.data.data_module.PinaDataModule` class. Default is + ``False``. + :param bool automatic_batching: If ``True``, automatic PyTorch batching + is performed, otherwise the items are retrieved from the dataset + all at once. For further details, see the + :class:`~pina.data.data_module.PinaDataModule` class. Default is + ``False``. + :param int num_workers: The number of worker threads for data loading. + Default is ``0`` (serial loading). + :param bool pin_memory: Whether to use pinned memory for faster data + transfer to GPU. Default is ``False``. + :param bool shuffle: Whether to shuffle the data during training. + Default is ``True``. + :param dict kwargs: Additional keyword arguments that specify the + training setup. These can be selected from the `pytorch-lightning + Trainer API + `_. """ + # check consistency for init types + self._check_input_consistency( + solver=solver, + train_size=train_size, + test_size=test_size, + val_size=val_size, + repeat=repeat, + automatic_batching=automatic_batching, + compile=compile, + ) + pin_memory, num_workers, shuffle, batch_size = ( + self._check_consistency_and_set_defaults( + pin_memory, num_workers, shuffle, batch_size + ) + ) - super().__init__(**kwargs) + # inference mode set to false when validating/testing PINNs otherwise + # gradient is not tracked and optimization_cycle fails + if isinstance(solver, PINNInterface): + kwargs["inference_mode"] = False - # check inheritance consistency for solver and batch size - check_consistency(solver, SolverInterface) - if batch_size is not None: - check_consistency(batch_size, int) + # Logging depends on the batch size, when batch_size is None then + # log_every_n_steps should be zero + if batch_size is None: + kwargs["log_every_n_steps"] = 0 + else: + kwargs.setdefault("log_every_n_steps", 50) # default for lightning - self._model = solver - self.batch_size = batch_size + # Setting default kwargs, overriding lightning defaults + kwargs.setdefault("enable_progress_bar", True) - # create dataloader - if solver.problem.have_sampled_points is False: - raise RuntimeError( - f"Input points in {solver.problem.not_sampled_points} " - "training are None. Please " - "sample points in your problem by calling " - "discretise_domain function before train " - "in the provided locations." - ) + super().__init__(**kwargs) - self._create_or_update_loader() + # checking compilation and automatic batching + if compile is None or sys.platform == "win32": + compile = False - def _create_or_update_loader(self): - """ - This method is used here because is resampling is needed - during training, there is no need to define to touch the - trainer dataloader, just call the method. - """ - devices = self._accelerator_connector._parallel_devices + repeat = repeat if repeat is not None else False - if len(devices) > 1: - raise RuntimeError("Parallel training is not supported yet.") + automatic_batching = ( + automatic_batching if automatic_batching is not None else False + ) - device = devices[0] - dataset_phys = SamplePointDataset(self._model.problem, device) - dataset_data = DataPointDataset(self._model.problem, device) - self._loader = SamplePointLoader( - dataset_phys, dataset_data, batch_size=self.batch_size, shuffle=True + # set attributes + self.compile = compile + self.solver = solver + self.batch_size = batch_size + self._move_to_device() + self.data_module = None + self._create_datamodule( + train_size=train_size, + test_size=test_size, + val_size=val_size, + batch_size=batch_size, + repeat=repeat, + automatic_batching=automatic_batching, + pin_memory=pin_memory, + num_workers=num_workers, + shuffle=shuffle, ) - pb = self._model.problem + + # logging + self.logging_kwargs = { + "sync_dist": bool( + len(self._accelerator_connector._parallel_devices) > 1 + ), + "on_step": bool(kwargs["log_every_n_steps"] > 0), + "prog_bar": bool(kwargs["enable_progress_bar"]), + "on_epoch": True, + } + + def _move_to_device(self): + """ + Moves the ``unknown_parameters`` of an instance of + :class:`~pina.problem.abstract_problem.AbstractProblem` to the + :class:`Trainer` device. + """ + device = self._accelerator_connector._parallel_devices[0] + # move parameters to device + pb = self.solver.problem if hasattr(pb, "unknown_parameters"): for key in pb.unknown_parameters: pb.unknown_parameters[key] = torch.nn.Parameter( pb.unknown_parameters[key].data.to(device) ) - def train(self, **kwargs): + def _create_datamodule( + self, + train_size, + test_size, + val_size, + batch_size, + repeat, + automatic_batching, + pin_memory, + num_workers, + shuffle, + ): """ - Train the solver method. + This method is designed to handle the creation of a data module when + resampling is needed during training. Instead of manually defining and + modifying the trainer's dataloaders, this method is called to + automatically configure the data module. + + :param float train_size: The percentage of elements to include in the + training dataset. + :param float test_size: The percentage of elements to include in the + test dataset. + :param float val_size: The percentage of elements to include in the + validation dataset. + :param int batch_size: The number of samples per batch to load. + :param bool repeat: Whether to repeat the dataset data in each + condition during training. + :param bool automatic_batching: Whether to perform automatic batching + with PyTorch. + :param bool pin_memory: Whether to use pinned memory for faster data + transfer to GPU. + :param int num_workers: The number of worker threads for data loading. + :param bool shuffle: Whether to shuffle the data during training. + :raises RuntimeError: If not all conditions are sampled. """ - return super().fit( - self._model, train_dataloaders=self._loader, **kwargs + if not self.solver.problem.are_all_domains_discretised: + error_message = "\n".join( + [ + f"""{" " * 13} ---> Domain {key} { + "sampled" if key in self.solver.problem.discretised_domains + else + "not sampled"}""" + for key in self.solver.problem.domains.keys() + ] + ) + raise RuntimeError( + "Cannot create Trainer if not all conditions " + "are sampled. The Trainer got the following:\n" + f"{error_message}" + ) + self.data_module = PinaDataModule( + self.solver.problem, + train_size=train_size, + test_size=test_size, + val_size=val_size, + batch_size=batch_size, + repeat=repeat, + automatic_batching=automatic_batching, + num_workers=num_workers, + pin_memory=pin_memory, + shuffle=shuffle, ) + def train(self, **kwargs): + """ + Manage the training process of the solver. + + :param dict kwargs: Additional keyword arguments. See `pytorch-lightning + Trainer API `_ + for details. + """ + return super().fit(self.solver, datamodule=self.data_module, **kwargs) + + def test(self, **kwargs): + """ + Manage the test process of the solver. + + :param dict kwargs: Additional keyword arguments. See `pytorch-lightning + Trainer API `_ + for details. + """ + return super().test(self.solver, datamodule=self.data_module, **kwargs) + @property def solver(self): """ - Returning trainer solver. + Get the solver. + + :return: The solver. + :rtype: SolverInterface """ - return self._model + return self._solver + + @solver.setter + def solver(self, solver): + """ + Set the solver. + + :param SolverInterface solver: The solver to set. + """ + self._solver = solver + + @staticmethod + def _check_input_consistency( + solver, + train_size, + test_size, + val_size, + repeat, + automatic_batching, + compile, + ): + """ + Verifies the consistency of the parameters for the solver configuration. + + :param SolverInterface solver: The solver. + :param float train_size: The percentage of elements to include in the + training dataset. + :param float test_size: The percentage of elements to include in the + test dataset. + :param float val_size: The percentage of elements to include in the + validation dataset. + :param bool repeat: Whether to repeat the dataset data in each + condition during training. + :param bool automatic_batching: Whether to perform automatic batching + with PyTorch. + :param bool compile: If ``True``, the model is compiled before training. + """ + + check_consistency(solver, SolverInterface) + check_consistency(train_size, float) + check_consistency(test_size, float) + check_consistency(val_size, float) + if repeat is not None: + check_consistency(repeat, bool) + if automatic_batching is not None: + check_consistency(automatic_batching, bool) + if compile is not None: + check_consistency(compile, bool) + + @staticmethod + def _check_consistency_and_set_defaults( + pin_memory, num_workers, shuffle, batch_size + ): + """ + Checks the consistency of input parameters and sets default values + for missing or invalid parameters. + + :param bool pin_memory: Whether to use pinned memory for faster data + transfer to GPU. + :param int num_workers: The number of worker threads for data loading. + :param bool shuffle: Whether to shuffle the data during training. + :param int batch_size: The number of samples per batch to load. + """ + if pin_memory is not None: + check_consistency(pin_memory, bool) + else: + pin_memory = False + if num_workers is not None: + check_consistency(pin_memory, int) + else: + num_workers = 0 + if shuffle is not None: + check_consistency(shuffle, bool) + else: + shuffle = True + if batch_size is not None: + check_consistency(batch_size, int) + return pin_memory, num_workers, shuffle, batch_size diff --git a/pina/utils.py b/pina/utils.py index 282dd5332..56b329bd9 100644 --- a/pina/utils.py +++ b/pina/utils.py @@ -1,81 +1,125 @@ -"""Utils module""" +"""Module for utility functions.""" -from torch.utils.data import Dataset, DataLoader -from functools import reduce import types - +from functools import reduce import torch -from torch.utils.data import DataLoader, default_collate, ConcatDataset from .label_tensor import LabelTensor -import torch +# Codacy error unused parameters +def custom_warning_format( + message, category, filename, lineno, file=None, line=None +): + """ + Custom warning formatting function. + + :param str message: The warning message. + :param Warning category: The warning category. + :param str filename: The filename where the warning is raised. + :param int lineno: The line number where the warning is raised. + :param str file: The file object where the warning is raised. + Default is None. + :param int line: The line where the warning is raised. + :return: The formatted warning message. + :rtype: str + """ + return f"{filename}: {category.__name__}: {message}\n" -def check_consistency(object, object_instance, subclass=False): - """Helper function to check object inheritance consistency. - Given a specific ``'object'`` we check if the object is - instance of a specific ``'object_instance'``, or in case - ``'subclass=True'`` we check if the object is subclass - if the ``'object_instance'``. - :param (iterable or class object) object: The object to check the inheritance - :param Object object_instance: The parent class from where the object - is expected to inherit - :param str object_name: The name of the object - :param bool subclass: Check if is a subclass and not instance - :raises ValueError: If the object does not inherit from the - specified class +def check_consistency(object_, object_instance, subclass=False): + """ + Check if an object maintains inheritance consistency. + + This function checks whether a given object is an instance of a specified + class or, if ``subclass=True``, whether it is a subclass of the specified + class. + + :param object: The object to check. + :type object: Iterable | Object + :param Object object_instance: The expected parent class. + :param bool subclass: If True, checks whether ``object_`` is a subclass + of ``object_instance`` instead of an instance. Default is ``False``. + :raises ValueError: If ``object_`` does not inherit from ``object_instance`` + as expected. """ - if not isinstance(object, (list, set, tuple)): - object = [object] + if not isinstance(object_, (list, set, tuple)): + object_ = [object_] - for obj in object: + for obj in object_: try: if not subclass: assert isinstance(obj, object_instance) else: assert issubclass(obj, object_instance) - except AssertionError: - raise ValueError(f"{type(obj).__name__} must be {object_instance}.") + except AssertionError as e: + raise ValueError( + f"{type(obj).__name__} must be {object_instance}." + ) from e -def number_parameters( - model, aggregate=True, only_trainable=True -): # TODO: check +def labelize_forward(forward, input_variables, output_variables): """ - Return the number of parameters of a given `model`. - - :param torch.nn.Module model: the torch module to inspect. - :param bool aggregate: if True the return values is an integer corresponding - to the total amount of parameters of whole model. If False, it returns a - dictionary whose keys are the names of layers and the values the - corresponding number of parameters. Default is True. - :param bool trainable: if True, only trainable parameters are count, - otherwise no. Default is True. - :return: the number of parameters of the model - :rtype: dict or int + Decorator to enable or disable the use of + :class:`~pina.label_tensor.LabelTensor` during the forward pass. + + :param Callable forward: The forward function of a :class:`torch.nn.Module`. + :param list[str] input_variables: The names of the input variables of a + :class:`~pina.problem.abstract_problem.AbstractProblem`. + :param list[str] output_variables: The names of the output variables of a + :class:`~pina.problem.abstract_problem.AbstractProblem`. + :return: The decorated forward function. + :rtype: Callable """ - tmp = {} - for name, parameter in model.named_parameters(): - if only_trainable and not parameter.requires_grad: - continue - tmp[name] = parameter.numel() + def wrapper(x): + """ + Decorated forward function. - if aggregate: - tmp = sum(tmp.values()) + :param LabelTensor x: The labelized input of the forward pass of an + instance of :class:`torch.nn.Module`. + :return: The labelized output of the forward pass of an instance of + :class:`torch.nn.Module`. + :rtype: LabelTensor + """ + x = x.extract(input_variables) + output = forward(x) + # keep it like this, directly using LabelTensor(...) raises errors + # when compiling the code + output = output.as_subclass(LabelTensor) + output.labels = output_variables + return output - return tmp + return wrapper -def merge_tensors(tensors): # name to be changed +def merge_tensors(tensors): + """ + Merge a list of :class:`~pina.label_tensor.LabelTensor` instances into a + single :class:`~pina.label_tensor.LabelTensor` tensor, by applying + iteratively the cartesian product. + + :param list[LabelTensor] tensors: The list of tensors to merge. + :raises ValueError: If the list of tensors is empty. + :return: The merged tensor. + :rtype: LabelTensor + """ if tensors: return reduce(merge_two_tensors, tensors[1:], tensors[0]) raise ValueError("Expected at least one tensor") def merge_two_tensors(tensor1, tensor2): + """ + Merge two :class:`~pina.label_tensor.LabelTensor` instances into a single + :class:`~pina.label_tensor.LabelTensor` tensor, by applying the cartesian + product. + + :param LabelTensor tensor1: The first tensor to merge. + :param LabelTensor tensor2: The second tensor to merge. + :return: The merged tensor. + :rtype: LabelTensor + """ n1 = tensor1.shape[0] n2 = tensor2.shape[0] @@ -87,12 +131,14 @@ def merge_two_tensors(tensor1, tensor2): def torch_lhs(n, dim): - """Latin Hypercube Sampling torch routine. - Sampling in range $[0, 1)^d$. + """ + The Latin Hypercube Sampling torch routine, sampling in :math:`[0, 1)`$. - :param int n: number of samples - :param int dim: dimensions of latin hypercube - :return: samples + :param int n: The number of points to sample. + :param int dim: The number of dimensions of the sampling space. + :raises TypeError: If `n` or `dim` are not integers. + :raises ValueError: If `dim` is less than 1. + :return: The sampled points. :rtype: torch.tensor """ @@ -122,77 +168,24 @@ def torch_lhs(n, dim): def is_function(f): """ - Checks whether the given object `f` is a function or lambda. + Check if the given object is a function or a lambda. - :param object f: The object to be checked. - :return: `True` if `f` is a function, `False` otherwise. + :param Object f: The object to be checked. + :return: ``True`` if ``f`` is a function, ``False`` otherwise. :rtype: bool """ - return type(f) == types.FunctionType or type(f) == types.LambdaType + return isinstance(f, (types.FunctionType, types.LambdaType)) def chebyshev_roots(n): """ - Return the roots of *n* Chebyshev polynomials (between [-1, 1]). + Compute the roots of the Chebyshev polynomial of degree ``n``. - :param int n: number of roots - :return: roots - :rtype: torch.tensor + :param int n: The number of roots to return. + :return: The roots of the Chebyshev polynomials. + :rtype: torch.Tensor """ pi = torch.acos(torch.zeros(1)).item() * 2 k = torch.arange(n) nodes = torch.sort(torch.cos(pi * (k + 0.5) / n))[0] return nodes - - -# class PinaDataset(): - -# def __init__(self, pinn) -> None: -# self.pinn = pinn - -# @property -# def dataloader(self): -# return self._create_dataloader() - -# @property -# def dataset(self): -# return [self.SampleDataset(key, val) -# for key, val in self.input_pts.items()] - -# def _create_dataloader(self): -# """Private method for creating dataloader - -# :return: dataloader -# :rtype: torch.utils.data.DataLoader -# """ -# if self.pinn.batch_size is None: -# return {key: [{key: val}] for key, val in self.pinn.input_pts.items()} - -# def custom_collate(batch): -# # extracting pts labels -# _, pts = list(batch[0].items())[0] -# labels = pts.labels -# # calling default torch collate -# collate_res = default_collate(batch) -# # save collate result in dict -# res = {} -# for key, val in collate_res.items(): -# val.labels = labels -# res[key] = val -# __init__(self, location, tensor): -# self._tensor = tensor -# self._location = location -# self._len = len(tensor) - -# def __getitem__(self, index): -# tensor = self._tensor.select(0, index) -# return {self._location: tensor} - -# def __len__(self): -# return self._len - - -class LabelTensorDataLoader(DataLoader): - - def collate_fn(self, data): - pass diff --git a/pina/writer.py b/pina/writer.py deleted file mode 100644 index 831c1cc6b..000000000 --- a/pina/writer.py +++ /dev/null @@ -1,50 +0,0 @@ -""" Module for plotting. """ - -import matplotlib.pyplot as plt -import numpy as np -import torch - -from pina import LabelTensor - - -class Writer: - """ - Implementation of a writer class, for textual output. - """ - - def __init__(self, frequency_print=10, header="any") -> None: - """ - The constructor of the class. - - :param int frequency_print: the frequency in epochs of printing. - :param ['any', 'begin', 'none'] header: the header of the output. - """ - - self._frequency_print = frequency_print - self._header = header - - def header(self, trainer): - """ - The method for printing the header. - """ - header = [] - for condition_name in trainer.problem.conditions: - header.append(f"{condition_name}") - - return header - - def write_loss(self, trainer): - """ - The method for writing the output. - """ - pass - - def write_loss_in_loop(self, trainer, loss): - """ - The method for writing the output within the training loop. - - :param pina.trainer.Trainer trainer: the trainer object. - """ - - if trainer.trained_epoch % self._frequency_print == 0: - print(f"Epoch {trainer.trained_epoch:05d}: {loss.item():.5e}") diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 000000000..6dc215c9d --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,64 @@ +[project] +name = "pina-mathlab" +version = "0.2.0" +description = "Physic Informed Neural networks for Advance modeling." +readme = "README.md" +authors = [ + {name = "PINA Contributors", email = "pina.mathlab@gmail.com"} +] +license = { text = "MIT" } +keywords = [ + "machine-learning", "deep-learning", "modeling", "pytorch", "ode", + "neural-networks", "differential-equations", "pde", "hacktoberfest", + "pinn", "physics-informed", "physics-informed-neural-networks", + "neural-operators", "equation-learning", "lightining" +] +dependencies = [ + "torch", + "lightning", + "torch_geometric", + "matplotlib", +] +requires-python = ">=3.8" + +[project.optional-dependencies] +doc = [ + "sphinx>5.0,<8.2", + "sphinx_rtd_theme", + "sphinx_copybutton", + "sphinx_design", + "pydata_sphinx_theme" +] +test = [ + "pytest", + "pytest-cov", + "scipy" +] +dev = [ + "black @ git+https://github.com/psf/black" +] +tutorial = [ + "jupyter", + "smithers @ git+https://github.com/mathLab/smithers.git", + "torchvision", + "tensorboard", + "scipy", + "numpy", +] + +[project.urls] +Homepage = "https://mathlab.github.io/PINA/" +Repository = "https://github.com/mathLab/PINA" + +[build-system] +requires = [ "setuptools>=41", "wheel", "setuptools-git-versioning>=2.0,<3", ] +build-backend = "setuptools.build_meta" + +[tool.setuptools.packages.find] +include = ["pina*"] + +[tool.black] +line-length = 80 + +[tool.isort] +profile = "black" \ No newline at end of file diff --git a/setup.py b/setup.py deleted file mode 100644 index 8c7b9ac44..000000000 --- a/setup.py +++ /dev/null @@ -1,73 +0,0 @@ -from setuptools import setup, find_packages - -meta = {} -with open("pina/meta.py") as fp: - exec(fp.read(), meta) - -# Package meta-data. -IMPORTNAME = meta['__title__'] -PIPNAME = meta['__packagename__'] -DESCRIPTION = 'Physic Informed Neural networks for Advance modeling.' -URL = 'https://github.com/mathLab/PINA' -MAIL = meta['__mail__'] -AUTHOR = meta['__author__'] -VERSION = meta['__version__'] -KEYWORDS = 'machine-learning deep-learning modeling pytorch ode neural-networks differential-equations pde hacktoberfest pinn physics-informed physics-informed-neural-networks neural-operators equation-learning lightining' - -REQUIRED = [ - 'numpy<2.0', 'matplotlib', 'torch', 'lightning', 'pytorch_lightning' -] - -EXTRAS = { - 'docs': [ - 'sphinx>5.0', - 'sphinx_rtd_theme', - 'sphinx_copybutton', - 'sphinx_design', - 'pydata_sphinx_theme' - ], - 'test': [ - 'pytest', - 'pytest-cov', - 'scipy' - ], -} - -LDESCRIPTION = ( - "PINA is a Python package providing an easy interface to deal with " - "physics-informed neural networks (PINN) for the approximation of " - "(differential, nonlinear, ...) functions. Based on Pytorch, PINA " - "offers a simple and intuitive way to formalize a specific problem " - "and solve it using PINN. The approximated solution of a differential " - "equation can be implemented using PINA in a few lines of code thanks " - "to the intuitive and user-friendly interface." -) - -setup( - name=PIPNAME, - version=VERSION, - description=DESCRIPTION, - long_description=LDESCRIPTION, - author=AUTHOR, - author_email=MAIL, - classifiers=[ - 'Development Status :: 3 - Alpha', - 'License :: OSI Approved :: MIT License', - 'Programming Language :: Python :: 3', - 'Programming Language :: Python :: 3.8', - 'Programming Language :: Python :: 3.9', - 'Programming Language :: Python :: 3.10', - 'Programming Language :: Python :: 3.11', - 'Programming Language :: Python :: 3.12', - 'Intended Audience :: Science/Research', - 'Topic :: Scientific/Engineering :: Mathematics' - ], - keywords=KEYWORDS, - url=URL, - license='MIT', - packages=find_packages(), - install_requires=REQUIRED, - extras_require=EXTRAS, - include_package_data=True, - zip_safe=False, -) diff --git a/tests/test_adaptive_function.py b/tests/test_adaptive_function.py new file mode 100644 index 000000000..bce5059d7 --- /dev/null +++ b/tests/test_adaptive_function.py @@ -0,0 +1,85 @@ +import torch +import pytest + +from pina.adaptive_function import ( + AdaptiveReLU, + AdaptiveSigmoid, + AdaptiveTanh, + AdaptiveSiLU, + AdaptiveMish, + AdaptiveELU, + AdaptiveCELU, + AdaptiveGELU, + AdaptiveSoftmin, + AdaptiveSoftmax, + AdaptiveSIREN, + AdaptiveExp, +) + + +adaptive_function = ( + AdaptiveReLU, + AdaptiveSigmoid, + AdaptiveTanh, + AdaptiveSiLU, + AdaptiveMish, + AdaptiveELU, + AdaptiveCELU, + AdaptiveGELU, + AdaptiveSoftmin, + AdaptiveSoftmax, + AdaptiveSIREN, + AdaptiveExp, +) +x = torch.rand(10, requires_grad=True) + + +@pytest.mark.parametrize("Func", adaptive_function) +def test_constructor(Func): + if Func.__name__ == "AdaptiveExp": + # simple + Func() + # setting values + af = Func(alpha=1.0, beta=2.0) + assert af.alpha.requires_grad + assert af.beta.requires_grad + assert af.alpha == 1.0 + assert af.beta == 2.0 + else: + # simple + Func() + # setting values + af = Func(alpha=1.0, beta=2.0, gamma=3.0) + assert af.alpha.requires_grad + assert af.beta.requires_grad + assert af.gamma.requires_grad + assert af.alpha == 1.0 + assert af.beta == 2.0 + assert af.gamma == 3.0 + + # fixed variables + af = Func(alpha=1.0, beta=2.0, fixed=["alpha"]) + assert af.alpha.requires_grad is False + assert af.beta.requires_grad + assert af.alpha == 1.0 + assert af.beta == 2.0 + + with pytest.raises(TypeError): + Func(alpha=1.0, beta=2.0, fixed=["delta"]) + + with pytest.raises(ValueError): + Func(alpha="s") + Func(alpha=1) + + +@pytest.mark.parametrize("Func", adaptive_function) +def test_forward(Func): + af = Func() + af(x) + + +@pytest.mark.parametrize("Func", adaptive_function) +def test_backward(Func): + af = Func() + y = af(x) + y.mean().backward() diff --git a/tests/test_adaptive_functions.py b/tests/test_adaptive_functions.py deleted file mode 100644 index 43d9c1bc7..000000000 --- a/tests/test_adaptive_functions.py +++ /dev/null @@ -1,62 +0,0 @@ -import torch -import pytest - -from pina.adaptive_functions import (AdaptiveReLU, AdaptiveSigmoid, AdaptiveTanh, - AdaptiveSiLU, AdaptiveMish, AdaptiveELU, - AdaptiveCELU, AdaptiveGELU, AdaptiveSoftmin, - AdaptiveSoftmax, AdaptiveSIREN, AdaptiveExp) - - -adaptive_functions = (AdaptiveReLU, AdaptiveSigmoid, AdaptiveTanh, - AdaptiveSiLU, AdaptiveMish, AdaptiveELU, - AdaptiveCELU, AdaptiveGELU, AdaptiveSoftmin, - AdaptiveSoftmax, AdaptiveSIREN, AdaptiveExp) -x = torch.rand(10, requires_grad=True) - -@pytest.mark.parametrize("Func", adaptive_functions) -def test_constructor(Func): - if Func.__name__ == 'AdaptiveExp': - # simple - Func() - # setting values - af = Func(alpha=1., beta=2.) - assert af.alpha.requires_grad - assert af.beta.requires_grad - assert af.alpha == 1. - assert af.beta == 2. - else: - # simple - Func() - # setting values - af = Func(alpha=1., beta=2., gamma=3.) - assert af.alpha.requires_grad - assert af.beta.requires_grad - assert af.gamma.requires_grad - assert af.alpha == 1. - assert af.beta == 2. - assert af.gamma == 3. - - # fixed variables - af = Func(alpha=1., beta=2., fixed=['alpha']) - assert af.alpha.requires_grad is False - assert af.beta.requires_grad - assert af.alpha == 1. - assert af.beta == 2. - - with pytest.raises(TypeError): - Func(alpha=1., beta=2., fixed=['delta']) - - with pytest.raises(ValueError): - Func(alpha='s') - Func(alpha=1) - -@pytest.mark.parametrize("Func", adaptive_functions) -def test_forward(Func): - af = Func() - af(x) - -@pytest.mark.parametrize("Func", adaptive_functions) -def test_backward(Func): - af = Func() - y = af(x) - y.mean().backward() \ No newline at end of file diff --git a/tests/test_layers/test_conv.py b/tests/test_blocks/test_convolution.py similarity index 54% rename from tests/test_layers/test_conv.py rename to tests/test_blocks/test_convolution.py index 8f322ac40..f8206196f 100644 --- a/tests/test_layers/test_conv.py +++ b/tests/test_blocks/test_convolution.py @@ -1,4 +1,4 @@ -from pina.model.layers import ContinuousConvBlock +from pina.model.block import ContinuousConvBlock import torch @@ -18,8 +18,8 @@ def _transform_image(image): # initializing transfomed image coordinates = torch.zeros( - [channels, prod(dimension), - len(dimension) + 1]).to(image.device) + [channels, prod(dimension), len(dimension) + 1] + ).to(image.device) # creating the n dimensional mesh grid values_mesh = [ @@ -43,9 +43,13 @@ class MLP(torch.nn.Module): def __init__(self) -> None: super().__init__() - self.model = torch.nn.Sequential(torch.nn.Linear(2, 8), torch.nn.ReLU(), - torch.nn.Linear(8, 8), torch.nn.ReLU(), - torch.nn.Linear(8, 1)) + self.model = torch.nn.Sequential( + torch.nn.Linear(2, 8), + torch.nn.ReLU(), + torch.nn.Linear(8, 8), + torch.nn.ReLU(), + torch.nn.Linear(8, 1), + ) def forward(self, x): return self.model(x) @@ -61,7 +65,7 @@ def forward(self, x): "domain": [10, 10], "start": [0, 0], "jumps": [3, 3], - "direction": [1, 1.] + "direction": [1, 1.0], } dim_filter = len(dim) dim_input = (batch, channel_input, 10, dim_filter) @@ -73,53 +77,42 @@ def forward(self, x): def test_constructor(): model = MLP - conv = ContinuousConvBlock(channel_input, - channel_output, - dim, - stride, - model=model) - conv = ContinuousConvBlock(channel_input, - channel_output, - dim, - stride, - model=None) + conv = ContinuousConvBlock( + channel_input, channel_output, dim, stride, model=model + ) + conv = ContinuousConvBlock( + channel_input, channel_output, dim, stride, model=None + ) def test_forward(): model = MLP # simple forward - conv = ContinuousConvBlock(channel_input, - channel_output, - dim, - stride, - model=model) + conv = ContinuousConvBlock( + channel_input, channel_output, dim, stride, model=model + ) conv(x) # simple forward with optimization - conv = ContinuousConvBlock(channel_input, - channel_output, - dim, - stride, - model=model, - optimize=True) + conv = ContinuousConvBlock( + channel_input, channel_output, dim, stride, model=model, optimize=True + ) conv(x) def test_backward(): model = MLP - + x = torch.rand(dim_input) x = make_grid(x) x.requires_grad = True # simple backward - conv = ContinuousConvBlock(channel_input, - channel_output, - dim, - stride, - model=model) + conv = ContinuousConvBlock( + channel_input, channel_output, dim, stride, model=model + ) conv(x) - l=torch.mean(conv(x)) + l = torch.mean(conv(x)) l.backward() assert x._grad.shape == torch.Size([2, 2, 20, 3]) x = torch.rand(dim_input) @@ -127,14 +120,11 @@ def test_backward(): x.requires_grad = True # simple backward with optimization - conv = ContinuousConvBlock(channel_input, - channel_output, - dim, - stride, - model=model, - optimize=True) + conv = ContinuousConvBlock( + channel_input, channel_output, dim, stride, model=model, optimize=True + ) conv(x) - l=torch.mean(conv(x)) + l = torch.mean(conv(x)) l.backward() assert x._grad.shape == torch.Size([2, 2, 20, 3]) @@ -143,17 +133,13 @@ def test_transpose(): model = MLP # simple transpose - conv = ContinuousConvBlock(channel_input, - channel_output, - dim, - stride, - model=model) - - conv2 = ContinuousConvBlock(channel_output, - channel_input, - dim, - stride, - model=model) + conv = ContinuousConvBlock( + channel_input, channel_output, dim, stride, model=model + ) + + conv2 = ContinuousConvBlock( + channel_output, channel_input, dim, stride, model=model + ) integrals = conv(x) conv2.transpose(integrals[..., -1], x) diff --git a/tests/test_layers/test_embedding.py b/tests/test_blocks/test_embedding.py similarity index 52% rename from tests/test_layers/test_embedding.py rename to tests/test_blocks/test_embedding.py index 29052d060..e8fa6ebce 100644 --- a/tests/test_layers/test_embedding.py +++ b/tests/test_blocks/test_embedding.py @@ -1,60 +1,71 @@ import torch import pytest -from pina.model.layers import PeriodicBoundaryEmbedding, FourierFeatureEmbedding +from pina.model.block import PeriodicBoundaryEmbedding, FourierFeatureEmbedding # test tolerance tol = 1e-6 + def check_same_columns(tensor): # Get the first column and compute residual residual = tensor - tensor[0] zeros = torch.zeros_like(residual) # Compare each column with the first column - all_same = torch.allclose(input=residual,other=zeros,atol=tol) + all_same = torch.allclose(input=residual, other=zeros, atol=tol) return all_same + def grad(u, x): """ Compute the first derivative of u with respect to x. """ - return torch.autograd.grad(u, x, grad_outputs=torch.ones_like(u), - create_graph=True, allow_unused=True, - retain_graph=True)[0] + return torch.autograd.grad( + u, + x, + grad_outputs=torch.ones_like(u), + create_graph=True, + allow_unused=True, + retain_graph=True, + )[0] + def test_constructor_PeriodicBoundaryEmbedding(): PeriodicBoundaryEmbedding(input_dimension=1, periods=2) - PeriodicBoundaryEmbedding(input_dimension=1, periods={'x': 3, 'y' : 4}) - PeriodicBoundaryEmbedding(input_dimension=1, periods={0: 3, 1 : 4}) + PeriodicBoundaryEmbedding(input_dimension=1, periods={"x": 3, "y": 4}) + PeriodicBoundaryEmbedding(input_dimension=1, periods={0: 3, 1: 4}) PeriodicBoundaryEmbedding(input_dimension=1, periods=2, output_dimension=10) with pytest.raises(TypeError): PeriodicBoundaryEmbedding() with pytest.raises(ValueError): - PeriodicBoundaryEmbedding(input_dimension=1., periods=1) - PeriodicBoundaryEmbedding(input_dimension=1, periods=1, - output_dimension=1.) - PeriodicBoundaryEmbedding(input_dimension=1, periods={'x':'x'}) - PeriodicBoundaryEmbedding(input_dimension=1, periods={0:'x'}) + PeriodicBoundaryEmbedding(input_dimension=1.0, periods=1) + PeriodicBoundaryEmbedding( + input_dimension=1, periods=1, output_dimension=1.0 + ) + PeriodicBoundaryEmbedding(input_dimension=1, periods={"x": "x"}) + PeriodicBoundaryEmbedding(input_dimension=1, periods={0: "x"}) @pytest.mark.parametrize("period", [1, 4, 10]) @pytest.mark.parametrize("input_dimension", [1, 2, 3]) -def test_forward_backward_same_period_PeriodicBoundaryEmbedding(input_dimension, - period): +def test_forward_backward_same_period_PeriodicBoundaryEmbedding( + input_dimension, period +): func = torch.nn.Sequential( - PeriodicBoundaryEmbedding(input_dimension=input_dimension, - output_dimension=60, periods=period), + PeriodicBoundaryEmbedding( + input_dimension=input_dimension, output_dimension=60, periods=period + ), torch.nn.Tanh(), torch.nn.Linear(60, 60), torch.nn.Tanh(), - torch.nn.Linear(60, 1) + torch.nn.Linear(60, 1), ) # coordinates - x = period * torch.tensor([[0.],[1.]]) + x = period * torch.tensor([[0.0], [1.0]]) if input_dimension == 2: - x = torch.cartesian_prod(x.flatten(),x.flatten()) + x = torch.cartesian_prod(x.flatten(), x.flatten()) elif input_dimension == 3: - x = torch.cartesian_prod(x.flatten(),x.flatten(),x.flatten()) + x = torch.cartesian_prod(x.flatten(), x.flatten(), x.flatten()) x.requires_grad = True # output f = func(x) @@ -63,29 +74,32 @@ def test_forward_backward_same_period_PeriodicBoundaryEmbedding(input_dimension, loss = f.mean() loss.backward() + def test_constructor_FourierFeatureEmbedding(): - FourierFeatureEmbedding(input_dimension=1, output_dimension=20, - sigma=1) - with pytest.raises(TypeError): + FourierFeatureEmbedding(input_dimension=1, output_dimension=20, sigma=1) + with pytest.raises(TypeError): FourierFeatureEmbedding() - with pytest.raises(RuntimeError): + with pytest.raises(RuntimeError): FourierFeatureEmbedding(input_dimension=1, output_dimension=3, sigma=1) with pytest.raises(ValueError): - FourierFeatureEmbedding(input_dimension='x', output_dimension=20, - sigma=1) - FourierFeatureEmbedding(input_dimension=1, output_dimension='x', - sigma=1) - FourierFeatureEmbedding(input_dimension=1, output_dimension=20, - sigma='x') + FourierFeatureEmbedding( + input_dimension="x", output_dimension=20, sigma=1 + ) + FourierFeatureEmbedding( + input_dimension=1, output_dimension="x", sigma=1 + ) + FourierFeatureEmbedding( + input_dimension=1, output_dimension=20, sigma="x" + ) + @pytest.mark.parametrize("output_dimension", [2, 4, 6]) @pytest.mark.parametrize("input_dimension", [1, 2, 3]) @pytest.mark.parametrize("sigma", [10, 1, 0.1]) -def test_forward_backward_FourierFeatureEmbedding(input_dimension, - output_dimension, - sigma): - func = FourierFeatureEmbedding(input_dimension, output_dimension, - sigma) +def test_forward_backward_FourierFeatureEmbedding( + input_dimension, output_dimension, sigma +): + func = FourierFeatureEmbedding(input_dimension, output_dimension, sigma) # coordinates x = torch.rand((10, input_dimension), requires_grad=True) # output @@ -93,4 +107,4 @@ def test_forward_backward_FourierFeatureEmbedding(input_dimension, assert f.shape[-1] == output_dimension # compute backward loss = f.mean() - loss.backward() \ No newline at end of file + loss.backward() diff --git a/tests/test_blocks/test_fourier.py b/tests/test_blocks/test_fourier.py new file mode 100644 index 000000000..75265fe33 --- /dev/null +++ b/tests/test_blocks/test_fourier.py @@ -0,0 +1,102 @@ +from pina.model.block import FourierBlock1D, FourierBlock2D, FourierBlock3D +import torch + +input_numb_fields = 3 +output_numb_fields = 4 +batch = 5 + + +def test_constructor_1d(): + FourierBlock1D( + input_numb_fields=input_numb_fields, + output_numb_fields=output_numb_fields, + n_modes=5, + ) + + +def test_forward_1d(): + sconv = FourierBlock1D( + input_numb_fields=input_numb_fields, + output_numb_fields=output_numb_fields, + n_modes=4, + ) + x = torch.rand(batch, input_numb_fields, 10) + sconv(x) + + +def test_backward_1d(): + sconv = FourierBlock1D( + input_numb_fields=input_numb_fields, + output_numb_fields=output_numb_fields, + n_modes=4, + ) + x = torch.rand(batch, input_numb_fields, 10) + x.requires_grad = True + sconv(x) + l = torch.mean(sconv(x)) + l.backward() + assert x._grad.shape == torch.Size([5, 3, 10]) + + +def test_constructor_2d(): + FourierBlock2D( + input_numb_fields=input_numb_fields, + output_numb_fields=output_numb_fields, + n_modes=[5, 4], + ) + + +def test_forward_2d(): + sconv = FourierBlock2D( + input_numb_fields=input_numb_fields, + output_numb_fields=output_numb_fields, + n_modes=[5, 4], + ) + x = torch.rand(batch, input_numb_fields, 10, 10) + sconv(x) + + +def test_backward_2d(): + sconv = FourierBlock2D( + input_numb_fields=input_numb_fields, + output_numb_fields=output_numb_fields, + n_modes=[5, 4], + ) + x = torch.rand(batch, input_numb_fields, 10, 10) + x.requires_grad = True + sconv(x) + l = torch.mean(sconv(x)) + l.backward() + assert x._grad.shape == torch.Size([5, 3, 10, 10]) + + +def test_constructor_3d(): + FourierBlock3D( + input_numb_fields=input_numb_fields, + output_numb_fields=output_numb_fields, + n_modes=[5, 4, 4], + ) + + +def test_forward_3d(): + sconv = FourierBlock3D( + input_numb_fields=input_numb_fields, + output_numb_fields=output_numb_fields, + n_modes=[5, 4, 4], + ) + x = torch.rand(batch, input_numb_fields, 10, 10, 10) + sconv(x) + + +def test_backward_3d(): + sconv = FourierBlock3D( + input_numb_fields=input_numb_fields, + output_numb_fields=output_numb_fields, + n_modes=[5, 4, 4], + ) + x = torch.rand(batch, input_numb_fields, 10, 10, 10) + x.requires_grad = True + sconv(x) + l = torch.mean(sconv(x)) + l.backward() + assert x._grad.shape == torch.Size([5, 3, 10, 10, 10]) diff --git a/tests/test_blocks/test_low_rank_block.py b/tests/test_blocks/test_low_rank_block.py new file mode 100644 index 000000000..0e6ddcb89 --- /dev/null +++ b/tests/test_blocks/test_low_rank_block.py @@ -0,0 +1,70 @@ +import torch +import pytest + +from pina.model.block import LowRankBlock +from pina import LabelTensor + + +input_dimensions = 2 +embedding_dimenion = 1 +rank = 4 +inner_size = 20 +n_layers = 2 +func = torch.nn.Tanh +bias = True + + +def test_constructor(): + LowRankBlock( + input_dimensions=input_dimensions, + embedding_dimenion=embedding_dimenion, + rank=rank, + inner_size=inner_size, + n_layers=n_layers, + func=func, + bias=bias, + ) + + +def test_constructor_wrong(): + with pytest.raises(ValueError): + LowRankBlock( + input_dimensions=input_dimensions, + embedding_dimenion=embedding_dimenion, + rank=0.5, + inner_size=inner_size, + n_layers=n_layers, + func=func, + bias=bias, + ) + + +def test_forward(): + block = LowRankBlock( + input_dimensions=input_dimensions, + embedding_dimenion=embedding_dimenion, + rank=rank, + inner_size=inner_size, + n_layers=n_layers, + func=func, + bias=bias, + ) + data = LabelTensor(torch.rand(10, 30, 3), labels=["x", "y", "u"]) + block(data.extract("u"), data.extract(["x", "y"])) + + +def test_backward(): + block = LowRankBlock( + input_dimensions=input_dimensions, + embedding_dimenion=embedding_dimenion, + rank=rank, + inner_size=inner_size, + n_layers=n_layers, + func=func, + bias=bias, + ) + data = LabelTensor(torch.rand(10, 30, 3), labels=["x", "y", "u"]) + data.requires_grad_(True) + out = block(data.extract("u"), data.extract(["x", "y"])) + loss = out.mean() + loss.backward() diff --git a/tests/test_layers/test_orthogonal.py b/tests/test_blocks/test_orthogonal.py similarity index 89% rename from tests/test_layers/test_orthogonal.py rename to tests/test_blocks/test_orthogonal.py index d443c1776..e222c6bb5 100644 --- a/tests/test_layers/test_orthogonal.py +++ b/tests/test_blocks/test_orthogonal.py @@ -1,6 +1,6 @@ import torch import pytest -from pina.model.layers import OrthogonalBlock +from pina.model.block import OrthogonalBlock torch.manual_seed(111) @@ -8,10 +8,11 @@ torch.randn(10, 3), torch.rand(100, 5), torch.randn(5, 5), - ] +] list_prohibited_matrices_dim0 = list_matrices[:-1] + @pytest.mark.parametrize("dim", [-1, 0, 1, None]) @pytest.mark.parametrize("requires_grad", [True, False, None]) def test_constructor(dim, requires_grad): @@ -29,11 +30,13 @@ def test_constructor(dim, requires_grad): if requires_grad is not None: assert block.requires_grad == requires_grad + def test_wrong_constructor(): with pytest.raises(IndexError): - OrthogonalBlock(2) + OrthogonalBlock(2) with pytest.raises(ValueError): - OrthogonalBlock('a') + OrthogonalBlock("a") + @pytest.mark.parametrize("V", list_matrices) def test_forward(V): @@ -42,7 +45,10 @@ def test_forward(V): V_orth = orth(V) V_orth_row = orth_row(V.T) assert torch.allclose(V_orth.T @ V_orth, torch.eye(V.shape[1]), atol=1e-6) - assert torch.allclose(V_orth_row @ V_orth_row.T, torch.eye(V.shape[1]), atol=1e-6) + assert torch.allclose( + V_orth_row @ V_orth_row.T, torch.eye(V.shape[1]), atol=1e-6 + ) + @pytest.mark.parametrize("V", list_matrices) def test_backward(V): @@ -51,6 +57,7 @@ def test_backward(V): loss = V_orth.mean() loss.backward() + @pytest.mark.parametrize("V", list_matrices) def test_wrong_backward(V): orth = OrthogonalBlock(requires_grad=False) @@ -59,10 +66,10 @@ def test_wrong_backward(V): with pytest.raises(RuntimeError): loss.backward() + @pytest.mark.parametrize("V", list_prohibited_matrices_dim0) def test_forward_prohibited(V): orth = OrthogonalBlock(0) with pytest.raises(Warning): V_orth = orth(V) assert V.shape[0] > V.shape[1] - diff --git a/tests/test_layers/test_pod.py b/tests/test_blocks/test_pod.py similarity index 81% rename from tests/test_layers/test_pod.py rename to tests/test_blocks/test_pod.py index 433fcafed..a0823bca0 100644 --- a/tests/test_layers/test_pod.py +++ b/tests/test_blocks/test_pod.py @@ -1,10 +1,13 @@ import torch import pytest -from pina.model.layers.pod import PODBlock +from pina.model.block.pod_block import PODBlock x = torch.linspace(-1, 1, 100) -toy_snapshots = torch.vstack([torch.exp(-x**2)*c for c in torch.linspace(0, 1, 10)]) +toy_snapshots = torch.vstack( + [torch.exp(-(x**2)) * c for c in torch.linspace(0, 1, 10)] +) + def test_constructor(): pod = PODBlock(2) @@ -23,6 +26,7 @@ def test_fit(rank, scale): assert pod.rank == rank assert pod.scale_coefficients == scale + @pytest.mark.parametrize("scale", [True, False]) @pytest.mark.parametrize("rank", [1, 2, 10]) @pytest.mark.parametrize("randomized", [True, False]) @@ -34,15 +38,16 @@ def test_fit(rank, scale, randomized): assert pod.basis.shape == (rank, dof) assert pod._basis.shape == (n_snap, dof) if scale is True: - assert pod._scaler['mean'].shape == (n_snap,) - assert pod._scaler['std'].shape == (n_snap,) - assert pod.scaler['mean'].shape == (rank,) - assert pod.scaler['std'].shape == (rank,) - assert pod.scaler['mean'].shape[0] == pod.basis.shape[0] + assert pod._scaler["mean"].shape == (n_snap,) + assert pod._scaler["std"].shape == (n_snap,) + assert pod.scaler["mean"].shape == (rank,) + assert pod.scaler["std"].shape == (rank,) + assert pod.scaler["mean"].shape[0] == pod.basis.shape[0] else: assert pod._scaler == None assert pod.scaler == None + def test_forward(): pod = PODBlock(1) pod.fit(toy_snapshots) @@ -64,6 +69,7 @@ def test_forward(): torch.testing.assert_close(c.mean(dim=0), torch.zeros(pod.rank)) torch.testing.assert_close(c.std(dim=0), torch.ones(pod.rank)) + @pytest.mark.parametrize("scale", [True, False]) @pytest.mark.parametrize("rank", [1, 2, 10]) @pytest.mark.parametrize("randomized", [True, False]) @@ -74,6 +80,7 @@ def test_expand(rank, scale, randomized): torch.testing.assert_close(pod.expand(c), toy_snapshots) torch.testing.assert_close(pod.expand(c[0]), toy_snapshots[0].unsqueeze(0)) + @pytest.mark.parametrize("scale", [True, False]) @pytest.mark.parametrize("rank", [1, 2, 10]) @pytest.mark.parametrize("randomized", [True, False]) @@ -81,9 +88,9 @@ def test_reduce_expand(rank, scale, randomized): pod = PODBlock(rank, scale) pod.fit(toy_snapshots, randomized) torch.testing.assert_close( - pod.expand(pod.reduce(toy_snapshots)), - toy_snapshots) + pod.expand(pod.reduce(toy_snapshots)), toy_snapshots + ) torch.testing.assert_close( - pod.expand(pod.reduce(toy_snapshots[0])), - toy_snapshots[0].unsqueeze(0)) - # torch.testing.assert_close(pod.expand(pod.reduce(c[0])), c[0]) \ No newline at end of file + pod.expand(pod.reduce(toy_snapshots[0])), toy_snapshots[0].unsqueeze(0) + ) + # torch.testing.assert_close(pod.expand(pod.reduce(c[0])), c[0]) diff --git a/tests/test_layers/test_rbf.py b/tests/test_blocks/test_rbf.py similarity index 68% rename from tests/test_layers/test_rbf.py rename to tests/test_blocks/test_rbf.py index 43f19f3f2..65912fb76 100644 --- a/tests/test_layers/test_rbf.py +++ b/tests/test_blocks/test_rbf.py @@ -2,30 +2,46 @@ import pytest import math -from pina.model.layers.rbf_layer import RBFBlock +from pina.model.block.rbf_block import RBFBlock x = torch.linspace(-1, 1, 100) toy_params = torch.linspace(0, 1, 10).unsqueeze(1) -toy_snapshots = torch.vstack([torch.exp(-x**2)*c for c in toy_params]) +toy_snapshots = torch.vstack([torch.exp(-(x**2)) * c for c in toy_params]) toy_params_test = torch.linspace(0, 1, 3).unsqueeze(1) -toy_snapshots_test = torch.vstack([torch.exp(-x**2)*c for c in toy_params_test]) +toy_snapshots_test = torch.vstack( + [torch.exp(-(x**2)) * c for c in toy_params_test] +) -kernels = ["linear", "thin_plate_spline", "cubic", "quintic", - "multiquadric", "inverse_multiquadric", "inverse_quadratic", "gaussian"] +kernels = [ + "linear", + "thin_plate_spline", + "cubic", + "quintic", + "multiquadric", + "inverse_multiquadric", + "inverse_quadratic", + "gaussian", +] -noscale_invariant_kernels = ["multiquadric", "inverse_multiquadric", - "inverse_quadratic", "gaussian"] +noscale_invariant_kernels = [ + "multiquadric", + "inverse_multiquadric", + "inverse_quadratic", + "gaussian", +] scale_invariant_kernels = ["linear", "thin_plate_spline", "cubic", "quintic"] + def test_constructor_default(): rbf = RBFBlock() assert rbf.kernel == "thin_plate_spline" assert rbf.epsilon == 1 - assert rbf.smoothing == 0. + assert rbf.smoothing == 0.0 + @pytest.mark.parametrize("kernel", kernels) -@pytest.mark.parametrize("epsilon", [0.1, 1., 10.]) +@pytest.mark.parametrize("epsilon", [0.1, 1.0, 10.0]) def test_constructor_epsilon(kernel, epsilon): if kernel in scale_invariant_kernels: rbf = RBFBlock(kernel=kernel) @@ -38,15 +54,17 @@ def test_constructor_epsilon(kernel, epsilon): assert rbf.kernel == kernel assert rbf.epsilon == epsilon - assert rbf.smoothing == 0. + assert rbf.smoothing == 0.0 + @pytest.mark.parametrize("kernel", kernels) -@pytest.mark.parametrize("epsilon", [0.1, 1., 10.]) +@pytest.mark.parametrize("epsilon", [0.1, 1.0, 10.0]) @pytest.mark.parametrize("degree", [2, 3, 4]) @pytest.mark.parametrize("smoothing", [1e-5, 1e-3, 1e-1]) def test_constructor_all(kernel, epsilon, degree, smoothing): - rbf = RBFBlock(kernel=kernel, epsilon=epsilon, degree=degree, - smoothing=smoothing) + rbf = RBFBlock( + kernel=kernel, epsilon=epsilon, degree=degree, smoothing=smoothing + ) assert rbf.kernel == kernel assert rbf.epsilon == epsilon assert rbf.degree == degree @@ -58,16 +76,21 @@ def test_constructor_all(kernel, epsilon, degree, smoothing): assert rbf._scale == None assert rbf._coeffs == None + def test_fit(): rbf = RBFBlock() rbf.fit(toy_params, toy_snapshots) ndim = toy_params.shape[1] torch.testing.assert_close(rbf.y, toy_params) torch.testing.assert_close(rbf.d, toy_snapshots) - assert rbf.powers.shape == (math.comb(rbf.degree+ndim, ndim), ndim) + assert rbf.powers.shape == (math.comb(rbf.degree + ndim, ndim), ndim) assert rbf._shift.shape == (ndim,) assert rbf._scale.shape == (ndim,) - assert rbf._coeffs.shape == (rbf.powers.shape[0]+toy_snapshots.shape[0], toy_snapshots.shape[1]) + assert rbf._coeffs.shape == ( + rbf.powers.shape[0] + toy_snapshots.shape[0], + toy_snapshots.shape[1], + ) + def test_forward(): rbf = RBFBlock() @@ -76,10 +99,10 @@ def test_forward(): assert c.shape == toy_snapshots.shape torch.testing.assert_close(c, toy_snapshots) + def test_forward_unseen_parameters(): rbf = RBFBlock() rbf.fit(toy_params, toy_snapshots) c = rbf(toy_params_test) assert c.shape == toy_snapshots_test.shape torch.testing.assert_close(c, toy_snapshots_test) - diff --git a/tests/test_layers/test_residual.py b/tests/test_blocks/test_residual.py similarity index 76% rename from tests/test_layers/test_residual.py rename to tests/test_blocks/test_residual.py index 03425a552..37f54f27d 100644 --- a/tests/test_layers/test_residual.py +++ b/tests/test_blocks/test_residual.py @@ -1,4 +1,4 @@ -from pina.model.layers import ResidualBlock, EnhancedLinear +from pina.model.block import ResidualBlock, EnhancedLinear import torch import torch.nn as nn @@ -7,10 +7,9 @@ def test_constructor_residual_block(): res_block = ResidualBlock(input_dim=10, output_dim=3, hidden_dim=4) - res_block = ResidualBlock(input_dim=10, - output_dim=3, - hidden_dim=4, - spectral_norm=True) + res_block = ResidualBlock( + input_dim=10, output_dim=3, hidden_dim=4, spectral_norm=True + ) def test_forward_residual_block(): @@ -22,8 +21,9 @@ def test_forward_residual_block(): assert y.shape[1] == 3 assert y.shape[0] == x.shape[0] + def test_backward_residual_block(): - + res_block = ResidualBlock(input_dim=10, output_dim=3, hidden_dim=4) x = torch.rand(size=(80, 10)) @@ -31,27 +31,37 @@ def test_backward_residual_block(): y = res_block(x) l = torch.mean(y) l.backward() - assert x._grad.shape == torch.Size([80,10]) + assert x._grad.shape == torch.Size([80, 10]) + def test_constructor_no_activation_no_dropout(): linear_layer = nn.Linear(10, 20) enhanced_linear = EnhancedLinear(linear_layer) - assert len(list(enhanced_linear.parameters())) == len(list(linear_layer.parameters())) + assert len(list(enhanced_linear.parameters())) == len( + list(linear_layer.parameters()) + ) + def test_constructor_with_activation_no_dropout(): linear_layer = nn.Linear(10, 20) activation = nn.ReLU() enhanced_linear = EnhancedLinear(linear_layer, activation) - assert len(list(enhanced_linear.parameters())) == len(list(linear_layer.parameters())) + len(list(activation.parameters())) + assert len(list(enhanced_linear.parameters())) == len( + list(linear_layer.parameters()) + ) + len(list(activation.parameters())) + def test_constructor_no_activation_with_dropout(): linear_layer = nn.Linear(10, 20) dropout_prob = 0.5 enhanced_linear = EnhancedLinear(linear_layer, dropout=dropout_prob) - assert len(list(enhanced_linear.parameters())) == len(list(linear_layer.parameters())) + assert len(list(enhanced_linear.parameters())) == len( + list(linear_layer.parameters()) + ) + def test_constructor_with_activation_with_dropout(): linear_layer = nn.Linear(10, 20) @@ -59,7 +69,10 @@ def test_constructor_with_activation_with_dropout(): dropout_prob = 0.5 enhanced_linear = EnhancedLinear(linear_layer, activation, dropout_prob) - assert len(list(enhanced_linear.parameters())) == len(list(linear_layer.parameters())) + len(list(activation.parameters())) + assert len(list(enhanced_linear.parameters())) == len( + list(linear_layer.parameters()) + ) + len(list(activation.parameters())) + def test_forward_enhanced_linear_no_dropout(): @@ -70,8 +83,9 @@ def test_forward_enhanced_linear_no_dropout(): assert y.shape[1] == 3 assert y.shape[0] == x.shape[0] + def test_backward_enhanced_linear_no_dropout(): - + enhanced_linear = EnhancedLinear(nn.Linear(10, 3)) x = torch.rand(size=(80, 10)) @@ -81,6 +95,7 @@ def test_backward_enhanced_linear_no_dropout(): l.backward() assert x._grad.shape == torch.Size([80, 10]) + def test_forward_enhanced_linear_dropout(): enhanced_linear = EnhancedLinear(nn.Linear(10, 3), dropout=0.5) @@ -90,8 +105,9 @@ def test_forward_enhanced_linear_dropout(): assert y.shape[1] == 3 assert y.shape[0] == x.shape[0] + def test_backward_enhanced_linear_dropout(): - + enhanced_linear = EnhancedLinear(nn.Linear(10, 3), dropout=0.5) x = torch.rand(size=(80, 10)) diff --git a/tests/test_blocks/test_spectral_convolution.py b/tests/test_blocks/test_spectral_convolution.py new file mode 100644 index 000000000..ba4b4a8c5 --- /dev/null +++ b/tests/test_blocks/test_spectral_convolution.py @@ -0,0 +1,106 @@ +from pina.model.block import ( + SpectralConvBlock1D, + SpectralConvBlock2D, + SpectralConvBlock3D, +) +import torch + +input_numb_fields = 3 +output_numb_fields = 4 +batch = 5 + + +def test_constructor_1d(): + SpectralConvBlock1D( + input_numb_fields=input_numb_fields, + output_numb_fields=output_numb_fields, + n_modes=5, + ) + + +def test_forward_1d(): + sconv = SpectralConvBlock1D( + input_numb_fields=input_numb_fields, + output_numb_fields=output_numb_fields, + n_modes=4, + ) + x = torch.rand(batch, input_numb_fields, 10) + sconv(x) + + +def test_backward_1d(): + sconv = SpectralConvBlock1D( + input_numb_fields=input_numb_fields, + output_numb_fields=output_numb_fields, + n_modes=4, + ) + x = torch.rand(batch, input_numb_fields, 10) + x.requires_grad = True + sconv(x) + l = torch.mean(sconv(x)) + l.backward() + assert x._grad.shape == torch.Size([5, 3, 10]) + + +def test_constructor_2d(): + SpectralConvBlock2D( + input_numb_fields=input_numb_fields, + output_numb_fields=output_numb_fields, + n_modes=[5, 4], + ) + + +def test_forward_2d(): + sconv = SpectralConvBlock2D( + input_numb_fields=input_numb_fields, + output_numb_fields=output_numb_fields, + n_modes=[5, 4], + ) + x = torch.rand(batch, input_numb_fields, 10, 10) + sconv(x) + + +def test_backward_2d(): + sconv = SpectralConvBlock2D( + input_numb_fields=input_numb_fields, + output_numb_fields=output_numb_fields, + n_modes=[5, 4], + ) + x = torch.rand(batch, input_numb_fields, 10, 10) + x.requires_grad = True + sconv(x) + l = torch.mean(sconv(x)) + l.backward() + assert x._grad.shape == torch.Size([5, 3, 10, 10]) + + +def test_constructor_3d(): + SpectralConvBlock3D( + input_numb_fields=input_numb_fields, + output_numb_fields=output_numb_fields, + n_modes=[5, 4, 4], + ) + + +def test_forward_3d(): + sconv = SpectralConvBlock3D( + input_numb_fields=input_numb_fields, + output_numb_fields=output_numb_fields, + n_modes=[5, 4, 4], + ) + x = torch.rand(batch, input_numb_fields, 10, 10, 10) + sconv(x) + + +def test_backward_3d(): + sconv = SpectralConvBlock3D( + input_numb_fields=input_numb_fields, + output_numb_fields=output_numb_fields, + n_modes=[5, 4, 4], + ) + x = torch.rand(batch, input_numb_fields, 10, 10, 10) + x.requires_grad = True + sconv(x) + l = torch.mean(sconv(x)) + l.backward() + assert x._grad.shape == torch.Size([5, 3, 10, 10, 10]) diff --git a/tests/test_callback/test_adaptive_refinement_callback.py b/tests/test_callback/test_adaptive_refinement_callback.py new file mode 100644 index 000000000..dcabef13a --- /dev/null +++ b/tests/test_callback/test_adaptive_refinement_callback.py @@ -0,0 +1,45 @@ +from pina.solver import PINN +from pina.trainer import Trainer +from pina.model import FeedForward +from pina.problem.zoo import Poisson2DSquareProblem as Poisson +from pina.callback import R3Refinement + + +# make the problem +poisson_problem = Poisson() +boundaries = ["g1", "g2", "g3", "g4"] +n = 10 +poisson_problem.discretise_domain(n, "grid", domains=boundaries) +poisson_problem.discretise_domain(n, "grid", domains="D") +model = FeedForward( + len(poisson_problem.input_variables), len(poisson_problem.output_variables) +) + +# make the solver +solver = PINN(problem=poisson_problem, model=model) + + +# def test_r3constructor(): +# R3Refinement(sample_every=10) + + +# def test_r3refinment_routine(): +# # make the trainer +# trainer = Trainer(solver=solver, +# callback=[R3Refinement(sample_every=1)], +# accelerator='cpu', +# max_epochs=5) +# trainer.train() + +# def test_r3refinment_routine(): +# model = FeedForward(len(poisson_problem.input_variables), +# len(poisson_problem.output_variables)) +# solver = PINN(problem=poisson_problem, model=model) +# trainer = Trainer(solver=solver, +# callback=[R3Refinement(sample_every=1)], +# accelerator='cpu', +# max_epochs=5) +# before_n_points = {loc : len(pts) for loc, pts in trainer.solver.problem.input_pts.items()} +# trainer.train() +# after_n_points = {loc : len(pts) for loc, pts in trainer.solver.problem.input_pts.items()} +# assert before_n_points == after_n_points diff --git a/tests/test_callback/test_linear_weight_update_callback.py b/tests/test_callback/test_linear_weight_update_callback.py new file mode 100644 index 000000000..c1f4cf357 --- /dev/null +++ b/tests/test_callback/test_linear_weight_update_callback.py @@ -0,0 +1,164 @@ +import pytest +import math +from pina.solver import PINN +from pina.loss import ScalarWeighting +from pina.trainer import Trainer +from pina.model import FeedForward +from pina.problem.zoo import Poisson2DSquareProblem as Poisson +from pina.callback import LinearWeightUpdate + + +# Define the problem +poisson_problem = Poisson() +poisson_problem.discretise_domain(50, "grid") +cond_name = list(poisson_problem.conditions.keys())[0] + +# Define the model +model = FeedForward( + input_dimensions=len(poisson_problem.input_variables), + output_dimensions=len(poisson_problem.output_variables), + layers=[32, 32], +) + +# Define the weighting schema +weights_dict = {key: 1 for key in poisson_problem.conditions.keys()} +weighting = ScalarWeighting(weights=weights_dict) + +# Define the solver +solver = PINN(problem=poisson_problem, model=model, weighting=weighting) + +# Value used for testing +epochs = 10 + + +@pytest.mark.parametrize("initial_value", [1, 5.5]) +@pytest.mark.parametrize("target_value", [10, 25.5]) +def test_constructor(initial_value, target_value): + LinearWeightUpdate( + target_epoch=epochs, + condition_name=cond_name, + initial_value=initial_value, + target_value=target_value, + ) + + # Target_epoch must be int + with pytest.raises(ValueError): + LinearWeightUpdate( + target_epoch=10.0, + condition_name=cond_name, + initial_value=0, + target_value=1, + ) + + # Condition_name must be str + with pytest.raises(ValueError): + LinearWeightUpdate( + target_epoch=epochs, + condition_name=100, + initial_value=0, + target_value=1, + ) + + # Initial_value must be float or int + with pytest.raises(ValueError): + LinearWeightUpdate( + target_epoch=epochs, + condition_name=cond_name, + initial_value="0", + target_value=1, + ) + + # Target_value must be float or int + with pytest.raises(ValueError): + LinearWeightUpdate( + target_epoch=epochs, + condition_name=cond_name, + initial_value=0, + target_value="1", + ) + + +@pytest.mark.parametrize("initial_value, target_value", [(1, 10), (10, 1)]) +def test_training(initial_value, target_value): + callback = LinearWeightUpdate( + target_epoch=epochs, + condition_name=cond_name, + initial_value=initial_value, + target_value=target_value, + ) + trainer = Trainer( + solver=solver, + callbacks=[callback], + accelerator="cpu", + max_epochs=epochs, + ) + trainer.train() + + # Check that the final weight value matches the target value + final_value = solver.weighting.weights[cond_name] + assert math.isclose(final_value, target_value) + + # Target_epoch must be greater than 0 + with pytest.raises(ValueError): + callback = LinearWeightUpdate( + target_epoch=0, + condition_name=cond_name, + initial_value=0, + target_value=1, + ) + trainer = Trainer( + solver=solver, + callbacks=[callback], + accelerator="cpu", + max_epochs=5, + ) + trainer.train() + + # Target_epoch must be less than or equal to max_epochs + with pytest.raises(ValueError): + callback = LinearWeightUpdate( + target_epoch=epochs, + condition_name=cond_name, + initial_value=0, + target_value=1, + ) + trainer = Trainer( + solver=solver, + callbacks=[callback], + accelerator="cpu", + max_epochs=epochs - 1, + ) + trainer.train() + + # Condition_name must be a problem condition + with pytest.raises(ValueError): + callback = LinearWeightUpdate( + target_epoch=epochs, + condition_name="not_a_condition", + initial_value=0, + target_value=1, + ) + trainer = Trainer( + solver=solver, + callbacks=[callback], + accelerator="cpu", + max_epochs=epochs, + ) + trainer.train() + + # Weighting schema must be ScalarWeighting + with pytest.raises(ValueError): + callback = LinearWeightUpdate( + target_epoch=epochs, + condition_name=cond_name, + initial_value=0, + target_value=1, + ) + unweighted_solver = PINN(problem=poisson_problem, model=model) + trainer = Trainer( + solver=unweighted_solver, + callbacks=[callback], + accelerator="cpu", + max_epochs=epochs, + ) + trainer.train() diff --git a/tests/test_callback/test_metric_tracker.py b/tests/test_callback/test_metric_tracker.py new file mode 100644 index 000000000..3e6fa4407 --- /dev/null +++ b/tests/test_callback/test_metric_tracker.py @@ -0,0 +1,40 @@ +from pina.solver import PINN +from pina.trainer import Trainer +from pina.model import FeedForward +from pina.callback import MetricTracker +from pina.problem.zoo import Poisson2DSquareProblem as Poisson + + +# make the problem +poisson_problem = Poisson() +boundaries = ["g1", "g2", "g3", "g4"] +n = 10 +poisson_problem.discretise_domain(n, "grid", domains=boundaries) +poisson_problem.discretise_domain(n, "grid", domains="D") +model = FeedForward( + len(poisson_problem.input_variables), len(poisson_problem.output_variables) +) + +# make the solver +solver = PINN(problem=poisson_problem, model=model) + + +def test_metric_tracker_constructor(): + MetricTracker() + + +def test_metric_tracker_routine(): + # make the trainer + trainer = Trainer( + solver=solver, + callbacks=[MetricTracker()], + accelerator="cpu", + max_epochs=5, + log_every_n_steps=1, + ) + trainer.train() + # get the tracked metrics + metrics = trainer.callbacks[0].metrics + # assert the logged metrics are correct + logged_metrics = sorted(list(metrics.keys())) + assert logged_metrics == ["train_loss"] diff --git a/tests/test_callback/test_optimizer_callback.py b/tests/test_callback/test_optimizer_callback.py new file mode 100644 index 000000000..785a9c3f4 --- /dev/null +++ b/tests/test_callback/test_optimizer_callback.py @@ -0,0 +1,45 @@ +from pina.callback import SwitchOptimizer +import torch +import pytest + +from pina.solver import PINN +from pina.trainer import Trainer +from pina.model import FeedForward +from pina.problem.zoo import Poisson2DSquareProblem as Poisson +from pina.optim import TorchOptimizer + +# make the problem +poisson_problem = Poisson() +boundaries = ["g1", "g2", "g3", "g4"] +n = 10 +poisson_problem.discretise_domain(n, "grid", domains=boundaries) +poisson_problem.discretise_domain(n, "grid", domains="D") +model = FeedForward( + len(poisson_problem.input_variables), len(poisson_problem.output_variables) +) + +# make the solver +solver = PINN(problem=poisson_problem, model=model) + +adam = TorchOptimizer(torch.optim.Adam, lr=0.01) +lbfgs = TorchOptimizer(torch.optim.LBFGS, lr=0.001) + + +def test_switch_optimizer_constructor(): + SwitchOptimizer(adam, epoch_switch=10) + + +def test_switch_optimizer_routine(): + # check initial optimizer + solver.configure_optimizers() + assert solver.optimizer.instance.__class__ == torch.optim.Adam + # make the trainer + switch_opt_callback = SwitchOptimizer(lbfgs, epoch_switch=3) + trainer = Trainer( + solver=solver, + callbacks=[switch_opt_callback], + accelerator="cpu", + max_epochs=5, + ) + trainer.train() + assert solver.optimizer.instance.__class__ == torch.optim.LBFGS diff --git a/tests/test_callback/test_progress_bar.py b/tests/test_callback/test_progress_bar.py new file mode 100644 index 000000000..d77408c42 --- /dev/null +++ b/tests/test_callback/test_progress_bar.py @@ -0,0 +1,36 @@ +from pina.solver import PINN +from pina.trainer import Trainer +from pina.model import FeedForward +from pina.callback.processing_callback import PINAProgressBar +from pina.problem.zoo import Poisson2DSquareProblem as Poisson + + +# make the problem +poisson_problem = Poisson() +boundaries = ["g1", "g2", "g3", "g4"] +n = 10 +condition_names = list(poisson_problem.conditions.keys()) +poisson_problem.discretise_domain(n, "grid", domains=boundaries) +poisson_problem.discretise_domain(n, "grid", domains="D") +model = FeedForward( + len(poisson_problem.input_variables), len(poisson_problem.output_variables) +) + +# make the solver +solver = PINN(problem=poisson_problem, model=model) + + +def test_progress_bar_constructor(): + PINAProgressBar() + + +def test_progress_bar_routine(): + # make the trainer + trainer = Trainer( + solver=solver, + callbacks=[PINAProgressBar(["val", condition_names[0]])], + accelerator="cpu", + max_epochs=5, + ) + trainer.train() + # TODO there should be a check that the correct metrics are displayed diff --git a/tests/test_callbacks/test_adaptive_refinment_callbacks.py b/tests/test_callbacks/test_adaptive_refinment_callbacks.py deleted file mode 100644 index e5c46a17d..000000000 --- a/tests/test_callbacks/test_adaptive_refinment_callbacks.py +++ /dev/null @@ -1,89 +0,0 @@ -from pina.callbacks import R3Refinement -import torch -import pytest - -from pina.problem import SpatialProblem -from pina.operators import laplacian -from pina.geometry import CartesianDomain -from pina import Condition, LabelTensor -from pina.solvers import PINN -from pina.trainer import Trainer -from pina.model import FeedForward -from pina.equation.equation import Equation -from pina.equation.equation_factory import FixedValue - - -def laplace_equation(input_, output_): - force_term = (torch.sin(input_.extract(['x']) * torch.pi) * - torch.sin(input_.extract(['y']) * torch.pi)) - delta_u = laplacian(output_.extract(['u']), input_) - return delta_u - force_term - - -my_laplace = Equation(laplace_equation) -in_ = LabelTensor(torch.tensor([[0., 1.]]), ['x', 'y']) -out_ = LabelTensor(torch.tensor([[0.]]), ['u']) - - -class Poisson(SpatialProblem): - output_variables = ['u'] - spatial_domain = CartesianDomain({'x': [0, 1], 'y': [0, 1]}) - - conditions = { - 'gamma1': Condition( - location=CartesianDomain({'x': [0, 1], 'y': 1}), - equation=FixedValue(0.0)), - 'gamma2': Condition( - location=CartesianDomain({'x': [0, 1], 'y': 0}), - equation=FixedValue(0.0)), - 'gamma3': Condition( - location=CartesianDomain({'x': 1, 'y': [0, 1]}), - equation=FixedValue(0.0)), - 'gamma4': Condition( - location=CartesianDomain({'x': 0, 'y': [0, 1]}), - equation=FixedValue(0.0)), - 'D': Condition( - input_points=LabelTensor(torch.rand(size=(100, 2)), ['x', 'y']), - equation=my_laplace), - # 'data': Condition( - # input_points=in_, - # output_points=out_) - } - - -# make the problem -poisson_problem = Poisson() -boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -n = 10 -poisson_problem.discretise_domain(n, 'grid', locations=boundaries) -model = FeedForward(len(poisson_problem.input_variables), - len(poisson_problem.output_variables)) - -# make the solver -solver = PINN(problem=poisson_problem, model=model) - - -def test_r3constructor(): - R3Refinement(sample_every=10) - - -def test_r3refinment_routine(): - # make the trainer - trainer = Trainer(solver=solver, - callbacks=[R3Refinement(sample_every=1)], - accelerator='cpu', - max_epochs=5) - trainer.train() - -def test_r3refinment_routine(): - model = FeedForward(len(poisson_problem.input_variables), - len(poisson_problem.output_variables)) - solver = PINN(problem=poisson_problem, model=model) - trainer = Trainer(solver=solver, - callbacks=[R3Refinement(sample_every=1)], - accelerator='cpu', - max_epochs=5) - before_n_points = {loc : len(pts) for loc, pts in trainer.solver.problem.input_pts.items()} - trainer.train() - after_n_points = {loc : len(pts) for loc, pts in trainer.solver.problem.input_pts.items()} - assert before_n_points == after_n_points diff --git a/tests/test_callbacks/test_metric_tracker.py b/tests/test_callbacks/test_metric_tracker.py deleted file mode 100644 index c38024587..000000000 --- a/tests/test_callbacks/test_metric_tracker.py +++ /dev/null @@ -1,87 +0,0 @@ -import torch -import pytest - -from pina.problem import SpatialProblem -from pina.operators import laplacian -from pina.geometry import CartesianDomain -from pina import Condition, LabelTensor -from pina.solvers import PINN -from pina.trainer import Trainer -from pina.model import FeedForward -from pina.equation.equation import Equation -from pina.equation.equation_factory import FixedValue -from pina.callbacks import MetricTracker - - -def laplace_equation(input_, output_): - force_term = (torch.sin(input_.extract(['x']) * torch.pi) * - torch.sin(input_.extract(['y']) * torch.pi)) - delta_u = laplacian(output_.extract(['u']), input_) - return delta_u - force_term - - -my_laplace = Equation(laplace_equation) -in_ = LabelTensor(torch.tensor([[0., 1.]]), ['x', 'y']) -out_ = LabelTensor(torch.tensor([[0.]]), ['u']) - - -class Poisson(SpatialProblem): - output_variables = ['u'] - spatial_domain = CartesianDomain({'x': [0, 1], 'y': [0, 1]}) - - conditions = { - 'gamma1': Condition( - location=CartesianDomain({'x': [0, 1], 'y': 1}), - equation=FixedValue(0.0)), - 'gamma2': Condition( - location=CartesianDomain({'x': [0, 1], 'y': 0}), - equation=FixedValue(0.0)), - 'gamma3': Condition( - location=CartesianDomain({'x': 1, 'y': [0, 1]}), - equation=FixedValue(0.0)), - 'gamma4': Condition( - location=CartesianDomain({'x': 0, 'y': [0, 1]}), - equation=FixedValue(0.0)), - 'D': Condition( - input_points=LabelTensor(torch.rand(size=(100, 2)), ['x', 'y']), - equation=my_laplace), - 'data': Condition( - input_points=in_, - output_points=out_) - } - - -# make the problem -poisson_problem = Poisson() -boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -n = 10 -poisson_problem.discretise_domain(n, 'grid', locations=boundaries) -model = FeedForward(len(poisson_problem.input_variables), - len(poisson_problem.output_variables)) - -# make the solver -solver = PINN(problem=poisson_problem, model=model) - - -def test_metric_tracker_constructor(): - MetricTracker() - -def test_metric_tracker_routine(): - # make the trainer - trainer = Trainer(solver=solver, - callbacks=[ - MetricTracker() - ], - accelerator='cpu', - max_epochs=5) - trainer.train() - # get the tracked metrics - metrics = trainer.callbacks[0].metrics - # assert the logged metrics are correct - logged_metrics = sorted(list(metrics.keys())) - total_metrics = sorted( - list([key + '_loss' for key in poisson_problem.conditions.keys()]) - + ['mean_loss']) - assert logged_metrics == total_metrics - - diff --git a/tests/test_callbacks/test_optimizer_callbacks.py b/tests/test_callbacks/test_optimizer_callbacks.py deleted file mode 100644 index 0b0aabaab..000000000 --- a/tests/test_callbacks/test_optimizer_callbacks.py +++ /dev/null @@ -1,89 +0,0 @@ -from pina.callbacks import SwitchOptimizer -import torch -import pytest - -from pina.problem import SpatialProblem -from pina.operators import laplacian -from pina.geometry import CartesianDomain -from pina import Condition, LabelTensor -from pina.solvers import PINN -from pina.trainer import Trainer -from pina.model import FeedForward -from pina.equation.equation import Equation -from pina.equation.equation_factory import FixedValue - - -def laplace_equation(input_, output_): - force_term = (torch.sin(input_.extract(['x']) * torch.pi) * - torch.sin(input_.extract(['y']) * torch.pi)) - delta_u = laplacian(output_.extract(['u']), input_) - return delta_u - force_term - - -my_laplace = Equation(laplace_equation) -in_ = LabelTensor(torch.tensor([[0., 1.]]), ['x', 'y']) -out_ = LabelTensor(torch.tensor([[0.]]), ['u']) - - -class Poisson(SpatialProblem): - output_variables = ['u'] - spatial_domain = CartesianDomain({'x': [0, 1], 'y': [0, 1]}) - - conditions = { - 'gamma1': Condition( - location=CartesianDomain({'x': [0, 1], 'y': 1}), - equation=FixedValue(0.0)), - 'gamma2': Condition( - location=CartesianDomain({'x': [0, 1], 'y': 0}), - equation=FixedValue(0.0)), - 'gamma3': Condition( - location=CartesianDomain({'x': 1, 'y': [0, 1]}), - equation=FixedValue(0.0)), - 'gamma4': Condition( - location=CartesianDomain({'x': 0, 'y': [0, 1]}), - equation=FixedValue(0.0)), - 'D': Condition( - input_points=LabelTensor(torch.rand(size=(100, 2)), ['x', 'y']), - equation=my_laplace), - # 'data': Condition( - # input_points=in_, - # output_points=out_) - } - - -# make the problem -poisson_problem = Poisson() -boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -n = 10 -poisson_problem.discretise_domain(n, 'grid', locations=boundaries) -model = FeedForward(len(poisson_problem.input_variables), - len(poisson_problem.output_variables)) - -# make the solver -solver = PINN(problem=poisson_problem, model=model) - - -def test_switch_optimizer_constructor(): - SwitchOptimizer(new_optimizers=torch.optim.Adam, - new_optimizers_kwargs={'lr': 0.01}, - epoch_switch=10) - - with pytest.raises(ValueError): - SwitchOptimizer(new_optimizers=[torch.optim.Adam, torch.optim.Adam], - new_optimizers_kwargs=[{ - 'lr': 0.01 - }], - epoch_switch=10) - - -def test_switch_optimizer_routine(): - # make the trainer - trainer = Trainer(solver=solver, - callbacks=[ - SwitchOptimizer(new_optimizers=torch.optim.LBFGS, - new_optimizers_kwargs={'lr': 0.01}, - epoch_switch=3) - ], - accelerator='cpu', - max_epochs=5) - trainer.train() diff --git a/tests/test_callbacks/test_progress_bar.py b/tests/test_callbacks/test_progress_bar.py deleted file mode 100644 index 990b471fc..000000000 --- a/tests/test_callbacks/test_progress_bar.py +++ /dev/null @@ -1,78 +0,0 @@ -import torch -import pytest - -from pina.problem import SpatialProblem -from pina.operators import laplacian -from pina.geometry import CartesianDomain -from pina import Condition, LabelTensor -from pina.solvers import PINN -from pina.trainer import Trainer -from pina.model import FeedForward -from pina.equation.equation import Equation -from pina.equation.equation_factory import FixedValue -from pina.callbacks.processing_callbacks import PINAProgressBar - - -def laplace_equation(input_, output_): - force_term = (torch.sin(input_.extract(['x']) * torch.pi) * - torch.sin(input_.extract(['y']) * torch.pi)) - delta_u = laplacian(output_.extract(['u']), input_) - return delta_u - force_term - - -my_laplace = Equation(laplace_equation) -in_ = LabelTensor(torch.tensor([[0., 1.]]), ['x', 'y']) -out_ = LabelTensor(torch.tensor([[0.]]), ['u']) - - -class Poisson(SpatialProblem): - output_variables = ['u'] - spatial_domain = CartesianDomain({'x': [0, 1], 'y': [0, 1]}) - - conditions = { - 'gamma1': Condition( - location=CartesianDomain({'x': [0, 1], 'y': 1}), - equation=FixedValue(0.0)), - 'gamma2': Condition( - location=CartesianDomain({'x': [0, 1], 'y': 0}), - equation=FixedValue(0.0)), - 'gamma3': Condition( - location=CartesianDomain({'x': 1, 'y': [0, 1]}), - equation=FixedValue(0.0)), - 'gamma4': Condition( - location=CartesianDomain({'x': 0, 'y': [0, 1]}), - equation=FixedValue(0.0)), - 'D': Condition( - input_points=LabelTensor(torch.rand(size=(100, 2)), ['x', 'y']), - equation=my_laplace), - 'data': Condition( - input_points=in_, - output_points=out_) - } - - -# make the problem -poisson_problem = Poisson() -boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -n = 10 -poisson_problem.discretise_domain(n, 'grid', locations=boundaries) -model = FeedForward(len(poisson_problem.input_variables), - len(poisson_problem.output_variables)) - -# make the solver -solver = PINN(problem=poisson_problem, model=model) - - -def test_progress_bar_constructor(): - PINAProgressBar(['mean_loss']) - -def test_progress_bar_routine(): - # make the trainer - trainer = Trainer(solver=solver, - callbacks=[ - PINAProgressBar(['mean', 'D']) - ], - accelerator='cpu', - max_epochs=5) - trainer.train() - # TODO there should be a check that the correct metrics are displayed \ No newline at end of file diff --git a/tests/test_collector.py b/tests/test_collector.py new file mode 100644 index 000000000..3119f9db0 --- /dev/null +++ b/tests/test_collector.py @@ -0,0 +1,135 @@ +import torch +import pytest +from pina import Condition, LabelTensor, Graph +from pina.condition import InputTargetCondition, DomainEquationCondition +from pina.graph import RadiusGraph +from pina.problem import AbstractProblem, SpatialProblem +from pina.domain import CartesianDomain +from pina.equation.equation import Equation +from pina.equation.equation_factory import FixedValue +from pina.operator import laplacian +from pina.collector import Collector + + +def test_supervised_tensor_collector(): + class SupervisedProblem(AbstractProblem): + output_variables = None + conditions = { + "data1": Condition( + input=torch.rand((10, 2)), + target=torch.rand((10, 2)), + ), + "data2": Condition( + input=torch.rand((20, 2)), + target=torch.rand((20, 2)), + ), + "data3": Condition( + input=torch.rand((30, 2)), + target=torch.rand((30, 2)), + ), + } + + problem = SupervisedProblem() + collector = Collector(problem) + for v in collector.conditions_name.values(): + assert v in problem.conditions.keys() + + +def test_pinn_collector(): + def laplace_equation(input_, output_): + force_term = torch.sin(input_.extract(["x"]) * torch.pi) * torch.sin( + input_.extract(["y"]) * torch.pi + ) + delta_u = laplacian(output_.extract(["u"]), input_) + return delta_u - force_term + + my_laplace = Equation(laplace_equation) + in_ = LabelTensor( + torch.tensor([[0.0, 1.0]], requires_grad=True), ["x", "y"] + ) + out_ = LabelTensor(torch.tensor([[0.0]], requires_grad=True), ["u"]) + + class Poisson(SpatialProblem): + output_variables = ["u"] + spatial_domain = CartesianDomain({"x": [0, 1], "y": [0, 1]}) + + conditions = { + "gamma1": Condition( + domain=CartesianDomain({"x": [0, 1], "y": 1}), + equation=FixedValue(0.0), + ), + "gamma2": Condition( + domain=CartesianDomain({"x": [0, 1], "y": 0}), + equation=FixedValue(0.0), + ), + "gamma3": Condition( + domain=CartesianDomain({"x": 1, "y": [0, 1]}), + equation=FixedValue(0.0), + ), + "gamma4": Condition( + domain=CartesianDomain({"x": 0, "y": [0, 1]}), + equation=FixedValue(0.0), + ), + "D": Condition( + domain=CartesianDomain({"x": [0, 1], "y": [0, 1]}), + equation=my_laplace, + ), + "data": Condition(input=in_, target=out_), + } + + def poisson_sol(self, pts): + return -( + torch.sin(pts.extract(["x"]) * torch.pi) + * torch.sin(pts.extract(["y"]) * torch.pi) + ) / (2 * torch.pi**2) + + truth_solution = poisson_sol + + problem = Poisson() + boundaries = ["gamma1", "gamma2", "gamma3", "gamma4"] + problem.discretise_domain(10, "grid", domains=boundaries) + problem.discretise_domain(10, "grid", domains="D") + + collector = Collector(problem) + collector.store_fixed_data() + collector.store_sample_domains() + + for k, v in problem.conditions.items(): + if isinstance(v, InputTargetCondition): + assert list(collector.data_collections[k].keys()) == [ + "input", + "target", + ] + + for k, v in problem.conditions.items(): + if isinstance(v, DomainEquationCondition): + assert list(collector.data_collections[k].keys()) == [ + "input", + "equation", + ] + + +def test_supervised_graph_collector(): + pos = torch.rand((100, 3)) + x = [torch.rand((100, 3)) for _ in range(10)] + graph_list_1 = [RadiusGraph(pos=pos, radius=0.4, x=x_) for x_ in x] + out_1 = torch.rand((10, 100, 3)) + + pos = torch.rand((50, 3)) + x = [torch.rand((50, 3)) for _ in range(10)] + graph_list_2 = [RadiusGraph(pos=pos, radius=0.4, x=x_) for x_ in x] + out_2 = torch.rand((10, 50, 3)) + + class SupervisedProblem(AbstractProblem): + output_variables = None + conditions = { + "data1": Condition(input=graph_list_1, target=out_1), + "data2": Condition(input=graph_list_2, target=out_2), + } + + problem = SupervisedProblem() + collector = Collector(problem) + collector.store_fixed_data() + # assert all(collector._is_conditions_ready.values()) + for v in collector.conditions_name.values(): + assert v in problem.conditions.keys() diff --git a/tests/test_condition.py b/tests/test_condition.py index 23c9d126b..9199f2bd9 100644 --- a/tests/test_condition.py +++ b/tests/test_condition.py @@ -2,43 +2,153 @@ import pytest from pina import LabelTensor, Condition -from pina.solvers import PINN -from pina.geometry import CartesianDomain -from pina.problem import SpatialProblem -from pina.model import FeedForward -from pina.operators import laplacian +from pina.condition import ( + TensorInputGraphTargetCondition, + TensorInputTensorTargetCondition, + GraphInputGraphTargetCondition, + GraphInputTensorTargetCondition, +) +from pina.condition import ( + InputTensorEquationCondition, + InputGraphEquationCondition, + DomainEquationCondition, +) +from pina.condition import ( + TensorDataCondition, + GraphDataCondition, +) +from pina.domain import CartesianDomain from pina.equation.equation_factory import FixedValue +from pina.graph import RadiusGraph -example_domain = CartesianDomain({'x': [0, 1], 'y': [0, 1]}) -example_input_pts = LabelTensor(torch.tensor([[0, 0, 0]]), ['x', 'y', 'z']) -example_output_pts = LabelTensor(torch.tensor([[1, 2]]), ['a', 'b']) +example_domain = CartesianDomain({"x": [0, 1], "y": [0, 1]}) +input_tensor = torch.rand((10, 3)) +target_tensor = torch.rand((10, 2)) +input_lt = LabelTensor(torch.rand((10, 3)), ["x", "y", "z"]) +target_lt = LabelTensor(torch.rand((10, 2)), ["a", "b"]) + +x = torch.rand(10, 20, 2) +pos = torch.rand(10, 20, 2) +radius = 0.1 +input_graph = [ + RadiusGraph( + x=x_, + pos=pos_, + radius=radius, + ) + for x_, pos_ in zip(x, pos) +] +target_graph = [ + RadiusGraph( + x=x_, + pos=pos_, + radius=radius, + ) + for x_, pos_ in zip(x, pos) +] + +x = LabelTensor(torch.rand(10, 20, 2), ["u", "v"]) +pos = LabelTensor(torch.rand(10, 20, 2), ["x", "y"]) +radius = 0.1 +input_graph_lt = [ + RadiusGraph( + x=x[i], + pos=pos[i], + radius=radius, + ) + for i in range(len(x)) +] +target_graph_lt = [ + RadiusGraph( + x=x[i], + pos=pos[i], + radius=radius, + ) + for i in range(len(x)) +] + +input_single_graph = input_graph[0] +target_single_graph = target_graph[0] + + +def test_init_input_target(): + cond = Condition(input=input_tensor, target=target_tensor) + assert isinstance(cond, TensorInputTensorTargetCondition) + cond = Condition(input=input_tensor, target=target_tensor) + assert isinstance(cond, TensorInputTensorTargetCondition) + cond = Condition(input=input_tensor, target=target_graph) + assert isinstance(cond, TensorInputGraphTargetCondition) + cond = Condition(input=input_graph, target=target_tensor) + assert isinstance(cond, GraphInputTensorTargetCondition) + cond = Condition(input=input_graph, target=target_graph) + assert isinstance(cond, GraphInputGraphTargetCondition) + + cond = Condition(input=input_lt, target=input_single_graph) + assert isinstance(cond, TensorInputGraphTargetCondition) + cond = Condition(input=input_single_graph, target=target_lt) + assert isinstance(cond, GraphInputTensorTargetCondition) + cond = Condition(input=input_graph, target=target_graph) + assert isinstance(cond, GraphInputGraphTargetCondition) + cond = Condition(input=input_single_graph, target=target_single_graph) + assert isinstance(cond, GraphInputGraphTargetCondition) -def test_init_inputoutput(): - Condition(input_points=example_input_pts, output_points=example_output_pts) with pytest.raises(ValueError): - Condition(example_input_pts, example_output_pts) - with pytest.raises(TypeError): - Condition(input_points=3., output_points='example') - with pytest.raises(TypeError): - Condition(input_points=example_domain, output_points=example_domain) + Condition(input_tensor, input_tensor) + with pytest.raises(ValueError): + Condition(input=3.0, target="example") + with pytest.raises(ValueError): + Condition(input=example_domain, target=example_domain) + # Test wrong graph condition initialisation + input = [input_graph[0], input_graph_lt[0]] + target = [target_graph[0], target_graph_lt[0]] + with pytest.raises(ValueError): + Condition(input=input, target=target) -def test_init_locfunc(): - Condition(location=example_domain, equation=FixedValue(0.0)) + input_graph_lt[0].x.labels = ["a", "b"] + with pytest.raises(ValueError): + Condition(input=input_graph_lt, target=target_graph_lt) + input_graph_lt[0].x.labels = ["u", "v"] + + +def test_init_domain_equation(): + cond = Condition(domain=example_domain, equation=FixedValue(0.0)) + assert isinstance(cond, DomainEquationCondition) with pytest.raises(ValueError): Condition(example_domain, FixedValue(0.0)) - with pytest.raises(TypeError): - Condition(location=3., equation='example') - with pytest.raises(TypeError): - Condition(location=example_input_pts, equation=example_output_pts) + with pytest.raises(ValueError): + Condition(domain=3.0, equation="example") + with pytest.raises(ValueError): + Condition(domain=input_tensor, equation=input_graph) -def test_init_inputfunc(): - Condition(input_points=example_input_pts, equation=FixedValue(0.0)) +def test_init_input_equation(): + cond = Condition(input=input_lt, equation=FixedValue(0.0)) + assert isinstance(cond, InputTensorEquationCondition) + cond = Condition(input=input_graph_lt, equation=FixedValue(0.0)) + assert isinstance(cond, InputGraphEquationCondition) + with pytest.raises(ValueError): + cond = Condition(input=input_tensor, equation=FixedValue(0.0)) with pytest.raises(ValueError): Condition(example_domain, FixedValue(0.0)) - with pytest.raises(TypeError): - Condition(input_points=3., equation='example') - with pytest.raises(TypeError): - Condition(input_points=example_domain, equation=example_output_pts) + with pytest.raises(ValueError): + Condition(input=3.0, equation="example") + with pytest.raises(ValueError): + Condition(input=example_domain, equation=input_graph) + + +test_init_input_equation() + + +def test_init_data_condition(): + cond = Condition(input=input_lt) + assert isinstance(cond, TensorDataCondition) + cond = Condition(input=input_tensor) + assert isinstance(cond, TensorDataCondition) + cond = Condition(input=input_tensor, conditional_variables=torch.tensor(1)) + assert isinstance(cond, TensorDataCondition) + cond = Condition(input=input_graph) + assert isinstance(cond, GraphDataCondition) + cond = Condition(input=input_graph, conditional_variables=torch.tensor(1)) + assert isinstance(cond, GraphDataCondition) diff --git a/tests/test_data/test_data_module.py b/tests/test_data/test_data_module.py new file mode 100644 index 000000000..fe7b3ebfd --- /dev/null +++ b/tests/test_data/test_data_module.py @@ -0,0 +1,240 @@ +import torch +import pytest +from pina.data import PinaDataModule +from pina.data.dataset import PinaTensorDataset, PinaGraphDataset +from pina.problem.zoo import SupervisedProblem +from pina.graph import RadiusGraph +from pina.data.data_module import DummyDataloader +from pina import Trainer +from pina.solver import SupervisedSolver +from torch_geometric.data import Batch +from torch.utils.data import DataLoader + +input_tensor = torch.rand((100, 10)) +output_tensor = torch.rand((100, 2)) + +x = torch.rand((100, 50, 10)) +pos = torch.rand((100, 50, 2)) +input_graph = [ + RadiusGraph(x=x_, pos=pos_, radius=0.2) for x_, pos_, in zip(x, pos) +] +output_graph = torch.rand((100, 50, 10)) + + +@pytest.mark.parametrize( + "input_, output_", + [(input_tensor, output_tensor), (input_graph, output_graph)], +) +def test_constructor(input_, output_): + problem = SupervisedProblem(input_=input_, output_=output_) + PinaDataModule(problem) + + +@pytest.mark.parametrize( + "input_, output_", + [(input_tensor, output_tensor), (input_graph, output_graph)], +) +@pytest.mark.parametrize( + "train_size, val_size, test_size", [(0.7, 0.2, 0.1), (0.7, 0.3, 0)] +) +def test_setup_train(input_, output_, train_size, val_size, test_size): + problem = SupervisedProblem(input_=input_, output_=output_) + dm = PinaDataModule( + problem, train_size=train_size, val_size=val_size, test_size=test_size + ) + dm.setup() + assert hasattr(dm, "train_dataset") + if isinstance(input_, torch.Tensor): + assert isinstance(dm.train_dataset, PinaTensorDataset) + else: + assert isinstance(dm.train_dataset, PinaGraphDataset) + # assert len(dm.train_dataset) == int(len(input_) * train_size) + if test_size > 0: + assert hasattr(dm, "test_dataset") + assert dm.test_dataset is None + else: + assert not hasattr(dm, "test_dataset") + assert hasattr(dm, "val_dataset") + if isinstance(input_, torch.Tensor): + assert isinstance(dm.val_dataset, PinaTensorDataset) + else: + assert isinstance(dm.val_dataset, PinaGraphDataset) + # assert len(dm.val_dataset) == int(len(input_) * val_size) + + +@pytest.mark.parametrize( + "input_, output_", + [(input_tensor, output_tensor), (input_graph, output_graph)], +) +@pytest.mark.parametrize( + "train_size, val_size, test_size", [(0.7, 0.2, 0.1), (0.0, 0.0, 1.0)] +) +def test_setup_test(input_, output_, train_size, val_size, test_size): + problem = SupervisedProblem(input_=input_, output_=output_) + dm = PinaDataModule( + problem, train_size=train_size, val_size=val_size, test_size=test_size + ) + dm.setup(stage="test") + if train_size > 0: + assert hasattr(dm, "train_dataset") + assert dm.train_dataset is None + else: + assert not hasattr(dm, "train_dataset") + if val_size > 0: + assert hasattr(dm, "val_dataset") + assert dm.val_dataset is None + else: + assert not hasattr(dm, "val_dataset") + + assert hasattr(dm, "test_dataset") + if isinstance(input_, torch.Tensor): + assert isinstance(dm.test_dataset, PinaTensorDataset) + else: + assert isinstance(dm.test_dataset, PinaGraphDataset) + # assert len(dm.test_dataset) == int(len(input_) * test_size) + + +@pytest.mark.parametrize( + "input_, output_", + [(input_tensor, output_tensor), (input_graph, output_graph)], +) +def test_dummy_dataloader(input_, output_): + problem = SupervisedProblem(input_=input_, output_=output_) + solver = SupervisedSolver(problem=problem, model=torch.nn.Linear(10, 10)) + trainer = Trainer( + solver, batch_size=None, train_size=0.7, val_size=0.3, test_size=0.0 + ) + dm = trainer.data_module + dm.setup() + dm.trainer = trainer + dataloader = dm.train_dataloader() + assert isinstance(dataloader, DummyDataloader) + assert len(dataloader) == 1 + data = next(dataloader) + assert isinstance(data, list) + assert isinstance(data[0], tuple) + if isinstance(input_, list): + assert isinstance(data[0][1]["input"], Batch) + else: + assert isinstance(data[0][1]["input"], torch.Tensor) + assert isinstance(data[0][1]["target"], torch.Tensor) + + dataloader = dm.val_dataloader() + assert isinstance(dataloader, DummyDataloader) + assert len(dataloader) == 1 + data = next(dataloader) + assert isinstance(data, list) + assert isinstance(data[0], tuple) + if isinstance(input_, list): + assert isinstance(data[0][1]["input"], Batch) + else: + assert isinstance(data[0][1]["input"], torch.Tensor) + assert isinstance(data[0][1]["target"], torch.Tensor) + + +@pytest.mark.parametrize( + "input_, output_", + [(input_tensor, output_tensor), (input_graph, output_graph)], +) +@pytest.mark.parametrize("automatic_batching", [True, False]) +def test_dataloader(input_, output_, automatic_batching): + problem = SupervisedProblem(input_=input_, output_=output_) + solver = SupervisedSolver(problem=problem, model=torch.nn.Linear(10, 10)) + trainer = Trainer( + solver, + batch_size=10, + train_size=0.7, + val_size=0.3, + test_size=0.0, + automatic_batching=automatic_batching, + ) + dm = trainer.data_module + dm.setup() + dm.trainer = trainer + dataloader = dm.train_dataloader() + assert isinstance(dataloader, DataLoader) + assert len(dataloader) == 7 + data = next(iter(dataloader)) + assert isinstance(data, dict) + if isinstance(input_, list): + assert isinstance(data["data"]["input"], Batch) + else: + assert isinstance(data["data"]["input"], torch.Tensor) + assert isinstance(data["data"]["target"], torch.Tensor) + + dataloader = dm.val_dataloader() + assert isinstance(dataloader, DataLoader) + assert len(dataloader) == 3 + data = next(iter(dataloader)) + assert isinstance(data, dict) + if isinstance(input_, list): + assert isinstance(data["data"]["input"], Batch) + else: + assert isinstance(data["data"]["input"], torch.Tensor) + assert isinstance(data["data"]["target"], torch.Tensor) + + +from pina import LabelTensor + +input_tensor = LabelTensor(torch.rand((100, 3)), ["u", "v", "w"]) +output_tensor = LabelTensor(torch.rand((100, 3)), ["u", "v", "w"]) + +x = LabelTensor(torch.rand((100, 50, 3)), ["u", "v", "w"]) +pos = LabelTensor(torch.rand((100, 50, 2)), ["x", "y"]) +input_graph = [ + RadiusGraph(x=x[i], pos=pos[i], radius=0.1) for i in range(len(x)) +] +output_graph = LabelTensor(torch.rand((100, 50, 3)), ["u", "v", "w"]) + + +@pytest.mark.parametrize( + "input_, output_", + [(input_tensor, output_tensor), (input_graph, output_graph)], +) +@pytest.mark.parametrize("automatic_batching", [True, False]) +def test_dataloader_labels(input_, output_, automatic_batching): + problem = SupervisedProblem(input_=input_, output_=output_) + solver = SupervisedSolver(problem=problem, model=torch.nn.Linear(10, 10)) + trainer = Trainer( + solver, + batch_size=10, + train_size=0.7, + val_size=0.3, + test_size=0.0, + automatic_batching=automatic_batching, + ) + dm = trainer.data_module + dm.setup() + dm.trainer = trainer + dataloader = dm.train_dataloader() + assert isinstance(dataloader, DataLoader) + assert len(dataloader) == 7 + data = next(iter(dataloader)) + assert isinstance(data, dict) + if isinstance(input_, list): + assert isinstance(data["data"]["input"], Batch) + assert isinstance(data["data"]["input"].x, LabelTensor) + assert data["data"]["input"].x.labels == ["u", "v", "w"] + assert data["data"]["input"].pos.labels == ["x", "y"] + else: + assert isinstance(data["data"]["input"], LabelTensor) + assert data["data"]["input"].labels == ["u", "v", "w"] + assert isinstance(data["data"]["target"], LabelTensor) + assert data["data"]["target"].labels == ["u", "v", "w"] + + dataloader = dm.val_dataloader() + assert isinstance(dataloader, DataLoader) + assert len(dataloader) == 3 + data = next(iter(dataloader)) + assert isinstance(data, dict) + if isinstance(input_, list): + assert isinstance(data["data"]["input"], Batch) + assert isinstance(data["data"]["input"].x, LabelTensor) + assert data["data"]["input"].x.labels == ["u", "v", "w"] + assert data["data"]["input"].pos.labels == ["x", "y"] + else: + assert isinstance(data["data"]["input"], torch.Tensor) + assert isinstance(data["data"]["input"], LabelTensor) + assert data["data"]["input"].labels == ["u", "v", "w"] + assert isinstance(data["data"]["target"], torch.Tensor) + assert data["data"]["target"].labels == ["u", "v", "w"] diff --git a/tests/test_data/test_graph_dataset.py b/tests/test_data/test_graph_dataset.py new file mode 100644 index 000000000..1fe0c890d --- /dev/null +++ b/tests/test_data/test_graph_dataset.py @@ -0,0 +1,112 @@ +import torch +import pytest +from pina.data.dataset import PinaDatasetFactory, PinaGraphDataset +from pina.graph import KNNGraph +from torch_geometric.data import Data + +x = torch.rand((100, 20, 10)) +pos = torch.rand((100, 20, 2)) +input_ = [ + KNNGraph(x=x_, pos=pos_, neighbours=3, edge_attr=True) + for x_, pos_ in zip(x, pos) +] +output_ = torch.rand((100, 20, 10)) + +x_2 = torch.rand((50, 20, 10)) +pos_2 = torch.rand((50, 20, 2)) +input_2_ = [ + KNNGraph(x=x_, pos=pos_, neighbours=3, edge_attr=True) + for x_, pos_ in zip(x_2, pos_2) +] +output_2_ = torch.rand((50, 20, 10)) + + +# Problem with a single condition +conditions_dict_single = { + "data": { + "input": input_, + "target": output_, + } +} +max_conditions_lengths_single = {"data": 100} + +# Problem with multiple conditions +conditions_dict_single_multi = { + "data_1": { + "input": input_, + "target": output_, + }, + "data_2": { + "input": input_2_, + "target": output_2_, + }, +} + +max_conditions_lengths_multi = {"data_1": 100, "data_2": 50} + + +@pytest.mark.parametrize( + "conditions_dict, max_conditions_lengths", + [ + (conditions_dict_single, max_conditions_lengths_single), + (conditions_dict_single_multi, max_conditions_lengths_multi), + ], +) +def test_constructor(conditions_dict, max_conditions_lengths): + dataset = PinaDatasetFactory( + conditions_dict, + max_conditions_lengths=max_conditions_lengths, + automatic_batching=True, + ) + assert isinstance(dataset, PinaGraphDataset) + assert len(dataset) == 100 + + +@pytest.mark.parametrize( + "conditions_dict, max_conditions_lengths", + [ + (conditions_dict_single, max_conditions_lengths_single), + (conditions_dict_single_multi, max_conditions_lengths_multi), + ], +) +def test_getitem(conditions_dict, max_conditions_lengths): + dataset = PinaDatasetFactory( + conditions_dict, + max_conditions_lengths=max_conditions_lengths, + automatic_batching=True, + ) + data = dataset[50] + assert isinstance(data, dict) + assert all([isinstance(d["input"], Data) for d in data.values()]) + assert all([isinstance(d["target"], torch.Tensor) for d in data.values()]) + assert all( + [d["input"].x.shape == torch.Size((20, 10)) for d in data.values()] + ) + assert all( + [d["target"].shape == torch.Size((20, 10)) for d in data.values()] + ) + assert all( + [ + d["input"].edge_index.shape == torch.Size((2, 60)) + for d in data.values() + ] + ) + assert all([d["input"].edge_attr.shape[0] == 60 for d in data.values()]) + + data = dataset.fetch_from_idx_list([i for i in range(20)]) + assert isinstance(data, dict) + assert all([isinstance(d["input"], Data) for d in data.values()]) + assert all([isinstance(d["target"], torch.Tensor) for d in data.values()]) + assert all( + [d["input"].x.shape == torch.Size((400, 10)) for d in data.values()] + ) + assert all( + [d["target"].shape == torch.Size((400, 10)) for d in data.values()] + ) + assert all( + [ + d["input"].edge_index.shape == torch.Size((2, 1200)) + for d in data.values() + ] + ) + assert all([d["input"].edge_attr.shape[0] == 1200 for d in data.values()]) diff --git a/tests/test_data/test_tensor_dataset.py b/tests/test_data/test_tensor_dataset.py new file mode 100644 index 000000000..81a122f2f --- /dev/null +++ b/tests/test_data/test_tensor_dataset.py @@ -0,0 +1,86 @@ +import torch +import pytest +from pina.data.dataset import PinaDatasetFactory, PinaTensorDataset + +input_tensor = torch.rand((100, 10)) +output_tensor = torch.rand((100, 2)) + +input_tensor_2 = torch.rand((50, 10)) +output_tensor_2 = torch.rand((50, 2)) + +conditions_dict_single = { + "data": { + "input": input_tensor, + "target": output_tensor, + } +} + +conditions_dict_single_multi = { + "data_1": { + "input": input_tensor, + "target": output_tensor, + }, + "data_2": { + "input": input_tensor_2, + "target": output_tensor_2, + }, +} + +max_conditions_lengths_single = {"data": 100} + +max_conditions_lengths_multi = {"data_1": 100, "data_2": 50} + + +@pytest.mark.parametrize( + "conditions_dict, max_conditions_lengths", + [ + (conditions_dict_single, max_conditions_lengths_single), + (conditions_dict_single_multi, max_conditions_lengths_multi), + ], +) +def test_constructor_tensor(conditions_dict, max_conditions_lengths): + dataset = PinaDatasetFactory( + conditions_dict, + max_conditions_lengths=max_conditions_lengths, + automatic_batching=True, + ) + assert isinstance(dataset, PinaTensorDataset) + + +def test_getitem_single(): + dataset = PinaDatasetFactory( + conditions_dict_single, + max_conditions_lengths=max_conditions_lengths_single, + automatic_batching=False, + ) + + tensors = dataset.fetch_from_idx_list([i for i in range(70)]) + assert isinstance(tensors, dict) + assert list(tensors.keys()) == ["data"] + assert sorted(list(tensors["data"].keys())) == ["input", "target"] + assert isinstance(tensors["data"]["input"], torch.Tensor) + assert tensors["data"]["input"].shape == torch.Size((70, 10)) + assert isinstance(tensors["data"]["target"], torch.Tensor) + assert tensors["data"]["target"].shape == torch.Size((70, 2)) + + +def test_getitem_multi(): + dataset = PinaDatasetFactory( + conditions_dict_single_multi, + max_conditions_lengths=max_conditions_lengths_multi, + automatic_batching=False, + ) + tensors = dataset.fetch_from_idx_list([i for i in range(70)]) + assert isinstance(tensors, dict) + assert list(tensors.keys()) == ["data_1", "data_2"] + assert sorted(list(tensors["data_1"].keys())) == ["input", "target"] + assert isinstance(tensors["data_1"]["input"], torch.Tensor) + assert tensors["data_1"]["input"].shape == torch.Size((70, 10)) + assert isinstance(tensors["data_1"]["target"], torch.Tensor) + assert tensors["data_1"]["target"].shape == torch.Size((70, 2)) + + assert sorted(list(tensors["data_2"].keys())) == ["input", "target"] + assert isinstance(tensors["data_2"]["input"], torch.Tensor) + assert tensors["data_2"]["input"].shape == torch.Size((50, 10)) + assert isinstance(tensors["data_2"]["target"], torch.Tensor) + assert tensors["data_2"]["target"].shape == torch.Size((50, 2)) diff --git a/tests/test_dataset.py b/tests/test_dataset.py deleted file mode 100644 index ff1b6c228..000000000 --- a/tests/test_dataset.py +++ /dev/null @@ -1,122 +0,0 @@ -import torch -import pytest - -from pina.dataset import SamplePointDataset, SamplePointLoader, DataPointDataset -from pina import LabelTensor, Condition -from pina.equation import Equation -from pina.geometry import CartesianDomain -from pina.problem import SpatialProblem -from pina.model import FeedForward -from pina.operators import laplacian -from pina.equation.equation_factory import FixedValue - - -def laplace_equation(input_, output_): - force_term = (torch.sin(input_.extract(['x'])*torch.pi) * - torch.sin(input_.extract(['y'])*torch.pi)) - delta_u = laplacian(output_.extract(['u']), input_) - return delta_u - force_term - -my_laplace = Equation(laplace_equation) -in_ = LabelTensor(torch.tensor([[0., 1.]]), ['x', 'y']) -out_ = LabelTensor(torch.tensor([[0.]]), ['u']) -in2_ = LabelTensor(torch.rand(60, 2), ['x', 'y']) -out2_ = LabelTensor(torch.rand(60, 1), ['u']) - -class Poisson(SpatialProblem): - output_variables = ['u'] - spatial_domain = CartesianDomain({'x': [0, 1], 'y': [0, 1]}) - - conditions = { - 'gamma1': Condition( - location=CartesianDomain({'x': [0, 1], 'y': 1}), - equation=FixedValue(0.0)), - 'gamma2': Condition( - location=CartesianDomain({'x': [0, 1], 'y': 0}), - equation=FixedValue(0.0)), - 'gamma3': Condition( - location=CartesianDomain({'x': 1, 'y': [0, 1]}), - equation=FixedValue(0.0)), - 'gamma4': Condition( - location=CartesianDomain({'x': 0, 'y': [0, 1]}), - equation=FixedValue(0.0)), - 'D': Condition( - input_points=LabelTensor(torch.rand(size=(100, 2)), ['x', 'y']), - equation=my_laplace), - 'data': Condition( - input_points=in_, - output_points=out_), - 'data2': Condition( - input_points=in2_, - output_points=out2_) - } - -boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -poisson = Poisson() -poisson.discretise_domain(10, 'grid', locations=boundaries) - -def test_sample(): - sample_dataset = SamplePointDataset(poisson, device='cpu') - assert len(sample_dataset) == 140 - assert sample_dataset.pts.shape == (140, 2) - assert sample_dataset.pts.labels == ['x', 'y'] - assert sample_dataset.condition_indeces.dtype == torch.int64 - assert sample_dataset.condition_indeces.max() == torch.tensor(4) - assert sample_dataset.condition_indeces.min() == torch.tensor(0) - -def test_data(): - dataset = DataPointDataset(poisson, device='cpu') - assert len(dataset) == 61 - assert dataset.input_pts.shape == (61, 2) - assert dataset.input_pts.labels == ['x', 'y'] - assert dataset.output_pts.shape == (61, 1 ) - assert dataset.output_pts.labels == ['u'] - assert dataset.condition_indeces.dtype == torch.int64 - assert dataset.condition_indeces.max() == torch.tensor(1) - assert dataset.condition_indeces.min() == torch.tensor(0) - -def test_loader(): - sample_dataset = SamplePointDataset(poisson, device='cpu') - data_dataset = DataPointDataset(poisson, device='cpu') - loader = SamplePointLoader(sample_dataset, data_dataset, batch_size=10) - - for batch in loader: - assert len(batch) in [2, 3] - assert batch['pts'].shape[0] <= 10 - assert batch['pts'].requires_grad == True - assert batch['pts'].labels == ['x', 'y'] - - loader2 = SamplePointLoader(sample_dataset, data_dataset, batch_size=None) - assert len(list(loader2)) == 2 - -def test_loader2(): - poisson2 = Poisson() - del poisson.conditions['data2'] - del poisson2.conditions['data'] - poisson2.discretise_domain(10, 'grid', locations=boundaries) - sample_dataset = SamplePointDataset(poisson, device='cpu') - data_dataset = DataPointDataset(poisson, device='cpu') - loader = SamplePointLoader(sample_dataset, data_dataset, batch_size=10) - - for batch in loader: - assert len(batch) == 2 # only phys condtions - assert batch['pts'].shape[0] <= 10 - assert batch['pts'].requires_grad == True - assert batch['pts'].labels == ['x', 'y'] - -def test_loader3(): - poisson2 = Poisson() - del poisson.conditions['gamma1'] - del poisson.conditions['gamma2'] - del poisson.conditions['gamma3'] - del poisson.conditions['gamma4'] - del poisson.conditions['D'] - sample_dataset = SamplePointDataset(poisson, device='cpu') - data_dataset = DataPointDataset(poisson, device='cpu') - loader = SamplePointLoader(sample_dataset, data_dataset, batch_size=10) - - for batch in loader: - assert len(batch) == 2 # only phys condtions - assert batch['pts'].shape[0] <= 10 - assert batch['pts'].requires_grad == True - assert batch['pts'].labels == ['x', 'y'] diff --git a/tests/test_equations/test_equation.py b/tests/test_equations/test_equation.py index aed4b096f..096b2d5e7 100644 --- a/tests/test_equations/test_equation.py +++ b/tests/test_equations/test_equation.py @@ -1,5 +1,5 @@ from pina.equation import Equation -from pina.operators import grad, laplacian +from pina.operator import grad, laplacian from pina import LabelTensor import torch import pytest @@ -7,15 +7,16 @@ def eq1(input_, output_): u_grad = grad(output_, input_) - u1_xx = grad(u_grad, input_, components=['du1dx'], d=['x']) - u2_xy = grad(u_grad, input_, components=['du2dx'], d=['y']) + u1_xx = grad(u_grad, input_, components=["du1dx"], d=["x"]) + u2_xy = grad(u_grad, input_, components=["du2dx"], d=["y"]) return torch.hstack([u1_xx, u2_xy]) def eq2(input_, output_): - force_term = (torch.sin(input_.extract(['x']) * torch.pi) * - torch.sin(input_.extract(['y']) * torch.pi)) - delta_u = laplacian(output_.extract(['u1']), input_) + force_term = torch.sin(input_.extract(["x"]) * torch.pi) * torch.sin( + input_.extract(["y"]) * torch.pi + ) + delta_u = laplacian(output_.extract(["u1"]), input_) return delta_u - force_term @@ -36,10 +37,10 @@ def test_residual(): eq_1 = Equation(eq1) eq_2 = Equation(eq2) - pts = LabelTensor(torch.rand(10, 2), labels=['x', 'y']) + pts = LabelTensor(torch.rand(10, 2), labels=["x", "y"]) pts.requires_grad = True u = torch.pow(pts, 2) - u.labels = ['u1', 'u2'] + u.labels = ["u1", "u2"] eq_1_res = eq_1.residual(pts, u) eq_2_res = eq_2.residual(pts, u) diff --git a/tests/test_equations/test_systemequation.py b/tests/test_equations/test_system_equation.py similarity index 58% rename from tests/test_equations/test_systemequation.py rename to tests/test_equations/test_system_equation.py index 7af90a78b..4a0a1163e 100644 --- a/tests/test_equations/test_systemequation.py +++ b/tests/test_equations/test_system_equation.py @@ -1,5 +1,5 @@ from pina.equation import SystemEquation -from pina.operators import grad, laplacian +from pina.operator import grad, laplacian from pina import LabelTensor import torch import pytest @@ -7,15 +7,16 @@ def eq1(input_, output_): u_grad = grad(output_, input_) - u1_xx = grad(u_grad, input_, components=['du1dx'], d=['x']) - u2_xy = grad(u_grad, input_, components=['du2dx'], d=['y']) + u1_xx = grad(u_grad, input_, components=["du1dx"], d=["x"]) + u2_xy = grad(u_grad, input_, components=["du2dx"], d=["y"]) return torch.hstack([u1_xx, u2_xy]) def eq2(input_, output_): - force_term = (torch.sin(input_.extract(['x']) * torch.pi) * - torch.sin(input_.extract(['y']) * torch.pi)) - delta_u = laplacian(output_.extract(['u1']), input_) + force_term = torch.sin(input_.extract(["x"]) * torch.pi) * torch.sin( + input_.extract(["y"]) * torch.pi + ) + delta_u = laplacian(output_.extract(["u1"]), input_) return delta_u - force_term @@ -25,25 +26,25 @@ def foo(): def test_constructor(): SystemEquation([eq1, eq2]) - SystemEquation([eq1, eq2], reduction='sum') + SystemEquation([eq1, eq2], reduction="sum") with pytest.raises(NotImplementedError): - SystemEquation([eq1, eq2], reduction='foo') + SystemEquation([eq1, eq2], reduction="foo") with pytest.raises(ValueError): SystemEquation(foo) def test_residual(): - pts = LabelTensor(torch.rand(10, 2), labels=['x', 'y']) + pts = LabelTensor(torch.rand(10, 2), labels=["x", "y"]) pts.requires_grad = True u = torch.pow(pts, 2) - u.labels = ['u1', 'u2'] + u.labels = ["u1", "u2"] - eq_1 = SystemEquation([eq1, eq2], reduction='mean') + eq_1 = SystemEquation([eq1, eq2], reduction="mean") res = eq_1.residual(pts, u) assert res.shape == torch.Size([10]) - eq_1 = SystemEquation([eq1, eq2], reduction='sum') + eq_1 = SystemEquation([eq1, eq2], reduction="sum") res = eq_1.residual(pts, u) assert res.shape == torch.Size([10]) diff --git a/tests/test_geometry/test_cartesian.py b/tests/test_geometry/test_cartesian.py index 3e7a8c900..1de06431c 100644 --- a/tests/test_geometry/test_cartesian.py +++ b/tests/test_geometry/test_cartesian.py @@ -1,34 +1,35 @@ import torch from pina import LabelTensor -from pina.geometry import CartesianDomain +from pina.domain import CartesianDomain + def test_constructor(): - CartesianDomain({'x': [0, 1], 'y': [0, 1]}) + CartesianDomain({"x": [0, 1], "y": [0, 1]}) def test_is_inside_check_border(): - pt_1 = LabelTensor(torch.tensor([[0.5, 0.5]]), ['x', 'y']) - pt_2 = LabelTensor(torch.tensor([[1.0, 0.5]]), ['x', 'y']) - pt_3 = LabelTensor(torch.tensor([[1.5, 0.5]]), ['x', 'y']) - domain = CartesianDomain({'x': [0, 1], 'y': [0, 1]}) + pt_1 = LabelTensor(torch.tensor([[0.5, 0.5]]), ["x", "y"]) + pt_2 = LabelTensor(torch.tensor([[1.0, 0.5]]), ["x", "y"]) + pt_3 = LabelTensor(torch.tensor([[1.5, 0.5]]), ["x", "y"]) + domain = CartesianDomain({"x": [0, 1], "y": [0, 1]}) for pt, exp_result in zip([pt_1, pt_2, pt_3], [True, True, False]): assert domain.is_inside(pt, check_border=True) == exp_result def test_is_inside_not_check_border(): - pt_1 = LabelTensor(torch.tensor([[0.5, 0.5]]), ['x', 'y']) - pt_2 = LabelTensor(torch.tensor([[1.0, 0.5]]), ['x', 'y']) - pt_3 = LabelTensor(torch.tensor([[1.5, 0.5]]), ['x', 'y']) - domain = CartesianDomain({'x': [0, 1], 'y': [0, 1]}) + pt_1 = LabelTensor(torch.tensor([[0.5, 0.5]]), ["x", "y"]) + pt_2 = LabelTensor(torch.tensor([[1.0, 0.5]]), ["x", "y"]) + pt_3 = LabelTensor(torch.tensor([[1.5, 0.5]]), ["x", "y"]) + domain = CartesianDomain({"x": [0, 1], "y": [0, 1]}) for pt, exp_result in zip([pt_1, pt_2, pt_3], [True, False, False]): assert domain.is_inside(pt, check_border=False) == exp_result def test_is_inside_fixed_variables(): - pt_1 = LabelTensor(torch.tensor([[0.5, 0.5]]), ['x', 'y']) - pt_2 = LabelTensor(torch.tensor([[1.0, 0.5]]), ['x', 'y']) - pt_3 = LabelTensor(torch.tensor([[1.0, 1.5]]), ['x', 'y']) - domain = CartesianDomain({'x': 1, 'y': [0, 1]}) + pt_1 = LabelTensor(torch.tensor([[0.5, 0.5]]), ["x", "y"]) + pt_2 = LabelTensor(torch.tensor([[1.0, 0.5]]), ["x", "y"]) + pt_3 = LabelTensor(torch.tensor([[1.0, 1.5]]), ["x", "y"]) + domain = CartesianDomain({"x": 1, "y": [0, 1]}) for pt, exp_result in zip([pt_1, pt_2, pt_3], [False, True, False]): assert domain.is_inside(pt, check_border=False) == exp_result diff --git a/tests/test_geometry/test_difference.py b/tests/test_geometry/test_difference.py index b165fa710..5e45836db 100644 --- a/tests/test_geometry/test_difference.py +++ b/tests/test_geometry/test_difference.py @@ -1,102 +1,71 @@ import torch from pina import LabelTensor -from pina.geometry import Difference, EllipsoidDomain, CartesianDomain +from pina.domain import Difference, EllipsoidDomain, CartesianDomain def test_constructor_two_CartesianDomains(): - Difference([ - CartesianDomain({ - 'x': [0, 2], - 'y': [0, 2] - }), - CartesianDomain({ - 'x': [1, 3], - 'y': [1, 3] - }) - ]) + Difference( + [ + CartesianDomain({"x": [0, 2], "y": [0, 2]}), + CartesianDomain({"x": [1, 3], "y": [1, 3]}), + ] + ) def test_constructor_two_3DCartesianDomain(): - Difference([ - CartesianDomain({ - 'x': [0, 2], - 'y': [0, 2], - 'z': [0, 2] - }), - CartesianDomain({ - 'x': [1, 3], - 'y': [1, 3], - 'z': [1, 3] - }) - ]) + Difference( + [ + CartesianDomain({"x": [0, 2], "y": [0, 2], "z": [0, 2]}), + CartesianDomain({"x": [1, 3], "y": [1, 3], "z": [1, 3]}), + ] + ) def test_constructor_three_CartesianDomains(): - Difference([ - CartesianDomain({ - 'x': [0, 2], - 'y': [0, 2] - }), - CartesianDomain({ - 'x': [1, 3], - 'y': [1, 3] - }), - CartesianDomain({ - 'x': [2, 4], - 'y': [2, 4] - }) - ]) + Difference( + [ + CartesianDomain({"x": [0, 2], "y": [0, 2]}), + CartesianDomain({"x": [1, 3], "y": [1, 3]}), + CartesianDomain({"x": [2, 4], "y": [2, 4]}), + ] + ) def test_is_inside_two_CartesianDomains(): - pt_1 = LabelTensor(torch.tensor([[0.5, 0.5]]), ['x', 'y']) - pt_2 = LabelTensor(torch.tensor([[-1, -0.5]]), ['x', 'y']) - domain = Difference([ - CartesianDomain({ - 'x': [0, 2], - 'y': [0, 2] - }), - CartesianDomain({ - 'x': [1, 3], - 'y': [1, 3] - }) - ]) + pt_1 = LabelTensor(torch.tensor([[0.5, 0.5]]), ["x", "y"]) + pt_2 = LabelTensor(torch.tensor([[-1, -0.5]]), ["x", "y"]) + domain = Difference( + [ + CartesianDomain({"x": [0, 2], "y": [0, 2]}), + CartesianDomain({"x": [1, 3], "y": [1, 3]}), + ] + ) assert domain.is_inside(pt_1) == True assert domain.is_inside(pt_2) == False def test_is_inside_two_3DCartesianDomain(): - pt_1 = LabelTensor(torch.tensor([[0.5, 0.5, 0.5]]), ['x', 'y', 'z']) - pt_2 = LabelTensor(torch.tensor([[-1, -0.5, -0.5]]), ['x', 'y', 'z']) - domain = Difference([ - CartesianDomain({ - 'x': [0, 2], - 'y': [0, 2], - 'z': [0, 2] - }), - CartesianDomain({ - 'x': [1, 3], - 'y': [1, 3], - 'z': [1, 3] - }) - ]) + pt_1 = LabelTensor(torch.tensor([[0.5, 0.5, 0.5]]), ["x", "y", "z"]) + pt_2 = LabelTensor(torch.tensor([[-1, -0.5, -0.5]]), ["x", "y", "z"]) + domain = Difference( + [ + CartesianDomain({"x": [0, 2], "y": [0, 2], "z": [0, 2]}), + CartesianDomain({"x": [1, 3], "y": [1, 3], "z": [1, 3]}), + ] + ) assert domain.is_inside(pt_1) == True assert domain.is_inside(pt_2) == False def test_sample(): n = 100 - domain = Difference([ - EllipsoidDomain({ - 'x': [-1, 1], - 'y': [-1, 1] - }), - CartesianDomain({ - 'x': [-0.5, 0.5], - 'y': [-0.5, 0.5] - }) - ]) + domain = Difference( + [ + EllipsoidDomain({"x": [-1, 1], "y": [-1, 1]}), + CartesianDomain({"x": [-0.5, 0.5], "y": [-0.5, 0.5]}), + ] + ) pts = domain.sample(n) assert isinstance(pts, LabelTensor) assert pts.shape[0] == n diff --git a/tests/test_geometry/test_ellipsoid.py b/tests/test_geometry/test_ellipsoid.py index 9ab0989ba..203010799 100644 --- a/tests/test_geometry/test_ellipsoid.py +++ b/tests/test_geometry/test_ellipsoid.py @@ -2,19 +2,19 @@ import pytest from pina import LabelTensor -from pina.geometry import EllipsoidDomain +from pina.domain import EllipsoidDomain def test_constructor(): - EllipsoidDomain({'x': [0, 1], 'y': [0, 1]}) - EllipsoidDomain({'x': [0, 1], 'y': [0, 1]}, sample_surface=True) + EllipsoidDomain({"x": [0, 1], "y": [0, 1]}) + EllipsoidDomain({"x": [0, 1], "y": [0, 1]}, sample_surface=True) def test_is_inside_sample_surface_false(): - domain = EllipsoidDomain({'x': [0, 1], 'y': [0, 1]}, sample_surface=False) - pt_1 = LabelTensor(torch.tensor([[0.5, 0.5]]), ['x', 'y']) - pt_2 = LabelTensor(torch.tensor([[1.0, 0.5]]), ['x', 'y']) - pt_3 = LabelTensor(torch.tensor([[1.5, 0.5]]), ['x', 'y']) + domain = EllipsoidDomain({"x": [0, 1], "y": [0, 1]}, sample_surface=False) + pt_1 = LabelTensor(torch.tensor([[0.5, 0.5]]), ["x", "y"]) + pt_2 = LabelTensor(torch.tensor([[1.0, 0.5]]), ["x", "y"]) + pt_3 = LabelTensor(torch.tensor([[1.5, 0.5]]), ["x", "y"]) for pt, exp_result in zip([pt_1, pt_2, pt_3], [True, False, False]): assert domain.is_inside(pt) == exp_result for pt, exp_result in zip([pt_1, pt_2, pt_3], [True, True, False]): @@ -22,9 +22,9 @@ def test_is_inside_sample_surface_false(): def test_is_inside_sample_surface_true(): - domain = EllipsoidDomain({'x': [0, 1], 'y': [0, 1]}, sample_surface=True) - pt_1 = LabelTensor(torch.tensor([[0.5, 0.5]]), ['x', 'y']) - pt_2 = LabelTensor(torch.tensor([[1.0, 0.5]]), ['x', 'y']) - pt_3 = LabelTensor(torch.tensor([[1.5, 0.5]]), ['x', 'y']) + domain = EllipsoidDomain({"x": [0, 1], "y": [0, 1]}, sample_surface=True) + pt_1 = LabelTensor(torch.tensor([[0.5, 0.5]]), ["x", "y"]) + pt_2 = LabelTensor(torch.tensor([[1.0, 0.5]]), ["x", "y"]) + pt_3 = LabelTensor(torch.tensor([[1.5, 0.5]]), ["x", "y"]) for pt, exp_result in zip([pt_1, pt_2, pt_3], [False, True, False]): assert domain.is_inside(pt) == exp_result diff --git a/tests/test_geometry/test_exclusion.py b/tests/test_geometry/test_exclusion.py index b6400cde6..95ada2c9d 100644 --- a/tests/test_geometry/test_exclusion.py +++ b/tests/test_geometry/test_exclusion.py @@ -1,102 +1,71 @@ import torch from pina import LabelTensor -from pina.geometry import Exclusion, EllipsoidDomain, CartesianDomain +from pina.domain import Exclusion, EllipsoidDomain, CartesianDomain def test_constructor_two_CartesianDomains(): - Exclusion([ - CartesianDomain({ - 'x': [0, 2], - 'y': [0, 2] - }), - CartesianDomain({ - 'x': [1, 3], - 'y': [1, 3] - }) - ]) + Exclusion( + [ + CartesianDomain({"x": [0, 2], "y": [0, 2]}), + CartesianDomain({"x": [1, 3], "y": [1, 3]}), + ] + ) def test_constructor_two_3DCartesianDomain(): - Exclusion([ - CartesianDomain({ - 'x': [0, 2], - 'y': [0, 2], - 'z': [0, 2] - }), - CartesianDomain({ - 'x': [1, 3], - 'y': [1, 3], - 'z': [1, 3] - }) - ]) + Exclusion( + [ + CartesianDomain({"x": [0, 2], "y": [0, 2], "z": [0, 2]}), + CartesianDomain({"x": [1, 3], "y": [1, 3], "z": [1, 3]}), + ] + ) def test_constructor_three_CartesianDomains(): - Exclusion([ - CartesianDomain({ - 'x': [0, 2], - 'y': [0, 2] - }), - CartesianDomain({ - 'x': [1, 3], - 'y': [1, 3] - }), - CartesianDomain({ - 'x': [2, 4], - 'y': [2, 4] - }) - ]) + Exclusion( + [ + CartesianDomain({"x": [0, 2], "y": [0, 2]}), + CartesianDomain({"x": [1, 3], "y": [1, 3]}), + CartesianDomain({"x": [2, 4], "y": [2, 4]}), + ] + ) def test_is_inside_two_CartesianDomains(): - pt_1 = LabelTensor(torch.tensor([[0.5, 0.5]]), ['x', 'y']) - pt_2 = LabelTensor(torch.tensor([[-1, -0.5]]), ['x', 'y']) - domain = Exclusion([ - CartesianDomain({ - 'x': [0, 2], - 'y': [0, 2] - }), - CartesianDomain({ - 'x': [1, 3], - 'y': [1, 3] - }) - ]) + pt_1 = LabelTensor(torch.tensor([[0.5, 0.5]]), ["x", "y"]) + pt_2 = LabelTensor(torch.tensor([[-1, -0.5]]), ["x", "y"]) + domain = Exclusion( + [ + CartesianDomain({"x": [0, 2], "y": [0, 2]}), + CartesianDomain({"x": [1, 3], "y": [1, 3]}), + ] + ) assert domain.is_inside(pt_1) == True assert domain.is_inside(pt_2) == False def test_is_inside_two_3DCartesianDomain(): - pt_1 = LabelTensor(torch.tensor([[0.5, 0.5, 0.5]]), ['x', 'y', 'z']) - pt_2 = LabelTensor(torch.tensor([[-1, -0.5, -0.5]]), ['x', 'y', 'z']) - domain = Exclusion([ - CartesianDomain({ - 'x': [0, 2], - 'y': [0, 2], - 'z': [0, 2] - }), - CartesianDomain({ - 'x': [1, 3], - 'y': [1, 3], - 'z': [1, 3] - }) - ]) + pt_1 = LabelTensor(torch.tensor([[0.5, 0.5, 0.5]]), ["x", "y", "z"]) + pt_2 = LabelTensor(torch.tensor([[-1, -0.5, -0.5]]), ["x", "y", "z"]) + domain = Exclusion( + [ + CartesianDomain({"x": [0, 2], "y": [0, 2], "z": [0, 2]}), + CartesianDomain({"x": [1, 3], "y": [1, 3], "z": [1, 3]}), + ] + ) assert domain.is_inside(pt_1) == True assert domain.is_inside(pt_2) == False def test_sample(): n = 100 - domain = Exclusion([ - EllipsoidDomain({ - 'x': [-1, 1], - 'y': [-1, 1] - }), - CartesianDomain({ - 'x': [0.3, 1.5], - 'y': [0.3, 1.5] - }) - ]) + domain = Exclusion( + [ + EllipsoidDomain({"x": [-1, 1], "y": [-1, 1]}), + CartesianDomain({"x": [0.3, 1.5], "y": [0.3, 1.5]}), + ] + ) pts = domain.sample(n) assert isinstance(pts, LabelTensor) assert pts.shape[0] == n diff --git a/tests/test_geometry/test_intersection.py b/tests/test_geometry/test_intersection.py index 61061072f..fe6921f16 100644 --- a/tests/test_geometry/test_intersection.py +++ b/tests/test_geometry/test_intersection.py @@ -1,90 +1,63 @@ import torch from pina import LabelTensor -from pina.geometry import Intersection, EllipsoidDomain, CartesianDomain +from pina.domain import Intersection, EllipsoidDomain, CartesianDomain def test_constructor_two_CartesianDomains(): - Intersection([ - CartesianDomain({ - 'x': [0, 2], - 'y': [0, 2] - }), - CartesianDomain({ - 'x': [1, 3], - 'y': [1, 3] - }) - ]) + Intersection( + [ + CartesianDomain({"x": [0, 2], "y": [0, 2]}), + CartesianDomain({"x": [1, 3], "y": [1, 3]}), + ] + ) def test_constructor_two_3DCartesianDomain(): - Intersection([ - CartesianDomain({ - 'x': [0, 2], - 'y': [0, 2], - 'z': [0, 2] - }), - CartesianDomain({ - 'x': [1, 3], - 'y': [1, 3], - 'z': [1, 3] - }) - ]) + Intersection( + [ + CartesianDomain({"x": [0, 2], "y": [0, 2], "z": [0, 2]}), + CartesianDomain({"x": [1, 3], "y": [1, 3], "z": [1, 3]}), + ] + ) def test_constructor_three_CartesianDomains(): - Intersection([ - CartesianDomain({ - 'x': [0, 2], - 'y': [0, 2] - }), - CartesianDomain({ - 'x': [1, 3], - 'y': [1, 3] - }), - CartesianDomain({ - 'x': [2, 4], - 'y': [2, 4] - }) - ]) + Intersection( + [ + CartesianDomain({"x": [0, 2], "y": [0, 2]}), + CartesianDomain({"x": [1, 3], "y": [1, 3]}), + CartesianDomain({"x": [2, 4], "y": [2, 4]}), + ] + ) def test_is_inside_two_CartesianDomains(): - pt_1 = LabelTensor(torch.tensor([[0.5, 0.5]]), ['x', 'y']) - pt_2 = LabelTensor(torch.tensor([[-1, -0.5]]), ['x', 'y']) - pt_3 = LabelTensor(torch.tensor([[1.5, 1.5]]), ['x', 'y']) + pt_1 = LabelTensor(torch.tensor([[0.5, 0.5]]), ["x", "y"]) + pt_2 = LabelTensor(torch.tensor([[-1, -0.5]]), ["x", "y"]) + pt_3 = LabelTensor(torch.tensor([[1.5, 1.5]]), ["x", "y"]) - domain = Intersection([ - CartesianDomain({ - 'x': [0, 2], - 'y': [0, 2] - }), - CartesianDomain({ - 'x': [1, 3], - 'y': [1, 3] - }) - ]) + domain = Intersection( + [ + CartesianDomain({"x": [0, 2], "y": [0, 2]}), + CartesianDomain({"x": [1, 3], "y": [1, 3]}), + ] + ) assert domain.is_inside(pt_1) == False assert domain.is_inside(pt_2) == False assert domain.is_inside(pt_3) == True def test_is_inside_two_3DCartesianDomain(): - pt_1 = LabelTensor(torch.tensor([[0.5, 0.5, 0.5]]), ['x', 'y', 'z']) - pt_2 = LabelTensor(torch.tensor([[-1, -0.5, -0.5]]), ['x', 'y', 'z']) - pt_3 = LabelTensor(torch.tensor([[1.5, 1.5, 1.5]]), ['x', 'y', 'z']) - domain = Intersection([ - CartesianDomain({ - 'x': [0, 2], - 'y': [0, 2], - 'z': [0, 2] - }), - CartesianDomain({ - 'x': [1, 3], - 'y': [1, 3], - 'z': [1, 3] - }) - ]) + pt_1 = LabelTensor(torch.tensor([[0.5, 0.5, 0.5]]), ["x", "y", "z"]) + pt_2 = LabelTensor(torch.tensor([[-1, -0.5, -0.5]]), ["x", "y", "z"]) + pt_3 = LabelTensor(torch.tensor([[1.5, 1.5, 1.5]]), ["x", "y", "z"]) + domain = Intersection( + [ + CartesianDomain({"x": [0, 2], "y": [0, 2], "z": [0, 2]}), + CartesianDomain({"x": [1, 3], "y": [1, 3], "z": [1, 3]}), + ] + ) assert domain.is_inside(pt_1) == False assert domain.is_inside(pt_2) == False assert domain.is_inside(pt_3) == True @@ -92,16 +65,12 @@ def test_is_inside_two_3DCartesianDomain(): def test_sample(): n = 100 - domain = Intersection([ - EllipsoidDomain({ - 'x': [-1, 1], - 'y': [-1, 1] - }), - CartesianDomain({ - 'x': [-0.5, 0.5], - 'y': [-0.5, 0.5] - }) - ]) + domain = Intersection( + [ + EllipsoidDomain({"x": [-1, 1], "y": [-1, 1]}), + CartesianDomain({"x": [-0.5, 0.5], "y": [-0.5, 0.5]}), + ] + ) pts = domain.sample(n) assert isinstance(pts, LabelTensor) assert pts.shape[0] == n diff --git a/tests/test_geometry/test_simplex.py b/tests/test_geometry/test_simplex.py index 1f59585c6..c03e1504e 100644 --- a/tests/test_geometry/test_simplex.py +++ b/tests/test_geometry/test_simplex.py @@ -2,15 +2,17 @@ import pytest from pina import LabelTensor -from pina.geometry import SimplexDomain +from pina.domain import SimplexDomain def test_constructor(): - SimplexDomain([ - LabelTensor(torch.tensor([[0, 0]]), labels=["x", "y"]), - LabelTensor(torch.tensor([[1, 1]]), labels=["x", "y"]), - LabelTensor(torch.tensor([[0, 2]]), labels=["x", "y"]), - ]) + SimplexDomain( + [ + LabelTensor(torch.tensor([[0, 0]]), labels=["x", "y"]), + LabelTensor(torch.tensor([[1, 1]]), labels=["x", "y"]), + LabelTensor(torch.tensor([[0, 2]]), labels=["x", "y"]), + ] + ) SimplexDomain( [ LabelTensor(torch.tensor([[0, 0]]), labels=["x", "y"]), @@ -21,33 +23,41 @@ def test_constructor(): ) with pytest.raises(ValueError): # different labels - SimplexDomain([ - LabelTensor(torch.tensor([[0, 0]]), labels=["x", "y"]), - LabelTensor(torch.tensor([[1, 1]]), labels=["x", "z"]), - LabelTensor(torch.tensor([[0, 2]]), labels=["x", "a"]), - ]) + SimplexDomain( + [ + LabelTensor(torch.tensor([[0, 0]]), labels=["x", "y"]), + LabelTensor(torch.tensor([[1, 1]]), labels=["x", "z"]), + LabelTensor(torch.tensor([[0, 2]]), labels=["x", "a"]), + ] + ) # not LabelTensor - SimplexDomain([ - LabelTensor(torch.tensor([[0, 0]]), labels=["x", "y"]), - [1, 1], - LabelTensor(torch.tensor([[0, 2]]), labels=["x", "y"]), - ]) + SimplexDomain( + [ + LabelTensor(torch.tensor([[0, 0]]), labels=["x", "y"]), + [1, 1], + LabelTensor(torch.tensor([[0, 2]]), labels=["x", "y"]), + ] + ) # different number of vertices - SimplexDomain([ - LabelTensor(torch.tensor([[0., -2.]]), labels=["x", "y"]), - LabelTensor(torch.tensor([[-.5, -.5]]), labels=["x", "y"]), - LabelTensor(torch.tensor([[-2., 0.]]), labels=["x", "y"]), - LabelTensor(torch.tensor([[-.5, .5]]), labels=["x", "y"]), - ]) + SimplexDomain( + [ + LabelTensor(torch.tensor([[0.0, -2.0]]), labels=["x", "y"]), + LabelTensor(torch.tensor([[-0.5, -0.5]]), labels=["x", "y"]), + LabelTensor(torch.tensor([[-2.0, 0.0]]), labels=["x", "y"]), + LabelTensor(torch.tensor([[-0.5, 0.5]]), labels=["x", "y"]), + ] + ) def test_sample(): # sampling inside - simplex = SimplexDomain([ - LabelTensor(torch.tensor([[0, 0]]), labels=["x", "y"]), - LabelTensor(torch.tensor([[1, 1]]), labels=["x", "y"]), - LabelTensor(torch.tensor([[0, 2]]), labels=["x", "y"]), - ]) + simplex = SimplexDomain( + [ + LabelTensor(torch.tensor([[0, 0]]), labels=["x", "y"]), + LabelTensor(torch.tensor([[1, 1]]), labels=["x", "y"]), + LabelTensor(torch.tensor([[0, 2]]), labels=["x", "y"]), + ] + ) pts = simplex.sample(10) assert isinstance(pts, LabelTensor) assert pts.size() == torch.Size([10, 2]) @@ -118,8 +128,9 @@ def test_is_inside_2D_check_border_false(): pt6 = LabelTensor(torch.tensor([[2.5, 1]]), ["x", "y"]) pt7 = LabelTensor(torch.tensor([[100, 100]]), ["x", "y"]) pts = [pt1, pt2, pt3, pt4, pt5, pt6, pt7] - for pt, exp_result in zip(pts, - [False, False, False, False, True, True, False]): + for pt, exp_result in zip( + pts, [False, False, False, False, True, True, False] + ): assert domain.is_inside(point=pt, check_border=False) == exp_result @@ -144,7 +155,8 @@ def test_is_inside_3D_check_border_true(): pt9 = LabelTensor(torch.tensor([[2, 1, 1]]), ["x", "y", "z"]) pts = [pt1, pt2, pt3, pt4, pt5, pt6, pt7, pt8, pt9] for pt, exp_result in zip( - pts, [True, True, True, True, True, False, True, True, False]): + pts, [True, True, True, True, True, False, True, True, False] + ): assert domain.is_inside(point=pt, check_border=True) == exp_result @@ -166,6 +178,7 @@ def test_is_inside_3D_check_border_false(): pt6 = LabelTensor(torch.tensor([[0, 0, 20]]), ["x", "y", "z"]) pt7 = LabelTensor(torch.tensor([[2, 1, 1]]), ["x", "y", "z"]) pts = [pt1, pt2, pt3, pt4, pt5, pt6, pt7] - for pt, exp_result in zip(pts, - [False, False, False, False, False, False, True]): + for pt, exp_result in zip( + pts, [False, False, False, False, False, False, True] + ): assert domain.is_inside(point=pt, check_border=False) == exp_result diff --git a/tests/test_geometry/test_union.py b/tests/test_geometry/test_union.py index 16f8bca2e..a2fd05f86 100644 --- a/tests/test_geometry/test_union.py +++ b/tests/test_geometry/test_union.py @@ -1,115 +1,92 @@ import torch from pina import LabelTensor -from pina.geometry import Union, EllipsoidDomain, CartesianDomain +from pina.domain import Union, EllipsoidDomain, CartesianDomain def test_constructor_two_CartesianDomains(): - Union([ - CartesianDomain({ - 'x': [0, 1], - 'y': [0, 1] - }), - CartesianDomain({ - 'x': [0.5, 2], - 'y': [-1, 0.1] - }) - ]) + Union( + [ + CartesianDomain({"x": [0, 1], "y": [0, 1]}), + CartesianDomain({"x": [0.5, 2], "y": [-1, 0.1]}), + ] + ) def test_constructor_two_EllipsoidDomains(): - Union([ - EllipsoidDomain({ - 'x': [-1, 1], - 'y': [-1, 1], - 'z': [-1, 1] - }), - EllipsoidDomain({ - 'x': [-0.5, 0.5], - 'y': [-0.5, 0.5], - 'z': [-0.5, 0.5] - }) - ]) + Union( + [ + EllipsoidDomain({"x": [-1, 1], "y": [-1, 1], "z": [-1, 1]}), + EllipsoidDomain( + {"x": [-0.5, 0.5], "y": [-0.5, 0.5], "z": [-0.5, 0.5]} + ), + ] + ) def test_constructor_EllipsoidDomain_CartesianDomain(): - Union([ - EllipsoidDomain({ - 'x': [-1, 1], - 'y': [-1, 1] - }), - CartesianDomain({ - 'x': [-0.5, 0.5], - 'y': [-0.5, 0.5] - }) - ]) + Union( + [ + EllipsoidDomain({"x": [-1, 1], "y": [-1, 1]}), + CartesianDomain({"x": [-0.5, 0.5], "y": [-0.5, 0.5]}), + ] + ) def test_is_inside_two_CartesianDomains(): - pt_1 = LabelTensor(torch.tensor([[0.5, 0.5]]), ['x', 'y']) - pt_2 = LabelTensor(torch.tensor([[-1, -1]]), ['x', 'y']) - domain = Union([ - CartesianDomain({ - 'x': [0, 1], - 'y': [0, 1] - }), - CartesianDomain({ - 'x': [0.5, 2], - 'y': [-1, 0.1] - }) - ]) + pt_1 = LabelTensor(torch.tensor([[0.5, 0.5]]), ["x", "y"]) + pt_2 = LabelTensor(torch.tensor([[-1, -1]]), ["x", "y"]) + domain = Union( + [ + CartesianDomain({"x": [0, 1], "y": [0, 1]}), + CartesianDomain({"x": [0.5, 2], "y": [-1, 0.1]}), + ] + ) assert domain.is_inside(pt_1) == True assert domain.is_inside(pt_2) == False def test_is_inside_two_EllipsoidDomains(): - pt_1 = LabelTensor(torch.tensor([[0.5, 0.5, 0.5]]), ['x', 'y', 'z']) - pt_2 = LabelTensor(torch.tensor([[-1, -1, -1]]), ['x', 'y', 'z']) - domain = Union([ - EllipsoidDomain({ - 'x': [-1, 1], - 'y': [-1, 1], - 'z': [-1, 1] - }), - EllipsoidDomain({ - 'x': [-0.5, 0.5], - 'y': [-0.5, 0.5], - 'z': [-0.5, 0.5] - }) - ]) + pt_1 = LabelTensor(torch.tensor([[0.5, 0.5, 0.5]]), ["x", "y", "z"]) + pt_2 = LabelTensor(torch.tensor([[-1, -1, -1]]), ["x", "y", "z"]) + domain = Union( + [ + EllipsoidDomain({"x": [-1, 1], "y": [-1, 1], "z": [-1, 1]}), + EllipsoidDomain( + {"x": [-0.5, 0.5], "y": [-0.5, 0.5], "z": [-0.5, 0.5]} + ), + ] + ) assert domain.is_inside(pt_1) == True assert domain.is_inside(pt_2) == False def test_is_inside_EllipsoidDomain_CartesianDomain(): - pt_1 = LabelTensor(torch.tensor([[0.5, 0.5]]), ['x', 'y']) - pt_2 = LabelTensor(torch.tensor([[-1, -1]]), ['x', 'y']) - domain = Union([ - EllipsoidDomain({ - 'x': [-1, 1], - 'y': [-1, 1], - }), - CartesianDomain({ - 'x': [0.6, 1.5], - 'y': [-2, 0] - }) - ]) + pt_1 = LabelTensor(torch.tensor([[0.5, 0.5]]), ["x", "y"]) + pt_2 = LabelTensor(torch.tensor([[-1, -1]]), ["x", "y"]) + domain = Union( + [ + EllipsoidDomain( + { + "x": [-1, 1], + "y": [-1, 1], + } + ), + CartesianDomain({"x": [0.6, 1.5], "y": [-2, 0]}), + ] + ) assert domain.is_inside(pt_1) == True assert domain.is_inside(pt_2) == False def test_sample(): n = 100 - domain = Union([ - EllipsoidDomain({ - 'x': [-1, 1], - 'y': [-1, 1] - }), - CartesianDomain({ - 'x': [-0.5, 0.5], - 'y': [-0.5, 0.5] - }) - ]) + domain = Union( + [ + EllipsoidDomain({"x": [-1, 1], "y": [-1, 1]}), + CartesianDomain({"x": [-0.5, 0.5], "y": [-0.5, 0.5]}), + ] + ) pts = domain.sample(n) assert isinstance(pts, LabelTensor) assert pts.shape[0] == n diff --git a/tests/test_graph.py b/tests/test_graph.py new file mode 100644 index 000000000..bf053a89f --- /dev/null +++ b/tests/test_graph.py @@ -0,0 +1,346 @@ +import pytest +import torch +from pina import LabelTensor +from pina.graph import RadiusGraph, KNNGraph, Graph +from torch_geometric.data import Data + + +def build_edge_attr(pos, edge_index): + return torch.cat([pos[edge_index[0]], pos[edge_index[1]]], dim=-1) + + +@pytest.mark.parametrize( + "x, pos", + [ + (torch.rand(10, 2), torch.rand(10, 3)), + ( + LabelTensor(torch.rand(10, 2), ["u", "v"]), + LabelTensor(torch.rand(10, 3), ["x", "y", "z"]), + ), + ], +) +def test_build_graph(x, pos): + edge_index = torch.tensor( + [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [1, 2, 3, 4, 5, 6, 7, 8, 9, 0]], + dtype=torch.int64, + ) + graph = Graph(x=x, pos=pos, edge_index=edge_index) + assert hasattr(graph, "x") + assert hasattr(graph, "pos") + assert hasattr(graph, "edge_index") + assert torch.isclose(graph.x, x).all() + if isinstance(x, LabelTensor): + assert isinstance(graph.x, LabelTensor) + assert graph.x.labels == x.labels + else: + assert isinstance(graph.pos, torch.Tensor) + assert torch.isclose(graph.pos, pos).all() + if isinstance(pos, LabelTensor): + assert isinstance(graph.pos, LabelTensor) + assert graph.pos.labels == pos.labels + else: + assert isinstance(graph.pos, torch.Tensor) + + edge_index = torch.tensor( + [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [1, 2, 3, 4, 5, 6, 7, 8, 9, 0]], + dtype=torch.int64, + ) + graph = Graph(x=x, edge_index=edge_index) + assert hasattr(graph, "x") + assert hasattr(graph, "pos") + assert hasattr(graph, "edge_index") + assert torch.isclose(graph.x, x).all() + if isinstance(x, LabelTensor): + assert isinstance(graph.x, LabelTensor) + assert graph.x.labels == x.labels + else: + assert isinstance(graph.x, torch.Tensor) + + +@pytest.mark.parametrize( + "x, pos", + [ + (torch.rand(10, 2), torch.rand(10, 3)), + ( + LabelTensor(torch.rand(10, 2), ["u", "v"]), + LabelTensor(torch.rand(10, 3), ["x", "y", "z"]), + ), + ], +) +def test_build_radius_graph(x, pos): + graph = RadiusGraph(x=x, pos=pos, radius=0.5) + assert hasattr(graph, "x") + assert hasattr(graph, "pos") + assert hasattr(graph, "edge_index") + assert torch.isclose(graph.x, x).all() + if isinstance(x, LabelTensor): + assert isinstance(graph.x, LabelTensor) + assert graph.x.labels == x.labels + else: + assert isinstance(graph.pos, torch.Tensor) + assert torch.isclose(graph.pos, pos).all() + if isinstance(pos, LabelTensor): + assert isinstance(graph.pos, LabelTensor) + assert graph.pos.labels == pos.labels + else: + assert isinstance(graph.pos, torch.Tensor) + + +@pytest.mark.parametrize( + "x, pos", + [ + (torch.rand(10, 2), torch.rand(10, 3)), + ( + LabelTensor(torch.rand(10, 2), ["u", "v"]), + LabelTensor(torch.rand(10, 3), ["x", "y", "z"]), + ), + ], +) +def test_build_radius_graph_edge_attr(x, pos): + graph = RadiusGraph(x=x, pos=pos, radius=0.5, edge_attr=True) + assert hasattr(graph, "x") + assert hasattr(graph, "pos") + assert hasattr(graph, "edge_index") + assert torch.isclose(graph.x, x).all() + if isinstance(x, LabelTensor): + assert isinstance(graph.x, LabelTensor) + assert graph.x.labels == x.labels + else: + assert isinstance(graph.pos, torch.Tensor) + assert torch.isclose(graph.pos, pos).all() + if isinstance(pos, LabelTensor): + assert isinstance(graph.pos, LabelTensor) + assert graph.pos.labels == pos.labels + else: + assert isinstance(graph.pos, torch.Tensor) + assert hasattr(graph, "edge_attr") + assert isinstance(graph.edge_attr, torch.Tensor) + assert graph.edge_attr.shape[-1] == 3 + assert graph.edge_attr.shape[0] == graph.edge_index.shape[1] + + +@pytest.mark.parametrize( + "x, pos", + [ + (torch.rand(10, 2), torch.rand(10, 3)), + ( + LabelTensor(torch.rand(10, 2), ["u", "v"]), + LabelTensor(torch.rand(10, 3), ["x", "y", "z"]), + ), + ], +) +def test_build_radius_graph_custom_edge_attr(x, pos): + graph = RadiusGraph( + x=x, + pos=pos, + radius=0.5, + edge_attr=True, + custom_edge_func=build_edge_attr, + ) + assert hasattr(graph, "x") + assert hasattr(graph, "pos") + assert hasattr(graph, "edge_index") + assert torch.isclose(graph.x, x).all() + if isinstance(x, LabelTensor): + assert isinstance(graph.x, LabelTensor) + assert graph.x.labels == x.labels + else: + assert isinstance(graph.pos, torch.Tensor) + assert torch.isclose(graph.pos, pos).all() + if isinstance(pos, LabelTensor): + assert isinstance(graph.pos, LabelTensor) + assert graph.pos.labels == pos.labels + else: + assert isinstance(graph.pos, torch.Tensor) + assert hasattr(graph, "edge_attr") + assert isinstance(graph.edge_attr, torch.Tensor) + assert graph.edge_attr.shape[-1] == 6 + assert graph.edge_attr.shape[0] == graph.edge_index.shape[1] + + +@pytest.mark.parametrize( + "x, pos", + [ + (torch.rand(10, 2), torch.rand(10, 3)), + ( + LabelTensor(torch.rand(10, 2), ["u", "v"]), + LabelTensor(torch.rand(10, 3), ["x", "y", "z"]), + ), + ], +) +def test_build_knn_graph(x, pos): + graph = KNNGraph(x=x, pos=pos, neighbours=2) + assert hasattr(graph, "x") + assert hasattr(graph, "pos") + assert hasattr(graph, "edge_index") + assert torch.isclose(graph.x, x).all() + if isinstance(x, LabelTensor): + assert isinstance(graph.x, LabelTensor) + assert graph.x.labels == x.labels + else: + assert isinstance(graph.pos, torch.Tensor) + assert torch.isclose(graph.pos, pos).all() + if isinstance(pos, LabelTensor): + assert isinstance(graph.pos, LabelTensor) + assert graph.pos.labels == pos.labels + else: + assert isinstance(graph.pos, torch.Tensor) + assert graph.edge_attr is None + + +@pytest.mark.parametrize( + "x, pos", + [ + (torch.rand(10, 2), torch.rand(10, 3)), + ( + LabelTensor(torch.rand(10, 2), ["u", "v"]), + LabelTensor(torch.rand(10, 3), ["x", "y", "z"]), + ), + ], +) +def test_build_knn_graph_edge_attr(x, pos): + graph = KNNGraph(x=x, pos=pos, neighbours=2, edge_attr=True) + assert hasattr(graph, "x") + assert hasattr(graph, "pos") + assert hasattr(graph, "edge_index") + assert torch.isclose(graph.x, x).all() + if isinstance(x, LabelTensor): + assert isinstance(graph.x, LabelTensor) + assert graph.x.labels == x.labels + else: + assert isinstance(graph.pos, torch.Tensor) + assert torch.isclose(graph.pos, pos).all() + if isinstance(pos, LabelTensor): + assert isinstance(graph.pos, LabelTensor) + assert graph.pos.labels == pos.labels + else: + assert isinstance(graph.pos, torch.Tensor) + assert isinstance(graph.edge_attr, torch.Tensor) + assert graph.edge_attr.shape[-1] == 3 + assert graph.edge_attr.shape[0] == graph.edge_index.shape[1] + + +@pytest.mark.parametrize( + "x, pos", + [ + (torch.rand(10, 2), torch.rand(10, 3)), + ( + LabelTensor(torch.rand(10, 2), ["u", "v"]), + LabelTensor(torch.rand(10, 3), ["x", "y", "z"]), + ), + ], +) +def test_build_knn_graph_custom_edge_attr(x, pos): + graph = KNNGraph( + x=x, + pos=pos, + neighbours=2, + edge_attr=True, + custom_edge_func=build_edge_attr, + ) + assert hasattr(graph, "x") + assert hasattr(graph, "pos") + assert hasattr(graph, "edge_index") + assert torch.isclose(graph.x, x).all() + if isinstance(x, LabelTensor): + assert isinstance(graph.x, LabelTensor) + assert graph.x.labels == x.labels + else: + assert isinstance(graph.pos, torch.Tensor) + assert torch.isclose(graph.pos, pos).all() + if isinstance(pos, LabelTensor): + assert isinstance(graph.pos, LabelTensor) + assert graph.pos.labels == pos.labels + else: + assert isinstance(graph.pos, torch.Tensor) + assert isinstance(graph.edge_attr, torch.Tensor) + assert graph.edge_attr.shape[-1] == 6 + assert graph.edge_attr.shape[0] == graph.edge_index.shape[1] + + +@pytest.mark.parametrize( + "x, pos, y", + [ + (torch.rand(10, 2), torch.rand(10, 3), torch.rand(10, 4)), + ( + LabelTensor(torch.rand(10, 2), ["u", "v"]), + LabelTensor(torch.rand(10, 3), ["x", "y", "z"]), + LabelTensor(torch.rand(10, 4), ["a", "b", "c", "d"]), + ), + ], +) +def test_additional_params(x, pos, y): + edge_index = torch.tensor( + [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [1, 2, 3, 4, 5, 6, 7, 8, 9, 0]], + dtype=torch.int64, + ) + graph = Graph(x=x, pos=pos, edge_index=edge_index, y=y) + assert hasattr(graph, "y") + assert torch.isclose(graph.y, y).all() + if isinstance(y, LabelTensor): + assert isinstance(graph.y, LabelTensor) + assert graph.y.labels == y.labels + else: + assert isinstance(graph.y, torch.Tensor) + assert torch.isclose(graph.y, y).all() + if isinstance(y, LabelTensor): + assert isinstance(graph.y, LabelTensor) + assert graph.y.labels == y.labels + else: + assert isinstance(graph.y, torch.Tensor) + + +@pytest.mark.parametrize( + "x, pos, y", + [ + (torch.rand(10, 2), torch.rand(10, 3), torch.rand(10, 4)), + ( + LabelTensor(torch.rand(10, 2), ["u", "v"]), + LabelTensor(torch.rand(10, 3), ["x", "y", "z"]), + LabelTensor(torch.rand(10, 4), ["a", "b", "c", "d"]), + ), + ], +) +def test_additional_params_radius_graph(x, pos, y): + graph = RadiusGraph(x=x, pos=pos, radius=0.5, y=y) + assert hasattr(graph, "y") + assert torch.isclose(graph.y, y).all() + if isinstance(y, LabelTensor): + assert isinstance(graph.y, LabelTensor) + assert graph.y.labels == y.labels + else: + assert isinstance(graph.y, torch.Tensor) + assert torch.isclose(graph.y, y).all() + if isinstance(y, LabelTensor): + assert isinstance(graph.y, LabelTensor) + assert graph.y.labels == y.labels + else: + assert isinstance(graph.y, torch.Tensor) + + +@pytest.mark.parametrize( + "x, pos, y", + [ + (torch.rand(10, 2), torch.rand(10, 3), torch.rand(10, 4)), + ( + LabelTensor(torch.rand(10, 2), ["u", "v"]), + LabelTensor(torch.rand(10, 3), ["x", "y", "z"]), + LabelTensor(torch.rand(10, 4), ["a", "b", "c", "d"]), + ), + ], +) +def test_additional_params_knn_graph(x, pos, y): + graph = KNNGraph(x=x, pos=pos, neighbours=3, y=y) + assert hasattr(graph, "y") + assert torch.isclose(graph.y, y).all() + if isinstance(y, LabelTensor): + assert isinstance(graph.y, LabelTensor) + assert graph.y.labels == y.labels + else: + assert isinstance(graph.y, torch.Tensor) + assert torch.isclose(graph.y, y).all() + if isinstance(y, LabelTensor): + assert isinstance(graph.y, LabelTensor) + assert graph.y.labels == y.labels + else: + assert isinstance(graph.y, torch.Tensor) diff --git a/tests/test_label_tensor/test_label_tensor.py b/tests/test_label_tensor/test_label_tensor.py new file mode 100644 index 000000000..556957b9d --- /dev/null +++ b/tests/test_label_tensor/test_label_tensor.py @@ -0,0 +1,280 @@ +import torch +import pytest + +from pina.label_tensor import LabelTensor + +data = torch.rand((20, 3)) +labels_column = {1: {"name": "space", "dof": ["x", "y", "z"]}} +labels_row = {0: {"name": "samples", "dof": range(20)}} +labels_list = ["x", "y", "z"] +labels_all = labels_column.copy() +labels_all.update(labels_row) + + +@pytest.mark.parametrize( + "labels", [labels_column, labels_row, labels_all, labels_list] +) +def test_constructor(labels): + print(LabelTensor(data, labels)) + + +def test_wrong_constructor(): + with pytest.raises(ValueError): + LabelTensor(data, ["a", "b"]) + + +@pytest.mark.parametrize("labels", [labels_column, labels_all]) +@pytest.mark.parametrize("labels_te", ["z", ["z"], {"space": ["z"]}]) +def test_extract_column(labels, labels_te): + tensor = LabelTensor(data, labels) + new = tensor.extract(labels_te) + assert new.ndim == tensor.ndim + assert new.shape[1] == 1 + assert new.shape[0] == 20 + assert torch.all(torch.isclose(data[:, 2].reshape(-1, 1), new)) + + +@pytest.mark.parametrize("labels", [labels_row, labels_all]) +@pytest.mark.parametrize("labels_te", [{"samples": [2]}]) +def test_extract_row(labels, labels_te): + tensor = LabelTensor(data, labels) + new = tensor.extract(labels_te) + assert new.ndim == tensor.ndim + assert new.shape[1] == 3 + assert new.shape[0] == 1 + assert torch.all(torch.isclose(data[2].reshape(1, -1), new)) + + +@pytest.mark.parametrize( + "labels_te", + [{"samples": [2], "space": ["z"]}, {"space": "z", "samples": 2}], +) +def test_extract_2D(labels_te): + labels = labels_all + tensor = LabelTensor(data, labels) + new = tensor.extract(labels_te) + assert new.ndim == tensor.ndim + assert new.shape[1] == 1 + assert new.shape[0] == 1 + assert torch.all(torch.isclose(data[2, 2].reshape(1, 1), new)) + + +def test_extract_3D(): + data = torch.rand(20, 3, 4) + labels = { + 1: {"name": "space", "dof": ["x", "y", "z"]}, + 2: {"name": "time", "dof": range(4)}, + } + labels_te = {"space": ["x", "z"], "time": range(1, 4)} + + tensor = LabelTensor(data, labels) + new = tensor.extract(labels_te) + tensor2 = LabelTensor(data, labels) + assert new.ndim == tensor.ndim + assert new.shape[0] == 20 + assert new.shape[1] == 2 + assert new.shape[2] == 3 + assert torch.all(torch.isclose(data[:, 0::2, 1:4].reshape(20, 2, 3), new)) + assert tensor2.ndim == tensor.ndim + assert tensor2.shape == tensor.shape + assert tensor.full_labels == tensor2.full_labels + assert new.shape != tensor.shape + + +def test_concatenation_3D(): + data_1 = torch.rand(20, 3, 4) + labels_1 = ["x", "y", "z", "w"] + lt1 = LabelTensor(data_1, labels_1) + data_2 = torch.rand(50, 3, 4) + labels_2 = ["x", "y", "z", "w"] + lt2 = LabelTensor(data_2, labels_2) + lt_cat = LabelTensor.cat([lt1, lt2]) + assert lt_cat.shape == (70, 3, 4) + assert lt_cat.full_labels[0]["dof"] == range(70) + assert lt_cat.full_labels[1]["dof"] == range(3) + assert lt_cat.full_labels[2]["dof"] == ["x", "y", "z", "w"] + + data_1 = torch.rand(20, 3, 4) + labels_1 = ["x", "y", "z", "w"] + lt1 = LabelTensor(data_1, labels_1) + data_2 = torch.rand(20, 2, 4) + labels_2 = ["x", "y", "z", "w"] + lt2 = LabelTensor(data_2, labels_2) + lt_cat = LabelTensor.cat([lt1, lt2], dim=1) + assert lt_cat.shape == (20, 5, 4) + assert lt_cat.full_labels[0]["dof"] == range(20) + assert lt_cat.full_labels[1]["dof"] == range(5) + assert lt_cat.full_labels[2]["dof"] == ["x", "y", "z", "w"] + + data_1 = torch.rand(20, 3, 2) + labels_1 = ["x", "y"] + lt1 = LabelTensor(data_1, labels_1) + data_2 = torch.rand(20, 3, 3) + labels_2 = ["z", "w", "a"] + lt2 = LabelTensor(data_2, labels_2) + lt_cat = LabelTensor.cat([lt1, lt2], dim=2) + assert lt_cat.shape == (20, 3, 5) + assert lt_cat.full_labels[2]["dof"] == ["x", "y", "z", "w", "a"] + assert lt_cat.full_labels[0]["dof"] == range(20) + assert lt_cat.full_labels[1]["dof"] == range(3) + + data_1 = torch.rand(20, 2, 4) + labels_1 = ["x", "y", "z", "w"] + lt1 = LabelTensor(data_1, labels_1) + data_2 = torch.rand(20, 3, 4) + labels_2 = ["x", "y", "z", "w"] + lt2 = LabelTensor(data_2, labels_2) + with pytest.raises(RuntimeError): + LabelTensor.cat([lt1, lt2], dim=2) + data_1 = torch.rand(20, 3, 2) + labels_1 = ["x", "y"] + lt1 = LabelTensor(data_1, labels_1) + data_2 = torch.rand(20, 3, 3) + labels_2 = ["z", "w", "a"] + lt2 = LabelTensor(data_2, labels_2) + lt_cat = LabelTensor.cat([lt1, lt2], dim=2) + assert lt_cat.shape == (20, 3, 5) + assert lt_cat.full_labels[2]["dof"] == ["x", "y", "z", "w", "a"] + assert lt_cat.full_labels[0]["dof"] == range(20) + assert lt_cat.full_labels[1]["dof"] == range(3) + + +def test_summation(): + lt1 = LabelTensor(torch.ones(20, 3), labels_all) + lt2 = LabelTensor(torch.ones(30, 3), ["x", "y", "z"]) + with pytest.raises(RuntimeError): + LabelTensor.summation([lt1, lt2]) + lt1 = LabelTensor(torch.ones(20, 3), labels_all) + lt2 = LabelTensor(torch.ones(20, 3), labels_all) + lt_sum = LabelTensor.summation([lt1, lt2]) + assert lt_sum.ndim == lt_sum.ndim + assert lt_sum.shape[0] == 20 + assert lt_sum.shape[1] == 3 + assert lt_sum.full_labels[0] == labels_all[0] + assert lt_sum.labels == ["x+x", "y+y", "z+z"] + assert torch.eq(lt_sum.tensor, torch.ones(20, 3) * 2).all() + lt1 = LabelTensor(torch.ones(20, 3), labels_all) + lt2 = LabelTensor(torch.ones(20, 3), labels_all) + lt3 = LabelTensor(torch.zeros(20, 3), labels_all) + lt_sum = LabelTensor.summation([lt1, lt2, lt3]) + assert lt_sum.ndim == lt_sum.ndim + assert lt_sum.shape[0] == 20 + assert lt_sum.shape[1] == 3 + assert lt_sum.full_labels[0] == labels_all[0] + assert lt_sum.labels == ["x+x+x", "y+y+y", "z+z+z"] + assert torch.eq(lt_sum.tensor, torch.ones(20, 3) * 2).all() + + +def test_append_3D(): + data_1 = torch.rand(20, 3, 2) + labels_1 = ["x", "y"] + lt1 = LabelTensor(data_1, labels_1) + data_2 = torch.rand(20, 3, 2) + labels_2 = ["z", "w"] + lt2 = LabelTensor(data_2, labels_2) + lt1 = lt1.append(lt2) + assert lt1.shape == (20, 3, 4) + assert lt1.full_labels[0]["dof"] == range(20) + assert lt1.full_labels[1]["dof"] == range(3) + assert lt1.full_labels[2]["dof"] == ["x", "y", "z", "w"] + + +def test_append_2D(): + data_1 = torch.rand(20, 2) + labels_1 = ["x", "y"] + lt1 = LabelTensor(data_1, labels_1) + data_2 = torch.rand(20, 2) + labels_2 = ["z", "w"] + lt2 = LabelTensor(data_2, labels_2) + lt1 = lt1.append(lt2, mode="cross") + assert lt1.shape == (400, 4) + assert lt1.full_labels[0]["dof"] == range(400) + assert lt1.full_labels[1]["dof"] == ["x", "y", "z", "w"] + + +def test_vstack_3D(): + data_1 = torch.rand(20, 3, 2) + labels_1 = { + 1: {"dof": ["a", "b", "c"], "name": "first"}, + 2: {"dof": ["x", "y"], "name": "second"}, + } + lt1 = LabelTensor(data_1, labels_1) + data_2 = torch.rand(20, 3, 2) + labels_1 = { + 1: {"dof": ["a", "b", "c"], "name": "first"}, + 2: {"dof": ["x", "y"], "name": "second"}, + } + lt2 = LabelTensor(data_2, labels_1) + lt_stacked = LabelTensor.vstack([lt1, lt2]) + assert lt_stacked.shape == (40, 3, 2) + assert lt_stacked.full_labels[0]["dof"] == range(40) + assert lt_stacked.full_labels[1]["dof"] == ["a", "b", "c"] + assert lt_stacked.full_labels[2]["dof"] == ["x", "y"] + assert lt_stacked.full_labels[1]["name"] == "first" + assert lt_stacked.full_labels[2]["name"] == "second" + + +def test_vstack_2D(): + data_1 = torch.rand(20, 2) + labels_1 = {1: {"dof": ["x", "y"], "name": "second"}} + lt1 = LabelTensor(data_1, labels_1) + data_2 = torch.rand(20, 2) + labels_1 = {1: {"dof": ["x", "y"], "name": "second"}} + lt2 = LabelTensor(data_2, labels_1) + lt_stacked = LabelTensor.vstack([lt1, lt2]) + assert lt_stacked.shape == (40, 2) + assert lt_stacked.full_labels[0]["dof"] == range(40) + assert lt_stacked.full_labels[1]["dof"] == ["x", "y"] + assert lt_stacked.full_labels[0]["name"] == 0 + assert lt_stacked.full_labels[1]["name"] == "second" + + +def test_sorting(): + data = torch.ones(20, 5) + data[:, 0] = data[:, 0] * 4 + data[:, 1] = data[:, 1] * 2 + data[:, 2] = data[:, 2] + data[:, 3] = data[:, 3] * 5 + data[:, 4] = data[:, 4] * 3 + labels = ["d", "b", "a", "e", "c"] + lt_data = LabelTensor(data, labels) + lt_sorted = LabelTensor.sort_labels(lt_data) + assert lt_sorted.shape == (20, 5) + assert lt_sorted.labels == ["a", "b", "c", "d", "e"] + assert torch.eq(lt_sorted.tensor[:, 0], torch.ones(20) * 1).all() + assert torch.eq(lt_sorted.tensor[:, 1], torch.ones(20) * 2).all() + assert torch.eq(lt_sorted.tensor[:, 2], torch.ones(20) * 3).all() + assert torch.eq(lt_sorted.tensor[:, 3], torch.ones(20) * 4).all() + assert torch.eq(lt_sorted.tensor[:, 4], torch.ones(20) * 5).all() + + data = torch.ones(20, 4, 5) + data[:, 0, :] = data[:, 0] * 4 + data[:, 1, :] = data[:, 1] * 2 + data[:, 2, :] = data[:, 2] + data[:, 3, :] = data[:, 3] * 3 + labels = {1: {"dof": ["d", "b", "a", "c"], "name": 1}} + lt_data = LabelTensor(data, labels) + lt_sorted = LabelTensor.sort_labels(lt_data, dim=1) + assert lt_sorted.shape == (20, 4, 5) + assert lt_sorted.full_labels[1]["dof"] == ["a", "b", "c", "d"] + assert torch.eq(lt_sorted.tensor[:, 0, :], torch.ones(20, 5) * 1).all() + assert torch.eq(lt_sorted.tensor[:, 1, :], torch.ones(20, 5) * 2).all() + assert torch.eq(lt_sorted.tensor[:, 2, :], torch.ones(20, 5) * 3).all() + assert torch.eq(lt_sorted.tensor[:, 3, :], torch.ones(20, 5) * 4).all() + + +@pytest.mark.parametrize( + "labels", + [ + [f"s{i}" for i in range(10)], + {0: {"dof": ["a", "b", "c"]}, 1: {"dof": [f"s{i}" for i in range(10)]}}, + ], +) +def test_cat_bool(labels): + out = torch.randn((3, 10)) + out = LabelTensor(out, labels) + selected = out[torch.tensor([True, True, False])] + assert selected.shape == (2, 10) + assert selected.stored_labels[1]["dof"] == [f"s{i}" for i in range(10)] + if isinstance(labels, dict): + assert selected.stored_labels[0]["dof"] == ["a", "b"] diff --git a/tests/test_label_tensor.py b/tests/test_label_tensor/test_label_tensor_01.py similarity index 76% rename from tests/test_label_tensor.py rename to tests/test_label_tensor/test_label_tensor_01.py index 05dace5e3..6806dd9e4 100644 --- a/tests/test_label_tensor.py +++ b/tests/test_label_tensor/test_label_tensor_01.py @@ -4,7 +4,7 @@ from pina import LabelTensor data = torch.rand((20, 3)) -labels = ['a', 'b', 'c'] +labels = ["a", "b", "c"] def test_constructor(): @@ -13,7 +13,7 @@ def test_constructor(): def test_wrong_constructor(): with pytest.raises(ValueError): - LabelTensor(data, ['a', 'b']) + LabelTensor(data, ["a", "b"]) def test_labels(): @@ -25,7 +25,7 @@ def test_labels(): def test_extract(): - label_to_extract = ['a', 'c'] + label_to_extract = ["a", "c"] tensor = LabelTensor(data, labels) new = tensor.extract(label_to_extract) assert new.labels == label_to_extract @@ -34,7 +34,7 @@ def test_extract(): def test_extract_onelabel(): - label_to_extract = ['a'] + label_to_extract = ["a"] tensor = LabelTensor(data, labels) new = tensor.extract(label_to_extract) assert new.ndim == 2 @@ -44,19 +44,19 @@ def test_extract_onelabel(): def test_wrong_extract(): - label_to_extract = ['a', 'cc'] + label_to_extract = ["a", "cc"] tensor = LabelTensor(data, labels) with pytest.raises(ValueError): tensor.extract(label_to_extract) def test_extract_order(): - label_to_extract = ['c', 'a'] + label_to_extract = ["c", "a"] tensor = LabelTensor(data, labels) new = tensor.extract(label_to_extract) expected = torch.cat( - (data[:, 2].reshape(-1, 1), data[:, 0].reshape(-1, 1)), - dim=1) + (data[:, 2].reshape(-1, 1), data[:, 0].reshape(-1, 1)), dim=1 + ) assert new.labels == label_to_extract assert new.shape[1] == len(label_to_extract) assert torch.all(torch.isclose(expected, new)) @@ -64,35 +64,34 @@ def test_extract_order(): def test_merge(): tensor = LabelTensor(data, labels) - tensor_a = tensor.extract('a') - tensor_b = tensor.extract('b') - tensor_c = tensor.extract('c') + tensor_a = tensor.extract("a") + tensor_b = tensor.extract("b") + tensor_c = tensor.extract("c") tensor_bc = tensor_b.append(tensor_c) - assert torch.allclose(tensor_bc, tensor.extract(['b', 'c'])) + assert torch.allclose(tensor_bc, tensor.extract(["b", "c"])) def test_merge2(): tensor = LabelTensor(data, labels) - tensor_b = tensor.extract('b') - tensor_c = tensor.extract('c') + tensor_b = tensor.extract("b") + tensor_c = tensor.extract("c") tensor_bc = tensor_b.append(tensor_c) - assert torch.allclose(tensor_bc, tensor.extract(['b', 'c'])) + assert torch.allclose(tensor_bc, tensor.extract(["b", "c"])) def test_getitem(): tensor = LabelTensor(data, labels) - tensor_view = tensor['a'] - - assert tensor_view.labels == ['a'] + tensor_view = tensor["a"] + assert tensor_view.labels == ["a"] assert torch.allclose(tensor_view.flatten(), data[:, 0]) - tensor_view = tensor['a', 'c'] - - assert tensor_view.labels == ['a', 'c'] + tensor_view = tensor["a", "c"] + assert tensor_view.labels == ["a", "c"] assert torch.allclose(tensor_view, data[:, 0::2]) + def test_getitem2(): tensor = LabelTensor(data, labels) tensor_view = tensor[:5] @@ -111,9 +110,10 @@ def test_slice(): assert torch.allclose(tensor_view, data[:5, :2]) tensor_view2 = tensor[3] + assert tensor_view2.labels == labels assert torch.allclose(tensor_view2, data[3]) tensor_view3 = tensor[:, 2] - assert tensor_view3.labels == labels[2] + assert tensor_view3.labels == [labels[2]] assert torch.allclose(tensor_view3, data[:, 2].reshape(-1, 1)) diff --git a/tests/test_layers/test_fourier.py b/tests/test_layers/test_fourier.py deleted file mode 100644 index f9c874bb4..000000000 --- a/tests/test_layers/test_fourier.py +++ /dev/null @@ -1,84 +0,0 @@ -from pina.model.layers import FourierBlock1D, FourierBlock2D, FourierBlock3D -import torch - -input_numb_fields = 3 -output_numb_fields = 4 -batch = 5 - - -def test_constructor_1d(): - FourierBlock1D(input_numb_fields=input_numb_fields, - output_numb_fields=output_numb_fields, - n_modes=5) - - -def test_forward_1d(): - sconv = FourierBlock1D(input_numb_fields=input_numb_fields, - output_numb_fields=output_numb_fields, - n_modes=4) - x = torch.rand(batch, input_numb_fields, 10) - sconv(x) - - -def test_backward_1d(): - sconv = FourierBlock1D(input_numb_fields=input_numb_fields, - output_numb_fields=output_numb_fields, - n_modes=4) - x = torch.rand(batch, input_numb_fields, 10) - x.requires_grad = True - sconv(x) - l = torch.mean(sconv(x)) - l.backward() - assert x._grad.shape == torch.Size([5, 3, 10]) - - -def test_constructor_2d(): - FourierBlock2D(input_numb_fields=input_numb_fields, - output_numb_fields=output_numb_fields, - n_modes=[5, 4]) - - -def test_forward_2d(): - sconv = FourierBlock2D(input_numb_fields=input_numb_fields, - output_numb_fields=output_numb_fields, - n_modes=[5, 4]) - x = torch.rand(batch, input_numb_fields, 10, 10) - sconv(x) - - -def test_backward_2d(): - sconv = FourierBlock2D(input_numb_fields=input_numb_fields, - output_numb_fields=output_numb_fields, - n_modes=[5, 4]) - x = torch.rand(batch, input_numb_fields, 10, 10) - x.requires_grad = True - sconv(x) - l = torch.mean(sconv(x)) - l.backward() - assert x._grad.shape == torch.Size([5, 3, 10, 10]) - - -def test_constructor_3d(): - FourierBlock3D(input_numb_fields=input_numb_fields, - output_numb_fields=output_numb_fields, - n_modes=[5, 4, 4]) - - -def test_forward_3d(): - sconv = FourierBlock3D(input_numb_fields=input_numb_fields, - output_numb_fields=output_numb_fields, - n_modes=[5, 4, 4]) - x = torch.rand(batch, input_numb_fields, 10, 10, 10) - sconv(x) - - -def test_backward_3d(): - sconv = FourierBlock3D(input_numb_fields=input_numb_fields, - output_numb_fields=output_numb_fields, - n_modes=[5, 4, 4]) - x = torch.rand(batch, input_numb_fields, 10, 10, 10) - x.requires_grad = True - sconv(x) - l = torch.mean(sconv(x)) - l.backward() - assert x._grad.shape == torch.Size([5, 3, 10, 10, 10]) diff --git a/tests/test_layers/test_lnolayer.py b/tests/test_layers/test_lnolayer.py deleted file mode 100644 index 28db849e8..000000000 --- a/tests/test_layers/test_lnolayer.py +++ /dev/null @@ -1,58 +0,0 @@ -import torch -import pytest - -from pina.model.layers import LowRankBlock -from pina import LabelTensor - - -input_dimensions=2 -embedding_dimenion=1 -rank=4 -inner_size=20 -n_layers=2 -func=torch.nn.Tanh -bias=True - -def test_constructor(): - LowRankBlock(input_dimensions=input_dimensions, - embedding_dimenion=embedding_dimenion, - rank=rank, - inner_size=inner_size, - n_layers=n_layers, - func=func, - bias=bias) - -def test_constructor_wrong(): - with pytest.raises(ValueError): - LowRankBlock(input_dimensions=input_dimensions, - embedding_dimenion=embedding_dimenion, - rank=0.5, - inner_size=inner_size, - n_layers=n_layers, - func=func, - bias=bias) - -def test_forward(): - block = LowRankBlock(input_dimensions=input_dimensions, - embedding_dimenion=embedding_dimenion, - rank=rank, - inner_size=inner_size, - n_layers=n_layers, - func=func, - bias=bias) - data = LabelTensor(torch.rand(10, 30, 3), labels=['x', 'y', 'u']) - block(data.extract('u'), data.extract(['x', 'y'])) - -def test_backward(): - block = LowRankBlock(input_dimensions=input_dimensions, - embedding_dimenion=embedding_dimenion, - rank=rank, - inner_size=inner_size, - n_layers=n_layers, - func=func, - bias=bias) - data = LabelTensor(torch.rand(10, 30, 3), labels=['x', 'y', 'u']) - data.requires_grad_(True) - out = block(data.extract('u'), data.extract(['x', 'y'])) - loss = out.mean() - loss.backward() \ No newline at end of file diff --git a/tests/test_layers/test_spectral_conv.py b/tests/test_layers/test_spectral_conv.py deleted file mode 100644 index 3ff1ee3bb..000000000 --- a/tests/test_layers/test_spectral_conv.py +++ /dev/null @@ -1,84 +0,0 @@ -from pina.model.layers import SpectralConvBlock1D, SpectralConvBlock2D, SpectralConvBlock3D -import torch - -input_numb_fields = 3 -output_numb_fields = 4 -batch = 5 - - -def test_constructor_1d(): - SpectralConvBlock1D(input_numb_fields=input_numb_fields, - output_numb_fields=output_numb_fields, - n_modes=5) - - -def test_forward_1d(): - sconv = SpectralConvBlock1D(input_numb_fields=input_numb_fields, - output_numb_fields=output_numb_fields, - n_modes=4) - x = torch.rand(batch, input_numb_fields, 10) - sconv(x) - - -def test_backward_1d(): - sconv = SpectralConvBlock1D(input_numb_fields=input_numb_fields, - output_numb_fields=output_numb_fields, - n_modes=4) - x = torch.rand(batch, input_numb_fields, 10) - x.requires_grad = True - sconv(x) - l=torch.mean(sconv(x)) - l.backward() - assert x._grad.shape == torch.Size([5,3,10]) - - -def test_constructor_2d(): - SpectralConvBlock2D(input_numb_fields=input_numb_fields, - output_numb_fields=output_numb_fields, - n_modes=[5, 4]) - - -def test_forward_2d(): - sconv = SpectralConvBlock2D(input_numb_fields=input_numb_fields, - output_numb_fields=output_numb_fields, - n_modes=[5, 4]) - x = torch.rand(batch, input_numb_fields, 10, 10) - sconv(x) - - -def test_backward_2d(): - sconv = SpectralConvBlock2D(input_numb_fields=input_numb_fields, - output_numb_fields=output_numb_fields, - n_modes=[5, 4]) - x = torch.rand(batch, input_numb_fields, 10, 10) - x.requires_grad = True - sconv(x) - l=torch.mean(sconv(x)) - l.backward() - assert x._grad.shape == torch.Size([5,3,10,10]) - - -def test_constructor_3d(): - SpectralConvBlock3D(input_numb_fields=input_numb_fields, - output_numb_fields=output_numb_fields, - n_modes=[5, 4, 4]) - - -def test_forward_3d(): - sconv = SpectralConvBlock3D(input_numb_fields=input_numb_fields, - output_numb_fields=output_numb_fields, - n_modes=[5, 4, 4]) - x = torch.rand(batch, input_numb_fields, 10, 10, 10) - sconv(x) - - -def test_backward_3d(): - sconv = SpectralConvBlock3D(input_numb_fields=input_numb_fields, - output_numb_fields=output_numb_fields, - n_modes=[5, 4, 4]) - x = torch.rand(batch, input_numb_fields, 10, 10, 10) - x.requires_grad = True - sconv(x) - l=torch.mean(sconv(x)) - l.backward() - assert x._grad.shape == torch.Size([5,3,10,10,10]) diff --git a/tests/test_loss/test_lploss.py b/tests/test_loss/test_lp_loss.py similarity index 67% rename from tests/test_loss/test_lploss.py rename to tests/test_loss/test_lp_loss.py index 3743970df..8f1f48d58 100644 --- a/tests/test_loss/test_lploss.py +++ b/tests/test_loss/test_lp_loss.py @@ -1,11 +1,10 @@ import torch -import pytest -from pina.loss import * +from pina.loss import LpLoss -input = torch.tensor([[3.], [1.], [-8.]]) -target = torch.tensor([[6.], [4.], [2.]]) -available_reductions = ['str', 'mean', 'none'] +input = torch.tensor([[3.0], [1.0], [-8.0]]) +target = torch.tensor([[6.0], [4.0], [2.0]]) +available_reductions = ["str", "mean", "none"] def test_LpLoss_constructor(): @@ -13,17 +12,17 @@ def test_LpLoss_constructor(): for reduction in available_reductions: LpLoss(reduction=reduction) # test p - for p in [float('inf'), -float('inf'), 1, 10, -8]: + for p in [float("inf"), -float("inf"), 1, 10, -8]: LpLoss(p=p) def test_LpLoss_forward(): # l2 loss - loss = LpLoss(p=2, reduction='mean') + loss = LpLoss(p=2, reduction="mean") l2_loss = torch.mean(torch.sqrt((input - target).pow(2))) assert loss(input, target) == l2_loss # l1 loss - loss = LpLoss(p=1, reduction='sum') + loss = LpLoss(p=1, reduction="sum") l1_loss = torch.sum(torch.abs(input - target)) assert loss(input, target) == l1_loss @@ -33,16 +32,16 @@ def test_LpRelativeLoss_constructor(): for reduction in available_reductions: LpLoss(reduction=reduction, relative=True) # test p - for p in [float('inf'), -float('inf'), 1, 10, -8]: + for p in [float("inf"), -float("inf"), 1, 10, -8]: LpLoss(p=p, relative=True) def test_LpRelativeLoss_forward(): # l2 relative loss - loss = LpLoss(p=2, reduction='mean', relative=True) + loss = LpLoss(p=2, reduction="mean", relative=True) l2_loss = torch.sqrt((input - target).pow(2)) / torch.sqrt(input.pow(2)) assert loss(input, target) == torch.mean(l2_loss) # l1 relative loss - loss = LpLoss(p=1, reduction='sum', relative=True) + loss = LpLoss(p=1, reduction="sum", relative=True) l1_loss = torch.abs(input - target) / torch.abs(input) assert loss(input, target) == torch.sum(l1_loss) diff --git a/tests/test_loss/test_powerloss.py b/tests/test_loss/test_power_loss.py similarity index 68% rename from tests/test_loss/test_powerloss.py rename to tests/test_loss/test_power_loss.py index 7ea26755d..4ea90282b 100644 --- a/tests/test_loss/test_powerloss.py +++ b/tests/test_loss/test_power_loss.py @@ -3,9 +3,9 @@ from pina.loss import PowerLoss -input = torch.tensor([[3.], [1.], [-8.]]) -target = torch.tensor([[6.], [4.], [2.]]) -available_reductions = ['str', 'mean', 'none'] +input = torch.tensor([[3.0], [1.0], [-8.0]]) +target = torch.tensor([[6.0], [4.0], [2.0]]) +available_reductions = ["str", "mean", "none"] def test_PowerLoss_constructor(): @@ -13,17 +13,17 @@ def test_PowerLoss_constructor(): for reduction in available_reductions: PowerLoss(reduction=reduction) # test p - for p in [float('inf'), -float('inf'), 1, 10, -8]: + for p in [float("inf"), -float("inf"), 1, 10, -8]: PowerLoss(p=p) def test_PowerLoss_forward(): # l2 loss - loss = PowerLoss(p=2, reduction='mean') + loss = PowerLoss(p=2, reduction="mean") l2_loss = torch.mean((input - target).pow(2)) assert loss(input, target) == l2_loss # l1 loss - loss = PowerLoss(p=1, reduction='sum') + loss = PowerLoss(p=1, reduction="sum") l1_loss = torch.sum(torch.abs(input - target)) assert loss(input, target) == l1_loss @@ -33,16 +33,16 @@ def test_LpRelativeLoss_constructor(): for reduction in available_reductions: PowerLoss(reduction=reduction, relative=True) # test p - for p in [float('inf'), -float('inf'), 1, 10, -8]: + for p in [float("inf"), -float("inf"), 1, 10, -8]: PowerLoss(p=p, relative=True) def test_LpRelativeLoss_forward(): # l2 relative loss - loss = PowerLoss(p=2, reduction='mean', relative=True) + loss = PowerLoss(p=2, reduction="mean", relative=True) l2_loss = (input - target).pow(2) / input.pow(2) assert loss(input, target) == torch.mean(l2_loss) # l1 relative loss - loss = PowerLoss(p=1, reduction='sum', relative=True) + loss = PowerLoss(p=1, reduction="sum", relative=True) l1_loss = torch.abs(input - target) / torch.abs(input) assert loss(input, target) == torch.sum(l1_loss) diff --git a/tests/test_model/test_average_neural_operator.py b/tests/test_model/test_average_neural_operator.py new file mode 100644 index 000000000..ded81c43d --- /dev/null +++ b/tests/test_model/test_average_neural_operator.py @@ -0,0 +1,173 @@ +import torch +from pina.model import AveragingNeuralOperator +from pina import LabelTensor +import pytest + + +batch_size = 15 +n_layers = 4 +embedding_dim = 24 +func = torch.nn.Tanh +coordinates_indices = ["p"] +field_indices = ["v"] + + +def test_constructor(): + # working constructor + lifting_net = torch.nn.Linear( + len(coordinates_indices) + len(field_indices), embedding_dim + ) + projecting_net = torch.nn.Linear( + embedding_dim + len(field_indices), len(field_indices) + ) + AveragingNeuralOperator( + lifting_net=lifting_net, + projecting_net=projecting_net, + coordinates_indices=coordinates_indices, + field_indices=field_indices, + n_layers=n_layers, + func=func, + ) + + # not working constructor + with pytest.raises(ValueError): + AveragingNeuralOperator( + lifting_net=lifting_net, + projecting_net=projecting_net, + coordinates_indices=coordinates_indices, + field_indices=field_indices, + n_layers=3.2, # wrong + func=func, + ) + + AveragingNeuralOperator( + lifting_net=lifting_net, + projecting_net=projecting_net, + coordinates_indices=coordinates_indices, + field_indices=field_indices, + n_layers=n_layers, + func=1, + ) # wrong + + AveragingNeuralOperator( + lifting_net=[0], # wrong + projecting_net=projecting_net, + coordinates_indices=coordinates_indices, + field_indices=field_indices, + n_layers=n_layers, + func=func, + ) + + AveragingNeuralOperator( + lifting_net=lifting_net, + projecting_net=[0], # wront + coordinates_indices=coordinates_indices, + field_indices=field_indices, + n_layers=n_layers, + func=func, + ) + + AveragingNeuralOperator( + lifting_net=lifting_net, + projecting_net=projecting_net, + coordinates_indices=[0], # wrong + field_indices=field_indices, + n_layers=n_layers, + func=func, + ) + + AveragingNeuralOperator( + lifting_net=lifting_net, + projecting_net=projecting_net, + coordinates_indices=coordinates_indices, + field_indices=[0], # wrong + n_layers=n_layers, + func=func, + ) + + lifting_net = torch.nn.Linear(len(coordinates_indices), embedding_dim) + AveragingNeuralOperator( + lifting_net=lifting_net, + projecting_net=projecting_net, + coordinates_indices=coordinates_indices, + field_indices=field_indices, + n_layers=n_layers, + func=func, + ) + + lifting_net = torch.nn.Linear( + len(coordinates_indices) + len(field_indices), embedding_dim + ) + projecting_net = torch.nn.Linear(embedding_dim, len(field_indices)) + AveragingNeuralOperator( + lifting_net=lifting_net, + projecting_net=projecting_net, + coordinates_indices=coordinates_indices, + field_indices=field_indices, + n_layers=n_layers, + func=func, + ) + + +def test_forward(): + lifting_net = torch.nn.Linear( + len(coordinates_indices) + len(field_indices), embedding_dim + ) + projecting_net = torch.nn.Linear( + embedding_dim + len(field_indices), len(field_indices) + ) + avno = AveragingNeuralOperator( + lifting_net=lifting_net, + projecting_net=projecting_net, + coordinates_indices=coordinates_indices, + field_indices=field_indices, + n_layers=n_layers, + func=func, + ) + + input_ = LabelTensor( + torch.rand( + batch_size, 100, len(coordinates_indices) + len(field_indices) + ), + ["p", "v"], + ) + + out = avno(input_) + assert out.shape == torch.Size( + [batch_size, input_.shape[1], len(field_indices)] + ) + + +def test_backward(): + lifting_net = torch.nn.Linear( + len(coordinates_indices) + len(field_indices), embedding_dim + ) + projecting_net = torch.nn.Linear( + embedding_dim + len(field_indices), len(field_indices) + ) + avno = AveragingNeuralOperator( + lifting_net=lifting_net, + projecting_net=projecting_net, + coordinates_indices=coordinates_indices, + field_indices=field_indices, + n_layers=n_layers, + func=func, + ) + input_ = LabelTensor( + torch.rand( + batch_size, 100, len(coordinates_indices) + len(field_indices) + ), + ["p", "v"], + ) + input_ = input_.requires_grad_() + out = avno(input_) + tmp = torch.linalg.norm(out) + tmp.backward() + grad = input_.grad + assert grad.shape == torch.Size( + [ + batch_size, + input_.shape[1], + len(coordinates_indices) + len(field_indices), + ] + ) diff --git a/tests/test_model/test_avno.py b/tests/test_model/test_avno.py deleted file mode 100644 index 1988bde2f..000000000 --- a/tests/test_model/test_avno.py +++ /dev/null @@ -1,146 +0,0 @@ -import torch -from pina.model import AveragingNeuralOperator -from pina import LabelTensor -import pytest - - -batch_size = 15 -n_layers = 4 -embedding_dim = 24 -func = torch.nn.Tanh -coordinates_indices = ['p'] -field_indices = ['v'] - - -def test_constructor(): - # working constructor - lifting_net = torch.nn.Linear(len(coordinates_indices) + len(field_indices), - embedding_dim) - projecting_net = torch.nn.Linear(embedding_dim + len(field_indices), - len(field_indices)) - AveragingNeuralOperator( - lifting_net=lifting_net, - projecting_net=projecting_net, - coordinates_indices=coordinates_indices, - field_indices=field_indices, - n_layers=n_layers, - func=func) - - # not working constructor - with pytest.raises(ValueError): - AveragingNeuralOperator( - lifting_net=lifting_net, - projecting_net=projecting_net, - coordinates_indices=coordinates_indices, - field_indices=field_indices, - n_layers=3.2, # wrong - func=func) - - AveragingNeuralOperator( - lifting_net=lifting_net, - projecting_net=projecting_net, - coordinates_indices=coordinates_indices, - field_indices=field_indices, - n_layers=n_layers, - func=1) # wrong - - AveragingNeuralOperator( - lifting_net=[0], # wrong - projecting_net=projecting_net, - coordinates_indices=coordinates_indices, - field_indices=field_indices, - n_layers=n_layers, - func=func) - - AveragingNeuralOperator( - lifting_net=lifting_net, - projecting_net=[0], # wront - coordinates_indices=coordinates_indices, - field_indices=field_indices, - n_layers=n_layers, - func=func) - - AveragingNeuralOperator( - lifting_net=lifting_net, - projecting_net=projecting_net, - coordinates_indices=[0], #wrong - field_indices=field_indices, - n_layers=n_layers, - func=func) - - AveragingNeuralOperator( - lifting_net=lifting_net, - projecting_net=projecting_net, - coordinates_indices=coordinates_indices, - field_indices=[0], #wrong - n_layers=n_layers, - func=func) - - lifting_net = torch.nn.Linear(len(coordinates_indices), - embedding_dim) - AveragingNeuralOperator( - lifting_net=lifting_net, - projecting_net=projecting_net, - coordinates_indices=coordinates_indices, - field_indices=field_indices, - n_layers=n_layers, - func=func) - - lifting_net = torch.nn.Linear(len(coordinates_indices) + len(field_indices), - embedding_dim) - projecting_net = torch.nn.Linear(embedding_dim, - len(field_indices)) - AveragingNeuralOperator( - lifting_net=lifting_net, - projecting_net=projecting_net, - coordinates_indices=coordinates_indices, - field_indices=field_indices, - n_layers=n_layers, - func=func) - - -def test_forward(): - lifting_net = torch.nn.Linear(len(coordinates_indices) + len(field_indices), - embedding_dim) - projecting_net = torch.nn.Linear(embedding_dim + len(field_indices), - len(field_indices)) - avno=AveragingNeuralOperator( - lifting_net=lifting_net, - projecting_net=projecting_net, - coordinates_indices=coordinates_indices, - field_indices=field_indices, - n_layers=n_layers, - func=func) - - input_ = LabelTensor( - torch.rand(batch_size, 100, - len(coordinates_indices) + len(field_indices)), ['p', 'v']) - - out = avno(input_) - assert out.shape == torch.Size( - [batch_size, input_.shape[1], len(field_indices)]) - - -def test_backward(): - lifting_net = torch.nn.Linear(len(coordinates_indices) + len(field_indices), - embedding_dim) - projecting_net = torch.nn.Linear(embedding_dim + len(field_indices), - len(field_indices)) - avno=AveragingNeuralOperator( - lifting_net=lifting_net, - projecting_net=projecting_net, - coordinates_indices=coordinates_indices, - field_indices=field_indices, - n_layers=n_layers, - func=func) - input_ = LabelTensor( - torch.rand(batch_size, 100, - len(coordinates_indices) + len(field_indices)), ['p', 'v']) - input_ = input_.requires_grad_() - out = avno(input_) - tmp = torch.linalg.norm(out) - tmp.backward() - grad = input_.grad - assert grad.shape == torch.Size( - [batch_size, input_.shape[1], - len(coordinates_indices) + len(field_indices)]) \ No newline at end of file diff --git a/tests/test_model/test_base_no.py b/tests/test_model/test_base_no.py deleted file mode 100644 index 4a14fd1e4..000000000 --- a/tests/test_model/test_base_no.py +++ /dev/null @@ -1,40 +0,0 @@ -import torch -from pina.model import KernelNeuralOperator, FeedForward - -input_dim = 2 -output_dim = 4 -embedding_dim = 24 -batch_size = 10 -numb = 256 -data = torch.rand(size=(batch_size, numb, input_dim), requires_grad=True) -output_shape = torch.Size([batch_size, numb, output_dim]) - - -lifting_operator = FeedForward(input_dimensions=input_dim, output_dimensions=embedding_dim) -projection_operator = FeedForward(input_dimensions=embedding_dim, output_dimensions=output_dim) -integral_kernels = torch.nn.Sequential(FeedForward(input_dimensions=embedding_dim, - output_dimensions=embedding_dim), - FeedForward(input_dimensions=embedding_dim, - output_dimensions=embedding_dim),) - -def test_constructor(): - KernelNeuralOperator(lifting_operator=lifting_operator, - integral_kernels=integral_kernels, - projection_operator=projection_operator) - -def test_forward(): - operator = KernelNeuralOperator(lifting_operator=lifting_operator, - integral_kernels=integral_kernels, - projection_operator=projection_operator) - out = operator(data) - assert out.shape == output_shape - -def test_backward(): - operator = KernelNeuralOperator(lifting_operator=lifting_operator, - integral_kernels=integral_kernels, - projection_operator=projection_operator) - out = operator(data) - loss = torch.nn.functional.mse_loss(out, torch.zeros_like(out)) - loss.backward() - grad = data.grad - assert grad.shape == data.shape diff --git a/tests/test_model/test_deeponet.py b/tests/test_model/test_deeponet.py index 9670424c7..8917811c5 100644 --- a/tests/test_model/test_deeponet.py +++ b/tests/test_model/test_deeponet.py @@ -7,42 +7,50 @@ from pina.model import FeedForward data = torch.rand((20, 3)) -input_vars = ['a', 'b', 'c'] +input_vars = ["a", "b", "c"] input_ = LabelTensor(data, input_vars) symbol_funcs_red = DeepONet._symbol_functions(dim=-1) output_dims = [1, 5, 10, 20] + def test_constructor(): branch_net = FeedForward(input_dimensions=1, output_dimensions=10) trunk_net = FeedForward(input_dimensions=2, output_dimensions=10) - DeepONet(branch_net=branch_net, - trunk_net=trunk_net, - input_indeces_branch_net=['a'], - input_indeces_trunk_net=['b', 'c'], - reduction='+', - aggregator='*') + DeepONet( + branch_net=branch_net, + trunk_net=trunk_net, + input_indeces_branch_net=["a"], + input_indeces_trunk_net=["b", "c"], + reduction="+", + aggregator="*", + ) def test_constructor_fails_when_invalid_inner_layer_size(): branch_net = FeedForward(input_dimensions=1, output_dimensions=10) trunk_net = FeedForward(input_dimensions=2, output_dimensions=8) with pytest.raises(ValueError): - DeepONet(branch_net=branch_net, - trunk_net=trunk_net, - input_indeces_branch_net=['a'], - input_indeces_trunk_net=['b', 'c'], - reduction='+', - aggregator='*') + DeepONet( + branch_net=branch_net, + trunk_net=trunk_net, + input_indeces_branch_net=["a"], + input_indeces_trunk_net=["b", "c"], + reduction="+", + aggregator="*", + ) + def test_forward_extract_str(): branch_net = FeedForward(input_dimensions=1, output_dimensions=10) trunk_net = FeedForward(input_dimensions=2, output_dimensions=10) - model = DeepONet(branch_net=branch_net, - trunk_net=trunk_net, - input_indeces_branch_net=['a'], - input_indeces_trunk_net=['b', 'c'], - reduction='+', - aggregator='*') + model = DeepONet( + branch_net=branch_net, + trunk_net=trunk_net, + input_indeces_branch_net=["a"], + input_indeces_trunk_net=["b", "c"], + reduction="+", + aggregator="*", + ) model(input_) assert model(input_).shape[-1] == 1 @@ -50,82 +58,99 @@ def test_forward_extract_str(): def test_forward_extract_int(): branch_net = FeedForward(input_dimensions=1, output_dimensions=10) trunk_net = FeedForward(input_dimensions=2, output_dimensions=10) - model = DeepONet(branch_net=branch_net, - trunk_net=trunk_net, - input_indeces_branch_net=[0], - input_indeces_trunk_net=[1, 2], - reduction='+', - aggregator='*') + model = DeepONet( + branch_net=branch_net, + trunk_net=trunk_net, + input_indeces_branch_net=[0], + input_indeces_trunk_net=[1, 2], + reduction="+", + aggregator="*", + ) model(data) + def test_backward_extract_int(): data = torch.rand((20, 3)) branch_net = FeedForward(input_dimensions=1, output_dimensions=10) trunk_net = FeedForward(input_dimensions=2, output_dimensions=10) - model = DeepONet(branch_net=branch_net, - trunk_net=trunk_net, - input_indeces_branch_net=[0], - input_indeces_trunk_net=[1, 2], - reduction='+', - aggregator='*') + model = DeepONet( + branch_net=branch_net, + trunk_net=trunk_net, + input_indeces_branch_net=[0], + input_indeces_trunk_net=[1, 2], + reduction="+", + aggregator="*", + ) data.requires_grad = True model(data) - l=torch.mean(model(data)) + l = torch.mean(model(data)) l.backward() - assert data._grad.shape == torch.Size([20,3]) + assert data._grad.shape == torch.Size([20, 3]) + def test_forward_extract_str_wrong(): branch_net = FeedForward(input_dimensions=1, output_dimensions=10) trunk_net = FeedForward(input_dimensions=2, output_dimensions=10) - model = DeepONet(branch_net=branch_net, - trunk_net=trunk_net, - input_indeces_branch_net=['a'], - input_indeces_trunk_net=['b', 'c'], - reduction='+', - aggregator='*') + model = DeepONet( + branch_net=branch_net, + trunk_net=trunk_net, + input_indeces_branch_net=["a"], + input_indeces_trunk_net=["b", "c"], + reduction="+", + aggregator="*", + ) with pytest.raises(RuntimeError): model(data) + def test_backward_extract_str_wrong(): data = torch.rand((20, 3)) branch_net = FeedForward(input_dimensions=1, output_dimensions=10) trunk_net = FeedForward(input_dimensions=2, output_dimensions=10) - model = DeepONet(branch_net=branch_net, - trunk_net=trunk_net, - input_indeces_branch_net=['a'], - input_indeces_trunk_net=['b', 'c'], - reduction='+', - aggregator='*') + model = DeepONet( + branch_net=branch_net, + trunk_net=trunk_net, + input_indeces_branch_net=["a"], + input_indeces_trunk_net=["b", "c"], + reduction="+", + aggregator="*", + ) data.requires_grad = True with pytest.raises(RuntimeError): model(data) - l=torch.mean(model(data)) + l = torch.mean(model(data)) l.backward() - assert data._grad.shape == torch.Size([20,3]) + assert data._grad.shape == torch.Size([20, 3]) + -@pytest.mark.parametrize('red', symbol_funcs_red) +@pytest.mark.parametrize("red", symbol_funcs_red) def test_forward_symbol_funcs(red): branch_net = FeedForward(input_dimensions=1, output_dimensions=10) trunk_net = FeedForward(input_dimensions=2, output_dimensions=10) - model = DeepONet(branch_net=branch_net, - trunk_net=trunk_net, - input_indeces_branch_net=['a'], - input_indeces_trunk_net=['b', 'c'], - reduction=red, - aggregator='*') + model = DeepONet( + branch_net=branch_net, + trunk_net=trunk_net, + input_indeces_branch_net=["a"], + input_indeces_trunk_net=["b", "c"], + reduction=red, + aggregator="*", + ) model(input_) assert model(input_).shape[-1] == 1 -@pytest.mark.parametrize('out_dim', output_dims) + +@pytest.mark.parametrize("out_dim", output_dims) def test_forward_callable_reduction(out_dim): branch_net = FeedForward(input_dimensions=1, output_dimensions=10) trunk_net = FeedForward(input_dimensions=2, output_dimensions=10) reduction_layer = Linear(10, out_dim) - model = DeepONet(branch_net=branch_net, - trunk_net=trunk_net, - input_indeces_branch_net=['a'], - input_indeces_trunk_net=['b', 'c'], - reduction=reduction_layer, - aggregator='*') + model = DeepONet( + branch_net=branch_net, + trunk_net=trunk_net, + input_indeces_branch_net=["a"], + input_indeces_trunk_net=["b", "c"], + reduction=reduction_layer, + aggregator="*", + ) model(input_) assert model(input_).shape[-1] == out_dim diff --git a/tests/test_model/test_fnn.py b/tests/test_model/test_feed_forward.py similarity index 56% rename from tests/test_model/test_fnn.py rename to tests/test_model/test_feed_forward.py index d02dcb820..3664130b8 100644 --- a/tests/test_model/test_fnn.py +++ b/tests/test_model/test_feed_forward.py @@ -12,22 +12,25 @@ def test_constructor(): FeedForward(input_vars, output_vars) FeedForward(input_vars, output_vars, inner_size=10, n_layers=20) FeedForward(input_vars, output_vars, layers=[10, 20, 5, 2]) - FeedForward(input_vars, - output_vars, - layers=[10, 20, 5, 2], - func=torch.nn.ReLU) - FeedForward(input_vars, - output_vars, - layers=[10, 20, 5, 2], - func=[torch.nn.ReLU, torch.nn.ReLU, None, torch.nn.Tanh]) + FeedForward( + input_vars, output_vars, layers=[10, 20, 5, 2], func=torch.nn.ReLU + ) + FeedForward( + input_vars, + output_vars, + layers=[10, 20, 5, 2], + func=[torch.nn.ReLU, torch.nn.ReLU, None, torch.nn.Tanh], + ) def test_constructor_wrong(): with pytest.raises(RuntimeError): - FeedForward(input_vars, - output_vars, - layers=[10, 20, 5, 2], - func=[torch.nn.ReLU, torch.nn.ReLU]) + FeedForward( + input_vars, + output_vars, + layers=[10, 20, 5, 2], + func=[torch.nn.ReLU, torch.nn.ReLU], + ) def test_forward(): @@ -36,11 +39,12 @@ def test_forward(): output_ = fnn(data) assert output_.shape == (data.shape[0], dim_out) + def test_backward(): dim_in, dim_out = 3, 2 fnn = FeedForward(dim_in, dim_out) data.requires_grad = True output_ = fnn(data) - l=torch.mean(output_) + l = torch.mean(output_) l.backward() - assert data._grad.shape == torch.Size([20,3]) + assert data._grad.shape == torch.Size([20, 3]) diff --git a/tests/test_model/test_fno.py b/tests/test_model/test_fourier_neural_operator.py similarity index 55% rename from tests/test_model/test_fno.py rename to tests/test_model/test_fourier_neural_operator.py index 3c8094bd3..f9082d24c 100644 --- a/tests/test_model/test_fno.py +++ b/tests/test_model/test_fourier_neural_operator.py @@ -2,9 +2,9 @@ from pina.model import FNO output_channels = 5 -batch_size = 15 -resolution = [30, 40, 50] -lifting_dim = 128 +batch_size = 4 +resolution = [4, 6, 8] +lifting_dim = 24 def test_constructor(): @@ -13,36 +13,44 @@ def test_constructor(): projecting_net = torch.nn.Linear(60, output_channels) # simple constructor - FNO(lifting_net=lifting_net, + FNO( + lifting_net=lifting_net, projecting_net=projecting_net, n_modes=5, dimensions=3, inner_size=60, - n_layers=5) + n_layers=5, + ) # simple constructor with n_modes list - FNO(lifting_net=lifting_net, + FNO( + lifting_net=lifting_net, projecting_net=projecting_net, n_modes=[5, 3, 2], dimensions=3, inner_size=60, - n_layers=5) + n_layers=5, + ) # simple constructor with n_modes list of list - FNO(lifting_net=lifting_net, + FNO( + lifting_net=lifting_net, projecting_net=projecting_net, n_modes=[[5, 3, 2], [5, 3, 2]], dimensions=3, inner_size=60, - n_layers=2) + n_layers=2, + ) # simple constructor with n_modes list of list projecting_net = torch.nn.Linear(50, output_channels) - FNO(lifting_net=lifting_net, + FNO( + lifting_net=lifting_net, projecting_net=projecting_net, n_modes=5, dimensions=3, - layers=[50, 50]) + layers=[50, 50], + ) def test_1d_forward(): @@ -50,12 +58,14 @@ def test_1d_forward(): input_ = torch.rand(batch_size, resolution[0], input_channels) lifting_net = torch.nn.Linear(input_channels, lifting_dim) projecting_net = torch.nn.Linear(60, output_channels) - fno = FNO(lifting_net=lifting_net, - projecting_net=projecting_net, - n_modes=5, - dimensions=1, - inner_size=60, - n_layers=2) + fno = FNO( + lifting_net=lifting_net, + projecting_net=projecting_net, + n_modes=5, + dimensions=1, + inner_size=60, + n_layers=2, + ) out = fno(input_) assert out.shape == torch.Size([batch_size, resolution[0], output_channels]) @@ -65,91 +75,120 @@ def test_1d_backward(): input_ = torch.rand(batch_size, resolution[0], input_channels) lifting_net = torch.nn.Linear(input_channels, lifting_dim) projecting_net = torch.nn.Linear(60, output_channels) - fno = FNO(lifting_net=lifting_net, - projecting_net=projecting_net, - n_modes=5, - dimensions=1, - inner_size=60, - n_layers=2) + fno = FNO( + lifting_net=lifting_net, + projecting_net=projecting_net, + n_modes=5, + dimensions=1, + inner_size=60, + n_layers=2, + ) input_.requires_grad = True out = fno(input_) l = torch.mean(out) l.backward() - assert input_.grad.shape == torch.Size([batch_size, resolution[0], input_channels]) + assert input_.grad.shape == torch.Size( + [batch_size, resolution[0], input_channels] + ) def test_2d_forward(): input_channels = 2 - input_ = torch.rand(batch_size, resolution[0], resolution[1], - input_channels) + input_ = torch.rand( + batch_size, resolution[0], resolution[1], input_channels + ) lifting_net = torch.nn.Linear(input_channels, lifting_dim) projecting_net = torch.nn.Linear(60, output_channels) - fno = FNO(lifting_net=lifting_net, - projecting_net=projecting_net, - n_modes=5, - dimensions=2, - inner_size=60, - n_layers=2) + fno = FNO( + lifting_net=lifting_net, + projecting_net=projecting_net, + n_modes=5, + dimensions=2, + inner_size=60, + n_layers=2, + ) out = fno(input_) assert out.shape == torch.Size( - [batch_size, resolution[0], resolution[1], output_channels]) + [batch_size, resolution[0], resolution[1], output_channels] + ) def test_2d_backward(): input_channels = 2 - input_ = torch.rand(batch_size, resolution[0], resolution[1], - input_channels) + input_ = torch.rand( + batch_size, resolution[0], resolution[1], input_channels + ) lifting_net = torch.nn.Linear(input_channels, lifting_dim) projecting_net = torch.nn.Linear(60, output_channels) - fno = FNO(lifting_net=lifting_net, - projecting_net=projecting_net, - n_modes=5, - dimensions=2, - inner_size=60, - n_layers=2) + fno = FNO( + lifting_net=lifting_net, + projecting_net=projecting_net, + n_modes=5, + dimensions=2, + inner_size=60, + n_layers=2, + ) input_.requires_grad = True out = fno(input_) l = torch.mean(out) l.backward() - assert input_.grad.shape == torch.Size([ - batch_size, resolution[0], resolution[1], input_channels - ]) + assert input_.grad.shape == torch.Size( + [batch_size, resolution[0], resolution[1], input_channels] + ) def test_3d_forward(): input_channels = 3 - input_ = torch.rand(batch_size, resolution[0], resolution[1], resolution[2], - input_channels) + input_ = torch.rand( + batch_size, resolution[0], resolution[1], resolution[2], input_channels + ) lifting_net = torch.nn.Linear(input_channels, lifting_dim) projecting_net = torch.nn.Linear(60, output_channels) - fno = FNO(lifting_net=lifting_net, - projecting_net=projecting_net, - n_modes=5, - dimensions=3, - inner_size=60, - n_layers=2) + fno = FNO( + lifting_net=lifting_net, + projecting_net=projecting_net, + n_modes=5, + dimensions=3, + inner_size=60, + n_layers=2, + ) out = fno(input_) - assert out.shape == torch.Size([ - batch_size, resolution[0], resolution[1], resolution[2], output_channels - ]) + assert out.shape == torch.Size( + [ + batch_size, + resolution[0], + resolution[1], + resolution[2], + output_channels, + ] + ) def test_3d_backward(): input_channels = 3 - input_ = torch.rand(batch_size, resolution[0], resolution[1], resolution[2], - input_channels) + input_ = torch.rand( + batch_size, resolution[0], resolution[1], resolution[2], input_channels + ) lifting_net = torch.nn.Linear(input_channels, lifting_dim) projecting_net = torch.nn.Linear(60, output_channels) - fno = FNO(lifting_net=lifting_net, - projecting_net=projecting_net, - n_modes=5, - dimensions=3, - inner_size=60, - n_layers=2) + fno = FNO( + lifting_net=lifting_net, + projecting_net=projecting_net, + n_modes=5, + dimensions=3, + inner_size=60, + n_layers=2, + ) input_.requires_grad = True out = fno(input_) l = torch.mean(out) l.backward() - assert input_.grad.shape == torch.Size([ - batch_size, resolution[0], resolution[1], resolution[2], input_channels - ]) + assert input_.grad.shape == torch.Size( + [ + batch_size, + resolution[0], + resolution[1], + resolution[2], + input_channels, + ] + ) diff --git a/tests/test_model/test_graph_neural_operator.py b/tests/test_model/test_graph_neural_operator.py new file mode 100644 index 000000000..e2ea3adcf --- /dev/null +++ b/tests/test_model/test_graph_neural_operator.py @@ -0,0 +1,116 @@ +import pytest +import torch +from pina.graph import KNNGraph +from pina.model import GraphNeuralOperator +from torch_geometric.data import Batch + +x = [torch.rand(100, 6) for _ in range(10)] +pos = [torch.rand(100, 3) for _ in range(10)] +graph = [ + KNNGraph(x=x_, pos=pos_, neighbours=6, edge_attr=True) + for x_, pos_ in zip(x, pos) +] +input_ = Batch.from_data_list(graph) + + +@pytest.mark.parametrize("shared_weights", [True, False]) +def test_constructor(shared_weights): + lifting_operator = torch.nn.Linear(6, 16) + projection_operator = torch.nn.Linear(16, 3) + GraphNeuralOperator( + lifting_operator=lifting_operator, + projection_operator=projection_operator, + edge_features=3, + internal_layers=[16, 16], + shared_weights=shared_weights, + ) + + GraphNeuralOperator( + lifting_operator=lifting_operator, + projection_operator=projection_operator, + edge_features=3, + inner_size=16, + internal_n_layers=10, + shared_weights=shared_weights, + ) + + int_func = torch.nn.Softplus + ext_func = torch.nn.ReLU + + GraphNeuralOperator( + lifting_operator=lifting_operator, + projection_operator=projection_operator, + edge_features=3, + internal_n_layers=10, + shared_weights=shared_weights, + internal_func=int_func, + external_func=ext_func, + ) + + +@pytest.mark.parametrize("shared_weights", [True, False]) +def test_forward_1(shared_weights): + lifting_operator = torch.nn.Linear(6, 16) + projection_operator = torch.nn.Linear(16, 3) + model = GraphNeuralOperator( + lifting_operator=lifting_operator, + projection_operator=projection_operator, + edge_features=3, + internal_layers=[16, 16], + shared_weights=shared_weights, + ) + output_ = model(input_) + assert output_.shape == torch.Size([1000, 3]) + + +@pytest.mark.parametrize("shared_weights", [True, False]) +def test_forward_2(shared_weights): + lifting_operator = torch.nn.Linear(6, 16) + projection_operator = torch.nn.Linear(16, 3) + model = GraphNeuralOperator( + lifting_operator=lifting_operator, + projection_operator=projection_operator, + edge_features=3, + inner_size=32, + internal_n_layers=2, + shared_weights=shared_weights, + ) + output_ = model(input_) + assert output_.shape == torch.Size([1000, 3]) + + +@pytest.mark.parametrize("shared_weights", [True, False]) +def test_backward(shared_weights): + lifting_operator = torch.nn.Linear(6, 16) + projection_operator = torch.nn.Linear(16, 3) + model = GraphNeuralOperator( + lifting_operator=lifting_operator, + projection_operator=projection_operator, + edge_features=3, + internal_layers=[16, 16], + shared_weights=shared_weights, + ) + input_.x.requires_grad = True + output_ = model(input_) + l = torch.mean(output_) + l.backward() + assert input_.x.grad.shape == torch.Size([1000, 6]) + + +@pytest.mark.parametrize("shared_weights", [True, False]) +def test_backward_2(shared_weights): + lifting_operator = torch.nn.Linear(6, 16) + projection_operator = torch.nn.Linear(16, 3) + model = GraphNeuralOperator( + lifting_operator=lifting_operator, + projection_operator=projection_operator, + edge_features=3, + inner_size=32, + internal_n_layers=2, + shared_weights=shared_weights, + ) + input_.x.requires_grad = True + output_ = model(input_) + l = torch.mean(output_) + l.backward() + assert input_.x.grad.shape == torch.Size([1000, 6]) diff --git a/tests/test_model/test_kernel_neural_operator.py b/tests/test_model/test_kernel_neural_operator.py new file mode 100644 index 000000000..d36f0aa8a --- /dev/null +++ b/tests/test_model/test_kernel_neural_operator.py @@ -0,0 +1,57 @@ +import torch +from pina.model import KernelNeuralOperator, FeedForward + +input_dim = 2 +output_dim = 4 +embedding_dim = 24 +batch_size = 10 +numb = 256 +data = torch.rand(size=(batch_size, numb, input_dim), requires_grad=True) +output_shape = torch.Size([batch_size, numb, output_dim]) + + +lifting_operator = FeedForward( + input_dimensions=input_dim, output_dimensions=embedding_dim +) +projection_operator = FeedForward( + input_dimensions=embedding_dim, output_dimensions=output_dim +) +integral_kernels = torch.nn.Sequential( + FeedForward( + input_dimensions=embedding_dim, output_dimensions=embedding_dim + ), + FeedForward( + input_dimensions=embedding_dim, output_dimensions=embedding_dim + ), +) + + +def test_constructor(): + KernelNeuralOperator( + lifting_operator=lifting_operator, + integral_kernels=integral_kernels, + projection_operator=projection_operator, + ) + + +def test_forward(): + operator = KernelNeuralOperator( + lifting_operator=lifting_operator, + integral_kernels=integral_kernels, + projection_operator=projection_operator, + ) + out = operator(data) + assert out.shape == output_shape + + +def test_backward(): + operator = KernelNeuralOperator( + lifting_operator=lifting_operator, + integral_kernels=integral_kernels, + projection_operator=projection_operator, + ) + out = operator(data) + loss = torch.nn.functional.mse_loss(out, torch.zeros_like(out)) + loss.backward() + grad = data.grad + assert grad.shape == data.shape diff --git a/tests/test_model/test_lno.py b/tests/test_model/test_lno.py deleted file mode 100644 index 1cd09a77f..000000000 --- a/tests/test_model/test_lno.py +++ /dev/null @@ -1,141 +0,0 @@ -import torch -from pina.model import LowRankNeuralOperator -from pina import LabelTensor -import pytest - - -batch_size = 15 -n_layers = 4 -embedding_dim = 24 -func = torch.nn.Tanh -rank = 4 -n_kernel_layers = 3 -field_indices = ['u'] -coordinates_indices = ['x', 'y'] - -def test_constructor(): - # working constructor - lifting_net = torch.nn.Linear(len(coordinates_indices) + len(field_indices), - embedding_dim) - projecting_net = torch.nn.Linear(embedding_dim + len(coordinates_indices), - len(field_indices)) - LowRankNeuralOperator( - lifting_net=lifting_net, - projecting_net=projecting_net, - coordinates_indices=coordinates_indices, - field_indices=field_indices, - n_kernel_layers=n_kernel_layers, - rank=rank) - - # not working constructor - with pytest.raises(ValueError): - LowRankNeuralOperator( - lifting_net=lifting_net, - projecting_net=projecting_net, - coordinates_indices=coordinates_indices, - field_indices=field_indices, - n_kernel_layers=3.2, # wrong - rank=rank) - - LowRankNeuralOperator( - lifting_net=[0], # wrong - projecting_net=projecting_net, - coordinates_indices=coordinates_indices, - field_indices=field_indices, - n_kernel_layers=n_kernel_layers, - rank=rank) - - LowRankNeuralOperator( - lifting_net=lifting_net, - projecting_net=[0], # wront - coordinates_indices=coordinates_indices, - field_indices=field_indices, - n_kernel_layers=n_kernel_layers, - rank=rank) - - LowRankNeuralOperator( - lifting_net=lifting_net, - projecting_net=projecting_net, - coordinates_indices=[0], #wrong - field_indices=field_indices, - n_kernel_layers=n_kernel_layers, - rank=rank) - - LowRankNeuralOperator( - lifting_net=lifting_net, - projecting_net=projecting_net, - coordinates_indices=coordinates_indices, - field_indices=[0], #wrong - n_kernel_layers=n_kernel_layers, - rank=rank) - - lifting_net = torch.nn.Linear(len(coordinates_indices), - embedding_dim) - LowRankNeuralOperator( - lifting_net=lifting_net, - projecting_net=projecting_net, - coordinates_indices=coordinates_indices, - field_indices=field_indices, - n_kernel_layers=n_kernel_layers, - rank=rank) - - lifting_net = torch.nn.Linear(len(coordinates_indices) + len(field_indices), - embedding_dim) - projecting_net = torch.nn.Linear(embedding_dim, - len(field_indices)) - LowRankNeuralOperator( - lifting_net=lifting_net, - projecting_net=projecting_net, - coordinates_indices=coordinates_indices, - field_indices=field_indices, - n_kernel_layers=n_kernel_layers, - rank=rank) - - -def test_forward(): - lifting_net = torch.nn.Linear(len(coordinates_indices) + len(field_indices), - embedding_dim) - projecting_net = torch.nn.Linear(embedding_dim + len(coordinates_indices), - len(field_indices)) - lno = LowRankNeuralOperator( - lifting_net=lifting_net, - projecting_net=projecting_net, - coordinates_indices=coordinates_indices, - field_indices=field_indices, - n_kernel_layers=n_kernel_layers, - rank=rank) - - input_ = LabelTensor( - torch.rand(batch_size, 100, - len(coordinates_indices) + len(field_indices)), - coordinates_indices + field_indices) - - out = lno(input_) - assert out.shape == torch.Size( - [batch_size, input_.shape[1], len(field_indices)]) - - -def test_backward(): - lifting_net = torch.nn.Linear(len(coordinates_indices) + len(field_indices), - embedding_dim) - projecting_net = torch.nn.Linear(embedding_dim + len(coordinates_indices), - len(field_indices)) - lno=LowRankNeuralOperator( - lifting_net=lifting_net, - projecting_net=projecting_net, - coordinates_indices=coordinates_indices, - field_indices=field_indices, - n_kernel_layers=n_kernel_layers, - rank=rank) - input_ = LabelTensor( - torch.rand(batch_size, 100, - len(coordinates_indices) + len(field_indices)), - coordinates_indices + field_indices) - input_ = input_.requires_grad_() - out = lno(input_) - tmp = torch.linalg.norm(out) - tmp.backward() - grad = input_.grad - assert grad.shape == torch.Size( - [batch_size, input_.shape[1], - len(coordinates_indices) + len(field_indices)]) \ No newline at end of file diff --git a/tests/test_model/test_low_rank_neural_operator.py b/tests/test_model/test_low_rank_neural_operator.py new file mode 100644 index 000000000..3702df91b --- /dev/null +++ b/tests/test_model/test_low_rank_neural_operator.py @@ -0,0 +1,166 @@ +import torch +from pina.model import LowRankNeuralOperator +from pina import LabelTensor +import pytest + + +batch_size = 15 +n_layers = 4 +embedding_dim = 24 +func = torch.nn.Tanh +rank = 4 +n_kernel_layers = 3 +field_indices = ["u"] +coordinates_indices = ["x", "y"] + + +def test_constructor(): + # working constructor + lifting_net = torch.nn.Linear( + len(coordinates_indices) + len(field_indices), embedding_dim + ) + projecting_net = torch.nn.Linear( + embedding_dim + len(coordinates_indices), len(field_indices) + ) + LowRankNeuralOperator( + lifting_net=lifting_net, + projecting_net=projecting_net, + coordinates_indices=coordinates_indices, + field_indices=field_indices, + n_kernel_layers=n_kernel_layers, + rank=rank, + ) + + # not working constructor + with pytest.raises(ValueError): + LowRankNeuralOperator( + lifting_net=lifting_net, + projecting_net=projecting_net, + coordinates_indices=coordinates_indices, + field_indices=field_indices, + n_kernel_layers=3.2, # wrong + rank=rank, + ) + + LowRankNeuralOperator( + lifting_net=[0], # wrong + projecting_net=projecting_net, + coordinates_indices=coordinates_indices, + field_indices=field_indices, + n_kernel_layers=n_kernel_layers, + rank=rank, + ) + + LowRankNeuralOperator( + lifting_net=lifting_net, + projecting_net=[0], # wront + coordinates_indices=coordinates_indices, + field_indices=field_indices, + n_kernel_layers=n_kernel_layers, + rank=rank, + ) + + LowRankNeuralOperator( + lifting_net=lifting_net, + projecting_net=projecting_net, + coordinates_indices=[0], # wrong + field_indices=field_indices, + n_kernel_layers=n_kernel_layers, + rank=rank, + ) + + LowRankNeuralOperator( + lifting_net=lifting_net, + projecting_net=projecting_net, + coordinates_indices=coordinates_indices, + field_indices=[0], # wrong + n_kernel_layers=n_kernel_layers, + rank=rank, + ) + + lifting_net = torch.nn.Linear(len(coordinates_indices), embedding_dim) + LowRankNeuralOperator( + lifting_net=lifting_net, + projecting_net=projecting_net, + coordinates_indices=coordinates_indices, + field_indices=field_indices, + n_kernel_layers=n_kernel_layers, + rank=rank, + ) + + lifting_net = torch.nn.Linear( + len(coordinates_indices) + len(field_indices), embedding_dim + ) + projecting_net = torch.nn.Linear(embedding_dim, len(field_indices)) + LowRankNeuralOperator( + lifting_net=lifting_net, + projecting_net=projecting_net, + coordinates_indices=coordinates_indices, + field_indices=field_indices, + n_kernel_layers=n_kernel_layers, + rank=rank, + ) + + +def test_forward(): + lifting_net = torch.nn.Linear( + len(coordinates_indices) + len(field_indices), embedding_dim + ) + projecting_net = torch.nn.Linear( + embedding_dim + len(coordinates_indices), len(field_indices) + ) + lno = LowRankNeuralOperator( + lifting_net=lifting_net, + projecting_net=projecting_net, + coordinates_indices=coordinates_indices, + field_indices=field_indices, + n_kernel_layers=n_kernel_layers, + rank=rank, + ) + + input_ = LabelTensor( + torch.rand( + batch_size, 100, len(coordinates_indices) + len(field_indices) + ), + coordinates_indices + field_indices, + ) + + out = lno(input_) + assert out.shape == torch.Size( + [batch_size, input_.shape[1], len(field_indices)] + ) + + +def test_backward(): + lifting_net = torch.nn.Linear( + len(coordinates_indices) + len(field_indices), embedding_dim + ) + projecting_net = torch.nn.Linear( + embedding_dim + len(coordinates_indices), len(field_indices) + ) + lno = LowRankNeuralOperator( + lifting_net=lifting_net, + projecting_net=projecting_net, + coordinates_indices=coordinates_indices, + field_indices=field_indices, + n_kernel_layers=n_kernel_layers, + rank=rank, + ) + input_ = LabelTensor( + torch.rand( + batch_size, 100, len(coordinates_indices) + len(field_indices) + ), + coordinates_indices + field_indices, + ) + input_ = input_.requires_grad_() + out = lno(input_) + tmp = torch.linalg.norm(out) + tmp.backward() + grad = input_.grad + assert grad.shape == torch.Size( + [ + batch_size, + input_.shape[1], + len(coordinates_indices) + len(field_indices), + ] + ) diff --git a/tests/test_model/test_mionet.py b/tests/test_model/test_mionet.py index 174251eed..4d59433bf 100644 --- a/tests/test_model/test_mionet.py +++ b/tests/test_model/test_mionet.py @@ -6,7 +6,7 @@ from pina.model import FeedForward data = torch.rand((20, 3)) -input_vars = ['a', 'b', 'c'] +input_vars = ["a", "b", "c"] input_ = LabelTensor(data, input_vars) @@ -14,42 +14,42 @@ def test_constructor(): branch_net1 = FeedForward(input_dimensions=1, output_dimensions=10) branch_net2 = FeedForward(input_dimensions=2, output_dimensions=10) trunk_net = FeedForward(input_dimensions=1, output_dimensions=10) - networks = {branch_net1: ['x'], branch_net2: ['x', 'y'], trunk_net: ['z']} - MIONet(networks=networks, reduction='+', aggregator='*') + networks = {branch_net1: ["x"], branch_net2: ["x", "y"], trunk_net: ["z"]} + MIONet(networks=networks, reduction="+", aggregator="*") def test_constructor_fails_when_invalid_inner_layer_size(): branch_net1 = FeedForward(input_dimensions=1, output_dimensions=10) branch_net2 = FeedForward(input_dimensions=2, output_dimensions=10) trunk_net = FeedForward(input_dimensions=1, output_dimensions=12) - networks = {branch_net1: ['x'], branch_net2: ['x', 'y'], trunk_net: ['z']} + networks = {branch_net1: ["x"], branch_net2: ["x", "y"], trunk_net: ["z"]} with pytest.raises(ValueError): - MIONet(networks=networks, reduction='+', aggregator='*') + MIONet(networks=networks, reduction="+", aggregator="*") def test_forward_extract_str(): branch_net1 = FeedForward(input_dimensions=1, output_dimensions=10) branch_net2 = FeedForward(input_dimensions=1, output_dimensions=10) trunk_net = FeedForward(input_dimensions=1, output_dimensions=10) - networks = {branch_net1: ['a'], branch_net2: ['b'], trunk_net: ['c']} - model = MIONet(networks=networks, reduction='+', aggregator='*') + networks = {branch_net1: ["a"], branch_net2: ["b"], trunk_net: ["c"]} + model = MIONet(networks=networks, reduction="+", aggregator="*") model(input_) def test_backward_extract_str(): data = torch.rand((20, 3)) data.requires_grad = True - input_vars = ['a', 'b', 'c'] + input_vars = ["a", "b", "c"] input_ = LabelTensor(data, input_vars) branch_net1 = FeedForward(input_dimensions=1, output_dimensions=10) branch_net2 = FeedForward(input_dimensions=1, output_dimensions=10) trunk_net = FeedForward(input_dimensions=1, output_dimensions=10) - networks = {branch_net1: ['a'], branch_net2: ['b'], trunk_net: ['c']} - model = MIONet(networks=networks, reduction='+', aggregator='*') + networks = {branch_net1: ["a"], branch_net2: ["b"], trunk_net: ["c"]} + model = MIONet(networks=networks, reduction="+", aggregator="*") model(input_) l = torch.mean(model(input_)) l.backward() - assert data._grad.shape == torch.Size([20,3]) + assert data._grad.shape == torch.Size([20, 3]) def test_forward_extract_int(): @@ -57,7 +57,7 @@ def test_forward_extract_int(): branch_net2 = FeedForward(input_dimensions=1, output_dimensions=10) trunk_net = FeedForward(input_dimensions=1, output_dimensions=10) networks = {branch_net1: [0], branch_net2: [1], trunk_net: [2]} - model = MIONet(networks=networks, reduction='+', aggregator='*') + model = MIONet(networks=networks, reduction="+", aggregator="*") model(data) @@ -68,19 +68,19 @@ def test_backward_extract_int(): branch_net2 = FeedForward(input_dimensions=1, output_dimensions=10) trunk_net = FeedForward(input_dimensions=1, output_dimensions=10) networks = {branch_net1: [0], branch_net2: [1], trunk_net: [2]} - model = MIONet(networks=networks, reduction='+', aggregator='*') + model = MIONet(networks=networks, reduction="+", aggregator="*") model(data) l = torch.mean(model(data)) l.backward() - assert data._grad.shape == torch.Size([20,3]) + assert data._grad.shape == torch.Size([20, 3]) def test_forward_extract_str_wrong(): branch_net1 = FeedForward(input_dimensions=1, output_dimensions=10) branch_net2 = FeedForward(input_dimensions=1, output_dimensions=10) trunk_net = FeedForward(input_dimensions=1, output_dimensions=10) - networks = {branch_net1: ['a'], branch_net2: ['b'], trunk_net: ['c']} - model = MIONet(networks=networks, reduction='+', aggregator='*') + networks = {branch_net1: ["a"], branch_net2: ["b"], trunk_net: ["c"]} + model = MIONet(networks=networks, reduction="+", aggregator="*") with pytest.raises(RuntimeError): model(data) @@ -91,10 +91,10 @@ def test_backward_extract_str_wrong(): branch_net1 = FeedForward(input_dimensions=1, output_dimensions=10) branch_net2 = FeedForward(input_dimensions=1, output_dimensions=10) trunk_net = FeedForward(input_dimensions=1, output_dimensions=10) - networks = {branch_net1: ['a'], branch_net2: ['b'], trunk_net: ['c']} - model = MIONet(networks=networks, reduction='+', aggregator='*') + networks = {branch_net1: ["a"], branch_net2: ["b"], trunk_net: ["c"]} + model = MIONet(networks=networks, reduction="+", aggregator="*") with pytest.raises(RuntimeError): model(data) l = torch.mean(model(data)) l.backward() - assert data._grad.shape == torch.Size([20,3]) + assert data._grad.shape == torch.Size([20, 3]) diff --git a/tests/test_model/test_network.py b/tests/test_model/test_network.py deleted file mode 100644 index 870480efc..000000000 --- a/tests/test_model/test_network.py +++ /dev/null @@ -1,49 +0,0 @@ -import torch -import pytest - -from pina.model.network import Network -from pina.model import FeedForward -from pina import LabelTensor - -data = torch.rand((20, 3)) -data_lt = LabelTensor(data, ['x', 'y', 'z']) -input_dim = 3 -output_dim = 4 -torchmodel = FeedForward(input_dim, output_dim) -extra_feat = [] - - -def test_constructor(): - Network(model=torchmodel, - input_variables=['x', 'y', 'z'], - output_variables=['a', 'b', 'c', 'd'], - extra_features=None) - -def test_forward(): - net = Network(model=torchmodel, - input_variables=['x', 'y', 'z'], - output_variables=['a', 'b', 'c', 'd'], - extra_features=None) - out = net.torchmodel(data) - out_lt = net(data_lt) - assert isinstance(out, torch.Tensor) - assert isinstance(out_lt, LabelTensor) - assert out.shape == (20, 4) - assert out_lt.shape == (20, 4) - assert torch.allclose(out_lt, out) - assert out_lt.labels == ['a', 'b', 'c', 'd'] - - with pytest.raises(AssertionError): - net(data) - -def test_backward(): - net = Network(model=torchmodel, - input_variables=['x', 'y', 'z'], - output_variables=['a', 'b', 'c', 'd'], - extra_features=None) - data = torch.rand((20, 3)) - data.requires_grad = True - out = net.torchmodel(data) - l = torch.mean(out) - l.backward() - assert data._grad.shape == torch.Size([20, 3]) \ No newline at end of file diff --git a/tests/test_model/test_residualfnn.py b/tests/test_model/test_residual_feed_forward.py similarity index 69% rename from tests/test_model/test_residualfnn.py rename to tests/test_model/test_residual_feed_forward.py index 1c0cbf8cf..8cad1c63c 100644 --- a/tests/test_model/test_residualfnn.py +++ b/tests/test_model/test_residual_feed_forward.py @@ -9,15 +9,17 @@ def test_constructor(): # wrong transformer nets (not 2) with pytest.raises(ValueError): - ResidualFeedForward(input_dimensions=2, - output_dimensions=1, - transformer_nets=[torch.nn.Linear(2, 20)]) + ResidualFeedForward( + input_dimensions=2, + output_dimensions=1, + transformer_nets=[torch.nn.Linear(2, 20)], + ) # wrong transformer nets (not nn.Module) with pytest.raises(ValueError): - ResidualFeedForward(input_dimensions=2, - output_dimensions=1, - transformer_nets=[2, 2]) + ResidualFeedForward( + input_dimensions=2, output_dimensions=1, transformer_nets=[2, 2] + ) def test_forward(): @@ -34,4 +36,3 @@ def test_backward(): l = torch.mean(model(x)) l.backward() assert x.grad.shape == torch.Size([10, 2]) - \ No newline at end of file diff --git a/tests/test_model/test_spline.py b/tests/test_model/test_spline.py index 4bb9a8035..d38b1610b 100644 --- a/tests/test_model/test_spline.py +++ b/tests/test_model/test_spline.py @@ -9,54 +9,61 @@ valid_args = [ { - 'knots': torch.tensor([0., 0., 0., 1., 2., 3., 3., 3.]), - 'control_points': torch.tensor([0., 0., 1., 0., 0.]), - 'order': 3 + "knots": torch.tensor([0.0, 0.0, 0.0, 1.0, 2.0, 3.0, 3.0, 3.0]), + "control_points": torch.tensor([0.0, 0.0, 1.0, 0.0, 0.0]), + "order": 3, }, { - 'knots': torch.tensor([-2., -2., -2., -2., -1., 0., 1., 2., 2., 2., 2.]), - 'control_points': torch.tensor([0., 0., 0., 6., 0., 0., 0.]), - 'order': 4 + "knots": torch.tensor( + [-2.0, -2.0, -2.0, -2.0, -1.0, 0.0, 1.0, 2.0, 2.0, 2.0, 2.0] + ), + "control_points": torch.tensor([0.0, 0.0, 0.0, 6.0, 0.0, 0.0, 0.0]), + "order": 4, }, # {'control_points': {'n': 5, 'dim': 1}, 'order': 2}, # {'control_points': {'n': 7, 'dim': 1}, 'order': 3} ] - + + def scipy_check(model, x, y): from scipy.interpolate._bsplines import BSpline import numpy as np + spline = BSpline( t=model.knots.detach().numpy(), c=model.control_points.detach().numpy(), - k=model.order-1 + k=model.order - 1, ) y_scipy = spline(x).flatten() y = y.detach().numpy() np.testing.assert_allclose(y, y_scipy, atol=1e-5) + @pytest.mark.parametrize("args", valid_args) def test_constructor(args): Spline(**args) + def test_constructor_wrong(): with pytest.raises(ValueError): Spline() + @pytest.mark.parametrize("args", valid_args) def test_forward(args): - min_x = args['knots'][0] - max_x = args['knots'][-1] + min_x = args["knots"][0] + max_x = args["knots"][-1] xi = torch.linspace(min_x, max_x, 1000) model = Spline(**args) yi = model(xi).squeeze() scipy_check(model, xi, yi) - return - + return + @pytest.mark.parametrize("args", valid_args) def test_backward(args): - min_x = args['knots'][0] - max_x = args['knots'][-1] + min_x = args["knots"][0] + max_x = args["knots"][-1] xi = torch.linspace(min_x, max_x, 100) model = Spline(**args) yi = model(xi) diff --git a/tests/test_operator.py b/tests/test_operator.py new file mode 100644 index 000000000..e274fda65 --- /dev/null +++ b/tests/test_operator.py @@ -0,0 +1,166 @@ +import torch +import pytest + +from pina import LabelTensor +from pina.operator import grad, div, laplacian + + +def func_vector(x): + return x**2 + + +def func_scalar(x): + x_ = x.extract(["x"]) + y_ = x.extract(["y"]) + z_ = x.extract(["z"]) + return x_**2 + y_**2 + z_**2 + + +data = torch.rand((20, 3)) +inp = LabelTensor(data, ["x", "y", "z"]).requires_grad_(True) +labels = ["a", "b", "c"] +tensor_v = LabelTensor(func_vector(inp), labels) +tensor_s = LabelTensor(func_scalar(inp).reshape(-1, 1), labels[0]) + + +def test_grad_scalar_output(): + grad_tensor_s = grad(tensor_s, inp) + true_val = 2 * inp + true_val.labels = inp.labels + assert grad_tensor_s.shape == inp.shape + assert grad_tensor_s.labels == [ + f"d{tensor_s.labels[0]}d{i}" for i in inp.labels + ] + assert torch.allclose(grad_tensor_s, true_val) + + grad_tensor_s = grad(tensor_s, inp, d=["x", "y"]) + assert grad_tensor_s.shape == (20, 2) + assert grad_tensor_s.labels == [ + f"d{tensor_s.labels[0]}d{i}" for i in ["x", "y"] + ] + assert torch.allclose(grad_tensor_s, true_val.extract(["x", "y"])) + + +def test_grad_vector_output(): + grad_tensor_v = grad(tensor_v, inp) + true_val = torch.cat( + ( + 2 * inp.extract(["x"]), + torch.zeros_like(inp.extract(["y"])), + torch.zeros_like(inp.extract(["z"])), + torch.zeros_like(inp.extract(["x"])), + 2 * inp.extract(["y"]), + torch.zeros_like(inp.extract(["z"])), + torch.zeros_like(inp.extract(["x"])), + torch.zeros_like(inp.extract(["y"])), + 2 * inp.extract(["z"]), + ), + dim=1, + ) + assert grad_tensor_v.shape == (20, 9) + assert grad_tensor_v.labels == [ + f"d{j}d{i}" for j in tensor_v.labels for i in inp.labels + ] + assert torch.allclose(grad_tensor_v, true_val) + + grad_tensor_v = grad(tensor_v, inp, d=["x", "y"]) + true_val = torch.cat( + ( + 2 * inp.extract(["x"]), + torch.zeros_like(inp.extract(["y"])), + torch.zeros_like(inp.extract(["x"])), + 2 * inp.extract(["y"]), + torch.zeros_like(inp.extract(["x"])), + torch.zeros_like(inp.extract(["y"])), + ), + dim=1, + ) + assert grad_tensor_v.shape == (inp.shape[0], 6) + assert grad_tensor_v.labels == [ + f"d{j}d{i}" for j in tensor_v.labels for i in ["x", "y"] + ] + assert torch.allclose(grad_tensor_v, true_val) + + +def test_div_vector_output(): + div_tensor_v = div(tensor_v, inp) + true_val = 2 * torch.sum(inp, dim=1).reshape(-1, 1) + assert div_tensor_v.shape == (20, 1) + assert div_tensor_v.labels == [f"dadx+dbdy+dcdz"] + assert torch.allclose(div_tensor_v, true_val) + + div_tensor_v = div(tensor_v, inp, components=["a", "b"], d=["x", "y"]) + true_val = 2 * torch.sum(inp.extract(["x", "y"]), dim=1).reshape(-1, 1) + assert div_tensor_v.shape == (inp.shape[0], 1) + assert div_tensor_v.labels == [f"dadx+dbdy"] + assert torch.allclose(div_tensor_v, true_val) + + +def test_laplacian_scalar_output(): + laplace_tensor_s = laplacian(tensor_s, inp) + true_val = 6 * torch.ones_like(laplace_tensor_s) + assert laplace_tensor_s.shape == tensor_s.shape + assert laplace_tensor_s.labels == [f"dd{tensor_s.labels[0]}"] + assert torch.allclose(laplace_tensor_s, true_val) + + laplace_tensor_s = laplacian(tensor_s, inp, components=["a"], d=["x", "y"]) + true_val = 4 * torch.ones_like(laplace_tensor_s) + assert laplace_tensor_s.shape == tensor_s.shape + assert laplace_tensor_s.labels == [f"dd{tensor_s.labels[0]}"] + assert torch.allclose(laplace_tensor_s, true_val) + + +def test_laplacian_vector_output(): + laplace_tensor_v = laplacian(tensor_v, inp) + print(laplace_tensor_v.labels) + print(tensor_v.labels) + true_val = 2 * torch.ones_like(tensor_v) + assert laplace_tensor_v.shape == tensor_v.shape + assert laplace_tensor_v.labels == [f"dd{i}" for i in tensor_v.labels] + assert torch.allclose(laplace_tensor_v, true_val) + + laplace_tensor_v = laplacian( + tensor_v, inp, components=["a", "b"], d=["x", "y"] + ) + true_val = 2 * torch.ones_like(tensor_v.extract(["a", "b"])) + assert laplace_tensor_v.shape == tensor_v.extract(["a", "b"]).shape + assert laplace_tensor_v.labels == [f"dd{i}" for i in ["a", "b"]] + assert torch.allclose(laplace_tensor_v, true_val) + + +def test_laplacian_vector_output2(): + x = LabelTensor( + torch.linspace(0, 1, 10, requires_grad=True).reshape(-1, 1), + labels=["x"], + ) + y = LabelTensor( + torch.linspace(3, 4, 10, requires_grad=True).reshape(-1, 1), + labels=["y"], + ) + input_ = LabelTensor(torch.cat((x, y), dim=1), labels=["x", "y"]) + + # Construct two scalar functions: + # u = x**2 + y**2 + # v = x**2 - y**2 + u = LabelTensor( + input_.extract("x") ** 2 + input_.extract("y") ** 2, labels="u" + ) + v = LabelTensor( + input_.extract("x") ** 2 - input_.extract("y") ** 2, labels="v" + ) + + # Define a vector-valued function, whose components are u and v. + f = LabelTensor(torch.cat((u, v), dim=1), labels=["u", "v"]) + + # Compute the scalar laplacian of both u and v: + # Lap(u) = [4, 4, 4, ..., 4] + # Lap(v) = [0, 0, 0, ..., 0] + lap_u = laplacian(u, input_, components=["u"]) + lap_v = laplacian(v, input_, components=["v"]) + + # Compute the laplacian of f: the two columns should correspond + # to the laplacians of u and v, respectively... + lap_f = laplacian(f, input_, components=["u", "v"]) + + assert torch.allclose(lap_f.extract("ddu"), lap_u) + assert torch.allclose(lap_f.extract("ddv"), lap_v) diff --git a/tests/test_operators.py b/tests/test_operators.py deleted file mode 100644 index 58e90ca33..000000000 --- a/tests/test_operators.py +++ /dev/null @@ -1,124 +0,0 @@ -import torch -import pytest - -from pina import LabelTensor -from pina.operators import grad, div, laplacian - - -def func_vector(x): - return x**2 - - -def func_scalar(x): - x_ = x.extract(['x']) - y_ = x.extract(['y']) - z_ = x.extract(['z']) - return x_**2 + y_**2 + z_**2 - - -inp = LabelTensor(torch.rand((20, 3), requires_grad=True), ['x', 'y', 'z']) -tensor_v = LabelTensor(func_vector(inp), ['a', 'b', 'c']) -tensor_s = LabelTensor(func_scalar(inp).reshape(-1, 1), ['a']) - -def test_grad_scalar_output(): - grad_tensor_s = grad(tensor_s, inp) - true_val = 2*inp - assert grad_tensor_s.shape == inp.shape - assert grad_tensor_s.labels == [ - f'd{tensor_s.labels[0]}d{i}' for i in inp.labels - ] - assert torch.allclose(grad_tensor_s, true_val) - - grad_tensor_s = grad(tensor_s, inp, d=['x', 'y']) - true_val = 2*inp.extract(['x', 'y']) - assert grad_tensor_s.shape == (inp.shape[0], 2) - assert grad_tensor_s.labels == [ - f'd{tensor_s.labels[0]}d{i}' for i in ['x', 'y'] - ] - assert torch.allclose(grad_tensor_s, true_val) - - -def test_grad_vector_output(): - grad_tensor_v = grad(tensor_v, inp) - true_val = torch.cat( - (2*inp.extract(['x']), - torch.zeros_like(inp.extract(['y'])), - torch.zeros_like(inp.extract(['z'])), - torch.zeros_like(inp.extract(['x'])), - 2*inp.extract(['y']), - torch.zeros_like(inp.extract(['z'])), - torch.zeros_like(inp.extract(['x'])), - torch.zeros_like(inp.extract(['y'])), - 2*inp.extract(['z']) - ), dim=1 - ) - assert grad_tensor_v.shape == (20, 9) - assert grad_tensor_v.labels == [ - f'd{j}d{i}' for j in tensor_v.labels for i in inp.labels - ] - assert torch.allclose(grad_tensor_v, true_val) - - grad_tensor_v = grad(tensor_v, inp, d=['x', 'y']) - true_val = torch.cat( - (2*inp.extract(['x']), - torch.zeros_like(inp.extract(['y'])), - torch.zeros_like(inp.extract(['x'])), - 2*inp.extract(['y']), - torch.zeros_like(inp.extract(['x'])), - torch.zeros_like(inp.extract(['y'])) - ), dim=1 - ) - assert grad_tensor_v.shape == (inp.shape[0], 6) - assert grad_tensor_v.labels == [ - f'd{j}d{i}' for j in tensor_v.labels for i in ['x', 'y'] - ] - assert torch.allclose(grad_tensor_v, true_val) - - -def test_div_vector_output(): - div_tensor_v = div(tensor_v, inp) - true_val = 2*torch.sum(inp, dim=1).reshape(-1,1) - assert div_tensor_v.shape == (20, 1) - assert div_tensor_v.labels == [f'dadx+dbdy+dcdz'] - assert torch.allclose(div_tensor_v, true_val) - - div_tensor_v = div(tensor_v, inp, components=['a', 'b'], d=['x', 'y']) - true_val = 2*torch.sum(inp.extract(['x', 'y']), dim=1).reshape(-1,1) - assert div_tensor_v.shape == (inp.shape[0], 1) - assert div_tensor_v.labels == [f'dadx+dbdy'] - assert torch.allclose(div_tensor_v, true_val) - - -def test_laplacian_scalar_output(): - laplace_tensor_s = laplacian(tensor_s, inp) - true_val = 6*torch.ones_like(laplace_tensor_s) - assert laplace_tensor_s.shape == tensor_s.shape - assert laplace_tensor_s.labels == [f"dd{tensor_s.labels[0]}"] - assert torch.allclose(laplace_tensor_s, true_val) - - laplace_tensor_s = laplacian(tensor_s, inp, components=['a'], d=['x', 'y']) - true_val = 4*torch.ones_like(laplace_tensor_s) - assert laplace_tensor_s.shape == tensor_s.shape - assert laplace_tensor_s.labels == [f"dd{tensor_s.labels[0]}"] - assert torch.allclose(laplace_tensor_s, true_val) - - -def test_laplacian_vector_output(): - laplace_tensor_v = laplacian(tensor_v, inp) - true_val = 2*torch.ones_like(tensor_v) - assert laplace_tensor_v.shape == tensor_v.shape - assert laplace_tensor_v.labels == [ - f'dd{i}' for i in tensor_v.labels - ] - assert torch.allclose(laplace_tensor_v, true_val) - - laplace_tensor_v = laplacian(tensor_v, - inp, - components=['a', 'b'], - d=['x', 'y']) - true_val = 2*torch.ones_like(tensor_v.extract(['a', 'b'])) - assert laplace_tensor_v.shape == tensor_v.extract(['a', 'b']).shape - assert laplace_tensor_v.labels == [ - f'dd{i}' for i in ['a', 'b'] - ] - assert torch.allclose(laplace_tensor_v, true_val) diff --git a/tests/test_optimizer.py b/tests/test_optimizer.py new file mode 100644 index 000000000..037de9929 --- /dev/null +++ b/tests/test_optimizer.py @@ -0,0 +1,21 @@ +import torch +import pytest +from pina.optim import TorchOptimizer + +opt_list = [ + torch.optim.Adam, + torch.optim.AdamW, + torch.optim.SGD, + torch.optim.RMSprop, +] + + +@pytest.mark.parametrize("optimizer_class", opt_list) +def test_constructor(optimizer_class): + TorchOptimizer(optimizer_class, lr=1e-3) + + +@pytest.mark.parametrize("optimizer_class", opt_list) +def test_hook(optimizer_class): + opt = TorchOptimizer(optimizer_class, lr=1e-3) + opt.hook(torch.nn.Linear(10, 10).parameters()) diff --git a/tests/test_plotter.py b/tests/test_plotter.py deleted file mode 100644 index 99f99bc7e..000000000 --- a/tests/test_plotter.py +++ /dev/null @@ -1,69 +0,0 @@ -from pina.geometry import CartesianDomain -from pina import Condition, Plotter -from matplotlib.testing.decorators import image_comparison -import matplotlib.pyplot as plt -from pina.problem import SpatialProblem -from pina.equation import FixedValue - - -class FooProblem1D(SpatialProblem): - - # assign output/ spatial and temporal variables - output_variables = ['u'] - spatial_domain = CartesianDomain({'x' : [-1, 1]}) - - # problem condition statement - conditions = { - 'D': Condition(location=CartesianDomain({'x': [-1, 1]}), equation=FixedValue(0.)), - } - -class FooProblem2D(SpatialProblem): - - # assign output/ spatial and temporal variables - output_variables = ['u'] - spatial_domain = CartesianDomain({'x' : [-1, 1], 'y': [-1, 1]}) - - # problem condition statement - conditions = { - 'D': Condition(location=CartesianDomain({'x' : [-1, 1], 'y': [-1, 1]}), equation=FixedValue(0.)), - } - -class FooProblem3D(SpatialProblem): - - # assign output/ spatial and temporal variables - output_variables = ['u'] - spatial_domain = CartesianDomain({'x' : [-1, 1], 'y': [-1, 1], 'z':[-1,1]}) - - # problem condition statement - conditions = { - 'D': Condition(location=CartesianDomain({'x' : [-1, 1], 'y': [-1, 1], 'z':[-1,1]}), equation=FixedValue(0.)), - } - - - -def test_constructor(): - Plotter() - -def test_plot_samples_1d(): - problem = FooProblem1D() - problem.discretise_domain(n=10, mode='grid', variables = 'x', locations=['D']) - pl = Plotter() - pl.plot_samples(problem=problem, filename='fig.png') - import os - os.remove('fig.png') - -def test_plot_samples_2d(): - problem = FooProblem2D() - problem.discretise_domain(n=10, mode='grid', variables = ['x', 'y'], locations=['D']) - pl = Plotter() - pl.plot_samples(problem=problem, filename='fig.png') - import os - os.remove('fig.png') - -def test_plot_samples_3d(): - problem = FooProblem3D() - problem.discretise_domain(n=10, mode='grid', variables = ['x', 'y', 'z'], locations=['D']) - pl = Plotter() - pl.plot_samples(problem=problem, filename='fig.png') - import os - os.remove('fig.png') \ No newline at end of file diff --git a/tests/test_problem.py b/tests/test_problem.py index 09133d4e2..069dc0620 100644 --- a/tests/test_problem.py +++ b/tests/test_problem.py @@ -1,144 +1,86 @@ import torch import pytest - -from pina.problem import SpatialProblem -from pina.operators import laplacian -from pina import LabelTensor, Condition -from pina.geometry import CartesianDomain -from pina.equation.equation import Equation -from pina.equation.equation_factory import FixedValue - - -def laplace_equation(input_, output_): - force_term = (torch.sin(input_.extract(['x']) * torch.pi) * - torch.sin(input_.extract(['y']) * torch.pi)) - delta_u = laplacian(output_.extract(['u']), input_) - return delta_u - force_term - - -my_laplace = Equation(laplace_equation) -in_ = LabelTensor(torch.tensor([[0., 1.]], requires_grad=True), ['x', 'y']) -out_ = LabelTensor(torch.tensor([[0.]], requires_grad=True), ['u']) - - -class Poisson(SpatialProblem): - output_variables = ['u'] - spatial_domain = CartesianDomain({'x': [0, 1], 'y': [0, 1]}) - - conditions = { - 'gamma1': - Condition(location=CartesianDomain({ - 'x': [0, 1], - 'y': 1 - }), - equation=FixedValue(0.0)), - 'gamma2': - Condition(location=CartesianDomain({ - 'x': [0, 1], - 'y': 0 - }), - equation=FixedValue(0.0)), - 'gamma3': - Condition(location=CartesianDomain({ - 'x': 1, - 'y': [0, 1] - }), - equation=FixedValue(0.0)), - 'gamma4': - Condition(location=CartesianDomain({ - 'x': 0, - 'y': [0, 1] - }), - equation=FixedValue(0.0)), - 'D': - Condition(location=CartesianDomain({ - 'x': [0, 1], - 'y': [0, 1] - }), - equation=my_laplace), - 'data': - Condition(input_points=in_, output_points=out_) - } - - def poisson_sol(self, pts): - return -(torch.sin(pts.extract(['x']) * torch.pi) * - torch.sin(pts.extract(['y']) * torch.pi)) / (2 * torch.pi**2) - - truth_solution = poisson_sol +from pina.problem.zoo import Poisson2DSquareProblem as Poisson +from pina import LabelTensor +from pina.domain import Union +from pina.domain import CartesianDomain def test_discretise_domain(): n = 10 poisson_problem = Poisson() - boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] - poisson_problem.discretise_domain(n, 'grid', locations=boundaries) + boundaries = ["g1", "g2", "g3", "g4"] + poisson_problem.discretise_domain(n, "grid", domains=boundaries) for b in boundaries: - assert poisson_problem.input_pts[b].shape[0] == n - poisson_problem.discretise_domain(n, 'random', locations=boundaries) + assert poisson_problem.discretised_domains[b].shape[0] == n + poisson_problem.discretise_domain(n, "random", domains=boundaries) for b in boundaries: - assert poisson_problem.input_pts[b].shape[0] == n + assert poisson_problem.discretised_domains[b].shape[0] == n - poisson_problem.discretise_domain(n, 'grid', locations=['D']) - assert poisson_problem.input_pts['D'].shape[0] == n**2 - poisson_problem.discretise_domain(n, 'random', locations=['D']) - assert poisson_problem.input_pts['D'].shape[0] == n + poisson_problem.discretise_domain(n, "grid", domains=["D"]) + assert poisson_problem.discretised_domains["D"].shape[0] == n**2 + poisson_problem.discretise_domain(n, "random", domains=["D"]) + assert poisson_problem.discretised_domains["D"].shape[0] == n - poisson_problem.discretise_domain(n, 'latin', locations=['D']) - assert poisson_problem.input_pts['D'].shape[0] == n + poisson_problem.discretise_domain(n, "latin", domains=["D"]) + assert poisson_problem.discretised_domains["D"].shape[0] == n - poisson_problem.discretise_domain(n, 'lh', locations=['D']) - assert poisson_problem.input_pts['D'].shape[0] == n + poisson_problem.discretise_domain(n, "lh", domains=["D"]) + assert poisson_problem.discretised_domains["D"].shape[0] == n - -def test_sampling_few_variables(): - n = 10 - poisson_problem = Poisson() - poisson_problem.discretise_domain(n, - 'grid', - locations=['D'], - variables=['x']) - assert poisson_problem.input_pts['D'].shape[1] == 1 - assert poisson_problem._have_sampled_points['D'] is False + poisson_problem.discretise_domain(n) def test_variables_correct_order_sampling(): n = 10 poisson_problem = Poisson() - poisson_problem.discretise_domain(n, - 'grid', - locations=['D'], - variables=['x']) - poisson_problem.discretise_domain(n, - 'grid', - locations=['D'], - variables=['y']) - assert poisson_problem.input_pts['D'].labels == sorted( - poisson_problem.input_variables) - - poisson_problem.discretise_domain(n, - 'grid', - locations=['D']) - assert poisson_problem.input_pts['D'].labels == sorted( - poisson_problem.input_variables) - - poisson_problem.discretise_domain(n, - 'grid', - locations=['D'], - variables=['y']) - poisson_problem.discretise_domain(n, - 'grid', - locations=['D'], - variables=['x']) - assert poisson_problem.input_pts['D'].labels == sorted( - poisson_problem.input_variables) + poisson_problem.discretise_domain(n, "grid", domains=["D"]) + assert poisson_problem.discretised_domains["D"].labels == sorted( + poisson_problem.input_variables + ) + + poisson_problem.discretise_domain(n, "grid", domains=["D"]) + assert poisson_problem.discretised_domains["D"].labels == sorted( + poisson_problem.input_variables + ) + def test_add_points(): poisson_problem = Poisson() - poisson_problem.discretise_domain(0, - 'random', - locations=['D'], - variables=['x','y']) - new_pts = LabelTensor(torch.tensor([[0.5,-0.5]]),labels=['x','y']) - poisson_problem.add_points({'D': new_pts}) - assert torch.isclose(poisson_problem.input_pts['D'].extract('x'),new_pts.extract('x')) - assert torch.isclose(poisson_problem.input_pts['D'].extract('y'),new_pts.extract('y')) + poisson_problem.discretise_domain(0, "random", domains=["D"]) + new_pts = LabelTensor(torch.tensor([[0.5, -0.5]]), labels=["x", "y"]) + poisson_problem.add_points({"D": new_pts}) + assert torch.isclose( + poisson_problem.discretised_domains["D"].extract("x"), + new_pts.extract("x"), + ) + assert torch.isclose( + poisson_problem.discretised_domains["D"].extract("y"), + new_pts.extract("y"), + ) + + +@pytest.mark.parametrize("mode", ["random", "grid"]) +def test_custom_sampling_logic(mode): + poisson_problem = Poisson() + sampling_rules = { + "x": {"n": 100, "mode": mode}, + "y": {"n": 50, "mode": mode}, + } + poisson_problem.discretise_domain(sample_rules=sampling_rules) + for domain in ["g1", "g2", "g3", "g4"]: + assert poisson_problem.discretised_domains[domain].shape[0] == 100 * 50 + assert poisson_problem.discretised_domains[domain].labels == ["x", "y"] + + +@pytest.mark.parametrize("mode", ["random", "grid"]) +def test_wrong_custom_sampling_logic(mode): + d2 = CartesianDomain({"x": [1, 2], "y": [0, 1]}) + poisson_problem = Poisson() + poisson_problem.domains["D"] = Union([poisson_problem.domains["D"], d2]) + sampling_rules = { + "x": {"n": 100, "mode": mode}, + "y": {"n": 50, "mode": mode}, + } + with pytest.raises(RuntimeError): + poisson_problem.discretise_domain(sample_rules=sampling_rules) diff --git a/tests/test_problem_zoo/test_advection.py b/tests/test_problem_zoo/test_advection.py new file mode 100644 index 000000000..4cfc27cd0 --- /dev/null +++ b/tests/test_problem_zoo/test_advection.py @@ -0,0 +1,18 @@ +import pytest +from pina.problem.zoo import AdvectionProblem +from pina.problem import SpatialProblem, TimeDependentProblem + + +@pytest.mark.parametrize("c", [1.5, 3]) +def test_constructor(c): + print(f"Testing with c = {c} (type: {type(c)})") + problem = AdvectionProblem(c=c) + problem.discretise_domain(n=10, mode="random", domains="all") + assert problem.are_all_domains_discretised + assert isinstance(problem, SpatialProblem) + assert isinstance(problem, TimeDependentProblem) + assert hasattr(problem, "conditions") + assert isinstance(problem.conditions, dict) + + with pytest.raises(ValueError): + AdvectionProblem(c="a") diff --git a/tests/test_problem_zoo/test_allen_cahn.py b/tests/test_problem_zoo/test_allen_cahn.py new file mode 100644 index 000000000..851348077 --- /dev/null +++ b/tests/test_problem_zoo/test_allen_cahn.py @@ -0,0 +1,12 @@ +from pina.problem.zoo import AllenCahnProblem +from pina.problem import SpatialProblem, TimeDependentProblem + + +def test_constructor(): + problem = AllenCahnProblem() + problem.discretise_domain(n=10, mode="random", domains="all") + assert problem.are_all_domains_discretised + assert isinstance(problem, SpatialProblem) + assert isinstance(problem, TimeDependentProblem) + assert hasattr(problem, "conditions") + assert isinstance(problem.conditions, dict) diff --git a/tests/test_problem_zoo/test_diffusion_reaction.py b/tests/test_problem_zoo/test_diffusion_reaction.py new file mode 100644 index 000000000..51709b29c --- /dev/null +++ b/tests/test_problem_zoo/test_diffusion_reaction.py @@ -0,0 +1,12 @@ +from pina.problem.zoo import DiffusionReactionProblem +from pina.problem import TimeDependentProblem, SpatialProblem + + +def test_constructor(): + problem = DiffusionReactionProblem() + problem.discretise_domain(n=10, mode="random", domains="all") + assert problem.are_all_domains_discretised + assert isinstance(problem, TimeDependentProblem) + assert isinstance(problem, SpatialProblem) + assert hasattr(problem, "conditions") + assert isinstance(problem.conditions, dict) diff --git a/tests/test_problem_zoo/test_helmholtz.py b/tests/test_problem_zoo/test_helmholtz.py new file mode 100644 index 000000000..ad8618a06 --- /dev/null +++ b/tests/test_problem_zoo/test_helmholtz.py @@ -0,0 +1,16 @@ +import pytest +from pina.problem.zoo import HelmholtzProblem +from pina.problem import SpatialProblem + + +@pytest.mark.parametrize("alpha", [1.5, 3]) +def test_constructor(alpha): + problem = HelmholtzProblem(alpha=alpha) + problem.discretise_domain(n=10, mode="random", domains="all") + assert problem.are_all_domains_discretised + assert isinstance(problem, SpatialProblem) + assert hasattr(problem, "conditions") + assert isinstance(problem.conditions, dict) + + with pytest.raises(ValueError): + HelmholtzProblem(alpha="a") diff --git a/tests/test_problem_zoo/test_inverse_poisson_2d_square.py b/tests/test_problem_zoo/test_inverse_poisson_2d_square.py new file mode 100644 index 000000000..20a60e636 --- /dev/null +++ b/tests/test_problem_zoo/test_inverse_poisson_2d_square.py @@ -0,0 +1,12 @@ +from pina.problem.zoo import InversePoisson2DSquareProblem +from pina.problem import InverseProblem, SpatialProblem + + +def test_constructor(): + problem = InversePoisson2DSquareProblem() + problem.discretise_domain(n=10, mode="random", domains="all") + assert problem.are_all_domains_discretised + assert isinstance(problem, InverseProblem) + assert isinstance(problem, SpatialProblem) + assert hasattr(problem, "conditions") + assert isinstance(problem.conditions, dict) diff --git a/tests/test_problem_zoo/test_poisson_2d_square.py b/tests/test_problem_zoo/test_poisson_2d_square.py new file mode 100644 index 000000000..ed7be0425 --- /dev/null +++ b/tests/test_problem_zoo/test_poisson_2d_square.py @@ -0,0 +1,11 @@ +from pina.problem.zoo import Poisson2DSquareProblem +from pina.problem import SpatialProblem + + +def test_constructor(): + problem = Poisson2DSquareProblem() + problem.discretise_domain(n=10, mode="random", domains="all") + assert problem.are_all_domains_discretised + assert isinstance(problem, SpatialProblem) + assert hasattr(problem, "conditions") + assert isinstance(problem.conditions, dict) diff --git a/tests/test_problem_zoo/test_supervised_problem.py b/tests/test_problem_zoo/test_supervised_problem.py new file mode 100644 index 000000000..19b3920ce --- /dev/null +++ b/tests/test_problem_zoo/test_supervised_problem.py @@ -0,0 +1,34 @@ +import torch +from pina.problem import AbstractProblem +from pina.condition import InputTargetCondition +from pina.problem.zoo.supervised_problem import SupervisedProblem +from pina.graph import RadiusGraph + + +def test_constructor(): + input_ = torch.rand((100, 10)) + output_ = torch.rand((100, 10)) + problem = SupervisedProblem(input_=input_, output_=output_) + assert isinstance(problem, AbstractProblem) + assert hasattr(problem, "conditions") + assert isinstance(problem.conditions, dict) + assert list(problem.conditions.keys()) == ["data"] + assert isinstance(problem.conditions["data"], InputTargetCondition) + + +def test_constructor_graph(): + x = torch.rand((20, 100, 10)) + pos = torch.rand((20, 100, 2)) + input_ = [ + RadiusGraph(x=x_, pos=pos_, radius=0.2, edge_attr=True) + for x_, pos_ in zip(x, pos) + ] + output_ = torch.rand((20, 100, 10)) + problem = SupervisedProblem(input_=input_, output_=output_) + assert isinstance(problem, AbstractProblem) + assert hasattr(problem, "conditions") + assert isinstance(problem.conditions, dict) + assert list(problem.conditions.keys()) == ["data"] + assert isinstance(problem.conditions["data"], InputTargetCondition) + assert isinstance(problem.conditions["data"].input, list) + assert isinstance(problem.conditions["data"].target, torch.Tensor) diff --git a/tests/test_scheduler.py b/tests/test_scheduler.py new file mode 100644 index 000000000..157a818d2 --- /dev/null +++ b/tests/test_scheduler.py @@ -0,0 +1,26 @@ +import torch +import pytest +from pina.optim import TorchOptimizer, TorchScheduler + +opt_list = [ + torch.optim.Adam, + torch.optim.AdamW, + torch.optim.SGD, + torch.optim.RMSprop, +] + +sch_list = [torch.optim.lr_scheduler.ConstantLR] + + +@pytest.mark.parametrize("scheduler_class", sch_list) +def test_constructor(scheduler_class): + TorchScheduler(scheduler_class) + + +@pytest.mark.parametrize("optimizer_class", opt_list) +@pytest.mark.parametrize("scheduler_class", sch_list) +def test_hook(optimizer_class, scheduler_class): + opt = TorchOptimizer(optimizer_class, lr=1e-3) + opt.hook(torch.nn.Linear(10, 10).parameters()) + sch = TorchScheduler(scheduler_class) + sch.hook(opt) diff --git a/tests/test_solver/test_causal_pinn.py b/tests/test_solver/test_causal_pinn.py new file mode 100644 index 000000000..4e72732d3 --- /dev/null +++ b/tests/test_solver/test_causal_pinn.py @@ -0,0 +1,160 @@ +import torch +import pytest + +from pina import LabelTensor, Condition +from pina.problem import SpatialProblem +from pina.solver import CausalPINN +from pina.trainer import Trainer +from pina.model import FeedForward +from pina.problem.zoo import DiffusionReactionProblem +from pina.condition import ( + InputTargetCondition, + InputEquationCondition, + DomainEquationCondition, +) +from torch._dynamo.eval_frame import OptimizedModule + + +class DummySpatialProblem(SpatialProblem): + """ + A mock spatial problem for testing purposes. + """ + + output_variables = ["u"] + conditions = {} + spatial_domain = None + + +# define problems +problem = DiffusionReactionProblem() +problem.discretise_domain(50) + +# add input-output condition to test supervised learning +input_pts = torch.rand(50, len(problem.input_variables)) +input_pts = LabelTensor(input_pts, problem.input_variables) +output_pts = torch.rand(50, len(problem.output_variables)) +output_pts = LabelTensor(output_pts, problem.output_variables) +problem.conditions["data"] = Condition(input=input_pts, target=output_pts) + +# define model +model = FeedForward(len(problem.input_variables), len(problem.output_variables)) + + +@pytest.mark.parametrize("problem", [problem]) +@pytest.mark.parametrize("eps", [100, 100.1]) +def test_constructor(problem, eps): + with pytest.raises(ValueError): + CausalPINN(model=model, problem=DummySpatialProblem()) + solver = CausalPINN(model=model, problem=problem, eps=eps) + + assert solver.accepted_conditions_types == ( + InputTargetCondition, + InputEquationCondition, + DomainEquationCondition, + ) + + +@pytest.mark.parametrize("problem", [problem]) +@pytest.mark.parametrize("batch_size", [None, 1, 5, 20]) +@pytest.mark.parametrize("compile", [True, False]) +def test_solver_train(problem, batch_size, compile): + solver = CausalPINN(model=model, problem=problem) + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=1.0, + val_size=0.0, + test_size=0.0, + compile=compile, + ) + trainer.train() + if trainer.compile: + assert isinstance(solver.model, OptimizedModule) + + +@pytest.mark.parametrize("problem", [problem]) +@pytest.mark.parametrize("batch_size", [None, 1, 5, 20]) +@pytest.mark.parametrize("compile", [True, False]) +def test_solver_validation(problem, batch_size, compile): + solver = CausalPINN(model=model, problem=problem) + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=0.9, + val_size=0.1, + test_size=0.0, + compile=compile, + ) + trainer.train() + if trainer.compile: + assert isinstance(solver.model, OptimizedModule) + + +@pytest.mark.parametrize("problem", [problem]) +@pytest.mark.parametrize("batch_size", [None, 1, 5, 20]) +@pytest.mark.parametrize("compile", [True, False]) +def test_solver_test(problem, batch_size, compile): + solver = CausalPINN(model=model, problem=problem) + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=0.7, + val_size=0.2, + test_size=0.1, + compile=compile, + ) + trainer.test() + if trainer.compile: + assert isinstance(solver.model, OptimizedModule) + + +@pytest.mark.parametrize("problem", [problem]) +def test_train_load_restore(problem): + dir = "tests/test_solver/tmp" + problem = problem + solver = CausalPINN(model=model, problem=problem) + trainer = Trainer( + solver=solver, + max_epochs=5, + accelerator="cpu", + batch_size=None, + train_size=0.7, + val_size=0.2, + test_size=0.1, + default_root_dir=dir, + ) + trainer.train() + + # restore + new_trainer = Trainer(solver=solver, max_epochs=5, accelerator="cpu") + new_trainer.train( + ckpt_path=f"{dir}/lightning_logs/version_0/checkpoints/" + + "epoch=4-step=5.ckpt" + ) + + # loading + new_solver = CausalPINN.load_from_checkpoint( + f"{dir}/lightning_logs/version_0/checkpoints/epoch=4-step=5.ckpt", + problem=problem, + model=model, + ) + + test_pts = LabelTensor(torch.rand(20, 2), problem.input_variables) + assert new_solver.forward(test_pts).shape == (20, 1) + assert new_solver.forward(test_pts).shape == ( + solver.forward(test_pts).shape + ) + torch.testing.assert_close( + new_solver.forward(test_pts), solver.forward(test_pts) + ) + + # rm directories + import shutil + + shutil.rmtree("tests/test_solver/tmp") diff --git a/tests/test_solver/test_competitive_pinn.py b/tests/test_solver/test_competitive_pinn.py new file mode 100644 index 000000000..64fb28058 --- /dev/null +++ b/tests/test_solver/test_competitive_pinn.py @@ -0,0 +1,164 @@ +import torch +import pytest + +from pina import LabelTensor, Condition +from pina.solver import CompetitivePINN as CompPINN +from pina.trainer import Trainer +from pina.model import FeedForward +from pina.problem.zoo import ( + Poisson2DSquareProblem as Poisson, + InversePoisson2DSquareProblem as InversePoisson, +) +from pina.condition import ( + InputTargetCondition, + InputEquationCondition, + DomainEquationCondition, +) +from torch._dynamo.eval_frame import OptimizedModule + + +# define problems +problem = Poisson() +problem.discretise_domain(50) +inverse_problem = InversePoisson() +inverse_problem.discretise_domain(50) + +# reduce the number of data points to speed up testing +data_condition = inverse_problem.conditions["data"] +data_condition.input = data_condition.input[:10] +data_condition.target = data_condition.target[:10] + +# add input-output condition to test supervised learning +input_pts = torch.rand(50, len(problem.input_variables)) +input_pts = LabelTensor(input_pts, problem.input_variables) +output_pts = torch.rand(50, len(problem.output_variables)) +output_pts = LabelTensor(output_pts, problem.output_variables) +problem.conditions["data"] = Condition(input=input_pts, target=output_pts) + +# define model +model = FeedForward(len(problem.input_variables), len(problem.output_variables)) + + +@pytest.mark.parametrize("problem", [problem, inverse_problem]) +@pytest.mark.parametrize("discr", [None, model]) +def test_constructor(problem, discr): + solver = CompPINN(problem=problem, model=model) + solver = CompPINN(problem=problem, model=model, discriminator=discr) + + assert solver.accepted_conditions_types == ( + InputTargetCondition, + InputEquationCondition, + DomainEquationCondition, + ) + + +@pytest.mark.parametrize("problem", [problem, inverse_problem]) +@pytest.mark.parametrize("batch_size", [None, 1, 5, 20]) +@pytest.mark.parametrize("compile", [True, False]) +def test_solver_train(problem, batch_size, compile): + solver = CompPINN(problem=problem, model=model) + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=1.0, + val_size=0.0, + test_size=0.0, + compile=compile, + ) + trainer.train() + if trainer.compile: + assert all( + [isinstance(model, OptimizedModule) for model in solver.models] + ) + + +@pytest.mark.parametrize("problem", [problem, inverse_problem]) +@pytest.mark.parametrize("batch_size", [None, 1, 5, 20]) +@pytest.mark.parametrize("compile", [True, False]) +def test_solver_validation(problem, batch_size, compile): + solver = CompPINN(problem=problem, model=model) + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=0.9, + val_size=0.1, + test_size=0.0, + compile=compile, + ) + trainer.train() + if trainer.compile: + assert all( + [isinstance(model, OptimizedModule) for model in solver.models] + ) + + +@pytest.mark.parametrize("problem", [problem, inverse_problem]) +@pytest.mark.parametrize("batch_size", [None, 1, 5, 20]) +@pytest.mark.parametrize("compile", [True, False]) +def test_solver_test(problem, batch_size, compile): + solver = CompPINN(problem=problem, model=model) + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=0.7, + val_size=0.2, + test_size=0.1, + compile=compile, + ) + trainer.test() + if trainer.compile: + assert all( + [isinstance(model, OptimizedModule) for model in solver.models] + ) + + +@pytest.mark.parametrize("problem", [problem, inverse_problem]) +def test_train_load_restore(problem): + dir = "tests/test_solver/tmp" + problem = problem + solver = CompPINN(problem=problem, model=model) + trainer = Trainer( + solver=solver, + max_epochs=5, + accelerator="cpu", + batch_size=None, + train_size=0.7, + val_size=0.2, + test_size=0.1, + default_root_dir=dir, + ) + trainer.train() + + # restore + new_trainer = Trainer(solver=solver, max_epochs=5, accelerator="cpu") + new_trainer.train( + ckpt_path=f"{dir}/lightning_logs/version_0/checkpoints/" + + "epoch=4-step=5.ckpt" + ) + + # loading + new_solver = CompPINN.load_from_checkpoint( + f"{dir}/lightning_logs/version_0/checkpoints/epoch=4-step=5.ckpt", + problem=problem, + model=model, + ) + + test_pts = LabelTensor(torch.rand(20, 2), problem.input_variables) + assert new_solver.forward(test_pts).shape == (20, 1) + assert new_solver.forward(test_pts).shape == ( + solver.forward(test_pts).shape + ) + torch.testing.assert_close( + new_solver.forward(test_pts), solver.forward(test_pts) + ) + + # rm directories + import shutil + + shutil.rmtree("tests/test_solver/tmp") diff --git a/tests/test_solver/test_garom.py b/tests/test_solver/test_garom.py new file mode 100644 index 000000000..ed147c809 --- /dev/null +++ b/tests/test_solver/test_garom.py @@ -0,0 +1,208 @@ +import torch +import torch.nn as nn + +import pytest +from pina import Condition, LabelTensor +from pina.solver import GAROM +from pina.condition import InputTargetCondition +from pina.problem import AbstractProblem +from pina.model import FeedForward +from pina.trainer import Trainer +from torch._dynamo.eval_frame import OptimizedModule + + +class TensorProblem(AbstractProblem): + input_variables = ["u_0", "u_1"] + output_variables = ["u"] + conditions = { + "data": Condition(target=torch.randn(50, 2), input=torch.randn(50, 1)) + } + + +# simple Generator Network +class Generator(nn.Module): + + def __init__( + self, + input_dimension=2, + parameters_dimension=1, + noise_dimension=2, + activation=torch.nn.SiLU, + ): + super().__init__() + + self._noise_dimension = noise_dimension + self._activation = activation + self.model = FeedForward(6 * noise_dimension, input_dimension) + self.condition = FeedForward(parameters_dimension, 5 * noise_dimension) + + def forward(self, param): + # uniform sampling in [-1, 1] + z = ( + 2 + * torch.rand( + size=(param.shape[0], self._noise_dimension), + device=param.device, + dtype=param.dtype, + requires_grad=True, + ) + - 1 + ) + return self.model(torch.cat((z, self.condition(param)), dim=-1)) + + +# Simple Discriminator Network + + +class Discriminator(nn.Module): + + def __init__( + self, + input_dimension=2, + parameter_dimension=1, + hidden_dimension=2, + activation=torch.nn.ReLU, + ): + super().__init__() + + self._activation = activation + self.encoding = FeedForward(input_dimension, hidden_dimension) + self.decoding = FeedForward(2 * hidden_dimension, input_dimension) + self.condition = FeedForward(parameter_dimension, hidden_dimension) + + def forward(self, data): + x, condition = data + encoding = self.encoding(x) + conditioning = torch.cat((encoding, self.condition(condition)), dim=-1) + decoding = self.decoding(conditioning) + return decoding + + +def test_constructor(): + GAROM( + problem=TensorProblem(), + generator=Generator(), + discriminator=Discriminator(), + ) + assert GAROM.accepted_conditions_types == (InputTargetCondition) + + +@pytest.mark.parametrize("batch_size", [None, 1, 5, 20]) +@pytest.mark.parametrize("compile", [True, False]) +def test_solver_train(batch_size, compile): + solver = GAROM( + problem=TensorProblem(), + generator=Generator(), + discriminator=Discriminator(), + ) + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=1.0, + test_size=0.0, + val_size=0.0, + compile=compile, + ) + trainer.train() + if trainer.compile: + assert all( + [isinstance(model, OptimizedModule) for model in solver.models] + ) + + +@pytest.mark.parametrize("batch_size", [None, 1, 5, 20]) +@pytest.mark.parametrize("compile", [True, False]) +def test_solver_validation(batch_size, compile): + solver = GAROM( + problem=TensorProblem(), + generator=Generator(), + discriminator=Discriminator(), + ) + + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=0.9, + val_size=0.1, + test_size=0.0, + compile=compile, + ) + trainer.train() + if trainer.compile: + assert all( + [isinstance(model, OptimizedModule) for model in solver.models] + ) + + +@pytest.mark.parametrize("batch_size", [None, 1, 5, 20]) +@pytest.mark.parametrize("compile", [True, False]) +def test_solver_test(batch_size, compile): + solver = GAROM( + problem=TensorProblem(), + generator=Generator(), + discriminator=Discriminator(), + ) + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=0.8, + val_size=0.1, + test_size=0.1, + compile=compile, + ) + trainer.test() + if trainer.compile: + assert all( + [isinstance(model, OptimizedModule) for model in solver.models] + ) + + +def test_train_load_restore(): + dir = "tests/test_solver/tmp/" + problem = TensorProblem() + solver = GAROM( + problem=TensorProblem(), + generator=Generator(), + discriminator=Discriminator(), + ) + trainer = Trainer( + solver=solver, + max_epochs=5, + accelerator="cpu", + batch_size=None, + train_size=0.9, + test_size=0.1, + val_size=0.0, + default_root_dir=dir, + ) + trainer.train() + + # restore + new_trainer = Trainer(solver=solver, max_epochs=5, accelerator="cpu") + new_trainer.train( + ckpt_path=f"{dir}/lightning_logs/version_0/checkpoints/" + + "epoch=4-step=5.ckpt" + ) + + # loading + new_solver = GAROM.load_from_checkpoint( + f"{dir}/lightning_logs/version_0/checkpoints/epoch=4-step=5.ckpt", + problem=TensorProblem(), + generator=Generator(), + discriminator=Discriminator(), + ) + + test_pts = torch.rand(20, 1) + assert new_solver.forward(test_pts).shape == (20, 2) + assert new_solver.forward(test_pts).shape == solver.forward(test_pts).shape + + # rm directories + import shutil + + shutil.rmtree("tests/test_solver/tmp") diff --git a/tests/test_solver/test_gradient_pinn.py b/tests/test_solver/test_gradient_pinn.py new file mode 100644 index 000000000..31666db3d --- /dev/null +++ b/tests/test_solver/test_gradient_pinn.py @@ -0,0 +1,169 @@ +import pytest +import torch + +from pina import LabelTensor, Condition +from pina.problem import TimeDependentProblem +from pina.solver import GradientPINN +from pina.model import FeedForward +from pina.trainer import Trainer +from pina.problem.zoo import ( + Poisson2DSquareProblem as Poisson, + InversePoisson2DSquareProblem as InversePoisson, +) +from pina.condition import ( + InputTargetCondition, + InputEquationCondition, + DomainEquationCondition, +) +from torch._dynamo.eval_frame import OptimizedModule + + +class DummyTimeProblem(TimeDependentProblem): + """ + A mock time-dependent problem for testing purposes. + """ + + output_variables = ["u"] + temporal_domain = None + conditions = {} + + +# define problems +problem = Poisson() +problem.discretise_domain(50) +inverse_problem = InversePoisson() +inverse_problem.discretise_domain(50) + +# reduce the number of data points to speed up testing +data_condition = inverse_problem.conditions["data"] +data_condition.input = data_condition.input[:10] +data_condition.target = data_condition.target[:10] + +# add input-output condition to test supervised learning +input_pts = torch.rand(50, len(problem.input_variables)) +input_pts = LabelTensor(input_pts, problem.input_variables) +output_pts = torch.rand(50, len(problem.output_variables)) +output_pts = LabelTensor(output_pts, problem.output_variables) +problem.conditions["data"] = Condition(input=input_pts, target=output_pts) + +# define model +model = FeedForward(len(problem.input_variables), len(problem.output_variables)) + + +@pytest.mark.parametrize("problem", [problem, inverse_problem]) +def test_constructor(problem): + with pytest.raises(ValueError): + GradientPINN(model=model, problem=DummyTimeProblem()) + solver = GradientPINN(model=model, problem=problem) + + assert solver.accepted_conditions_types == ( + InputTargetCondition, + InputEquationCondition, + DomainEquationCondition, + ) + + +@pytest.mark.parametrize("problem", [problem, inverse_problem]) +@pytest.mark.parametrize("batch_size", [None, 1, 5, 20]) +@pytest.mark.parametrize("compile", [True, False]) +def test_solver_train(problem, batch_size, compile): + solver = GradientPINN(model=model, problem=problem) + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=1.0, + val_size=0.0, + test_size=0.0, + compile=compile, + ) + trainer.train() + if trainer.compile: + assert isinstance(solver.model, OptimizedModule) + + +@pytest.mark.parametrize("problem", [problem, inverse_problem]) +@pytest.mark.parametrize("batch_size", [None, 1, 5, 20]) +@pytest.mark.parametrize("compile", [True, False]) +def test_solver_validation(problem, batch_size, compile): + solver = GradientPINN(model=model, problem=problem) + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=0.9, + val_size=0.1, + test_size=0.0, + compile=compile, + ) + trainer.train() + if trainer.compile: + assert isinstance(solver.model, OptimizedModule) + + +@pytest.mark.parametrize("problem", [problem, inverse_problem]) +@pytest.mark.parametrize("batch_size", [None, 1, 5, 20]) +@pytest.mark.parametrize("compile", [True, False]) +def test_solver_test(problem, batch_size, compile): + solver = GradientPINN(model=model, problem=problem) + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=0.7, + val_size=0.2, + test_size=0.1, + compile=compile, + ) + trainer.test() + if trainer.compile: + assert isinstance(solver.model, OptimizedModule) + + +@pytest.mark.parametrize("problem", [problem, inverse_problem]) +def test_train_load_restore(problem): + dir = "tests/test_solver/tmp" + problem = problem + solver = GradientPINN(model=model, problem=problem) + trainer = Trainer( + solver=solver, + max_epochs=5, + accelerator="cpu", + batch_size=None, + train_size=0.7, + val_size=0.2, + test_size=0.1, + default_root_dir=dir, + ) + trainer.train() + + # restore + new_trainer = Trainer(solver=solver, max_epochs=5, accelerator="cpu") + new_trainer.train( + ckpt_path=f"{dir}/lightning_logs/version_0/checkpoints/" + + "epoch=4-step=5.ckpt" + ) + + # loading + new_solver = GradientPINN.load_from_checkpoint( + f"{dir}/lightning_logs/version_0/checkpoints/epoch=4-step=5.ckpt", + problem=problem, + model=model, + ) + + test_pts = LabelTensor(torch.rand(20, 2), problem.input_variables) + assert new_solver.forward(test_pts).shape == (20, 1) + assert new_solver.forward(test_pts).shape == ( + solver.forward(test_pts).shape + ) + torch.testing.assert_close( + new_solver.forward(test_pts), solver.forward(test_pts) + ) + + # rm directories + import shutil + + shutil.rmtree("tests/test_solver/tmp") diff --git a/tests/test_solver/test_pinn.py b/tests/test_solver/test_pinn.py new file mode 100644 index 000000000..97511cb14 --- /dev/null +++ b/tests/test_solver/test_pinn.py @@ -0,0 +1,150 @@ +import pytest +import torch + +from pina import LabelTensor, Condition +from pina.model import FeedForward +from pina.trainer import Trainer +from pina.solver import PINN +from pina.condition import ( + InputTargetCondition, + InputEquationCondition, + DomainEquationCondition, +) +from pina.problem.zoo import ( + Poisson2DSquareProblem as Poisson, + InversePoisson2DSquareProblem as InversePoisson, +) +from torch._dynamo.eval_frame import OptimizedModule + + +# define problems +problem = Poisson() +problem.discretise_domain(50) +inverse_problem = InversePoisson() +inverse_problem.discretise_domain(50) + +# reduce the number of data points to speed up testing +data_condition = inverse_problem.conditions["data"] +data_condition.input = data_condition.input[:10] +data_condition.target = data_condition.target[:10] + +# add input-output condition to test supervised learning +input_pts = torch.rand(50, len(problem.input_variables)) +input_pts = LabelTensor(input_pts, problem.input_variables) +output_pts = torch.rand(50, len(problem.output_variables)) +output_pts = LabelTensor(output_pts, problem.output_variables) +problem.conditions["data"] = Condition(input=input_pts, target=output_pts) + +# define model +model = FeedForward(len(problem.input_variables), len(problem.output_variables)) + + +@pytest.mark.parametrize("problem", [problem, inverse_problem]) +def test_constructor(problem): + solver = PINN(problem=problem, model=model) + + assert solver.accepted_conditions_types == ( + InputTargetCondition, + InputEquationCondition, + DomainEquationCondition, + ) + + +@pytest.mark.parametrize("problem", [problem, inverse_problem]) +@pytest.mark.parametrize("batch_size", [None, 1, 5, 20]) +@pytest.mark.parametrize("compile", [True, False]) +def test_solver_train(problem, batch_size, compile): + solver = PINN(model=model, problem=problem) + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=1.0, + val_size=0.0, + test_size=0.0, + compile=compile, + ) + trainer.train() + + +@pytest.mark.parametrize("problem", [problem, inverse_problem]) +@pytest.mark.parametrize("batch_size", [None, 1, 5, 20]) +@pytest.mark.parametrize("compile", [True, False]) +def test_solver_validation(problem, batch_size, compile): + solver = PINN(model=model, problem=problem) + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=0.9, + val_size=0.1, + test_size=0.0, + compile=compile, + ) + trainer.train() + if trainer.compile: + assert isinstance(solver.model, OptimizedModule) + + +@pytest.mark.parametrize("problem", [problem, inverse_problem]) +@pytest.mark.parametrize("batch_size", [None, 1, 5, 20]) +@pytest.mark.parametrize("compile", [True, False]) +def test_solver_test(problem, batch_size, compile): + solver = PINN(model=model, problem=problem) + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=0.7, + val_size=0.2, + test_size=0.1, + compile=compile, + ) + trainer.test() + + +@pytest.mark.parametrize("problem", [problem, inverse_problem]) +def test_train_load_restore(problem): + dir = "tests/test_solver/tmp" + problem = problem + solver = PINN(model=model, problem=problem) + trainer = Trainer( + solver=solver, + max_epochs=5, + accelerator="cpu", + batch_size=None, + train_size=0.7, + val_size=0.2, + test_size=0.1, + default_root_dir=dir, + ) + trainer.train() + + # restore + new_trainer = Trainer(solver=solver, max_epochs=5, accelerator="cpu") + new_trainer.train( + ckpt_path=f"{dir}/lightning_logs/version_0/checkpoints/" + + "epoch=4-step=5.ckpt" + ) + + # loading + new_solver = PINN.load_from_checkpoint( + f"{dir}/lightning_logs/version_0/checkpoints/epoch=4-step=5.ckpt", + problem=problem, + model=model, + ) + + test_pts = LabelTensor(torch.rand(20, 2), problem.input_variables) + assert new_solver.forward(test_pts).shape == (20, 1) + assert new_solver.forward(test_pts).shape == solver.forward(test_pts).shape + torch.testing.assert_close( + new_solver.forward(test_pts), solver.forward(test_pts) + ) + + # rm directories + import shutil + + shutil.rmtree("tests/test_solver/tmp") diff --git a/tests/test_solver/test_rba_pinn.py b/tests/test_solver/test_rba_pinn.py new file mode 100644 index 000000000..f355aab02 --- /dev/null +++ b/tests/test_solver/test_rba_pinn.py @@ -0,0 +1,172 @@ +import pytest +import torch + +from pina import LabelTensor, Condition +from pina.model import FeedForward +from pina.trainer import Trainer +from pina.solver import RBAPINN +from pina.condition import ( + InputTargetCondition, + InputEquationCondition, + DomainEquationCondition, +) +from pina.problem.zoo import ( + Poisson2DSquareProblem as Poisson, + InversePoisson2DSquareProblem as InversePoisson, +) +from torch._dynamo.eval_frame import OptimizedModule + +# define problems +problem = Poisson() +problem.discretise_domain(50) +inverse_problem = InversePoisson() +inverse_problem.discretise_domain(50) + +# reduce the number of data points to speed up testing +data_condition = inverse_problem.conditions["data"] +data_condition.input = data_condition.input[:10] +data_condition.target = data_condition.target[:10] + +# add input-output condition to test supervised learning +input_pts = torch.rand(50, len(problem.input_variables)) +input_pts = LabelTensor(input_pts, problem.input_variables) +output_pts = torch.rand(50, len(problem.output_variables)) +output_pts = LabelTensor(output_pts, problem.output_variables) +problem.conditions["data"] = Condition(input=input_pts, target=output_pts) + +# define model +model = FeedForward(len(problem.input_variables), len(problem.output_variables)) + + +@pytest.mark.parametrize("problem", [problem, inverse_problem]) +@pytest.mark.parametrize("eta", [1, 0.001]) +@pytest.mark.parametrize("gamma", [0.5, 0.9]) +def test_constructor(problem, eta, gamma): + with pytest.raises(AssertionError): + solver = RBAPINN(model=model, problem=problem, gamma=1.5) + solver = RBAPINN(model=model, problem=problem, eta=eta, gamma=gamma) + + assert solver.accepted_conditions_types == ( + InputTargetCondition, + InputEquationCondition, + DomainEquationCondition, + ) + + +@pytest.mark.parametrize("problem", [problem, inverse_problem]) +def test_wrong_batch(problem): + with pytest.raises(NotImplementedError): + solver = RBAPINN(model=model, problem=problem) + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=10, + train_size=1.0, + val_size=0.0, + test_size=0.0, + ) + trainer.train() + + +@pytest.mark.parametrize("problem", [problem, inverse_problem]) +@pytest.mark.parametrize("compile", [True, False]) +def test_solver_train(problem, compile): + solver = RBAPINN(model=model, problem=problem) + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=None, + train_size=1.0, + val_size=0.0, + test_size=0.0, + compile=compile, + ) + trainer.train() + if trainer.compile: + assert isinstance(solver.model, OptimizedModule) + + +@pytest.mark.parametrize("problem", [problem, inverse_problem]) +@pytest.mark.parametrize("compile", [True, False]) +def test_solver_validation(problem, compile): + solver = RBAPINN(model=model, problem=problem) + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=None, + train_size=0.9, + val_size=0.1, + test_size=0.0, + compile=compile, + ) + trainer.train() + if trainer.compile: + assert isinstance(solver.model, OptimizedModule) + + +@pytest.mark.parametrize("problem", [problem, inverse_problem]) +@pytest.mark.parametrize("compile", [True, False]) +def test_solver_test(problem, compile): + solver = RBAPINN(model=model, problem=problem) + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=None, + train_size=0.7, + val_size=0.2, + test_size=0.1, + compile=compile, + ) + trainer.test() + if trainer.compile: + assert isinstance(solver.model, OptimizedModule) + + +@pytest.mark.parametrize("problem", [problem, inverse_problem]) +def test_train_load_restore(problem): + dir = "tests/test_solver/tmp" + problem = problem + solver = RBAPINN(model=model, problem=problem) + trainer = Trainer( + solver=solver, + max_epochs=5, + accelerator="cpu", + batch_size=None, + train_size=0.7, + val_size=0.2, + test_size=0.1, + default_root_dir=dir, + ) + trainer.train() + + # restore + new_trainer = Trainer(solver=solver, max_epochs=5, accelerator="cpu") + new_trainer.train( + ckpt_path=f"{dir}/lightning_logs/version_0/checkpoints/" + + "epoch=4-step=5.ckpt" + ) + + # loading + new_solver = RBAPINN.load_from_checkpoint( + f"{dir}/lightning_logs/version_0/checkpoints/epoch=4-step=5.ckpt", + problem=problem, + model=model, + ) + + test_pts = LabelTensor(torch.rand(20, 2), problem.input_variables) + assert new_solver.forward(test_pts).shape == (20, 1) + assert new_solver.forward(test_pts).shape == ( + solver.forward(test_pts).shape + ) + torch.testing.assert_close( + new_solver.forward(test_pts), solver.forward(test_pts) + ) + + # rm directories + import shutil + + shutil.rmtree("tests/test_solver/tmp") diff --git a/tests/test_solver/test_reduced_order_model_solver.py b/tests/test_solver/test_reduced_order_model_solver.py new file mode 100644 index 000000000..5427ec7a2 --- /dev/null +++ b/tests/test_solver/test_reduced_order_model_solver.py @@ -0,0 +1,228 @@ +import torch +import pytest + +from pina import Condition, LabelTensor +from pina.problem import AbstractProblem +from pina.condition import InputTargetCondition +from pina.solver import ReducedOrderModelSolver +from pina.trainer import Trainer +from pina.model import FeedForward +from pina.problem.zoo import Poisson2DSquareProblem +from torch._dynamo.eval_frame import OptimizedModule + + +class LabelTensorProblem(AbstractProblem): + input_variables = ["u_0", "u_1"] + output_variables = ["u"] + conditions = { + "data": Condition( + input=LabelTensor(torch.randn(20, 2), ["u_0", "u_1"]), + target=LabelTensor(torch.randn(20, 1), ["u"]), + ), + } + + +class TensorProblem(AbstractProblem): + input_variables = ["u_0", "u_1"] + output_variables = ["u"] + conditions = { + "data": Condition(input=torch.randn(20, 2), target=torch.randn(20, 1)) + } + + +class AE(torch.nn.Module): + def __init__(self, input_dimensions, rank): + super().__init__() + self.encode = FeedForward( + input_dimensions, rank, layers=[input_dimensions // 4] + ) + self.decode = FeedForward( + rank, input_dimensions, layers=[input_dimensions // 4] + ) + + +class AE_missing_encode(torch.nn.Module): + def __init__(self, input_dimensions, rank): + super().__init__() + self.encode = FeedForward( + input_dimensions, rank, layers=[input_dimensions // 4] + ) + + +class AE_missing_decode(torch.nn.Module): + def __init__(self, input_dimensions, rank): + super().__init__() + self.decode = FeedForward( + rank, input_dimensions, layers=[input_dimensions // 4] + ) + + +rank = 10 +model = AE(2, 1) +interpolation_net = FeedForward(2, rank) +reduction_net = AE(1, rank) + + +def test_constructor(): + problem = TensorProblem() + ReducedOrderModelSolver( + problem=problem, + interpolation_network=interpolation_net, + reduction_network=reduction_net, + ) + ReducedOrderModelSolver( + problem=LabelTensorProblem(), + reduction_network=reduction_net, + interpolation_network=interpolation_net, + ) + assert ( + ReducedOrderModelSolver.accepted_conditions_types + == InputTargetCondition + ) + with pytest.raises(SyntaxError): + ReducedOrderModelSolver( + problem=problem, + reduction_network=AE_missing_encode( + len(problem.output_variables), rank + ), + interpolation_network=interpolation_net, + ) + ReducedOrderModelSolver( + problem=problem, + reduction_network=AE_missing_decode( + len(problem.output_variables), rank + ), + interpolation_network=interpolation_net, + ) + with pytest.raises(ValueError): + ReducedOrderModelSolver( + problem=Poisson2DSquareProblem(), + reduction_network=reduction_net, + interpolation_network=interpolation_net, + ) + + +@pytest.mark.parametrize("batch_size", [None, 1, 5, 20]) +@pytest.mark.parametrize("use_lt", [True, False]) +@pytest.mark.parametrize("compile", [True, False]) +def test_solver_train(use_lt, batch_size, compile): + problem = LabelTensorProblem() if use_lt else TensorProblem() + solver = ReducedOrderModelSolver( + problem=problem, + reduction_network=reduction_net, + interpolation_network=interpolation_net, + use_lt=use_lt, + ) + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=1.0, + test_size=0.0, + val_size=0.0, + compile=compile, + ) + trainer.train() + if trainer.compile: + for v in solver.model.values(): + assert isinstance(v, OptimizedModule) + + +@pytest.mark.parametrize("use_lt", [True, False]) +@pytest.mark.parametrize("compile", [True, False]) +def test_solver_validation(use_lt, compile): + problem = LabelTensorProblem() if use_lt else TensorProblem() + solver = ReducedOrderModelSolver( + problem=problem, + reduction_network=reduction_net, + interpolation_network=interpolation_net, + use_lt=use_lt, + ) + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=None, + train_size=0.9, + val_size=0.1, + test_size=0.0, + compile=compile, + ) + trainer.train() + if trainer.compile: + for v in solver.model.values(): + assert isinstance(v, OptimizedModule) + + +@pytest.mark.parametrize("use_lt", [True, False]) +@pytest.mark.parametrize("compile", [True, False]) +def test_solver_test(use_lt, compile): + problem = LabelTensorProblem() if use_lt else TensorProblem() + solver = ReducedOrderModelSolver( + problem=problem, + reduction_network=reduction_net, + interpolation_network=interpolation_net, + use_lt=use_lt, + ) + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=None, + train_size=0.8, + val_size=0.1, + test_size=0.1, + compile=compile, + ) + trainer.train() + if trainer.compile: + for v in solver.model.values(): + assert isinstance(v, OptimizedModule) + + +def test_train_load_restore(): + dir = "tests/test_solver/tmp/" + problem = LabelTensorProblem() + solver = ReducedOrderModelSolver( + problem=problem, + reduction_network=reduction_net, + interpolation_network=interpolation_net, + ) + trainer = Trainer( + solver=solver, + max_epochs=5, + accelerator="cpu", + batch_size=None, + train_size=0.9, + test_size=0.1, + val_size=0.0, + default_root_dir=dir, + ) + trainer.train() + # restore + ntrainer = Trainer( + solver=solver, + max_epochs=5, + accelerator="cpu", + ) + ntrainer.train( + ckpt_path=f"{dir}/lightning_logs/version_0/checkpoints/epoch=4-step=5.ckpt" + ) + # loading + new_solver = ReducedOrderModelSolver.load_from_checkpoint( + f"{dir}/lightning_logs/version_0/checkpoints/epoch=4-step=5.ckpt", + problem=problem, + reduction_network=reduction_net, + interpolation_network=interpolation_net, + ) + test_pts = LabelTensor(torch.rand(20, 2), problem.input_variables) + assert new_solver.forward(test_pts).shape == (20, 1) + assert new_solver.forward(test_pts).shape == solver.forward(test_pts).shape + torch.testing.assert_close( + new_solver.forward(test_pts), solver.forward(test_pts) + ) + # rm directories + import shutil + + shutil.rmtree("tests/test_solver/tmp") diff --git a/tests/test_solver/test_self_adaptive_pinn.py b/tests/test_solver/test_self_adaptive_pinn.py new file mode 100644 index 000000000..48e3d9f8b --- /dev/null +++ b/tests/test_solver/test_self_adaptive_pinn.py @@ -0,0 +1,186 @@ +import torch +import pytest + +from pina import LabelTensor, Condition +from pina.solver import SelfAdaptivePINN as SAPINN +from pina.trainer import Trainer +from pina.model import FeedForward +from pina.problem.zoo import ( + Poisson2DSquareProblem as Poisson, + InversePoisson2DSquareProblem as InversePoisson, +) +from pina.condition import ( + InputTargetCondition, + InputEquationCondition, + DomainEquationCondition, +) +from torch._dynamo.eval_frame import OptimizedModule + + +# define problems +problem = Poisson() +problem.discretise_domain(50) +inverse_problem = InversePoisson() +inverse_problem.discretise_domain(50) + +# reduce the number of data points to speed up testing +data_condition = inverse_problem.conditions["data"] +data_condition.input = data_condition.input[:10] +data_condition.target = data_condition.target[:10] + +# add input-output condition to test supervised learning +input_pts = torch.rand(50, len(problem.input_variables)) +input_pts = LabelTensor(input_pts, problem.input_variables) +output_pts = torch.rand(50, len(problem.output_variables)) +output_pts = LabelTensor(output_pts, problem.output_variables) +problem.conditions["data"] = Condition(input=input_pts, target=output_pts) + +# define model +model = FeedForward(len(problem.input_variables), len(problem.output_variables)) + + +@pytest.mark.parametrize("problem", [problem, inverse_problem]) +@pytest.mark.parametrize("weight_fn", [torch.nn.Sigmoid(), torch.nn.Tanh()]) +def test_constructor(problem, weight_fn): + with pytest.raises(ValueError): + SAPINN(model=model, problem=problem, weight_function=1) + solver = SAPINN(problem=problem, model=model, weight_function=weight_fn) + + assert solver.accepted_conditions_types == ( + InputTargetCondition, + InputEquationCondition, + DomainEquationCondition, + ) + + +@pytest.mark.parametrize("problem", [problem, inverse_problem]) +def test_wrong_batch(problem): + with pytest.raises(NotImplementedError): + solver = SAPINN(model=model, problem=problem) + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=10, + train_size=1.0, + val_size=0.0, + test_size=0.0, + ) + trainer.train() + + +@pytest.mark.parametrize("problem", [problem, inverse_problem]) +@pytest.mark.parametrize("compile", [True, False]) +def test_solver_train(problem, compile): + solver = SAPINN(model=model, problem=problem) + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=None, + train_size=1.0, + val_size=0.0, + test_size=0.0, + compile=compile, + ) + trainer.train() + if trainer.compile: + assert all( + [ + isinstance(model, (OptimizedModule, torch.nn.ModuleDict)) + for model in solver.models + ] + ) + + +@pytest.mark.parametrize("problem", [problem, inverse_problem]) +@pytest.mark.parametrize("compile", [True, False]) +def test_solver_validation(problem, compile): + solver = SAPINN(model=model, problem=problem) + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=None, + train_size=0.9, + val_size=0.1, + test_size=0.0, + compile=compile, + ) + trainer.train() + if trainer.compile: + assert all( + [ + isinstance(model, (OptimizedModule, torch.nn.ModuleDict)) + for model in solver.models + ] + ) + + +@pytest.mark.parametrize("problem", [problem, inverse_problem]) +@pytest.mark.parametrize("compile", [True, False]) +def test_solver_test(problem, compile): + solver = SAPINN(model=model, problem=problem) + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=None, + train_size=0.7, + val_size=0.2, + test_size=0.1, + compile=compile, + ) + trainer.test() + if trainer.compile: + assert all( + [ + isinstance(model, (OptimizedModule, torch.nn.ModuleDict)) + for model in solver.models + ] + ) + + +@pytest.mark.parametrize("problem", [problem, inverse_problem]) +def test_train_load_restore(problem): + dir = "tests/test_solver/tmp" + problem = problem + solver = SAPINN(model=model, problem=problem) + trainer = Trainer( + solver=solver, + max_epochs=5, + accelerator="cpu", + batch_size=None, + train_size=0.7, + val_size=0.2, + test_size=0.1, + default_root_dir=dir, + ) + trainer.train() + # restore + new_trainer = Trainer(solver=solver, max_epochs=5, accelerator="cpu") + new_trainer.train( + ckpt_path=f"{dir}/lightning_logs/version_0/checkpoints/" + + "epoch=4-step=5.ckpt" + ) + + # loading + new_solver = SAPINN.load_from_checkpoint( + f"{dir}/lightning_logs/version_0/checkpoints/epoch=4-step=5.ckpt", + problem=problem, + model=model, + ) + + test_pts = LabelTensor(torch.rand(20, 2), problem.input_variables) + assert new_solver.forward(test_pts).shape == (20, 1) + assert new_solver.forward(test_pts).shape == ( + solver.forward(test_pts).shape + ) + torch.testing.assert_close( + new_solver.forward(test_pts), solver.forward(test_pts) + ) + + # rm directories + import shutil + + shutil.rmtree("tests/test_solver/tmp") diff --git a/tests/test_solver/test_supervised_solver.py b/tests/test_solver/test_supervised_solver.py new file mode 100644 index 000000000..30ae08064 --- /dev/null +++ b/tests/test_solver/test_supervised_solver.py @@ -0,0 +1,253 @@ +import torch +import pytest +from torch._dynamo.eval_frame import OptimizedModule +from torch_geometric.nn import GCNConv +from pina import Condition, LabelTensor +from pina.condition import InputTargetCondition +from pina.problem import AbstractProblem +from pina.solver import SupervisedSolver +from pina.model import FeedForward +from pina.trainer import Trainer +from pina.graph import KNNGraph + + +class LabelTensorProblem(AbstractProblem): + input_variables = ["u_0", "u_1"] + output_variables = ["u"] + conditions = { + "data": Condition( + input=LabelTensor(torch.randn(20, 2), ["u_0", "u_1"]), + target=LabelTensor(torch.randn(20, 1), ["u"]), + ), + } + + +class TensorProblem(AbstractProblem): + input_variables = ["u_0", "u_1"] + output_variables = ["u"] + conditions = { + "data": Condition(input=torch.randn(20, 2), target=torch.randn(20, 1)) + } + + +x = torch.rand((100, 20, 5)) +pos = torch.rand((100, 20, 2)) +output_ = torch.rand((100, 20, 1)) +input_ = [ + KNNGraph(x=x_, pos=pos_, neighbours=3, edge_attr=True) + for x_, pos_ in zip(x, pos) +] + + +class GraphProblem(AbstractProblem): + output_variables = None + conditions = {"data": Condition(input=input_, target=output_)} + + +x = LabelTensor(torch.rand((100, 20, 5)), ["a", "b", "c", "d", "e"]) +pos = LabelTensor(torch.rand((100, 20, 2)), ["x", "y"]) +output_ = LabelTensor(torch.rand((100, 20, 1)), ["u"]) +input_ = [ + KNNGraph(x=x[i], pos=pos[i], neighbours=3, edge_attr=True) + for i in range(len(x)) +] + + +class GraphProblemLT(AbstractProblem): + output_variables = ["u"] + input_variables = ["a", "b", "c", "d", "e"] + conditions = {"data": Condition(input=input_, target=output_)} + + +model = FeedForward(2, 1) + + +class Model(torch.nn.Module): + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.lift = torch.nn.Linear(5, 10) + self.activation = torch.nn.Tanh() + self.output = torch.nn.Linear(10, 1) + + self.conv = GCNConv(10, 10) + + def forward(self, batch): + + x = batch.x + edge_index = batch.edge_index + for _ in range(1): + y = self.lift(x) + y = self.activation(y) + y = self.conv(y, edge_index) + y = self.activation(y) + y = self.output(y) + return y + + +graph_model = Model() + + +def test_constructor(): + SupervisedSolver(problem=TensorProblem(), model=model) + SupervisedSolver(problem=LabelTensorProblem(), model=model) + assert SupervisedSolver.accepted_conditions_types == (InputTargetCondition) + + +@pytest.mark.parametrize("batch_size", [None, 1, 5, 20]) +@pytest.mark.parametrize("use_lt", [True, False]) +@pytest.mark.parametrize("compile", [True, False]) +def test_solver_train(use_lt, batch_size, compile): + problem = LabelTensorProblem() if use_lt else TensorProblem() + solver = SupervisedSolver(problem=problem, model=model, use_lt=use_lt) + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=1.0, + test_size=0.0, + val_size=0.0, + compile=compile, + ) + + trainer.train() + if trainer.compile: + assert isinstance(solver.model, OptimizedModule) + + +@pytest.mark.parametrize("batch_size", [None, 1, 5, 20]) +@pytest.mark.parametrize("use_lt", [True, False]) +def test_solver_train_graph(batch_size, use_lt): + problem = GraphProblemLT() if use_lt else GraphProblem() + solver = SupervisedSolver(problem=problem, model=graph_model, use_lt=use_lt) + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=1.0, + test_size=0.0, + val_size=0.0, + ) + + trainer.train() + + +@pytest.mark.parametrize("use_lt", [True, False]) +@pytest.mark.parametrize("compile", [True, False]) +def test_solver_validation(use_lt, compile): + problem = LabelTensorProblem() if use_lt else TensorProblem() + solver = SupervisedSolver(problem=problem, model=model, use_lt=use_lt) + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=None, + train_size=0.9, + val_size=0.1, + test_size=0.0, + compile=compile, + ) + trainer.train() + if trainer.compile: + assert isinstance(solver.model, OptimizedModule) + + +@pytest.mark.parametrize("batch_size", [None, 1, 5, 20]) +@pytest.mark.parametrize("use_lt", [True, False]) +def test_solver_validation_graph(batch_size, use_lt): + problem = GraphProblemLT() if use_lt else GraphProblem() + solver = SupervisedSolver(problem=problem, model=graph_model, use_lt=use_lt) + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=0.9, + val_size=0.1, + test_size=0.0, + ) + + trainer.train() + + +@pytest.mark.parametrize("use_lt", [True, False]) +@pytest.mark.parametrize("compile", [True, False]) +def test_solver_test(use_lt, compile): + problem = LabelTensorProblem() if use_lt else TensorProblem() + solver = SupervisedSolver(problem=problem, model=model, use_lt=use_lt) + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=None, + train_size=0.8, + val_size=0.1, + test_size=0.1, + compile=compile, + ) + trainer.test() + if trainer.compile: + assert isinstance(solver.model, OptimizedModule) + + +@pytest.mark.parametrize("batch_size", [None, 1, 5, 20]) +@pytest.mark.parametrize("use_lt", [True, False]) +def test_solver_test_graph(batch_size, use_lt): + problem = GraphProblemLT() if use_lt else GraphProblem() + solver = SupervisedSolver(problem=problem, model=graph_model, use_lt=use_lt) + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=0.8, + val_size=0.1, + test_size=0.1, + ) + + trainer.test() + + +def test_train_load_restore(): + dir = "tests/test_solver/tmp/" + problem = LabelTensorProblem() + solver = SupervisedSolver(problem=problem, model=model) + trainer = Trainer( + solver=solver, + max_epochs=5, + accelerator="cpu", + batch_size=None, + train_size=0.9, + test_size=0.1, + val_size=0.0, + default_root_dir=dir, + ) + trainer.train() + + # restore + new_trainer = Trainer(solver=solver, max_epochs=5, accelerator="cpu") + new_trainer.train( + ckpt_path=f"{dir}/lightning_logs/version_0/checkpoints/" + + "epoch=4-step=5.ckpt" + ) + + # loading + new_solver = SupervisedSolver.load_from_checkpoint( + f"{dir}/lightning_logs/version_0/checkpoints/epoch=4-step=5.ckpt", + problem=problem, + model=model, + ) + + test_pts = LabelTensor(torch.rand(20, 2), problem.input_variables) + assert new_solver.forward(test_pts).shape == (20, 1) + assert new_solver.forward(test_pts).shape == solver.forward(test_pts).shape + torch.testing.assert_close( + new_solver.forward(test_pts), solver.forward(test_pts) + ) + + # rm directories + import shutil + + shutil.rmtree("tests/test_solver/tmp") diff --git a/tests/test_solvers/test_basepinn.py b/tests/test_solvers/test_basepinn.py deleted file mode 100644 index e7f820d08..000000000 --- a/tests/test_solvers/test_basepinn.py +++ /dev/null @@ -1,113 +0,0 @@ -import torch -import pytest - -from pina import Condition, LabelTensor, Trainer -from pina.problem import SpatialProblem -from pina.operators import laplacian -from pina.geometry import CartesianDomain -from pina.model import FeedForward -from pina.solvers import PINNInterface -from pina.equation.equation import Equation -from pina.equation.equation_factory import FixedValue - -def laplace_equation(input_, output_): - force_term = (torch.sin(input_.extract(['x']) * torch.pi) * - torch.sin(input_.extract(['y']) * torch.pi)) - delta_u = laplacian(output_.extract(['u']), input_) - return delta_u - force_term - - -my_laplace = Equation(laplace_equation) -in_ = LabelTensor(torch.tensor([[0., 1.]]), ['x', 'y']) -out_ = LabelTensor(torch.tensor([[0.]]), ['u']) -in2_ = LabelTensor(torch.rand(60, 2), ['x', 'y']) -out2_ = LabelTensor(torch.rand(60, 1), ['u']) - - - -class Poisson(SpatialProblem): - output_variables = ['u'] - spatial_domain = CartesianDomain({'x': [0, 1], 'y': [0, 1]}) - - conditions = { - 'gamma1': Condition( - location=CartesianDomain({'x': [0, 1], 'y': 1}), - equation=FixedValue(0.0)), - 'gamma2': Condition( - location=CartesianDomain({'x': [0, 1], 'y': 0}), - equation=FixedValue(0.0)), - 'gamma3': Condition( - location=CartesianDomain({'x': 1, 'y': [0, 1]}), - equation=FixedValue(0.0)), - 'gamma4': Condition( - location=CartesianDomain({'x': 0, 'y': [0, 1]}), - equation=FixedValue(0.0)), - 'D': Condition( - input_points=LabelTensor(torch.rand(size=(100, 2)), ['x', 'y']), - equation=my_laplace), - 'data': Condition( - input_points=in_, - output_points=out_), - 'data2': Condition( - input_points=in2_, - output_points=out2_) - } - - def poisson_sol(self, pts): - return -(torch.sin(pts.extract(['x']) * torch.pi) * - torch.sin(pts.extract(['y']) * torch.pi)) / (2 * torch.pi**2) - - truth_solution = poisson_sol - -class FOOPINN(PINNInterface): - def __init__(self, model, problem): - super().__init__(models=[model], problem=problem, - optimizers=[torch.optim.Adam], - optimizers_kwargs=[{'lr' : 0.001}], - extra_features=None, - loss=torch.nn.MSELoss()) - def forward(self, x): - return self.models[0](x) - - def loss_phys(self, samples, equation): - residual = self.compute_residual(samples=samples, equation=equation) - loss_value = self.loss( - torch.zeros_like(residual, requires_grad=True), residual - ) - self.store_log(loss_value=float(loss_value)) - return loss_value - - def configure_optimizers(self): - return self.optimizers, [] - -# make the problem -poisson_problem = Poisson() -poisson_problem.discretise_domain(100) -model = FeedForward(len(poisson_problem.input_variables), - len(poisson_problem.output_variables)) -model_extra_feats = FeedForward( - len(poisson_problem.input_variables) + 1, - len(poisson_problem.output_variables)) - - -def test_constructor(): - with pytest.raises(TypeError): - PINNInterface() - # a simple pinn built with PINNInterface - FOOPINN(model, poisson_problem) - -def test_train_step(): - solver = FOOPINN(model, poisson_problem) - trainer = Trainer(solver, max_epochs=2, accelerator='cpu') - trainer.train() - -def test_log(): - solver = FOOPINN(model, poisson_problem) - trainer = Trainer(solver, max_epochs=2, accelerator='cpu') - trainer.train() - # assert the logged metrics are correct - logged_metrics = sorted(list(trainer.logged_metrics.keys())) - total_metrics = sorted( - list([key + '_loss' for key in poisson_problem.conditions.keys()]) - + ['mean_loss']) - assert logged_metrics == total_metrics \ No newline at end of file diff --git a/tests/test_solvers/test_causalpinn.py b/tests/test_solvers/test_causalpinn.py deleted file mode 100644 index bad5255d3..000000000 --- a/tests/test_solvers/test_causalpinn.py +++ /dev/null @@ -1,278 +0,0 @@ -import torch -import pytest - -from pina.problem import TimeDependentProblem, InverseProblem, SpatialProblem -from pina.operators import grad -from pina.geometry import CartesianDomain -from pina import Condition, LabelTensor -from pina.solvers import CausalPINN -from pina.trainer import Trainer -from pina.model import FeedForward -from pina.equation.equation import Equation -from pina.equation.equation_factory import FixedValue -from pina.loss import LpLoss - - - -class FooProblem(SpatialProblem): - ''' - Foo problem formulation. - ''' - output_variables = ['u'] - conditions = {} - spatial_domain = None - - -class InverseDiffusionReactionSystem(TimeDependentProblem, SpatialProblem, InverseProblem): - - def diffusionreaction(input_, output_, params_): - x = input_.extract('x') - t = input_.extract('t') - u_t = grad(output_, input_, d='t') - u_x = grad(output_, input_, d='x') - u_xx = grad(u_x, input_, d='x') - r = torch.exp(-t) * (1.5 * torch.sin(2*x) + (8/3)*torch.sin(3*x) + - (15/4)*torch.sin(4*x) + (63/8)*torch.sin(8*x)) - return u_t - params_['mu']*u_xx - r - - def _solution(self, pts): - t = pts.extract('t') - x = pts.extract('x') - return torch.exp(-t) * (torch.sin(x) + (1/2)*torch.sin(2*x) + - (1/3)*torch.sin(3*x) + (1/4)*torch.sin(4*x) + - (1/8)*torch.sin(8*x)) - - # assign output/ spatial and temporal variables - output_variables = ['u'] - spatial_domain = CartesianDomain({'x': [-torch.pi, torch.pi]}) - temporal_domain = CartesianDomain({'t': [0, 1]}) - unknown_parameter_domain = CartesianDomain({'mu': [-1, 1]}) - - # problem condition statement - conditions = { - 'D': Condition(location=CartesianDomain({'x': [-torch.pi, torch.pi], - 't': [0, 1]}), - equation=Equation(diffusionreaction)), - 'data' : Condition(input_points=LabelTensor(torch.tensor([[0., 0.]]), ['x', 't']), - output_points=LabelTensor(torch.tensor([[0.]]), ['u'])), - } - -class DiffusionReactionSystem(TimeDependentProblem, SpatialProblem): - - def diffusionreaction(input_, output_): - x = input_.extract('x') - t = input_.extract('t') - u_t = grad(output_, input_, d='t') - u_x = grad(output_, input_, d='x') - u_xx = grad(u_x, input_, d='x') - r = torch.exp(-t) * (1.5 * torch.sin(2*x) + (8/3)*torch.sin(3*x) + - (15/4)*torch.sin(4*x) + (63/8)*torch.sin(8*x)) - return u_t - u_xx - r - - def _solution(self, pts): - t = pts.extract('t') - x = pts.extract('x') - return torch.exp(-t) * (torch.sin(x) + (1/2)*torch.sin(2*x) + - (1/3)*torch.sin(3*x) + (1/4)*torch.sin(4*x) + - (1/8)*torch.sin(8*x)) - - # assign output/ spatial and temporal variables - output_variables = ['u'] - spatial_domain = CartesianDomain({'x': [-torch.pi, torch.pi]}) - temporal_domain = CartesianDomain({'t': [0, 1]}) - - # problem condition statement - conditions = { - 'D': Condition(location=CartesianDomain({'x': [-torch.pi, torch.pi], - 't': [0, 1]}), - equation=Equation(diffusionreaction)), - } - -class myFeature(torch.nn.Module): - """ - Feature: sin(x) - """ - - def __init__(self): - super(myFeature, self).__init__() - - def forward(self, x): - t = (torch.sin(x.extract(['x']) * torch.pi)) - return LabelTensor(t, ['sin(x)']) - - -# make the problem -problem = DiffusionReactionSystem() -model = FeedForward(len(problem.input_variables), - len(problem.output_variables)) -model_extra_feats = FeedForward( - len(problem.input_variables) + 1, - len(problem.output_variables)) -extra_feats = [myFeature()] - - -def test_constructor(): - CausalPINN(problem=problem, model=model, extra_features=None) - - with pytest.raises(ValueError): - CausalPINN(FooProblem(), model=model, extra_features=None) - - -def test_constructor_extra_feats(): - model_extra_feats = FeedForward( - len(problem.input_variables) + 1, - len(problem.output_variables)) - CausalPINN(problem=problem, - model=model_extra_feats, - extra_features=extra_feats) - - -def test_train_cpu(): - problem = DiffusionReactionSystem() - boundaries = ['D'] - n = 10 - problem.discretise_domain(n, 'grid', locations=boundaries) - pinn = CausalPINN(problem = problem, - model=model, extra_features=None, loss=LpLoss()) - trainer = Trainer(solver=pinn, max_epochs=1, - accelerator='cpu', batch_size=20) - trainer.train() - -def test_log(): - problem.discretise_domain(100) - solver = CausalPINN(problem = problem, - model=model, extra_features=None, loss=LpLoss()) - trainer = Trainer(solver, max_epochs=2, accelerator='cpu') - trainer.train() - # assert the logged metrics are correct - logged_metrics = sorted(list(trainer.logged_metrics.keys())) - total_metrics = sorted( - list([key + '_loss' for key in problem.conditions.keys()]) - + ['mean_loss']) - assert logged_metrics == total_metrics - -def test_train_restore(): - tmpdir = "tests/tmp_restore" - problem = DiffusionReactionSystem() - boundaries = ['D'] - n = 10 - problem.discretise_domain(n, 'grid', locations=boundaries) - pinn = CausalPINN(problem=problem, - model=model, - extra_features=None, - loss=LpLoss()) - trainer = Trainer(solver=pinn, - max_epochs=5, - accelerator='cpu', - default_root_dir=tmpdir) - trainer.train() - ntrainer = Trainer(solver=pinn, max_epochs=15, accelerator='cpu') - t = ntrainer.train( - ckpt_path=f'{tmpdir}/lightning_logs/version_0/' - 'checkpoints/epoch=4-step=5.ckpt') - import shutil - shutil.rmtree(tmpdir) - - -def test_train_load(): - tmpdir = "tests/tmp_load" - problem = DiffusionReactionSystem() - boundaries = ['D'] - n = 10 - problem.discretise_domain(n, 'grid', locations=boundaries) - pinn = CausalPINN(problem=problem, - model=model, - extra_features=None, - loss=LpLoss()) - trainer = Trainer(solver=pinn, - max_epochs=15, - accelerator='cpu', - default_root_dir=tmpdir) - trainer.train() - new_pinn = CausalPINN.load_from_checkpoint( - f'{tmpdir}/lightning_logs/version_0/checkpoints/epoch=14-step=15.ckpt', - problem = problem, model=model) - test_pts = CartesianDomain({'x': [0, 1], 't': [0, 1]}).sample(10) - assert new_pinn.forward(test_pts).extract(['u']).shape == (10, 1) - assert new_pinn.forward(test_pts).extract( - ['u']).shape == pinn.forward(test_pts).extract(['u']).shape - torch.testing.assert_close( - new_pinn.forward(test_pts).extract(['u']), - pinn.forward(test_pts).extract(['u'])) - import shutil - shutil.rmtree(tmpdir) - -def test_train_inverse_problem_cpu(): - problem = InverseDiffusionReactionSystem() - boundaries = ['D'] - n = 100 - problem.discretise_domain(n, 'random', locations=boundaries) - pinn = CausalPINN(problem = problem, - model=model, extra_features=None, loss=LpLoss()) - trainer = Trainer(solver=pinn, max_epochs=1, - accelerator='cpu', batch_size=20) - trainer.train() - - -# # TODO does not currently work -# def test_train_inverse_problem_restore(): -# tmpdir = "tests/tmp_restore_inv" -# problem = InverseDiffusionReactionSystem() -# boundaries = ['D'] -# n = 100 -# problem.discretise_domain(n, 'random', locations=boundaries) -# pinn = CausalPINN(problem=problem, -# model=model, -# extra_features=None, -# loss=LpLoss()) -# trainer = Trainer(solver=pinn, -# max_epochs=5, -# accelerator='cpu', -# default_root_dir=tmpdir) -# trainer.train() -# ntrainer = Trainer(solver=pinn, max_epochs=5, accelerator='cpu') -# t = ntrainer.train( -# ckpt_path=f'{tmpdir}/lightning_logs/version_0/checkpoints/epoch=4-step=5.ckpt') -# import shutil -# shutil.rmtree(tmpdir) - - -def test_train_inverse_problem_load(): - tmpdir = "tests/tmp_load_inv" - problem = InverseDiffusionReactionSystem() - boundaries = ['D'] - n = 100 - problem.discretise_domain(n, 'random', locations=boundaries) - pinn = CausalPINN(problem=problem, - model=model, - extra_features=None, - loss=LpLoss()) - trainer = Trainer(solver=pinn, - max_epochs=15, - accelerator='cpu', - default_root_dir=tmpdir) - trainer.train() - new_pinn = CausalPINN.load_from_checkpoint( - f'{tmpdir}/lightning_logs/version_0/checkpoints/epoch=14-step=30.ckpt', - problem = problem, model=model) - test_pts = CartesianDomain({'x': [0, 1], 't': [0, 1]}).sample(10) - assert new_pinn.forward(test_pts).extract(['u']).shape == (10, 1) - assert new_pinn.forward(test_pts).extract( - ['u']).shape == pinn.forward(test_pts).extract(['u']).shape - torch.testing.assert_close( - new_pinn.forward(test_pts).extract(['u']), - pinn.forward(test_pts).extract(['u'])) - import shutil - shutil.rmtree(tmpdir) - - -def test_train_extra_feats_cpu(): - problem = DiffusionReactionSystem() - boundaries = ['D'] - n = 10 - problem.discretise_domain(n, 'grid', locations=boundaries) - pinn = CausalPINN(problem=problem, - model=model_extra_feats, - extra_features=extra_feats) - trainer = Trainer(solver=pinn, max_epochs=5, accelerator='cpu') - trainer.train() \ No newline at end of file diff --git a/tests/test_solvers/test_competitive_pinn.py b/tests/test_solvers/test_competitive_pinn.py deleted file mode 100644 index fae6d43be..000000000 --- a/tests/test_solvers/test_competitive_pinn.py +++ /dev/null @@ -1,429 +0,0 @@ -import torch -import pytest - -from pina.problem import SpatialProblem, InverseProblem -from pina.operators import laplacian -from pina.geometry import CartesianDomain -from pina import Condition, LabelTensor -from pina.solvers import CompetitivePINN as PINN -from pina.trainer import Trainer -from pina.model import FeedForward -from pina.equation.equation import Equation -from pina.equation.equation_factory import FixedValue -from pina.loss import LpLoss - - -def laplace_equation(input_, output_): - force_term = (torch.sin(input_.extract(['x']) * torch.pi) * - torch.sin(input_.extract(['y']) * torch.pi)) - delta_u = laplacian(output_.extract(['u']), input_) - return delta_u - force_term - - -my_laplace = Equation(laplace_equation) -in_ = LabelTensor(torch.tensor([[0., 1.]]), ['x', 'y']) -out_ = LabelTensor(torch.tensor([[0.]]), ['u']) -in2_ = LabelTensor(torch.rand(60, 2), ['x', 'y']) -out2_ = LabelTensor(torch.rand(60, 1), ['u']) - - -class InversePoisson(SpatialProblem, InverseProblem): - ''' - Problem definition for the Poisson equation. - ''' - output_variables = ['u'] - x_min = -2 - x_max = 2 - y_min = -2 - y_max = 2 - data_input = LabelTensor(torch.rand(10, 2), ['x', 'y']) - data_output = LabelTensor(torch.rand(10, 1), ['u']) - spatial_domain = CartesianDomain({'x': [x_min, x_max], 'y': [y_min, y_max]}) - # define the ranges for the parameters - unknown_parameter_domain = CartesianDomain({'mu1': [-1, 1], 'mu2': [-1, 1]}) - - def laplace_equation(input_, output_, params_): - ''' - Laplace equation with a force term. - ''' - force_term = torch.exp( - - 2*(input_.extract(['x']) - params_['mu1'])**2 - - 2*(input_.extract(['y']) - params_['mu2'])**2) - delta_u = laplacian(output_, input_, components=['u'], d=['x', 'y']) - - return delta_u - force_term - - # define the conditions for the loss (boundary conditions, equation, data) - conditions = { - 'gamma1': Condition(location=CartesianDomain({'x': [x_min, x_max], - 'y': y_max}), - equation=FixedValue(0.0, components=['u'])), - 'gamma2': Condition(location=CartesianDomain( - {'x': [x_min, x_max], 'y': y_min - }), - equation=FixedValue(0.0, components=['u'])), - 'gamma3': Condition(location=CartesianDomain( - {'x': x_max, 'y': [y_min, y_max] - }), - equation=FixedValue(0.0, components=['u'])), - 'gamma4': Condition(location=CartesianDomain( - {'x': x_min, 'y': [y_min, y_max] - }), - equation=FixedValue(0.0, components=['u'])), - 'D': Condition(location=CartesianDomain( - {'x': [x_min, x_max], 'y': [y_min, y_max] - }), - equation=Equation(laplace_equation)), - 'data': Condition(input_points=data_input.extract(['x', 'y']), - output_points=data_output) - } - - -class Poisson(SpatialProblem): - output_variables = ['u'] - spatial_domain = CartesianDomain({'x': [0, 1], 'y': [0, 1]}) - - conditions = { - 'gamma1': Condition( - location=CartesianDomain({'x': [0, 1], 'y': 1}), - equation=FixedValue(0.0)), - 'gamma2': Condition( - location=CartesianDomain({'x': [0, 1], 'y': 0}), - equation=FixedValue(0.0)), - 'gamma3': Condition( - location=CartesianDomain({'x': 1, 'y': [0, 1]}), - equation=FixedValue(0.0)), - 'gamma4': Condition( - location=CartesianDomain({'x': 0, 'y': [0, 1]}), - equation=FixedValue(0.0)), - 'D': Condition( - input_points=LabelTensor(torch.rand(size=(100, 2)), ['x', 'y']), - equation=my_laplace), - 'data': Condition( - input_points=in_, - output_points=out_), - 'data2': Condition( - input_points=in2_, - output_points=out2_) - } - - def poisson_sol(self, pts): - return -(torch.sin(pts.extract(['x']) * torch.pi) * - torch.sin(pts.extract(['y']) * torch.pi)) / (2 * torch.pi**2) - - truth_solution = poisson_sol - - -class myFeature(torch.nn.Module): - """ - Feature: sin(x) - """ - - def __init__(self): - super(myFeature, self).__init__() - - def forward(self, x): - t = (torch.sin(x.extract(['x']) * torch.pi) * - torch.sin(x.extract(['y']) * torch.pi)) - return LabelTensor(t, ['sin(x)sin(y)']) - - -# make the problem -poisson_problem = Poisson() -model = FeedForward(len(poisson_problem.input_variables), - len(poisson_problem.output_variables)) -model_extra_feats = FeedForward( - len(poisson_problem.input_variables) + 1, - len(poisson_problem.output_variables)) -extra_feats = [myFeature()] - - -def test_constructor(): - PINN(problem=poisson_problem, model=model) - PINN(problem=poisson_problem, model=model, discriminator = model) - - -def test_constructor_extra_feats(): - with pytest.raises(TypeError): - model_extra_feats = FeedForward( - len(poisson_problem.input_variables) + 1, - len(poisson_problem.output_variables)) - PINN(problem=poisson_problem, - model=model_extra_feats, - extra_features=extra_feats) - - -def test_train_cpu(): - poisson_problem = Poisson() - boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] - n = 10 - poisson_problem.discretise_domain(n, 'grid', locations=boundaries) - pinn = PINN(problem = poisson_problem, model=model, loss=LpLoss()) - trainer = Trainer(solver=pinn, max_epochs=1, - accelerator='cpu', batch_size=20) - trainer.train() - -def test_log(): - poisson_problem.discretise_domain(100) - solver = PINN(problem = poisson_problem, model=model, loss=LpLoss()) - trainer = Trainer(solver, max_epochs=2, accelerator='cpu') - trainer.train() - # assert the logged metrics are correct - logged_metrics = sorted(list(trainer.logged_metrics.keys())) - total_metrics = sorted( - list([key + '_loss' for key in poisson_problem.conditions.keys()]) - + ['mean_loss']) - assert logged_metrics == total_metrics - -def test_train_restore(): - tmpdir = "tests/tmp_restore" - poisson_problem = Poisson() - boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] - n = 10 - poisson_problem.discretise_domain(n, 'grid', locations=boundaries) - pinn = PINN(problem=poisson_problem, - model=model, - loss=LpLoss()) - trainer = Trainer(solver=pinn, - max_epochs=5, - accelerator='cpu', - default_root_dir=tmpdir) - trainer.train() - ntrainer = Trainer(solver=pinn, max_epochs=15, accelerator='cpu') - t = ntrainer.train( - ckpt_path=f'{tmpdir}/lightning_logs/version_0/' - 'checkpoints/epoch=4-step=10.ckpt') - import shutil - shutil.rmtree(tmpdir) - - -def test_train_load(): - tmpdir = "tests/tmp_load" - poisson_problem = Poisson() - boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] - n = 10 - poisson_problem.discretise_domain(n, 'grid', locations=boundaries) - pinn = PINN(problem=poisson_problem, - model=model, - loss=LpLoss()) - trainer = Trainer(solver=pinn, - max_epochs=15, - accelerator='cpu', - default_root_dir=tmpdir) - trainer.train() - new_pinn = PINN.load_from_checkpoint( - f'{tmpdir}/lightning_logs/version_0/checkpoints/epoch=14-step=30.ckpt', - problem = poisson_problem, model=model) - test_pts = CartesianDomain({'x': [0, 1], 'y': [0, 1]}).sample(10) - assert new_pinn.forward(test_pts).extract(['u']).shape == (10, 1) - assert new_pinn.forward(test_pts).extract( - ['u']).shape == pinn.forward(test_pts).extract(['u']).shape - torch.testing.assert_close( - new_pinn.forward(test_pts).extract(['u']), - pinn.forward(test_pts).extract(['u'])) - import shutil - shutil.rmtree(tmpdir) - -def test_train_inverse_problem_cpu(): - poisson_problem = InversePoisson() - boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4', 'D'] - n = 100 - poisson_problem.discretise_domain(n, 'random', locations=boundaries) - pinn = PINN(problem = poisson_problem, model=model, loss=LpLoss()) - trainer = Trainer(solver=pinn, max_epochs=1, - accelerator='cpu', batch_size=20) - trainer.train() - - -# # TODO does not currently work -# def test_train_inverse_problem_restore(): -# tmpdir = "tests/tmp_restore_inv" -# poisson_problem = InversePoisson() -# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4', 'D'] -# n = 100 -# poisson_problem.discretise_domain(n, 'random', locations=boundaries) -# pinn = PINN(problem=poisson_problem, -# model=model, -# loss=LpLoss()) -# trainer = Trainer(solver=pinn, -# max_epochs=5, -# accelerator='cpu', -# default_root_dir=tmpdir) -# trainer.train() -# ntrainer = Trainer(solver=pinn, max_epochs=5, accelerator='cpu') -# t = ntrainer.train( -# ckpt_path=f'{tmpdir}/lightning_logs/version_0/checkpoints/epoch=4-step=10.ckpt') -# import shutil -# shutil.rmtree(tmpdir) - - -def test_train_inverse_problem_load(): - tmpdir = "tests/tmp_load_inv" - poisson_problem = InversePoisson() - boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4', 'D'] - n = 100 - poisson_problem.discretise_domain(n, 'random', locations=boundaries) - pinn = PINN(problem=poisson_problem, - model=model, - loss=LpLoss()) - trainer = Trainer(solver=pinn, - max_epochs=15, - accelerator='cpu', - default_root_dir=tmpdir) - trainer.train() - new_pinn = PINN.load_from_checkpoint( - f'{tmpdir}/lightning_logs/version_0/checkpoints/epoch=14-step=30.ckpt', - problem = poisson_problem, model=model) - test_pts = CartesianDomain({'x': [0, 1], 'y': [0, 1]}).sample(10) - assert new_pinn.forward(test_pts).extract(['u']).shape == (10, 1) - assert new_pinn.forward(test_pts).extract( - ['u']).shape == pinn.forward(test_pts).extract(['u']).shape - torch.testing.assert_close( - new_pinn.forward(test_pts).extract(['u']), - pinn.forward(test_pts).extract(['u'])) - import shutil - shutil.rmtree(tmpdir) - -# # TODO fix asap. Basically sampling few variables -# # works only if both variables are in a range. -# # if one is fixed and the other not, this will -# # not work. This test also needs to be fixed and -# # insert in test problem not in test pinn. -# def test_train_cpu_sampling_few_vars(): -# poisson_problem = Poisson() -# boundaries = ['gamma1', 'gamma2', 'gamma3'] -# n = 10 -# poisson_problem.discretise_domain(n, 'grid', locations=boundaries) -# poisson_problem.discretise_domain(n, 'random', locations=['gamma4'], variables=['x']) -# poisson_problem.discretise_domain(n, 'random', locations=['gamma4'], variables=['y']) -# pinn = PINN(problem = poisson_problem, model=model, extra_features=None, loss=LpLoss()) -# trainer = Trainer(solver=pinn, kwargs={'max_epochs' : 5, 'accelerator':'cpu'}) -# trainer.train() - - -# TODO, fix GitHub actions to run also on GPU -# def test_train_gpu(): -# poisson_problem = Poisson() -# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# n = 10 -# poisson_problem.discretise_domain(n, 'grid', locations=boundaries) -# pinn = PINN(problem = poisson_problem, model=model, extra_features=None, loss=LpLoss()) -# trainer = Trainer(solver=pinn, kwargs={'max_epochs' : 5, 'accelerator':'gpu'}) -# trainer.train() - -# def test_train_gpu(): #TODO fix ASAP -# poisson_problem = Poisson() -# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# n = 10 -# poisson_problem.discretise_domain(n, 'grid', locations=boundaries) -# poisson_problem.conditions.pop('data') # The input/output pts are allocated on cpu -# pinn = PINN(problem = poisson_problem, model=model, extra_features=None, loss=LpLoss()) -# trainer = Trainer(solver=pinn, kwargs={'max_epochs' : 5, 'accelerator':'gpu'}) -# trainer.train() - -# def test_train_2(): -# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# n = 10 -# expected_keys = [[], list(range(0, 50, 3))] -# param = [0, 3] -# for i, truth_key in zip(param, expected_keys): -# pinn = PINN(problem, model) -# pinn.discretise_domain(n, 'grid', locations=boundaries) -# pinn.discretise_domain(n, 'grid', locations=['D']) -# pinn.train(50, save_loss=i) -# assert list(pinn.history_loss.keys()) == truth_key - - -# def test_train_extra_feats(): -# pinn = PINN(problem, model_extra_feat, [myFeature()]) -# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# n = 10 -# pinn.discretise_domain(n, 'grid', locations=boundaries) -# pinn.discretise_domain(n, 'grid', locations=['D']) -# pinn.train(5) - - -# def test_train_2_extra_feats(): -# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# n = 10 -# expected_keys = [[], list(range(0, 50, 3))] -# param = [0, 3] -# for i, truth_key in zip(param, expected_keys): -# pinn = PINN(problem, model_extra_feat, [myFeature()]) -# pinn.discretise_domain(n, 'grid', locations=boundaries) -# pinn.discretise_domain(n, 'grid', locations=['D']) -# pinn.train(50, save_loss=i) -# assert list(pinn.history_loss.keys()) == truth_key - - -# def test_train_with_optimizer_kwargs(): -# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# n = 10 -# expected_keys = [[], list(range(0, 50, 3))] -# param = [0, 3] -# for i, truth_key in zip(param, expected_keys): -# pinn = PINN(problem, model, optimizer_kwargs={'lr' : 0.3}) -# pinn.discretise_domain(n, 'grid', locations=boundaries) -# pinn.discretise_domain(n, 'grid', locations=['D']) -# pinn.train(50, save_loss=i) -# assert list(pinn.history_loss.keys()) == truth_key - - -# def test_train_with_lr_scheduler(): -# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# n = 10 -# expected_keys = [[], list(range(0, 50, 3))] -# param = [0, 3] -# for i, truth_key in zip(param, expected_keys): -# pinn = PINN( -# problem, -# model, -# lr_scheduler_type=torch.optim.lr_scheduler.CyclicLR, -# lr_scheduler_kwargs={'base_lr' : 0.1, 'max_lr' : 0.3, 'cycle_momentum': False} -# ) -# pinn.discretise_domain(n, 'grid', locations=boundaries) -# pinn.discretise_domain(n, 'grid', locations=['D']) -# pinn.train(50, save_loss=i) -# assert list(pinn.history_loss.keys()) == truth_key - - -# # def test_train_batch(): -# # pinn = PINN(problem, model, batch_size=6) -# # boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# # n = 10 -# # pinn.discretise_domain(n, 'grid', locations=boundaries) -# # pinn.discretise_domain(n, 'grid', locations=['D']) -# # pinn.train(5) - - -# # def test_train_batch_2(): -# # boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# # n = 10 -# # expected_keys = [[], list(range(0, 50, 3))] -# # param = [0, 3] -# # for i, truth_key in zip(param, expected_keys): -# # pinn = PINN(problem, model, batch_size=6) -# # pinn.discretise_domain(n, 'grid', locations=boundaries) -# # pinn.discretise_domain(n, 'grid', locations=['D']) -# # pinn.train(50, save_loss=i) -# # assert list(pinn.history_loss.keys()) == truth_key - - -# if torch.cuda.is_available(): - -# # def test_gpu_train(): -# # pinn = PINN(problem, model, batch_size=20, device='cuda') -# # boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# # n = 100 -# # pinn.discretise_domain(n, 'grid', locations=boundaries) -# # pinn.discretise_domain(n, 'grid', locations=['D']) -# # pinn.train(5) - -# def test_gpu_train_nobatch(): -# pinn = PINN(problem, model, batch_size=None, device='cuda') -# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# n = 100 -# pinn.discretise_domain(n, 'grid', locations=boundaries) -# pinn.discretise_domain(n, 'grid', locations=['D']) -# pinn.train(5) - diff --git a/tests/test_solvers/test_garom.py b/tests/test_solvers/test_garom.py deleted file mode 100644 index 4ff4e1c9d..000000000 --- a/tests/test_solvers/test_garom.py +++ /dev/null @@ -1,167 +0,0 @@ -import torch - -from pina.problem import AbstractProblem -from pina import Condition, LabelTensor -from pina.solvers import GAROM -from pina.trainer import Trainer -import torch.nn as nn -import matplotlib.tri as tri - - -def func(x, mu1, mu2): - import torch - x_m1 = (x[:, 0] - mu1).pow(2) - x_m2 = (x[:, 1] - mu2).pow(2) - norm = x[:, 0]**2 + x[:, 1]**2 - return torch.exp(-(x_m1 + x_m2)) - - -class ParametricGaussian(AbstractProblem): - output_variables = [f'u_{i}' for i in range(900)] - - # params - xx = torch.linspace(-1, 1, 20) - yy = xx - params = LabelTensor(torch.cartesian_prod(xx, yy), labels=['mu1', 'mu2']) - - # define domain - x = torch.linspace(-1, 1, 30) - domain = torch.cartesian_prod(x, x) - triang = tri.Triangulation(domain[:, 0], domain[:, 1]) - sol = [] - for p in params: - sol.append(func(domain, p[0], p[1])) - snapshots = LabelTensor(torch.stack(sol), labels=output_variables) - - # define conditions - conditions = { - 'data': Condition(input_points=params, output_points=snapshots) - } - - -# simple Generator Network -class Generator(nn.Module): - - def __init__(self, - input_dimension, - parameters_dimension, - noise_dimension, - activation=torch.nn.SiLU): - super().__init__() - - self._noise_dimension = noise_dimension - self._activation = activation - - self.model = torch.nn.Sequential( - torch.nn.Linear(6 * self._noise_dimension, input_dimension // 6), - self._activation(), - torch.nn.Linear(input_dimension // 6, input_dimension // 3), - self._activation(), - torch.nn.Linear(input_dimension // 3, input_dimension)) - self.condition = torch.nn.Sequential( - torch.nn.Linear(parameters_dimension, 2 * self._noise_dimension), - self._activation(), - torch.nn.Linear(2 * self._noise_dimension, - 5 * self._noise_dimension)) - - def forward(self, param): - # uniform sampling in [-1, 1] - z = torch.rand(size=(param.shape[0], self._noise_dimension), - device=param.device, - dtype=param.dtype, - requires_grad=True) - z = 2. * z - 1. - - # conditioning by concatenation of mapped parameters - input_ = torch.cat((z, self.condition(param)), dim=-1) - out = self.model(input_) - - return out - - -# Simple Discriminator Network -class Discriminator(nn.Module): - - def __init__(self, - input_dimension, - parameter_dimension, - hidden_dimension, - activation=torch.nn.ReLU): - super().__init__() - - self._activation = activation - self.encoding = torch.nn.Sequential( - torch.nn.Linear(input_dimension, input_dimension // 3), - self._activation(), - torch.nn.Linear(input_dimension // 3, input_dimension // 6), - self._activation(), - torch.nn.Linear(input_dimension // 6, hidden_dimension)) - self.decoding = torch.nn.Sequential( - torch.nn.Linear(2 * hidden_dimension, input_dimension // 6), - self._activation(), - torch.nn.Linear(input_dimension // 6, input_dimension // 3), - self._activation(), - torch.nn.Linear(input_dimension // 3, input_dimension), - ) - - self.condition = torch.nn.Sequential( - torch.nn.Linear(parameter_dimension, hidden_dimension // 2), - self._activation(), - torch.nn.Linear(hidden_dimension // 2, hidden_dimension)) - - def forward(self, data): - x, condition = data - encoding = self.encoding(x) - conditioning = torch.cat((encoding, self.condition(condition)), dim=-1) - decoding = self.decoding(conditioning) - return decoding - - -problem = ParametricGaussian() - - -def test_constructor(): - GAROM(problem=problem, - generator=Generator(input_dimension=900, - parameters_dimension=2, - noise_dimension=12), - discriminator=Discriminator(input_dimension=900, - parameter_dimension=2, - hidden_dimension=64)) - - -def test_train_cpu(): - solver = GAROM(problem=problem, - generator=Generator(input_dimension=900, - parameters_dimension=2, - noise_dimension=12), - discriminator=Discriminator(input_dimension=900, - parameter_dimension=2, - hidden_dimension=64)) - - trainer = Trainer(solver=solver, max_epochs=4, accelerator='cpu', batch_size=20) - trainer.train() - - -def test_sample(): - solver = GAROM(problem=problem, - generator=Generator(input_dimension=900, - parameters_dimension=2, - noise_dimension=12), - discriminator=Discriminator(input_dimension=900, - parameter_dimension=2, - hidden_dimension=64)) - solver.sample(problem.params) - assert solver.sample(problem.params).shape == problem.snapshots.shape - - -def test_forward(): - solver = GAROM(problem=problem, - generator=Generator(input_dimension=900, - parameters_dimension=2, - noise_dimension=12), - discriminator=Discriminator(input_dimension=900, - parameter_dimension=2, - hidden_dimension=64)) - solver(problem.params, mc_steps=100, variance=True) - assert solver(problem.params).shape == problem.snapshots.shape diff --git a/tests/test_solvers/test_gpinn.py b/tests/test_solvers/test_gpinn.py deleted file mode 100644 index 7c2bb50f6..000000000 --- a/tests/test_solvers/test_gpinn.py +++ /dev/null @@ -1,444 +0,0 @@ -import torch - -from pina.problem import SpatialProblem, InverseProblem -from pina.operators import laplacian -from pina.geometry import CartesianDomain -from pina import Condition, LabelTensor -from pina.solvers import GPINN -from pina.trainer import Trainer -from pina.model import FeedForward -from pina.equation.equation import Equation -from pina.equation.equation_factory import FixedValue -from pina.loss import LpLoss - - -def laplace_equation(input_, output_): - force_term = (torch.sin(input_.extract(['x']) * torch.pi) * - torch.sin(input_.extract(['y']) * torch.pi)) - delta_u = laplacian(output_.extract(['u']), input_) - return delta_u - force_term - - -my_laplace = Equation(laplace_equation) -in_ = LabelTensor(torch.tensor([[0., 1.]]), ['x', 'y']) -out_ = LabelTensor(torch.tensor([[0.]]), ['u']) -in2_ = LabelTensor(torch.rand(60, 2), ['x', 'y']) -out2_ = LabelTensor(torch.rand(60, 1), ['u']) - - -class InversePoisson(SpatialProblem, InverseProblem): - ''' - Problem definition for the Poisson equation. - ''' - output_variables = ['u'] - x_min = -2 - x_max = 2 - y_min = -2 - y_max = 2 - data_input = LabelTensor(torch.rand(10, 2), ['x', 'y']) - data_output = LabelTensor(torch.rand(10, 1), ['u']) - spatial_domain = CartesianDomain({'x': [x_min, x_max], 'y': [y_min, y_max]}) - # define the ranges for the parameters - unknown_parameter_domain = CartesianDomain({'mu1': [-1, 1], 'mu2': [-1, 1]}) - - def laplace_equation(input_, output_, params_): - ''' - Laplace equation with a force term. - ''' - force_term = torch.exp( - - 2*(input_.extract(['x']) - params_['mu1'])**2 - - 2*(input_.extract(['y']) - params_['mu2'])**2) - delta_u = laplacian(output_, input_, components=['u'], d=['x', 'y']) - - return delta_u - force_term - - # define the conditions for the loss (boundary conditions, equation, data) - conditions = { - 'gamma1': Condition(location=CartesianDomain({'x': [x_min, x_max], - 'y': y_max}), - equation=FixedValue(0.0, components=['u'])), - 'gamma2': Condition(location=CartesianDomain( - {'x': [x_min, x_max], 'y': y_min}), - equation=FixedValue(0.0, components=['u'])), - 'gamma3': Condition(location=CartesianDomain( - {'x': x_max, 'y': [y_min, y_max]}), - equation=FixedValue(0.0, components=['u'])), - 'gamma4': Condition(location=CartesianDomain( - {'x': x_min, 'y': [y_min, y_max] - }), - equation=FixedValue(0.0, components=['u'])), - 'D': Condition(location=CartesianDomain( - {'x': [x_min, x_max], 'y': [y_min, y_max] - }), - equation=Equation(laplace_equation)), - 'data': Condition( - input_points=data_input.extract(['x', 'y']), - output_points=data_output) - } - - -class Poisson(SpatialProblem): - output_variables = ['u'] - spatial_domain = CartesianDomain({'x': [0, 1], 'y': [0, 1]}) - - conditions = { - 'gamma1': Condition( - location=CartesianDomain({'x': [0, 1], 'y': 1}), - equation=FixedValue(0.0)), - 'gamma2': Condition( - location=CartesianDomain({'x': [0, 1], 'y': 0}), - equation=FixedValue(0.0)), - 'gamma3': Condition( - location=CartesianDomain({'x': 1, 'y': [0, 1]}), - equation=FixedValue(0.0)), - 'gamma4': Condition( - location=CartesianDomain({'x': 0, 'y': [0, 1]}), - equation=FixedValue(0.0)), - 'D': Condition( - input_points=LabelTensor(torch.rand(size=(100, 2)), ['x', 'y']), - equation=my_laplace), - 'data': Condition( - input_points=in_, - output_points=out_), - 'data2': Condition( - input_points=in2_, - output_points=out2_) - } - - def poisson_sol(self, pts): - return -(torch.sin(pts.extract(['x']) * torch.pi) * - torch.sin(pts.extract(['y']) * torch.pi)) / (2 * torch.pi**2) - - truth_solution = poisson_sol - - -class myFeature(torch.nn.Module): - """ - Feature: sin(x) - """ - - def __init__(self): - super(myFeature, self).__init__() - - def forward(self, x): - t = (torch.sin(x.extract(['x']) * torch.pi) * - torch.sin(x.extract(['y']) * torch.pi)) - return LabelTensor(t, ['sin(x)sin(y)']) - - -# make the problem -poisson_problem = Poisson() -model = FeedForward(len(poisson_problem.input_variables), - len(poisson_problem.output_variables)) -model_extra_feats = FeedForward( - len(poisson_problem.input_variables) + 1, - len(poisson_problem.output_variables)) -extra_feats = [myFeature()] - - -def test_constructor(): - GPINN(problem=poisson_problem, model=model, extra_features=None) - - -def test_constructor_extra_feats(): - model_extra_feats = FeedForward( - len(poisson_problem.input_variables) + 1, - len(poisson_problem.output_variables)) - GPINN(problem=poisson_problem, - model=model_extra_feats, - extra_features=extra_feats) - - -def test_train_cpu(): - poisson_problem = Poisson() - boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] - n = 10 - poisson_problem.discretise_domain(n, 'grid', locations=boundaries) - pinn = GPINN(problem = poisson_problem, - model=model, extra_features=None, loss=LpLoss()) - trainer = Trainer(solver=pinn, max_epochs=1, - accelerator='cpu', batch_size=20) - trainer.train() - -def test_log(): - poisson_problem.discretise_domain(100) - solver = GPINN(problem = poisson_problem, model=model, - extra_features=None, loss=LpLoss()) - trainer = Trainer(solver, max_epochs=2, accelerator='cpu') - trainer.train() - # assert the logged metrics are correct - logged_metrics = sorted(list(trainer.logged_metrics.keys())) - total_metrics = sorted( - list([key + '_loss' for key in poisson_problem.conditions.keys()]) - + ['mean_loss']) - assert logged_metrics == total_metrics - -def test_train_restore(): - tmpdir = "tests/tmp_restore" - poisson_problem = Poisson() - boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] - n = 10 - poisson_problem.discretise_domain(n, 'grid', locations=boundaries) - pinn = GPINN(problem=poisson_problem, - model=model, - extra_features=None, - loss=LpLoss()) - trainer = Trainer(solver=pinn, - max_epochs=5, - accelerator='cpu', - default_root_dir=tmpdir) - trainer.train() - ntrainer = Trainer(solver=pinn, max_epochs=15, accelerator='cpu') - t = ntrainer.train( - ckpt_path=f'{tmpdir}/lightning_logs/version_0/' - 'checkpoints/epoch=4-step=10.ckpt') - import shutil - shutil.rmtree(tmpdir) - - -def test_train_load(): - tmpdir = "tests/tmp_load" - poisson_problem = Poisson() - boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] - n = 10 - poisson_problem.discretise_domain(n, 'grid', locations=boundaries) - pinn = GPINN(problem=poisson_problem, - model=model, - extra_features=None, - loss=LpLoss()) - trainer = Trainer(solver=pinn, - max_epochs=15, - accelerator='cpu', - default_root_dir=tmpdir) - trainer.train() - new_pinn = GPINN.load_from_checkpoint( - f'{tmpdir}/lightning_logs/version_0/checkpoints/epoch=14-step=30.ckpt', - problem = poisson_problem, model=model) - test_pts = CartesianDomain({'x': [0, 1], 'y': [0, 1]}).sample(10) - assert new_pinn.forward(test_pts).extract(['u']).shape == (10, 1) - assert new_pinn.forward(test_pts).extract( - ['u']).shape == pinn.forward(test_pts).extract(['u']).shape - torch.testing.assert_close( - new_pinn.forward(test_pts).extract(['u']), - pinn.forward(test_pts).extract(['u'])) - import shutil - shutil.rmtree(tmpdir) - -def test_train_inverse_problem_cpu(): - poisson_problem = InversePoisson() - boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4', 'D'] - n = 100 - poisson_problem.discretise_domain(n, 'random', locations=boundaries) - pinn = GPINN(problem = poisson_problem, - model=model, extra_features=None, loss=LpLoss()) - trainer = Trainer(solver=pinn, max_epochs=1, - accelerator='cpu', batch_size=20) - trainer.train() - - -# # TODO does not currently work -# def test_train_inverse_problem_restore(): -# tmpdir = "tests/tmp_restore_inv" -# poisson_problem = InversePoisson() -# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4', 'D'] -# n = 100 -# poisson_problem.discretise_domain(n, 'random', locations=boundaries) -# pinn = GPINN(problem=poisson_problem, -# model=model, -# extra_features=None, -# loss=LpLoss()) -# trainer = Trainer(solver=pinn, -# max_epochs=5, -# accelerator='cpu', -# default_root_dir=tmpdir) -# trainer.train() -# ntrainer = Trainer(solver=pinn, max_epochs=5, accelerator='cpu') -# t = ntrainer.train( -# ckpt_path=f'{tmpdir}/lightning_logs/version_0/checkpoints/epoch=4-step=10.ckpt') -# import shutil -# shutil.rmtree(tmpdir) - - -def test_train_inverse_problem_load(): - tmpdir = "tests/tmp_load_inv" - poisson_problem = InversePoisson() - boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4', 'D'] - n = 100 - poisson_problem.discretise_domain(n, 'random', locations=boundaries) - pinn = GPINN(problem=poisson_problem, - model=model, - extra_features=None, - loss=LpLoss()) - trainer = Trainer(solver=pinn, - max_epochs=15, - accelerator='cpu', - default_root_dir=tmpdir) - trainer.train() - new_pinn = GPINN.load_from_checkpoint( - f'{tmpdir}/lightning_logs/version_0/checkpoints/epoch=14-step=30.ckpt', - problem = poisson_problem, model=model) - test_pts = CartesianDomain({'x': [0, 1], 'y': [0, 1]}).sample(10) - assert new_pinn.forward(test_pts).extract(['u']).shape == (10, 1) - assert new_pinn.forward(test_pts).extract( - ['u']).shape == pinn.forward(test_pts).extract(['u']).shape - torch.testing.assert_close( - new_pinn.forward(test_pts).extract(['u']), - pinn.forward(test_pts).extract(['u'])) - import shutil - shutil.rmtree(tmpdir) - -# # TODO fix asap. Basically sampling few variables -# # works only if both variables are in a range. -# # if one is fixed and the other not, this will -# # not work. This test also needs to be fixed and -# # insert in test problem not in test pinn. -# def test_train_cpu_sampling_few_vars(): -# poisson_problem = Poisson() -# boundaries = ['gamma1', 'gamma2', 'gamma3'] -# n = 10 -# poisson_problem.discretise_domain(n, 'grid', locations=boundaries) -# poisson_problem.discretise_domain(n, 'random', locations=['gamma4'], variables=['x']) -# poisson_problem.discretise_domain(n, 'random', locations=['gamma4'], variables=['y']) -# pinn = GPINN(problem = poisson_problem, model=model, extra_features=None, loss=LpLoss()) -# trainer = Trainer(solver=pinn, kwargs={'max_epochs' : 5, 'accelerator':'cpu'}) -# trainer.train() - - -def test_train_extra_feats_cpu(): - poisson_problem = Poisson() - boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] - n = 10 - poisson_problem.discretise_domain(n, 'grid', locations=boundaries) - pinn = GPINN(problem=poisson_problem, - model=model_extra_feats, - extra_features=extra_feats) - trainer = Trainer(solver=pinn, max_epochs=5, accelerator='cpu') - trainer.train() - - -# TODO, fix GitHub actions to run also on GPU -# def test_train_gpu(): -# poisson_problem = Poisson() -# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# n = 10 -# poisson_problem.discretise_domain(n, 'grid', locations=boundaries) -# pinn = GPINN(problem = poisson_problem, model=model, extra_features=None, loss=LpLoss()) -# trainer = Trainer(solver=pinn, kwargs={'max_epochs' : 5, 'accelerator':'gpu'}) -# trainer.train() - -# def test_train_gpu(): #TODO fix ASAP -# poisson_problem = Poisson() -# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# n = 10 -# poisson_problem.discretise_domain(n, 'grid', locations=boundaries) -# poisson_problem.conditions.pop('data') # The input/output pts are allocated on cpu -# pinn = GPINN(problem = poisson_problem, model=model, extra_features=None, loss=LpLoss()) -# trainer = Trainer(solver=pinn, kwargs={'max_epochs' : 5, 'accelerator':'gpu'}) -# trainer.train() - -# def test_train_2(): -# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# n = 10 -# expected_keys = [[], list(range(0, 50, 3))] -# param = [0, 3] -# for i, truth_key in zip(param, expected_keys): -# pinn = GPINN(problem, model) -# pinn.discretise_domain(n, 'grid', locations=boundaries) -# pinn.discretise_domain(n, 'grid', locations=['D']) -# pinn.train(50, save_loss=i) -# assert list(pinn.history_loss.keys()) == truth_key - - -# def test_train_extra_feats(): -# pinn = GPINN(problem, model_extra_feat, [myFeature()]) -# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# n = 10 -# pinn.discretise_domain(n, 'grid', locations=boundaries) -# pinn.discretise_domain(n, 'grid', locations=['D']) -# pinn.train(5) - - -# def test_train_2_extra_feats(): -# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# n = 10 -# expected_keys = [[], list(range(0, 50, 3))] -# param = [0, 3] -# for i, truth_key in zip(param, expected_keys): -# pinn = GPINN(problem, model_extra_feat, [myFeature()]) -# pinn.discretise_domain(n, 'grid', locations=boundaries) -# pinn.discretise_domain(n, 'grid', locations=['D']) -# pinn.train(50, save_loss=i) -# assert list(pinn.history_loss.keys()) == truth_key - - -# def test_train_with_optimizer_kwargs(): -# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# n = 10 -# expected_keys = [[], list(range(0, 50, 3))] -# param = [0, 3] -# for i, truth_key in zip(param, expected_keys): -# pinn = GPINN(problem, model, optimizer_kwargs={'lr' : 0.3}) -# pinn.discretise_domain(n, 'grid', locations=boundaries) -# pinn.discretise_domain(n, 'grid', locations=['D']) -# pinn.train(50, save_loss=i) -# assert list(pinn.history_loss.keys()) == truth_key - - -# def test_train_with_lr_scheduler(): -# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# n = 10 -# expected_keys = [[], list(range(0, 50, 3))] -# param = [0, 3] -# for i, truth_key in zip(param, expected_keys): -# pinn = GPINN( -# problem, -# model, -# lr_scheduler_type=torch.optim.lr_scheduler.CyclicLR, -# lr_scheduler_kwargs={'base_lr' : 0.1, 'max_lr' : 0.3, 'cycle_momentum': False} -# ) -# pinn.discretise_domain(n, 'grid', locations=boundaries) -# pinn.discretise_domain(n, 'grid', locations=['D']) -# pinn.train(50, save_loss=i) -# assert list(pinn.history_loss.keys()) == truth_key - - -# # def test_train_batch(): -# # pinn = GPINN(problem, model, batch_size=6) -# # boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# # n = 10 -# # pinn.discretise_domain(n, 'grid', locations=boundaries) -# # pinn.discretise_domain(n, 'grid', locations=['D']) -# # pinn.train(5) - - -# # def test_train_batch_2(): -# # boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# # n = 10 -# # expected_keys = [[], list(range(0, 50, 3))] -# # param = [0, 3] -# # for i, truth_key in zip(param, expected_keys): -# # pinn = GPINN(problem, model, batch_size=6) -# # pinn.discretise_domain(n, 'grid', locations=boundaries) -# # pinn.discretise_domain(n, 'grid', locations=['D']) -# # pinn.train(50, save_loss=i) -# # assert list(pinn.history_loss.keys()) == truth_key - - -# if torch.cuda.is_available(): - -# # def test_gpu_train(): -# # pinn = GPINN(problem, model, batch_size=20, device='cuda') -# # boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# # n = 100 -# # pinn.discretise_domain(n, 'grid', locations=boundaries) -# # pinn.discretise_domain(n, 'grid', locations=['D']) -# # pinn.train(5) - -# def test_gpu_train_nobatch(): -# pinn = GPINN(problem, model, batch_size=None, device='cuda') -# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# n = 100 -# pinn.discretise_domain(n, 'grid', locations=boundaries) -# pinn.discretise_domain(n, 'grid', locations=['D']) -# pinn.train(5) - diff --git a/tests/test_solvers/test_pinn.py b/tests/test_solvers/test_pinn.py deleted file mode 100644 index f3cf275bd..000000000 --- a/tests/test_solvers/test_pinn.py +++ /dev/null @@ -1,445 +0,0 @@ -import torch - -from pina.problem import SpatialProblem, InverseProblem -from pina.operators import laplacian -from pina.geometry import CartesianDomain -from pina import Condition, LabelTensor -from pina.solvers import PINN -from pina.trainer import Trainer -from pina.model import FeedForward -from pina.equation.equation import Equation -from pina.equation.equation_factory import FixedValue -from pina.loss import LpLoss - - -def laplace_equation(input_, output_): - force_term = (torch.sin(input_.extract(['x']) * torch.pi) * - torch.sin(input_.extract(['y']) * torch.pi)) - delta_u = laplacian(output_.extract(['u']), input_) - return delta_u - force_term - - -my_laplace = Equation(laplace_equation) -in_ = LabelTensor(torch.tensor([[0., 1.]]), ['x', 'y']) -out_ = LabelTensor(torch.tensor([[0.]]), ['u']) -in2_ = LabelTensor(torch.rand(60, 2), ['x', 'y']) -out2_ = LabelTensor(torch.rand(60, 1), ['u']) - - -class InversePoisson(SpatialProblem, InverseProblem): - ''' - Problem definition for the Poisson equation. - ''' - output_variables = ['u'] - x_min = -2 - x_max = 2 - y_min = -2 - y_max = 2 - data_input = LabelTensor(torch.rand(10, 2), ['x', 'y']) - data_output = LabelTensor(torch.rand(10, 1), ['u']) - spatial_domain = CartesianDomain({'x': [x_min, x_max], 'y': [y_min, y_max]}) - # define the ranges for the parameters - unknown_parameter_domain = CartesianDomain({'mu1': [-1, 1], 'mu2': [-1, 1]}) - - def laplace_equation(input_, output_, params_): - ''' - Laplace equation with a force term. - ''' - force_term = torch.exp( - - 2*(input_.extract(['x']) - params_['mu1'])**2 - - 2*(input_.extract(['y']) - params_['mu2'])**2) - delta_u = laplacian(output_, input_, components=['u'], d=['x', 'y']) - - return delta_u - force_term - - # define the conditions for the loss (boundary conditions, equation, data) - conditions = { - 'gamma1': Condition(location=CartesianDomain({'x': [x_min, x_max], - 'y': y_max}), - equation=FixedValue(0.0, components=['u'])), - 'gamma2': Condition(location=CartesianDomain( - {'x': [x_min, x_max], 'y': y_min - }), - equation=FixedValue(0.0, components=['u'])), - 'gamma3': Condition(location=CartesianDomain( - {'x': x_max, 'y': [y_min, y_max] - }), - equation=FixedValue(0.0, components=['u'])), - 'gamma4': Condition(location=CartesianDomain( - {'x': x_min, 'y': [y_min, y_max] - }), - equation=FixedValue(0.0, components=['u'])), - 'D': Condition(location=CartesianDomain( - {'x': [x_min, x_max], 'y': [y_min, y_max] - }), - equation=Equation(laplace_equation)), - 'data': Condition(input_points=data_input.extract(['x', 'y']), - output_points=data_output) - } - - -class Poisson(SpatialProblem): - output_variables = ['u'] - spatial_domain = CartesianDomain({'x': [0, 1], 'y': [0, 1]}) - - conditions = { - 'gamma1': Condition( - location=CartesianDomain({'x': [0, 1], 'y': 1}), - equation=FixedValue(0.0)), - 'gamma2': Condition( - location=CartesianDomain({'x': [0, 1], 'y': 0}), - equation=FixedValue(0.0)), - 'gamma3': Condition( - location=CartesianDomain({'x': 1, 'y': [0, 1]}), - equation=FixedValue(0.0)), - 'gamma4': Condition( - location=CartesianDomain({'x': 0, 'y': [0, 1]}), - equation=FixedValue(0.0)), - 'D': Condition( - input_points=LabelTensor(torch.rand(size=(100, 2)), ['x', 'y']), - equation=my_laplace), - 'data': Condition( - input_points=in_, - output_points=out_), - 'data2': Condition( - input_points=in2_, - output_points=out2_) - } - - def poisson_sol(self, pts): - return -(torch.sin(pts.extract(['x']) * torch.pi) * - torch.sin(pts.extract(['y']) * torch.pi)) / (2 * torch.pi**2) - - truth_solution = poisson_sol - - -class myFeature(torch.nn.Module): - """ - Feature: sin(x) - """ - - def __init__(self): - super(myFeature, self).__init__() - - def forward(self, x): - t = (torch.sin(x.extract(['x']) * torch.pi) * - torch.sin(x.extract(['y']) * torch.pi)) - return LabelTensor(t, ['sin(x)sin(y)']) - - -# make the problem -poisson_problem = Poisson() -model = FeedForward(len(poisson_problem.input_variables), - len(poisson_problem.output_variables)) -model_extra_feats = FeedForward( - len(poisson_problem.input_variables) + 1, - len(poisson_problem.output_variables)) -extra_feats = [myFeature()] - - -def test_constructor(): - PINN(problem=poisson_problem, model=model, extra_features=None) - - -def test_constructor_extra_feats(): - model_extra_feats = FeedForward( - len(poisson_problem.input_variables) + 1, - len(poisson_problem.output_variables)) - PINN(problem=poisson_problem, - model=model_extra_feats, - extra_features=extra_feats) - - -def test_train_cpu(): - poisson_problem = Poisson() - boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] - n = 10 - poisson_problem.discretise_domain(n, 'grid', locations=boundaries) - pinn = PINN(problem = poisson_problem, model=model, - extra_features=None, loss=LpLoss()) - trainer = Trainer(solver=pinn, max_epochs=1, - accelerator='cpu', batch_size=20) - trainer.train() - -def test_log(): - poisson_problem.discretise_domain(100) - solver = PINN(problem = poisson_problem, model=model, - extra_features=None, loss=LpLoss()) - trainer = Trainer(solver, max_epochs=2, accelerator='cpu') - trainer.train() - # assert the logged metrics are correct - logged_metrics = sorted(list(trainer.logged_metrics.keys())) - total_metrics = sorted( - list([key + '_loss' for key in poisson_problem.conditions.keys()]) - + ['mean_loss']) - assert logged_metrics == total_metrics - -def test_train_restore(): - tmpdir = "tests/tmp_restore" - poisson_problem = Poisson() - boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] - n = 10 - poisson_problem.discretise_domain(n, 'grid', locations=boundaries) - pinn = PINN(problem=poisson_problem, - model=model, - extra_features=None, - loss=LpLoss()) - trainer = Trainer(solver=pinn, - max_epochs=5, - accelerator='cpu', - default_root_dir=tmpdir) - trainer.train() - ntrainer = Trainer(solver=pinn, max_epochs=15, accelerator='cpu') - t = ntrainer.train( - ckpt_path=f'{tmpdir}/lightning_logs/version_0/' - 'checkpoints/epoch=4-step=10.ckpt') - import shutil - shutil.rmtree(tmpdir) - - -def test_train_load(): - tmpdir = "tests/tmp_load" - poisson_problem = Poisson() - boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] - n = 10 - poisson_problem.discretise_domain(n, 'grid', locations=boundaries) - pinn = PINN(problem=poisson_problem, - model=model, - extra_features=None, - loss=LpLoss()) - trainer = Trainer(solver=pinn, - max_epochs=15, - accelerator='cpu', - default_root_dir=tmpdir) - trainer.train() - new_pinn = PINN.load_from_checkpoint( - f'{tmpdir}/lightning_logs/version_0/checkpoints/epoch=14-step=30.ckpt', - problem = poisson_problem, model=model) - test_pts = CartesianDomain({'x': [0, 1], 'y': [0, 1]}).sample(10) - assert new_pinn.forward(test_pts).extract(['u']).shape == (10, 1) - assert new_pinn.forward(test_pts).extract( - ['u']).shape == pinn.forward(test_pts).extract(['u']).shape - torch.testing.assert_close( - new_pinn.forward(test_pts).extract(['u']), - pinn.forward(test_pts).extract(['u'])) - import shutil - shutil.rmtree(tmpdir) - -def test_train_inverse_problem_cpu(): - poisson_problem = InversePoisson() - boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4', 'D'] - n = 100 - poisson_problem.discretise_domain(n, 'random', locations=boundaries) - pinn = PINN(problem = poisson_problem, model=model, - extra_features=None, loss=LpLoss()) - trainer = Trainer(solver=pinn, max_epochs=1, - accelerator='cpu', batch_size=20) - trainer.train() - - -# # TODO does not currently work -# def test_train_inverse_problem_restore(): -# tmpdir = "tests/tmp_restore_inv" -# poisson_problem = InversePoisson() -# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4', 'D'] -# n = 100 -# poisson_problem.discretise_domain(n, 'random', locations=boundaries) -# pinn = PINN(problem=poisson_problem, -# model=model, -# extra_features=None, -# loss=LpLoss()) -# trainer = Trainer(solver=pinn, -# max_epochs=5, -# accelerator='cpu', -# default_root_dir=tmpdir) -# trainer.train() -# ntrainer = Trainer(solver=pinn, max_epochs=5, accelerator='cpu') -# t = ntrainer.train( -# ckpt_path=f'{tmpdir}/lightning_logs/version_0/checkpoints/epoch=4-step=10.ckpt') -# import shutil -# shutil.rmtree(tmpdir) - - -def test_train_inverse_problem_load(): - tmpdir = "tests/tmp_load_inv" - poisson_problem = InversePoisson() - boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4', 'D'] - n = 100 - poisson_problem.discretise_domain(n, 'random', locations=boundaries) - pinn = PINN(problem=poisson_problem, - model=model, - extra_features=None, - loss=LpLoss()) - trainer = Trainer(solver=pinn, - max_epochs=15, - accelerator='cpu', - default_root_dir=tmpdir) - trainer.train() - new_pinn = PINN.load_from_checkpoint( - f'{tmpdir}/lightning_logs/version_0/checkpoints/epoch=14-step=30.ckpt', - problem = poisson_problem, model=model) - test_pts = CartesianDomain({'x': [0, 1], 'y': [0, 1]}).sample(10) - assert new_pinn.forward(test_pts).extract(['u']).shape == (10, 1) - assert new_pinn.forward(test_pts).extract( - ['u']).shape == pinn.forward(test_pts).extract(['u']).shape - torch.testing.assert_close( - new_pinn.forward(test_pts).extract(['u']), - pinn.forward(test_pts).extract(['u'])) - import shutil - shutil.rmtree(tmpdir) - -# # TODO fix asap. Basically sampling few variables -# # works only if both variables are in a range. -# # if one is fixed and the other not, this will -# # not work. This test also needs to be fixed and -# # insert in test problem not in test pinn. -# def test_train_cpu_sampling_few_vars(): -# poisson_problem = Poisson() -# boundaries = ['gamma1', 'gamma2', 'gamma3'] -# n = 10 -# poisson_problem.discretise_domain(n, 'grid', locations=boundaries) -# poisson_problem.discretise_domain(n, 'random', locations=['gamma4'], variables=['x']) -# poisson_problem.discretise_domain(n, 'random', locations=['gamma4'], variables=['y']) -# pinn = PINN(problem = poisson_problem, model=model, extra_features=None, loss=LpLoss()) -# trainer = Trainer(solver=pinn, kwargs={'max_epochs' : 5, 'accelerator':'cpu'}) -# trainer.train() - - -def test_train_extra_feats_cpu(): - poisson_problem = Poisson() - boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] - n = 10 - poisson_problem.discretise_domain(n, 'grid', locations=boundaries) - pinn = PINN(problem=poisson_problem, - model=model_extra_feats, - extra_features=extra_feats) - trainer = Trainer(solver=pinn, max_epochs=5, accelerator='cpu') - trainer.train() - - -# TODO, fix GitHub actions to run also on GPU -# def test_train_gpu(): -# poisson_problem = Poisson() -# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# n = 10 -# poisson_problem.discretise_domain(n, 'grid', locations=boundaries) -# pinn = PINN(problem = poisson_problem, model=model, extra_features=None, loss=LpLoss()) -# trainer = Trainer(solver=pinn, kwargs={'max_epochs' : 5, 'accelerator':'gpu'}) -# trainer.train() - -# def test_train_gpu(): #TODO fix ASAP -# poisson_problem = Poisson() -# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# n = 10 -# poisson_problem.discretise_domain(n, 'grid', locations=boundaries) -# poisson_problem.conditions.pop('data') # The input/output pts are allocated on cpu -# pinn = PINN(problem = poisson_problem, model=model, extra_features=None, loss=LpLoss()) -# trainer = Trainer(solver=pinn, kwargs={'max_epochs' : 5, 'accelerator':'gpu'}) -# trainer.train() - -# def test_train_2(): -# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# n = 10 -# expected_keys = [[], list(range(0, 50, 3))] -# param = [0, 3] -# for i, truth_key in zip(param, expected_keys): -# pinn = PINN(problem, model) -# pinn.discretise_domain(n, 'grid', locations=boundaries) -# pinn.discretise_domain(n, 'grid', locations=['D']) -# pinn.train(50, save_loss=i) -# assert list(pinn.history_loss.keys()) == truth_key - - -# def test_train_extra_feats(): -# pinn = PINN(problem, model_extra_feat, [myFeature()]) -# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# n = 10 -# pinn.discretise_domain(n, 'grid', locations=boundaries) -# pinn.discretise_domain(n, 'grid', locations=['D']) -# pinn.train(5) - - -# def test_train_2_extra_feats(): -# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# n = 10 -# expected_keys = [[], list(range(0, 50, 3))] -# param = [0, 3] -# for i, truth_key in zip(param, expected_keys): -# pinn = PINN(problem, model_extra_feat, [myFeature()]) -# pinn.discretise_domain(n, 'grid', locations=boundaries) -# pinn.discretise_domain(n, 'grid', locations=['D']) -# pinn.train(50, save_loss=i) -# assert list(pinn.history_loss.keys()) == truth_key - - -# def test_train_with_optimizer_kwargs(): -# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# n = 10 -# expected_keys = [[], list(range(0, 50, 3))] -# param = [0, 3] -# for i, truth_key in zip(param, expected_keys): -# pinn = PINN(problem, model, optimizer_kwargs={'lr' : 0.3}) -# pinn.discretise_domain(n, 'grid', locations=boundaries) -# pinn.discretise_domain(n, 'grid', locations=['D']) -# pinn.train(50, save_loss=i) -# assert list(pinn.history_loss.keys()) == truth_key - - -# def test_train_with_lr_scheduler(): -# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# n = 10 -# expected_keys = [[], list(range(0, 50, 3))] -# param = [0, 3] -# for i, truth_key in zip(param, expected_keys): -# pinn = PINN( -# problem, -# model, -# lr_scheduler_type=torch.optim.lr_scheduler.CyclicLR, -# lr_scheduler_kwargs={'base_lr' : 0.1, 'max_lr' : 0.3, 'cycle_momentum': False} -# ) -# pinn.discretise_domain(n, 'grid', locations=boundaries) -# pinn.discretise_domain(n, 'grid', locations=['D']) -# pinn.train(50, save_loss=i) -# assert list(pinn.history_loss.keys()) == truth_key - - -# # def test_train_batch(): -# # pinn = PINN(problem, model, batch_size=6) -# # boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# # n = 10 -# # pinn.discretise_domain(n, 'grid', locations=boundaries) -# # pinn.discretise_domain(n, 'grid', locations=['D']) -# # pinn.train(5) - - -# # def test_train_batch_2(): -# # boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# # n = 10 -# # expected_keys = [[], list(range(0, 50, 3))] -# # param = [0, 3] -# # for i, truth_key in zip(param, expected_keys): -# # pinn = PINN(problem, model, batch_size=6) -# # pinn.discretise_domain(n, 'grid', locations=boundaries) -# # pinn.discretise_domain(n, 'grid', locations=['D']) -# # pinn.train(50, save_loss=i) -# # assert list(pinn.history_loss.keys()) == truth_key - - -# if torch.cuda.is_available(): - -# # def test_gpu_train(): -# # pinn = PINN(problem, model, batch_size=20, device='cuda') -# # boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# # n = 100 -# # pinn.discretise_domain(n, 'grid', locations=boundaries) -# # pinn.discretise_domain(n, 'grid', locations=['D']) -# # pinn.train(5) - -# def test_gpu_train_nobatch(): -# pinn = PINN(problem, model, batch_size=None, device='cuda') -# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# n = 100 -# pinn.discretise_domain(n, 'grid', locations=boundaries) -# pinn.discretise_domain(n, 'grid', locations=['D']) -# pinn.train(5) - diff --git a/tests/test_solvers/test_rba_pinn.py b/tests/test_solvers/test_rba_pinn.py deleted file mode 100644 index aad47bbb7..000000000 --- a/tests/test_solvers/test_rba_pinn.py +++ /dev/null @@ -1,449 +0,0 @@ -import torch -import pytest - -from pina.problem import SpatialProblem, InverseProblem -from pina.operators import laplacian -from pina.geometry import CartesianDomain -from pina import Condition, LabelTensor -from pina.solvers import RBAPINN as PINN -from pina.trainer import Trainer -from pina.model import FeedForward -from pina.equation.equation import Equation -from pina.equation.equation_factory import FixedValue -from pina.loss import LpLoss - - -def laplace_equation(input_, output_): - force_term = (torch.sin(input_.extract(['x']) * torch.pi) * - torch.sin(input_.extract(['y']) * torch.pi)) - delta_u = laplacian(output_.extract(['u']), input_) - return delta_u - force_term - - -my_laplace = Equation(laplace_equation) -in_ = LabelTensor(torch.tensor([[0., 1.]]), ['x', 'y']) -out_ = LabelTensor(torch.tensor([[0.]]), ['u']) -in2_ = LabelTensor(torch.rand(60, 2), ['x', 'y']) -out2_ = LabelTensor(torch.rand(60, 1), ['u']) - - -class InversePoisson(SpatialProblem, InverseProblem): - ''' - Problem definition for the Poisson equation. - ''' - output_variables = ['u'] - x_min = -2 - x_max = 2 - y_min = -2 - y_max = 2 - data_input = LabelTensor(torch.rand(10, 2), ['x', 'y']) - data_output = LabelTensor(torch.rand(10, 1), ['u']) - spatial_domain = CartesianDomain({'x': [x_min, x_max], 'y': [y_min, y_max]}) - # define the ranges for the parameters - unknown_parameter_domain = CartesianDomain({'mu1': [-1, 1], 'mu2': [-1, 1]}) - - def laplace_equation(input_, output_, params_): - ''' - Laplace equation with a force term. - ''' - force_term = torch.exp( - - 2*(input_.extract(['x']) - params_['mu1'])**2 - - 2*(input_.extract(['y']) - params_['mu2'])**2) - delta_u = laplacian(output_, input_, components=['u'], d=['x', 'y']) - - return delta_u - force_term - - # define the conditions for the loss (boundary conditions, equation, data) - conditions = { - 'gamma1': Condition(location=CartesianDomain({'x': [x_min, x_max], - 'y': y_max}), - equation=FixedValue(0.0, components=['u'])), - 'gamma2': Condition(location=CartesianDomain( - {'x': [x_min, x_max], 'y': y_min - }), - equation=FixedValue(0.0, components=['u'])), - 'gamma3': Condition(location=CartesianDomain( - {'x': x_max, 'y': [y_min, y_max] - }), - equation=FixedValue(0.0, components=['u'])), - 'gamma4': Condition(location=CartesianDomain( - {'x': x_min, 'y': [y_min, y_max] - }), - equation=FixedValue(0.0, components=['u'])), - 'D': Condition(location=CartesianDomain( - {'x': [x_min, x_max], 'y': [y_min, y_max] - }), - equation=Equation(laplace_equation)), - 'data': Condition(input_points=data_input.extract(['x', 'y']), - output_points=data_output) - } - - -class Poisson(SpatialProblem): - output_variables = ['u'] - spatial_domain = CartesianDomain({'x': [0, 1], 'y': [0, 1]}) - - conditions = { - 'gamma1': Condition( - location=CartesianDomain({'x': [0, 1], 'y': 1}), - equation=FixedValue(0.0)), - 'gamma2': Condition( - location=CartesianDomain({'x': [0, 1], 'y': 0}), - equation=FixedValue(0.0)), - 'gamma3': Condition( - location=CartesianDomain({'x': 1, 'y': [0, 1]}), - equation=FixedValue(0.0)), - 'gamma4': Condition( - location=CartesianDomain({'x': 0, 'y': [0, 1]}), - equation=FixedValue(0.0)), - 'D': Condition( - input_points=LabelTensor(torch.rand(size=(100, 2)), ['x', 'y']), - equation=my_laplace), - 'data': Condition( - input_points=in_, - output_points=out_), - 'data2': Condition( - input_points=in2_, - output_points=out2_) - } - - def poisson_sol(self, pts): - return -(torch.sin(pts.extract(['x']) * torch.pi) * - torch.sin(pts.extract(['y']) * torch.pi)) / (2 * torch.pi**2) - - truth_solution = poisson_sol - - -class myFeature(torch.nn.Module): - """ - Feature: sin(x) - """ - - def __init__(self): - super(myFeature, self).__init__() - - def forward(self, x): - t = (torch.sin(x.extract(['x']) * torch.pi) * - torch.sin(x.extract(['y']) * torch.pi)) - return LabelTensor(t, ['sin(x)sin(y)']) - - -# make the problem -poisson_problem = Poisson() -model = FeedForward(len(poisson_problem.input_variables), - len(poisson_problem.output_variables)) -model_extra_feats = FeedForward( - len(poisson_problem.input_variables) + 1, - len(poisson_problem.output_variables)) -extra_feats = [myFeature()] - - -def test_constructor(): - PINN(problem=poisson_problem, model=model, extra_features=None) - with pytest.raises(ValueError): - PINN(problem=poisson_problem, model=model, eta='x') - PINN(problem=poisson_problem, model=model, gamma='x') - - -def test_constructor_extra_feats(): - model_extra_feats = FeedForward( - len(poisson_problem.input_variables) + 1, - len(poisson_problem.output_variables)) - PINN(problem=poisson_problem, - model=model_extra_feats, - extra_features=extra_feats) - - -def test_train_cpu(): - poisson_problem = Poisson() - boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] - n = 10 - poisson_problem.discretise_domain(n, 'grid', locations=boundaries) - pinn = PINN(problem = poisson_problem, model=model, - extra_features=None, loss=LpLoss()) - trainer = Trainer(solver=pinn, max_epochs=1, - accelerator='cpu', batch_size=20) - trainer.train() - -def test_log(): - poisson_problem.discretise_domain(100) - solver = PINN(problem = poisson_problem, model=model, - extra_features=None, loss=LpLoss()) - trainer = Trainer(solver, max_epochs=2, accelerator='cpu') - trainer.train() - # assert the logged metrics are correct - logged_metrics = sorted(list(trainer.logged_metrics.keys())) - total_metrics = sorted( - list([key + '_loss' for key in poisson_problem.conditions.keys()]) - + ['mean_loss']) - assert logged_metrics == total_metrics - -def test_train_restore(): - tmpdir = "tests/tmp_restore" - poisson_problem = Poisson() - boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] - n = 10 - poisson_problem.discretise_domain(n, 'grid', locations=boundaries) - pinn = PINN(problem=poisson_problem, - model=model, - extra_features=None, - loss=LpLoss()) - trainer = Trainer(solver=pinn, - max_epochs=5, - accelerator='cpu', - default_root_dir=tmpdir) - trainer.train() - ntrainer = Trainer(solver=pinn, max_epochs=15, accelerator='cpu') - t = ntrainer.train( - ckpt_path=f'{tmpdir}/lightning_logs/version_0/' - 'checkpoints/epoch=4-step=10.ckpt') - import shutil - shutil.rmtree(tmpdir) - - -def test_train_load(): - tmpdir = "tests/tmp_load" - poisson_problem = Poisson() - boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] - n = 10 - poisson_problem.discretise_domain(n, 'grid', locations=boundaries) - pinn = PINN(problem=poisson_problem, - model=model, - extra_features=None, - loss=LpLoss()) - trainer = Trainer(solver=pinn, - max_epochs=15, - accelerator='cpu', - default_root_dir=tmpdir) - trainer.train() - new_pinn = PINN.load_from_checkpoint( - f'{tmpdir}/lightning_logs/version_0/checkpoints/epoch=14-step=30.ckpt', - problem = poisson_problem, model=model) - test_pts = CartesianDomain({'x': [0, 1], 'y': [0, 1]}).sample(10) - assert new_pinn.forward(test_pts).extract(['u']).shape == (10, 1) - assert new_pinn.forward(test_pts).extract( - ['u']).shape == pinn.forward(test_pts).extract(['u']).shape - torch.testing.assert_close( - new_pinn.forward(test_pts).extract(['u']), - pinn.forward(test_pts).extract(['u'])) - import shutil - shutil.rmtree(tmpdir) - -def test_train_inverse_problem_cpu(): - poisson_problem = InversePoisson() - boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4', 'D'] - n = 100 - poisson_problem.discretise_domain(n, 'random', locations=boundaries) - pinn = PINN(problem = poisson_problem, model=model, - extra_features=None, loss=LpLoss()) - trainer = Trainer(solver=pinn, max_epochs=1, - accelerator='cpu', batch_size=20) - trainer.train() - - -# # TODO does not currently work -# def test_train_inverse_problem_restore(): -# tmpdir = "tests/tmp_restore_inv" -# poisson_problem = InversePoisson() -# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4', 'D'] -# n = 100 -# poisson_problem.discretise_domain(n, 'random', locations=boundaries) -# pinn = PINN(problem=poisson_problem, -# model=model, -# extra_features=None, -# loss=LpLoss()) -# trainer = Trainer(solver=pinn, -# max_epochs=5, -# accelerator='cpu', -# default_root_dir=tmpdir) -# trainer.train() -# ntrainer = Trainer(solver=pinn, max_epochs=5, accelerator='cpu') -# t = ntrainer.train( -# ckpt_path=f'{tmpdir}/lightning_logs/version_0/checkpoints/epoch=4-step=10.ckpt') -# import shutil -# shutil.rmtree(tmpdir) - - -def test_train_inverse_problem_load(): - tmpdir = "tests/tmp_load_inv" - poisson_problem = InversePoisson() - boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4', 'D'] - n = 100 - poisson_problem.discretise_domain(n, 'random', locations=boundaries) - pinn = PINN(problem=poisson_problem, - model=model, - extra_features=None, - loss=LpLoss()) - trainer = Trainer(solver=pinn, - max_epochs=15, - accelerator='cpu', - default_root_dir=tmpdir) - trainer.train() - new_pinn = PINN.load_from_checkpoint( - f'{tmpdir}/lightning_logs/version_0/checkpoints/epoch=14-step=30.ckpt', - problem = poisson_problem, model=model) - test_pts = CartesianDomain({'x': [0, 1], 'y': [0, 1]}).sample(10) - assert new_pinn.forward(test_pts).extract(['u']).shape == (10, 1) - assert new_pinn.forward(test_pts).extract( - ['u']).shape == pinn.forward(test_pts).extract(['u']).shape - torch.testing.assert_close( - new_pinn.forward(test_pts).extract(['u']), - pinn.forward(test_pts).extract(['u'])) - import shutil - shutil.rmtree(tmpdir) - -# # TODO fix asap. Basically sampling few variables -# # works only if both variables are in a range. -# # if one is fixed and the other not, this will -# # not work. This test also needs to be fixed and -# # insert in test problem not in test pinn. -# def test_train_cpu_sampling_few_vars(): -# poisson_problem = Poisson() -# boundaries = ['gamma1', 'gamma2', 'gamma3'] -# n = 10 -# poisson_problem.discretise_domain(n, 'grid', locations=boundaries) -# poisson_problem.discretise_domain(n, 'random', locations=['gamma4'], variables=['x']) -# poisson_problem.discretise_domain(n, 'random', locations=['gamma4'], variables=['y']) -# pinn = PINN(problem = poisson_problem, model=model, extra_features=None, loss=LpLoss()) -# trainer = Trainer(solver=pinn, kwargs={'max_epochs' : 5, 'accelerator':'cpu'}) -# trainer.train() - - -def test_train_extra_feats_cpu(): - poisson_problem = Poisson() - boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] - n = 10 - poisson_problem.discretise_domain(n, 'grid', locations=boundaries) - pinn = PINN(problem=poisson_problem, - model=model_extra_feats, - extra_features=extra_feats) - trainer = Trainer(solver=pinn, max_epochs=5, accelerator='cpu') - trainer.train() - - -# TODO, fix GitHub actions to run also on GPU -# def test_train_gpu(): -# poisson_problem = Poisson() -# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# n = 10 -# poisson_problem.discretise_domain(n, 'grid', locations=boundaries) -# pinn = PINN(problem = poisson_problem, model=model, extra_features=None, loss=LpLoss()) -# trainer = Trainer(solver=pinn, kwargs={'max_epochs' : 5, 'accelerator':'gpu'}) -# trainer.train() - -# def test_train_gpu(): #TODO fix ASAP -# poisson_problem = Poisson() -# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# n = 10 -# poisson_problem.discretise_domain(n, 'grid', locations=boundaries) -# poisson_problem.conditions.pop('data') # The input/output pts are allocated on cpu -# pinn = PINN(problem = poisson_problem, model=model, extra_features=None, loss=LpLoss()) -# trainer = Trainer(solver=pinn, kwargs={'max_epochs' : 5, 'accelerator':'gpu'}) -# trainer.train() - -# def test_train_2(): -# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# n = 10 -# expected_keys = [[], list(range(0, 50, 3))] -# param = [0, 3] -# for i, truth_key in zip(param, expected_keys): -# pinn = PINN(problem, model) -# pinn.discretise_domain(n, 'grid', locations=boundaries) -# pinn.discretise_domain(n, 'grid', locations=['D']) -# pinn.train(50, save_loss=i) -# assert list(pinn.history_loss.keys()) == truth_key - - -# def test_train_extra_feats(): -# pinn = PINN(problem, model_extra_feat, [myFeature()]) -# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# n = 10 -# pinn.discretise_domain(n, 'grid', locations=boundaries) -# pinn.discretise_domain(n, 'grid', locations=['D']) -# pinn.train(5) - - -# def test_train_2_extra_feats(): -# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# n = 10 -# expected_keys = [[], list(range(0, 50, 3))] -# param = [0, 3] -# for i, truth_key in zip(param, expected_keys): -# pinn = PINN(problem, model_extra_feat, [myFeature()]) -# pinn.discretise_domain(n, 'grid', locations=boundaries) -# pinn.discretise_domain(n, 'grid', locations=['D']) -# pinn.train(50, save_loss=i) -# assert list(pinn.history_loss.keys()) == truth_key - - -# def test_train_with_optimizer_kwargs(): -# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# n = 10 -# expected_keys = [[], list(range(0, 50, 3))] -# param = [0, 3] -# for i, truth_key in zip(param, expected_keys): -# pinn = PINN(problem, model, optimizer_kwargs={'lr' : 0.3}) -# pinn.discretise_domain(n, 'grid', locations=boundaries) -# pinn.discretise_domain(n, 'grid', locations=['D']) -# pinn.train(50, save_loss=i) -# assert list(pinn.history_loss.keys()) == truth_key - - -# def test_train_with_lr_scheduler(): -# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# n = 10 -# expected_keys = [[], list(range(0, 50, 3))] -# param = [0, 3] -# for i, truth_key in zip(param, expected_keys): -# pinn = PINN( -# problem, -# model, -# lr_scheduler_type=torch.optim.lr_scheduler.CyclicLR, -# lr_scheduler_kwargs={'base_lr' : 0.1, 'max_lr' : 0.3, 'cycle_momentum': False} -# ) -# pinn.discretise_domain(n, 'grid', locations=boundaries) -# pinn.discretise_domain(n, 'grid', locations=['D']) -# pinn.train(50, save_loss=i) -# assert list(pinn.history_loss.keys()) == truth_key - - -# # def test_train_batch(): -# # pinn = PINN(problem, model, batch_size=6) -# # boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# # n = 10 -# # pinn.discretise_domain(n, 'grid', locations=boundaries) -# # pinn.discretise_domain(n, 'grid', locations=['D']) -# # pinn.train(5) - - -# # def test_train_batch_2(): -# # boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# # n = 10 -# # expected_keys = [[], list(range(0, 50, 3))] -# # param = [0, 3] -# # for i, truth_key in zip(param, expected_keys): -# # pinn = PINN(problem, model, batch_size=6) -# # pinn.discretise_domain(n, 'grid', locations=boundaries) -# # pinn.discretise_domain(n, 'grid', locations=['D']) -# # pinn.train(50, save_loss=i) -# # assert list(pinn.history_loss.keys()) == truth_key - - -# if torch.cuda.is_available(): - -# # def test_gpu_train(): -# # pinn = PINN(problem, model, batch_size=20, device='cuda') -# # boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# # n = 100 -# # pinn.discretise_domain(n, 'grid', locations=boundaries) -# # pinn.discretise_domain(n, 'grid', locations=['D']) -# # pinn.train(5) - -# def test_gpu_train_nobatch(): -# pinn = PINN(problem, model, batch_size=None, device='cuda') -# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# n = 100 -# pinn.discretise_domain(n, 'grid', locations=boundaries) -# pinn.discretise_domain(n, 'grid', locations=['D']) -# pinn.train(5) - diff --git a/tests/test_solvers/test_rom_solver.py b/tests/test_solvers/test_rom_solver.py deleted file mode 100644 index a16ffcaae..000000000 --- a/tests/test_solvers/test_rom_solver.py +++ /dev/null @@ -1,105 +0,0 @@ -import torch -import pytest - -from pina.problem import AbstractProblem -from pina import Condition, LabelTensor -from pina.solvers import ReducedOrderModelSolver -from pina.trainer import Trainer -from pina.model import FeedForward -from pina.loss import LpLoss - - -class NeuralOperatorProblem(AbstractProblem): - input_variables = ['u_0', 'u_1'] - output_variables = [f'u_{i}' for i in range(100)] - conditions = {'data' : Condition(input_points= - LabelTensor(torch.rand(10, 2), - input_variables), - output_points= - LabelTensor(torch.rand(10, 100), - output_variables))} - - -# make the problem + extra feats -class AE(torch.nn.Module): - def __init__(self, input_dimensions, rank): - super().__init__() - self.encode = FeedForward(input_dimensions, rank, layers=[input_dimensions//4]) - self.decode = FeedForward(rank, input_dimensions, layers=[input_dimensions//4]) -class AE_missing_encode(torch.nn.Module): - def __init__(self, input_dimensions, rank): - super().__init__() - self.encode = FeedForward(input_dimensions, rank, layers=[input_dimensions//4]) -class AE_missing_decode(torch.nn.Module): - def __init__(self, input_dimensions, rank): - super().__init__() - self.decode = FeedForward(rank, input_dimensions, layers=[input_dimensions//4]) - -rank = 10 -problem = NeuralOperatorProblem() -interpolation_net = FeedForward(len(problem.input_variables), - rank) -reduction_net = AE(len(problem.output_variables), rank) - -def test_constructor(): - ReducedOrderModelSolver(problem=problem,reduction_network=reduction_net, - interpolation_network=interpolation_net) - with pytest.raises(SyntaxError): - ReducedOrderModelSolver(problem=problem, - reduction_network=AE_missing_encode( - len(problem.output_variables), rank), - interpolation_network=interpolation_net) - ReducedOrderModelSolver(problem=problem, - reduction_network=AE_missing_decode( - len(problem.output_variables), rank), - interpolation_network=interpolation_net) - - -def test_train_cpu(): - solver = ReducedOrderModelSolver(problem = problem,reduction_network=reduction_net, - interpolation_network=interpolation_net, loss=LpLoss()) - trainer = Trainer(solver=solver, max_epochs=3, accelerator='cpu', batch_size=20) - trainer.train() - - -def test_train_restore(): - tmpdir = "tests/tmp_restore" - solver = ReducedOrderModelSolver(problem=problem, - reduction_network=reduction_net, - interpolation_network=interpolation_net, - loss=LpLoss()) - trainer = Trainer(solver=solver, - max_epochs=5, - accelerator='cpu', - default_root_dir=tmpdir) - trainer.train() - ntrainer = Trainer(solver=solver, max_epochs=15, accelerator='cpu') - t = ntrainer.train( - ckpt_path=f'{tmpdir}/lightning_logs/version_0/checkpoints/epoch=4-step=5.ckpt') - import shutil - shutil.rmtree(tmpdir) - - -def test_train_load(): - tmpdir = "tests/tmp_load" - solver = ReducedOrderModelSolver(problem=problem, - reduction_network=reduction_net, - interpolation_network=interpolation_net, - loss=LpLoss()) - trainer = Trainer(solver=solver, - max_epochs=15, - accelerator='cpu', - default_root_dir=tmpdir) - trainer.train() - new_solver = ReducedOrderModelSolver.load_from_checkpoint( - f'{tmpdir}/lightning_logs/version_0/checkpoints/epoch=14-step=15.ckpt', - problem = problem,reduction_network=reduction_net, - interpolation_network=interpolation_net) - test_pts = LabelTensor(torch.rand(20, 2), problem.input_variables) - assert new_solver.forward(test_pts).shape == (20, 100) - assert new_solver.forward(test_pts).shape == solver.forward(test_pts).shape - torch.testing.assert_close( - new_solver.forward(test_pts), - solver.forward(test_pts)) - import shutil - shutil.rmtree(tmpdir) \ No newline at end of file diff --git a/tests/test_solvers/test_sapinn.py b/tests/test_solvers/test_sapinn.py deleted file mode 100644 index 45475fc42..000000000 --- a/tests/test_solvers/test_sapinn.py +++ /dev/null @@ -1,449 +0,0 @@ -import torch -import pytest - -from pina.problem import SpatialProblem, InverseProblem -from pina.operators import laplacian -from pina.geometry import CartesianDomain -from pina import Condition, LabelTensor -from pina.solvers import SAPINN as PINN -from pina.trainer import Trainer -from pina.model import FeedForward -from pina.equation.equation import Equation -from pina.equation.equation_factory import FixedValue -from pina.loss import LpLoss - - -def laplace_equation(input_, output_): - force_term = (torch.sin(input_.extract(['x']) * torch.pi) * - torch.sin(input_.extract(['y']) * torch.pi)) - delta_u = laplacian(output_.extract(['u']), input_) - return delta_u - force_term - - -my_laplace = Equation(laplace_equation) -in_ = LabelTensor(torch.tensor([[0., 1.]]), ['x', 'y']) -out_ = LabelTensor(torch.tensor([[0.]]), ['u']) -in2_ = LabelTensor(torch.rand(60, 2), ['x', 'y']) -out2_ = LabelTensor(torch.rand(60, 1), ['u']) - - -class InversePoisson(SpatialProblem, InverseProblem): - ''' - Problem definition for the Poisson equation. - ''' - output_variables = ['u'] - x_min = -2 - x_max = 2 - y_min = -2 - y_max = 2 - data_input = LabelTensor(torch.rand(10, 2), ['x', 'y']) - data_output = LabelTensor(torch.rand(10, 1), ['u']) - spatial_domain = CartesianDomain({'x': [x_min, x_max], 'y': [y_min, y_max]}) - # define the ranges for the parameters - unknown_parameter_domain = CartesianDomain({'mu1': [-1, 1], 'mu2': [-1, 1]}) - - def laplace_equation(input_, output_, params_): - ''' - Laplace equation with a force term. - ''' - force_term = torch.exp( - - 2*(input_.extract(['x']) - params_['mu1'])**2 - - 2*(input_.extract(['y']) - params_['mu2'])**2) - delta_u = laplacian(output_, input_, components=['u'], d=['x', 'y']) - - return delta_u - force_term - - # define the conditions for the loss (boundary conditions, equation, data) - conditions = { - 'gamma1': Condition(location=CartesianDomain({'x': [x_min, x_max], - 'y': y_max}), - equation=FixedValue(0.0, components=['u'])), - 'gamma2': Condition(location=CartesianDomain( - {'x': [x_min, x_max], 'y': y_min - }), - equation=FixedValue(0.0, components=['u'])), - 'gamma3': Condition(location=CartesianDomain( - {'x': x_max, 'y': [y_min, y_max] - }), - equation=FixedValue(0.0, components=['u'])), - 'gamma4': Condition(location=CartesianDomain( - {'x': x_min, 'y': [y_min, y_max] - }), - equation=FixedValue(0.0, components=['u'])), - 'D': Condition(location=CartesianDomain( - {'x': [x_min, x_max], 'y': [y_min, y_max] - }), - equation=Equation(laplace_equation)), - 'data': Condition(input_points=data_input.extract(['x', 'y']), - output_points=data_output) - } - - -class Poisson(SpatialProblem): - output_variables = ['u'] - spatial_domain = CartesianDomain({'x': [0, 1], 'y': [0, 1]}) - - conditions = { - 'gamma1': Condition( - location=CartesianDomain({'x': [0, 1], 'y': 1}), - equation=FixedValue(0.0)), - 'gamma2': Condition( - location=CartesianDomain({'x': [0, 1], 'y': 0}), - equation=FixedValue(0.0)), - 'gamma3': Condition( - location=CartesianDomain({'x': 1, 'y': [0, 1]}), - equation=FixedValue(0.0)), - 'gamma4': Condition( - location=CartesianDomain({'x': 0, 'y': [0, 1]}), - equation=FixedValue(0.0)), - 'D': Condition( - input_points=LabelTensor(torch.rand(size=(100, 2)), ['x', 'y']), - equation=my_laplace), - 'data': Condition( - input_points=in_, - output_points=out_), - 'data2': Condition( - input_points=in2_, - output_points=out2_) - } - - def poisson_sol(self, pts): - return -(torch.sin(pts.extract(['x']) * torch.pi) * - torch.sin(pts.extract(['y']) * torch.pi)) / (2 * torch.pi**2) - - truth_solution = poisson_sol - - -class myFeature(torch.nn.Module): - """ - Feature: sin(x) - """ - - def __init__(self): - super(myFeature, self).__init__() - - def forward(self, x): - t = (torch.sin(x.extract(['x']) * torch.pi) * - torch.sin(x.extract(['y']) * torch.pi)) - return LabelTensor(t, ['sin(x)sin(y)']) - - -# make the problem -poisson_problem = Poisson() -model = FeedForward(len(poisson_problem.input_variables), - len(poisson_problem.output_variables)) -model_extra_feats = FeedForward( - len(poisson_problem.input_variables) + 1, - len(poisson_problem.output_variables)) -extra_feats = [myFeature()] - - -def test_constructor(): - PINN(problem=poisson_problem, model=model, extra_features=None) - with pytest.raises(ValueError): - PINN(problem=poisson_problem, model=model, extra_features=None, - weights_function=1) - - -def test_constructor_extra_feats(): - model_extra_feats = FeedForward( - len(poisson_problem.input_variables) + 1, - len(poisson_problem.output_variables)) - PINN(problem=poisson_problem, - model=model_extra_feats, - extra_features=extra_feats) - - -def test_train_cpu(): - poisson_problem = Poisson() - boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] - n = 10 - poisson_problem.discretise_domain(n, 'grid', locations=boundaries) - pinn = PINN(problem = poisson_problem, model=model, - extra_features=None, loss=LpLoss()) - trainer = Trainer(solver=pinn, max_epochs=1, - accelerator='cpu', batch_size=20) - trainer.train() - -def test_log(): - poisson_problem.discretise_domain(100) - solver = PINN(problem = poisson_problem, model=model, - extra_features=None, loss=LpLoss()) - trainer = Trainer(solver, max_epochs=2, accelerator='cpu') - trainer.train() - # assert the logged metrics are correct - logged_metrics = sorted(list(trainer.logged_metrics.keys())) - total_metrics = sorted( - list([key + '_loss' for key in poisson_problem.conditions.keys()]) - + ['mean_loss']) - assert logged_metrics == total_metrics - -def test_train_restore(): - tmpdir = "tests/tmp_restore" - poisson_problem = Poisson() - boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] - n = 10 - poisson_problem.discretise_domain(n, 'grid', locations=boundaries) - pinn = PINN(problem=poisson_problem, - model=model, - extra_features=None, - loss=LpLoss()) - trainer = Trainer(solver=pinn, - max_epochs=5, - accelerator='cpu', - default_root_dir=tmpdir) - trainer.train() - ntrainer = Trainer(solver=pinn, max_epochs=15, accelerator='cpu') - t = ntrainer.train( - ckpt_path=f'{tmpdir}/lightning_logs/version_0/' - 'checkpoints/epoch=4-step=10.ckpt') - import shutil - shutil.rmtree(tmpdir) - - -def test_train_load(): - tmpdir = "tests/tmp_load" - poisson_problem = Poisson() - boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] - n = 10 - poisson_problem.discretise_domain(n, 'grid', locations=boundaries) - pinn = PINN(problem=poisson_problem, - model=model, - extra_features=None, - loss=LpLoss()) - trainer = Trainer(solver=pinn, - max_epochs=15, - accelerator='cpu', - default_root_dir=tmpdir) - trainer.train() - new_pinn = PINN.load_from_checkpoint( - f'{tmpdir}/lightning_logs/version_0/checkpoints/epoch=14-step=30.ckpt', - problem = poisson_problem, model=model) - test_pts = CartesianDomain({'x': [0, 1], 'y': [0, 1]}).sample(10) - assert new_pinn.forward(test_pts).extract(['u']).shape == (10, 1) - assert new_pinn.forward(test_pts).extract( - ['u']).shape == pinn.forward(test_pts).extract(['u']).shape - torch.testing.assert_close( - new_pinn.forward(test_pts).extract(['u']), - pinn.forward(test_pts).extract(['u'])) - import shutil - shutil.rmtree(tmpdir) - -def test_train_inverse_problem_cpu(): - poisson_problem = InversePoisson() - boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4', 'D'] - n = 100 - poisson_problem.discretise_domain(n, 'random', locations=boundaries) - pinn = PINN(problem = poisson_problem, model=model, - extra_features=None, loss=LpLoss()) - trainer = Trainer(solver=pinn, max_epochs=1, - accelerator='cpu', batch_size=20) - trainer.train() - - -# # TODO does not currently work -# def test_train_inverse_problem_restore(): -# tmpdir = "tests/tmp_restore_inv" -# poisson_problem = InversePoisson() -# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4', 'D'] -# n = 100 -# poisson_problem.discretise_domain(n, 'random', locations=boundaries) -# pinn = PINN(problem=poisson_problem, -# model=model, -# extra_features=None, -# loss=LpLoss()) -# trainer = Trainer(solver=pinn, -# max_epochs=5, -# accelerator='cpu', -# default_root_dir=tmpdir) -# trainer.train() -# ntrainer = Trainer(solver=pinn, max_epochs=5, accelerator='cpu') -# t = ntrainer.train( -# ckpt_path=f'{tmpdir}/lightning_logs/version_0/checkpoints/epoch=4-step=10.ckpt') -# import shutil -# shutil.rmtree(tmpdir) - - -def test_train_inverse_problem_load(): - tmpdir = "tests/tmp_load_inv" - poisson_problem = InversePoisson() - boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4', 'D'] - n = 100 - poisson_problem.discretise_domain(n, 'random', locations=boundaries) - pinn = PINN(problem=poisson_problem, - model=model, - extra_features=None, - loss=LpLoss()) - trainer = Trainer(solver=pinn, - max_epochs=15, - accelerator='cpu', - default_root_dir=tmpdir) - trainer.train() - new_pinn = PINN.load_from_checkpoint( - f'{tmpdir}/lightning_logs/version_0/checkpoints/epoch=14-step=30.ckpt', - problem = poisson_problem, model=model) - test_pts = CartesianDomain({'x': [0, 1], 'y': [0, 1]}).sample(10) - assert new_pinn.forward(test_pts).extract(['u']).shape == (10, 1) - assert new_pinn.forward(test_pts).extract( - ['u']).shape == pinn.forward(test_pts).extract(['u']).shape - torch.testing.assert_close( - new_pinn.forward(test_pts).extract(['u']), - pinn.forward(test_pts).extract(['u'])) - import shutil - shutil.rmtree(tmpdir) - -# # TODO fix asap. Basically sampling few variables -# # works only if both variables are in a range. -# # if one is fixed and the other not, this will -# # not work. This test also needs to be fixed and -# # insert in test problem not in test pinn. -# def test_train_cpu_sampling_few_vars(): -# poisson_problem = Poisson() -# boundaries = ['gamma1', 'gamma2', 'gamma3'] -# n = 10 -# poisson_problem.discretise_domain(n, 'grid', locations=boundaries) -# poisson_problem.discretise_domain(n, 'random', locations=['gamma4'], variables=['x']) -# poisson_problem.discretise_domain(n, 'random', locations=['gamma4'], variables=['y']) -# pinn = PINN(problem = poisson_problem, model=model, extra_features=None, loss=LpLoss()) -# trainer = Trainer(solver=pinn, kwargs={'max_epochs' : 5, 'accelerator':'cpu'}) -# trainer.train() - - -def test_train_extra_feats_cpu(): - poisson_problem = Poisson() - boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] - n = 10 - poisson_problem.discretise_domain(n, 'grid', locations=boundaries) - pinn = PINN(problem=poisson_problem, - model=model_extra_feats, - extra_features=extra_feats) - trainer = Trainer(solver=pinn, max_epochs=5, accelerator='cpu') - trainer.train() - - -# TODO, fix GitHub actions to run also on GPU -# def test_train_gpu(): -# poisson_problem = Poisson() -# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# n = 10 -# poisson_problem.discretise_domain(n, 'grid', locations=boundaries) -# pinn = PINN(problem = poisson_problem, model=model, extra_features=None, loss=LpLoss()) -# trainer = Trainer(solver=pinn, kwargs={'max_epochs' : 5, 'accelerator':'gpu'}) -# trainer.train() - -# def test_train_gpu(): #TODO fix ASAP -# poisson_problem = Poisson() -# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# n = 10 -# poisson_problem.discretise_domain(n, 'grid', locations=boundaries) -# poisson_problem.conditions.pop('data') # The input/output pts are allocated on cpu -# pinn = PINN(problem = poisson_problem, model=model, extra_features=None, loss=LpLoss()) -# trainer = Trainer(solver=pinn, kwargs={'max_epochs' : 5, 'accelerator':'gpu'}) -# trainer.train() - -# def test_train_2(): -# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# n = 10 -# expected_keys = [[], list(range(0, 50, 3))] -# param = [0, 3] -# for i, truth_key in zip(param, expected_keys): -# pinn = PINN(problem, model) -# pinn.discretise_domain(n, 'grid', locations=boundaries) -# pinn.discretise_domain(n, 'grid', locations=['D']) -# pinn.train(50, save_loss=i) -# assert list(pinn.history_loss.keys()) == truth_key - - -# def test_train_extra_feats(): -# pinn = PINN(problem, model_extra_feat, [myFeature()]) -# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# n = 10 -# pinn.discretise_domain(n, 'grid', locations=boundaries) -# pinn.discretise_domain(n, 'grid', locations=['D']) -# pinn.train(5) - - -# def test_train_2_extra_feats(): -# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# n = 10 -# expected_keys = [[], list(range(0, 50, 3))] -# param = [0, 3] -# for i, truth_key in zip(param, expected_keys): -# pinn = PINN(problem, model_extra_feat, [myFeature()]) -# pinn.discretise_domain(n, 'grid', locations=boundaries) -# pinn.discretise_domain(n, 'grid', locations=['D']) -# pinn.train(50, save_loss=i) -# assert list(pinn.history_loss.keys()) == truth_key - - -# def test_train_with_optimizer_kwargs(): -# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# n = 10 -# expected_keys = [[], list(range(0, 50, 3))] -# param = [0, 3] -# for i, truth_key in zip(param, expected_keys): -# pinn = PINN(problem, model, optimizer_kwargs={'lr' : 0.3}) -# pinn.discretise_domain(n, 'grid', locations=boundaries) -# pinn.discretise_domain(n, 'grid', locations=['D']) -# pinn.train(50, save_loss=i) -# assert list(pinn.history_loss.keys()) == truth_key - - -# def test_train_with_lr_scheduler(): -# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# n = 10 -# expected_keys = [[], list(range(0, 50, 3))] -# param = [0, 3] -# for i, truth_key in zip(param, expected_keys): -# pinn = PINN( -# problem, -# model, -# lr_scheduler_type=torch.optim.lr_scheduler.CyclicLR, -# lr_scheduler_kwargs={'base_lr' : 0.1, 'max_lr' : 0.3, 'cycle_momentum': False} -# ) -# pinn.discretise_domain(n, 'grid', locations=boundaries) -# pinn.discretise_domain(n, 'grid', locations=['D']) -# pinn.train(50, save_loss=i) -# assert list(pinn.history_loss.keys()) == truth_key - - -# # def test_train_batch(): -# # pinn = PINN(problem, model, batch_size=6) -# # boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# # n = 10 -# # pinn.discretise_domain(n, 'grid', locations=boundaries) -# # pinn.discretise_domain(n, 'grid', locations=['D']) -# # pinn.train(5) - - -# # def test_train_batch_2(): -# # boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# # n = 10 -# # expected_keys = [[], list(range(0, 50, 3))] -# # param = [0, 3] -# # for i, truth_key in zip(param, expected_keys): -# # pinn = PINN(problem, model, batch_size=6) -# # pinn.discretise_domain(n, 'grid', locations=boundaries) -# # pinn.discretise_domain(n, 'grid', locations=['D']) -# # pinn.train(50, save_loss=i) -# # assert list(pinn.history_loss.keys()) == truth_key - - -# if torch.cuda.is_available(): - -# # def test_gpu_train(): -# # pinn = PINN(problem, model, batch_size=20, device='cuda') -# # boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# # n = 100 -# # pinn.discretise_domain(n, 'grid', locations=boundaries) -# # pinn.discretise_domain(n, 'grid', locations=['D']) -# # pinn.train(5) - -# def test_gpu_train_nobatch(): -# pinn = PINN(problem, model, batch_size=None, device='cuda') -# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] -# n = 100 -# pinn.discretise_domain(n, 'grid', locations=boundaries) -# pinn.discretise_domain(n, 'grid', locations=['D']) -# pinn.train(5) - diff --git a/tests/test_solvers/test_supervised_solver.py b/tests/test_solvers/test_supervised_solver.py deleted file mode 100644 index dfe0bd867..000000000 --- a/tests/test_solvers/test_supervised_solver.py +++ /dev/null @@ -1,101 +0,0 @@ -import torch - -from pina.problem import AbstractProblem -from pina import Condition, LabelTensor -from pina.solvers import SupervisedSolver -from pina.trainer import Trainer -from pina.model import FeedForward -from pina.loss import LpLoss - - -class NeuralOperatorProblem(AbstractProblem): - input_variables = ['u_0', 'u_1'] - output_variables = ['u'] - conditions = {'data' : Condition(input_points=LabelTensor(torch.rand(100, 2), input_variables), - output_points=LabelTensor(torch.rand(100, 1), output_variables))} - -class myFeature(torch.nn.Module): - """ - Feature: sin(x) - """ - - def __init__(self): - super(myFeature, self).__init__() - - def forward(self, x): - t = (torch.sin(x.extract(['u_0']) * torch.pi) * - torch.sin(x.extract(['u_1']) * torch.pi)) - return LabelTensor(t, ['sin(x)sin(y)']) - - -# make the problem + extra feats -problem = NeuralOperatorProblem() -extra_feats = [myFeature()] -model = FeedForward(len(problem.input_variables), - len(problem.output_variables)) -model_extra_feats = FeedForward( - len(problem.input_variables) + 1, - len(problem.output_variables)) - - -def test_constructor(): - SupervisedSolver(problem=problem, model=model, extra_features=None) - - -def test_constructor_extra_feats(): - SupervisedSolver(problem=problem, model=model_extra_feats, extra_features=extra_feats) - - -def test_train_cpu(): - solver = SupervisedSolver(problem = problem, model=model, extra_features=None, loss=LpLoss()) - trainer = Trainer(solver=solver, max_epochs=3, accelerator='cpu', batch_size=20) - trainer.train() - - -def test_train_restore(): - tmpdir = "tests/tmp_restore" - solver = SupervisedSolver(problem=problem, - model=model, - extra_features=None, - loss=LpLoss()) - trainer = Trainer(solver=solver, - max_epochs=5, - accelerator='cpu', - default_root_dir=tmpdir) - trainer.train() - ntrainer = Trainer(solver=solver, max_epochs=15, accelerator='cpu') - t = ntrainer.train( - ckpt_path=f'{tmpdir}/lightning_logs/version_0/checkpoints/epoch=4-step=5.ckpt') - import shutil - shutil.rmtree(tmpdir) - - -def test_train_load(): - tmpdir = "tests/tmp_load" - solver = SupervisedSolver(problem=problem, - model=model, - extra_features=None, - loss=LpLoss()) - trainer = Trainer(solver=solver, - max_epochs=15, - accelerator='cpu', - default_root_dir=tmpdir) - trainer.train() - new_solver = SupervisedSolver.load_from_checkpoint( - f'{tmpdir}/lightning_logs/version_0/checkpoints/epoch=14-step=15.ckpt', - problem = problem, model=model) - test_pts = LabelTensor(torch.rand(20, 2), problem.input_variables) - assert new_solver.forward(test_pts).shape == (20, 1) - assert new_solver.forward(test_pts).shape == solver.forward(test_pts).shape - torch.testing.assert_close( - new_solver.forward(test_pts), - solver.forward(test_pts)) - import shutil - shutil.rmtree(tmpdir) - -def test_train_extra_feats_cpu(): - pinn = SupervisedSolver(problem=problem, - model=model_extra_feats, - extra_features=extra_feats) - trainer = Trainer(solver=pinn, max_epochs=5, accelerator='cpu') - trainer.train() \ No newline at end of file diff --git a/tests/test_utils.py b/tests/test_utils.py index 46305f647..a641c3838 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -3,41 +3,50 @@ from pina.utils import merge_tensors from pina.label_tensor import LabelTensor from pina import LabelTensor -from pina.geometry import EllipsoidDomain, CartesianDomain +from pina.domain import EllipsoidDomain, CartesianDomain from pina.utils import check_consistency import pytest -from pina.geometry import Location +from pina.domain import DomainInterface def test_merge_tensors(): - tensor1 = LabelTensor(torch.rand((20, 3)), ['a', 'b', 'c']) - tensor2 = LabelTensor(torch.zeros((20, 3)), ['d', 'e', 'f']) - tensor3 = LabelTensor(torch.ones((30, 3)), ['g', 'h', 'i']) + tensor1 = LabelTensor(torch.rand((20, 3)), ["a", "b", "c"]) + tensor2 = LabelTensor(torch.zeros((20, 3)), ["d", "e", "f"]) + tensor3 = LabelTensor(torch.ones((30, 3)), ["g", "h", "i"]) merged_tensor = merge_tensors((tensor1, tensor2, tensor3)) - assert tuple(merged_tensor.labels) == ('a', 'b', 'c', 'd', 'e', 'f', 'g', - 'h', 'i') + assert tuple(merged_tensor.labels) == ( + "a", + "b", + "c", + "d", + "e", + "f", + "g", + "h", + "i", + ) assert merged_tensor.shape == (20 * 20 * 30, 9) - assert torch.all(merged_tensor.extract(('d', 'e', 'f')) == 0) - assert torch.all(merged_tensor.extract(('g', 'h', 'i')) == 1) + assert torch.all(merged_tensor.extract(("d", "e", "f")) == 0) + assert torch.all(merged_tensor.extract(("g", "h", "i")) == 1) def test_check_consistency_correct(): - ellipsoid1 = EllipsoidDomain({'x': [1, 2], 'y': [-2, 1]}) - example_input_pts = LabelTensor(torch.tensor([[0, 0, 0]]), ['x', 'y', 'z']) + ellipsoid1 = EllipsoidDomain({"x": [1, 2], "y": [-2, 1]}) + example_input_pts = LabelTensor(torch.tensor([[0, 0, 0]]), ["x", "y", "z"]) check_consistency(example_input_pts, torch.Tensor) - check_consistency(CartesianDomain, Location, subclass=True) - check_consistency(ellipsoid1, Location) + check_consistency(CartesianDomain, DomainInterface, subclass=True) + check_consistency(ellipsoid1, DomainInterface) def test_check_consistency_incorrect(): - ellipsoid1 = EllipsoidDomain({'x': [1, 2], 'y': [-2, 1]}) - example_input_pts = LabelTensor(torch.tensor([[0, 0, 0]]), ['x', 'y', 'z']) + ellipsoid1 = EllipsoidDomain({"x": [1, 2], "y": [-2, 1]}) + example_input_pts = LabelTensor(torch.tensor([[0, 0, 0]]), ["x", "y", "z"]) with pytest.raises(ValueError): - check_consistency(example_input_pts, Location) + check_consistency(example_input_pts, DomainInterface) with pytest.raises(ValueError): - check_consistency(torch.Tensor, Location, subclass=True) + check_consistency(torch.Tensor, DomainInterface, subclass=True) with pytest.raises(ValueError): check_consistency(ellipsoid1, torch.Tensor) diff --git a/tests/test_weighting/test_ntk_weighting.py b/tests/test_weighting/test_ntk_weighting.py new file mode 100644 index 000000000..840237fb4 --- /dev/null +++ b/tests/test_weighting/test_ntk_weighting.py @@ -0,0 +1,65 @@ +import pytest +from pina import Trainer +from pina.solver import PINN +from pina.model import FeedForward +from pina.problem.zoo import Poisson2DSquareProblem +from pina.loss import NeuralTangentKernelWeighting + +problem = Poisson2DSquareProblem() +condition_names = problem.conditions.keys() + + +@pytest.mark.parametrize( + "model,alpha", + [ + ( + FeedForward( + len(problem.input_variables), len(problem.output_variables) + ), + 0.5, + ) + ], +) +def test_constructor(model, alpha): + NeuralTangentKernelWeighting(model=model, alpha=alpha) + + +@pytest.mark.parametrize("model", [0.5]) +def test_wrong_constructor1(model): + with pytest.raises(ValueError): + NeuralTangentKernelWeighting(model) + + +@pytest.mark.parametrize( + "model,alpha", + [ + ( + FeedForward( + len(problem.input_variables), len(problem.output_variables) + ), + 1.2, + ) + ], +) +def test_wrong_constructor2(model, alpha): + with pytest.raises(ValueError): + NeuralTangentKernelWeighting(model, alpha) + + +@pytest.mark.parametrize( + "model,alpha", + [ + ( + FeedForward( + len(problem.input_variables), len(problem.output_variables) + ), + 0.5, + ) + ], +) +def test_train_aggregation(model, alpha): + weighting = NeuralTangentKernelWeighting(model=model, alpha=alpha) + problem.discretise_domain(50) + solver = PINN(problem=problem, model=model, weighting=weighting) + trainer = Trainer(solver=solver, max_epochs=5, accelerator="cpu") + trainer.train() diff --git a/tests/test_weighting/test_standard_weighting.py b/tests/test_weighting/test_standard_weighting.py new file mode 100644 index 000000000..9caa89ae1 --- /dev/null +++ b/tests/test_weighting/test_standard_weighting.py @@ -0,0 +1,51 @@ +import pytest +import torch + +from pina import Trainer +from pina.solver import PINN +from pina.model import FeedForward +from pina.problem.zoo import Poisson2DSquareProblem +from pina.loss import ScalarWeighting + +problem = Poisson2DSquareProblem() +model = FeedForward(len(problem.input_variables), len(problem.output_variables)) +condition_names = problem.conditions.keys() +print(problem.conditions.keys()) + + +@pytest.mark.parametrize( + "weights", [1, 1.0, dict(zip(condition_names, [1] * len(condition_names)))] +) +def test_constructor(weights): + ScalarWeighting(weights=weights) + + +@pytest.mark.parametrize("weights", ["a", [1, 2, 3]]) +def test_wrong_constructor(weights): + with pytest.raises(ValueError): + ScalarWeighting(weights=weights) + + +@pytest.mark.parametrize( + "weights", [1, 1.0, dict(zip(condition_names, [1] * len(condition_names)))] +) +def test_aggregate(weights): + weighting = ScalarWeighting(weights=weights) + losses = dict( + zip( + condition_names, + [torch.randn(1) for _ in range(len(condition_names))], + ) + ) + weighting.aggregate(losses=losses) + + +@pytest.mark.parametrize( + "weights", [1, 1.0, dict(zip(condition_names, [1] * len(condition_names)))] +) +def test_train_aggregation(weights): + weighting = ScalarWeighting(weights=weights) + problem.discretise_domain(50) + solver = PINN(problem=problem, model=model, weighting=weighting) + trainer = Trainer(solver=solver, max_epochs=5, accelerator="cpu") + trainer.train() diff --git a/tutorials/README.md b/tutorials/README.md index 5838a1fff..3129dd9b7 100644 --- a/tutorials/README.md +++ b/tutorials/README.md @@ -33,3 +33,4 @@ Time dependent Kuramoto Sivashinsky equation using the Averaging Neural Operator |---------------|-----------| Unstructured convolutional autoencoder via continuous convolution |[[.ipynb](tutorial4/tutorial.ipynb), [.py](tutorial4/tutorial.py), [.html](http://mathlab.github.io/PINA/_rst/tutorials/tutorial4/tutorial.html)]| POD-RBF and POD-NN for reduced order modeling| [[.ipynb](tutorial8/tutorial.ipynb), [.py](tutorial8/tutorial.py), [.html](http://mathlab.github.io/PINA/_rst/tutorials/tutorial8/tutorial.html)]| +POD-RBF for modelling Lid Cavity| [[.ipynb](tutorial14/tutorial.ipynb), [.py](tutorial14/tutorial.py), [.html](http://mathlab.github.io/PINA/_rst/tutorials/tutorial14/tutorial.html)]| diff --git a/tutorials/tutorial1/tutorial.ipynb b/tutorials/tutorial1/tutorial.ipynb index a09cf4621..f92dc4d6c 100644 --- a/tutorials/tutorial1/tutorial.ipynb +++ b/tutorials/tutorial1/tutorial.ipynb @@ -63,7 +63,7 @@ "\n", "```python\n", "from pina.problem import SpatialProblem\n", - "from pina.geometry import CartesianProblem\n", + "from pina.domain import CartesianProblem\n", "\n", "class SimpleODE(SpatialProblem):\n", " \n", @@ -87,21 +87,27 @@ "source": [ "## routine needed to run the notebook on Google Colab\n", "try:\n", - " import google.colab\n", - " IN_COLAB = True\n", + " import google.colab\n", + "\n", + " IN_COLAB = True\n", "except:\n", - " IN_COLAB = False\n", + " IN_COLAB = False\n", "if IN_COLAB:\n", - " !pip install \"pina-mathlab\"\n", + " !pip install \"pina-mathlab\"\n", + "\n", + "import warnings\n", "\n", "from pina.problem import SpatialProblem, TimeDependentProblem\n", - "from pina.geometry import CartesianDomain\n", + "from pina.domain import CartesianDomain\n", + "\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", "\n", "class TimeSpaceODE(SpatialProblem, TimeDependentProblem):\n", - " \n", - " output_variables = ['u']\n", - " spatial_domain = CartesianDomain({'x': [0, 1]})\n", - " temporal_domain = CartesianDomain({'t': [0, 1]})\n", + "\n", + " output_variables = [\"u\"]\n", + " spatial_domain = CartesianDomain({\"x\": [0, 1]})\n", + " temporal_domain = CartesianDomain({\"t\": [0, 1]})\n", "\n", " # other stuff ..." ] @@ -129,55 +135,60 @@ "source": [ "### Write the problem class\n", "\n", - "Once the `Problem` class is initialized, we need to represent the differential equation in **PINA**. In order to do this, we need to load the **PINA** operators from `pina.operators` module. Again, we'll consider Equation (1) and represent it in **PINA**:" + "Once the `Problem` class is initialized, we need to represent the differential equation in **PINA**. In order to do this, we need to load the **PINA** operators from `pina.operator` module. Again, we'll consider Equation (1) and represent it in **PINA**:" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "f2608e2e", "metadata": {}, "outputs": [], "source": [ + "import torch\n", + "import matplotlib.pyplot as plt\n", + "\n", "from pina.problem import SpatialProblem\n", - "from pina.operators import grad\n", + "from pina.operator import grad\n", "from pina import Condition\n", - "from pina.geometry import CartesianDomain\n", + "from pina.domain import CartesianDomain\n", "from pina.equation import Equation, FixedValue\n", "\n", - "import torch\n", "\n", + "# defining the ode equation\n", + "def ode_equation(input_, output_):\n", "\n", - "class SimpleODE(SpatialProblem):\n", + " # computing the derivative\n", + " u_x = grad(output_, input_, components=[\"u\"], d=[\"x\"])\n", "\n", - " output_variables = ['u']\n", - " spatial_domain = CartesianDomain({'x': [0, 1]})\n", + " # extracting the u input variable\n", + " u = output_.extract([\"u\"])\n", "\n", - " # defining the ode equation\n", - " def ode_equation(input_, output_):\n", + " # calculate the residual and return it\n", + " return u_x - u\n", "\n", - " # computing the derivative\n", - " u_x = grad(output_, input_, components=['u'], d=['x'])\n", "\n", - " # extracting the u input variable\n", - " u = output_.extract(['u'])\n", + "class SimpleODE(SpatialProblem):\n", "\n", - " # calculate the residual and return it\n", - " return u_x - u\n", + " output_variables = [\"u\"]\n", + " spatial_domain = CartesianDomain({\"x\": [0, 1]})\n", + "\n", + " domains = {\n", + " \"x0\": CartesianDomain({\"x\": 0.0}),\n", + " \"D\": CartesianDomain({\"x\": [0, 1]}),\n", + " }\n", "\n", " # conditions to hold\n", " conditions = {\n", - " 'x0': Condition(location=CartesianDomain({'x': 0.}), equation=FixedValue(1)), # We fix initial condition to value 1\n", - " 'D': Condition(location=CartesianDomain({'x': [0, 1]}), equation=Equation(ode_equation)), # We wrap the python equation using Equation\n", + " \"bound_cond\": Condition(domain=\"x0\", equation=FixedValue(1.0)),\n", + " \"phys_cond\": Condition(domain=\"D\", equation=Equation(ode_equation)),\n", " }\n", "\n", - " # sampled points (see below)\n", - " input_pts = None\n", - "\n", " # defining the true solution\n", - " def truth_solution(self, pts):\n", - " return torch.exp(pts.extract(['x']))\n", - " \n", + " def solution(self, pts):\n", + " return torch.exp(pts.extract([\"x\"]))\n", + "\n", + "\n", "problem = SimpleODE()" ] }, @@ -191,7 +202,7 @@ "\n", "Once we have defined the function, we need to tell the neural network where these methods are to be applied. To do so, we use the `Condition` class. In the `Condition` class, we pass the location points and the equation we want minimized on those points (other possibilities are allowed, see the documentation for reference).\n", "\n", - "Finally, it's possible to define a `truth_solution` function, which can be useful if we want to plot the results and see how the real solution compares to the expected (true) solution. Notice that the `truth_solution` function is a method of the `PINN` class, but it is not mandatory for problem definition.\n" + "Finally, it's possible to define a `solution` function, which can be useful if we want to plot the results and see how the real solution compares to the expected (true) solution. Notice that the `solution` function is a method of the `PINN` class, but it is not mandatory for problem definition.\n" ] }, { @@ -207,20 +218,20 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "09ce5c3a", "metadata": {}, "outputs": [], "source": [ "# sampling 20 points in [0, 1] through discretization in all locations\n", - "problem.discretise_domain(n=20, mode='grid', variables=['x'], locations='all')\n", + "problem.discretise_domain(n=20, mode=\"grid\", domains=\"all\")\n", "\n", "# sampling 20 points in (0, 1) through latin hypercube sampling in D, and 1 point in x0\n", - "problem.discretise_domain(n=20, mode='latin', variables=['x'], locations=['D'])\n", - "problem.discretise_domain(n=1, mode='random', variables=['x'], locations=['x0'])\n", + "problem.discretise_domain(n=20, mode=\"latin\", domains=[\"D\"])\n", + "problem.discretise_domain(n=1, mode=\"random\", domains=[\"x0\"])\n", "\n", "# sampling 20 points in (0, 1) randomly\n", - "problem.discretise_domain(n=20, mode='random', variables=['x'])" + "problem.discretise_domain(n=20, mode=\"random\")" ] }, { @@ -233,14 +244,14 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "329962b6", "metadata": {}, "outputs": [], "source": [ "# sampling for training\n", - "problem.discretise_domain(1, 'random', locations=['x0'])\n", - "problem.discretise_domain(20, 'lh', locations=['D'])" + "problem.discretise_domain(1, \"random\", domains=[\"x0\"])\n", + "problem.discretise_domain(20, \"lh\", domains=[\"D\"])" ] }, { @@ -253,7 +264,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "d6ed9aaf", "metadata": {}, "outputs": [ @@ -261,33 +272,33 @@ "name": "stdout", "output_type": "stream", "text": [ - "Input points: {'x0': LabelTensor([[[0.]]]), 'D': LabelTensor([[[0.7644]],\n", - " [[0.2028]],\n", - " [[0.1789]],\n", - " [[0.4294]],\n", - " [[0.3239]],\n", - " [[0.6531]],\n", - " [[0.1406]],\n", - " [[0.6062]],\n", - " [[0.4969]],\n", - " [[0.7429]],\n", - " [[0.8681]],\n", - " [[0.3800]],\n", - " [[0.5357]],\n", - " [[0.0152]],\n", - " [[0.9679]],\n", - " [[0.8101]],\n", - " [[0.0662]],\n", - " [[0.9095]],\n", - " [[0.2503]],\n", - " [[0.5580]]])}\n", + "Input points: {'x0': LabelTensor([[0.]]), 'D': LabelTensor([[0.3097],\n", + " [0.9524],\n", + " [0.6227],\n", + " [0.9200],\n", + " [0.1549],\n", + " [0.8729],\n", + " [0.8064],\n", + " [0.3929],\n", + " [0.1100],\n", + " [0.4493],\n", + " [0.2909],\n", + " [0.6947],\n", + " [0.0141],\n", + " [0.4516],\n", + " [0.5632],\n", + " [0.5328],\n", + " [0.7851],\n", + " [0.0829],\n", + " [0.7144],\n", + " [0.2229]])}\n", "Input points labels: ['x']\n" ] } ], "source": [ - "print('Input points:', problem.input_pts)\n", - "print('Input points labels:', problem.input_pts['D'].labels)" + "print(\"Input points:\", problem.discretised_domains)\n", + "print(\"Input points labels:\", problem.discretised_domains[\"D\"].labels)" ] }, { @@ -295,18 +306,28 @@ "id": "669e8534", "metadata": {}, "source": [ - "To visualize the sampled points we can use the `.plot_samples` method of the `Plotter` class" + "To visualize the sampled points we can use `matplotlib.pyplot`:" ] }, { "cell_type": "code", - "execution_count": 5, - "id": "33cc80bc", + "execution_count": null, + "id": "3802e22a", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" ] @@ -316,10 +337,12 @@ } ], "source": [ - "from pina import Plotter\n", - "\n", - "pl = Plotter()\n", - "pl.plot_samples(problem=problem)" + "for location in problem.input_pts:\n", + " coords = (\n", + " problem.input_pts[location].extract(problem.spatial_variables).flatten()\n", + " )\n", + " plt.scatter(coords, torch.zeros_like(coords), s=10, label=location)\n", + "plt.legend()" ] }, { @@ -337,7 +360,7 @@ "id": "075f43f5", "metadata": {}, "source": [ - "Once we have defined the problem and generated the data we can start the modelling. Here we will choose a `FeedForward` neural network available in `pina.model`, and we will train using the `PINN` solver from `pina.solvers`. We highlight that this training is fairly simple, for more advanced stuff consider the tutorials in the ***Physics Informed Neural Networks*** section of ***Tutorials***. For training we use the `Trainer` class from `pina.trainer`. Here we show a very short training and some method for plotting the results. Notice that by default all relevant metrics (e.g. MSE error during training) are going to be tracked using a `lightining` logger, by default `CSVLogger`. If you want to track the metric by yourself without a logger, use `pina.callbacks.MetricTracker`." + "Once we have defined the problem and generated the data we can start the modelling. Here we will choose a `FeedForward` neural network available in `pina.model`, and we will train using the `PINN` solver from `pina.solver`. We highlight that this training is fairly simple, for more advanced stuff consider the tutorials in the ***Physics Informed Neural Networks*** section of ***Tutorials***. For training we use the `Trainer` class from `pina.trainer`. Here we show a very short training and some method for plotting the results. Notice that by default all relevant metrics (e.g. MSE error during training) are going to be tracked using a `lightning` logger, by default `CSVLogger`. If you want to track the metric by yourself without a logger, use `pina.callback.MetricTracker`." ] }, { @@ -345,12 +368,44 @@ "execution_count": null, "id": "3bb4dc9b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: True (mps), used: False\n", + "TPU available: False, using: 0 TPU cores\n", + "HPU available: False, using: 0 HPUs\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1499: 100%|██████████| 1/1 [00:00<00:00, 149.92it/s, v_num=0, bound_cond_loss=1.52e-8, phys_cond_loss=7.68e-6, train_loss=7.69e-6] " + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "`Trainer.fit` stopped: `max_epochs=1500` reached.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1499: 100%|██████████| 1/1 [00:00<00:00, 100.10it/s, v_num=0, bound_cond_loss=1.52e-8, phys_cond_loss=7.68e-6, train_loss=7.69e-6]\n" + ] + } + ], "source": [ "from pina import Trainer\n", - "from pina.solvers import PINN\n", + "from pina.solver import PINN\n", "from pina.model import FeedForward\n", - "from pina.callbacks import MetricTracker\n", + "from lightning.pytorch.loggers import TensorBoardLogger\n", + "from pina.optim import TorchOptimizer\n", "\n", "\n", "# build the model\n", @@ -358,14 +413,23 @@ " layers=[10, 10],\n", " func=torch.nn.Tanh,\n", " output_dimensions=len(problem.output_variables),\n", - " input_dimensions=len(problem.input_variables)\n", + " input_dimensions=len(problem.input_variables),\n", ")\n", "\n", "# create the PINN object\n", - "pinn = PINN(problem, model)\n", + "pinn = PINN(problem, model, TorchOptimizer(torch.optim.Adam, lr=0.005))\n", "\n", "# create the trainer\n", - "trainer = Trainer(solver=pinn, max_epochs=1500, callbacks=[MetricTracker()], accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional)\n", + "trainer = Trainer(\n", + " solver=pinn,\n", + " max_epochs=1500,\n", + " logger=TensorBoardLogger(\"tutorial_logs\"),\n", + " accelerator=\"cpu\",\n", + " train_size=1.0,\n", + " test_size=0.0,\n", + " val_size=0.0,\n", + " enable_model_summary=False,\n", + ") # we train on CPU and avoid model summary at beginning of training (optional)\n", "\n", "# train\n", "trainer.train()" @@ -376,24 +440,24 @@ "id": "f8b4f496", "metadata": {}, "source": [ - "After the training we can inspect trainer logged metrics (by default **PINA** logs mean square error residual loss). The logged metrics can be accessed online using one of the `Lightinig` loggers. The final loss can be accessed by `trainer.logged_metrics`" + "After the training we can inspect trainer logged metrics (by default **PINA** logs mean square error residual loss). The logged metrics can be accessed online using one of the `Lightning` loggers. The final loss can be accessed by `trainer.logged_metrics`" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 27, "id": "f5fbf362", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'x0_loss': tensor(1.0674e-05),\n", - " 'D_loss': tensor(0.0008),\n", - " 'mean_loss': tensor(0.0004)}" + "{'bound_cond_loss': tensor(1.5208e-08),\n", + " 'phys_cond_loss': tensor(7.6781e-06),\n", + " 'train_loss': tensor(7.6933e-06)}" ] }, - "execution_count": 7, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -408,36 +472,30 @@ "id": "0963d7d2", "metadata": {}, "source": [ - "By using the `Plotter` class from **PINA** we can also do some quatitative plots of the solution. " + "By using `matplotlib` we can also do some qualitative plots of the solution. " ] }, { "cell_type": "code", - "execution_count": 8, - "id": "19078eb5", + "execution_count": null, + "id": "ffbf0d5e", "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n" - ] - }, { "data": { - "image/png": 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", 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" + "" ] }, + "execution_count": 28, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" }, { "data": { + "image/png": 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", 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" ] }, "metadata": {}, @@ -445,8 +503,13 @@ } ], "source": [ - "# plotting the solution\n", - "pl.plot(solver=pinn)" + "pts = pinn.problem.spatial_domain.sample(256, \"grid\", variables=\"x\")\n", + "predicted_output = pinn.forward(pts).extract(\"u\").tensor.detach()\n", + "true_output = pinn.problem.solution(pts).detach()\n", + "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8, 8))\n", + "ax.plot(pts.extract([\"x\"]), predicted_output, label=\"Neural Network solution\")\n", + "ax.plot(pts.extract([\"x\"]), true_output, label=\"True solution\")\n", + "plt.legend()" ] }, { @@ -454,20 +517,57 @@ "id": "bf47b98a", "metadata": {}, "source": [ - "The solution is overlapped with the actual one, and they are barely indistinguishable. We can also plot easily the loss:" + "The solution is overlapped with the actual one, and they are barely indistinguishable. We can also take a look at the loss using `TensorBoard`:" ] }, { "cell_type": "code", - "execution_count": 9, - "id": "bf6211e6", + "execution_count": null, + "id": "fcac93e4", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "To load TensorBoard run load_ext tensorboard on your terminal\n", + "To visualize the loss you can run tensorboard --logdir 'tutorial_logs' on your terminal\n", + "\n", + "The tensorboard extension is already loaded. To reload it, use:\n", + " %reload_ext tensorboard\n" + ] + }, { "data": { - "image/png": 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", "text/plain": [ - "
" + "Reusing TensorBoard on port 6007 (pid 55149), started 0:00:03 ago. (Use '!kill 55149' to kill it.)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " " + ], + "text/plain": [ + "" ] }, "metadata": {}, @@ -475,7 +575,13 @@ } ], "source": [ - "pl.plot_loss(trainer=trainer, label = 'mean_loss', logy=True)" + "print(\"\\nTo load TensorBoard run load_ext tensorboard on your terminal\")\n", + "print(\n", + " \"To visualize the loss you can run tensorboard --logdir 'tutorial_logs' on your terminal\\n\"\n", + ")\n", + "# # uncomment for running tensorboard\n", + "# %load_ext tensorboard\n", + "# %tensorboard --logdir=tutorial_logs" ] }, { @@ -483,7 +589,97 @@ "id": "58172899", "metadata": {}, "source": [ - "As we can see the loss has not reached a minimum, suggesting that we could train for longer" + "As we can see the loss has not reached a minimum, suggesting that we could train for longer! Alternatively, we can also take look at the loss using callbacks. Here we use `MetricTracker` from `pina.callback`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "03398692", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: True (mps), used: False\n", + "TPU available: False, using: 0 TPU cores\n", + "HPU available: False, using: 0 HPUs\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1499: 100%|██████████| 1/1 [00:00<00:00, 211.36it/s, v_num=0, bound_cond_loss=3.6e-8, phys_cond_loss=2.13e-5, train_loss=2.13e-5] " + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "`Trainer.fit` stopped: `max_epochs=1500` reached.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1499: 100%|██████████| 1/1 [00:00<00:00, 134.97it/s, v_num=0, bound_cond_loss=3.6e-8, phys_cond_loss=2.13e-5, train_loss=2.13e-5]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pina.callback import MetricTracker\n", + "\n", + "# create the model\n", + "newmodel = FeedForward(\n", + " layers=[10, 10],\n", + " func=torch.nn.Tanh,\n", + " output_dimensions=len(problem.output_variables),\n", + " input_dimensions=len(problem.input_variables),\n", + ")\n", + "\n", + "# create the PINN object\n", + "newpinn = PINN(\n", + " problem, newmodel, optimizer=TorchOptimizer(torch.optim.Adam, lr=0.005)\n", + ")\n", + "\n", + "# create the trainer\n", + "newtrainer = Trainer(\n", + " solver=newpinn,\n", + " max_epochs=1500,\n", + " logger=True, # enable parameter logging\n", + " callbacks=[MetricTracker()],\n", + " accelerator=\"cpu\",\n", + " train_size=1.0,\n", + " test_size=0.0,\n", + " val_size=0.0,\n", + " enable_model_summary=False,\n", + ") # we train on CPU and avoid model summary at beginning of training (optional)\n", + "\n", + "# train\n", + "newtrainer.train()\n", + "\n", + "# plot loss\n", + "trainer_metrics = newtrainer.callbacks[0].metrics\n", + "loss = trainer_metrics[\"train_loss\"]\n", + "epochs = range(len(loss))\n", + "plt.plot(epochs, loss.cpu())\n", + "# plotting\n", + "plt.xlabel(\"epoch\")\n", + "plt.ylabel(\"loss\")\n", + "plt.yscale(\"log\")" ] }, { @@ -506,11 +702,8 @@ } ], "metadata": { - "interpreter": { - "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49" - }, "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "pina", "language": "python", "name": "python3" }, @@ -524,7 +717,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.7" + "version": "3.9.21" } }, "nbformat": 4, diff --git a/tutorials/tutorial1/tutorial.py b/tutorials/tutorial1/tutorial.py index aa18b7fd8..b6cb93c8e 100644 --- a/tutorials/tutorial1/tutorial.py +++ b/tutorials/tutorial1/tutorial.py @@ -2,21 +2,21 @@ # coding: utf-8 # # Tutorial: Physics Informed Neural Networks on PINA -# +# # [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial1/tutorial.ipynb) -# +# -# In this tutorial, we will demonstrate a typical use case of **PINA** on a toy problem, following the standard API procedure. -# +# In this tutorial, we will demonstrate a typical use case of **PINA** on a toy problem, following the standard API procedure. +# #

# PINA API #

-# +# # Specifically, the tutorial aims to introduce the following topics: -# +# # * Explaining how to build **PINA** Problems, # * Showing how to generate data for `PINN` training -# +# # These are the two main steps needed **before** starting the modelling optimization (choose model and solver, and train). We will show each step in detail, and at the end, we will solve a simple Ordinary Differential Equation (ODE) problem using the `PINN` solver. # ## Build a PINA problem @@ -24,7 +24,7 @@ # Problem definition in the **PINA** framework is done by building a python `class`, which inherits from one or more problem classes (`SpatialProblem`, `TimeDependentProblem`, `ParametricProblem`, ...) depending on the nature of the problem. Below is an example: # ### Simple Ordinary Differential Equation # Consider the following: -# +# # $$ # \begin{equation} # \begin{cases} @@ -33,52 +33,58 @@ # \end{cases} # \end{equation} # $$ -# +# # with the analytical solution $u(x) = e^x$. In this case, our ODE depends only on the spatial variable $x\in(0,1)$ , meaning that our `Problem` class is going to be inherited from the `SpatialProblem` class: -# +# # ```python # from pina.problem import SpatialProblem -# from pina.geometry import CartesianProblem -# +# from pina.domain import CartesianProblem +# # class SimpleODE(SpatialProblem): -# +# # output_variables = ['u'] # spatial_domain = CartesianProblem({'x': [0, 1]}) -# +# # # other stuff ... # ``` -# +# # Notice that we define `output_variables` as a list of symbols, indicating the output variables of our equation (in this case only $u$), this is done because in **PINA** the `torch.Tensor`s are labelled, allowing the user maximal flexibility for the manipulation of the tensor. The `spatial_domain` variable indicates where the sample points are going to be sampled in the domain, in this case $x\in[0,1]$. -# +# # What if our equation is also time-dependent? In this case, our `class` will inherit from both `SpatialProblem` and `TimeDependentProblem`: -# +# # In[ ]: ## routine needed to run the notebook on Google Colab try: - import google.colab - IN_COLAB = True + import google.colab + + IN_COLAB = True except: - IN_COLAB = False + IN_COLAB = False if IN_COLAB: - get_ipython().system('pip install "pina-mathlab"') + get_ipython().system('pip install "pina-mathlab"') + +import warnings from pina.problem import SpatialProblem, TimeDependentProblem -from pina.geometry import CartesianDomain +from pina.domain import CartesianDomain + +warnings.filterwarnings("ignore") + class TimeSpaceODE(SpatialProblem, TimeDependentProblem): - - output_variables = ['u'] - spatial_domain = CartesianDomain({'x': [0, 1]}) - temporal_domain = CartesianDomain({'t': [0, 1]}) + + output_variables = ["u"] + spatial_domain = CartesianDomain({"x": [0, 1]}) + temporal_domain = CartesianDomain({"t": [0, 1]}) # other stuff ... # where we have included the `temporal_domain` variable, indicating the time domain wanted for the solution. -# +# # In summary, using **PINA**, we can initialize a problem with a class which inherits from different base classes: `SpatialProblem`, `TimeDependentProblem`, `ParametricProblem`, and so on depending on the type of problem we are considering. Here are some examples (more on the official documentation): # * ``SpatialProblem`` $\rightarrow$ a differential equation with spatial variable(s) ``spatial_domain`` # * ``TimeDependentProblem`` $\rightarrow$ a time-dependent differential equation with temporal variable(s) ``temporal_domain`` @@ -86,120 +92,128 @@ class TimeSpaceODE(SpatialProblem, TimeDependentProblem): # * ``AbstractProblem`` $\rightarrow$ any **PINA** problem inherits from here # ### Write the problem class -# -# Once the `Problem` class is initialized, we need to represent the differential equation in **PINA**. In order to do this, we need to load the **PINA** operators from `pina.operators` module. Again, we'll consider Equation (1) and represent it in **PINA**: +# +# Once the `Problem` class is initialized, we need to represent the differential equation in **PINA**. In order to do this, we need to load the **PINA** operators from `pina.operator` module. Again, we'll consider Equation (1) and represent it in **PINA**: -# In[2]: +# In[ ]: +import torch +import matplotlib.pyplot as plt + from pina.problem import SpatialProblem -from pina.operators import grad +from pina.operator import grad from pina import Condition -from pina.geometry import CartesianDomain +from pina.domain import CartesianDomain from pina.equation import Equation, FixedValue -import torch +# defining the ode equation +def ode_equation(input_, output_): -class SimpleODE(SpatialProblem): + # computing the derivative + u_x = grad(output_, input_, components=["u"], d=["x"]) - output_variables = ['u'] - spatial_domain = CartesianDomain({'x': [0, 1]}) + # extracting the u input variable + u = output_.extract(["u"]) - # defining the ode equation - def ode_equation(input_, output_): + # calculate the residual and return it + return u_x - u - # computing the derivative - u_x = grad(output_, input_, components=['u'], d=['x']) - # extracting the u input variable - u = output_.extract(['u']) +class SimpleODE(SpatialProblem): - # calculate the residual and return it - return u_x - u + output_variables = ["u"] + spatial_domain = CartesianDomain({"x": [0, 1]}) + + domains = { + "x0": CartesianDomain({"x": 0.0}), + "D": CartesianDomain({"x": [0, 1]}), + } # conditions to hold conditions = { - 'x0': Condition(location=CartesianDomain({'x': 0.}), equation=FixedValue(1)), # We fix initial condition to value 1 - 'D': Condition(location=CartesianDomain({'x': [0, 1]}), equation=Equation(ode_equation)), # We wrap the python equation using Equation + "bound_cond": Condition(domain="x0", equation=FixedValue(1.0)), + "phys_cond": Condition(domain="D", equation=Equation(ode_equation)), } - # sampled points (see below) - input_pts = None - # defining the true solution - def truth_solution(self, pts): - return torch.exp(pts.extract(['x'])) - + def solution(self, pts): + return torch.exp(pts.extract(["x"])) + + problem = SimpleODE() # After we define the `Problem` class, we need to write different class methods, where each method is a function returning a residual. These functions are the ones minimized during PINN optimization, given the initial conditions. For example, in the domain $[0,1]$, the ODE equation (`ode_equation`) must be satisfied. We represent this by returning the difference between subtracting the variable `u` from its gradient (the residual), which we hope to minimize to 0. This is done for all conditions. Notice that we do not pass directly a `python` function, but an `Equation` object, which is initialized with the `python` function. This is done so that all the computations and internal checks are done inside **PINA**. -# +# # Once we have defined the function, we need to tell the neural network where these methods are to be applied. To do so, we use the `Condition` class. In the `Condition` class, we pass the location points and the equation we want minimized on those points (other possibilities are allowed, see the documentation for reference). -# -# Finally, it's possible to define a `truth_solution` function, which can be useful if we want to plot the results and see how the real solution compares to the expected (true) solution. Notice that the `truth_solution` function is a method of the `PINN` class, but it is not mandatory for problem definition. -# +# +# Finally, it's possible to define a `solution` function, which can be useful if we want to plot the results and see how the real solution compares to the expected (true) solution. Notice that the `solution` function is a method of the `PINN` class, but it is not mandatory for problem definition. +# -# ## Generate data -# +# ## Generate data +# # Data for training can come in form of direct numerical simulation results, or points in the domains. In case we perform unsupervised learning, we just need the collocation points for training, i.e. points where we want to evaluate the neural network. Sampling point in **PINA** is very easy, here we show three examples using the `.discretise_domain` method of the `AbstractProblem` class. -# In[3]: +# In[ ]: # sampling 20 points in [0, 1] through discretization in all locations -problem.discretise_domain(n=20, mode='grid', variables=['x'], locations='all') +problem.discretise_domain(n=20, mode="grid", domains="all") # sampling 20 points in (0, 1) through latin hypercube sampling in D, and 1 point in x0 -problem.discretise_domain(n=20, mode='latin', variables=['x'], locations=['D']) -problem.discretise_domain(n=1, mode='random', variables=['x'], locations=['x0']) +problem.discretise_domain(n=20, mode="latin", domains=["D"]) +problem.discretise_domain(n=1, mode="random", domains=["x0"]) # sampling 20 points in (0, 1) randomly -problem.discretise_domain(n=20, mode='random', variables=['x']) +problem.discretise_domain(n=20, mode="random") # We are going to use latin hypercube points for sampling. We need to sample in all the conditions domains. In our case we sample in `D` and `x0`. -# In[4]: +# In[ ]: # sampling for training -problem.discretise_domain(1, 'random', locations=['x0']) -problem.discretise_domain(20, 'lh', locations=['D']) - +problem.discretise_domain(1, "random", domains=["x0"]) +problem.discretise_domain(20, "lh", domains=["D"]) -# The points are saved in a python `dict`, and can be accessed by calling the attribute `input_pts` of the problem -# In[5]: +# The points are saved in a python `dict`, and can be accessed by calling the attribute `input_pts` of the problem +# In[ ]: -print('Input points:', problem.input_pts) -print('Input points labels:', problem.input_pts['D'].labels) +print("Input points:", problem.discretised_domains) +print("Input points labels:", problem.discretised_domains["D"].labels) -# To visualize the sampled points we can use the `.plot_samples` method of the `Plotter` class -# In[5]: +# To visualize the sampled points we can use `matplotlib.pyplot`: +# In[ ]: -from pina import Plotter -pl = Plotter() -pl.plot_samples(problem=problem) +for location in problem.input_pts: + coords = ( + problem.input_pts[location].extract(problem.spatial_variables).flatten() + ) + plt.scatter(coords, torch.zeros_like(coords), s=10, label=location) +plt.legend() # ## Perform a small training -# Once we have defined the problem and generated the data we can start the modelling. Here we will choose a `FeedForward` neural network available in `pina.model`, and we will train using the `PINN` solver from `pina.solvers`. We highlight that this training is fairly simple, for more advanced stuff consider the tutorials in the ***Physics Informed Neural Networks*** section of ***Tutorials***. For training we use the `Trainer` class from `pina.trainer`. Here we show a very short training and some method for plotting the results. Notice that by default all relevant metrics (e.g. MSE error during training) are going to be tracked using a `lightining` logger, by default `CSVLogger`. If you want to track the metric by yourself without a logger, use `pina.callbacks.MetricTracker`. +# Once we have defined the problem and generated the data we can start the modelling. Here we will choose a `FeedForward` neural network available in `pina.model`, and we will train using the `PINN` solver from `pina.solver`. We highlight that this training is fairly simple, for more advanced stuff consider the tutorials in the ***Physics Informed Neural Networks*** section of ***Tutorials***. For training we use the `Trainer` class from `pina.trainer`. Here we show a very short training and some method for plotting the results. Notice that by default all relevant metrics (e.g. MSE error during training) are going to be tracked using a `lightning` logger, by default `CSVLogger`. If you want to track the metric by yourself without a logger, use `pina.callback.MetricTracker`. # In[ ]: from pina import Trainer -from pina.solvers import PINN +from pina.solver import PINN from pina.model import FeedForward -from pina.callbacks import MetricTracker +from lightning.pytorch.loggers import TensorBoardLogger +from pina.optim import TorchOptimizer # build the model @@ -207,55 +221,120 @@ def truth_solution(self, pts): layers=[10, 10], func=torch.nn.Tanh, output_dimensions=len(problem.output_variables), - input_dimensions=len(problem.input_variables) + input_dimensions=len(problem.input_variables), ) # create the PINN object -pinn = PINN(problem, model) +pinn = PINN(problem, model, TorchOptimizer(torch.optim.Adam, lr=0.005)) # create the trainer -trainer = Trainer(solver=pinn, max_epochs=1500, callbacks=[MetricTracker()], accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional) +trainer = Trainer( + solver=pinn, + max_epochs=1500, + logger=TensorBoardLogger("tutorial_logs"), + accelerator="cpu", + train_size=1.0, + test_size=0.0, + val_size=0.0, + enable_model_summary=False, +) # we train on CPU and avoid model summary at beginning of training (optional) # train trainer.train() -# After the training we can inspect trainer logged metrics (by default **PINA** logs mean square error residual loss). The logged metrics can be accessed online using one of the `Lightinig` loggers. The final loss can be accessed by `trainer.logged_metrics` +# After the training we can inspect trainer logged metrics (by default **PINA** logs mean square error residual loss). The logged metrics can be accessed online using one of the `Lightning` loggers. The final loss can be accessed by `trainer.logged_metrics` -# In[7]: +# In[27]: # inspecting final loss trainer.logged_metrics -# By using the `Plotter` class from **PINA** we can also do some quatitative plots of the solution. +# By using `matplotlib` we can also do some qualitative plots of the solution. -# In[8]: +# In[ ]: + + +pts = pinn.problem.spatial_domain.sample(256, "grid", variables="x") +predicted_output = pinn.forward(pts).extract("u").tensor.detach() +true_output = pinn.problem.solution(pts).detach() +fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8, 8)) +ax.plot(pts.extract(["x"]), predicted_output, label="Neural Network solution") +ax.plot(pts.extract(["x"]), true_output, label="True solution") +plt.legend() -# plotting the solution -pl.plot(solver=pinn) +# The solution is overlapped with the actual one, and they are barely indistinguishable. We can also take a look at the loss using `TensorBoard`: + +# In[ ]: -# The solution is overlapped with the actual one, and they are barely indistinguishable. We can also plot easily the loss: +print("\nTo load TensorBoard run load_ext tensorboard on your terminal") +print( + "To visualize the loss you can run tensorboard --logdir 'tutorial_logs' on your terminal\n" +) +# # uncomment for running tensorboard +# %load_ext tensorboard +# %tensorboard --logdir=tutorial_logs -# In[9]: +# As we can see the loss has not reached a minimum, suggesting that we could train for longer! Alternatively, we can also take look at the loss using callbacks. Here we use `MetricTracker` from `pina.callback`: -pl.plot_loss(trainer=trainer, label = 'mean_loss', logy=True) +# In[ ]: + + +from pina.callback import MetricTracker + +# create the model +newmodel = FeedForward( + layers=[10, 10], + func=torch.nn.Tanh, + output_dimensions=len(problem.output_variables), + input_dimensions=len(problem.input_variables), +) + +# create the PINN object +newpinn = PINN( + problem, newmodel, optimizer=TorchOptimizer(torch.optim.Adam, lr=0.005) +) + +# create the trainer +newtrainer = Trainer( + solver=newpinn, + max_epochs=1500, + logger=True, # enable parameter logging + callbacks=[MetricTracker()], + accelerator="cpu", + train_size=1.0, + test_size=0.0, + val_size=0.0, + enable_model_summary=False, +) # we train on CPU and avoid model summary at beginning of training (optional) + +# train +newtrainer.train() +# plot loss +trainer_metrics = newtrainer.callbacks[0].metrics +loss = trainer_metrics["train_loss"] +epochs = range(len(loss)) +plt.plot(epochs, loss.cpu()) +# plotting +plt.xlabel("epoch") +plt.ylabel("loss") +plt.yscale("log") -# As we can see the loss has not reached a minimum, suggesting that we could train for longer # ## What's next? -# +# # Congratulations on completing the introductory tutorial of **PINA**! There are several directions you can go now: -# +# # 1. Train the network for longer or with different layer sizes and assert the finaly accuracy -# +# # 2. Train the network using other types of models (see `pina.model`) -# +# # 3. GPU training and speed benchmarking -# +# # 4. Many more... diff --git a/tutorials/tutorial10/tutorial.ipynb b/tutorials/tutorial10/tutorial.ipynb index d361109c5..fa0642d5e 100644 --- a/tutorials/tutorial10/tutorial.ipynb +++ b/tutorials/tutorial10/tutorial.ipynb @@ -19,32 +19,35 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "## routine needed to run the notebook on Google Colab\n", "try:\n", - " import google.colab\n", - " IN_COLAB = True\n", + " import google.colab\n", + "\n", + " IN_COLAB = True\n", "except:\n", - " IN_COLAB = False\n", + " IN_COLAB = False\n", "if IN_COLAB:\n", - " !pip install \"pina-mathlab\"\n", - " # get the data\n", - " !mkdir \"data\"\n", - " !wget \"https://github.com/mathLab/PINA/raw/refs/heads/master/tutorials/tutorial10/data/Data_KS.mat\" -O \"data/Data_KS.mat\"\n", - " !wget \"https://github.com/mathLab/PINA/raw/refs/heads/master/tutorials/tutorial10/data/Data_KS2.mat\" -O \"data/Data_KS2.mat\"\n", + " !pip install \"pina-mathlab\"\n", + " # get the data\n", + " !mkdir \"data\"\n", + " !wget \"https://github.com/mathLab/PINA/raw/refs/heads/master/tutorials/tutorial10/data/Data_KS.mat\" -O \"data/Data_KS.mat\"\n", + " !wget \"https://github.com/mathLab/PINA/raw/refs/heads/master/tutorials/tutorial10/data/Data_KS2.mat\" -O \"data/Data_KS2.mat\"\n", "\n", "import torch\n", "import matplotlib.pyplot as plt\n", - "plt.style.use('tableau-colorblind10')\n", + "import warnings\n", + "\n", "from scipy import io\n", - "from pina import Condition, LabelTensor\n", - "from pina.problem import AbstractProblem\n", + "from pina import Condition, Trainer, LabelTensor\n", "from pina.model import AveragingNeuralOperator\n", - "from pina.solvers import SupervisedSolver\n", - "from pina.trainer import Trainer" + "from pina.solver import SupervisedSolver\n", + "from pina.problem.zoo import SupervisedProblem\n", + "\n", + "warnings.filterwarnings(\"ignore\")" ] }, { @@ -104,17 +107,24 @@ ], "source": [ "# load data\n", - "data=io.loadmat(\"data/Data_KS.mat\")\n", + "data = io.loadmat(\"data/Data_KS.mat\")\n", "\n", "# converting to label tensor\n", - "initial_cond_train = LabelTensor(torch.tensor(data['initial_cond_train'], dtype=torch.float), ['t','x','u0'])\n", - "initial_cond_test = LabelTensor(torch.tensor(data['initial_cond_test'], dtype=torch.float), ['t','x','u0'])\n", - "sol_train = LabelTensor(torch.tensor(data['sol_train'], dtype=torch.float), ['u'])\n", - "sol_test = LabelTensor(torch.tensor(data['sol_test'], dtype=torch.float), ['u'])\n", - "\n", - "print('Data Loaded')\n", - "print(f' shape initial condition: {initial_cond_train.shape}')\n", - "print(f' shape solution: {sol_train.shape}')" + "initial_cond_train = LabelTensor(\n", + " torch.tensor(data[\"initial_cond_train\"], dtype=torch.float),\n", + " [\"t\", \"x\", \"u0\"],\n", + ")\n", + "initial_cond_test = LabelTensor(\n", + " torch.tensor(data[\"initial_cond_test\"], dtype=torch.float), [\"t\", \"x\", \"u0\"]\n", + ")\n", + "sol_train = LabelTensor(\n", + " torch.tensor(data[\"sol_train\"], dtype=torch.float), [\"u\"]\n", + ")\n", + "sol_test = LabelTensor(torch.tensor(data[\"sol_test\"], dtype=torch.float), [\"u\"])\n", + "\n", + "print(\"Data Loaded\")\n", + "print(f\" shape initial condition: {initial_cond_train.shape}\")\n", + "print(f\" shape solution: {sol_train.shape}\")" ] }, { @@ -136,7 +146,7 @@ "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -149,35 +159,58 @@ "# helper function\n", "def plot_trajectory(coords, real, no_sol=None):\n", " # find the x-t shapes\n", - " dim_x = len(torch.unique(coords.extract('x')))\n", - " dim_t = len(torch.unique(coords.extract('t')))\n", + " dim_x = len(torch.unique(coords.extract(\"x\")))\n", + " dim_t = len(torch.unique(coords.extract(\"t\")))\n", " # if we don't have the Neural Operator solution we simply plot the real one\n", " if no_sol is None:\n", " fig, axs = plt.subplots(1, 1, figsize=(15, 5), sharex=True, sharey=True)\n", - " c = axs.imshow(real.reshape(dim_t, dim_x).T.detach(),extent=[0, 50, 0, 64], cmap='PuOr_r', aspect='auto')\n", - " axs.set_title('Real solution')\n", + " c = axs.imshow(\n", + " real.reshape(dim_t, dim_x).T.detach(),\n", + " extent=[0, 50, 0, 64],\n", + " cmap=\"PuOr_r\",\n", + " aspect=\"auto\",\n", + " )\n", + " axs.set_title(\"Real solution\")\n", " fig.colorbar(c, ax=axs)\n", - " axs.set_xlabel('t')\n", - " axs.set_ylabel('x')\n", + " axs.set_xlabel(\"t\")\n", + " axs.set_ylabel(\"x\")\n", " # otherwise we plot the real one, the Neural Operator one, and their difference\n", " else:\n", " fig, axs = plt.subplots(1, 3, figsize=(15, 5), sharex=True, sharey=True)\n", - " axs[0].imshow(real.reshape(dim_t, dim_x).T.detach(),extent=[0, 50, 0, 64], cmap='PuOr_r', aspect='auto')\n", - " axs[0].set_title('Real solution')\n", - " axs[1].imshow(no_sol.reshape(dim_t, dim_x).T.detach(),extent=[0, 50, 0, 64], cmap='PuOr_r', aspect='auto')\n", - " axs[1].set_title('NO solution')\n", - " c = axs[2].imshow((real - no_sol).abs().reshape(dim_t, dim_x).T.detach(),extent=[0, 50, 0, 64], cmap='PuOr_r', aspect='auto')\n", - " axs[2].set_title('Absolute difference')\n", + " axs[0].imshow(\n", + " real.reshape(dim_t, dim_x).T.detach(),\n", + " extent=[0, 50, 0, 64],\n", + " cmap=\"PuOr_r\",\n", + " aspect=\"auto\",\n", + " )\n", + " axs[0].set_title(\"Real solution\")\n", + " axs[1].imshow(\n", + " no_sol.reshape(dim_t, dim_x).T.detach(),\n", + " extent=[0, 50, 0, 64],\n", + " cmap=\"PuOr_r\",\n", + " aspect=\"auto\",\n", + " )\n", + " axs[1].set_title(\"NO solution\")\n", + " c = axs[2].imshow(\n", + " (real - no_sol).abs().reshape(dim_t, dim_x).T.detach(),\n", + " extent=[0, 50, 0, 64],\n", + " cmap=\"PuOr_r\",\n", + " aspect=\"auto\",\n", + " )\n", + " axs[2].set_title(\"Absolute difference\")\n", " fig.colorbar(c, ax=axs.ravel().tolist())\n", " for ax in axs:\n", - " ax.set_xlabel('t')\n", - " ax.set_ylabel('x')\n", + " ax.set_xlabel(\"t\")\n", + " ax.set_ylabel(\"x\")\n", " plt.show()\n", "\n", + "\n", "# a sample trajectory (we use the sample 5, feel free to change)\n", "sample_number = 20\n", - "plot_trajectory(coords=initial_cond_train[sample_number].extract(['x', 't']),\n", - " real=sol_train[sample_number].extract('u'))\n" + "plot_trajectory(\n", + " coords=initial_cond_train[sample_number].extract([\"x\", \"t\"]),\n", + " real=sol_train[sample_number].extract(\"u\"),\n", + ")" ] }, { @@ -231,19 +264,21 @@ "class SIREN(torch.nn.Module):\n", " def forward(self, x):\n", " return torch.sin(x)\n", - " \n", - "embedding_dimesion = 40 # hyperparameter embedding dimension\n", - "input_dimension = 3 # ['u', 'x', 't']\n", - "number_of_coordinates = 2 # ['x', 't']\n", - "lifting_net = torch.nn.Linear(input_dimension, embedding_dimesion) # simple linear layers for lifting and projecting nets\n", + "\n", + "\n", + "embedding_dimesion = 40 # hyperparameter embedding dimension\n", + "input_dimension = 3 # ['u', 'x', 't']\n", + "number_of_coordinates = 2 # ['x', 't']\n", + "lifting_net = torch.nn.Linear(input_dimension, embedding_dimesion)\n", "projecting_net = torch.nn.Linear(embedding_dimesion + number_of_coordinates, 1)\n", - "model = AveragingNeuralOperator(lifting_net=lifting_net,\n", - " projecting_net=projecting_net,\n", - " coordinates_indices=['x', 't'],\n", - " field_indices=['u0'],\n", - " n_layers=4,\n", - " func=SIREN\n", - " ) " + "model = AveragingNeuralOperator(\n", + " lifting_net=lifting_net,\n", + " projecting_net=projecting_net,\n", + " coordinates_indices=[\"x\", \"t\"],\n", + " field_indices=[\"u0\"],\n", + " n_layers=4,\n", + " func=SIREN,\n", + ")" ] }, { @@ -255,12 +290,12 @@ "## Solving the KS problem\n", "\n", "We will now focus on solving the KS equation using the `SupervisedSolver` class\n", - "and the `AveragingNeuralOperator` model. As done in the [FNO tutorial](https://github.com/mathLab/PINA/blob/master/tutorials/tutorial5/tutorial.ipynb) we now create the `NeuralOperatorProblem` class with `AbstractProblem`." + "and the `AveragingNeuralOperator` model. As done in the [FNO tutorial](https://github.com/mathLab/PINA/blob/master/tutorials/tutorial5/tutorial.ipynb) we now create the Neural Operator problem class with `SupervisedProblem`." ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -269,7 +304,6 @@ "text": [ "GPU available: True (mps), used: False\n", "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", "HPU available: False, using: 0 HPUs\n" ] }, @@ -277,7 +311,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 39: 100%|██████████| 20/20 [00:01<00:00, 13.59it/s, v_num=3, mean_loss=0.118]" + "Epoch 39: 100%|██████████| 20/20 [00:01<00:00, 18.75it/s, v_num=9, data_loss_step=0.0809, train_loss_step=0.0809, data_loss_epoch=0.108, train_loss_epoch=0.108]" ] }, { @@ -291,27 +325,32 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 39: 100%|██████████| 20/20 [00:01<00:00, 13.56it/s, v_num=3, mean_loss=0.118]\n" + "Epoch 39: 100%|██████████| 20/20 [00:01<00:00, 18.70it/s, v_num=9, data_loss_step=0.0809, train_loss_step=0.0809, data_loss_epoch=0.108, train_loss_epoch=0.108]\n" ] } ], "source": [ - "# expected running time ~ 1 minute\n", - "\n", - "class NeuralOperatorProblem(AbstractProblem):\n", - " input_variables = initial_cond_train.labels\n", - " output_variables = sol_train.labels\n", - " conditions = {'data' : Condition(input_points=initial_cond_train, \n", - " output_points=sol_train)}\n", - "\n", - "\n", "# initialize problem\n", - "problem = NeuralOperatorProblem()\n", + "problem = SupervisedProblem(\n", + " initial_cond_train,\n", + " sol_train,\n", + " input_variables=initial_cond_train.labels,\n", + " output_variables=sol_train.labels,\n", + ")\n", "# initialize solver\n", - "solver = SupervisedSolver(problem=problem, model=model,optimizer_kwargs={\"lr\":0.001})\n", + "solver = SupervisedSolver(problem=problem, model=model)\n", "# train, only CPU and avoid model summary at beginning of training (optional)\n", - "trainer = Trainer(solver=solver, max_epochs=40, accelerator='cpu', enable_model_summary=False, log_every_n_steps=-1, batch_size=5) # we train on CPU and avoid model summary at beginning of training (optional)\n", - "trainer.train()\n" + "trainer = Trainer(\n", + " solver=solver,\n", + " max_epochs=40,\n", + " accelerator=\"cpu\",\n", + " enable_model_summary=False,\n", + " batch_size=5, # we train on CPU and avoid model summary at beginning of training (optional)\n", + " train_size=1.0,\n", + " val_size=0.0,\n", + " test_size=0.0,\n", + ")\n", + "trainer.train()" ] }, { @@ -323,12 +362,12 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -340,52 +379,58 @@ "source": [ "sample_number = 2\n", "no_sol = solver(initial_cond_test)\n", - "plot_trajectory(coords=initial_cond_test[sample_number].extract(['x', 't']),\n", - " real=sol_test[sample_number].extract('u'),\n", - " no_sol=no_sol[5])" + "plot_trajectory(\n", + " coords=initial_cond_test[sample_number].extract([\"x\", \"t\"]),\n", + " real=sol_test[sample_number].extract(\"u\"),\n", + " no_sol=no_sol[5],\n", + ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "As we can see we can obtain nice result considering the small trainint time and the difficulty of the problem!\n", - "Let's see how the training and testing error:" + "As we can see we can obtain nice result considering the small training time and the difficulty of the problem!\n", + "Let's take a look at the training and testing error:" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Training error: 0.128\n", - "Testing error: 0.119\n" + "Training error: 0.107\n", + "Testing error: 0.097\n" ] } ], "source": [ "from pina.loss import PowerLoss\n", "\n", - "error_metric = PowerLoss(p=2) # we use the MSE loss\n", + "error_metric = PowerLoss(p=2) # we use the MSE loss\n", "\n", "with torch.no_grad():\n", " no_sol_train = solver(initial_cond_train)\n", - " err_train = error_metric(sol_train.extract('u'), no_sol_train).mean() # we average the error over trajectories\n", + " err_train = error_metric(\n", + " sol_train.extract(\"u\"), no_sol_train\n", + " ).mean() # we average the error over trajectories\n", " no_sol_test = solver(initial_cond_test)\n", - " err_test = error_metric(sol_test.extract('u'),no_sol_test).mean() # we average the error over trajectories\n", - " print(f'Training error: {float(err_train):.3f}')\n", - " print(f'Testing error: {float(err_test):.3f}')" + " err_test = error_metric(\n", + " sol_test.extract(\"u\"), no_sol_test\n", + " ).mean() # we average the error over trajectories\n", + " print(f\"Training error: {float(err_train):.3f}\")\n", + " print(f\"Testing error: {float(err_test):.3f}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "as we can see the error is pretty small, which agrees with what we can see from the previous plots." + "As we can see the error is pretty small, which agrees with what we can see from the previous plots." ] }, { @@ -396,9 +441,9 @@ "\n", "Now you know how to solve a time dependent neural operator problem in **PINA**! There are multiple directions you can go now:\n", "\n", - "1. Train the network for longer or with different layer sizes and assert the finaly accuracy\n", + "1. Train the network for longer or with different layer sizes and assert the final accuracy\n", "\n", - "2. We left a more challenging dataset [Data_KS2.mat](dat/Data_KS2.mat) where $A_k \\in [-0.5, 0.5]$, $\\ell_k \\in [1, 2, 3]$, $\\phi_k \\in [0, 2\\pi]$ for loger training\n", + "2. We left a more challenging dataset [Data_KS2.mat](dat/Data_KS2.mat) where $A_k \\in [-0.5, 0.5]$, $\\ell_k \\in [1, 2, 3]$, $\\phi_k \\in [0, 2\\pi]$ for longer training\n", "\n", "3. Compare the performance between the different neural operators (you can even try to implement your favourite one!)" ] @@ -420,7 +465,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.9.21" } }, "nbformat": 4, diff --git a/tutorials/tutorial10/tutorial.py b/tutorials/tutorial10/tutorial.py index 637dd0560..f5f57db70 100644 --- a/tutorials/tutorial10/tutorial.py +++ b/tutorials/tutorial10/tutorial.py @@ -2,103 +2,117 @@ # coding: utf-8 # # Tutorial: Averaging Neural Operator for solving Kuramoto Sivashinsky equation -# +# # [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial10/tutorial.ipynb) -# +# # In this tutorial we will build a Neural Operator using the # `AveragingNeuralOperator` model and the `SupervisedSolver`. At the end of the # tutorial you will be able to train a Neural Operator for learning # the operator of time dependent PDEs. -# -# +# +# # First of all, some useful imports. Note we use `scipy` for i/o operations. -# +# -# In[1]: +# In[ ]: ## routine needed to run the notebook on Google Colab try: - import google.colab - IN_COLAB = True + import google.colab + + IN_COLAB = True except: - IN_COLAB = False + IN_COLAB = False if IN_COLAB: - get_ipython().system('pip install "pina-mathlab"') - # get the data - get_ipython().system('mkdir "data"') - get_ipython().system('wget "https://github.com/mathLab/PINA/raw/refs/heads/master/tutorials/tutorial10/data/Data_KS.mat" -O "data/Data_KS.mat"') - get_ipython().system('wget "https://github.com/mathLab/PINA/raw/refs/heads/master/tutorials/tutorial10/data/Data_KS2.mat" -O "data/Data_KS2.mat"') + get_ipython().system('pip install "pina-mathlab"') + # get the data + get_ipython().system('mkdir "data"') + get_ipython().system( + 'wget "https://github.com/mathLab/PINA/raw/refs/heads/master/tutorials/tutorial10/data/Data_KS.mat" -O "data/Data_KS.mat"' + ) + get_ipython().system( + 'wget "https://github.com/mathLab/PINA/raw/refs/heads/master/tutorials/tutorial10/data/Data_KS2.mat" -O "data/Data_KS2.mat"' + ) import torch import matplotlib.pyplot as plt -plt.style.use('tableau-colorblind10') +import warnings + from scipy import io -from pina import Condition, LabelTensor -from pina.problem import AbstractProblem +from pina import Condition, Trainer, LabelTensor from pina.model import AveragingNeuralOperator -from pina.solvers import SupervisedSolver -from pina.trainer import Trainer +from pina.solver import SupervisedSolver +from pina.problem.zoo import SupervisedProblem + +warnings.filterwarnings("ignore") # ## Data Generation -# +# # We will focus on solving a specific PDE, the **Kuramoto Sivashinsky** (KS) equation. # The KS PDE is a fourth-order nonlinear PDE with the following form: -# +# # $$ # \frac{\partial u}{\partial t}(x,t) = -u(x,t)\frac{\partial u}{\partial x}(x,t)- \frac{\partial^{4}u}{\partial x^{4}}(x,t) - \frac{\partial^{2}u}{\partial x^{2}}(x,t). # $$ -# +# # In the above $x\in \Omega=[0, 64]$ represents a spatial location, $t\in\mathbb{T}=[0,50]$ the time and $u(x, t)$ is the value of the function $u:\Omega \times\mathbb{T}\in\mathbb{R}$. We indicate with $\mathbb{U}$ a suitable space for $u$, i.e. we have that the solution $u\in\mathbb{U}$. -# -# +# +# # We impose Dirichlet boundary conditions on the derivative of $u$ on the border of the domain $\partial \Omega$ # $$ # \frac{\partial u}{\partial x}(x,t)=0 \quad \forall (x,t)\in \partial \Omega\times\mathbb{T}. # $$ -# -# Initial conditions are sampled from a distribution over truncated Fourier series with random coefficients +# +# Initial conditions are sampled from a distribution over truncated Fourier series with random coefficients # $\{A_k, \ell_k, \phi_k\}_k$ as # $$ # u(x,0) = \sum_{k=1}^N A_k \sin(2 \pi \ell_k x / L + \phi_k) \ , # $$ -# -# where $A_k \in [-0.4, -0.3]$, $\ell_k = 2$, $\phi_k = 2\pi \quad \forall k=1,\dots,N$. -# -# +# +# where $A_k \in [-0.4, -0.3]$, $\ell_k = 2$, $\phi_k = 2\pi \quad \forall k=1,\dots,N$. +# +# # We have already generated some data for differenti initial conditions, and our objective will # be to build a Neural Operator that, given $u(x, t)$ will output $u(x, t+\delta)$, where # $\delta$ is a fixed time step. We will come back on the Neural Operator architecture, for now # we first need to import the data. -# +# # **Note:** # *The numerical integration is obtained by using pseudospectral method for spatial derivative discratization and # implicit Runge Kutta 5 for temporal dynamics.* -# +# # In[2]: # load data -data=io.loadmat("dat/Data_KS.mat") +data = io.loadmat("data/Data_KS.mat") # converting to label tensor -initial_cond_train = LabelTensor(torch.tensor(data['initial_cond_train'], dtype=torch.float), ['t','x','u0']) -initial_cond_test = LabelTensor(torch.tensor(data['initial_cond_test'], dtype=torch.float), ['t','x','u0']) -sol_train = LabelTensor(torch.tensor(data['sol_train'], dtype=torch.float), ['u']) -sol_test = LabelTensor(torch.tensor(data['sol_test'], dtype=torch.float), ['u']) - -print('Data Loaded') -print(f' shape initial condition: {initial_cond_train.shape}') -print(f' shape solution: {sol_train.shape}') +initial_cond_train = LabelTensor( + torch.tensor(data["initial_cond_train"], dtype=torch.float), + ["t", "x", "u0"], +) +initial_cond_test = LabelTensor( + torch.tensor(data["initial_cond_test"], dtype=torch.float), ["t", "x", "u0"] +) +sol_train = LabelTensor( + torch.tensor(data["sol_train"], dtype=torch.float), ["u"] +) +sol_test = LabelTensor(torch.tensor(data["sol_test"], dtype=torch.float), ["u"]) + +print("Data Loaded") +print(f" shape initial condition: {initial_cond_train.shape}") +print(f" shape solution: {sol_train.shape}") # The data are saved in the form `B \times N \times D`, where `B` is the batch_size # (basically how many initial conditions we sample), `N` the number of points in the mesh -# (which is the product of the discretization in `x` timese the one in `t`), and +# (which is the product of the discretization in `x` timese the one in `t`), and # `D` the dimension of the problem (in this case we have three variables `[u, t, x]`). -# +# # We are now going to plot some trajectories! # In[3]: @@ -107,43 +121,66 @@ # helper function def plot_trajectory(coords, real, no_sol=None): # find the x-t shapes - dim_x = len(torch.unique(coords.extract('x'))) - dim_t = len(torch.unique(coords.extract('t'))) + dim_x = len(torch.unique(coords.extract("x"))) + dim_t = len(torch.unique(coords.extract("t"))) # if we don't have the Neural Operator solution we simply plot the real one if no_sol is None: fig, axs = plt.subplots(1, 1, figsize=(15, 5), sharex=True, sharey=True) - c = axs.imshow(real.reshape(dim_t, dim_x).T.detach(),extent=[0, 50, 0, 64], cmap='PuOr_r', aspect='auto') - axs.set_title('Real solution') + c = axs.imshow( + real.reshape(dim_t, dim_x).T.detach(), + extent=[0, 50, 0, 64], + cmap="PuOr_r", + aspect="auto", + ) + axs.set_title("Real solution") fig.colorbar(c, ax=axs) - axs.set_xlabel('t') - axs.set_ylabel('x') + axs.set_xlabel("t") + axs.set_ylabel("x") # otherwise we plot the real one, the Neural Operator one, and their difference else: fig, axs = plt.subplots(1, 3, figsize=(15, 5), sharex=True, sharey=True) - axs[0].imshow(real.reshape(dim_t, dim_x).T.detach(),extent=[0, 50, 0, 64], cmap='PuOr_r', aspect='auto') - axs[0].set_title('Real solution') - axs[1].imshow(no_sol.reshape(dim_t, dim_x).T.detach(),extent=[0, 50, 0, 64], cmap='PuOr_r', aspect='auto') - axs[1].set_title('NO solution') - c = axs[2].imshow((real - no_sol).abs().reshape(dim_t, dim_x).T.detach(),extent=[0, 50, 0, 64], cmap='PuOr_r', aspect='auto') - axs[2].set_title('Absolute difference') + axs[0].imshow( + real.reshape(dim_t, dim_x).T.detach(), + extent=[0, 50, 0, 64], + cmap="PuOr_r", + aspect="auto", + ) + axs[0].set_title("Real solution") + axs[1].imshow( + no_sol.reshape(dim_t, dim_x).T.detach(), + extent=[0, 50, 0, 64], + cmap="PuOr_r", + aspect="auto", + ) + axs[1].set_title("NO solution") + c = axs[2].imshow( + (real - no_sol).abs().reshape(dim_t, dim_x).T.detach(), + extent=[0, 50, 0, 64], + cmap="PuOr_r", + aspect="auto", + ) + axs[2].set_title("Absolute difference") fig.colorbar(c, ax=axs.ravel().tolist()) for ax in axs: - ax.set_xlabel('t') - ax.set_ylabel('x') + ax.set_xlabel("t") + ax.set_ylabel("x") plt.show() + # a sample trajectory (we use the sample 5, feel free to change) sample_number = 20 -plot_trajectory(coords=initial_cond_train[sample_number].extract(['x', 't']), - real=sol_train[sample_number].extract('u')) +plot_trajectory( + coords=initial_cond_train[sample_number].extract(["x", "t"]), + real=sol_train[sample_number].extract("u"), +) # As we can see, as the time progresses the solution becomes chaotic, which makes # it really hard to learn! We will now focus on building a Neural Operator using the # `SupervisedSolver` class to tackle the problem. -# +# # ## Averaging Neural Operator -# +# # We will build a neural operator $\texttt{NO}$ which takes the solution at time $t=0$ for any $x\in\Omega$, # the time $(t)$ at which we want to compute the solution, and gives back the solution to the KS equation $u(x, t)$, mathematically: # $$ @@ -153,26 +190,26 @@ def plot_trajectory(coords, real, no_sol=None): # $$ # \texttt{NO}_\theta[u(t=0)](x, t) \rightarrow u(x, t). # $$ -# +# # There are many ways on approximating the following operator, e.g. by 2D [FNO](https://mathlab.github.io/PINA/_rst/models/fno.html) (for regular meshes), # a [DeepOnet](https://mathlab.github.io/PINA/_rst/models/deeponet.html), [Continuous Convolutional Neural Operator](https://mathlab.github.io/PINA/_rst/layers/convolution.html), -# [MIONet](https://mathlab.github.io/PINA/_rst/models/mionet.html). +# [MIONet](https://mathlab.github.io/PINA/_rst/models/mionet.html). # In this tutorial we will use the *Averaging Neural Operator* presented in [*The Nonlocal Neural Operator: Universal Approximation*](https://arxiv.org/abs/2304.13221) # which is a [Kernel Neural Operator](https://mathlab.github.io/PINA/_rst/models/base_no.html) with integral kernel: -# +# # $$ # K(v) = \sigma\left(Wv(x) + b + \frac{1}{|\Omega|}\int_\Omega v(y)dy\right) # $$ -# +# # where: -# +# # * $v(x)\in\mathbb{R}^{\rm{emb}}$ is the update for a function $v$ with $\mathbb{R}^{\rm{emb}}$ the embedding (hidden) size # * $\sigma$ is a non-linear activation # * $W\in\mathbb{R}^{\rm{emb}\times\rm{emb}}$ is a tunable matrix. # * $b\in\mathbb{R}^{\rm{emb}}$ is a tunable bias. -# +# # If PINA many Kernel Neural Operators are already implemented, and the modular componets of the [Kernel Neural Operator](https://mathlab.github.io/PINA/_rst/models/base_no.html) class permits to create new ones by composing base kernel layers. -# +# # **Note:*** We will use the already built class* `AveragingNeuralOperator`, *as constructive excercise try to use the* [KernelNeuralOperator](https://mathlab.github.io/PINA/_rst/models/base_no.html) *class for building a kernel neural operator from scratch. You might employ the different layers that we have in pina, e.g.* [FeedForward](https://mathlab.github.io/PINA/_rst/models/fnn.html), *and* [AveragingNeuralOperator](https://mathlab.github.io/PINA/_rst/layers/avno_layer.html) *layers*. # In[4]: @@ -181,88 +218,101 @@ def plot_trajectory(coords, real, no_sol=None): class SIREN(torch.nn.Module): def forward(self, x): return torch.sin(x) - -embedding_dimesion = 40 # hyperparameter embedding dimension -input_dimension = 3 # ['u', 'x', 't'] -number_of_coordinates = 2 # ['x', 't'] -lifting_net = torch.nn.Linear(input_dimension, embedding_dimesion) # simple linear layers for lifting and projecting nets + + +embedding_dimesion = 40 # hyperparameter embedding dimension +input_dimension = 3 # ['u', 'x', 't'] +number_of_coordinates = 2 # ['x', 't'] +lifting_net = torch.nn.Linear(input_dimension, embedding_dimesion) projecting_net = torch.nn.Linear(embedding_dimesion + number_of_coordinates, 1) -model = AveragingNeuralOperator(lifting_net=lifting_net, - projecting_net=projecting_net, - coordinates_indices=['x', 't'], - field_indices=['u0'], - n_layers=4, - func=SIREN - ) +model = AveragingNeuralOperator( + lifting_net=lifting_net, + projecting_net=projecting_net, + coordinates_indices=["x", "t"], + field_indices=["u0"], + n_layers=4, + func=SIREN, +) # Super easy! Notice that we use the `SIREN` activation function, more on [Implicit Neural Representations with Periodic Activation Functions](https://arxiv.org/abs/2006.09661). -# +# # ## Solving the KS problem -# +# # We will now focus on solving the KS equation using the `SupervisedSolver` class -# and the `AveragingNeuralOperator` model. As done in the [FNO tutorial](https://github.com/mathLab/PINA/blob/master/tutorials/tutorial5/tutorial.ipynb) we now create the `NeuralOperatorProblem` class with `AbstractProblem`. +# and the `AveragingNeuralOperator` model. As done in the [FNO tutorial](https://github.com/mathLab/PINA/blob/master/tutorials/tutorial5/tutorial.ipynb) we now create the Neural Operator problem class with `SupervisedProblem`. -# In[6]: - - -# expected running time ~ 1 minute - -class NeuralOperatorProblem(AbstractProblem): - input_variables = initial_cond_train.labels - output_variables = sol_train.labels - conditions = {'data' : Condition(input_points=initial_cond_train, - output_points=sol_train)} +# In[5]: # initialize problem -problem = NeuralOperatorProblem() +problem = SupervisedProblem( + initial_cond_train, + sol_train, + input_variables=initial_cond_train.labels, + output_variables=sol_train.labels, +) # initialize solver -solver = SupervisedSolver(problem=problem, model=model,optimizer_kwargs={"lr":0.001}) +solver = SupervisedSolver(problem=problem, model=model) # train, only CPU and avoid model summary at beginning of training (optional) -trainer = Trainer(solver=solver, max_epochs=40, accelerator='cpu', enable_model_summary=False, log_every_n_steps=-1, batch_size=5) # we train on CPU and avoid model summary at beginning of training (optional) +trainer = Trainer( + solver=solver, + max_epochs=40, + accelerator="cpu", + enable_model_summary=False, + batch_size=5, # we train on CPU and avoid model summary at beginning of training (optional) + train_size=1.0, + val_size=0.0, + test_size=0.0, +) trainer.train() # We can now see some plots for the solutions -# In[7]: +# In[6]: sample_number = 2 no_sol = solver(initial_cond_test) -plot_trajectory(coords=initial_cond_test[sample_number].extract(['x', 't']), - real=sol_test[sample_number].extract('u'), - no_sol=no_sol[5]) +plot_trajectory( + coords=initial_cond_test[sample_number].extract(["x", "t"]), + real=sol_test[sample_number].extract("u"), + no_sol=no_sol[5], +) -# As we can see we can obtain nice result considering the small trainint time and the difficulty of the problem! -# Let's see how the training and testing error: +# As we can see we can obtain nice result considering the small training time and the difficulty of the problem! +# Let's take a look at the training and testing error: -# In[8]: +# In[7]: from pina.loss import PowerLoss -error_metric = PowerLoss(p=2) # we use the MSE loss +error_metric = PowerLoss(p=2) # we use the MSE loss with torch.no_grad(): no_sol_train = solver(initial_cond_train) - err_train = error_metric(sol_train.extract('u'), no_sol_train).mean() # we average the error over trajectories + err_train = error_metric( + sol_train.extract("u"), no_sol_train + ).mean() # we average the error over trajectories no_sol_test = solver(initial_cond_test) - err_test = error_metric(sol_test.extract('u'),no_sol_test).mean() # we average the error over trajectories - print(f'Training error: {float(err_train):.3f}') - print(f'Testing error: {float(err_test):.3f}') + err_test = error_metric( + sol_test.extract("u"), no_sol_test + ).mean() # we average the error over trajectories + print(f"Training error: {float(err_train):.3f}") + print(f"Testing error: {float(err_test):.3f}") -# as we can see the error is pretty small, which agrees with what we can see from the previous plots. +# As we can see the error is pretty small, which agrees with what we can see from the previous plots. # ## What's next? -# +# # Now you know how to solve a time dependent neural operator problem in **PINA**! There are multiple directions you can go now: -# -# 1. Train the network for longer or with different layer sizes and assert the finaly accuracy -# -# 2. We left a more challenging dataset [Data_KS2.mat](dat/Data_KS2.mat) where $A_k \in [-0.5, 0.5]$, $\ell_k \in [1, 2, 3]$, $\phi_k \in [0, 2\pi]$ for loger training -# +# +# 1. Train the network for longer or with different layer sizes and assert the final accuracy +# +# 2. We left a more challenging dataset [Data_KS2.mat](dat/Data_KS2.mat) where $A_k \in [-0.5, 0.5]$, $\ell_k \in [1, 2, 3]$, $\phi_k \in [0, 2\pi]$ for longer training +# # 3. Compare the performance between the different neural operators (you can even try to implement your favourite one!) diff --git a/tutorials/tutorial11/tutorial.ipynb b/tutorials/tutorial11/tutorial.ipynb index f42d427dc..b9acb6d0c 100644 --- a/tutorials/tutorial11/tutorial.ipynb +++ b/tutorials/tutorial11/tutorial.ipynb @@ -19,62 +19,91 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "## routine needed to run the notebook on Google Colab\n", "try:\n", - " import google.colab\n", - " IN_COLAB = True\n", + " import google.colab\n", + "\n", + " IN_COLAB = True\n", "except:\n", - " IN_COLAB = False\n", + " IN_COLAB = False\n", "if IN_COLAB:\n", - " !pip install \"pina-mathlab\"\n", + " !pip install \"pina-mathlab\"\n", "\n", "import torch\n", + "import warnings\n", "\n", "from pina import Condition, Trainer\n", - "from pina.solvers import PINN\n", + "from pina.solver import PINN\n", "from pina.model import FeedForward\n", "from pina.problem import SpatialProblem\n", - "from pina.operators import grad\n", - "from pina.geometry import CartesianDomain\n", + "from pina.operator import grad\n", + "from pina.domain import CartesianDomain\n", "from pina.equation import Equation, FixedValue\n", "\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Define problem and solver." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# defining the ode equation\n", + "def ode_equation(input_, output_):\n", + "\n", + " # computing the derivative\n", + " u_x = grad(output_, input_, components=[\"u\"], d=[\"x\"])\n", + "\n", + " # extracting the u input variable\n", + " u = output_.extract([\"u\"])\n", + "\n", + " # calculate the residual and return it\n", + " return u_x - u\n", + "\n", + "\n", "class SimpleODE(SpatialProblem):\n", "\n", - " output_variables = ['u']\n", - " spatial_domain = CartesianDomain({'x': [0, 1]})\n", + " output_variables = [\"u\"]\n", + " spatial_domain = CartesianDomain({\"x\": [0, 1]})\n", "\n", - " # defining the ode equation\n", - " def ode_equation(input_, output_):\n", - " u_x = grad(output_, input_, components=['u'], d=['x'])\n", - " u = output_.extract(['u'])\n", - " return u_x - u\n", + " domains = {\n", + " \"x0\": CartesianDomain({\"x\": 0.0}),\n", + " \"D\": CartesianDomain({\"x\": [0, 1]}),\n", + " }\n", "\n", " # conditions to hold\n", " conditions = {\n", - " 'x0': Condition(location=CartesianDomain({'x': 0.}), equation=FixedValue(1)), # We fix initial condition to value 1\n", - " 'D': Condition(location=CartesianDomain({'x': [0, 1]}), equation=Equation(ode_equation)), # We wrap the python equation using Equation\n", + " \"bound_cond\": Condition(domain=\"x0\", equation=FixedValue(1.0)),\n", + " \"phys_cond\": Condition(domain=\"D\", equation=Equation(ode_equation)),\n", " }\n", "\n", " # defining the true solution\n", - " def truth_solution(self, pts):\n", - " return torch.exp(pts.extract(['x']))\n", - " \n", + " def solution(self, pts):\n", + " return torch.exp(pts.extract([\"x\"]))\n", + "\n", "\n", "# sampling for training\n", "problem = SimpleODE()\n", - "problem.discretise_domain(1, 'random', locations=['x0'])\n", - "problem.discretise_domain(20, 'lh', locations=['D'])\n", + "problem.discretise_domain(1, \"random\", domains=[\"x0\"])\n", + "problem.discretise_domain(20, \"lh\", domains=[\"D\"])\n", "\n", "# build the model\n", "model = FeedForward(\n", " layers=[10, 10],\n", " func=torch.nn.Tanh,\n", " output_dimensions=len(problem.output_variables),\n", - " input_dimensions=len(problem.input_variables)\n", + " input_dimensions=len(problem.input_variables),\n", ")\n", "\n", "# create the PINN object\n", @@ -100,7 +129,6 @@ "text": [ "GPU available: True (mps), used: True\n", "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", "HPU available: False, using: 0 HPUs\n" ] } @@ -134,7 +162,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -143,14 +171,12 @@ "text": [ "GPU available: True (mps), used: False\n", "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", "HPU available: False, using: 0 HPUs\n" ] } ], "source": [ - "trainer = Trainer(solver=pinn,\n", - " accelerator='cpu')" + "trainer = Trainer(solver=pinn, accelerator=\"cpu\")" ] }, { @@ -175,7 +201,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -184,7 +210,6 @@ "text": [ "GPU available: True (mps), used: False\n", "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", "HPU available: False, using: 0 HPUs\n" ] }, @@ -192,14 +217,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 8: 100%|██████████| 1/1 [00:00<00:00, 232.78it/s, v_num=6, x0_loss=0.436, D_loss=0.129, mean_loss=0.283] " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 999: 100%|██████████| 1/1 [00:00<00:00, 222.52it/s, v_num=6, x0_loss=1.48e-5, D_loss=0.000655, mean_loss=0.000335]" + "Epoch 999: 100%|██████████| 1/1 [00:00<00:00, 233.15it/s, v_num=0, bound_cond_loss=1.22e-5, phys_cond_loss=0.000517, train_loss=0.000529]" ] }, { @@ -213,7 +231,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 999: 100%|██████████| 1/1 [00:00<00:00, 133.46it/s, v_num=6, x0_loss=1.48e-5, D_loss=0.000655, mean_loss=0.000335]\n" + "Epoch 999: 100%|██████████| 1/1 [00:00<00:00, 137.95it/s, v_num=0, bound_cond_loss=1.22e-5, phys_cond_loss=0.000517, train_loss=0.000529]\n" ] }, { @@ -222,7 +240,6 @@ "text": [ "GPU available: True (mps), used: False\n", "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", "HPU available: False, using: 0 HPUs\n" ] }, @@ -230,7 +247,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 999: 100%|██████████| 1/1 [00:00<00:00, 274.80it/s, v_num=7, x0_loss=6.21e-6, D_loss=0.000221, mean_loss=0.000114]" + "Epoch 999: 100%|██████████| 1/1 [00:00<00:00, 248.63it/s, v_num=1, bound_cond_loss=2.29e-5, phys_cond_loss=0.00106, train_loss=0.00108] " ] }, { @@ -244,7 +261,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 999: 100%|██████████| 1/1 [00:00<00:00, 154.49it/s, v_num=7, x0_loss=6.21e-6, D_loss=0.000221, mean_loss=0.000114]\n" + "Epoch 999: 100%|██████████| 1/1 [00:00<00:00, 149.06it/s, v_num=1, bound_cond_loss=2.29e-5, phys_cond_loss=0.00106, train_loss=0.00108]\n" ] }, { @@ -253,7 +270,6 @@ "text": [ "GPU available: True (mps), used: False\n", "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", "HPU available: False, using: 0 HPUs\n" ] }, @@ -261,7 +277,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 999: 100%|██████████| 1/1 [00:00<00:00, 78.56it/s, v_num=8, x0_loss=1.44e-5, D_loss=0.000572, mean_loss=0.000293] " + "Epoch 999: 100%|██████████| 1/1 [00:00<00:00, 254.65it/s, v_num=2, bound_cond_loss=0.00029, phys_cond_loss=0.00253, train_loss=0.00282] " ] }, { @@ -275,12 +291,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 999: 100%|██████████| 1/1 [00:00<00:00, 62.60it/s, v_num=8, x0_loss=1.44e-5, D_loss=0.000572, mean_loss=0.000293]\n" + "Epoch 999: 100%|██████████| 1/1 [00:00<00:00, 150.72it/s, v_num=2, bound_cond_loss=0.00029, phys_cond_loss=0.00253, train_loss=0.00282]\n" ] } ], "source": [ - "from pytorch_lightning.loggers import TensorBoardLogger\n", + "from lightning.pytorch.loggers import TensorBoardLogger\n", "\n", "# three run of training, by default it trains for 1000 epochs\n", "# we reinitialize the model each time otherwise the same parameters will be optimized\n", @@ -289,13 +305,18 @@ " layers=[10, 10],\n", " func=torch.nn.Tanh,\n", " output_dimensions=len(problem.output_variables),\n", - " input_dimensions=len(problem.input_variables)\n", + " input_dimensions=len(problem.input_variables),\n", " )\n", " pinn = PINN(problem, model)\n", - " trainer = Trainer(solver=pinn,\n", - " accelerator='cpu',\n", - " logger=TensorBoardLogger(save_dir='simpleode'),\n", - " enable_model_summary=False)\n", + " trainer = Trainer(\n", + " solver=pinn,\n", + " accelerator=\"cpu\",\n", + " logger=TensorBoardLogger(save_dir=\"training_log\"),\n", + " enable_model_summary=False,\n", + " train_size=1.0,\n", + " val_size=0.0,\n", + " test_size=0.0,\n", + " )\n", " trainer.train()" ] }, @@ -303,7 +324,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can now visualize the logs by simply running `tensorboard --logdir=simpleode/` on terminal, you should obtain a webpage as the one shown below:" + "We can now visualize the logs by simply running `tensorboard --logdir=training_log/` on terminal, you should obtain a webpage as the one shown below:" ] }, { @@ -351,19 +372,23 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ - "from pytorch_lightning.callbacks import Callback\n", + "from lightning.pytorch.callbacks import Callback\n", + "from lightning.pytorch.callbacks import EarlyStopping\n", "import torch\n", "\n", + "\n", "# define a simple callback\n", "class NaiveMetricTracker(Callback):\n", " def __init__(self):\n", " self.saved_metrics = []\n", "\n", - " def on_train_epoch_end(self, trainer, __): # function called at the end of each epoch\n", + " def on_train_epoch_end(\n", + " self, trainer, __\n", + " ): # function called at the end of each epoch\n", " self.saved_metrics.append(\n", " {key: value for key, value in trainer.logged_metrics.items()}\n", " )" @@ -378,7 +403,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -386,14 +411,7 @@ "output_type": "stream", "text": [ "GPU available: True (mps), used: False\n", - "TPU available: False, using: 0 TPU cores\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "IPU available: False, using: 0 IPUs\n", + "TPU available: False, using: 0 TPU cores\n", "HPU available: False, using: 0 HPUs\n" ] }, @@ -401,7 +419,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 999: 100%|██████████| 1/1 [00:00<00:00, 241.30it/s, v_num=1, x0_loss=7.27e-5, D_loss=0.0016, mean_loss=0.000838] " + "Epoch 999: 100%|██████████| 1/1 [00:00<00:00, 278.93it/s, v_num=0, bound_cond_loss=6.94e-5, phys_cond_loss=0.00116, train_loss=0.00123] " ] }, { @@ -415,22 +433,28 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 999: 100%|██████████| 1/1 [00:00<00:00, 149.27it/s, v_num=1, x0_loss=7.27e-5, D_loss=0.0016, mean_loss=0.000838]\n" + "Epoch 999: 100%|██████████| 1/1 [00:00<00:00, 140.62it/s, v_num=0, bound_cond_loss=6.94e-5, phys_cond_loss=0.00116, train_loss=0.00123]\n" ] } ], "source": [ "model = FeedForward(\n", - " layers=[10, 10],\n", - " func=torch.nn.Tanh,\n", - " output_dimensions=len(problem.output_variables),\n", - " input_dimensions=len(problem.input_variables)\n", - " )\n", + " layers=[10, 10],\n", + " func=torch.nn.Tanh,\n", + " output_dimensions=len(problem.output_variables),\n", + " input_dimensions=len(problem.input_variables),\n", + ")\n", "pinn = PINN(problem, model)\n", - "trainer = Trainer(solver=pinn,\n", - " accelerator='cpu',\n", - " enable_model_summary=False,\n", - " callbacks=[NaiveMetricTracker()]) # adding a callbacks\n", + "trainer = Trainer(\n", + " solver=pinn,\n", + " accelerator=\"cpu\",\n", + " logger=True,\n", + " callbacks=[NaiveMetricTracker()], # adding a callbacks\n", + " enable_model_summary=False,\n", + " train_size=1.0,\n", + " val_size=0.0,\n", + " test_size=0.0,\n", + ")\n", "trainer.train()" ] }, @@ -443,30 +467,30 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[{'x0_loss': tensor(0.9141),\n", - " 'D_loss': tensor(0.0304),\n", - " 'mean_loss': tensor(0.4722)},\n", - " {'x0_loss': tensor(0.8906),\n", - " 'D_loss': tensor(0.0287),\n", - " 'mean_loss': tensor(0.4596)},\n", - " {'x0_loss': tensor(0.8674),\n", - " 'D_loss': tensor(0.0274),\n", - " 'mean_loss': tensor(0.4474)}]" + "[{'bound_cond_loss': tensor(0.9935),\n", + " 'phys_cond_loss': tensor(0.0303),\n", + " 'train_loss': tensor(1.0239)},\n", + " {'bound_cond_loss': tensor(0.9875),\n", + " 'phys_cond_loss': tensor(0.0293),\n", + " 'train_loss': tensor(1.0169)},\n", + " {'bound_cond_loss': tensor(0.9815),\n", + " 'phys_cond_loss': tensor(0.0284),\n", + " 'train_loss': tensor(1.0099)}]" ] }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "trainer.callbacks[0].saved_metrics[:3] # only the first three epochs" + "trainer.callbacks[0].saved_metrics[:3] # only the first three epochs" ] }, { @@ -475,12 +499,12 @@ "source": [ "PyTorch Lightning also has some built in `Callbacks` which can be used in **PINA**, [here an extensive list](https://lightning.ai/docs/pytorch/stable/extensions/callbacks.html#built-in-callbacks). \n", "\n", - "We can for example try the `EarlyStopping` routine, which automatically stops the training when a specific metric converged (here the `mean_loss`). In order to let the training keep going forever set `max_epochs=-1`." + "We can for example try the `EarlyStopping` routine, which automatically stops the training when a specific metric converged (here the `train_loss`). In order to let the training keep going forever set `max_epochs=-1`." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -489,7 +513,6 @@ "text": [ "GPU available: True (mps), used: False\n", "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", "HPU available: False, using: 0 HPUs\n" ] }, @@ -497,33 +520,29 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 4: 100%|██████████| 1/1 [00:00<00:00, 255.67it/s, v_num=9, x0_loss=0.876, D_loss=0.00542, mean_loss=0.441]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 6157: 100%|██████████| 1/1 [00:00<00:00, 139.84it/s, v_num=9, x0_loss=4.21e-9, D_loss=9.93e-6, mean_loss=4.97e-6] \n" + "Epoch 2343: 100%|██████████| 1/1 [00:00<00:00, 64.24it/s, v_num=1, val_loss=4.79e-6, bound_cond_loss=1.15e-7, phys_cond_loss=2.33e-5, train_loss=2.34e-5] \n" ] } ], "source": [ - "# ~2 mins\n", - "from pytorch_lightning.callbacks import EarlyStopping\n", - "\n", "model = FeedForward(\n", - " layers=[10, 10],\n", - " func=torch.nn.Tanh,\n", - " output_dimensions=len(problem.output_variables),\n", - " input_dimensions=len(problem.input_variables)\n", - " )\n", + " layers=[10, 10],\n", + " func=torch.nn.Tanh,\n", + " output_dimensions=len(problem.output_variables),\n", + " input_dimensions=len(problem.input_variables),\n", + ")\n", "pinn = PINN(problem, model)\n", - "trainer = Trainer(solver=pinn,\n", - " accelerator='cpu',\n", - " max_epochs = -1,\n", - " enable_model_summary=False,\n", - " callbacks=[EarlyStopping('mean_loss')]) # adding a callbacks\n", + "trainer = Trainer(\n", + " solver=pinn,\n", + " accelerator=\"cpu\",\n", + " max_epochs=-1,\n", + " enable_model_summary=False,\n", + " enable_progress_bar=False,\n", + " val_size=0.2,\n", + " train_size=0.8,\n", + " test_size=0.0,\n", + " callbacks=[EarlyStopping(\"val_loss\")],\n", + ") # adding a callbacks\n", "trainer.train()" ] }, @@ -557,7 +576,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -567,7 +586,6 @@ "Seed set to 42\n", "GPU available: True (mps), used: False\n", "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", "HPU available: False, using: 0 HPUs\n" ] }, @@ -575,7 +593,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1999: 100%|██████████| 1/1 [00:00<00:00, 275.87it/s, v_num=31, x0_loss=1.12e-6, D_loss=0.000127, mean_loss=6.4e-5] " + "Epoch 1999: 100%|██████████| 1/1 [00:00<00:00, 156.69it/s, v_num=2, bound_cond_loss=1.53e-6, phys_cond_loss=0.000169, train_loss=0.000171]" ] }, { @@ -589,32 +607,34 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1999: 100%|██████████| 1/1 [00:00<00:00, 163.58it/s, v_num=31, x0_loss=1.12e-6, D_loss=0.000127, mean_loss=6.4e-5]\n", - "Total training time 17.36381 s\n" + "Epoch 1999: 100%|██████████| 1/1 [00:00<00:00, 108.75it/s, v_num=2, bound_cond_loss=1.53e-6, phys_cond_loss=0.000169, train_loss=0.000171]\n", + "Total training time 15.36648 s\n" ] } ], "source": [ - "from pytorch_lightning.callbacks import Timer\n", - "from pytorch_lightning import seed_everything\n", + "from lightning.pytorch.callbacks import Timer\n", + "from lightning.pytorch import seed_everything\n", "\n", "# setting the seed for reproducibility\n", "seed_everything(42, workers=True)\n", "\n", "model = FeedForward(\n", - " layers=[10, 10],\n", - " func=torch.nn.Tanh,\n", - " output_dimensions=len(problem.output_variables),\n", - " input_dimensions=len(problem.input_variables)\n", - " )\n", + " layers=[10, 10],\n", + " func=torch.nn.Tanh,\n", + " output_dimensions=len(problem.output_variables),\n", + " input_dimensions=len(problem.input_variables),\n", + ")\n", "\n", "pinn = PINN(problem, model)\n", - "trainer = Trainer(solver=pinn,\n", - " accelerator='cpu',\n", - " deterministic=True, # setting deterministic=True ensure reproducibility when a seed is imposed\n", - " max_epochs = 2000,\n", - " enable_model_summary=False,\n", - " callbacks=[Timer()]) # adding a callbacks\n", + "trainer = Trainer(\n", + " solver=pinn,\n", + " accelerator=\"cpu\",\n", + " deterministic=True, # setting deterministic=True ensure reproducibility when a seed is imposed\n", + " max_epochs=2000,\n", + " enable_model_summary=False,\n", + " callbacks=[Timer()],\n", + ") # adding a callbacks\n", "trainer.train()\n", "print(f'Total training time {trainer.callbacks[0].time_elapsed(\"train\"):.5f} s')" ] @@ -628,7 +648,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -638,7 +658,6 @@ "Seed set to 42\n", "GPU available: True (mps), used: False\n", "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", "HPU available: False, using: 0 HPUs\n" ] }, @@ -646,7 +665,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1598: 100%|██████████| 1/1 [00:00<00:00, 210.04it/s, v_num=47, x0_loss=4.17e-6, D_loss=0.000204, mean_loss=0.000104]" + "Epoch 1598: 100%|██████████| 1/1 [00:00<00:00, 224.16it/s, v_num=3, bound_cond_loss=5.7e-6, phys_cond_loss=0.000257, train_loss=0.000263] " ] }, { @@ -660,7 +679,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1999: 100%|██████████| 1/1 [00:00<00:00, 259.39it/s, v_num=47, x0_loss=1.56e-7, D_loss=7.49e-5, mean_loss=3.75e-5] " + "Epoch 1999: 100%|██████████| 1/1 [00:00<00:00, 261.43it/s, v_num=3, bound_cond_loss=2.58e-7, phys_cond_loss=9.4e-5, train_loss=9.43e-5] " ] }, { @@ -674,31 +693,32 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1999: 100%|██████████| 1/1 [00:00<00:00, 120.85it/s, v_num=47, x0_loss=1.56e-7, D_loss=7.49e-5, mean_loss=3.75e-5]\n", - "Total training time 17.10627 s\n" + "Epoch 1999: 100%|██████████| 1/1 [00:00<00:00, 145.96it/s, v_num=3, bound_cond_loss=2.58e-7, phys_cond_loss=9.4e-5, train_loss=9.43e-5]\n", + "Total training time 17.78182 s\n" ] } ], "source": [ - "from pytorch_lightning.callbacks import StochasticWeightAveraging\n", + "from lightning.pytorch.callbacks import StochasticWeightAveraging\n", "\n", "# setting the seed for reproducibility\n", "seed_everything(42, workers=True)\n", "\n", "model = FeedForward(\n", - " layers=[10, 10],\n", - " func=torch.nn.Tanh,\n", - " output_dimensions=len(problem.output_variables),\n", - " input_dimensions=len(problem.input_variables)\n", - " )\n", + " layers=[10, 10],\n", + " func=torch.nn.Tanh,\n", + " output_dimensions=len(problem.output_variables),\n", + " input_dimensions=len(problem.input_variables),\n", + ")\n", "pinn = PINN(problem, model)\n", - "trainer = Trainer(solver=pinn,\n", - " accelerator='cpu',\n", - " deterministic=True,\n", - " max_epochs = 2000,\n", - " enable_model_summary=False,\n", - " callbacks=[Timer(),\n", - " StochasticWeightAveraging(swa_lrs=0.005)]) # adding StochasticWeightAveraging callbacks\n", + "trainer = Trainer(\n", + " solver=pinn,\n", + " accelerator=\"cpu\",\n", + " deterministic=True,\n", + " max_epochs=2000,\n", + " enable_model_summary=False,\n", + " callbacks=[Timer(), StochasticWeightAveraging(swa_lrs=0.005)],\n", + ") # adding StochasticWeightAveraging callbacks\n", "trainer.train()\n", "print(f'Total training time {trainer.callbacks[0].time_elapsed(\"train\"):.5f} s')" ] @@ -716,7 +736,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -726,7 +746,6 @@ "Seed set to 42\n", "GPU available: True (mps), used: False\n", "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", "HPU available: False, using: 0 HPUs\n" ] }, @@ -734,7 +753,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1598: 100%|██████████| 1/1 [00:00<00:00, 261.80it/s, v_num=46, x0_loss=9e-8, D_loss=2.39e-5, mean_loss=1.2e-5] " + "Epoch 1598: 100%|██████████| 1/1 [00:00<00:00, 251.76it/s, v_num=4, bound_cond_loss=5.98e-8, phys_cond_loss=3.88e-5, train_loss=3.88e-5] " ] }, { @@ -748,7 +767,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1999: 100%|██████████| 1/1 [00:00<00:00, 261.78it/s, v_num=46, x0_loss=7.08e-7, D_loss=1.77e-5, mean_loss=9.19e-6] " + "Epoch 1999: 100%|██████████| 1/1 [00:00<00:00, 239.11it/s, v_num=4, bound_cond_loss=0.000333, phys_cond_loss=0.000676, train_loss=0.00101] " ] }, { @@ -762,8 +781,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1999: 100%|██████████| 1/1 [00:00<00:00, 148.99it/s, v_num=46, x0_loss=7.08e-7, D_loss=1.77e-5, mean_loss=9.19e-6]\n", - "Total training time 17.01149 s\n" + "Epoch 1999: 100%|██████████| 1/1 [00:00<00:00, 127.88it/s, v_num=4, bound_cond_loss=0.000333, phys_cond_loss=0.000676, train_loss=0.00101]\n", + "Total training time 15.12576 s\n" ] } ], @@ -772,19 +791,20 @@ "seed_everything(42, workers=True)\n", "\n", "model = FeedForward(\n", - " layers=[10, 10],\n", - " func=torch.nn.Tanh,\n", - " output_dimensions=len(problem.output_variables),\n", - " input_dimensions=len(problem.input_variables)\n", - " )\n", + " layers=[10, 10],\n", + " func=torch.nn.Tanh,\n", + " output_dimensions=len(problem.output_variables),\n", + " input_dimensions=len(problem.input_variables),\n", + ")\n", "pinn = PINN(problem, model)\n", - "trainer = Trainer(solver=pinn,\n", - " accelerator='cpu',\n", - " max_epochs = 2000,\n", - " enable_model_summary=False,\n", - " gradient_clip_val=0.1, # clipping the gradient\n", - " callbacks=[Timer(),\n", - " StochasticWeightAveraging(swa_lrs=0.005)])\n", + "trainer = Trainer(\n", + " solver=pinn,\n", + " accelerator=\"cpu\",\n", + " max_epochs=2000,\n", + " enable_model_summary=False,\n", + " gradient_clip_val=0.1, # clipping the gradient\n", + " callbacks=[Timer(), StochasticWeightAveraging(swa_lrs=0.005)],\n", + ")\n", "trainer.train()\n", "print(f'Total training time {trainer.callbacks[0].time_elapsed(\"train\"):.5f} s')" ] @@ -823,7 +843,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.9.21" } }, "nbformat": 4, diff --git a/tutorials/tutorial11/tutorial.py b/tutorials/tutorial11/tutorial.py index 9bbabfea6..df36aa18a 100644 --- a/tutorials/tutorial11/tutorial.py +++ b/tutorials/tutorial11/tutorial.py @@ -1,73 +1,94 @@ #!/usr/bin/env python # coding: utf-8 -# # Tutorial: PINA and PyTorch Lightning, training tips and visualizations -# +# # Tutorial: PINA and PyTorch Lightning, training tips and visualizations +# # [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial11/tutorial.ipynb) -# -# In this tutorial, we will delve deeper into the functionality of the `Trainer` class, which serves as the cornerstone for training **PINA** [Solvers](https://mathlab.github.io/PINA/_rst/_code.html#solvers). -# +# +# In this tutorial, we will delve deeper into the functionality of the `Trainer` class, which serves as the cornerstone for training **PINA** [Solvers](https://mathlab.github.io/PINA/_rst/_code.html#solvers). +# # The `Trainer` class offers a plethora of features aimed at improving model accuracy, reducing training time and memory usage, facilitating logging visualization, and more thanks to the amazing job done by the PyTorch Lightning team! -# +# # Our leading example will revolve around solving the `SimpleODE` problem, as outlined in the [*Introduction to PINA for Physics Informed Neural Networks training*](https://github.com/mathLab/PINA/blob/master/tutorials/tutorial1/tutorial.ipynb). If you haven't already explored it, we highly recommend doing so before diving into this tutorial. -# +# # Let's start by importing useful modules, define the `SimpleODE` problem and the `PINN` solver. -# In[18]: +# In[ ]: -## routine needed to run the notebook on Google Colab try: - import google.colab - IN_COLAB = True + import google.colab + + IN_COLAB = True except: - IN_COLAB = False + IN_COLAB = False if IN_COLAB: - get_ipython().system('pip install "pina-mathlab"') + get_ipython().system('pip install "pina-mathlab"') import torch +import warnings from pina import Condition, Trainer -from pina.solvers import PINN +from pina.solver import PINN from pina.model import FeedForward from pina.problem import SpatialProblem -from pina.operators import grad -from pina.geometry import CartesianDomain +from pina.operator import grad +from pina.domain import CartesianDomain from pina.equation import Equation, FixedValue +warnings.filterwarnings("ignore") + + +# Define problem and solver. + +# In[2]: + + +# defining the ode equation +def ode_equation(input_, output_): + + # computing the derivative + u_x = grad(output_, input_, components=["u"], d=["x"]) + + # extracting the u input variable + u = output_.extract(["u"]) + + # calculate the residual and return it + return u_x - u + + class SimpleODE(SpatialProblem): - output_variables = ['u'] - spatial_domain = CartesianDomain({'x': [0, 1]}) + output_variables = ["u"] + spatial_domain = CartesianDomain({"x": [0, 1]}) - # defining the ode equation - def ode_equation(input_, output_): - u_x = grad(output_, input_, components=['u'], d=['x']) - u = output_.extract(['u']) - return u_x - u + domains = { + "x0": CartesianDomain({"x": 0.0}), + "D": CartesianDomain({"x": [0, 1]}), + } # conditions to hold conditions = { - 'x0': Condition(location=CartesianDomain({'x': 0.}), equation=FixedValue(1)), # We fix initial condition to value 1 - 'D': Condition(location=CartesianDomain({'x': [0, 1]}), equation=Equation(ode_equation)), # We wrap the python equation using Equation + "bound_cond": Condition(domain="x0", equation=FixedValue(1.0)), + "phys_cond": Condition(domain="D", equation=Equation(ode_equation)), } # defining the true solution - def truth_solution(self, pts): - return torch.exp(pts.extract(['x'])) - + def solution(self, pts): + return torch.exp(pts.extract(["x"])) + # sampling for training problem = SimpleODE() -problem.discretise_domain(1, 'random', locations=['x0']) -problem.discretise_domain(20, 'lh', locations=['D']) +problem.discretise_domain(1, "random", domains=["x0"]) +problem.discretise_domain(20, "lh", domains=["D"]) # build the model model = FeedForward( layers=[10, 10], func=torch.nn.Tanh, output_dimensions=len(problem.output_variables), - input_dimensions=len(problem.input_variables) + input_dimensions=len(problem.input_variables), ) # create the PINN object @@ -84,7 +105,7 @@ def truth_solution(self, pts): # ## Trainer Accelerator -# +# # When creating the trainer, **by defualt** the `Trainer` will choose the most performing `accelerator` for training which is available in your system, ranked as follow: # 1. [TPU](https://cloud.google.com/tpu/docs/intro-to-tpu) # 2. [IPU](https://www.graphcore.ai/products/ipu) @@ -93,31 +114,30 @@ def truth_solution(self, pts): # 5. CPU # For setting manually the `accelerator` run: -# +# # * `accelerator = {'gpu', 'cpu', 'hpu', 'mps', 'cpu', 'ipu'}` sets the accelerator to a specific one -# In[5]: +# In[4]: -trainer = Trainer(solver=pinn, - accelerator='cpu') +trainer = Trainer(solver=pinn, accelerator="cpu") # as you can see, even if in the used system `GPU` is available, it is not used since we set `accelerator='cpu'`. # ## Trainer Logging -# +# # In **PINA** you can log metrics in different ways. The simplest approach is to use the `MetricTraker` class from `pina.callbacks` as seen in the [*Introduction to PINA for Physics Informed Neural Networks training*](https://github.com/mathLab/PINA/blob/master/tutorials/tutorial1/tutorial.ipynb) tutorial. -# +# # However, expecially when we need to train multiple times to get an average of the loss across multiple runs, `pytorch_lightning.loggers` might be useful. Here we will use `TensorBoardLogger` (more on [logging](https://lightning.ai/docs/pytorch/stable/extensions/logging.html) here), but you can choose the one you prefer (or make your own one). -# +# # We will now import `TensorBoardLogger`, do three runs of training and then visualize the results. Notice we set `enable_model_summary=False` to avoid model summary specifications (e.g. number of parameters), set it to true if needed. -# +# -# In[7]: +# In[5]: -from pytorch_lightning.loggers import TensorBoardLogger +from lightning.pytorch.loggers import TensorBoardLogger # three run of training, by default it trains for 1000 epochs # we reinitialize the model each time otherwise the same parameters will be optimized @@ -126,17 +146,22 @@ def truth_solution(self, pts): layers=[10, 10], func=torch.nn.Tanh, output_dimensions=len(problem.output_variables), - input_dimensions=len(problem.input_variables) + input_dimensions=len(problem.input_variables), ) pinn = PINN(problem, model) - trainer = Trainer(solver=pinn, - accelerator='cpu', - logger=TensorBoardLogger(save_dir='simpleode'), - enable_model_summary=False) + trainer = Trainer( + solver=pinn, + accelerator="cpu", + logger=TensorBoardLogger(save_dir="training_log"), + enable_model_summary=False, + train_size=1.0, + val_size=0.0, + test_size=0.0, + ) trainer.train() -# We can now visualize the logs by simply running `tensorboard --logdir=simpleode/` on terminal, you should obtain a webpage as the one shown below: +# We can now visualize the logs by simply running `tensorboard --logdir=training_log/` on terminal, you should obtain a webpage as the one shown below: #

# \"Logging @@ -148,157 +173,173 @@ def truth_solution(self, pts): # Whenever we need to access certain steps of the training for logging, do static modifications (i.e. not changing the `Solver`) or updating `Problem` hyperparameters (static variables), we can use `Callabacks`. Notice that `Callbacks` allow you to add arbitrary self-contained programs to your training. At specific points during the flow of execution (hooks), the Callback interface allows you to design programs that encapsulate a full set of functionality. It de-couples functionality that does not need to be in **PINA** `Solver`s. # Lightning has a callback system to execute them when needed. Callbacks should capture NON-ESSENTIAL logic that is NOT required for your lightning module to run. -# +# # The following are best practices when using/designing callbacks. -# +# # * Callbacks should be isolated in their functionality. # * Your callback should not rely on the behavior of other callbacks in order to work properly. # * Do not manually call methods from the callback. # * Directly calling methods (eg. on_validation_end) is strongly discouraged. # * Whenever possible, your callbacks should not depend on the order in which they are executed. -# +# # We will try now to implement a naive version of `MetricTraker` to show how callbacks work. Notice that this is a very easy application of callbacks, fortunately in **PINA** we already provide more advanced callbacks in `pina.callbacks`. -# +# # -# In[8]: +# In[6]: -from pytorch_lightning.callbacks import Callback +from lightning.pytorch.callbacks import Callback +from lightning.pytorch.callbacks import EarlyStopping import torch + # define a simple callback class NaiveMetricTracker(Callback): def __init__(self): self.saved_metrics = [] - def on_train_epoch_end(self, trainer, __): # function called at the end of each epoch + def on_train_epoch_end( + self, trainer, __ + ): # function called at the end of each epoch self.saved_metrics.append( {key: value for key, value in trainer.logged_metrics.items()} ) -# Let's see the results when applyed to the `SimpleODE` problem. You can define callbacks when initializing the `Trainer` by the `callbacks` argument, which expects a list of callbacks. +# Let's see the results when applyed to the `SimpleODE` problem. You can define callbacks when initializing the `Trainer` by the `callbacks` argument, which expects a list of callbacks. -# In[10]: +# In[7]: model = FeedForward( - layers=[10, 10], - func=torch.nn.Tanh, - output_dimensions=len(problem.output_variables), - input_dimensions=len(problem.input_variables) - ) + layers=[10, 10], + func=torch.nn.Tanh, + output_dimensions=len(problem.output_variables), + input_dimensions=len(problem.input_variables), +) pinn = PINN(problem, model) -trainer = Trainer(solver=pinn, - accelerator='cpu', - enable_model_summary=False, - callbacks=[NaiveMetricTracker()]) # adding a callbacks +trainer = Trainer( + solver=pinn, + accelerator="cpu", + logger=True, + callbacks=[NaiveMetricTracker()], # adding a callbacks + enable_model_summary=False, + train_size=1.0, + val_size=0.0, + test_size=0.0, +) trainer.train() # We can easily access the data by calling `trainer.callbacks[0].saved_metrics` (notice the zero representing the first callback in the list given at initialization). -# In[9]: - +# In[8]: -trainer.callbacks[0].saved_metrics[:3] # only the first three epochs +trainer.callbacks[0].saved_metrics[:3] # only the first three epochs -# PyTorch Lightning also has some built in `Callbacks` which can be used in **PINA**, [here an extensive list](https://lightning.ai/docs/pytorch/stable/extensions/callbacks.html#built-in-callbacks). -# -# We can for example try the `EarlyStopping` routine, which automatically stops the training when a specific metric converged (here the `mean_loss`). In order to let the training keep going forever set `max_epochs=-1`. -# In[7]: +# PyTorch Lightning also has some built in `Callbacks` which can be used in **PINA**, [here an extensive list](https://lightning.ai/docs/pytorch/stable/extensions/callbacks.html#built-in-callbacks). +# +# We can for example try the `EarlyStopping` routine, which automatically stops the training when a specific metric converged (here the `train_loss`). In order to let the training keep going forever set `max_epochs=-1`. +# In[ ]: -# ~2 mins -from pytorch_lightning.callbacks import EarlyStopping model = FeedForward( - layers=[10, 10], - func=torch.nn.Tanh, - output_dimensions=len(problem.output_variables), - input_dimensions=len(problem.input_variables) - ) + layers=[10, 10], + func=torch.nn.Tanh, + output_dimensions=len(problem.output_variables), + input_dimensions=len(problem.input_variables), +) pinn = PINN(problem, model) -trainer = Trainer(solver=pinn, - accelerator='cpu', - max_epochs = -1, - enable_model_summary=False, - callbacks=[EarlyStopping('mean_loss')]) # adding a callbacks +trainer = Trainer( + solver=pinn, + accelerator="cpu", + max_epochs=-1, + enable_model_summary=False, + enable_progress_bar=False, + val_size=0.2, + train_size=0.8, + test_size=0.0, + callbacks=[EarlyStopping("val_loss")], +) # adding a callbacks trainer.train() # As we can see the model automatically stop when the logging metric stopped improving! # ## Trainer Tips to Boost Accuracy, Save Memory and Speed Up Training -# +# # Untill now we have seen how to choose the right `accelerator`, how to log and visualize the results, and how to interface with the program in order to add specific parts of code at specific points by `callbacks`. # Now, we well focus on how boost your training by saving memory and speeding it up, while mantaining the same or even better degree of accuracy! -# -# +# +# # There are several built in methods developed in PyTorch Lightning which can be applied straight forward in **PINA**, here we report some: -# +# # * [Stochastic Weight Averaging](https://pytorch.org/blog/pytorch-1.6-now-includes-stochastic-weight-averaging/) to boost accuracy # * [Gradient Clippling](https://deepgram.com/ai-glossary/gradient-clipping) to reduce computational time (and improve accuracy) -# * [Gradient Accumulation](https://lightning.ai/docs/pytorch/stable/common/optimization.html#id3) to save memory consumption -# * [Mixed Precision Training](https://lightning.ai/docs/pytorch/stable/common/optimization.html#id3) to save memory consumption -# +# * [Gradient Accumulation](https://lightning.ai/docs/pytorch/stable/common/optimization.html#id3) to save memory consumption +# * [Mixed Precision Training](https://lightning.ai/docs/pytorch/stable/common/optimization.html#id3) to save memory consumption +# # We will just demonstrate how to use the first two, and see the results compared to a standard training. # We use the [`Timer`](https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.callbacks.Timer.html#lightning.pytorch.callbacks.Timer) callback from `pytorch_lightning.callbacks` to take the times. Let's start by training a simple model without any optimization (train for 2000 epochs). -# In[19]: +# In[10]: -from pytorch_lightning.callbacks import Timer -from pytorch_lightning import seed_everything +from lightning.pytorch.callbacks import Timer +from lightning.pytorch import seed_everything # setting the seed for reproducibility seed_everything(42, workers=True) model = FeedForward( - layers=[10, 10], - func=torch.nn.Tanh, - output_dimensions=len(problem.output_variables), - input_dimensions=len(problem.input_variables) - ) + layers=[10, 10], + func=torch.nn.Tanh, + output_dimensions=len(problem.output_variables), + input_dimensions=len(problem.input_variables), +) pinn = PINN(problem, model) -trainer = Trainer(solver=pinn, - accelerator='cpu', - deterministic=True, # setting deterministic=True ensure reproducibility when a seed is imposed - max_epochs = 2000, - enable_model_summary=False, - callbacks=[Timer()]) # adding a callbacks +trainer = Trainer( + solver=pinn, + accelerator="cpu", + deterministic=True, # setting deterministic=True ensure reproducibility when a seed is imposed + max_epochs=2000, + enable_model_summary=False, + callbacks=[Timer()], +) # adding a callbacks trainer.train() print(f'Total training time {trainer.callbacks[0].time_elapsed("train"):.5f} s') # Now we do the same but with StochasticWeightAveraging -# In[36]: +# In[11]: -from pytorch_lightning.callbacks import StochasticWeightAveraging +from lightning.pytorch.callbacks import StochasticWeightAveraging # setting the seed for reproducibility seed_everything(42, workers=True) model = FeedForward( - layers=[10, 10], - func=torch.nn.Tanh, - output_dimensions=len(problem.output_variables), - input_dimensions=len(problem.input_variables) - ) + layers=[10, 10], + func=torch.nn.Tanh, + output_dimensions=len(problem.output_variables), + input_dimensions=len(problem.input_variables), +) pinn = PINN(problem, model) -trainer = Trainer(solver=pinn, - accelerator='cpu', - deterministic=True, - max_epochs = 2000, - enable_model_summary=False, - callbacks=[Timer(), - StochasticWeightAveraging(swa_lrs=0.005)]) # adding StochasticWeightAveraging callbacks +trainer = Trainer( + solver=pinn, + accelerator="cpu", + deterministic=True, + max_epochs=2000, + enable_model_summary=False, + callbacks=[Timer(), StochasticWeightAveraging(swa_lrs=0.005)], +) # adding StochasticWeightAveraging callbacks trainer.train() print(f'Total training time {trainer.callbacks[0].time_elapsed("train"):.5f} s') @@ -306,41 +347,42 @@ def on_train_epoch_end(self, trainer, __): # function called at the end of each # As you can see, the training time does not change at all! Notice that around epoch `1600` # the scheduler is switched from the defalut one `ConstantLR` to the Stochastic Weight Average Learning Rate (`SWALR`). # This is because by default `StochasticWeightAveraging` will be activated after `int(swa_epoch_start * max_epochs)` with `swa_epoch_start=0.7` by default. Finally, the final `mean_loss` is lower when `StochasticWeightAveraging` is used. -# +# # We will now now do the same but clippling the gradient to be relatively small. -# In[35]: +# In[12]: # setting the seed for reproducibility seed_everything(42, workers=True) model = FeedForward( - layers=[10, 10], - func=torch.nn.Tanh, - output_dimensions=len(problem.output_variables), - input_dimensions=len(problem.input_variables) - ) + layers=[10, 10], + func=torch.nn.Tanh, + output_dimensions=len(problem.output_variables), + input_dimensions=len(problem.input_variables), +) pinn = PINN(problem, model) -trainer = Trainer(solver=pinn, - accelerator='cpu', - max_epochs = 2000, - enable_model_summary=False, - gradient_clip_val=0.1, # clipping the gradient - callbacks=[Timer(), - StochasticWeightAveraging(swa_lrs=0.005)]) +trainer = Trainer( + solver=pinn, + accelerator="cpu", + max_epochs=2000, + enable_model_summary=False, + gradient_clip_val=0.1, # clipping the gradient + callbacks=[Timer(), StochasticWeightAveraging(swa_lrs=0.005)], +) trainer.train() print(f'Total training time {trainer.callbacks[0].time_elapsed("train"):.5f} s') # As we can see we by applying gradient clipping we were able to even obtain lower error! -# +# # ## What's next? -# +# # Now you know how to use efficiently the `Trainer` class **PINA**! There are multiple directions you can go now: -# -# 1. Explore training times on different devices (e.g.) `TPU` -# +# +# 1. Explore training times on different devices (e.g.) `TPU` +# # 2. Try to reduce memory cost by mixed precision training and gradient accumulation (especially useful when training Neural Operators) -# +# # 3. Benchmark `Trainer` speed for different precisions. diff --git a/tutorials/tutorial12/tutorial.ipynb b/tutorials/tutorial12/tutorial.ipynb index d374bb10c..0223da5ae 100644 --- a/tutorials/tutorial12/tutorial.ipynb +++ b/tutorials/tutorial12/tutorial.ipynb @@ -47,63 +47,80 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "## routine needed to run the notebook on Google Colab\n", "try:\n", - " import google.colab\n", - " IN_COLAB = True\n", + " import google.colab\n", + "\n", + " IN_COLAB = True\n", "except:\n", - " IN_COLAB = False\n", + " IN_COLAB = False\n", "if IN_COLAB:\n", - " !pip install \"pina-mathlab\"\n", + " !pip install \"pina-mathlab\"\n", "\n", - "#useful imports\n", - "from pina.problem import SpatialProblem, TimeDependentProblem\n", - "from pina.equation import Equation, FixedValue, FixedGradient, FixedFlux\n", - "from pina.geometry import CartesianDomain\n", "import torch\n", - "from pina.operators import grad, laplacian\n", + "\n", + "# useful imports\n", "from pina import Condition\n", - "\n" + "from pina.problem import SpatialProblem, TimeDependentProblem\n", + "from pina.equation import Equation, FixedValue\n", + "from pina.domain import CartesianDomain\n", + "from pina.operator import grad, laplacian" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "class Burgers1D(TimeDependentProblem, SpatialProblem):\n", + "# define the burger equation\n", + "def burger_equation(input_, output_):\n", + " du = grad(output_, input_)\n", + " ddu = grad(du, input_, components=[\"dudx\"])\n", + " return (\n", + " du.extract([\"dudt\"])\n", + " + output_.extract([\"u\"]) * du.extract([\"dudx\"])\n", + " - (0.01 / torch.pi) * ddu.extract([\"ddudxdx\"])\n", + " )\n", "\n", - " # define the burger equation\n", - " def burger_equation(input_, output_):\n", - " du = grad(output_, input_)\n", - " ddu = grad(du, input_, components=['dudx'])\n", - " return (\n", - " du.extract(['dudt']) +\n", - " output_.extract(['u'])*du.extract(['dudx']) -\n", - " (0.01/torch.pi)*ddu.extract(['ddudxdx'])\n", - " )\n", "\n", - " # define initial condition\n", - " def initial_condition(input_, output_):\n", - " u_expected = -torch.sin(torch.pi*input_.extract(['x']))\n", - " return output_.extract(['u']) - u_expected\n", + "# define initial condition\n", + "def initial_condition(input_, output_):\n", + " u_expected = -torch.sin(torch.pi * input_.extract([\"x\"]))\n", + " return output_.extract([\"u\"]) - u_expected\n", + "\n", + "\n", + "class Burgers1D(TimeDependentProblem, SpatialProblem):\n", "\n", " # assign output/ spatial and temporal variables\n", - " output_variables = ['u']\n", - " spatial_domain = CartesianDomain({'x': [-1, 1]})\n", - " temporal_domain = CartesianDomain({'t': [0, 1]})\n", + " output_variables = [\"u\"]\n", + " spatial_domain = CartesianDomain({\"x\": [-1, 1]})\n", + " temporal_domain = CartesianDomain({\"t\": [0, 1]})\n", "\n", + " domains = {\n", + " \"bound_cond1\": CartesianDomain({\"x\": -1, \"t\": [0, 1]}),\n", + " \"bound_cond2\": CartesianDomain({\"x\": 1, \"t\": [0, 1]}),\n", + " \"time_cond\": CartesianDomain({\"x\": [-1, 1], \"t\": 0}),\n", + " \"phys_cond\": CartesianDomain({\"x\": [-1, 1], \"t\": [0, 1]}),\n", + " }\n", " # problem condition statement\n", " conditions = {\n", - " 'gamma1': Condition(location=CartesianDomain({'x': -1, 't': [0, 1]}), equation=FixedValue(0.)),\n", - " 'gamma2': Condition(location=CartesianDomain({'x': 1, 't': [0, 1]}), equation=FixedValue(0.)),\n", - " 't0': Condition(location=CartesianDomain({'x': [-1, 1], 't': 0}), equation=Equation(initial_condition)),\n", - " 'D': Condition(location=CartesianDomain({'x': [-1, 1], 't': [0, 1]}), equation=Equation(burger_equation)),\n", + " \"bound_cond1\": Condition(\n", + " domain=\"bound_cond1\", equation=FixedValue(0.0)\n", + " ),\n", + " \"bound_cond2\": Condition(\n", + " domain=\"bound_cond2\", equation=FixedValue(0.0)\n", + " ),\n", + " \"time_cond\": Condition(\n", + " domain=\"time_cond\", equation=Equation(initial_condition)\n", + " ),\n", + " \"phys_cond\": Condition(\n", + " domain=\"phys_cond\", equation=Equation(burger_equation)\n", + " ),\n", " }" ] }, @@ -114,7 +131,7 @@ "\n", "The `Equation` class takes as input a function (in this case it happens twice, with `initial_condition` and `burger_equation`) which computes a residual of an equation, such as a PDE. In a problem class such as the one above, the `Equation` class with such a given input is passed as a parameter in the specified `Condition`. \n", "\n", - "The `FixedValue` class takes as input a value of same dimensions of the output functions; this class can be used to enforced a fixed value for a specific condition, e.g. Dirichlet boundary conditions, as it happens for instance in our example.\n", + "The `FixedValue` class takes as input a value of same dimensions of the output functions; this class can be used to enforce a fixed value for a specific condition, e.g. Dirichlet boundary conditions, as it happens for instance in our example.\n", "\n", "Once the equations are set as above in the problem conditions, the PINN solver will aim to minimize the residuals described in each equation in the training phase. " ] @@ -145,27 +162,28 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "class Burgers1DEquation(Equation):\n", - " \n", - " def __init__(self, nu = 0.):\n", + "\n", + " def __init__(self, nu=0.0):\n", " \"\"\"\n", " Burgers1D class. This class can be\n", " used to enforce the solution u to solve the viscous Burgers 1D Equation.\n", - " \n", + "\n", " :param torch.float32 nu: the viscosity coefficient. Default value is set to 0.\n", " \"\"\"\n", - " self.nu = nu \n", - " \n", + " self.nu = nu\n", + "\n", " def equation(input_, output_):\n", - " return grad(output_, input_, d='t') +\\\n", - " output_*grad(output_, input_, d='x') -\\\n", - " self.nu*laplacian(output_, input_, d='x')\n", + " return (\n", + " grad(output_, input_, d=\"t\")\n", + " + output_ * grad(output_, input_, d=\"x\")\n", + " - self.nu * laplacian(output_, input_, d=\"x\")\n", + " )\n", "\n", - " \n", " super().__init__(equation)" ] }, @@ -178,7 +196,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -186,20 +204,34 @@ "\n", " # define initial condition\n", " def initial_condition(input_, output_):\n", - " u_expected = -torch.sin(torch.pi*input_.extract(['x']))\n", - " return output_.extract(['u']) - u_expected\n", + " u_expected = -torch.sin(torch.pi * input_.extract([\"x\"]))\n", + " return output_.extract([\"u\"]) - u_expected\n", "\n", " # assign output/ spatial and temporal variables\n", - " output_variables = ['u']\n", - " spatial_domain = CartesianDomain({'x': [-1, 1]})\n", - " temporal_domain = CartesianDomain({'t': [0, 1]})\n", + " output_variables = [\"u\"]\n", + " spatial_domain = CartesianDomain({\"x\": [-1, 1]})\n", + " temporal_domain = CartesianDomain({\"t\": [0, 1]})\n", "\n", + " domains = {\n", + " \"bound_cond1\": CartesianDomain({\"x\": -1, \"t\": [0, 1]}),\n", + " \"bound_cond2\": CartesianDomain({\"x\": 1, \"t\": [0, 1]}),\n", + " \"time_cond\": CartesianDomain({\"x\": [-1, 1], \"t\": 0}),\n", + " \"phys_cond\": CartesianDomain({\"x\": [-1, 1], \"t\": [0, 1]}),\n", + " }\n", " # problem condition statement\n", " conditions = {\n", - " 'gamma1': Condition(location=CartesianDomain({'x': -1, 't': [0, 1]}), equation=FixedValue(0.)),\n", - " 'gamma2': Condition(location=CartesianDomain({'x': 1, 't': [0, 1]}), equation=FixedValue(0.)),\n", - " 't0': Condition(location=CartesianDomain({'x': [-1, 1], 't': 0}), equation=Equation(initial_condition)),\n", - " 'D': Condition(location=CartesianDomain({'x': [-1, 1], 't': [0, 1]}), equation=Burgers1DEquation(0.01/torch.pi)),\n", + " \"bound_cond1\": Condition(\n", + " domain=\"bound_cond1\", equation=FixedValue(0.0)\n", + " ),\n", + " \"bound_cond2\": Condition(\n", + " domain=\"bound_cond2\", equation=FixedValue(0.0)\n", + " ),\n", + " \"time_cond\": Condition(\n", + " domain=\"time_cond\", equation=Equation(initial_condition)\n", + " ),\n", + " \"phys_cond\": Condition(\n", + " domain=\"phys_cond\", equation=Burgers1DEquation(nu=0.01 / torch.pi)\n", + " ),\n", " }" ] }, @@ -214,7 +246,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Congratulations on completing the `Equation` class tutorial of **PINA**! As we have seen, you can build new classes that inherits `Equation` to store more complex equations, as the Burgers 1D equation, only requiring to pass the characteristic coefficients of the problem. \n", + "Congratulations on completing the `Equation` class tutorial of **PINA**! As we have seen, you can build new classes that inherit `Equation` to store more complex equations, as the Burgers 1D equation, only requiring to pass the characteristic coefficients of the problem. \n", "From now on, you can:\n", "- define additional complex equation classes (e.g. `SchrodingerEquation`, `NavierStokeEquation`..)\n", "- define more `FixedOperator` (e.g. `FixedCurl`)" @@ -237,7 +269,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.1.0" + "version": "3.9.21" }, "orig_nbformat": 4 }, diff --git a/tutorials/tutorial12/tutorial.py b/tutorials/tutorial12/tutorial.py index 515841a4e..300744081 100644 --- a/tutorials/tutorial12/tutorial.py +++ b/tutorials/tutorial12/tutorial.py @@ -2,7 +2,7 @@ # coding: utf-8 # # Tutorial: The `Equation` Class -# +# # [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial12/tutorial.ipynb) # In this tutorial, we will show how to use the `Equation` Class in PINA. Specifically, we will see how use the Class and its inherited classes to enforce residuals minimization in PINNs. @@ -10,8 +10,8 @@ # # Example: The Burgers 1D equation # We will start implementing the viscous Burgers 1D problem Class, described as follows: -# -# +# +# # $$ # \begin{equation} # \begin{cases} @@ -21,133 +21,168 @@ # \end{cases} # \end{equation} # $$ -# +# # where we set $ \nu = \frac{0.01}{\pi}$. -# -# In the class that models this problem we will see in action the `Equation` class and one of its inherited classes, the `FixedValue` class. +# +# In the class that models this problem we will see in action the `Equation` class and one of its inherited classes, the `FixedValue` class. -# In[7]: +# In[1]: ## routine needed to run the notebook on Google Colab try: - import google.colab - IN_COLAB = True + import google.colab + + IN_COLAB = True except: - IN_COLAB = False + IN_COLAB = False if IN_COLAB: - get_ipython().system('pip install "pina-mathlab"') + get_ipython().system('pip install "pina-mathlab"') -#useful imports -from pina.problem import SpatialProblem, TimeDependentProblem -from pina.equation import Equation, FixedValue, FixedGradient, FixedFlux -from pina.geometry import CartesianDomain import torch -from pina.operators import grad, laplacian + +# useful imports from pina import Condition +from pina.problem import SpatialProblem, TimeDependentProblem +from pina.equation import Equation, FixedValue +from pina.domain import CartesianDomain +from pina.operator import grad, laplacian -# In[6]: +# In[2]: -class Burgers1D(TimeDependentProblem, SpatialProblem): +# define the burger equation +def burger_equation(input_, output_): + du = grad(output_, input_) + ddu = grad(du, input_, components=["dudx"]) + return ( + du.extract(["dudt"]) + + output_.extract(["u"]) * du.extract(["dudx"]) + - (0.01 / torch.pi) * ddu.extract(["ddudxdx"]) + ) - # define the burger equation - def burger_equation(input_, output_): - du = grad(output_, input_) - ddu = grad(du, input_, components=['dudx']) - return ( - du.extract(['dudt']) + - output_.extract(['u'])*du.extract(['dudx']) - - (0.01/torch.pi)*ddu.extract(['ddudxdx']) - ) - # define initial condition - def initial_condition(input_, output_): - u_expected = -torch.sin(torch.pi*input_.extract(['x'])) - return output_.extract(['u']) - u_expected +# define initial condition +def initial_condition(input_, output_): + u_expected = -torch.sin(torch.pi * input_.extract(["x"])) + return output_.extract(["u"]) - u_expected - # assign output/ spatial and temporal variables - output_variables = ['u'] - spatial_domain = CartesianDomain({'x': [-1, 1]}) - temporal_domain = CartesianDomain({'t': [0, 1]}) +class Burgers1D(TimeDependentProblem, SpatialProblem): + + # assign output/ spatial and temporal variables + output_variables = ["u"] + spatial_domain = CartesianDomain({"x": [-1, 1]}) + temporal_domain = CartesianDomain({"t": [0, 1]}) + + domains = { + "bound_cond1": CartesianDomain({"x": -1, "t": [0, 1]}), + "bound_cond2": CartesianDomain({"x": 1, "t": [0, 1]}), + "time_cond": CartesianDomain({"x": [-1, 1], "t": 0}), + "phys_cond": CartesianDomain({"x": [-1, 1], "t": [0, 1]}), + } # problem condition statement conditions = { - 'gamma1': Condition(location=CartesianDomain({'x': -1, 't': [0, 1]}), equation=FixedValue(0.)), - 'gamma2': Condition(location=CartesianDomain({'x': 1, 't': [0, 1]}), equation=FixedValue(0.)), - 't0': Condition(location=CartesianDomain({'x': [-1, 1], 't': 0}), equation=Equation(initial_condition)), - 'D': Condition(location=CartesianDomain({'x': [-1, 1], 't': [0, 1]}), equation=Equation(burger_equation)), + "bound_cond1": Condition( + domain="bound_cond1", equation=FixedValue(0.0) + ), + "bound_cond2": Condition( + domain="bound_cond2", equation=FixedValue(0.0) + ), + "time_cond": Condition( + domain="time_cond", equation=Equation(initial_condition) + ), + "phys_cond": Condition( + domain="phys_cond", equation=Equation(burger_equation) + ), } -# -# The `Equation` class takes as input a function (in this case it happens twice, with `initial_condition` and `burger_equation`) which computes a residual of an equation, such as a PDE. In a problem class such as the one above, the `Equation` class with such a given input is passed as a parameter in the specified `Condition`. -# -# The `FixedValue` class takes as input a value of same dimensions of the output functions; this class can be used to enforced a fixed value for a specific condition, e.g. Dirichlet boundary conditions, as it happens for instance in our example. -# -# Once the equations are set as above in the problem conditions, the PINN solver will aim to minimize the residuals described in each equation in the training phase. +# +# The `Equation` class takes as input a function (in this case it happens twice, with `initial_condition` and `burger_equation`) which computes a residual of an equation, such as a PDE. In a problem class such as the one above, the `Equation` class with such a given input is passed as a parameter in the specified `Condition`. +# +# The `FixedValue` class takes as input a value of same dimensions of the output functions; this class can be used to enforce a fixed value for a specific condition, e.g. Dirichlet boundary conditions, as it happens for instance in our example. +# +# Once the equations are set as above in the problem conditions, the PINN solver will aim to minimize the residuals described in each equation in the training phase. # Available classes of equations include also: # - `FixedGradient` and `FixedFlux`: they work analogously to `FixedValue` class, where we can require a constant value to be enforced, respectively, on the gradient of the solution or the divergence of the solution; # - `Laplace`: it can be used to enforce the laplacian of the solution to be zero; # - `SystemEquation`: we can enforce multiple conditions on the same subdomain through this class, passing a list of residual equations defined in the problem. -# +# # # Defining a new Equation class # `Equation` classes can be also inherited to define a new class. As example, we can see how to rewrite the above problem introducing a new class `Burgers1D`; during the class call, we can pass the viscosity parameter $\nu$: -# In[13]: +# In[3]: class Burgers1DEquation(Equation): - - def __init__(self, nu = 0.): + + def __init__(self, nu=0.0): """ Burgers1D class. This class can be used to enforce the solution u to solve the viscous Burgers 1D Equation. - + :param torch.float32 nu: the viscosity coefficient. Default value is set to 0. """ - self.nu = nu - + self.nu = nu + def equation(input_, output_): - return grad(output_, input_, d='t') + output_*grad(output_, input_, d='x') - self.nu*laplacian(output_, input_, d='x') + return ( + grad(output_, input_, d="t") + + output_ * grad(output_, input_, d="x") + - self.nu * laplacian(output_, input_, d="x") + ) - super().__init__(equation) # Now we can just pass the above class as input for the last condition, setting $\nu= \frac{0.01}{\pi}$: -# In[14]: +# In[4]: class Burgers1D(TimeDependentProblem, SpatialProblem): # define initial condition def initial_condition(input_, output_): - u_expected = -torch.sin(torch.pi*input_.extract(['x'])) - return output_.extract(['u']) - u_expected + u_expected = -torch.sin(torch.pi * input_.extract(["x"])) + return output_.extract(["u"]) - u_expected # assign output/ spatial and temporal variables - output_variables = ['u'] - spatial_domain = CartesianDomain({'x': [-1, 1]}) - temporal_domain = CartesianDomain({'t': [0, 1]}) - + output_variables = ["u"] + spatial_domain = CartesianDomain({"x": [-1, 1]}) + temporal_domain = CartesianDomain({"t": [0, 1]}) + + domains = { + "bound_cond1": CartesianDomain({"x": -1, "t": [0, 1]}), + "bound_cond2": CartesianDomain({"x": 1, "t": [0, 1]}), + "time_cond": CartesianDomain({"x": [-1, 1], "t": 0}), + "phys_cond": CartesianDomain({"x": [-1, 1], "t": [0, 1]}), + } # problem condition statement conditions = { - 'gamma1': Condition(location=CartesianDomain({'x': -1, 't': [0, 1]}), equation=FixedValue(0.)), - 'gamma2': Condition(location=CartesianDomain({'x': 1, 't': [0, 1]}), equation=FixedValue(0.)), - 't0': Condition(location=CartesianDomain({'x': [-1, 1], 't': 0}), equation=Equation(initial_condition)), - 'D': Condition(location=CartesianDomain({'x': [-1, 1], 't': [0, 1]}), equation=Burgers1DEquation(0.01/torch.pi)), + "bound_cond1": Condition( + domain="bound_cond1", equation=FixedValue(0.0) + ), + "bound_cond2": Condition( + domain="bound_cond2", equation=FixedValue(0.0) + ), + "time_cond": Condition( + domain="time_cond", equation=Equation(initial_condition) + ), + "phys_cond": Condition( + domain="phys_cond", equation=Burgers1DEquation(nu=0.01 / torch.pi) + ), } # # What's next? -# Congratulations on completing the `Equation` class tutorial of **PINA**! As we have seen, you can build new classes that inherits `Equation` to store more complex equations, as the Burgers 1D equation, only requiring to pass the characteristic coefficients of the problem. +# Congratulations on completing the `Equation` class tutorial of **PINA**! As we have seen, you can build new classes that inherit `Equation` to store more complex equations, as the Burgers 1D equation, only requiring to pass the characteristic coefficients of the problem. # From now on, you can: # - define additional complex equation classes (e.g. `SchrodingerEquation`, `NavierStokeEquation`..) # - define more `FixedOperator` (e.g. `FixedCurl`) diff --git a/tutorials/tutorial13/tutorial.ipynb b/tutorials/tutorial13/tutorial.ipynb index ca8c7213e..765ca479f 100644 --- a/tutorials/tutorial13/tutorial.ipynb +++ b/tutorials/tutorial13/tutorial.ipynb @@ -19,30 +19,35 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "## routine needed to run the notebook on Google Colab\n", "try:\n", - " import google.colab\n", - " IN_COLAB = True\n", + " import google.colab\n", + "\n", + " IN_COLAB = True\n", "except:\n", - " IN_COLAB = False\n", + " IN_COLAB = False\n", "if IN_COLAB:\n", - " !pip install \"pina-mathlab\"\n", + " !pip install \"pina-mathlab\"\n", "\n", "import torch\n", + "import matplotlib.pyplot as plt\n", + "import warnings\n", "\n", - "from pina import Condition, Plotter, Trainer, Plotter\n", + "from pina import Condition, Trainer\n", "from pina.problem import SpatialProblem\n", - "from pina.operators import laplacian\n", - "from pina.solvers import PINN, SAPINN\n", - "from pina.model.layers import FourierFeatureEmbedding\n", + "from pina.operator import laplacian\n", + "from pina.solver import PINN, SelfAdaptivePINN as SAPINN\n", "from pina.loss import LpLoss\n", - "from pina.geometry import CartesianDomain\n", + "from pina.domain import CartesianDomain\n", "from pina.equation import Equation, FixedValue\n", - "from pina.model import FeedForward\n" + "from pina.model import FeedForward\n", + "from pina.model.block import FourierFeatureEmbedding\n", + "\n", + "warnings.filterwarnings(\"ignore\")" ] }, { @@ -78,32 +83,44 @@ "outputs": [], "source": [ "class Poisson(SpatialProblem):\n", - " output_variables = ['u']\n", - " spatial_domain = CartesianDomain({'x': [0, 1]})\n", + " output_variables = [\"u\"]\n", + " spatial_domain = CartesianDomain({\"x\": [0, 1]})\n", "\n", " def poisson_equation(input_, output_):\n", - " x = input_.extract('x')\n", - " u_xx = laplacian(output_, input_, components=['u'], d=['x'])\n", - " f = ((2*torch.pi)**2)*torch.sin(2*torch.pi*x) + 0.1*((50*torch.pi)**2)*torch.sin(50*torch.pi*x)\n", + " x = input_.extract(\"x\")\n", + " u_xx = laplacian(output_, input_, components=[\"u\"], d=[\"x\"])\n", + " f = ((2 * torch.pi) ** 2) * torch.sin(2 * torch.pi * x) + 0.1 * (\n", + " (50 * torch.pi) ** 2\n", + " ) * torch.sin(50 * torch.pi * x)\n", " return u_xx + f\n", "\n", + " domains = {\n", + " \"bound_cond0\": CartesianDomain({\"x\": 0.0}),\n", + " \"bound_cond1\": CartesianDomain({\"x\": 1.0}),\n", + " \"phys_cond\": spatial_domain,\n", + " }\n", " # here we write the problem conditions\n", " conditions = {\n", - " 'gamma0' : Condition(location=CartesianDomain({'x': 0}),\n", - " equation=FixedValue(0)),\n", - " 'gamma1' : Condition(location=CartesianDomain({'x': 1}),\n", - " equation=FixedValue(0)),\n", - " 'D': Condition(location=spatial_domain,\n", - " equation=Equation(poisson_equation)),\n", + " \"bound_cond0\": Condition(\n", + " domain=\"bound_cond0\", equation=FixedValue(0.0)\n", + " ),\n", + " \"bound_cond1\": Condition(\n", + " domain=\"bound_cond1\", equation=FixedValue(0.0)\n", + " ),\n", + " \"phys_cond\": Condition(\n", + " domain=\"phys_cond\", equation=Equation(poisson_equation)\n", + " ),\n", " }\n", "\n", - " def truth_solution(self, x):\n", - " return torch.sin(2*torch.pi*x) + 0.1*torch.sin(50*torch.pi*x)\n", + " def solution(self, x):\n", + " return torch.sin(2 * torch.pi * x) + 0.1 * torch.sin(50 * torch.pi * x)\n", + "\n", "\n", "problem = Poisson()\n", "\n", "# let's discretise the domain\n", - "problem.discretise_domain(128, 'grid')" + "problem.discretise_domain(128, \"grid\", domains=[\"phys_cond\"])\n", + "problem.discretise_domain(1, \"grid\", domains=[\"bound_cond0\", \"bound_cond1\"])" ] }, { @@ -113,12 +130,12 @@ "A standard PINN approach would be to fit this model using a Feed Forward (fully connected) Neural Network. For a conventional fully-connected neural network is easy to\n", "approximate a function $u$, given sufficient data inside the computational domain. However solving high-frequency or multi-scale problems presents great challenges to PINNs especially when the number of data cannot capture the different scales.\n", "\n", - "Below we run a simulation using the `PINN` solver and the self adaptive `SAPINN` solver, using a [`FeedForward`](https://mathlab.github.io/PINA/_modules/pina/model/feed_forward.html#FeedForward) model. We used a `MultiStepLR` scheduler to decrease the learning rate slowly during training (it takes around 2 minutes to run on CPU)." + "Below we run a simulation using the `PINN` solver and the self adaptive `SAPINN` solver, using a [`FeedForward`](https://mathlab.github.io/PINA/_modules/pina/model/feed_forward.html#FeedForward) model. " ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -127,7 +144,6 @@ "text": [ "GPU available: True (mps), used: False\n", "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", "HPU available: False, using: 0 HPUs\n" ] }, @@ -135,21 +151,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 4999: 100%|██████████| 1/1 [00:00<00:00, 150.58it/s, v_num=69, gamma0_loss=2.61e+3, gamma1_loss=2.61e+3, D_loss=409.0, mean_loss=1.88e+3] " + "Epoch 1499: 100%|██████████| 1/1 [00:00<00:00, 161.89it/s, v_num=2, bound_cond0_loss=3.12e+3, bound_cond1_loss=3.12e+3, phys_cond_loss=1.21e+3, train_loss=7.46e+3]" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "`Trainer.fit` stopped: `max_epochs=5000` reached.\n" + "`Trainer.fit` stopped: `max_epochs=1500` reached.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 4999: 100%|██████████| 1/1 [00:00<00:00, 97.66it/s, v_num=69, gamma0_loss=2.61e+3, gamma1_loss=2.61e+3, D_loss=409.0, mean_loss=1.88e+3] \n" + "Epoch 1499: 100%|██████████| 1/1 [00:00<00:00, 104.39it/s, v_num=2, bound_cond0_loss=3.12e+3, bound_cond1_loss=3.12e+3, phys_cond_loss=1.21e+3, train_loss=7.46e+3]" ] }, { @@ -158,7 +174,6 @@ "text": [ "GPU available: True (mps), used: False\n", "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", "HPU available: False, using: 0 HPUs\n" ] }, @@ -166,28 +181,74 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 4999: 100%|██████████| 1/1 [00:00<00:00, 88.18it/s, v_num=70, gamma0_loss=151.0, gamma1_loss=148.0, D_loss=6.38e+5, mean_loss=2.13e+5] " + "\n", + "Epoch 1499: 100%|██████████| 1/1 [00:00<00:00, 82.62it/s, v_num=3, bound_cond0_loss=1.06e+3, bound_cond1_loss=1.01e+3, phys_cond_loss=2.91e+3, train_loss=4.98e+3] " ] }, { "name": "stderr", "output_type": "stream", "text": [ - "`Trainer.fit` stopped: `max_epochs=5000` reached.\n" + "`Trainer.fit` stopped: `max_epochs=1500` reached.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 4999: 100%|██████████| 1/1 [00:00<00:00, 65.77it/s, v_num=70, gamma0_loss=151.0, gamma1_loss=148.0, D_loss=6.38e+5, mean_loss=2.13e+5]\n" + "Epoch 1499: 100%|██████████| 1/1 [00:00<00:00, 63.19it/s, v_num=3, bound_cond0_loss=1.06e+3, bound_cond1_loss=1.01e+3, phys_cond_loss=2.91e+3, train_loss=4.98e+3]\n" ] - }, + } + ], + "source": [ + "# training with PINN and visualize results\n", + "pinn = PINN(\n", + " problem=problem,\n", + " model=FeedForward(\n", + " input_dimensions=1, output_dimensions=1, layers=[100, 100, 100]\n", + " ),\n", + ")\n", + "\n", + "trainer = Trainer(\n", + " pinn,\n", + " max_epochs=1500,\n", + " accelerator=\"cpu\",\n", + " enable_model_summary=False,\n", + " val_size=0.0,\n", + " train_size=1.0,\n", + " test_size=0.0,\n", + ")\n", + "trainer.train()\n", + "\n", + "# training with PINN and visualize results\n", + "sapinn = SAPINN(\n", + " problem=problem,\n", + " model=FeedForward(\n", + " input_dimensions=1, output_dimensions=1, layers=[100, 100, 100]\n", + " ),\n", + ")\n", + "trainer_sapinn = Trainer(\n", + " sapinn,\n", + " max_epochs=1500,\n", + " accelerator=\"cpu\",\n", + " enable_model_summary=False,\n", + " val_size=0.0,\n", + " train_size=1.0,\n", + " test_size=0.0,\n", + ")\n", + "trainer_sapinn.train()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ { "data": { - "image/png": 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", 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", 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", 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", 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" + "
" ] }, "metadata": {}, @@ -205,26 +266,23 @@ } ], "source": [ - "# training with PINN and visualize results\n", - "pinn = PINN(problem=problem,\n", - " model=FeedForward(input_dimensions=1, output_dimensions=1, layers=[100, 100, 100]),\n", - " scheduler=torch.optim.lr_scheduler.MultiStepLR,\n", - " scheduler_kwargs={'milestones' : [1000, 2000, 3000, 4000], 'gamma':0.9})\n", - "trainer = Trainer(pinn, max_epochs=5000, accelerator='cpu', enable_model_summary=False)\n", - "trainer.train()\n", - "\n", - "# training with PINN and visualize results\n", - "sapinn = SAPINN(problem=problem,\n", - " model=FeedForward(input_dimensions=1, output_dimensions=1, layers=[100, 100, 100]),\n", - " scheduler_model=torch.optim.lr_scheduler.MultiStepLR,\n", - " scheduler_model_kwargs={'milestones' : [1000, 2000, 3000, 4000], 'gamma':0.9})\n", - "trainer_sapinn = Trainer(sapinn, max_epochs=5000, accelerator='cpu', enable_model_summary=False)\n", - "trainer_sapinn.train()\n", - "\n", - "# plot results\n", - "pl = Plotter()\n", - "pl.plot(pinn, title='PINN Solution')\n", - "pl.plot(sapinn, title='Self Adaptive PINN Solution')\n" + "# define the function to plot the solution obtained using matplotlib\n", + "def plot_solution(pinn_to_use, title):\n", + " pts = pinn_to_use.problem.spatial_domain.sample(256, \"grid\", variables=\"x\")\n", + " predicted_output = pinn_to_use.forward(pts).extract(\"u\").tensor.detach()\n", + " true_output = pinn_to_use.problem.solution(pts).detach()\n", + " plt.plot(\n", + " pts.extract([\"x\"]), predicted_output, label=\"Neural Network solution\"\n", + " )\n", + " plt.plot(pts.extract([\"x\"]), true_output, label=\"True solution\")\n", + " plt.title(title)\n", + " plt.legend()\n", + "\n", + "\n", + "# plot the solution of the two PINNs\n", + "plot_solution(pinn, \"PINN solution\")\n", + "plt.figure()\n", + "plot_solution(sapinn, \"Self Adaptive PINN solution\")" ] }, { @@ -238,26 +296,30 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Relative l2 error PINN 95.76%\n", - "Relative l2 error SAPINN 124.26%\n" + "Relative l2 error PINN 2833.18%\n", + "Relative l2 error SAPINN 1921.98%\n" ] } ], "source": [ "# l2 loss from PINA losses\n", - "l2_loss = LpLoss(p=2, relative=True)\n", + "l2_loss = LpLoss(p=2, relative=False)\n", "\n", "# sample new test points\n", - "pts = pts = problem.spatial_domain.sample(100, 'grid')\n", - "print(f'Relative l2 error PINN {l2_loss(pinn(pts), problem.truth_solution(pts)).item():.2%}')\n", - "print(f'Relative l2 error SAPINN {l2_loss(sapinn(pts), problem.truth_solution(pts)).item():.2%}')" + "pts = pts = problem.spatial_domain.sample(100, \"grid\")\n", + "print(\n", + " f\"Relative l2 error PINN {l2_loss(pinn(pts), problem.solution(pts)).item():.2%}\"\n", + ")\n", + "print(\n", + " f\"Relative l2 error SAPINN {l2_loss(sapinn(pts), problem.solution(pts)).item():.2%}\"\n", + ")" ] }, { @@ -293,50 +355,28 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 6, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "MultiscaleFourierNet(\n", - " (embedding1): FourierFeatureEmbedding()\n", - " (embedding2): FourierFeatureEmbedding()\n", - " (layers): FeedForward(\n", - " (model): Sequential(\n", - " (0): Linear(in_features=100, out_features=100, bias=True)\n", - " (1): Tanh()\n", - " (2): Linear(in_features=100, out_features=100, bias=True)\n", - " )\n", - " )\n", - " (final_layer): Linear(in_features=200, out_features=1, bias=True)\n", - ")" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "class MultiscaleFourierNet(torch.nn.Module):\n", " def __init__(self):\n", " super().__init__()\n", - " self.embedding1 = FourierFeatureEmbedding(input_dimension=1, \n", - " output_dimension=100,\n", - " sigma=1)\n", - " self.embedding2 = FourierFeatureEmbedding(input_dimension=1, \n", - " output_dimension=100,\n", - " sigma=10)\n", - " self.layers = FeedForward(input_dimensions=100, output_dimensions=100, layers=[100])\n", - " self.final_layer = torch.nn.Linear(2*100, 1)\n", + " self.embedding1 = FourierFeatureEmbedding(\n", + " input_dimension=1, output_dimension=100, sigma=1\n", + " )\n", + " self.embedding2 = FourierFeatureEmbedding(\n", + " input_dimension=1, output_dimension=100, sigma=10\n", + " )\n", + " self.layers = FeedForward(\n", + " input_dimensions=100, output_dimensions=100, layers=[100]\n", + " )\n", + " self.final_layer = torch.nn.Linear(2 * 100, 1)\n", "\n", " def forward(self, x):\n", " e1 = self.layers(self.embedding1(x))\n", " e2 = self.layers(self.embedding2(x))\n", - " return self.final_layer(torch.cat([e1, e2], dim=-1))\n", - "\n", - "MultiscaleFourierNet()" + " return self.final_layer(torch.cat([e1, e2], dim=-1))" ] }, { @@ -348,7 +388,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -357,7 +397,6 @@ "text": [ "GPU available: True (mps), used: False\n", "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", "HPU available: False, using: 0 HPUs\n" ] }, @@ -365,30 +404,35 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 4999: 100%|██████████| 1/1 [00:00<00:00, 94.64it/s, v_num=71, gamma0_loss=3.91e-5, gamma1_loss=3.91e-5, D_loss=0.000151, mean_loss=0.000113] " + "Epoch 1499: 100%|██████████| 1/1 [00:00<00:00, 144.03it/s, v_num=4, bound_cond0_loss=0.00252, bound_cond1_loss=0.00252, phys_cond_loss=0.00678, train_loss=0.0118] " ] }, { "name": "stderr", "output_type": "stream", "text": [ - "`Trainer.fit` stopped: `max_epochs=5000` reached.\n" + "`Trainer.fit` stopped: `max_epochs=1500` reached.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 4999: 100%|██████████| 1/1 [00:00<00:00, 72.21it/s, v_num=71, gamma0_loss=3.91e-5, gamma1_loss=3.91e-5, D_loss=0.000151, mean_loss=0.000113]\n" + "Epoch 1499: 100%|██████████| 1/1 [00:00<00:00, 97.91it/s, v_num=4, bound_cond0_loss=0.00252, bound_cond1_loss=0.00252, phys_cond_loss=0.00678, train_loss=0.0118] \n" ] } ], "source": [ - "multiscale_pinn = PINN(problem=problem,\n", - " model=MultiscaleFourierNet(),\n", - " scheduler=torch.optim.lr_scheduler.MultiStepLR,\n", - " scheduler_kwargs={'milestones' : [1000, 2000, 3000, 4000], 'gamma':0.9})\n", - "trainer = Trainer(multiscale_pinn, max_epochs=5000, accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional)\n", + "multiscale_pinn = PINN(problem=problem, model=MultiscaleFourierNet())\n", + "trainer = Trainer(\n", + " multiscale_pinn,\n", + " max_epochs=1500,\n", + " accelerator=\"cpu\",\n", + " enable_model_summary=False,\n", + " val_size=0.0,\n", + " train_size=1.0,\n", + " test_size=0.0,\n", + ")\n", "trainer.train()" ] }, @@ -401,41 +445,43 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 8, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Relative l2 error PINN with MultiscaleFourierNet: 2.53%\n" + ] + }, { "data": { - "image/png": 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", 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", 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" ] }, "metadata": {}, "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Relative l2 error PINN with MultiscaleFourierNet 2.72%\n" - ] } ], "source": [ - "# plot the solution\n", - "pl.plot(multiscale_pinn, title='Solution PINN with MultiscaleFourierNet')\n", + "# plot solution obtained\n", + "plot_solution(multiscale_pinn, \"Multiscale PINN solution\")\n", "\n", "# sample new test points\n", - "pts = pts = problem.spatial_domain.sample(100, 'grid')\n", - "print(f'Relative l2 error PINN with MultiscaleFourierNet {l2_loss(multiscale_pinn(pts), problem.truth_solution(pts)).item():.2%}')" + "pts = pts = problem.spatial_domain.sample(100, \"grid\")\n", + "print(\n", + " f\"Relative l2 error PINN with MultiscaleFourierNet: {l2_loss(multiscale_pinn(pts), problem.solution(pts)).item():.2%}\"\n", + ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "It is pretty clear that the network has learned the correct solution, with also a very law error. Obviously a longer training and a more expressive neural network could improve the results!\n", + "It is pretty clear that the network has learned the correct solution, with also a very low error. Obviously a longer training and a more expressive neural network could improve the results!\n", "\n", "## What's next?\n", "\n", @@ -467,7 +513,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.9.21" } }, "nbformat": 4, diff --git a/tutorials/tutorial13/tutorial.py b/tutorials/tutorial13/tutorial.py index 27d4d6e22..257e79537 100644 --- a/tutorials/tutorial13/tutorial.py +++ b/tutorials/tutorial13/tutorial.py @@ -2,139 +2,198 @@ # coding: utf-8 # # Tutorial: Multiscale PDE learning with Fourier Feature Network -# +# # [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial13/tutorial.ipynb) -# +# # This tutorial presents how to solve with Physics-Informed Neural Networks (PINNs) # a PDE characterized by multiscale behaviour, as # presented in [*On the eigenvector bias of Fourier feature networks: From regression to solving # multi-scale PDEs with physics-informed neural networks*]( -# https://doi.org/10.1016/j.cma.2021.113938). -# +# https://doi.org/10.1016/j.cma.2021.113938). +# # First of all, some useful imports. -# In[1]: +# In[ ]: ## routine needed to run the notebook on Google Colab try: - import google.colab - IN_COLAB = True + import google.colab + + IN_COLAB = True except: - IN_COLAB = False + IN_COLAB = False if IN_COLAB: - get_ipython().system('pip install "pina-mathlab"') + get_ipython().system('pip install "pina-mathlab"') import torch +import matplotlib.pyplot as plt +import warnings -from pina import Condition, Plotter, Trainer, Plotter +from pina import Condition, Trainer from pina.problem import SpatialProblem -from pina.operators import laplacian -from pina.solvers import PINN, SAPINN -from pina.model.layers import FourierFeatureEmbedding +from pina.operator import laplacian +from pina.solver import PINN, SelfAdaptivePINN as SAPINN from pina.loss import LpLoss -from pina.geometry import CartesianDomain +from pina.domain import CartesianDomain from pina.equation import Equation, FixedValue from pina.model import FeedForward +from pina.model.block import FourierFeatureEmbedding + +warnings.filterwarnings("ignore") # ## Multiscale Problem -# +# # We begin by presenting the problem which also can be found in Section 2 of [*On the eigenvector bias of Fourier feature networks: From regression to solving # multi-scale PDEs with physics-informed neural networks*]( # https://doi.org/10.1016/j.cma.2021.113938). The one-dimensional Poisson problem we aim to solve is mathematically written as: -# +# # \begin{equation} # \begin{cases} # \Delta u (x) + f(x) = 0 \quad x \in [0,1], \\ # u(x) = 0 \quad x \in \partial[0,1], \\ # \end{cases} # \end{equation} -# +# # We impose the solution as $u(x) = \sin(2\pi x) + 0.1 \sin(50\pi x)$ and obtain the force term $f(x) = (2\pi)^2 \sin(2\pi x) + 0.1 (50 \pi)^2 \sin(50\pi x)$. # Though this example is simple and pedagogical, it is worth noting that # the solution exhibits low frequency in the macro-scale and high frequency in the micro-scale, which resembles many # practical scenarios. -# -# +# +# # In **PINA** this problem is written, as always, as a class [see here for a tutorial on the Problem class](https://mathlab.github.io/PINA/_rst/tutorials/tutorial1/tutorial.html). Below you can find the `Poisson` problem which is mathmatically described above. # In[2]: class Poisson(SpatialProblem): - output_variables = ['u'] - spatial_domain = CartesianDomain({'x': [0, 1]}) + output_variables = ["u"] + spatial_domain = CartesianDomain({"x": [0, 1]}) def poisson_equation(input_, output_): - x = input_.extract('x') - u_xx = laplacian(output_, input_, components=['u'], d=['x']) - f = ((2*torch.pi)**2)*torch.sin(2*torch.pi*x) + 0.1*((50*torch.pi)**2)*torch.sin(50*torch.pi*x) + x = input_.extract("x") + u_xx = laplacian(output_, input_, components=["u"], d=["x"]) + f = ((2 * torch.pi) ** 2) * torch.sin(2 * torch.pi * x) + 0.1 * ( + (50 * torch.pi) ** 2 + ) * torch.sin(50 * torch.pi * x) return u_xx + f + domains = { + "bound_cond0": CartesianDomain({"x": 0.0}), + "bound_cond1": CartesianDomain({"x": 1.0}), + "phys_cond": spatial_domain, + } # here we write the problem conditions conditions = { - 'gamma0' : Condition(location=CartesianDomain({'x': 0}), - equation=FixedValue(0)), - 'gamma1' : Condition(location=CartesianDomain({'x': 1}), - equation=FixedValue(0)), - 'D': Condition(location=spatial_domain, - equation=Equation(poisson_equation)), + "bound_cond0": Condition( + domain="bound_cond0", equation=FixedValue(0.0) + ), + "bound_cond1": Condition( + domain="bound_cond1", equation=FixedValue(0.0) + ), + "phys_cond": Condition( + domain="phys_cond", equation=Equation(poisson_equation) + ), } - def truth_solution(self, x): - return torch.sin(2*torch.pi*x) + 0.1*torch.sin(50*torch.pi*x) + def solution(self, x): + return torch.sin(2 * torch.pi * x) + 0.1 * torch.sin(50 * torch.pi * x) + problem = Poisson() # let's discretise the domain -problem.discretise_domain(128, 'grid') +problem.discretise_domain(128, "grid", domains=["phys_cond"]) +problem.discretise_domain(1, "grid", domains=["bound_cond0", "bound_cond1"]) # A standard PINN approach would be to fit this model using a Feed Forward (fully connected) Neural Network. For a conventional fully-connected neural network is easy to # approximate a function $u$, given sufficient data inside the computational domain. However solving high-frequency or multi-scale problems presents great challenges to PINNs especially when the number of data cannot capture the different scales. -# -# Below we run a simulation using the `PINN` solver and the self adaptive `SAPINN` solver, using a [`FeedForward`](https://mathlab.github.io/PINA/_modules/pina/model/feed_forward.html#FeedForward) model. We used a `MultiStepLR` scheduler to decrease the learning rate slowly during training (it takes around 2 minutes to run on CPU). +# +# Below we run a simulation using the `PINN` solver and the self adaptive `SAPINN` solver, using a [`FeedForward`](https://mathlab.github.io/PINA/_modules/pina/model/feed_forward.html#FeedForward) model. -# In[19]: +# In[3]: # training with PINN and visualize results -pinn = PINN(problem=problem, - model=FeedForward(input_dimensions=1, output_dimensions=1, layers=[100, 100, 100]), - scheduler=torch.optim.lr_scheduler.MultiStepLR, - scheduler_kwargs={'milestones' : [1000, 2000, 3000, 4000], 'gamma':0.9}) -trainer = Trainer(pinn, max_epochs=5000, accelerator='cpu', enable_model_summary=False) +pinn = PINN( + problem=problem, + model=FeedForward( + input_dimensions=1, output_dimensions=1, layers=[100, 100, 100] + ), +) + +trainer = Trainer( + pinn, + max_epochs=1500, + accelerator="cpu", + enable_model_summary=False, + val_size=0.0, + train_size=1.0, + test_size=0.0, +) trainer.train() # training with PINN and visualize results -sapinn = SAPINN(problem=problem, - model=FeedForward(input_dimensions=1, output_dimensions=1, layers=[100, 100, 100]), - scheduler_model=torch.optim.lr_scheduler.MultiStepLR, - scheduler_model_kwargs={'milestones' : [1000, 2000, 3000, 4000], 'gamma':0.9}) -trainer_sapinn = Trainer(sapinn, max_epochs=5000, accelerator='cpu', enable_model_summary=False) +sapinn = SAPINN( + problem=problem, + model=FeedForward( + input_dimensions=1, output_dimensions=1, layers=[100, 100, 100] + ), +) +trainer_sapinn = Trainer( + sapinn, + max_epochs=1500, + accelerator="cpu", + enable_model_summary=False, + val_size=0.0, + train_size=1.0, + test_size=0.0, +) trainer_sapinn.train() -# plot results -pl = Plotter() -pl.plot(pinn, title='PINN Solution') -pl.plot(sapinn, title='Self Adaptive PINN Solution') + +# In[4]: + + +# define the function to plot the solution obtained using matplotlib +def plot_solution(pinn_to_use, title): + pts = pinn_to_use.problem.spatial_domain.sample(256, "grid", variables="x") + predicted_output = pinn_to_use.forward(pts).extract("u").tensor.detach() + true_output = pinn_to_use.problem.solution(pts).detach() + plt.plot( + pts.extract(["x"]), predicted_output, label="Neural Network solution" + ) + plt.plot(pts.extract(["x"]), true_output, label="True solution") + plt.title(title) + plt.legend() + + +# plot the solution of the two PINNs +plot_solution(pinn, "PINN solution") +plt.figure() +plot_solution(sapinn, "Self Adaptive PINN solution") # We can clearly see that the solution has not been learned by the two different solvers. Indeed the big problem is not in the optimization strategy (i.e. the solver), but in the model used to solve the problem. A simple `FeedForward` network can hardly handle multiscales if not enough collocation points are used! -# +# # We can also compute the $l_2$ relative error for the `PINN` and `SAPINN` solutions: -# In[20]: +# In[5]: # l2 loss from PINA losses -l2_loss = LpLoss(p=2, relative=True) +l2_loss = LpLoss(p=2, relative=False) # sample new test points -pts = pts = problem.spatial_domain.sample(100, 'grid') -print(f'Relative l2 error PINN {l2_loss(pinn(pts), problem.truth_solution(pts)).item():.2%}') -print(f'Relative l2 error SAPINN {l2_loss(sapinn(pts), problem.truth_solution(pts)).item():.2%}') +pts = pts = problem.spatial_domain.sample(100, "grid") +print( + f"Relative l2 error PINN {l2_loss(pinn(pts), problem.solution(pts)).item():.2%}" +) +print( + f"Relative l2 error SAPINN {l2_loss(sapinn(pts), problem.solution(pts)).item():.2%}" +) # Which is indeed very high! @@ -145,73 +204,80 @@ def truth_solution(self, x): # first introduced in [*On the eigenvector bias of Fourier feature networks: From regression to solving # multi-scale PDEs with physics-informed neural networks*]( # https://doi.org/10.1016/j.cma.2021.113938) showing great results for multiscale problems. The basic idea is to map the input $\mathbf{x}$ into an embedding $\tilde{\mathbf{x}}$ where: -# +# # $$ \tilde{\mathbf{x}} =\left[\cos\left( \mathbf{B} \mathbf{x} \right), \sin\left( \mathbf{B} \mathbf{x} \right)\right] $$ -# -# and $\mathbf{B}_{ij} \sim \mathcal{N}(0, \sigma^2)$. This simple operation allow the network to learn on multiple scales! -# +# +# and $\mathbf{B}_{ij} \sim \mathcal{N}(0, \sigma^2)$. This simple operation allow the network to learn on multiple scales! +# # In PINA we already have implemented the feature as a `layer` called [`FourierFeatureEmbedding`](https://mathlab.github.io/PINA/_rst/layers/fourier_embedding.html). Below we will build the *Multi-scale Fourier Feature Architecture*. In this architecture multiple Fourier feature embeddings (initialized with different $\sigma$) # are applied to input coordinates and then passed through the same fully-connected neural network, before the outputs are finally concatenated with a linear layer. -# In[21]: +# In[6]: class MultiscaleFourierNet(torch.nn.Module): def __init__(self): super().__init__() - self.embedding1 = FourierFeatureEmbedding(input_dimension=1, - output_dimension=100, - sigma=1) - self.embedding2 = FourierFeatureEmbedding(input_dimension=1, - output_dimension=100, - sigma=10) - self.layers = FeedForward(input_dimensions=100, output_dimensions=100, layers=[100]) - self.final_layer = torch.nn.Linear(2*100, 1) + self.embedding1 = FourierFeatureEmbedding( + input_dimension=1, output_dimension=100, sigma=1 + ) + self.embedding2 = FourierFeatureEmbedding( + input_dimension=1, output_dimension=100, sigma=10 + ) + self.layers = FeedForward( + input_dimensions=100, output_dimensions=100, layers=[100] + ) + self.final_layer = torch.nn.Linear(2 * 100, 1) def forward(self, x): e1 = self.layers(self.embedding1(x)) e2 = self.layers(self.embedding2(x)) return self.final_layer(torch.cat([e1, e2], dim=-1)) -MultiscaleFourierNet() - # We will train the `MultiscaleFourierNet` with the `PINN` solver (and feel free to try also with our PINN variants (`SAPINN`, `GPINN`, `CompetitivePINN`, ...). -# In[22]: +# In[7]: -multiscale_pinn = PINN(problem=problem, - model=MultiscaleFourierNet(), - scheduler=torch.optim.lr_scheduler.MultiStepLR, - scheduler_kwargs={'milestones' : [1000, 2000, 3000, 4000], 'gamma':0.9}) -trainer = Trainer(multiscale_pinn, max_epochs=5000, accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional) +multiscale_pinn = PINN(problem=problem, model=MultiscaleFourierNet()) +trainer = Trainer( + multiscale_pinn, + max_epochs=1500, + accelerator="cpu", + enable_model_summary=False, + val_size=0.0, + train_size=1.0, + test_size=0.0, +) trainer.train() # Let us now plot the solution and compute the relative $l_2$ again! -# In[24]: +# In[8]: -# plot the solution -pl.plot(multiscale_pinn, title='Solution PINN with MultiscaleFourierNet') +# plot solution obtained +plot_solution(multiscale_pinn, "Multiscale PINN solution") # sample new test points -pts = pts = problem.spatial_domain.sample(100, 'grid') -print(f'Relative l2 error PINN with MultiscaleFourierNet {l2_loss(multiscale_pinn(pts), problem.truth_solution(pts)).item():.2%}') +pts = pts = problem.spatial_domain.sample(100, "grid") +print( + f"Relative l2 error PINN with MultiscaleFourierNet: {l2_loss(multiscale_pinn(pts), problem.solution(pts)).item():.2%}" +) -# It is pretty clear that the network has learned the correct solution, with also a very law error. Obviously a longer training and a more expressive neural network could improve the results! -# +# It is pretty clear that the network has learned the correct solution, with also a very low error. Obviously a longer training and a more expressive neural network could improve the results! +# # ## What's next? -# +# # Congratulations on completing the one dimensional Poisson tutorial of **PINA** using `FourierFeatureEmbedding`! There are multiple directions you can go now: -# +# # 1. Train the network for longer or with different layer sizes and assert the finaly accuracy -# +# # 2. Understand the role of `sigma` in `FourierFeatureEmbedding` (see original paper for a nice reference) -# +# # 3. Code the *Spatio-temporal multi-scale Fourier feature architecture* for a more complex time dependent PDE (section 3 of the original reference) -# +# # 4. Many more... diff --git a/tutorials/tutorial14/tutorial.ipynb b/tutorials/tutorial14/tutorial.ipynb new file mode 100644 index 000000000..da1c02013 --- /dev/null +++ b/tutorials/tutorial14/tutorial.ipynb @@ -0,0 +1,572 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tutorial: Predicting Lid-driven cavity problem parameters with POD-RBF\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial14/tutorial.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this tutorial we will show how to use the **PINA** library to predict the distributions of velocity and pressure the Lid-driven Cavity problem, a benchmark in Computational Fluid Dynamics. The problem consists of a square cavity with a lid on top moving with tangential velocity (by convention to the right), with the addition of no-slip conditions on the walls of the cavity and null static pressure on the lower left angle. \n", + "\n", + "Our goal is to predict the distributions of velocity and pressure of the fluid inside the cavity as the Reynolds number of the inlet fluid varies. To do so we're using a Reduced Order Model (ROM) based on Proper Orthogonal Decomposition (POD). The parametric solution manifold is approximated here with Radial Basis Function (RBF) Interpolation, a common mesh-free interpolation method that doesn't require trainers or solvers as the found radial basis functions are used to interpolate new points." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's start with the necessary imports. We're particularly interested in the `PODBlock` and `RBFBlock` classes which will allow us to define the POD-RBF model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## routine needed to run the notebook on Google Colab\n", + "try:\n", + " import google.colab\n", + "\n", + " IN_COLAB = True\n", + "except:\n", + " IN_COLAB = False\n", + "if IN_COLAB:\n", + " !pip install \"pina-mathlab\"\n", + "\n", + "%matplotlib inline\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import torch\n", + "import pina\n", + "import warnings\n", + "\n", + "from pina.model.block import PODBlock, RBFBlock\n", + "from pina import LabelTensor\n", + "\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this tutorial we're gonna use the `LidCavity` class from the [Smithers](https://github.com/mathLab/Smithers) library, which contains a set of parametric solutions of the Lid-driven cavity problem in a square domain. The dataset consists of 300 snapshots of the parameter fields, which in this case are the magnitude of velocity and the pressure, and the corresponding parameter values $u$ and $p$. Each snapshot corresponds to a different value of the tangential velocity $\\mu$ of the lid, which has been sampled uniformly between 0.01 m/s and 1 m/s.\n", + "\n", + "Let's start by importing the dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import smithers\n", + "from smithers.dataset import LidCavity\n", + "\n", + "dataset = LidCavity()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's plot two the data points and the corresponding solution for both parameters at different snapshots, in order to better visualise the data we're using:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(1, 3, figsize=(14, 3))\n", + "for ax, par, u in zip(axs, dataset.params[:3], dataset.snapshots[\"mag(v)\"][:3]):\n", + " ax.tricontourf(dataset.triang, u, levels=16)\n", + " ax.set_title(f\"$u$ field for $\\mu$ = {par[0]:.4f}\")\n", + "fig, axs = plt.subplots(1, 3, figsize=(14, 3))\n", + "for ax, par, u in zip(axs, dataset.params[:3], dataset.snapshots[\"p\"][:3]):\n", + " ax.tricontourf(dataset.triang, u, levels=16)\n", + " ax.set_title(f\"$p$ field for $\\mu$ = {par[0]:.4f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To train the model we only need the snapshots for the two parameters. In order to be able to work with the snapshots in **PINA** we first need to assure they're in a compatible format, hence why we start by casting them into `LabelTensor` objects:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"velocity magnitude data, 5041 for each snapshot\"\"\"\n", + "\n", + "u = torch.tensor(dataset.snapshots[\"mag(v)\"]).float()\n", + "u = LabelTensor(u, labels=[f\"s{i}\" for i in range(u.shape[1])])\n", + "\"\"\"pressure data, 5041 for each snapshot\"\"\"\n", + "p = torch.tensor(dataset.snapshots[\"p\"]).float()\n", + "p = LabelTensor(p, labels=[f\"s{i}\" for i in range(p.shape[1])])\n", + "\"\"\"mu corresponding to each snapshot\"\"\"\n", + "mu = torch.tensor(dataset.params).float()\n", + "mu = LabelTensor(mu, labels=[\"mu\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The goal of our training is to be able to predict the solution for new test parameters. The first thing we need to do is validate the accuracy of the model, and in order to do so we split the 300 snapshots in training and testing dataset. In the example we set the training `ratio` to 0.9, which means that 90% of the total snapshots is used for training and the remaining 10% for testing." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"number of snapshots\"\"\"\n", + "\n", + "n = u.shape[0]\n", + "\"\"\"training over total snapshots ratio and number of training snapshots\"\"\"\n", + "ratio = 0.9\n", + "n_train = int(n * ratio)\n", + "\"\"\"split u and p data\"\"\"\n", + "u_train, u_test = u[:n_train], u[n_train:] # for mag(v)\n", + "p_train, p_test = p[:n_train], p[n_train:] # for p\n", + "\"\"\"split snapshots\"\"\"\n", + "mu_train, mu_test = mu[:n_train], mu[n_train:]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now proceed by defining the model we intend to use. We inherit from the `torch.nn.Module` class, but in addition we require a `pod_rank` for the POD part and a function `rbf_kernel` in order to perform the RBF part:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "class PODRBF(torch.nn.Module):\n", + " \"\"\"\n", + " Proper orthogonal decomposition with Radial Basis Function interpolation model.\n", + " \"\"\"\n", + "\n", + " def __init__(self, pod_rank, rbf_kernel):\n", + "\n", + " super().__init__()\n", + " self.pod = PODBlock(pod_rank)\n", + " self.rbf = RBFBlock(kernel=rbf_kernel)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We complete our model by adding two crucial methods. The first is `forward`, and it expands the input POD coefficients. After being expanded the POD layer needs to be fit, hence why we add a `fit` method that gives us the POD basis (current **PINA** default is by performing truncated Singular Value Decomposition). The same method then uses the basis to fit the RBF interpolation. Overall, the completed class looks like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "class PODRBF(torch.nn.Module):\n", + " \"\"\"\n", + " Proper orthogonal decomposition with Radial Basis Function interpolation model.\n", + " \"\"\"\n", + "\n", + " def __init__(self, pod_rank, rbf_kernel):\n", + "\n", + " super().__init__()\n", + " self.pod = PODBlock(pod_rank)\n", + " self.rbf = RBFBlock(kernel=rbf_kernel)\n", + "\n", + " def forward(self, x):\n", + " \"\"\"\n", + " Defines the computation performed at every call.\n", + " :param x: The tensor to apply the forward pass.\n", + " :type x: torch.Tensor\n", + " :return: the output computed by the model.\n", + " :rtype: torch.Tensor\n", + " \"\"\"\n", + " coefficients = self.rbf(x)\n", + " return self.pod.expand(coefficients)\n", + "\n", + " def fit(self, p, x):\n", + " \"\"\"\n", + " Call the :meth:`pina.model.layers.PODBlock.fit` method of the\n", + " :attr:`pina.model.layers.PODBlock` attribute to perform the POD,\n", + " and the :meth:`pina.model.layers.RBFBlock.fit` method of the\n", + " :attr:`pina.model.layers.RBFBlock` attribute to fit the interpolation.\n", + " \"\"\"\n", + " self.pod.fit(x)\n", + " self.rbf.fit(p, self.pod.reduce(x))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we've built our class, we can fit the model and ask it to predict the parameters for the remaining snapshots. We remember that we don't need to train the model, as it doesn't involve any learnable parameter. The only things we have to set are the rank of the decomposition and the radial basis function (here we use thin plate). Here we focus on predicting the magnitude of velocity:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"create the model\"\"\"\n", + "\n", + "pod_rbfu = PODRBF(pod_rank=20, rbf_kernel=\"thin_plate_spline\")\n", + "\n", + "\"\"\"fit the model to velocity training data\"\"\"\n", + "pod_rbfu.fit(mu_train, u_train)\n", + "\n", + "\"\"\"predict the parameter using the fitted model\"\"\"\n", + "u_train_rbf = pod_rbfu(mu_train)\n", + "u_test_rbf = pod_rbfu(mu_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally we can calculate the relative error for our model:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Error summary for POD-RBF model:\n", + " Train: 8.186829e-03\n", + " Test: 5.143083e-02\n" + ] + } + ], + "source": [ + "relative_u_error_train = torch.norm(u_train_rbf - u_train) / torch.norm(u_train)\n", + "relative_u_error_test = torch.norm(u_test_rbf - u_test) / torch.norm(u_test)\n", + "\n", + "print(\"Error summary for POD-RBF model:\")\n", + "print(f\" Train: {relative_u_error_train.item():e}\")\n", + "print(f\" Test: {relative_u_error_test.item():e}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The results are promising! Now let's visualise them, comparing four random predicted snapshots to the true ones:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "\n", + "idx = torch.randint(0, len(u_test), (4,))\n", + "u_idx_rbf = pod_rbfu(mu_test[idx])\n", + "fig, axs = plt.subplots(3, 4, figsize=(14, 10))\n", + "\n", + "relative_u_error_rbf = np.abs(u_test[idx] - u_idx_rbf.detach())\n", + "relative_u_error_rbf = np.where(\n", + " u_test[idx] < 1e-7, 1e-7, relative_u_error_rbf / u_test[idx]\n", + ")\n", + "\n", + "for i, (idx_, rbf_, rbf_err_) in enumerate(\n", + " zip(idx, u_idx_rbf, relative_u_error_rbf)\n", + "):\n", + " axs[0, i].set_title(\"Prediction for \" f\"$\\mu$ = {mu_test[idx_].item():.4f}\")\n", + " axs[1, i].set_title(\n", + " \"True snapshot for \" f\"$\\mu$ = {mu_test[idx_].item():.4f}\"\n", + " )\n", + " axs[2, i].set_title(\"Error for \" f\"$\\mu$ = {mu_test[idx_].item():.4f}\")\n", + "\n", + " cm = axs[0, i].tricontourf(\n", + " dataset.triang, rbf_.detach()\n", + " ) # POD-RBF prediction\n", + " plt.colorbar(cm, ax=axs[0, i])\n", + "\n", + " cm = axs[1, i].tricontourf(dataset.triang, u_test[idx_].flatten()) # Truth\n", + " plt.colorbar(cm, ax=axs[1, i])\n", + "\n", + " cm = axs[2, i].tripcolor(\n", + " dataset.triang, rbf_err_, norm=matplotlib.colors.LogNorm()\n", + " ) # Error for POD-RBF\n", + " plt.colorbar(cm, ax=axs[2, i])\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Overall we have reached a good level of approximation while avoiding time-consuming training procedures. Let's try doing the same to predict the pressure snapshots:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Error summary for POD-RBF model:\n", + " Train: 5.242423e-02\n", + " Test: 2.334622e+06\n" + ] + } + ], + "source": [ + "\"\"\"create the model\"\"\"\n", + "\n", + "pod_rbfp = PODRBF(pod_rank=20, rbf_kernel=\"thin_plate_spline\")\n", + "\n", + "\"\"\"fit the model to pressure training data\"\"\"\n", + "pod_rbfp.fit(mu_train, p_train)\n", + "\n", + "\"\"\"predict the parameter using the fitted model\"\"\"\n", + "p_train_rbf = pod_rbfp(mu_train)\n", + "p_test_rbf = pod_rbfp(mu_test)\n", + "\n", + "relative_p_error_train = torch.norm(p_train_rbf - p_train) / torch.norm(p_train)\n", + "relative_p_error_test = torch.norm(p_test_rbf - p_test) / torch.norm(p_test)\n", + "\n", + "print(\"Error summary for POD-RBF model:\")\n", + "print(f\" Train: {relative_p_error_train.item():e}\")\n", + "print(f\" Test: {relative_p_error_test.item():e}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Unfortunately here we obtain a very high relative test error, although this is likely due to the nature of the available data. Looking at the plots we can see that the pressure field is subject to high variations between subsequent snapshots, especially here: " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(2, 3, figsize=(14, 6))\n", + "for ax, par, u in zip(\n", + " axs.ravel(), dataset.params[66:72], dataset.snapshots[\"p\"][66:72]\n", + "):\n", + " cm = ax.tricontourf(dataset.triang, u, levels=16)\n", + " plt.colorbar(cm, ax=ax)\n", + " ax.set_title(f\"$p$ field for $\\mu$ = {par[0]:.4f}\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Or here:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(2, 3, figsize=(14, 6))\n", + "for ax, par, u in zip(\n", + " axs.ravel(), dataset.params[98:104], dataset.snapshots[\"p\"][98:104]\n", + "):\n", + " cm = ax.tricontourf(dataset.triang, u, levels=16)\n", + " plt.colorbar(cm, ax=ax)\n", + " ax.set_title(f\"$p$ field for $\\mu$ = {par[0]:.4f}\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Scrolling through the velocity snapshots we can observe a more regular behaviour, with no such variations in subsequent snapshots. Moreover, if we decide not to consider the abovementioned \"problematic\" snapshots, we can already observe a huge improvement:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Error summary for POD-RBF model:\n", + " Train: 3.672517e-02\n", + " Test: 1.686272e-01\n" + ] + } + ], + "source": [ + "\"\"\"excluding problematic snapshots\"\"\"\n", + "\n", + "data = list(range(300))\n", + "data_to_consider = data[:67] + data[71:100] + data[102:]\n", + "\"\"\"proceed as before\"\"\"\n", + "newp = torch.tensor(dataset.snapshots[\"p\"][data_to_consider]).float()\n", + "newp = LabelTensor(newp, labels=[f\"s{i}\" for i in range(newp.shape[1])])\n", + "\n", + "newmu = torch.tensor(dataset.params[data_to_consider]).float()\n", + "newmu = LabelTensor(newmu, labels=[\"mu\"])\n", + "\n", + "newn = newp.shape[0]\n", + "ratio = 0.9\n", + "new_train = int(newn * ratio)\n", + "\n", + "new_p_train, new_p_test = newp[:new_train], newp[new_train:]\n", + "\n", + "new_mu_train, new_mu_test = newmu[:new_train], newmu[new_train:]\n", + "\n", + "new_pod_rbfp = PODRBF(pod_rank=20, rbf_kernel=\"thin_plate_spline\")\n", + "\n", + "new_pod_rbfp.fit(new_mu_train, new_p_train)\n", + "\n", + "new_p_train_rbf = new_pod_rbfp(new_mu_train)\n", + "new_p_test_rbf = new_pod_rbfp(new_mu_test)\n", + "\n", + "new_relative_p_error_train = torch.norm(\n", + " new_p_train_rbf - new_p_train\n", + ") / torch.norm(new_p_train)\n", + "new_relative_p_error_test = torch.norm(\n", + " new_p_test_rbf - new_p_test\n", + ") / torch.norm(new_p_test)\n", + "\n", + "print(\"Error summary for POD-RBF model:\")\n", + "print(f\" Train: {new_relative_p_error_train.item():e}\")\n", + "print(f\" Test: {new_relative_p_error_test.item():e}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What's next?\n", + "\n", + "Congratulations on completing the **PINA** tutorial on building and using a custom POD class! Now you can try:\n", + "\n", + "1. Varying the inputs of the model (for a list of the supported RB functions look at the `rbf_layer.py` file in `pina.layers`)\n", + "\n", + "2. Changing the POD model, for example using Artificial Neural Networks. For a more in depth overview of POD-NN and a comparison with the POD-RBF model already shown, look at [Tutorial: Reduced order model (POD-RBF or POD-NN) for parametric problems](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial9/tutorial.ipynb)\n", + "\n", + "3. Building your own classes or adapt the one shown to other datasets/problems" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tutorials/tutorial14/tutorial.py b/tutorials/tutorial14/tutorial.py new file mode 100644 index 000000000..ed423b4b5 --- /dev/null +++ b/tutorials/tutorial14/tutorial.py @@ -0,0 +1,338 @@ +#!/usr/bin/env python +# coding: utf-8 + +# # Tutorial: Predicting Lid-driven cavity problem parameters with POD-RBF +# +# [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial14/tutorial.ipynb) + +# In this tutorial we will show how to use the **PINA** library to predict the distributions of velocity and pressure the Lid-driven Cavity problem, a benchmark in Computational Fluid Dynamics. The problem consists of a square cavity with a lid on top moving with tangential velocity (by convention to the right), with the addition of no-slip conditions on the walls of the cavity and null static pressure on the lower left angle. +# +# Our goal is to predict the distributions of velocity and pressure of the fluid inside the cavity as the Reynolds number of the inlet fluid varies. To do so we're using a Reduced Order Model (ROM) based on Proper Orthogonal Decomposition (POD). The parametric solution manifold is approximated here with Radial Basis Function (RBF) Interpolation, a common mesh-free interpolation method that doesn't require trainers or solvers as the found radial basis functions are used to interpolate new points. + +# Let's start with the necessary imports. We're particularly interested in the `PODBlock` and `RBFBlock` classes which will allow us to define the POD-RBF model. + +# In[ ]: + + +## routine needed to run the notebook on Google Colab +try: + import google.colab + + IN_COLAB = True +except: + IN_COLAB = False +if IN_COLAB: + get_ipython().system('pip install "pina-mathlab"') + +get_ipython().run_line_magic("matplotlib", "inline") + +import matplotlib.pyplot as plt +import torch +import pina +import warnings + +from pina.model.block import PODBlock, RBFBlock +from pina import LabelTensor + +warnings.filterwarnings("ignore") + + +# In this tutorial we're gonna use the `LidCavity` class from the [Smithers](https://github.com/mathLab/Smithers) library, which contains a set of parametric solutions of the Lid-driven cavity problem in a square domain. The dataset consists of 300 snapshots of the parameter fields, which in this case are the magnitude of velocity and the pressure, and the corresponding parameter values $u$ and $p$. Each snapshot corresponds to a different value of the tangential velocity $\mu$ of the lid, which has been sampled uniformly between 0.01 m/s and 1 m/s. +# +# Let's start by importing the dataset: + +# In[2]: + + +import smithers +from smithers.dataset import LidCavity + +dataset = LidCavity() + + +# Let's plot two the data points and the corresponding solution for both parameters at different snapshots, in order to better visualise the data we're using: + +# In[3]: + + +fig, axs = plt.subplots(1, 3, figsize=(14, 3)) +for ax, par, u in zip(axs, dataset.params[:3], dataset.snapshots["mag(v)"][:3]): + ax.tricontourf(dataset.triang, u, levels=16) + ax.set_title(f"$u$ field for $\mu$ = {par[0]:.4f}") +fig, axs = plt.subplots(1, 3, figsize=(14, 3)) +for ax, par, u in zip(axs, dataset.params[:3], dataset.snapshots["p"][:3]): + ax.tricontourf(dataset.triang, u, levels=16) + ax.set_title(f"$p$ field for $\mu$ = {par[0]:.4f}") + + +# To train the model we only need the snapshots for the two parameters. In order to be able to work with the snapshots in **PINA** we first need to assure they're in a compatible format, hence why we start by casting them into `LabelTensor` objects: + +# In[4]: + + +"""velocity magnitude data, 5041 for each snapshot""" + +u = torch.tensor(dataset.snapshots["mag(v)"]).float() +u = LabelTensor(u, labels=[f"s{i}" for i in range(u.shape[1])]) +"""pressure data, 5041 for each snapshot""" +p = torch.tensor(dataset.snapshots["p"]).float() +p = LabelTensor(p, labels=[f"s{i}" for i in range(p.shape[1])]) +"""mu corresponding to each snapshot""" +mu = torch.tensor(dataset.params).float() +mu = LabelTensor(mu, labels=["mu"]) + + +# The goal of our training is to be able to predict the solution for new test parameters. The first thing we need to do is validate the accuracy of the model, and in order to do so we split the 300 snapshots in training and testing dataset. In the example we set the training `ratio` to 0.9, which means that 90% of the total snapshots is used for training and the remaining 10% for testing. + +# In[5]: + + +"""number of snapshots""" + +n = u.shape[0] +"""training over total snapshots ratio and number of training snapshots""" +ratio = 0.9 +n_train = int(n * ratio) +"""split u and p data""" +u_train, u_test = u[:n_train], u[n_train:] # for mag(v) +p_train, p_test = p[:n_train], p[n_train:] # for p +"""split snapshots""" +mu_train, mu_test = mu[:n_train], mu[n_train:] + + +# We now proceed by defining the model we intend to use. We inherit from the `torch.nn.Module` class, but in addition we require a `pod_rank` for the POD part and a function `rbf_kernel` in order to perform the RBF part: + +# In[6]: + + +class PODRBF(torch.nn.Module): + """ + Proper orthogonal decomposition with Radial Basis Function interpolation model. + """ + + def __init__(self, pod_rank, rbf_kernel): + + super().__init__() + self.pod = PODBlock(pod_rank) + self.rbf = RBFBlock(kernel=rbf_kernel) + + +# We complete our model by adding two crucial methods. The first is `forward`, and it expands the input POD coefficients. After being expanded the POD layer needs to be fit, hence why we add a `fit` method that gives us the POD basis (current **PINA** default is by performing truncated Singular Value Decomposition). The same method then uses the basis to fit the RBF interpolation. Overall, the completed class looks like this: + +# In[7]: + + +class PODRBF(torch.nn.Module): + """ + Proper orthogonal decomposition with Radial Basis Function interpolation model. + """ + + def __init__(self, pod_rank, rbf_kernel): + + super().__init__() + self.pod = PODBlock(pod_rank) + self.rbf = RBFBlock(kernel=rbf_kernel) + + def forward(self, x): + """ + Defines the computation performed at every call. + :param x: The tensor to apply the forward pass. + :type x: torch.Tensor + :return: the output computed by the model. + :rtype: torch.Tensor + """ + coefficients = self.rbf(x) + return self.pod.expand(coefficients) + + def fit(self, p, x): + """ + Call the :meth:`pina.model.layers.PODBlock.fit` method of the + :attr:`pina.model.layers.PODBlock` attribute to perform the POD, + and the :meth:`pina.model.layers.RBFBlock.fit` method of the + :attr:`pina.model.layers.RBFBlock` attribute to fit the interpolation. + """ + self.pod.fit(x) + self.rbf.fit(p, self.pod.reduce(x)) + + +# Now that we've built our class, we can fit the model and ask it to predict the parameters for the remaining snapshots. We remember that we don't need to train the model, as it doesn't involve any learnable parameter. The only things we have to set are the rank of the decomposition and the radial basis function (here we use thin plate). Here we focus on predicting the magnitude of velocity: + +# In[8]: + + +"""create the model""" + +pod_rbfu = PODRBF(pod_rank=20, rbf_kernel="thin_plate_spline") + +"""fit the model to velocity training data""" +pod_rbfu.fit(mu_train, u_train) + +"""predict the parameter using the fitted model""" +u_train_rbf = pod_rbfu(mu_train) +u_test_rbf = pod_rbfu(mu_test) + + +# Finally we can calculate the relative error for our model: + +# In[9]: + + +relative_u_error_train = torch.norm(u_train_rbf - u_train) / torch.norm(u_train) +relative_u_error_test = torch.norm(u_test_rbf - u_test) / torch.norm(u_test) + +print("Error summary for POD-RBF model:") +print(f" Train: {relative_u_error_train.item():e}") +print(f" Test: {relative_u_error_test.item():e}") + + +# The results are promising! Now let's visualise them, comparing four random predicted snapshots to the true ones: + +# In[10]: + + +import numpy as np +import matplotlib +import matplotlib.pyplot as plt + +idx = torch.randint(0, len(u_test), (4,)) +u_idx_rbf = pod_rbfu(mu_test[idx]) +fig, axs = plt.subplots(3, 4, figsize=(14, 10)) + +relative_u_error_rbf = np.abs(u_test[idx] - u_idx_rbf.detach()) +relative_u_error_rbf = np.where( + u_test[idx] < 1e-7, 1e-7, relative_u_error_rbf / u_test[idx] +) + +for i, (idx_, rbf_, rbf_err_) in enumerate( + zip(idx, u_idx_rbf, relative_u_error_rbf) +): + axs[0, i].set_title("Prediction for " f"$\mu$ = {mu_test[idx_].item():.4f}") + axs[1, i].set_title( + "True snapshot for " f"$\mu$ = {mu_test[idx_].item():.4f}" + ) + axs[2, i].set_title("Error for " f"$\mu$ = {mu_test[idx_].item():.4f}") + + cm = axs[0, i].tricontourf( + dataset.triang, rbf_.detach() + ) # POD-RBF prediction + plt.colorbar(cm, ax=axs[0, i]) + + cm = axs[1, i].tricontourf(dataset.triang, u_test[idx_].flatten()) # Truth + plt.colorbar(cm, ax=axs[1, i]) + + cm = axs[2, i].tripcolor( + dataset.triang, rbf_err_, norm=matplotlib.colors.LogNorm() + ) # Error for POD-RBF + plt.colorbar(cm, ax=axs[2, i]) + +plt.show() + + +# Overall we have reached a good level of approximation while avoiding time-consuming training procedures. Let's try doing the same to predict the pressure snapshots: + +# In[11]: + + +"""create the model""" + +pod_rbfp = PODRBF(pod_rank=20, rbf_kernel="thin_plate_spline") + +"""fit the model to pressure training data""" +pod_rbfp.fit(mu_train, p_train) + +"""predict the parameter using the fitted model""" +p_train_rbf = pod_rbfp(mu_train) +p_test_rbf = pod_rbfp(mu_test) + +relative_p_error_train = torch.norm(p_train_rbf - p_train) / torch.norm(p_train) +relative_p_error_test = torch.norm(p_test_rbf - p_test) / torch.norm(p_test) + +print("Error summary for POD-RBF model:") +print(f" Train: {relative_p_error_train.item():e}") +print(f" Test: {relative_p_error_test.item():e}") + + +# Unfortunately here we obtain a very high relative test error, although this is likely due to the nature of the available data. Looking at the plots we can see that the pressure field is subject to high variations between subsequent snapshots, especially here: + +# In[12]: + + +fig, axs = plt.subplots(2, 3, figsize=(14, 6)) +for ax, par, u in zip( + axs.ravel(), dataset.params[66:72], dataset.snapshots["p"][66:72] +): + cm = ax.tricontourf(dataset.triang, u, levels=16) + plt.colorbar(cm, ax=ax) + ax.set_title(f"$p$ field for $\mu$ = {par[0]:.4f}") +plt.tight_layout() +plt.show() + + +# Or here: + +# In[13]: + + +fig, axs = plt.subplots(2, 3, figsize=(14, 6)) +for ax, par, u in zip( + axs.ravel(), dataset.params[98:104], dataset.snapshots["p"][98:104] +): + cm = ax.tricontourf(dataset.triang, u, levels=16) + plt.colorbar(cm, ax=ax) + ax.set_title(f"$p$ field for $\mu$ = {par[0]:.4f}") +plt.tight_layout() +plt.show() + + +# Scrolling through the velocity snapshots we can observe a more regular behaviour, with no such variations in subsequent snapshots. Moreover, if we decide not to consider the abovementioned "problematic" snapshots, we can already observe a huge improvement: + +# In[14]: + + +"""excluding problematic snapshots""" + +data = list(range(300)) +data_to_consider = data[:67] + data[71:100] + data[102:] +"""proceed as before""" +newp = torch.tensor(dataset.snapshots["p"][data_to_consider]).float() +newp = LabelTensor(newp, labels=[f"s{i}" for i in range(newp.shape[1])]) + +newmu = torch.tensor(dataset.params[data_to_consider]).float() +newmu = LabelTensor(newmu, labels=["mu"]) + +newn = newp.shape[0] +ratio = 0.9 +new_train = int(newn * ratio) + +new_p_train, new_p_test = newp[:new_train], newp[new_train:] + +new_mu_train, new_mu_test = newmu[:new_train], newmu[new_train:] + +new_pod_rbfp = PODRBF(pod_rank=20, rbf_kernel="thin_plate_spline") + +new_pod_rbfp.fit(new_mu_train, new_p_train) + +new_p_train_rbf = new_pod_rbfp(new_mu_train) +new_p_test_rbf = new_pod_rbfp(new_mu_test) + +new_relative_p_error_train = torch.norm( + new_p_train_rbf - new_p_train +) / torch.norm(new_p_train) +new_relative_p_error_test = torch.norm( + new_p_test_rbf - new_p_test +) / torch.norm(new_p_test) + +print("Error summary for POD-RBF model:") +print(f" Train: {new_relative_p_error_train.item():e}") +print(f" Test: {new_relative_p_error_test.item():e}") + + +# ## What's next? +# +# Congratulations on completing the **PINA** tutorial on building and using a custom POD class! Now you can try: +# +# 1. Varying the inputs of the model (for a list of the supported RB functions look at the `rbf_layer.py` file in `pina.layers`) +# +# 2. Changing the POD model, for example using Artificial Neural Networks. For a more in depth overview of POD-NN and a comparison with the POD-RBF model already shown, look at [Tutorial: Reduced order model (POD-RBF or POD-NN) for parametric problems](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial9/tutorial.ipynb) +# +# 3. Building your own classes or adapt the one shown to other datasets/problems diff --git a/tutorials/tutorial2/tutorial.ipynb b/tutorials/tutorial2/tutorial.ipynb index e375035ea..d0d891c77 100644 --- a/tutorials/tutorial2/tutorial.ipynb +++ b/tutorials/tutorial2/tutorial.ipynb @@ -16,33 +16,31 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "ad0b8dd7", "metadata": {}, "outputs": [], "source": [ "## routine needed to run the notebook on Google Colab\n", "try:\n", - " import google.colab\n", - " IN_COLAB = True\n", + " import google.colab\n", + "\n", + " IN_COLAB = True\n", "except:\n", - " IN_COLAB = False\n", + " IN_COLAB = False\n", "if IN_COLAB:\n", - " !pip install \"pina-mathlab\"\n", + " !pip install \"pina-mathlab\"\n", "\n", "import torch\n", - "from torch.nn import Softplus\n", + "import matplotlib.pyplot as plt\n", + "import warnings\n", "\n", - "from pina.problem import SpatialProblem\n", - "from pina.operators import laplacian\n", + "from pina import LabelTensor, Trainer\n", "from pina.model import FeedForward\n", - "from pina.solvers import PINN\n", - "from pina.trainer import Trainer\n", - "from pina.plotter import Plotter\n", - "from pina.geometry import CartesianDomain\n", - "from pina.equation import Equation, FixedValue\n", - "from pina import Condition, LabelTensor\n", - "from pina.callbacks import MetricTracker" + "from pina.solver import PINN\n", + "from torch.nn import Softplus\n", + "\n", + "warnings.filterwarnings(\"ignore\")" ] }, { @@ -61,14 +59,16 @@ "The two-dimensional Poisson problem is mathematically written as:\n", "\\begin{equation}\n", "\\begin{cases}\n", - "\\Delta u = \\sin{(\\pi x)} \\sin{(\\pi y)} \\text{ in } D, \\\\\n", + "\\Delta u = 2\\pi^2\\sin{(\\pi x)} \\sin{(\\pi y)} \\text{ in } D, \\\\\n", "u = 0 \\text{ on } \\Gamma_1 \\cup \\Gamma_2 \\cup \\Gamma_3 \\cup \\Gamma_4,\n", "\\end{cases}\n", "\\end{equation}\n", "where $D$ is a square domain $[0,1]^2$, and $\\Gamma_i$, with $i=1,...,4$, are the boundaries of the square.\n", "\n", - "The Poisson problem is written in **PINA** code as a class. The equations are written as *conditions* that should be satisfied in the corresponding domains. The *truth_solution*\n", - "is the exact solution which will be compared with the predicted one." + "The Poisson problem is written in **PINA** code as a class. The equations are written as *conditions* that should be satisfied in the corresponding domains. The *solution*\n", + "is the exact solution which will be compared with the predicted one. If interested in how to write problems see [this tutorial](https://mathlab.github.io/PINA/_rst/tutorials/tutorial1/tutorial.html).\n", + "\n", + "We will directly import the problem from `pina.problem.zoo`, which contains a vast list of PINN problems and more." ] }, { @@ -76,40 +76,35 @@ "execution_count": 2, "id": "82c24040", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The problem is made of 5 conditions: \n", + "They are: ['g1', 'g2', 'g3', 'g4', 'D']\n" + ] + } + ], "source": [ - "class Poisson(SpatialProblem):\n", - " output_variables = ['u']\n", - " spatial_domain = CartesianDomain({'x': [0, 1], 'y': [0, 1]})\n", - "\n", - " def laplace_equation(input_, output_):\n", - " force_term = (torch.sin(input_.extract(['x'])*torch.pi) *\n", - " torch.sin(input_.extract(['y'])*torch.pi))\n", - " laplacian_u = laplacian(output_, input_, components=['u'], d=['x', 'y'])\n", - " return laplacian_u - force_term\n", - "\n", - " # here we write the problem conditions\n", - " conditions = {\n", - " 'gamma1': Condition(location=CartesianDomain({'x': [0, 1], 'y': 1}), equation=FixedValue(0.)),\n", - " 'gamma2': Condition(location=CartesianDomain({'x': [0, 1], 'y': 0}), equation=FixedValue(0.)),\n", - " 'gamma3': Condition(location=CartesianDomain({'x': 1, 'y': [0, 1]}), equation=FixedValue(0.)),\n", - " 'gamma4': Condition(location=CartesianDomain({'x': 0, 'y': [0, 1]}), equation=FixedValue(0.)),\n", - " 'D': Condition(location=CartesianDomain({'x': [0, 1], 'y': [0, 1]}), equation=Equation(laplace_equation)),\n", - " }\n", - "\n", - " def poisson_sol(self, pts):\n", - " return -(\n", - " torch.sin(pts.extract(['x'])*torch.pi)*\n", - " torch.sin(pts.extract(['y'])*torch.pi)\n", - " )/(2*torch.pi**2)\n", - " \n", - " truth_solution = poisson_sol\n", + "from pina.problem.zoo import Poisson2DSquareProblem as Poisson\n", "\n", + "# initialize the problem\n", "problem = Poisson()\n", "\n", + "# print the conditions\n", + "print(\n", + " f\"The problem is made of {len(problem.conditions.keys())} conditions: \\n\"\n", + " f\"They are: {list(problem.conditions.keys())}\"\n", + ")\n", + "\n", "# let's discretise the domain\n", - "problem.discretise_domain(25, 'grid', locations=['D'])\n", - "problem.discretise_domain(25, 'grid', locations=['gamma1', 'gamma2', 'gamma3', 'gamma4'])" + "problem.discretise_domain(30, \"grid\", domains=[\"D\"])\n", + "problem.discretise_domain(\n", + " 100,\n", + " \"grid\",\n", + " domains=[\"g1\", \"g2\", \"g3\", \"g4\"],\n", + ")" ] }, { @@ -125,9 +120,9 @@ "id": "72ba4501", "metadata": {}, "source": [ - "After the problem, the feed-forward neural network is defined, through the class `FeedForward`. This neural network takes as input the coordinates (in this case $x$ and $y$) and provides the unkwown field of the Poisson problem. The residual of the equations are evaluated at several sampling points (which the user can manipulate using the method `CartesianDomain_pts`) and the loss minimized by the neural network is the sum of the residuals.\n", + "After the problem, the feed-forward neural network is defined, through the class `FeedForward`. This neural network takes as input the coordinates (in this case $x$ and $y$) and provides the unkwown field of the Poisson problem. The residual of the equations are evaluated at several sampling points and the loss minimized by the neural network is the sum of the residuals.\n", "\n", - "In this tutorial, the neural network is composed by two hidden layers of 10 neurons each, and it is trained for 1000 epochs with a learning rate of 0.006 and $l_2$ weight regularization set to $10^{-8}$. These parameters can be modified as desired. We use the `MetricTracker` class to track the metrics during training." + "In this tutorial, the neural network is composed by two hidden layers of 10 neurons each, and it is trained for 1000 epochs with a learning rate of 0.006 and $l_2$ weight regularization set to $10^{-8}$. These parameters can be modified as desired. We set the `train_size` to 0.8 and `test_size` to 0.2, this mean that the discretised points will be divided in a 80%-20% fashion, where 80% will be used for training and the remaining 20% for testing." ] }, { @@ -142,9 +137,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "GPU available: False, used: False\n", + "GPU available: True (mps), used: False\n", "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", "HPU available: False, using: 0 HPUs\n" ] }, @@ -152,7 +146,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 999: : 1it [00:00, 158.53it/s, v_num=3, gamma1_loss=5.29e-5, gamma2_loss=4.09e-5, gamma3_loss=4.73e-5, gamma4_loss=4.18e-5, D_loss=0.00134, mean_loss=0.000304] " + "Epoch 999: 100%|██████████| 1/1 [00:00<00:00, 143.27it/s, v_num=41, g1_loss=0.0148, g2_loss=0.0118, g3_loss=0.0346, g4_loss=0.00393, D_loss=0.206, train_loss=0.271] " ] }, { @@ -166,23 +160,38 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 999: : 1it [00:00, 105.33it/s, v_num=3, gamma1_loss=5.29e-5, gamma2_loss=4.09e-5, gamma3_loss=4.73e-5, gamma4_loss=4.18e-5, D_loss=0.00134, mean_loss=0.000304]\n" + "Epoch 999: 100%|██████████| 1/1 [00:00<00:00, 99.69it/s, v_num=41, g1_loss=0.0148, g2_loss=0.0118, g3_loss=0.0346, g4_loss=0.00393, D_loss=0.206, train_loss=0.271] \n" ] } ], "source": [ "# make model + solver + trainer\n", + "from pina.optim import TorchOptimizer\n", + "\n", "model = FeedForward(\n", " layers=[10, 10],\n", " func=Softplus,\n", " output_dimensions=len(problem.output_variables),\n", - " input_dimensions=len(problem.input_variables)\n", + " input_dimensions=len(problem.input_variables),\n", + ")\n", + "pinn = PINN(\n", + " problem,\n", + " model,\n", + " optimizer=TorchOptimizer(torch.optim.Adam, lr=0.006, weight_decay=1e-8),\n", + ")\n", + "trainer_base = Trainer(\n", + " solver=pinn, # setting the solver, i.e. PINN\n", + " max_epochs=1000, # setting max epochs in training\n", + " accelerator=\"cpu\", # we train on cpu, also other are available\n", + " enable_model_summary=False, # model summary statistics not printed\n", + " train_size=0.8, # set train size\n", + " val_size=0.0, # set validation size\n", + " test_size=0.2, # set testing size\n", + " shuffle=True, # shuffle the data\n", ")\n", - "pinn = PINN(problem, model, optimizer_kwargs={'lr':0.006, 'weight_decay':1e-8})\n", - "trainer = Trainer(pinn, max_epochs=1000, callbacks=[MetricTracker()], accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional)\n", "\n", "# train\n", - "trainer.train()" + "trainer_base.train()" ] }, { @@ -190,7 +199,7 @@ "id": "eb83cc7a", "metadata": {}, "source": [ - "Now the `Plotter` class is used to plot the results.\n", + "Now we plot the results using `matplotlib`.\n", "The solution predicted by the neural network is plotted on the left, the exact one is represented at the center and on the right the error between the exact and the predicted solutions is showed. " ] }, @@ -199,12 +208,53 @@ "execution_count": 4, "id": "1ab83c03", "metadata": {}, + "outputs": [], + "source": [ + "@torch.no_grad()\n", + "def plot_solution(solver):\n", + " # get the problem\n", + " problem = solver.problem\n", + " # get spatial points\n", + " spatial_samples = problem.spatial_domain.sample(30, \"grid\")\n", + " # compute pinn solution, true solution and absolute difference\n", + " data = {\n", + " \"PINN solution\": solver(spatial_samples),\n", + " \"True solution\": problem.solution(spatial_samples),\n", + " \"Absolute Difference\": torch.abs(\n", + " solver(spatial_samples) - problem.solution(spatial_samples)\n", + " ),\n", + " }\n", + " # plot the solution\n", + " for idx, (title, field) in enumerate(data.items()):\n", + " plt.subplot(1, 3, idx + 1)\n", + " plt.title(title)\n", + " plt.tricontourf( # convert to torch tensor + flatten\n", + " spatial_samples.extract(\"x\").tensor.flatten(),\n", + " spatial_samples.extract(\"y\").tensor.flatten(),\n", + " field.tensor.flatten(),\n", + " )\n", + " plt.colorbar(), plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "dfec566d", + "metadata": {}, + "source": [ + "Here the solution:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "7db10610", + "metadata": {}, "outputs": [ { "data": { - "image/png": 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -212,8 +262,16 @@ } ], "source": [ - "plotter = Plotter()\n", - "plotter.plot(solver=pinn)" + "plt.figure(figsize=(12, 6))\n", + "plot_solution(solver=pinn)" + ] + }, + { + "cell_type": "markdown", + "id": "49142e7f", + "metadata": {}, + "source": [ + "As you can see the solution is not very accurate, in what follows we will use **Extra Feature** as introduced in [*An extended physics informed neural network for preliminary analysis of parametric optimal control problems*](https://www.sciencedirect.com/science/article/abs/pii/S0898122123002018) to boost the training accuracy. Of course, even extra training will benefit, this tutorial is just to show that convergence using Extra Features is usally faster." ] }, { @@ -234,20 +292,19 @@ "The set of input variables to the neural network is:\n", "\n", "\\begin{equation}\n", - "[x, y, k(x, y)], \\text{ with } k(x, y)=\\sin{(\\pi x)}\\sin{(\\pi y)},\n", + "[x, y, k(x, y)], \\text{ with } k(x, y)= 2\\pi^2\\sin{(\\pi x)}\\sin{(\\pi y)},\n", "\\end{equation}\n", "\n", - "where $x$ and $y$ are the spatial coordinates and $k(x, y)$ is the added feature. \n", + "where $x$ and $y$ are the spatial coordinates and $k(x, y)$ is the added feature which is equal to the forcing term.\n", "\n", - "This feature is initialized in the class `SinSin`, which needs to be inherited by the `torch.nn.Module` class and to have the `forward` method. After declaring such feature, we can just incorporate in the `FeedForward` class thanks to the `extra_features` argument.\n", - "**NB**: `extra_features` always needs a `list` as input, you you have one feature just encapsulated it in a class, as in the next cell.\n", + "This feature is initialized in the class `SinSin`, which is a simple `torch.nn.Module`. After declaring such feature, we can just adjust the `FeedForward` class by creating a subclass `FeedForwardWithExtraFeatures` with an adjusted forward method and the additional attribute `extra_features`.\n", "\n", "Finally, we perform the same training as before: the problem is `Poisson`, the network is composed by the same number of neurons and optimizer parameters are equal to previous test, the only change is the new extra feature." ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "ef3ad372", "metadata": {}, "outputs": [ @@ -255,9 +312,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "GPU available: False, used: False\n", + "GPU available: True (mps), used: False\n", "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", "HPU available: False, using: 0 HPUs\n" ] }, @@ -265,7 +321,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 999: : 1it [00:00, 111.88it/s, v_num=4, gamma1_loss=2.54e-7, gamma2_loss=2.17e-7, gamma3_loss=1.94e-7, gamma4_loss=2.69e-7, D_loss=9.2e-6, mean_loss=2.03e-6] " + "Epoch 999: 100%|██████████| 1/1 [00:00<00:00, 121.03it/s, v_num=42, g1_loss=7.75e-5, g2_loss=6.85e-5, g3_loss=0.000217, g4_loss=0.000195, D_loss=0.000491, train_loss=0.00105] " ] }, { @@ -279,33 +335,58 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 999: : 1it [00:00, 85.62it/s, v_num=4, gamma1_loss=2.54e-7, gamma2_loss=2.17e-7, gamma3_loss=1.94e-7, gamma4_loss=2.69e-7, D_loss=9.2e-6, mean_loss=2.03e-6] \n" + "Epoch 999: 100%|██████████| 1/1 [00:00<00:00, 86.63it/s, v_num=42, g1_loss=7.75e-5, g2_loss=6.85e-5, g3_loss=0.000217, g4_loss=0.000195, D_loss=0.000491, train_loss=0.00105] \n" ] } ], "source": [ "class SinSin(torch.nn.Module):\n", " \"\"\"Feature: sin(x)*sin(y)\"\"\"\n", + "\n", " def __init__(self):\n", " super().__init__()\n", "\n", + " def forward(self, pts):\n", + " x, y = pts.extract([\"x\"]), pts.extract([\"y\"])\n", + " f = 2 * torch.pi**2 * torch.sin(x * torch.pi) * torch.sin(y * torch.pi)\n", + " return LabelTensor(f, [\"feat\"])\n", + "\n", + "\n", + "class FeedForwardWithExtraFeatures(FeedForward):\n", + " def __init__(self, *args, extra_features, **kwargs):\n", + " super().__init__(*args, **kwargs)\n", + " self.extra_features = extra_features\n", + "\n", " def forward(self, x):\n", - " t = (torch.sin(x.extract(['x'])*torch.pi) *\n", - " torch.sin(x.extract(['y'])*torch.pi))\n", - " return LabelTensor(t, ['sin(x)sin(y)'])\n", + " extra_feature = self.extra_features(x) # we append extra features\n", + " x = x.append(extra_feature)\n", + " return super().forward(x)\n", "\n", "\n", - "# make model + solver + trainer\n", - "model_feat = FeedForward(\n", - " layers=[10, 10],\n", - " func=Softplus,\n", + "model_feat = FeedForwardWithExtraFeatures(\n", + " input_dimensions=len(problem.input_variables) + 1,\n", " output_dimensions=len(problem.output_variables),\n", - " input_dimensions=len(problem.input_variables)+1\n", + " func=Softplus,\n", + " layers=[10, 10],\n", + " extra_features=SinSin(),\n", + ")\n", + "\n", + "pinn_feat = PINN(\n", + " problem,\n", + " model_feat,\n", + " optimizer=TorchOptimizer(torch.optim.Adam, lr=0.006, weight_decay=1e-8),\n", + ")\n", + "trainer_feat = Trainer(\n", + " solver=pinn_feat, # setting the solver, i.e. PINN\n", + " max_epochs=1000, # setting max epochs in training\n", + " accelerator=\"cpu\", # we train on cpu, also other are available\n", + " enable_model_summary=False, # model summary statistics not printed\n", + " train_size=0.8, # set train size\n", + " val_size=0.0, # set validation size\n", + " test_size=0.2, # set testing size\n", + " shuffle=True, # shuffle the data\n", ")\n", - "pinn_feat = PINN(problem, model_feat, extra_features=[SinSin()], optimizer_kwargs={'lr':0.006, 'weight_decay':1e-8})\n", - "trainer_feat = Trainer(pinn_feat, max_epochs=1000, callbacks=[MetricTracker()], accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional)\n", "\n", - "# train\n", "trainer_feat.train()" ] }, @@ -320,15 +401,15 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "2be6b145", "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -336,7 +417,8 @@ } ], "source": [ - "plotter.plot(solver=pinn_feat)" + "plt.figure(figsize=(12, 6))\n", + "plot_solution(solver=pinn_feat)" ] }, { @@ -367,7 +449,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "ae8716e7", "metadata": {}, "outputs": [ @@ -375,9 +457,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "GPU available: False, used: False\n", + "GPU available: True (mps), used: False\n", "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", "HPU available: False, using: 0 HPUs\n" ] }, @@ -385,7 +466,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 999: : 1it [00:00, 119.29it/s, v_num=5, gamma1_loss=3.26e-8, gamma2_loss=7.84e-8, gamma3_loss=1.13e-7, gamma4_loss=3.02e-8, D_loss=2.66e-6, mean_loss=5.82e-7] " + "Epoch 999: 100%|██████████| 1/1 [00:00<00:00, 102.62it/s, v_num=43, g1_loss=7.54e-6, g2_loss=2.9e-5, g3_loss=3.65e-5, g4_loss=1.22e-5, D_loss=0.00208, train_loss=0.00217] " ] }, { @@ -399,37 +480,53 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 999: : 1it [00:00, 85.94it/s, v_num=5, gamma1_loss=3.26e-8, gamma2_loss=7.84e-8, gamma3_loss=1.13e-7, gamma4_loss=3.02e-8, D_loss=2.66e-6, mean_loss=5.82e-7] \n" + "Epoch 999: 100%|██████████| 1/1 [00:00<00:00, 68.69it/s, v_num=43, g1_loss=7.54e-6, g2_loss=2.9e-5, g3_loss=3.65e-5, g4_loss=1.22e-5, D_loss=0.00208, train_loss=0.00217] \n" ] } ], "source": [ "class SinSinAB(torch.nn.Module):\n", " \"\"\" \"\"\"\n", + "\n", " def __init__(self):\n", " super().__init__()\n", " self.alpha = torch.nn.Parameter(torch.tensor([1.0]))\n", " self.beta = torch.nn.Parameter(torch.tensor([1.0]))\n", "\n", - "\n", " def forward(self, x):\n", - " t = (\n", - " self.beta*torch.sin(self.alpha*x.extract(['x'])*torch.pi)*\n", - " torch.sin(self.alpha*x.extract(['y'])*torch.pi)\n", + " t = (\n", + " self.beta\n", + " * torch.sin(self.alpha * x.extract([\"x\"]) * torch.pi)\n", + " * torch.sin(self.alpha * x.extract([\"y\"]) * torch.pi)\n", " )\n", - " return LabelTensor(t, ['b*sin(a*x)sin(a*y)'])\n", + " return LabelTensor(t, [\"b*sin(a*x)sin(a*y)\"])\n", "\n", "\n", "# make model + solver + trainer\n", - "model_lean= FeedForward(\n", - " layers=[10, 10],\n", - " func=Softplus,\n", + "model_learn = FeedForwardWithExtraFeatures(\n", + " input_dimensions=len(problem.input_variables)\n", + " + 1, # we add one as also we consider the extra feature dimension\n", " output_dimensions=len(problem.output_variables),\n", - " input_dimensions=len(problem.input_variables)+1\n", + " func=Softplus,\n", + " layers=[10, 10],\n", + " extra_features=SinSinAB(),\n", ")\n", - "pinn_lean = PINN(problem, model_lean, extra_features=[SinSinAB()], optimizer_kwargs={'lr':0.006, 'weight_decay':1e-8})\n", - "trainer_learn = Trainer(pinn_lean, max_epochs=1000, accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional)\n", "\n", + "pinn_learn = PINN(\n", + " problem,\n", + " model_learn,\n", + " optimizer=TorchOptimizer(torch.optim.Adam, lr=0.006, weight_decay=1e-8),\n", + ")\n", + "trainer_learn = Trainer(\n", + " solver=pinn_learn, # setting the solver, i.e. PINN\n", + " max_epochs=1000, # setting max epochs in training\n", + " accelerator=\"cpu\", # we train on cpu, also other are available\n", + " enable_model_summary=False, # model summary statistics not printed\n", + " train_size=0.8, # set train size\n", + " val_size=0.0, # set validation size\n", + " test_size=0.2, # set testing size\n", + " shuffle=True, # shuffle the data\n", + ")\n", "# train\n", "trainer_learn.train()" ] @@ -444,7 +541,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "daa9cf17", "metadata": {}, "outputs": [ @@ -452,9 +549,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "GPU available: False, used: False\n", + "GPU available: True (mps), used: False\n", "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", "HPU available: False, using: 0 HPUs\n" ] }, @@ -462,14 +558,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 0: : 0it [00:00, ?it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 999: : 1it [00:00, 131.20it/s, v_num=6, gamma1_loss=2.55e-16, gamma2_loss=4.76e-17, gamma3_loss=2.55e-16, gamma4_loss=4.76e-17, D_loss=1.74e-13, mean_loss=3.5e-14] " + "Epoch 999: 100%|██████████| 1/1 [00:00<00:00, 146.35it/s, v_num=44, g1_loss=1.48e-14, g2_loss=4.34e-15, g3_loss=2.04e-14, g4_loss=3.06e-15, D_loss=9.71e-12, train_loss=9.75e-12]" ] }, { @@ -483,21 +572,34 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 999: : 1it [00:00, 98.81it/s, v_num=6, gamma1_loss=2.55e-16, gamma2_loss=4.76e-17, gamma3_loss=2.55e-16, gamma4_loss=4.76e-17, D_loss=1.74e-13, mean_loss=3.5e-14] \n" + "Epoch 999: 100%|██████████| 1/1 [00:00<00:00, 103.91it/s, v_num=44, g1_loss=1.48e-14, g2_loss=4.34e-15, g3_loss=2.04e-14, g4_loss=3.06e-15, D_loss=9.71e-12, train_loss=9.75e-12]\n" ] } ], "source": [ "# make model + solver + trainer\n", - "model_lean= FeedForward(\n", + "model_learn = FeedForwardWithExtraFeatures(\n", " layers=[],\n", " func=Softplus,\n", " output_dimensions=len(problem.output_variables),\n", - " input_dimensions=len(problem.input_variables)+1\n", + " input_dimensions=len(problem.input_variables) + 1,\n", + " extra_features=SinSinAB(),\n", + ")\n", + "pinn_learn = PINN(\n", + " problem,\n", + " model_learn,\n", + " optimizer=TorchOptimizer(torch.optim.Adam, lr=0.006, weight_decay=1e-8),\n", + ")\n", + "trainer_learn = Trainer(\n", + " solver=pinn_learn, # setting the solver, i.e. PINN\n", + " max_epochs=1000, # setting max epochs in training\n", + " accelerator=\"cpu\", # we train on cpu, also other are available\n", + " enable_model_summary=False, # model summary statistics not printed\n", + " train_size=0.8, # set train size\n", + " val_size=0.0, # set validation size\n", + " test_size=0.2, # set testing size\n", + " shuffle=True, # shuffle the data\n", ")\n", - "pinn_learn = PINN(problem, model_lean, extra_features=[SinSinAB()], optimizer_kwargs={'lr':0.01, 'weight_decay':1e-8})\n", - "trainer_learn = Trainer(pinn_learn, max_epochs=1000, callbacks=[MetricTracker()], accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional)\n", - "\n", "# train\n", "trainer_learn.train()" ] @@ -508,30 +610,7 @@ "metadata": {}, "source": [ "In such a way, the model is able to reach a very high accuracy!\n", - "Of course, this is a toy problem for understanding the usage of extra features: similar precision could be obtained if the extra features are very similar to the true solution. The analyzed Poisson problem shows a forcing term very close to the solution, resulting in a perfect problem to address with such an approach.\n", - "\n", - "We conclude here by showing the graphical comparison of the unknown field and the loss trend for all the test cases presented here: the standard PINN, PINN with extra features, and PINN with learnable extra features." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "96e51c43", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plotter.plot(solver=pinn_learn)" + "Of course, this is a toy problem for understanding the usage of extra features: similar precision could be obtained if the extra features are very similar to the true solution. The analyzed Poisson problem shows a forcing term very close to the solution, resulting in a perfect problem to address with such an approach." ] }, { @@ -539,30 +618,53 @@ "id": "8c64fcb4", "metadata": {}, "source": [ - "Let us compare the training losses for the various types of training" + "We conclude here by showing the test error for the analysed methodologies: the standard PINN, PINN with extra features, and PINN with learnable extra features." ] }, { "cell_type": "code", - "execution_count": 10, - "id": "2855cea1", + "execution_count": 12, + "id": "a04e8a5d", "metadata": {}, "outputs": [ { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "PINN\n", + "Testing DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 77.71it/s] \n", + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + " Test metric DataLoader 0\n", + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + " test_loss 0.27009159326553345\n", + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + "PINN with extra features\n", + "Testing DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 111.93it/s]\n", + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + " Test metric DataLoader 0\n", + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + " test_loss 0.0012132360134273767\n", + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + "PINN with learnable extra features\n", + "Testing DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 155.77it/s]\n", + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + " Test metric DataLoader 0\n", + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + " test_loss 2.0213873977437125e-11\n", + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n" + ] } ], "source": [ - "plotter.plot_loss(trainer, logy=True, label='Standard')\n", - "plotter.plot_loss(trainer_feat, logy=True,label='Static Features')\n", - "plotter.plot_loss(trainer_learn, logy=True, label='Learnable Features')\n" + "# test error base pinn\n", + "print(\"PINN\")\n", + "trainer_base.test()\n", + "# test error extra features pinn\n", + "print(\"PINN with extra features\")\n", + "trainer_feat.test()\n", + "# test error learnable extra features pinn\n", + "print(\"PINN with learnable extra features\")\n", + "_ = trainer_learn.test()" ] }, { @@ -585,11 +687,8 @@ } ], "metadata": { - "interpreter": { - "hash": "56be7540488f3dc66429ddf54a0fa9de50124d45fcfccfaf04c4c3886d735a3a" - }, "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "pina", "language": "python", "name": "python3" }, @@ -603,7 +702,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.9.21" } }, "nbformat": 4, diff --git a/tutorials/tutorial2/tutorial.py b/tutorials/tutorial2/tutorial.py index b5132b442..622783aaa 100644 --- a/tutorials/tutorial2/tutorial.py +++ b/tutorials/tutorial2/tutorial.py @@ -2,38 +2,36 @@ # coding: utf-8 # # Tutorial: Two dimensional Poisson problem using Extra Features Learning -# +# # [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial2/tutorial.ipynb) -# +# # This tutorial presents how to solve with Physics-Informed Neural Networks (PINNs) a 2D Poisson problem with Dirichlet boundary conditions. We will train with standard PINN's training, and with extrafeatures. For more insights on extrafeature learning please read [*An extended physics informed neural network for preliminary analysis of parametric optimal control problems*](https://www.sciencedirect.com/science/article/abs/pii/S0898122123002018). -# +# # First of all, some useful imports. -# In[1]: +# In[ ]: ## routine needed to run the notebook on Google Colab try: - import google.colab - IN_COLAB = True + import google.colab + + IN_COLAB = True except: - IN_COLAB = False + IN_COLAB = False if IN_COLAB: - get_ipython().system('pip install "pina-mathlab"') + get_ipython().system('pip install "pina-mathlab"') import torch -from torch.nn import Softplus +import matplotlib.pyplot as plt +import warnings -from pina.problem import SpatialProblem -from pina.operators import laplacian +from pina import LabelTensor, Trainer from pina.model import FeedForward -from pina.solvers import PINN -from pina.trainer import Trainer -from pina.plotter import Plotter -from pina.geometry import CartesianDomain -from pina.equation import Equation, FixedValue -from pina import Condition, LabelTensor -from pina.callbacks import MetricTracker +from pina.solver import PINN +from torch.nn import Softplus + +warnings.filterwarnings("ignore") # ## The problem definition @@ -41,234 +39,322 @@ # The two-dimensional Poisson problem is mathematically written as: # \begin{equation} # \begin{cases} -# \Delta u = \sin{(\pi x)} \sin{(\pi y)} \text{ in } D, \\ +# \Delta u = 2\pi^2\sin{(\pi x)} \sin{(\pi y)} \text{ in } D, \\ # u = 0 \text{ on } \Gamma_1 \cup \Gamma_2 \cup \Gamma_3 \cup \Gamma_4, # \end{cases} # \end{equation} # where $D$ is a square domain $[0,1]^2$, and $\Gamma_i$, with $i=1,...,4$, are the boundaries of the square. -# -# The Poisson problem is written in **PINA** code as a class. The equations are written as *conditions* that should be satisfied in the corresponding domains. The *truth_solution* -# is the exact solution which will be compared with the predicted one. +# +# The Poisson problem is written in **PINA** code as a class. The equations are written as *conditions* that should be satisfied in the corresponding domains. The *solution* +# is the exact solution which will be compared with the predicted one. If interested in how to write problems see [this tutorial](https://mathlab.github.io/PINA/_rst/tutorials/tutorial1/tutorial.html). +# +# We will directly import the problem from `pina.problem.zoo`, which contains a vast list of PINN problems and more. # In[2]: -class Poisson(SpatialProblem): - output_variables = ['u'] - spatial_domain = CartesianDomain({'x': [0, 1], 'y': [0, 1]}) - - def laplace_equation(input_, output_): - force_term = (torch.sin(input_.extract(['x'])*torch.pi) * - torch.sin(input_.extract(['y'])*torch.pi)) - laplacian_u = laplacian(output_, input_, components=['u'], d=['x', 'y']) - return laplacian_u - force_term - - # here we write the problem conditions - conditions = { - 'gamma1': Condition(location=CartesianDomain({'x': [0, 1], 'y': 1}), equation=FixedValue(0.)), - 'gamma2': Condition(location=CartesianDomain({'x': [0, 1], 'y': 0}), equation=FixedValue(0.)), - 'gamma3': Condition(location=CartesianDomain({'x': 1, 'y': [0, 1]}), equation=FixedValue(0.)), - 'gamma4': Condition(location=CartesianDomain({'x': 0, 'y': [0, 1]}), equation=FixedValue(0.)), - 'D': Condition(location=CartesianDomain({'x': [0, 1], 'y': [0, 1]}), equation=Equation(laplace_equation)), - } - - def poisson_sol(self, pts): - return -( - torch.sin(pts.extract(['x'])*torch.pi)* - torch.sin(pts.extract(['y'])*torch.pi) - )/(2*torch.pi**2) - - truth_solution = poisson_sol +from pina.problem.zoo import Poisson2DSquareProblem as Poisson +# initialize the problem problem = Poisson() +# print the conditions +print( + f"The problem is made of {len(problem.conditions.keys())} conditions: \n" + f"They are: {list(problem.conditions.keys())}" +) + # let's discretise the domain -problem.discretise_domain(25, 'grid', locations=['D']) -problem.discretise_domain(25, 'grid', locations=['gamma1', 'gamma2', 'gamma3', 'gamma4']) +problem.discretise_domain(30, "grid", domains=["D"]) +problem.discretise_domain( + 100, + "grid", + domains=["g1", "g2", "g3", "g4"], +) # ## Solving the problem with standard PINNs -# After the problem, the feed-forward neural network is defined, through the class `FeedForward`. This neural network takes as input the coordinates (in this case $x$ and $y$) and provides the unkwown field of the Poisson problem. The residual of the equations are evaluated at several sampling points (which the user can manipulate using the method `CartesianDomain_pts`) and the loss minimized by the neural network is the sum of the residuals. -# -# In this tutorial, the neural network is composed by two hidden layers of 10 neurons each, and it is trained for 1000 epochs with a learning rate of 0.006 and $l_2$ weight regularization set to $10^{-8}$. These parameters can be modified as desired. We use the `MetricTracker` class to track the metrics during training. +# After the problem, the feed-forward neural network is defined, through the class `FeedForward`. This neural network takes as input the coordinates (in this case $x$ and $y$) and provides the unkwown field of the Poisson problem. The residual of the equations are evaluated at several sampling points and the loss minimized by the neural network is the sum of the residuals. +# +# In this tutorial, the neural network is composed by two hidden layers of 10 neurons each, and it is trained for 1000 epochs with a learning rate of 0.006 and $l_2$ weight regularization set to $10^{-8}$. These parameters can be modified as desired. We set the `train_size` to 0.8 and `test_size` to 0.2, this mean that the discretised points will be divided in a 80%-20% fashion, where 80% will be used for training and the remaining 20% for testing. # In[3]: # make model + solver + trainer +from pina.optim import TorchOptimizer + model = FeedForward( layers=[10, 10], func=Softplus, output_dimensions=len(problem.output_variables), - input_dimensions=len(problem.input_variables) + input_dimensions=len(problem.input_variables), +) +pinn = PINN( + problem, + model, + optimizer=TorchOptimizer(torch.optim.Adam, lr=0.006, weight_decay=1e-8), +) +trainer_base = Trainer( + solver=pinn, # setting the solver, i.e. PINN + max_epochs=1000, # setting max epochs in training + accelerator="cpu", # we train on cpu, also other are available + enable_model_summary=False, # model summary statistics not printed + train_size=0.8, # set train size + val_size=0.0, # set validation size + test_size=0.2, # set testing size + shuffle=True, # shuffle the data ) -pinn = PINN(problem, model, optimizer_kwargs={'lr':0.006, 'weight_decay':1e-8}) -trainer = Trainer(pinn, max_epochs=1000, callbacks=[MetricTracker()], accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional) # train -trainer.train() +trainer_base.train() -# Now the `Plotter` class is used to plot the results. -# The solution predicted by the neural network is plotted on the left, the exact one is represented at the center and on the right the error between the exact and the predicted solutions is showed. +# Now we plot the results using `matplotlib`. +# The solution predicted by the neural network is plotted on the left, the exact one is represented at the center and on the right the error between the exact and the predicted solutions is showed. # In[4]: -plotter = Plotter() -plotter.plot(solver=pinn) +@torch.no_grad() +def plot_solution(solver): + # get the problem + problem = solver.problem + # get spatial points + spatial_samples = problem.spatial_domain.sample(30, "grid") + # compute pinn solution, true solution and absolute difference + data = { + "PINN solution": solver(spatial_samples), + "True solution": problem.solution(spatial_samples), + "Absolute Difference": torch.abs( + solver(spatial_samples) - problem.solution(spatial_samples) + ), + } + # plot the solution + for idx, (title, field) in enumerate(data.items()): + plt.subplot(1, 3, idx + 1) + plt.title(title) + plt.tricontourf( # convert to torch tensor + flatten + spatial_samples.extract("x").tensor.flatten(), + spatial_samples.extract("y").tensor.flatten(), + field.tensor.flatten(), + ) + plt.colorbar(), plt.tight_layout() + + +# Here the solution: + +# In[5]: + + +plt.figure(figsize=(12, 6)) +plot_solution(solver=pinn) + +# As you can see the solution is not very accurate, in what follows we will use **Extra Feature** as introduced in [*An extended physics informed neural network for preliminary analysis of parametric optimal control problems*](https://www.sciencedirect.com/science/article/abs/pii/S0898122123002018) to boost the training accuracy. Of course, even extra training will benefit, this tutorial is just to show that convergence using Extra Features is usally faster. # ## Solving the problem with extra-features PINNs # Now, the same problem is solved in a different way. -# A new neural network is now defined, with an additional input variable, named extra-feature, which coincides with the forcing term in the Laplace equation. +# A new neural network is now defined, with an additional input variable, named extra-feature, which coincides with the forcing term in the Laplace equation. # The set of input variables to the neural network is: -# +# # \begin{equation} -# [x, y, k(x, y)], \text{ with } k(x, y)=\sin{(\pi x)}\sin{(\pi y)}, +# [x, y, k(x, y)], \text{ with } k(x, y)= 2\pi^2\sin{(\pi x)}\sin{(\pi y)}, # \end{equation} -# -# where $x$ and $y$ are the spatial coordinates and $k(x, y)$ is the added feature. -# -# This feature is initialized in the class `SinSin`, which needs to be inherited by the `torch.nn.Module` class and to have the `forward` method. After declaring such feature, we can just incorporate in the `FeedForward` class thanks to the `extra_features` argument. -# **NB**: `extra_features` always needs a `list` as input, you you have one feature just encapsulated it in a class, as in the next cell. -# +# +# where $x$ and $y$ are the spatial coordinates and $k(x, y)$ is the added feature which is equal to the forcing term. +# +# This feature is initialized in the class `SinSin`, which is a simple `torch.nn.Module`. After declaring such feature, we can just adjust the `FeedForward` class by creating a subclass `FeedForwardWithExtraFeatures` with an adjusted forward method and the additional attribute `extra_features`. +# # Finally, we perform the same training as before: the problem is `Poisson`, the network is composed by the same number of neurons and optimizer parameters are equal to previous test, the only change is the new extra feature. -# In[5]: +# In[6]: class SinSin(torch.nn.Module): """Feature: sin(x)*sin(y)""" + def __init__(self): super().__init__() + def forward(self, pts): + x, y = pts.extract(["x"]), pts.extract(["y"]) + f = 2 * torch.pi**2 * torch.sin(x * torch.pi) * torch.sin(y * torch.pi) + return LabelTensor(f, ["feat"]) + + +class FeedForwardWithExtraFeatures(FeedForward): + def __init__(self, *args, extra_features, **kwargs): + super().__init__(*args, **kwargs) + self.extra_features = extra_features + def forward(self, x): - t = (torch.sin(x.extract(['x'])*torch.pi) * - torch.sin(x.extract(['y'])*torch.pi)) - return LabelTensor(t, ['sin(x)sin(y)']) + extra_feature = self.extra_features(x) # we append extra features + x = x.append(extra_feature) + return super().forward(x) -# make model + solver + trainer -model_feat = FeedForward( - layers=[10, 10], - func=Softplus, +model_feat = FeedForwardWithExtraFeatures( + input_dimensions=len(problem.input_variables) + 1, output_dimensions=len(problem.output_variables), - input_dimensions=len(problem.input_variables)+1 + func=Softplus, + layers=[10, 10], + extra_features=SinSin(), +) + +pinn_feat = PINN( + problem, + model_feat, + optimizer=TorchOptimizer(torch.optim.Adam, lr=0.006, weight_decay=1e-8), +) +trainer_feat = Trainer( + solver=pinn_feat, # setting the solver, i.e. PINN + max_epochs=1000, # setting max epochs in training + accelerator="cpu", # we train on cpu, also other are available + enable_model_summary=False, # model summary statistics not printed + train_size=0.8, # set train size + val_size=0.0, # set validation size + test_size=0.2, # set testing size + shuffle=True, # shuffle the data ) -pinn_feat = PINN(problem, model_feat, extra_features=[SinSin()], optimizer_kwargs={'lr':0.006, 'weight_decay':1e-8}) -trainer_feat = Trainer(pinn_feat, max_epochs=1000, callbacks=[MetricTracker()], accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional) -# train trainer_feat.train() # The predicted and exact solutions and the error between them are represented below. # We can easily note that now our network, having almost the same condition as before, is able to reach additional order of magnitudes in accuracy. -# In[6]: +# In[7]: -plotter.plot(solver=pinn_feat) +plt.figure(figsize=(12, 6)) +plot_solution(solver=pinn_feat) # ## Solving the problem with learnable extra-features PINNs # We can still do better! -# +# # Another way to exploit the extra features is the addition of learnable parameter inside them. # In this way, the added parameters are learned during the training phase of the neural network. In this case, we use: -# +# # \begin{equation} # k(x, \mathbf{y}) = \beta \sin{(\alpha x)} \sin{(\alpha y)}, # \end{equation} -# +# # where $\alpha$ and $\beta$ are the abovementioned parameters. # Their implementation is quite trivial: by using the class `torch.nn.Parameter` we cam define all the learnable parameters we need, and they are managed by `autograd` module! -# In[7]: +# In[8]: class SinSinAB(torch.nn.Module): """ """ + def __init__(self): super().__init__() self.alpha = torch.nn.Parameter(torch.tensor([1.0])) self.beta = torch.nn.Parameter(torch.tensor([1.0])) - def forward(self, x): - t = ( - self.beta*torch.sin(self.alpha*x.extract(['x'])*torch.pi)* - torch.sin(self.alpha*x.extract(['y'])*torch.pi) + t = ( + self.beta + * torch.sin(self.alpha * x.extract(["x"]) * torch.pi) + * torch.sin(self.alpha * x.extract(["y"]) * torch.pi) ) - return LabelTensor(t, ['b*sin(a*x)sin(a*y)']) + return LabelTensor(t, ["b*sin(a*x)sin(a*y)"]) # make model + solver + trainer -model_lean= FeedForward( - layers=[10, 10], - func=Softplus, +model_learn = FeedForwardWithExtraFeatures( + input_dimensions=len(problem.input_variables) + + 1, # we add one as also we consider the extra feature dimension output_dimensions=len(problem.output_variables), - input_dimensions=len(problem.input_variables)+1 + func=Softplus, + layers=[10, 10], + extra_features=SinSinAB(), ) -pinn_lean = PINN(problem, model_lean, extra_features=[SinSinAB()], optimizer_kwargs={'lr':0.006, 'weight_decay':1e-8}) -trainer_learn = Trainer(pinn_lean, max_epochs=1000, accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional) +pinn_learn = PINN( + problem, + model_learn, + optimizer=TorchOptimizer(torch.optim.Adam, lr=0.006, weight_decay=1e-8), +) +trainer_learn = Trainer( + solver=pinn_learn, # setting the solver, i.e. PINN + max_epochs=1000, # setting max epochs in training + accelerator="cpu", # we train on cpu, also other are available + enable_model_summary=False, # model summary statistics not printed + train_size=0.8, # set train size + val_size=0.0, # set validation size + test_size=0.2, # set testing size + shuffle=True, # shuffle the data +) # train trainer_learn.train() # Umh, the final loss is not appreciabily better than previous model (with static extra features), despite the usage of learnable parameters. This is mainly due to the over-parametrization of the network: there are many parameter to optimize during the training, and the model in unable to understand automatically that only the parameters of the extra feature (and not the weights/bias of the FFN) should be tuned in order to fit our problem. A longer training can be helpful, but in this case the faster way to reach machine precision for solving the Poisson problem is removing all the hidden layers in the `FeedForward`, keeping only the $\alpha$ and $\beta$ parameters of the extra feature. -# In[8]: +# In[9]: # make model + solver + trainer -model_lean= FeedForward( +model_learn = FeedForwardWithExtraFeatures( layers=[], func=Softplus, output_dimensions=len(problem.output_variables), - input_dimensions=len(problem.input_variables)+1 + input_dimensions=len(problem.input_variables) + 1, + extra_features=SinSinAB(), +) +pinn_learn = PINN( + problem, + model_learn, + optimizer=TorchOptimizer(torch.optim.Adam, lr=0.006, weight_decay=1e-8), +) +trainer_learn = Trainer( + solver=pinn_learn, # setting the solver, i.e. PINN + max_epochs=1000, # setting max epochs in training + accelerator="cpu", # we train on cpu, also other are available + enable_model_summary=False, # model summary statistics not printed + train_size=0.8, # set train size + val_size=0.0, # set validation size + test_size=0.2, # set testing size + shuffle=True, # shuffle the data ) -pinn_learn = PINN(problem, model_lean, extra_features=[SinSinAB()], optimizer_kwargs={'lr':0.01, 'weight_decay':1e-8}) -trainer_learn = Trainer(pinn_learn, max_epochs=1000, callbacks=[MetricTracker()], accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional) - # train trainer_learn.train() # In such a way, the model is able to reach a very high accuracy! # Of course, this is a toy problem for understanding the usage of extra features: similar precision could be obtained if the extra features are very similar to the true solution. The analyzed Poisson problem shows a forcing term very close to the solution, resulting in a perfect problem to address with such an approach. -# -# We conclude here by showing the graphical comparison of the unknown field and the loss trend for all the test cases presented here: the standard PINN, PINN with extra features, and PINN with learnable extra features. - -# In[9]: - - -plotter.plot(solver=pinn_learn) - -# Let us compare the training losses for the various types of training +# We conclude here by showing the test error for the analysed methodologies: the standard PINN, PINN with extra features, and PINN with learnable extra features. -# In[10]: +# In[12]: -plotter.plot_loss(trainer, logy=True, label='Standard') -plotter.plot_loss(trainer_feat, logy=True,label='Static Features') -plotter.plot_loss(trainer_learn, logy=True, label='Learnable Features') +# test error base pinn +print("PINN") +trainer_base.test() +# test error extra features pinn +print("PINN with extra features") +trainer_feat.test() +# test error learnable extra features pinn +print("PINN with learnable extra features") +_ = trainer_learn.test() # ## What's next? -# +# # Congratulations on completing the two dimensional Poisson tutorial of **PINA**! There are multiple directions you can go now: -# +# # 1. Train the network for longer or with different layer sizes and assert the finaly accuracy -# +# # 2. Propose new types of extrafeatures and see how they affect the learning -# +# # 3. Exploit extrafeature training in more complex problems -# +# # 4. Many more... diff --git a/tutorials/tutorial3/tutorial.ipynb b/tutorials/tutorial3/tutorial.ipynb index 196cb3ecf..3ef328ea7 100644 --- a/tutorials/tutorial3/tutorial.ipynb +++ b/tutorials/tutorial3/tutorial.ipynb @@ -16,30 +16,34 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "d93daba0", "metadata": {}, "outputs": [], "source": [ "## routine needed to run the notebook on Google Colab\n", "try:\n", - " import google.colab\n", - " IN_COLAB = True\n", + " import google.colab\n", + "\n", + " IN_COLAB = True\n", "except:\n", - " IN_COLAB = False\n", + " IN_COLAB = False\n", "if IN_COLAB:\n", - " !pip install \"pina-mathlab\"\n", - " \n", + " !pip install \"pina-mathlab\"\n", + "\n", "import torch\n", + "import matplotlib.pyplot as plt\n", + "import warnings\n", "\n", + "from pina import Condition, LabelTensor, Trainer\n", "from pina.problem import SpatialProblem, TimeDependentProblem\n", - "from pina.operators import laplacian, grad\n", - "from pina.geometry import CartesianDomain\n", - "from pina.solvers import PINN\n", - "from pina.trainer import Trainer\n", - "from pina.equation import Equation\n", - "from pina.equation.equation_factory import FixedValue\n", - "from pina import Condition, Plotter" + "from pina.operator import laplacian, grad\n", + "from pina.domain import CartesianDomain\n", + "from pina.solver import PINN\n", + "from pina.equation import Equation, FixedValue\n", + "from pina.callback import MetricTracker\n", + "\n", + "warnings.filterwarnings(\"ignore\")" ] }, { @@ -73,7 +77,7 @@ "id": "cbc50741", "metadata": {}, "source": [ - "Now, the wave problem is written in PINA code as a class, inheriting from `SpatialProblem` and `TimeDependentProblem` since we deal with spatial, and time dependent variables. The equations are written as `conditions` that should be satisfied in the corresponding domains. `truth_solution` is the exact solution which will be compared with the predicted one." + "Now, the wave problem is written in PINA code as a class, inheriting from `SpatialProblem` and `TimeDependentProblem` since we deal with spatial, and time dependent variables. The equations are written as `conditions` that should be satisfied in the corresponding domains. `solution` is the exact solution which will be compared with the predicted one." ] }, { @@ -83,38 +87,55 @@ "metadata": {}, "outputs": [], "source": [ - "class Wave(TimeDependentProblem, SpatialProblem):\n", - " output_variables = ['u']\n", - " spatial_domain = CartesianDomain({'x': [0, 1], 'y': [0, 1]})\n", - " temporal_domain = CartesianDomain({'t': [0, 1]})\n", + "def wave_equation(input_, output_):\n", + " u_t = grad(output_, input_, components=[\"u\"], d=[\"t\"])\n", + " u_tt = grad(u_t, input_, components=[\"dudt\"], d=[\"t\"])\n", + " nabla_u = laplacian(output_, input_, components=[\"u\"], d=[\"x\", \"y\"])\n", + " return nabla_u - u_tt\n", "\n", - " def wave_equation(input_, output_):\n", - " u_t = grad(output_, input_, components=['u'], d=['t'])\n", - " u_tt = grad(u_t, input_, components=['dudt'], d=['t'])\n", - " nabla_u = laplacian(output_, input_, components=['u'], d=['x', 'y'])\n", - " return nabla_u - u_tt\n", "\n", - " def initial_condition(input_, output_):\n", - " u_expected = (torch.sin(torch.pi*input_.extract(['x'])) *\n", - " torch.sin(torch.pi*input_.extract(['y'])))\n", - " return output_.extract(['u']) - u_expected\n", + "def initial_condition(input_, output_):\n", + " u_expected = torch.sin(torch.pi * input_.extract([\"x\"])) * torch.sin(\n", + " torch.pi * input_.extract([\"y\"])\n", + " )\n", + " return output_.extract([\"u\"]) - u_expected\n", "\n", + "\n", + "class Wave(TimeDependentProblem, SpatialProblem):\n", + " output_variables = [\"u\"]\n", + " spatial_domain = CartesianDomain({\"x\": [0, 1], \"y\": [0, 1]})\n", + " temporal_domain = CartesianDomain({\"t\": [0, 1]})\n", + " domains = {\n", + " \"g1\": CartesianDomain({\"x\": 1, \"y\": [0, 1], \"t\": [0, 1]}),\n", + " \"g2\": CartesianDomain({\"x\": 0, \"y\": [0, 1], \"t\": [0, 1]}),\n", + " \"g3\": CartesianDomain({\"x\": [0, 1], \"y\": 0, \"t\": [0, 1]}),\n", + " \"g4\": CartesianDomain({\"x\": [0, 1], \"y\": 1, \"t\": [0, 1]}),\n", + " \"initial\": CartesianDomain({\"x\": [0, 1], \"y\": [0, 1], \"t\": 0}),\n", + " \"D\": CartesianDomain({\"x\": [0, 1], \"y\": [0, 1], \"t\": [0, 1]}),\n", + " }\n", " conditions = {\n", - " 'gamma1': Condition(location=CartesianDomain({'x': [0, 1], 'y': 1, 't': [0, 1]}), equation=FixedValue(0.)),\n", - " 'gamma2': Condition(location=CartesianDomain({'x': [0, 1], 'y': 0, 't': [0, 1]}), equation=FixedValue(0.)),\n", - " 'gamma3': Condition(location=CartesianDomain({'x': 1, 'y': [0, 1], 't': [0, 1]}), equation=FixedValue(0.)),\n", - " 'gamma4': Condition(location=CartesianDomain({'x': 0, 'y': [0, 1], 't': [0, 1]}), equation=FixedValue(0.)),\n", - " 't0': Condition(location=CartesianDomain({'x': [0, 1], 'y': [0, 1], 't': 0}), equation=Equation(initial_condition)),\n", - " 'D': Condition(location=CartesianDomain({'x': [0, 1], 'y': [0, 1], 't': [0, 1]}), equation=Equation(wave_equation)),\n", + " \"g1\": Condition(domain=\"g1\", equation=FixedValue(0.0)),\n", + " \"g2\": Condition(domain=\"g2\", equation=FixedValue(0.0)),\n", + " \"g3\": Condition(domain=\"g3\", equation=FixedValue(0.0)),\n", + " \"g4\": Condition(domain=\"g4\", equation=FixedValue(0.0)),\n", + " \"initial\": Condition(\n", + " domain=\"initial\", equation=Equation(initial_condition)\n", + " ),\n", + " \"D\": Condition(domain=\"D\", equation=Equation(wave_equation)),\n", " }\n", "\n", - " def wave_sol(self, pts):\n", - " return (torch.sin(torch.pi*pts.extract(['x'])) *\n", - " torch.sin(torch.pi*pts.extract(['y'])) *\n", - " torch.cos(torch.sqrt(torch.tensor(2.))*torch.pi*pts.extract(['t'])))\n", + " def solution(self, pts):\n", + " f = (\n", + " torch.sin(torch.pi * pts.extract([\"x\"]))\n", + " * torch.sin(torch.pi * pts.extract([\"y\"]))\n", + " * torch.cos(\n", + " torch.sqrt(torch.tensor(2.0)) * torch.pi * pts.extract([\"t\"])\n", + " )\n", + " )\n", + " return LabelTensor(f, self.output_variables)\n", "\n", - " truth_solution = wave_sol\n", "\n", + "# define problem\n", "problem = Wave()" ] }, @@ -150,16 +171,23 @@ " def __init__(self, input_dim, output_dim):\n", " super().__init__()\n", "\n", - " self.layers = torch.nn.Sequential(torch.nn.Linear(input_dim, 40),\n", - " torch.nn.ReLU(),\n", - " torch.nn.Linear(40, 40),\n", - " torch.nn.ReLU(),\n", - " torch.nn.Linear(40, output_dim))\n", - " \n", + " self.layers = torch.nn.Sequential(\n", + " torch.nn.Linear(input_dim, 40),\n", + " torch.nn.ReLU(),\n", + " torch.nn.Linear(40, 40),\n", + " torch.nn.ReLU(),\n", + " torch.nn.Linear(40, output_dim),\n", + " )\n", + "\n", " # here in the foward we implement the hard constraints\n", " def forward(self, x):\n", - " hard = x.extract(['x'])*(1-x.extract(['x']))*x.extract(['y'])*(1-x.extract(['y']))\n", - " return hard*self.layers(x)" + " hard = (\n", + " x.extract([\"x\"])\n", + " * (1 - x.extract([\"x\"]))\n", + " * x.extract([\"y\"])\n", + " * (1 - x.extract([\"y\"]))\n", + " )\n", + " return hard * self.layers(x)" ] }, { @@ -175,7 +203,7 @@ "id": "b465bebd", "metadata": {}, "source": [ - "In this tutorial, the neural network is trained for 1000 epochs with a learning rate of 0.001 (default in `PINN`). Training takes approximately 3 minutes." + "In this tutorial, the neural network is trained for 1000 epochs with a learning rate of 0.001 (default in `PINN`). As always, we will log using `Tensorboard`." ] }, { @@ -188,18 +216,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "GPU available: False, used: False\n", + "GPU available: True (mps), used: False\n", "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n", - "Missing logger folder: /Users/dariocoscia/Desktop/PINA/tutorials/tutorial3/lightning_logs\n" + "HPU available: False, using: 0 HPUs\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 999: : 1it [00:00, 84.47it/s, v_num=0, gamma1_loss=0.000, gamma2_loss=0.000, gamma3_loss=0.000, gamma4_loss=0.000, t0_loss=0.0419, D_loss=0.0307, mean_loss=0.0121]" + "Epoch 999: 100%|██████████| 1/1 [00:00<00:00, 37.78it/s, v_num=2, g1_loss=0.000, g2_loss=0.000, g3_loss=0.000, g4_loss=0.000, initial_loss=0.0711, D_loss=0.0291, train_loss=0.100]" ] }, { @@ -213,82 +239,165 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 999: : 1it [00:00, 68.69it/s, v_num=0, gamma1_loss=0.000, gamma2_loss=0.000, gamma3_loss=0.000, gamma4_loss=0.000, t0_loss=0.0419, D_loss=0.0307, mean_loss=0.0121]\n" + "Epoch 999: 100%|██████████| 1/1 [00:00<00:00, 32.87it/s, v_num=2, g1_loss=0.000, g2_loss=0.000, g3_loss=0.000, g4_loss=0.000, initial_loss=0.0711, D_loss=0.0291, train_loss=0.100]\n" ] } ], "source": [ "# generate the data\n", - "problem.discretise_domain(1000, 'random', locations=['D', 't0', 'gamma1', 'gamma2', 'gamma3', 'gamma4'])\n", + "problem.discretise_domain(1000, \"random\", domains=\"all\")\n", + "\n", + "# define model\n", + "model = HardMLP(len(problem.input_variables), len(problem.output_variables))\n", "\n", "# crete the solver\n", - "pinn = PINN(problem, HardMLP(len(problem.input_variables), len(problem.output_variables)))\n", + "pinn = PINN(problem=problem, model=model)\n", "\n", "# create trainer and train\n", - "trainer = Trainer(pinn, max_epochs=1000, accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional)\n", + "trainer = Trainer(\n", + " solver=pinn,\n", + " max_epochs=1000,\n", + " accelerator=\"cpu\",\n", + " enable_model_summary=False,\n", + " train_size=1.0,\n", + " val_size=0.0,\n", + " test_size=0.0,\n", + " callbacks=[MetricTracker([\"train_loss\", \"initial_loss\", \"D_loss\"])],\n", + ")\n", "trainer.train()" ] }, { "cell_type": "markdown", - "id": "c2a5c405", + "id": "4c6dbfac", "metadata": {}, "source": [ - "Notice that the loss on the boundaries of the spatial domain is exactly zero, as expected! After the training is completed one can now plot some results using the `Plotter` class of **PINA**." + "Let's now plot the losses inside `MetricTracker` to see how they vary during training." ] }, { "cell_type": "code", "execution_count": 5, - "id": "c086c05f", + "id": "77bfcb6e", "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Plotting at t=0\n" - ] + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" }, { "data": { - "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Plotting at t=0.5\n" - ] - }, + } + ], + "source": [ + "trainer_metrics = trainer.callbacks[0].metrics\n", + "for metric, loss in trainer_metrics.items():\n", + " plt.plot(range(len(loss)), loss, label=metric)\n", + "# plotting\n", + "plt.xlabel(\"epoch\")\n", + "plt.ylabel(\"loss\")\n", + "plt.yscale(\"log\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "id": "c2a5c405", + "metadata": {}, + "source": [ + "Notice that the loss on the boundaries of the spatial domain is exactly zero, as expected! After the training is completed one can now plot some results using the `matplotlib`. We plot the predicted output on the left side, the true solution at the center and the difference on the right side using the `plot_solution` function." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "c086c05f", + "metadata": {}, + "outputs": [], + "source": [ + "@torch.no_grad()\n", + "def plot_solution(solver, time):\n", + " # get the problem\n", + " problem = solver.problem\n", + " # get spatial points\n", + " spatial_samples = problem.spatial_domain.sample(30, \"grid\")\n", + " # get temporal value\n", + " time = LabelTensor(torch.tensor([[time]]), \"t\")\n", + " # cross data\n", + " points = spatial_samples.append(time, mode=\"cross\")\n", + " # compute pinn solution, true solution and absolute difference\n", + " data = {\n", + " \"PINN solution\": solver(points),\n", + " \"True solution\": problem.solution(points),\n", + " \"Absolute Difference\": torch.abs(\n", + " solver(points) - problem.solution(points)\n", + " ),\n", + " }\n", + " # plot the solution\n", + " plt.suptitle(f\"Solution for time {time.item()}\")\n", + " for idx, (title, field) in enumerate(data.items()):\n", + " plt.subplot(1, 3, idx + 1)\n", + " plt.title(title)\n", + " plt.tricontourf( # convert to torch tensor + flatten\n", + " points.extract(\"x\").tensor.flatten(),\n", + " points.extract(\"y\").tensor.flatten(),\n", + " field.tensor.flatten(),\n", + " )\n", + " plt.colorbar(), plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "910c55d8", + "metadata": {}, + "source": [ + "Let's take a look at the results at different times, for example `0.0`, `0.5` and `1.0`:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "0265003f", + "metadata": {}, + "outputs": [ { "data": { - "image/png": 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afuedd8owDP3zn/+sd93r49tvv9X27dt1yy23eK11MXDgQCUnJ1e6BkvSTTfd5PX7OeecwzUYAIBjpKWlKS4uTm3bttUVV1yhxo0ba+HChWrTpo0KCwu1dOlSXXXVVdq9e7cn3vjf//6n9PR0/fzzz55pGmNjY7V27Vr9/PPPtX7uiraDY+ONipkX6uvo9qNdu3Zp586d6tu3r3799Vft2rWrQccG4AwkLRxk8uTJKi0trXJti59//lmGYahLly6Ki4vz+snLy9P27dvr/dwdOnSotK28vFzPPPOMunTposjISLVo0UJxcXH697//7ZeL0O23367Y2Ngq17aouBBfcMEFlV7vp59+WqvXGx8fry5dungSFF9++aXOOeccnXvuudq2bZt+/fVXffXVVyovL/ckLXbs2KF9+/bpxBNPrHS8rl27qry8vNIaIr7OX4VBgwapadOm+uSTTxQTE1NjnY/WpUsXr987deqk0NDQSmtAHPv8O3bsUFFRkV566aVK566iAafi/FWss3FsksHX6z/Whg0blJiYqObNm9fpdVXlt99+8/ncERER6tixo+fxCm3atKlU7+OPP15//PGHX+oDALCv1q1bKyIiot77//zzz8rKyqp0HU1LS5OkauOQvn37asiQIZo2bZpatGihSy+9VK+99prXelG//fabEhMT1bRpU699K6aqPPaaF2hVXYMlKTk5uVJ9KtaxOhrXYAAAKps1a5ays7P1/vvva8CAAdq5c6ciIyMlSb/88osMw9ADDzxQKeao6BxYEXNMnz5dRUVF+tOf/qTu3bvr7rvv9lqr05fffvtNoaGhlaZors39fnW++uorpaWlqXHjxoqNjVVcXJxn/TCSFgAk1rRwlI4dO+raa6/VSy+9pIkTJ1Z6vLy8XCEhIfrnP//ps1d/kyZNPP+vqpd7WVmZz32PHWUhSY888ogeeOAB3XDDDXrwwQfVvHlzhYaGavz48bVagLImFaMtpk6d6nO0RcVzvPXWW0pISKj0eHh47T7+ffr00ZIlS7R//37l5uZqypQpOvnkkxUbG6svv/xSeXl5atKkiU499dR6vxZf56/CkCFD9MYbb2ju3Ln6y1/+Uu/nkKp+X499/opzd+2112rkyJE+96lYz8POqhrdYtSwgCkAwPmquzb7cuwC0uXl5brwwgt1zz33+Cz/pz/9qcpjhYSE6P3339fXX3+tjz76SJ988oluuOEGPfXUU/r666+9Yrb6qC7OC5aqrsEAAMDbGWecod69e0uSBg8erD59+uiaa67R+vXrPffud911l9LT033u37lzZ0nSueeeqw0bNugf//iHPv30U73yyit65plnNHv2bN14440Nrmdt44sNGzaoX79+Sk5O1tNPP622bdsqIiJCixcv1jPPPOOX9iIA9kfSwmEmT56st99+W4899lilxzp16iTDMNShQ4dqb5Slwz3dioqKKm3/7bffvKY8qs7777+v888/X6+++qrX9qKiolotIl0b48eP14wZMzRt2rRKi4hX9ARo2bKlp1djVaqbiuicc87Ra6+9pnfeeUdlZWU666yzFBoaqj59+niSFmeddZbn5jsuLk7R0dFav359pWOtW7dOoaGhatu2ba1f4xNPPKHw8HDdcsstatq0qa655ppa7/vzzz97jaL45ZdfVF5erqSkpGr3i4uLU9OmTVVWVlbjuWvfvr1+/PFHGYbhdR59vf5jderUSZ988okKCwurHW1R26mi2rdv73nuoz+nJSUl2rhxY42vBQCAmviKkUpKSipNz9mpUyft2bOnQdeeM888U2eeeaYefvhhzZs3T8OHD9c777yjG2+8Ue3bt9dnn32m3bt3e422WLdunaQj18SqXoN0OCY7On7yNTqjPtfgCy64wOux9evXV1sfAABQO2FhYXr00Ud1/vnn6/nnn9cNN9wgSTruuONqFXM0b95co0aN0qhRo7Rnzx6de+65mjp1apVJi/bt26u8vFwbNmzwGl3h636/unako3300Uc6ePCgFi5cqHbt2nm2N2TKcgDOw/RQDtOpUydde+21+tvf/qb8/Hyvxy6//HKFhYVp2rRplXqSG4ah//3vf17H+frrr1VSUuLZtmjRokrTGlUnLCys0vO89957nvkU/aFitMU//vEPrVmzxuux9PR0xcTE6JFHHvE5H/SOHTs8/2/cuLEk+bzAVkz79Nhjj+mUU05Rs2bNPNuXLFmib7/91lNGOvy6L7roIv3jH//wmoapoKBA8+bNU58+feo0zVNISIheeuklXXHFFRo5cqQWLlxY631nzZrl9ftzzz0nSbr44our3S8sLExDhgzRBx98oB9//LHS40efuwEDBmjbtm16//33Pdv27dunl156qcb6DRkyRIZhaNq0aZUeO/qz07hxY5/vzbHS0tIUERGhmTNneu3/6quvateuXRo4cGCNxwAAoDqdOnWqtB7FSy+9VKkX4VVXXaWcnBx98sknlY5RVFSk0tLSKp/jjz/+qBRD9ezZU5I8U0QNGDBAZWVlev75573KPfPMMwoJCan2Wl/RsePo17F371698cYblcrW9hrcu3dvtWzZUrNnz/aaxuqf//yn8vLyuAYDAOAn5513ns444wzNmDFDMTExOu+88/S3v/3N5/qmR9+7H93mIx2ebaNz585e1+1jVcQTM2fO9No+Y8aMSmU7deqkXbt2eU059d///lcffvihV7mKDp9Hxzq7du3Sa6+9VmU9ALgPIy0c6P7779dbb72l9evX66STTvJs79Spkx566CFNmjRJmzZt0uDBg9W0aVNt3LhRH374ocaOHau77rpLknTjjTfq/fffV//+/XXVVVdpw4YNevvttyvNY1idP//5z5o+fbpGjRqls846Sz/88IPmzp1b65EatXX77bfrmWee0ffff+9JPkhSTEyMXnzxRV133XU67bTTNGzYMMXFxWnz5s36+OOPdfbZZ3tu9Hv16iXp8OJS6enpCgsL07BhwyQdHkqZkJCg9evXexaylg4Prbz33nslyStpIUkPPfSQsrOz1adPH91yyy0KDw/X3/72Nx08eFCPP/54nV9jaGio3n77bQ0ePFhXXXWVFi9eXKkXoy8bN27UJZdcov79+ysnJ0dvv/22rrnmGvXo0aPGff/617/q888/V0pKisaMGaNu3bqpsLBQq1ev1meffabCwkJJhxe5fv755zVixAjl5uaqVatWeuuttxQdHV3jc5x//vm67rrrNHPmTP3888/q37+/ysvL9eWXX+r8889XRkaGpMPvz2effaann35aiYmJ6tChg1JSUiodLy4uTpMmTdK0adPUv39/XXLJJVq/fr1eeOEFnX766V6LbgMAUB833nijbrrpJg0ZMkQXXnihvv/+e33yySeVRpHefffdWrhwof785z/r+uuvV69evbR371798MMPev/997Vp06YqR56+8cYbeuGFF3TZZZepU6dO2r17t15++WXFxMRowIABkg6veXX++efr/vvv16ZNm9SjRw99+umn+sc//qHx48dXG7NddNFFateunUaPHq27775bYWFhmjNnjidOOlqvXr304osv6qGHHlLnzp3VsmVLnzHIcccdp8cee0yjRo1S3759dfXVV6ugoEDPPvuskpKSdMcdd9T1VAMAgCrcfffduvLKK/X6669r1qxZ6tOnj7p3764xY8aoY8eOKigoUE5Ojn7//Xd9//33kqRu3brpvPPOU69evdS8eXN9++23ev/99z333b707NlTV199tV544QXt2rVLZ511lpYsWaJffvmlUtlhw4bp3nvv1WWXXabbbrtN+/bt04svvqg//elPWr16tafcRRddpIiICA0aNEh/+ctftGfPHr388stq2bKlz8QLAJcyYFuvvfaaIcn45ptvKj02cuRIQ5Jx0kknVXrsgw8+MPr06WM0btzYaNy4sZGcnGyMGzfOWL9+vVe5p556ymjdurURGRlpnH322ca3335r9O3b1+jbt6+nzOeff25IMt57771Kz3PgwAHjzjvvNFq1amU0atTIOPvss42cnJxKx9i4caMhyXjttdeqfb3VPVdmZqYhyWjcuLHP/dLT041mzZoZUVFRRqdOnYzrr7/e+Pbbbz1lSktLjVtvvdWIi4szQkJCjGO/GldeeaUhyZg/f75nW0lJiREdHW1EREQY+/fvr/S8q1evNtLT040mTZoY0dHRxvnnn2+sWLHCq0x172HFa9qxY4dn2759+4y+ffsaTZo0Mb7++usqz1XFvj/99JNxxRVXGE2bNjWOP/54IyMjo1JdJRnjxo3zeZyCggJj3LhxRtu2bY3jjjvOSEhIMPr162e89NJLXuV+++0345JLLjGio6ONFi1aGLfffruRlZVlSDI+//xzT7mRI0ca7du399q3tLTUeOKJJ4zk5GQjIiLCiIuLMy6++GIjNzfXU2bdunXGueeeazRq1MiQZIwcOdLr/G3cuNHrmM8//7yRnJxsHHfccUZ8fLxx8803G3/88YdXmb59+/r8fviqIwDAucaNG1fpul/VNcIwDKOsrMy49957jRYtWhjR0dFGenq68csvvxjt27f3XJ8q7N6925g0aZLRuXNnIyIiwmjRooVx1llnGU8++aRRUlJSZZ1Wr15tXH311Ua7du2MyMhIo2XLlsaf//xnr9il4vh33HGHkZiYaBx33HFGly5djCeeeMIoLy/3Kuerbrm5uUZKSooRERFhtGvXznj66ad9Xlfz8/ONgQMHGk2bNjUkeWK4irjs6Ou8YRjG/PnzjVNPPdWIjIw0mjdvbgwfPtz4/fffvcqMHDnSZ8xWEb8AAIDq2wvKysqMTp06GZ06dTJKS0uNDRs2GCNGjDASEhKM4447zmjdurXx5z//2Xj//fc9+zz00EPGGWecYcTGxhqNGjUykpOTjYcfftgrJvF1Ld6/f79x2223GSeccILRuHFjY9CgQcaWLVsMSUZmZqZX2U8//dQ4+eSTjYi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"text/plain": [ - "
" + "
" ] }, "metadata": {}, "output_type": "display_data" }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Plotting at t=1\n" - ] + "data": { + "image/png": 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"text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -296,19 +405,14 @@ } ], "source": [ - "plotter = Plotter()\n", - "\n", - "# plotting at fixed time t = 0.0\n", - "print('Plotting at t=0')\n", - "plotter.plot(pinn, fixed_variables={'t': 0.0})\n", + "plt.figure(figsize=(12, 6))\n", + "plot_solution(solver=pinn, time=0)\n", "\n", - "# plotting at fixed time t = 0.5\n", - "print('Plotting at t=0.5')\n", - "plotter.plot(pinn, fixed_variables={'t': 0.5})\n", + "plt.figure(figsize=(12, 6))\n", + "plot_solution(solver=pinn, time=0.5)\n", "\n", - "# plotting at fixed time t = 1.\n", - "print('Plotting at t=1')\n", - "plotter.plot(pinn, fixed_variables={'t': 1.0})" + "plt.figure(figsize=(12, 6))\n", + "plot_solution(solver=pinn, time=1)" ] }, { @@ -316,7 +420,7 @@ "id": "35e51649", "metadata": {}, "source": [ - "The results are not so great, and we can clearly see that as time progress the solution gets worse.... Can we do better?\n", + "The results are not so great, and we can clearly see that as time progresses the solution gets worse.... Can we do better?\n", "\n", "A valid option is to impose the initial condition as hard constraint as well. Specifically, our solution is written as:\n", "\n", @@ -327,7 +431,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "id": "33e43412", "metadata": {}, "outputs": [], @@ -337,17 +441,30 @@ " def __init__(self, input_dim, output_dim):\n", " super().__init__()\n", "\n", - " self.layers = torch.nn.Sequential(torch.nn.Linear(input_dim, 40),\n", - " torch.nn.ReLU(),\n", - " torch.nn.Linear(40, 40),\n", - " torch.nn.ReLU(),\n", - " torch.nn.Linear(40, output_dim))\n", - " \n", + " self.layers = torch.nn.Sequential(\n", + " torch.nn.Linear(input_dim, 40),\n", + " torch.nn.ReLU(),\n", + " torch.nn.Linear(40, 40),\n", + " torch.nn.ReLU(),\n", + " torch.nn.Linear(40, output_dim),\n", + " )\n", + "\n", " # here in the foward we implement the hard constraints\n", " def forward(self, x):\n", - " hard_space = x.extract(['x'])*(1-x.extract(['x']))*x.extract(['y'])*(1-x.extract(['y']))\n", - " hard_t = torch.sin(torch.pi*x.extract(['x'])) * torch.sin(torch.pi*x.extract(['y'])) * torch.cos(torch.sqrt(torch.tensor(2.))*torch.pi*x.extract(['t']))\n", - " return hard_space * self.layers(x) * x.extract(['t']) + hard_t" + " hard_space = (\n", + " x.extract([\"x\"])\n", + " * (1 - x.extract([\"x\"]))\n", + " * x.extract([\"y\"])\n", + " * (1 - x.extract([\"y\"]))\n", + " )\n", + " hard_t = (\n", + " torch.sin(torch.pi * x.extract([\"x\"]))\n", + " * torch.sin(torch.pi * x.extract([\"y\"]))\n", + " * torch.cos(\n", + " torch.sqrt(torch.tensor(2.0)) * torch.pi * x.extract([\"t\"])\n", + " )\n", + " )\n", + " return hard_space * self.layers(x) * x.extract([\"t\"]) + hard_t" ] }, { @@ -355,12 +472,12 @@ "id": "5d3dc67b", "metadata": {}, "source": [ - "Now let's train with the same configuration as thre previous test" + "Now let's train with the same configuration as the previous test" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "id": "f4bc6be2", "metadata": {}, "outputs": [ @@ -368,9 +485,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "GPU available: False, used: False\n", + "GPU available: True (mps), used: False\n", "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", "HPU available: False, using: 0 HPUs\n" ] }, @@ -378,14 +494,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 0: : 0it [00:00, ?it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 999: : 1it [00:00, 52.10it/s, v_num=1, gamma1_loss=1.97e-15, gamma2_loss=0.000, gamma3_loss=2.14e-15, gamma4_loss=0.000, t0_loss=0.000, D_loss=1.25e-7, mean_loss=2.09e-8]" + "Epoch 999: 100%|██████████| 1/1 [00:00<00:00, 58.13it/s, v_num=3, g1_loss=2.02e-15, g2_loss=0.000, g3_loss=0.000, g4_loss=2.01e-15, initial_loss=0.000, D_loss=6.88e-8, train_loss=6.88e-8]" ] }, { @@ -399,19 +508,28 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 999: : 1it [00:00, 45.78it/s, v_num=1, gamma1_loss=1.97e-15, gamma2_loss=0.000, gamma3_loss=2.14e-15, gamma4_loss=0.000, t0_loss=0.000, D_loss=1.25e-7, mean_loss=2.09e-8]\n" + "Epoch 999: 100%|██████████| 1/1 [00:00<00:00, 48.30it/s, v_num=3, g1_loss=2.02e-15, g2_loss=0.000, g3_loss=0.000, g4_loss=2.01e-15, initial_loss=0.000, D_loss=6.88e-8, train_loss=6.88e-8]\n" ] } ], "source": [ - "# generate the data\n", - "problem.discretise_domain(1000, 'random', locations=['D', 't0', 'gamma1', 'gamma2', 'gamma3', 'gamma4'])\n", + "# define model\n", + "model = HardMLPtime(len(problem.input_variables), len(problem.output_variables))\n", "\n", "# crete the solver\n", - "pinn = PINN(problem, HardMLPtime(len(problem.input_variables), len(problem.output_variables)))\n", + "pinn = PINN(problem=problem, model=model)\n", "\n", "# create trainer and train\n", - "trainer = Trainer(pinn, max_epochs=1000, accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional)\n", + "trainer = Trainer(\n", + " solver=pinn,\n", + " max_epochs=1000,\n", + " accelerator=\"cpu\",\n", + " enable_model_summary=False,\n", + " train_size=1.0,\n", + " val_size=0.0,\n", + " test_size=0.0,\n", + " callbacks=[MetricTracker([\"train_loss\", \"initial_loss\", \"D_loss\"])],\n", + ")\n", "trainer.train()" ] }, @@ -425,56 +543,35 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "id": "019767e5", "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Plotting at t=0\n" - ] - }, { "data": { - "image/png": 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" + "
" ] }, "metadata": {}, "output_type": "display_data" }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Plotting at t=0.5\n" - ] - }, { "data": { - "image/png": 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l+ytbs2ZNtR0gSVKzZk1TiQwAtvj8889l2rRp8uSTT8qZZ55pklSVkyXYnA630w6ItMsvv9wcu1NPPVWOP/54EyerajssWbJEOnfunPX+LbfcMrDt3LBhg0mK0SECFzCUL2EOPfTQ8kBUnUsuuUQ2btxoEk350N4WPSFrMkurjoqh1Vma4NLGzFZbbWWSag8//HCFx7zzzjsm4da4cWPTW9GrV68KAaDQOUN0aKLelA5F+cUvfmH+f8CAAeWluulhGdnGia9evVouuOAC0ztSt25d2WWXXcw4/FQqVeFx+jo6lPLpp582QyH1sbqvEydOLOJIAYA90kO8Z86caSqD9ByuMSR97svWa53tnLxs2TIz10b6fKo999dee21elapvv/229OnTxzQW6tevb6qFtQe7mPN1ruEblaWHvuscLOl9+vDDD+XVV18tjx+Z8SXbkMfHH39cunXrZrZZt10TfpWH0etx0nin9x977LHm/7fZZhvTq65xGgCKockUvd4+6qijTLJFf67KTTfdJNttt505Xx100EFmBEamRYsWmevntm3bmnNsq1atTAVQ+hyZWVmq18D6GO38HjJkiDn/VyXXOTQ9B1bmtbpWS6nMYXdpGk+0sknfXzsLWrRoYZJy33//vZSiX79+pgjgzTffNJ03aZlth/Q+aDvsueeeq9DO0H81Fum2V97mfGJj5lxgun877LCDeexHH31kfj9nzhzzGTdt2tTst7axnnnmmQr7kN6O119/Xc4//3wTZ7bYYgv59a9/Ld98881m+/zvf//bfA8aNWpk2mXahqrcbtPjoUMSmzRpYq4N9PH6+kBlVEwlzKeffmr+1cqp6uhFfTrRNGzYsLyqpi699FIzzKOYqil9nz/+8Y/mpHnuueea4Yf//e9/zQnt5JNPNo/RC/4DDjjAnPwuuugi0wvx17/+1Vz4a0Og1PHxOhRFhz9qz8fgwYPNe6l999036+M1gPzqV7+SV155RQYOHGiqzLSc98ILLzQNCA3gmXQ4pfZKnXPOOeYkrsfouOOOk/nz5+f1mQCArb799lvTaXDiiSea5Ipe7BdCK6z0glXPndpI0Hk7tCdfq3a//vrrKieB1V7mww8/3FxEa7zSHme9SNfzbbHn62LoNupci5o40nioqjoO2gjQRpxezI8ePdrMAXnLLbeYi3bthMnsOdcElCbeNM5pw+Oll16SG264wTQ+dJ4SACiUJqJ+85vfmIoaHUmhQ9Teeuut8k7aTHp9v3LlSpNE0mt0PVdph/f7779ffp7Ta1q9VtfzoCZj9NysSRq9zk0nZzTRr1Nm9O7d25y7dLRF+n313Ffq6ASNH9pBnm3Iefr36XOvtjs0SXTbbbeZc26p769zRd11113y4osvymGHHZa1naHbpHNJafJOO0rUnnvuWT7XlD5P21/Fxkad60o/H23HaGJKE1H6mey3337Spk0bEyM12fTYY4+Zjo5//OMfJvGUST8/TVhq9ZzGUn0P7Vx/9NFHyx+jx1A7fzTBp9ui8UqPoXa4p9ttOjRRrwu080VfSyuHdfv0e/Of//xH9t5776KPNTyUgpfuu+8+7f5NvfTSS6lvvvkmtWDBgtQjjzyS2nrrrVP169dPffnll+Zxr7zyinnc448/vtlz33rrrdSnn36aKisrS/3xj38s//1BBx2U2nXXXSu833bbbZc66qijzP8PGDAgVa9evdRXX32V8z2yOeaYYzZ73cqOPfbYVJ06dcx2pen7NGrUKHXggQeW35d+T/03cxv79++/2Wvq/ugtTfdbn6vHoTJ9vr5O2tNPP20e+5e//KXC437729+matSokZo3b175ffo43fbM+9577z1z/9ixY6vcbwCwxZAhQ8x5K5OeQ/W+cePGbfZ4vX/EiBGb3V/5nDxq1KjUFltskfr4448rPG7YsGGpWrVqpebPn59zm5566qnyuJVLIefrytum25/tkikdLz///PPy+zSOZcaUXHFp/fr1qebNm6e6dOmS+uGHH8of9+yzz5rHXX755eX36bbofVdeeWWF19xzzz1T3bp1y7nPAJDL22+/bc4rkyZNMj9v2rQp1bZt29S5555b4XF6ftPHZbYf1JtvvmnuP++888zP33//vfn5+uuvz/meS5YsMdfChx9+eGrjxo3l9992223muePHj895zZ3t2j5z+zKv27PFKfWf//zH3P/QQw9VuH/ixIlZ768sHQu0bZVN+hj8+te/zrkfldtNmfS5uu2Z8o2N6ePQuHFjc5wz9erVK7Xbbrul1q5dW36fft777rtvaqeddtospvXu3dv8Pk0/Y32vZcuWmZ/1X2179ejRo0L8Sr9u+l997T59+lR4rTVr1qQ6dOiQOuyww7IeQyQXQ/k8p70R2oOspZ/ai629uE899ZTJmOdj++23L8/+a1Y+H5dddpkZ05zvEMA0zbR/+eWXpsckG+0t1h4Ize7rdqVpmbBm5rUaacWKFRL15Iq1atUyPS6ZtAdE44uWuFb+PLR3O61r166m+uuzzz6LbJsBIAzaM6s90MXSIW1apaq9tEuXLi2/6XlTz/9Tp07N+dx0ZZFOOvvjjz8Gcr4Omw491GoCraDNnHtKh9R07NjRDPOo7Kyzzqrwsx4v4geAYqultNLpkEMOMT/rEK4TTjhBHnnkkaxDhPX6O7P9oNUuWsGp51alw/u08kqHq+UaFqeVnuvXrzfD0rR6Jm3QoEHmejjbeS9IGmd0SJlWJWXGGa3o0TaSVtSWQl9DaWVZUAqNjVq1pm2/tO+++85ULv3ud78z25V+vlY5axXuJ598stnwca22yhxKqO+v7/W///3P/KzVaPpaWn1Vee7E9PN0dXN9bW2j6Xul31eH1Os0LLrdLCiFTCSmPKfjlPXkoSdaHWOsF7B6EipEoYmmYpJZ6uKLLzYndA10O+20kykVzhyDrGObtZxV5wTJVhqrJ7cFCxZIlPQErUMcdVhe5e1J/z6Tlt9WpoGm1HHtABA3bbCUMsGqXsDqEAC9oM686cW30iROLjrMQS/GdXiIztOkc5pUXrq70PN12NLvly2maWKq8vboxX9mY0MRPwAUQ5MMmoDSpJQOZdPV+PSmiSYdUvzyyy9v9hy9Nq9s5513Lp8/SjsndN4jTfJrwkvnG7zuuuvMvFPVnfc0dmj7IezzsMaZ5cuXS/PmzTeLNatWraoyzuRDX0NVjjOlKDQ26lQsmfRz1c6XP//5z5u9Rnqi+8qvUbm9orFGpeNNemoYnVuyqu1W/fv33+x977nnHhOf9bPwlSbejj76aHPdock6nWM4bAsXLjRTKej0MJoo3m233UwnmCuYY8pzmuRJr8pXLA0U+iXXRJNmxvOhc2voWGkNUNrDkg9tHOg4c+3x1hOwjnnWyRF1vidtbJQq28S16eCsvehRyPU+1U28CwC204ugQlTukdfOBe3F1vkDs9EGUFXn9yeeeMIshPGvf/2rfOlunYNJ70v3YocRP6ISVZwC4D+toNHOY01O6S1bNZXO21corYTSxrg2wvU8rMkQnT9P30/nUYr7PKxxRpNSuSZ5r5z8L1R6MnidnDwohcbGyrE4XZWki2XkKk6ovL1BtFfS73v99debOR2zKTU220wrw3bffXdzLaLzuIXt+++/N/OIabJZk8P6XdbkYDqp6AISU8i7aurBBx80iaZ86HA1TWbpxOSFTEiuk/FpGbHetNRX/5CvuuoqM6me/oHpag6avKpMV5rQkmAdspiL/mFmW/FDe2cyhwbmCnzZ6MokWpas5ayZvSO6PenfA0CSZTv36vm9ckWtxg3tbU73Ahdjn332MTeNG7oykK6SpI0uXSmplPN1+sJO9yNzQvJsvfv5xpD0+2lMS6+Ym6b3ET8AhEUTM5qgSa9el0kXjdBpP8aNG1chyZGugMn08ccfb7ZatZ7LdYi03vQ5mpTQTgJtR2Se9zKvvTUmaOVWVef/zPNwpkLOw7ptGge0AV9oZ0o+0pOtFzo6pSqlxsb0cdZJ3UuJr5W3KZ2Iy5WESz9Gh2gG9b4u0Unf9ZaLVoxpIcff//53853u0qWLaWenV/ItlD5X28FaLZ6res52DOVDwYmmzJLc6pJZOteHlvHmQ8cfVy7r7dy5s8nO6+to9l57b/75z39WWHZWS461AbL//vubk19V+6A95xr80rQ6q/LwP02OqeqWrVV9+/Y1PTW6mkcmXd1Jg2JVJyQASAI991aeA0MrcCv3cuv8F9OnTze97JXp+ViHlFfVU1i5JzfdQ5sezlfK+Tp9gZ25H9obev/992/2WI0h+cQPrWbWhqE2/jKHHGpP5+zZs81cUwAQtB9++MEkn375y1+albAr33T1NU3gP/PMMxWep1VQmXMRzZgxw6ycnT536nQbuhpc5XOndgSkz3GaoNDre12VOvOcfe+995phXVWd9zSppW2ByvFER1dUlutaXuOMxoFRo0Zt9hyNMfmcu3PRtogOUevZs6eZQykopcRGpXFGkx3ahss2xYpOlVIobY/p56rVcJU/8/TnqvN26eevq8imhziW+r4+0b8z/Vy180xXoT/++OPliCOOyJoAzof+vep1hb6OfuZaoagr3ruEiinkLT08T3s5dGnQfJNZ2S7cc53kWrZsaXoxdGy6XphrA0KDVLp3+y9/+YuZM0uTUDphbFlZmTnRasCrLgGmPeY61EP/6PUkr+OjtfcmczLy9HZrj7g2FvR9Nbhp1Ve2rLOWK2vJpB4bTZZpyaZO0K7JMy1nrvzaAJA0eu7VSbt1DigdjvDee++ZC2ydCyrThRdeaC6stLF02mmnmYtaTf7oUuR67tZzbOXnpGmc0caJLnmt511tVOkFmXZWaEKq1PO1xiedc2PgwIFmO7VxNH78eFPJq8ugZ9Lt1qXPNV5pT7JeIFauiEr3XmsPp04Yr3Nk6VLt2tGiS7BrBYIuJw4AQdPzrJ4jf/WrX2X9vVad6rlNq6p0BEOans/0+vvss882190333yzmcsmPcRMq6c0IaPX2NqxrNfoWnml5zVdgEnp6+ooCJ2iQ6/HdRu0XaHn71/84hem3ZCLTlquje6xY8eazgQ9Z2sHc7Z5ofQ8rHSxC61e0nO2boOea88880yTUNHJufXcrudiTQboJON6/tXkXHU0JukwNO3s1mSdxjSdF1fjir5OkEqJjWlaGaefnc45pBPNaxWVfi6aGNGFpzQuF0Jjq3bqaHzXz00nONeKNn0dTVBqTNaRLJqo08Slths11ulclHq8dO5jfQ0dep9Eet2glU36r85BlR5qOXHiRHP/1VdfXfBr6jzSeu1x/vnnyyWXXGIWE9PvvyaCdZ4vJ8S9LCDCkV7us6qlszOXXn388cfzem56yWpdDjufZU8/+eQTs7xo5ffI5q9//WvqwAMPTG299dapunXrpnbYYYfUhRdemFq+fHmFx82aNcssPdqwYcNUgwYNUoccckhq2rRpWfer8pKyN9xwQ6pNmzbm9ffbbz+zVK4u6115ae9//vOfqc6dO6fKysoqLEGbbcnXlStXmmVUW7dunapdu7ZZGlWXys1cGjXXErDZliUHAJtlW4Zbz6GV40KaLgl+8cUXp5o1a2bO2Xr+njdvXtZzn55Phw8fntpxxx3NkuL6HF3OesyYMan169fn3CaNCyeddFJq2223Nef35s2bp375y1+ac3wx5+ts2zZz5kyzNLZul77PjTfeWB4vdZnutEWLFpl4qEtp6+/S8SVXXHr00UdTe+65p9nupk2bpvr161dhSXal26LLhedauhwA8nX00Uen6tWrl1q9enXOx5x22mnmHLl06VJzftPzjJ4r9Tq6Xbt25nx1wAEHpN57773y5+hjNT507NjRnK+aNGlizpmPPfbYZq9/2223mcfpe7Ro0SJ19tlnp77//vsKj8l2zf3NN9+kjjvuOBNLttpqq9SZZ56Z+uCDDypcq6sNGzak/vCHP6S22WabVI0aNTY7T951112pbt26perXr2/O1bvttlvqoosuSn311VdVHrv0OTd90+PYtm1bE2/Gjx+fWrt27WbPybYfudpNudoK+cTGzM8pm08//TR16qmnplq2bGmOu7aHdLufeOKJatuAueLXM888Y7ZDj2Pjxo1Te++9d+rvf/97hce88847qd/85jfl7Tvd99/97nepl19+OZUUeuyeeuqp8p+fffZZc5/+nWTeysrKzLFRs2fPrvBdy3bTa6s0/Ux79uxZ4X31b2CfffZJuaKG/ifu5BgAAAAAAIBPtMJPqwfTC4I9+uijZg7MDz/8cLOJ5hs2bGhGEGk1nlZBVUUrFtMT9utQV61K1yq1tHT1duYQXJsxlA8AAAAAACBkOv+TznWmw1APOOCArI+pU6eOdOzYMe/X1KlwKi8QpsNrXVpIhcQUAAAAAABAAHTC93nz5pX/rKtO6rxmTZs2lZ133tlUTJ166qlmxUpNVH3zzTfy8ssvS9euXYta/ETnpdx3333N/FQ6z5suTqALzejNFQzlAwAAAAAACMCUKVPMgiuV6UTkEyZMMCvO6zC7Bx54wAy1a9asmVl4QBcG0Enqi6GLAejiAjqZvy7apROh62T3rqhZ6BN0iU5d2UZnkNfxkrp0aD4fzF577SV169Y1KzrohwEAyL16iq7KVa9ePbMipPZ6VEVXgNFyX328BrPnn39+s14bXZa2bdu2Ur9+fbNajq46GQdiCACEjzhSEXEEQJQOPvhgnXF/s1v63KOrQWoSSiupdD6pr776Sp588smik1JKV27U1RrXrl1rVrcvNimlq3bqasU6DFDP91qJpav8hX2OLTgxpctT6lKYGvDyoQdby9E0Y6jla7qTurSkLqsJAKhIJ0TUHo4RI0bIrFmzzPlWlzrOthyymjZtmllmXpexf+edd8zEinr74IMPyh+jr6dL0D744IMmUOl5WBsYuvxw1IghABAu4khFxBEAyJ+eHydNmiR/+9vfTKLr8MMPl969e+ecRD2wc2yQSx9mo0tvVl5C+oQTTjDLRQMAKtKldjOXCt64caNZ2n706NFZH6/LylZecliXZ9YllNP0HHzllVdWeMxee+2VuvTSS1NxIoYAQPCIIxURRwAgP2vWrEnVqlUr9eyzz+Z9vg/qHBv65OfTp083GbZM2mujmbRc1q1bZ25pmzZtku+++84siagluwCg16Naaqql/DVrFlz8WU7LXbWENsztrHze0jJXvVWm2zFz5kwzPjxN903PoXouzUbv157syufYzKENWoKrvdqnn366OV5abqsrddx0001iu2JiiCKOAIgijoQdQxRxpDS0RQCExbc4smHDBrNioA7rzqRD+l577bVAr9UrCz0xtWjRImnRokWF+/TnFStWyA8//GB2srLRo0ebMZcAUJ0FCxaYOS+KDQTbbreFfLNkk4SlYcOGZm6OTDq8YuTIkZs9dunSpSYYZDtnzpkzp6BzrN6fNnbsWBk8eLA5TmVlZSZw3n333XLggQeK7YqJIYo4AiDsOBJFDFHEkdLQFgFgcxxpu21D+fabjWJDHGnUqJH07NlTRo0aJZ06dTLnyr///e8m+aRzRwV5rR55YqoY2suT2XOzfPly2XbbbWXqjG2kYcPcmcgXV3eMaAsBhO3wLbJfQKetWrVJDtz7G3MCLZb2TmiDYuqM5tKwYfA9oKtWpeTAvZeYYNW4cePy+7P1codJGxRvvPGG6e3WiQx14tghQ4aY3p3KPRy+II4ACDuOhB1DFHHErhhycJuBUlazTqzbBsAeGzatlykL7y0pjmhS6pnpbWWLKq5PS7F61Sb5Vc8v844jOreUVse2adNGatWqZSY113kItRo3TKEnplq2bCmLFy+ucJ/+rAclV/YsV3myNiYaNsr+gT2/qrPUaxjQRgOI3VTpIn0bflTt44IoqdcGRa5zS2l+6kXX811mIMhFl4rVAJDtnKnn0kLOsenHa0/FJZdcIk899ZSZmFB17drVTE44ZswY6xsUxcQQRRwBUFUSOsg4El4MUcQRm9oimpQqqxltUhCA/UqNI1s0rClbhBZHpKA4ssMOO8irr75qFprQqqdWrVrJCSecINtvv32g1+qVhbv3IqYU7OWXX65wn87yrvcDAH5Wp04d6datW4Vzps5roT/nOmdWd4798ccfza3yuHdtuOhr244YAqBYmmxOGuLI5ogjAFC4LbbYwiSlvv/+e7PC3jHHHBPqObbgxJSOTdQeEr2llwfU/58/f3556eupp55a/vizzjpLPvvsM7nooovM2PY77rhDHnvsMTnvvPMkKEm88ACSIIl/2zp0QOftuP/++82S3GeffbbpsRgwYID5vZ5fMye1Pffcc80S3jfccIM5x+pY8bffftss4620t+Kggw6SCy+80ExWq+fsCRMmyAMPPCC//vWvI98/G2NIUr9rQBIk8W+bOBJPHAEAH7zwwgsmJui5VRNMhxxyiHTs2LE8hoR1ji14KJ8GKt24tPT46/79+5sg9fXXX5cHBtWhQwd57rnnzIbdcsstZlKwe+65x8zUDgCoSEtlv/nmG7n88svNZIJ77LGHCQ7pSQX1/JrZa60rJT388MNy2WWXmaEWO+20k1lJqUuXLuWPeeSRR0wQ6devn1lVSOcHueqqq0wgiRoxBADCRRwhjgBAsXROPT3ff/nll9K0aVM57rjjzPm+du3a5vdhnWNrpHTtQMvp2MYmTZrIrI9abDaGP4k9YUDSZJtratXKTbJX58Xm5JnPeOlCzy1BCGIbEQziCJBsYcSRsGNIENuIYKQ/697tzmaOKQDlNmxaJy8tuLPkOPLy+9uGNsfU6pWbpNdu862PI6HPMQUAgK1ISgEAAADxcjoxRYMCSAb+1gEApSCOAABgL6cTUwAAFIuGKgAAABA/ZxNTNCiAZOFvHgBQCuIIAAB2cjYxBQBAsWigAgAAAHZwMjFFgwJIJv72AQClII4AAGAf5xJTXFAAAEpBHAEAAADs4VxiCkCykVQAAJSCOAIAgF2cSky9uLpj3JsAwAI0KlAs4ggAAABgF6cSUwAAAECp6OAAALus7NbG3JBMZXFvAAAUX/myOO7NAAAAAFCgXEmofJJTjWYuDGGLECcSUwAAAEgcOjiA6kVdwULCwV9BfpfSr8X3xR8kpgAAAAAgIWweLkXCwT9hft8yX5vvjNtITAEAAACAZ2xOQFWHBJUfovwO8p1xG4kpAAAAAHCUywmo6lAR4664vpckqNxEYgqBmrzU7aXYD202J+5NAIBEI44AQH58TkhlQ5LKDbZ8L0lQuYXEFBLRUAh6P2l4AEBhiCMVEUcAuN7wjxNJBzvZ+N3UbeJ7Yj8SU0hkwyGK40SjA0ASEUfyQxwB4EOjP24kHexg+3eTRKb9SEwlGI2HeI4vDQ0AviCOhIs4AsCVhn+cSE7Fi+8mgkBiKiFoPNj5WdC4AOACYohdiCNAMtDgzx8VMfFw7TtKEtNeJKY8RSPCzc+JBgYAWxBH3EAcAfziWkPfNiSoADeRmHIcDQe/MGwDQNSII34hjgBuIiEVLBJU4XP1O0vVlJ1ITDmIRkTyMGwDQJCII8n9zIkhgH1cbOB/27lWIK+z9UcbA3kdAG4jMeUQGhJQNC4AFIs4Ajo6ALvYnpQKKgFV3euHlaCiOiaZ39vq8L2wD4kpy9GIQC40LgDkgziCXIgjQLxsbNyHnYiKI0FFEgKwH4kpS9GQQCGoogJQGXEEhSCOAMkTVxKqum0iOWU3GxOqxeA7YRcSU5ahIYFS0LAAQBxBKYgjgN+NexuTUVFVT5GIKJ0vSSnYh8SUJWhIIEg0LIDkIY4gSMQRwI/GvQuJqCirp1itD5lIVtqDxFTMaEggTDQsAP8RRxAm4gjgZlLK5YRUJqqngGSoGfcGJBmNCUSF7xrg5981f9uICt81wP6klCZx0jffhLFPDEsD7EHFVAy4uEMc6PUG/EEcQRyII4CdSRAfE1FRVU9ROQXYgYqpiNGYQNz4DgLuokoKNuA7CMSflPK5Oqo6SdxnhIfKOTtQMRURLuJgE3q9AfcQR2AT4ggQT6PXlqTM2o5ri35uvTn1rKqeomoKiB8VUxGgMQFb8d0E3MDfKmzFdxOIJikVR3WUJp9y3Up93aAEdUyomgHiRcVUiLhYgwvo9QbsRRyBC4gjQHiiSkYFmSzK9/2CqJwKsnqKyikgPlRMhYTGBFzDdxawC3+TcA3fWSDYCpwoklJBVECV8t4+DnP0lc9VZT7vmyuomAoBF2bVm72oRWzv3anl4tje23b0egN2II5Ujzhi73eXGALYn2SJKxkVZuVU+riVUjlF1RQQDxJTAaMxEX+DoZRto7HxExoWQHyII3bHEEUcqRqdHICdbElGVUZyCgCJKXjfgCh1X2hkAIhKUpNSxBEACKdaytZkVK7tDHLeqSBW7AMQDRJTAUpCg8KnxkOp++x7Q4OqKSBaxBB/EUeAZCllvpqgklKuJKTCTFCVkpyiasrOOZhIOPqLxFRAfG1QJLURUcyx8bGBQaMCiIavMUQRR/I7Nj7GEEUcAaJNSkWdkDpwp3mb3Tf1kx2tGd5Hcsq/vwmSU34iMRUAnxoUNCCK52sDg0YFgEIQR4rjc2cHcQTwJymVLRFV3WOKTVSRnEqmfP4WwkhO8VnHi8RUiXxIStGICJ5vSSoaFUB4iCOo6pj6EEMAhJuUCishlU8SqpjXKSRRFdTQPqps/Psb4DP1S824N8BlLjcm9II3fUO4OM4o1O233y7t27eXevXqSY8ePWTGjBlVPv7xxx+Xjh07msfvtttu8vzzz1f4fSqVkssvv1xatWol9evXl969e8snn3wS8l4gH8QRJOU4u/xddxFxJDlz8YSRlNJEUlBJqVyvH8d+FpsAtGV+pbjYvP9BLhSAeJGYStgFlg8Xt65yvXHh6nfeNY8++qicf/75MmLECJk1a5bsvvvu0qdPH1myZEnWx0+bNk1OOukkGThwoLzzzjty7LHHmtsHH3xQ/pjrrrtObr31Vhk3bpy8+eabssUWW5jXXLvWzYlRfeHq35TL5zHXEUeQD+KIW4ptWGuiJuikVNgJqaRO6I5wkZzyA4mphFxYuXwh6yNXPw8Xv/uuufHGG2XQoEEyYMAA6dy5s2kENGjQQMaPH5/18bfccoscccQRcuGFF0qnTp1k1KhRstdee8ltt91W3st98803y2WXXSbHHHOMdO3aVR544AH56quv5Omnn4547+Dq35LrCREf8XkgF+JIMpJSriekin2/UvedJEbhmHcJUSAxlZDGBOzE54NM69evl5kzZ5ohEmk1a9Y0P0+fPj3rc/T+zMcr7cVOP/7zzz+XRYsWVXhMkyZNzNCOXK+JcLkURzhH2c+1z8il77+LiCNwqUIqruQU/EPC0X1Mfu4hly5Q4d5k6UyEXrgVK1ZU+Llu3brmVtnSpUtl48aN0qJFxb9h/XnOnOzHXBsL2R6v96d/n74v12OAyogj7mGydL8RR/ybiyfuaimbh+yFvWIfk2YD9iEx5VkvH40J9+lnSMMiWi+u7ij1agR/Oly7eoOILJZ27dpVuF/n/Rg5cmTg7wf72R5HiCHucyFB5VsHR1gxRBFHEHRSyraElG5PIav0Ib7hfGFOgq6JQqqeko3EVJ5oTCBKtjcsfGtUhG3BggXSuHHj8p+z9XKrZs2aSa1atWTx4oqfu/7csmXLrM/R+6t6fPpfvU9XU8p8zB577FHCXqFQxBFEiTjiF+KIX4ppgPuYlCo1OVVK1RT8QyWc25hjygM0JvzFZ+sHbUxk3nI1KOrUqSPdunWTl19+ufy+TZs2mZ979uyZ9Tl6f+bj1aRJk8of36FDB9OoyHyMDgnRVZVyvSaCR1IKceGz9QNxJNlKTUrFPZeUjceG6hzALlRMOd6g4ILTf7YO7aO3Oxy6xHf//v2le/fusvfee5uVkFavXm1WV1KnnnqqtGnTRkaPHm1+Pvfcc+Wggw6SG264QY466ih55JFH5O2335a77rrL/L5GjRrypz/9Sf7yl7/ITjvtZBoYf/7zn6V169ZmOXAkGzEkGYgjyUIc8UsQSSkXMKTPLmEO2wOyITHlKBoTyWL7kAwE54QTTpBvvvlGLr/8cjOprA6TmDhxYvmks/PnzzcrLKXtu+++8vDDD5tlvC+55BLTaNDlu7t06VL+mIsuusg0SgYPHizLli2T/fff37xmvXqUvye5c4M4kizEkeQgjtjdkI+yUieKpNQ5LSZvdt8diw8t6rUY0hc/1xNSDOdzV41UKpUSy2m5sC5Le81bB0m9htHm0mxsUNCYSDbbGhVx9XavXbVBhv3iVVm+fHmFeTdsOrcEsY0IBnHkZ8QQEEeCOUdHcV4hjtgh/Vn3bne2lNWsG1liqpRqqbCTUtkSUkEkp1QxyaliElOFJDB08m+f2ZCQCippW0piqtDPecOmdfLSgjtLjiMvv7+tbNEonFmWVq/cJL12m299HGGOKYdoY4IGBfgeACgW5w4o4gjgtzCTUpqQqi4plX5clNsfxOTwSWVDUgogMeVILzcXkLD1O2HT3wlgG1v+PkhEIBtbvhO2/J0AYYuiWiqMpFQ6GVVossn25FQhn4evyRsf94uJ7d3EHFOWs+WiEXaydUJbAPYgjqAqxBHAPrYkpUpJLFV+jWKG9jEhenh8TEjBbVRMWYzGBFyphKC3G7Dz7yLucwPcYMP3xIa/F8DlRr6NSakgXq/Q/WJIX/XfVRu+r0BlVExZeoFkw0Vi2DZ81SCy9yprvUZ8F3evN8t+A3YhjgTL9zgSdwwBfJfv8KJiEitBJqWCTkhle+1SJkZHcUhGwXYkpizkY2MiysZDIe/vW0ODhgUARRyJ7v19iiPp701ccYQODkBiS0qFmZAqVaFD+jS5V8wqfT4iIQVXkJiyrFrKh8ZE3I2HUrfV9UZGnMkpGhXAT4gjpSGOxItODiAehVZLBZGUiiMhpe9ZaNVUWPNNaSXb1h9tFN+QkIJrmGPKIi43JvTCPH1znQ/74vJ3CUDy/vYzz7sun3vTfNiXuL5LcU+lAACuIykFF1ExZQnXGhMuX2yXsp8u9YLT4w0kC3HETsQRALYuX2/z8L2o5Fst1WjmQrGd6wkp/Sxs/VtB+KiYsqCnzqXGhOs9wEnb/zi+W/R2I+mII36dR5O+/y59t4AkCXoVPuRGUio6Pg6rRH5ITMXMlQs+1y6kw8bxAGALF+KID0PbgsbxyI0ODgBR8CUJ4ktSCslGYgo50ZDw4xi50GgF4Cfbz482II4AiAPD+PzgY1Kq1IShLwnHpCExFWMPna0XerZfINvK5uNm63cN8A1xxP7zoc1sPm62ftcAoLJ6c+olYm4pH5NSaSSXkofEVExsvMCz+YLYJRxHhmEAUSCO+IvjSBxB8qztuDay93KxWmrqJzvGvQlW8TkpVUpyqpSEls1JyCQgMQUugENi2zG1sRELwA/EkXDYdkyJI4AdmPi8NFTj+PlZ8bm6jcRUgi/saEgk7xjb8t0DfBRlhYctf8u2neN8ZNsxtuW7BwDFDuNzvYImCdVSSB4SUxE3KGy5oLPpIjcJbGtYRIFhGEA4iCPJlMQ4AiB4Lg7jC5LrVTUkpfz8XEFiKnG4sI2XDcfflkYtADfZcB5LMhuOfVRxhA4OAABJp2i1b99eatSosdltyJAhWR8/YcKEzR5br17hlYtlAWw7HEkI2HAxi58/i7LWa2L9LnZquTi29wd8k5SqW+KIHdKfA3EEsN+3nWsF+nrMLxXNMD4bUS2VHYmrYL311luycePPx/SDDz6Qww47TI4//vicz2ncuLHMnTu3/GdNThWKxFREDQoaE7CtYRFFo0L/tg5tNifU9wCSIs44QgyxU9xxBIBbK/K5OowvjhX5bJtfiqQUorLNNttU+Pmaa66RHXbYQQ466KCcz9FEVMuWLUt6X4byeY4hF/bj8wFgM85R9ovrM4oiWcpwPsA/dyw+1OrKGpJS7qBaKn8rVqyocFu3bl21z1m/fr08+OCDcvrpp1dZBbVq1SrZbrvtpF27dnLMMcfIhx9+KIWiYsrjXm4aE+6Ia2gfQzEANxBHUB3iCAAfq6WAdAIq6GGxNrl7yYFSZ02dUF57/ar1IvKgSRplGjFihIwcObLK5z799NOybNkyOe2003I+ZpdddpHx48dL165dZfny5TJmzBjZd999TXKqbdu2eW9n4hNTYffE0ZhAvmhUAG4ijsAWcc9fCACuzy9FtZQ7qJYqzIIFC8xcUGl169at9jn33nuvHHnkkdK6deucj+nZs6e5pWlSqlOnTvLXv/5VRo0alff2MZTPQzQm3OXjZ8cwDMAtDAF3WxyfXdjJU+II8BMmPk8WklLR8v14N27cuMKtusTU//73P3nppZfkjDPOKOh9ateuLXvuuafMm1fY+YrElEe93DQm/BDH5xj35PwA7IkjcJ+PySkAyRTUxOf5VNfYVi2F3KiWCt99990nzZs3l6OOOqqg5+mKfu+//760atWqoOeRmPIEjQn/8JkC9guzkoOkFErB5wkgKfNLBTWMzya+V++UkowiKRW+TZs2mcRU//79pays4uxPp556qgwfPrz85yuvvFJefPFF+eyzz2TWrFny+9//3lRbFVpplejElE8NCvgpyoZFmN9ZhmEAdiOJ4Sc+VwAuS2q1FEmp3EhKRUOH8M2fP9+sxleZ3v/111+X//z999/LoEGDzLxSffv2NSv+TZs2TTp37lzQeyZ+8nMf+Hbh2eDL0vKla9puCmxbACAOdG7EF0d8iyFRToge5mIa2sFxaLM5obw2gOjcsfhQ66qlbEpKATY4/PDDJZVKZf3dlClTKvx80003mVupSEw53qBwOSlVagKq0Nd1tbHBKksAwuRqHIk6hijiCAC4xbUKG6qlkFQkphzmWmMirEZEse/vUgMjqkZFmL3dgG/CGqJK54adMSTbNhBHNkccAWDTML7qUC0F2CGxiSnXGxQuNCZsaET41MBwvcebYRiAXYgjpSOOAEB8qhvG51q1FJBkdl/xwcnGhF6o296YcHW7o/jsmdsGiA+dGz+fi20/H2fjwna7HEdYSAM+WttxrSRF0PNL+VYtxTA+JFliK6ZcbVDY2piw/UK8mH2xteebHm8ApSCOhI84AgAAkL+irgJvv/12ad++vdSrV0969OghM2bMqPLxN998s+yyyy5Sv359adeunZx33nmydm1yegd85kLvsI+99mE3LKmaQtiII/GwMSll63nW9zgCuM6WOEKVSzzzS/k2jI/vEZKu4CulRx99VM4//3wZMWKEzJo1S3bffXfp06ePLFmyJOvjH374YRk2bJh5/OzZs+Xee+81r3HJJZeIT5JWLZW0C+2k7S8QJuJIdklLCCftvGrb/tLBAZcRR9zDMD4AVSn4CunGG2+UQYMGyYABA6Rz584ybtw4adCggYwfPz7r46dNmyb77befnHzyyaZX4/DDD5eTTjqp2l4N2JmUsu3COsn7b8t3AkhiHHFxrhtbzhk2nUeTvv+2fCeAJMYRFM+3aikABSam1q9fLzNnzpTevXv//AI1a5qfp0+fnvU5++67r3lO+sT/2WefyfPPPy99+/aVuLjWoLDhwtGmC2kb2HI8wvxuhNHb7drfHoLnSxxxrbqEOGIfW46Ha3EEII4U5pwWk8W3YXy+VUsxjA8ocPLzpUuXysaNG6VFi4oXGvrznDnZl4HXngl93v777y+pVEo2bNggZ511VpWls+vWrTO3tBUrVojNwrzwsqUxgdzHxtbJbQEbEUeiF3ccIYZUjThSeAfHoc2ynyuQDFHEEWKIvcP4qJYC/BT61eKUKVPk6quvljvuuMOMAX/yySflueeek1GjRuV8zujRo6VJkyblN52gEMntzbVd3MeJ3m74jjhSPJJSbiCOAHbFERtjyIE7zRMXMbdU1aiWAn5S0FVQs2bNpFatWrJ48eIK9+vPLVu2zPqcP//5z3LKKafIGWecIbvttpv8+te/NoFBT/ibNmXvIRw+fLgsX768/LZgwQJJorgaFHFfILvK10YFECTiSDIa7sSR4hBHADviiEsxxCdRDOMDYKeCroDq1Kkj3bp1k5dffrn8Pj2Z6889e/bM+pw1a9aYcd+ZNJgoLaXNpm7dutK4ceMKt6Q1KOJMSqF4PjbGfGw0Iz7EkegQR9zkYxwBXIsjxBA3q6WqG8ZnW7UUgCLnmFK6NGv//v2le/fusvfee8vNN98sq1evNqtiqFNPPVXatGljeiDU0UcfbVbO2HPPPaVHjx4yb94802uh96cDAuzAhbDbc4ZoI7Ss9ZpI3xMohutxhEn8cyOOBMenOKIdHJ1aVqxuAZIcRxDO/FKuYRgfUEJi6oQTTpBvvvlGLr/8clm0aJHsscceMnHixPIJCOfPn1+hR+Kyyy6TGjVqmH8XLlwo22yzjQkCV111lcTBhQZFHL3cNCaC51OjIkhMXAvX40iQfKq6JY4EjzgCZEcc8a9aqtRhfFRLAQlLTKmhQ4eaW67JBSu8QVmZjBgxwtx848sQJxoT4fFltSV6uxE04ohfiCPhIY4A2RFHkjWEr5RqKRuTUlRLARVxJWmZqHu5aUz4d4yZwDYZvvvuO+nXr5+Z92LLLbeUgQMHyqpVq6p8ztq1a2XIkCGy9dZbS8OGDeW4447bbPLYt956S3r16mVec6uttpI+ffrIe++9F/LewNVzAPMhRYM44l71uwuII/BlFb7qqqUA2I+ryQSjMREdGhUImjYmPvzwQ5k0aZI8++yzMnXqVBk8eHCVzznvvPPkX//6lzz++OPy6quvyldffSW/+c1vyn+vDZIjjjhCtt12W3nzzTfltddek0aNGplGxY8//hjBXiWL61W3xJBocbwRNOJIMsSRKCpUdcP4fKuWArA5rnIsalBElUyghzserh9z1xvRPpk9e7aZS+Oee+4xk7juv//+MnbsWHnkkUdMIyEbXe763nvvNZO/HnrooWZFo/vuu0+mTZsmb7zxhnnMnDlzTA/6lVdeKbvssovsuuuuZtiD9ob/73//i3gvYXscQfSiPO50cPiNOOKeUudgioILSTAbMIwP2BxXlglDYyJeUSYFaVTYY8WKFRVu69atK+n1pk+fboZI6GpEab179zYTvWoPdTYzZ840vdX6uLSOHTuaXm19PaWNCB2eoQ2P9evXyw8//GD+v1OnTtK+ffuSthnhIymVDC4ffzo4ikccga9KrZaqahgf1VKA55Ofw80GhcsXs77xZTJbX7z67c5Se22dwF/3x9Xr9dWlXbt2Fe7X3uORI0cW/bq6AlHz5s03m9i1adOm5ne5nlOnTh3TEMmkKxiln6PDLXTC2GOPPVZGjRpl7ttpp53khRdeMK8P5rYhjiQrhrBCX7wxRBFHACAaJDLjlagrzCQ3KGhM2IfPJDkWLFhghkCkb8OHD8/6uGHDhpnlrKu66TCJsGjPtk5+u99++5lhGa+//rp06dJFjjrqKPM72Fs5QudG8vB5JAtxBD4O40titRTD+IDs6LpIQIOCi9fkCrq3O6jlvjVJfGiz8C6MbaMrHumtOhdccIGcdtppVT5m++23l5YtW8qSJUsq3L9hwwYzr4f+Lhu9X4dVLFu2rEJvt877kX7Oww8/LF988YUZkqHDOdL36apK//znP+XEE0/Ma3/hH+JIciunqJqyA3EEAOAzElNAjBjSh0zbbLONuVWnZ8+epmGg833o5LNq8uTJsmnTJjOJbTb6uNq1a8vLL79slvdWc+fOlfnz55vXU2vWrDENCe1RT0v/rK8NO9G5kWyuxRE6OMJFHEFSJj13sVoKQG5cbcaMBgXC/oyYBN0/OomsLsc9aNAgmTFjhhkqMXToUNMT3bp1a/OYhQsXmklp9feqSZMmZnjF+eefL6+88oppjAwYMMA0JvbZZx/zmMMOO0y+//57GTJkiFmxSZcR18fovCCHHHJIrPsMIDfiCApFHEHcSh3GB/t827lWXjcgGyqmPF5RxtWkVMOFpfWorWrj3n671uON+D300EOmEdGrVy/TG62917feemv573XlJO3J1t7rtJtuuqn8sbqiU58+feSOO+4o/702QP71r3/JFVdcYRoa+tg999zTLCneqlWryPfRV0HGETo3go8jLsYQRRxBoYgjCALVUsmeX6qYRFP6OVV9jkgeElMxCrNB4UpjotQkVL6v6UJDI8xGRZBzhAQ1DAOl0ZWTdN6OXHRZ7lQqVeG+evXqye23325uuWhvt96wuaQtoJHUOJLr9YgjxBHfEEcQV0KqlGopkhl2CKLyyaYElc0JzaQgMQUvklGFvKcLjQsAqE6SOzeIIwCQnAqpfJFcCF8YQ/FsSlAhPolJTNnW0520BkUcjQgXGxeu9HYD8H84uG1sjCO2xRDFkD4A2ZJJ57SYXPJrINnD+MKeH4oEVbLZd0VlMRcaFLYlpfTi3abGhAvbZ9tn6OrfAgA/zkE2nqcrb5tt2xfWZ8gk6EAyBZmUCmsYH9VS4WHSckTBrqvPhAjrws6mxoSNF+ouba9Nn2VSqhgBl/geR2xN+FTFte2NGx0cgBurz1EplWwkpBAVO65A4Q3XL8xd3/7q0NsNwIWklMtsiSNJqJqigwM2iLJSp7pqI9uTUlRLuSUpSSm+P3aw4yoUXjQobLgQ96lhYcNnCiA4VIjYf94Nkg37QhwB4EpSCnZJSlIK9uCKJeIGRRg9jXFfePrWmMjk437Z1NsNoHC+xhEf+RwfAfibaIpj+B7VUkgjKZZMJKZQtKRccMe5n3E3FqtC9QeAUhFH3IwjdHAA/gojKVXKED4fuLYiX5ISQyQ27WFvqxdWJy6S0JCoLIn7DMB/xJHoJHGfq0IHB2CXOJJS1aFaKlpJSkrBLiSmIuRLD2OSL6zj2Hd6u4H42TLJctB/uySlokccAWBj4imupFSx1VIkpQC/kJhyuEcvjgZFkhsTvjUqAETH1jgSB+IIxwCAXeKYU6rUaikEL2nVUiQ37VImCWBLT3eQSErFS4/FqjYki4L42zy02Zy4NwNIpKjjCDEk3jiin/eatnwGgOu0CunAneZZn4SiWsotSUtKwT60rCPiesk7DYr4j0nQjUjXv5NA0rj8N0sM8fO4uPydBBCeUueVUlRLJTspFfY2kdy0D4kpB0XZy52UFZOKlfRjw/AkwE1RxxHYcXx8HBbuY1U8gNxIKAB+8u8KBYGhMWFf8s7HRgUAfxFH8pPk40QHB5DMaqmqhvH5VC21slsbsZmN1VJIJlq5jpW6R5WYSPJFcpKOGcMwADca3sQR/0XVyUEHBxCtfJIsxc615OMQvqpQLRUsklKwCVcn1UhiTx6NieLRqAAA4kiS4ggdHAAKTUolpVoKdiLBaSdauKiAxkTpknYMk5i8BVwVRWI7aefAMHAMAcRZmWTj9pBMSF61lAvbiOCQmAqZS8MvuBB251jS2w0kh0t/o8SRZMaRUtHBAbir0KQU1VIAsrHnqgSxojEBAH6jcwNhcSl5CsC9yi2qpYKV5Eokvkv2IjHlCJt6NpGfJPV2AwCCRxwB4FNSimopALmU5fwNEoNe7nCP7ao2XPgDSVXqEKWgKlGolnIXcQSAD1VSvqw8WIiV3dqIbZJcLQW7caWT8AYFjYnwhXmMg/pulPpdZX4QILmIIwBQukITN3FMgB5WUopqKQAkpgAAqMLkpR3j3gQkHB0cAOJm20qAAPxCYirB6OWODsc6Nxr9QLiouvUDxxpAXAmpUpJSVEvZg2F8sBmJqYQ2KLjAjV5Yx5zJawE/2b7SGXHEH8QRwE1hVzGV+vpJnFcKQHG8vxKJqxrD9gYFUBnfWQD5IikVD457dlTeAm4mpaiWig7VUrAdq/JZjGop/4S1upJ+V9a05XMFUBGVMP6xeZU+7eAoa70m7s0A4PhcUo1mLox7EwBEjMRUwriWlGr0+Q95PW5lh/riCpsbFQCC4+tkzj7GEZdiSFjo4ADcTSQduNO8wF4rCFRLiazs1kZsQbUUXEBiKmENCl8SUVU9jwZGfH8znVoujnszAIRYLeVCUqqYOFL5ObbHETo4AD9pQmdtx7WJrJJKo1oKSCYSU5bO1ZOkBkWxySiXGxhhNCqC6O1mGAZgB+Z8KwxxxI+qKTo4APcTUVRL2YVqKbiC7raEsDEppRf+QTcm4nwfAPCZbXEkfW4njtiFpCoQfSKmkESTTdVRlVEtBSQXiSlELq4LfNsaFrY18gD4w/dJz+M6lxNHANjKhoRTKdVSJKWCR7UUXMJQvgQ0KGy5cLXlYj69HTYMzQh6KEbcwzAA+Ik4Ym8cAYB8MYQvOROf+5CUCvr7QvLTbn53qcIKtvUw275dAJKNoVDunK9t2K6gk4a+V9sBSU082ZyUImEAgKsPzxsUcfZy23DB7sJ22lKJYOt3GHBRnCu7+lR1G/f5OV8ubCMAN+ST5LFpSF+p24vg+VAtheQhMeV5gyIuLl6ku7jNtn2H4vzbAcIweWnHuDchsVw7J8eZRKODA0BVyakwklWakMo3KUW1FIDq+JEFgVUXqq41JjLRqACAnxFHkrXtvnWSAUmWTkaFlZSCnaiWgqu48kCgfLgg92EfAMBVPpyD49gHXzo4qLwFgmNDUopqKQD5IDHlaQ9lHBeoPjQmfNkXersBN5U6BIo4Yg+f9gVA1YJOsNhakRRkUgrBo1oKLvO69Rr13CBJnlPBxwvwqPfJl95uACgGccSuOEIHB4Awk1JUSwHIxFUHSuZjYyIJ+1aVJCdZAUSfKPf5XOvzvgHwv3qokEnOIbKyW5tY3pdqKbiOxJSHomxQJOGCO8p9TGrVFCufAcmtcCGO+IkODqA0NiSDit0GqqUAFMrNq1gPudigSNKFtov76uJ3Cvn77rvvpF+/ftK4cWPZcsstZeDAgbJq1aoqn3PXXXfJwQcfbJ5To0YNWbZsWdbHPffcc9KjRw+pX7++bLXVVnLssceGtBdIOhfPrbbva1I7OFA44ghyoUrKLVRLIWgLFy6U3//+97L11lub8/huu+0mb7/9dpXPmTJliuy1115St25d2XHHHWXChAkFvSctV89EdUGapMZEkvcZ9tLGxIcffiiTJk2SZ599VqZOnSqDBw+u8jlr1qyRI444Qi655JKcj/nHP/4hp5xyigwYMEDee+89ef311+Xkk08OYQ/85fqqYsSR8Li2z3Rw+I04gmxKTUhRLQW47fvvv5f99ttPateuLf/+97/lo48+khtuuMF0MuTy+eefy1FHHSWHHHKIvPvuu/KnP/1JzjjjDHnhhRfyft+ygLbfG643KKLg2oV10Pu+skP90BuFq9rUdPpvqFPLxXFvhtdmz54tEydOlLfeeku6d+9u7hs7dqz07dtXxowZI61bt876PA0S6R6NbDZs2CDnnnuuXH/99abnPK1z586h7AeSO/QpyXEkCq7HEYSPOOJOkmhtx7WRvl8pSEoh6XOy+eDaa6+Vdu3ayX333Vd+X4cOHap8zrhx48xjNIGlOnXqJK+99prcdNNN0qdPn7zel6uWgCSpQQEgXtOnTzfDLtKNCdW7d2+pWbOmvPnmm0W/7qxZs0zprr7OnnvuKa1atZIjjzxSPvjgg4C2HLZXtkRRLZX0pFTS9x92II6gsrCH7pGUCgfD+BC0Z555xsSG448/Xpo3b27O5XfffXe1MUVjSCZNSOn9+SIxZQEaFG6J4hgE9VmW8t0i2RqcFStWVLitW7eupNdbtGiRCRSZysrKpGnTpuZ3xfrss8/MvyNHjpTLLrvMDO3Qsl2dT0TnIgGQvDjiSvW674toEEdge1KKChfAjzjy2WefyZ133ik77bSTGYp39tlnyx//+Ee5//77c762xo0WLSrGbv1Z3+eHH/K75mEoH/JGUiraIX2IztzF20itBsH3FG5c81P5vZbDZhoxYoS5aK9s2LBhpny2uuEXYdm06aeG7KWXXirHHXec+X8t423btq08/vjjcuaZZ4b23kgG4oh7cUQ7ONa03VR0B0dZ6zXiu7BiiCKO+EWTN4VWuIQ5nC+oKimG8AGlmfbp9lIzpDiyqcA4oudxrZi6+uqrzc9aMaVVrzpcr3///hIWElOecLlnFNkxR4g/FixYYFYwStPVKrK54IIL5LTTTqvytbbffntp2bKlLFmyZLN5PbQ3Wn9XLB1yUXkuEN1Wfc/58+cX/bpwQ9hxhKQUUDziCIJGUsp9DONDGHFEz+OV5wXUOaN0YYtcNG4sXlxxjmH9Wd9PV/XLB4kp5IUGhbu93YifnpQzA0Eu22yzjblVp2fPnmaJ7pkzZ0q3bt3MfZMnTzY9HLo8d7H0tTRIzZ07V/bff39z348//ihffPGFbLfddkW/LsLFymnuCjuO0MHhD+JIsgVZNRXkXFIM3wP8iyP77befOYdn+vjjj6s8h2tMef755yvcpyu+6v354molZi40KEhKuX1sXPiOoTDaa6HLdQ8aNEhmzJhhluIeOnSonHjiieUrKenksx07djS/zxz/rUu4zps3z/z8/vvvm5/T835osDrrrLNMae+LL75ogpKOK1c6ASLC4/ucbi6cK+PCsUEciCPJowmpqJNSVEsB7jnvvPPkjTfeMEP59Fz/8MMPy1133SVDhgwpf8zw4cPl1FNPLf9Zz/s6N9VFF10kc+bMkTvuuEMee+wx81r5omLKgwZFmMMvuGCOV9y93UmZH8RFDz30kGlE9OrVy6x+pHN53HrrreW/1x5qbRCsWfPz56djw6+44orynw888MDy+T/SQz90iW+dAPeUU04xkxVqz7n2ouvktfAXcQRhzTMFexFHkiGM1faolMpuZbc2kb0Xw/gQll/84hfy1FNPmeTTlVdeKR06dJCbb75Z+vXrV/6Yr7/+usLwbH3Mc889ZxJRt9xyi5lX8J577jEr8+WLxBRQIob0IQ66cpL2YOTSvn17SaVSFe7TCQ6zTXKYqXbt2jJmzBhzQzSriQFhxhE6OJALccTv4XxhJKQKSUpRLYVikfiM3y9/+Utzy2XChAmb3aerr77zzjtFvydjfJATvdz541gBwOY4N+aPYwUkQ9iN7qCH7GUiYQAgLFRMOY7V+PwXd283AL/nkAsrjpBoAYDoqqbCSkYVk5CiWipcDOMrHN9J+9HajXEIhs2TUtOg8OuY2fxdAwCEG0foxAL8ZlNSCuEiKQVfUTEFIJQkb6eWi+PeDMAJcS+gkbREPYrDBOhA9RUZUUx+HWYSKoikFJUpAIpBGUWJaFAgE8cO8MfkpR3j3gQkEHEE8J/tVUi6fSSl7EO1FHxGYsphlOYnR5yftY/JVwDhnVtIrCAIrHAJxMP2pBn8xvcvuUhMoQIaFKXjGBaH6hQACC+O0MEBIMykANVS4aJaCr4jMRUTJqMGAPgWR0jMA4CbSEoBiJN9V7WIDQ0Kv4+ljY1YAPFhOLjdbIwjAPxEUspuVEshCWipOooGRfLwmQMA4owjdHAAwbFlLh2SUgBs4O0VBvPVFIae2eBxTAEkCec8AHALSSkAtvA2MQUAgO2YFBpVIdkHICwkpdyQpGF8tlQRIh4kpgAAcEypQ6qCHhpMAgUA3GiI63uSlAJgGxJTMfR006BIjqCPLfNMAfaavahF3JsADxFH8sc0DkC4iTCSUnAR31s3kJgCEqTYpCjDjQAAANxFUgqAzUhMJbynm2qp8HGMAfiMc1yy0MEBBJu8CXs4X6lD9xRJKQBhKwv9HYAilX2afxDcsEObULcFxSV7O7VcHPdmAEjQUC5f44gm/1Z2qB/3ZgBwSFAJL5JS8WHicyQJiSnHJKVBUUhjIvPxNjcsgvwOrGpDsSOA+NlaLVVoDElaHAFgX6M8qCREkA18klIAokLrNsFsbFBow6CYBkVQz0/SsQbgplIX0PBdqTHA1jiSxE6upE6zAMQ1ZC8TSSkAUaJiKmI0KLILuhFAzzcAJIvvcYThfIDfiq2aCmMIFEkpAFEjS4JYhd0z7XuvdzFIjgLJ5UqlTCGIIwBsUkpSp5AkU9AVUmkkpRA15peComIqoQ0KG4aWRXWxb1uvNwD4sEpZ0uIIMaRiB8eatv4lOYGkN95JStkjSROfA6qo0onbb79d2rdvL/Xq1ZMePXrIjBkzqnz8smXLZMiQIdKqVSupW7eu7LzzzvL888/zCSBRgmzE+Vj1gGQhjsC1SiYbKqdsSAYCtvA1jqQroSrfwkZSyh4kpYLD99rjxNSjjz4q559/vowYMUJmzZolu+++u/Tp00eWLFmS9fHr16+Xww47TL744gt54oknZO7cuXL33XdLmzZu9zy63tMdtzgu8G1oVAAgjsBdPsUROjjgMuJIsGi8Iy4M40PRQ/luvPFGGTRokAwYMMD8PG7cOHnuuedk/PjxMmzYsM0er/d/9913Mm3aNKldu7a5T3s3kFxxXtgzHKO0ZGxZ6zVxbwY8QBxxX9yVO8QRINmII8EgIWUfqqWQVAVVTGlvw8yZM6V3794/v0DNmubn6dOnZ33OM888Iz179jSlsy1atJAuXbrI1VdfLRs3kh1NYoPCht7mOLch7sYcEDfiCEpFHCGOINlciCMuJHxc2MakISmFJCuoYmrp0qXmBK4n9Ez685w5c7I+57PPPpPJkydLv379zDjuefPmyTnnnCM//vijKb/NZt26deaWtmLFikI2E5ayoTGRRo83EA/iSHyravowdIs44jYqb+FKHPE1hqSRlIINGMaHTKGvG79p0yZp3ry53HXXXdKtWzc54YQT5NJLLzUlt7mMHj1amjRpUn5r166d+CDJDQqbGhM2bxOAzRFH7BJXxQ7nbABRxRGfYwhJKQA2KihT0qxZM6lVq5YsXry4wv36c8uWLbM+R1e+0FUv9HlpnTp1kkWLFplS3GyGDx8uy5cvL78tWLCgkM1EFRgC4M9nUEqyspQkKVAKX+PI7EUVe+6RjKSUrduVlE4vJFMUccTXtghJKXsxjA9JV1DrtE6dOqaX4eWXX67QA6E/67jtbPbbbz9TLquPS/v4449NgNDXy0aXcG3cuHGFG9xl84W7zdsG+Ig4At/EEUds6GSigwM+xxEfYwhJKQA2K/iqQpdm1eVV77//fpk9e7acffbZsnr16vJVMU499VTTy5Cmv9dVMM4991wTAHTFDJ1sUCcfhP9cSPy4sI2AT4gj8O0c7cI2Aj4hjhSGpBRsw/xSKGnyc6Vjsr/55hu5/PLLTfnrHnvsIRMnTiyfgHD+/PlmZYw0HZP9wgsvyHnnnSddu3aVNm3amKBw8cUXF/rWcIxLF+pMYgtEhzjirqgrdYgjAFyNI5oMWtkt/nMCSSn7MYwPKCIxpYYOHWpu2UyZMmWz+7Ss9o033hBbJWVukCgbFC41JuL6LFZ2qB/3ZgCx8S2O2M7FuYSIIwCqQhypHkkpAK5gggAHuNigcJGLjSC+G4CbNnzVIO5NgONxxIZ5pgDYi6RUtIqtjktitRTD+JANiSkAoUlKNSKAYLnYUeAqOjgA/5CUAuAaElMInMsNCpe3HYDfWAXNDcQRAHEiKQX8hL8Ft3CVWwSGYAAAkoahYwBgNxribmEYH/AzElMJ6emmQWEfPhMA2BwVRwB8EWWiiKQUAJeRmEKgfGhQ+LAPYSVLqRZEUkxe2jHuTYDDooojdHAAAFxBtRSqUlblbwEAAIAYF9Ho1HJx3JsBWIkqKXclcRgfUBUSUwiMT5VGui8bdihu2VcAsIlLq675FEcAICwkpAD4hsSU5VxqUAAAgOR0cOiQ8DVtNxU8JLys9ZrQtgnwFckoe63s5ve5PggM40N1mGMKcBzJSwBhYy4jAIgvoURSyi8M4wM2R2IqAaJoUPg4/CKKfaKxBwD+xhEAKDWxRFIKrqNaCvkgMQUADvruu++kX79+0rhxY9lyyy1l4MCBsmrVqiof/4c//EF22WUXqV+/vmy77bbyxz/+UZYvX5718d9++620bdtWatSoIcuWLQtxTwB/uZJso/I2mYgj9iMp5R+qpYDsSEwBgIO0MfHhhx/KpEmT5Nlnn5WpU6fK4MGDcz7+q6++MrcxY8bIBx98IBMmTJCJEyeahkg2en/Xrl1D3AMAQaDyFsUijtidZCIpBR9QLYV8Mfk5EtMjXIwkTF4L98yePds0Bt566y3p3r27uW/s2LHSt29f02Bo3br1Zs/p0qWL/OMf/yj/eYcddpCrrrpKfv/738uGDRukrOzncHDnnXea3u3LL79c/v3vf0e0V0gyn+MIYCPiiJ1IRgHB4G/JPVRMAYBjpk+fboZdpBsTqnfv3lKzZk158803834dHX6hQzgyGxMfffSRXHnllfLAAw+Y1wNQGpJusBFxxK7Gst5PQ9r/FfmSNoyPailI0iumJi/tGPcmAEC5FStWVPi5bt265lasRYsWSfPmzSvcp42Cpk2bmt/lY+nSpTJq1KgKwzbWrVsnJ510klx//fVm7pDPPvus6G1EsBp8WdPboWIkboDqEUf8RDIKviIphUJ5mZjCz2hQlI7hfP7buKiBpOrVC/x1N639KZnQrl27CvePGDFCRo4cudnjhw0bJtdee221wy+CaOAcddRR0rlz5wrbMXz4cOnUqZMZlgE/MKk14G4MUcQR/5CMAoDNkZjyvKcbbiQPV3aoH/dmIEQLFiwwQx3ScvVyX3DBBXLaaadV+Vrbb7+9tGzZUpYsWVLhfp3fQ1dM0t9VZeXKlXLEEUdIo0aN5KmnnpLatWuX/27y5Mny/vvvyxNPPGF+TqVS5t9mzZrJpZdeKldccUUeewugMjo4UCriCOC2JA3jo1oKxSAxBQAh08ZEZoMil2222cbcqtOzZ08zqezMmTOlW7du5Y2BTZs2SY8ePars4e7Tp49p0DzzzDNSr1IPv05q+8MPP1dZ6qS4p59+uvznP/8xk9wCsBMdHP4jjgBuzy8FoGokpizGEAxEUc23pi3fM9foMAntrR40aJCMGzdOfvzxRxk6dKiceOKJ5SspLVy4UHr16mUmn917771NY+Lwww+XNWvWyIMPPmh+Ts9Zoo2YWrVqbdZo0PlD0u+nk+QCQUvCcHDARsQRoDQkpbKjWgrFIjGFoiWpQWH7MAxNYq5qw5DRJHnooYdMI0IbDbrq0XHHHSe33npr+e+1kTF37lzTgFCzZs0qX2lpxx13rPBan3/+ubRv3z7iPQAAxIk4AkSTlErKMD6SUigFiSkAVq2oeWizOXFvhhN05aSHH3445++1gZCe20MdfPDBFX7ORzHPAeBmBweShzgCFI5KqexISqFUlFgAABChDV81iHsTAAAAvMTKl24iMQUAAJBwxc5rycrDAJBsVEshCFxNAAAAAAAQMN/nlyIphaCQmAIAAFk1+vznZd+DlqQFNAAA8A1JKQSJxBSKksQGRRL3GQAAAACAMJGY8liYPd0AAKAwdHAAAHxAtRSCRmIKsABJRAAAAMAfvs4vRVIKYSAxVSCW+QYAAAAAJA1JKYSFxBQAAAAAAMiJpBTCRGIKQEGoGgQqmr2oRdyb4BzmWgoeQ8IBAElPSjWayfWFq0hMoWA0KAAAAADA//mlXElKwW0kpoACkJQDAAAAkAQkpRAVElMAAAARoYMDANy0slsbSRKSUogSiSkAAAAAAALgwzA+klKIGokpAAAAAABAUgqxKIvnbQEAAAAAsF8ShvG5npBiRT63UTEFAAAAAEBCuZ6UQjiuueYaqVGjhvzpT3/K+ZgJEyaYx2Te6tWrV/B7UTEFAAAAAEAC55ciKYVs3nrrLfnrX/8qXbt2leo0btxY5s6dW/6zJqcKRcUUAAAAAAAJG8ZHUgrZrFq1Svr16yd33323bLXVVlIdTUS1bNmy/NaiRQspFIkpAAAAAAASQhNSPiWlmF8qWEOGDJGjjjpKevfunXcia7vttpN27drJMcccIx9++GHB78lQPgAAAAAAEsCnhBTyt2LFigo/161b19wqe+SRR2TWrFlmKF8+dtllFxk/frwZ8rd8+XIZM2aM7LvvviY51bZt27y3j8QUAAAAAACezy9FUspOdT+pJ7XqFj5heD42rvvpX61myjRixAgZOXJkhfsWLFgg5557rkyaNCnvCcx79uxpbmmalOrUqZOZn2rUqFF5byeJKQAAAAAAPJxfimQU0kknnaQ8LVu11MyZM2XJkiWy1157ld+3ceNGmTp1qtx2222ybt06qVWr6gRs7dq1Zc8995R58+ZJIUhMAQAAAADgERJSyKRJqczEVDa9evWS999/v8J9AwYMkI4dO8rFF19cbVIqncjS1+jbt68UgsQUAAAAAACOV0uRjEIpGjVqJF26dKlw3xZbbCFbb711+f2nnnqqtGnTRkaPHm1+vvLKK2WfffaRHXfcUZYtWybXX3+9/O9//5MzzjijoPcmMQUAAAAAgKPzSyU5IcWKfNGaP3++1KxZs/zn77//XgYNGiSLFi2SrbbaSrp16ybTpk2Tzp07F/S6JKYAAAAAAHBMkhNSiMaUKVOq/Pmmm24yt1KRmAIAAAAAwPJhfCSi4CsSUwAAAAAAWDaMj0QUkoLEFAAAAAAAMSIJhSQjMQUAABCRDTvYOTwEABAdklBARSSmgALQoAAAAAD8Vej8UiSZ4sOKfP74eZ0/IE8kZwAAAAAAQBBITAEoSFnrNXFvAmCVTi0Xx70JzqGDI3grO9SPexMAAACKQmIKAAAAAAAAsSAxVSCqRRAGeroBAAAAAElEYspjJDsAAAAAIJyJzxEfJj73C4kpAACACDC3FgAAwOZITKEoSby4TuI+A0g2Km8BAAAQNhJTAAAgciT7AQA2YRgfEB8SUwAAAAAAwAnML+UfElMAAESI1V1ho1VtirskXNN2U+DbAgAAkoXEFAAAAAAgsRjGB8SLxBSQB+ZCAQCUgjgCAACQHYkpFI2LbHsUOwTDNoc2mxP3Jjjju+++k379+knjxo1lyy23lIEDB8qqVauqfM6ZZ54pO+ywg9SvX1+22WYbOeaYY2TOnJ+P+XvvvScnnXSStGvXzjymU6dOcsstt0SwNwCAqBFHALiI+aX85Edr1lO+JBtgL+YGcZc2Jj788EOZNGmSPPvsszJ16lQZPHhwlc/p1q2b3HfffTJ79mx54YUXJJVKyeGHHy4bN240v585c6Y0b95cHnzwQfPal156qQwfPlxuu+22iPYKSUMHBxAf4gjwE4bxAfEri3sDktL4b/AlSSYAwdAGwcSJE+Wtt96S7t27m/vGjh0rffv2lTFjxkjr1q2zPi+zwdG+fXv5y1/+Irvvvrt88cUXpgf89NNPr/D47bffXqZPny5PPvmkDB06NOS9AlCslR3qx70JcAxxBIBtqIRKNrIlnuNi1f4efT4j/61YsaLCbd26dSW9nl7k67CLdGNC9e7dW2rWrClvvvlmXq+xevVq0+vdoUMHM+Qil+XLl0vTpk1L2l7Eh8pbO1AZhlIRRwD4moxK35BsVEyh5Ivtsk85kcBt9RfWlFp1g2/Ab1z302tWvmAfMWKEjBw5sujXXbRokRkqkamsrMxc+OvvqnLHHXfIRRddZBoUu+yyixnCUadOnayPnTZtmjz66KPy3HPPFb2tCIbPlbfEEbgurBiiiCMAfEICCrn4eZULABZZsGCB6TFO33S+jWyGDRsmNWrUqPKWOclssXOKvPPOO/Lqq6/KzjvvLL/73e9k7dq1mz3ugw8+MJPaauNH5w9BclHVCcSPOAKEg/mlokFVFBJZMaUre01e2jHuzYAHGH6BIOiKR3qrzgUXXCCnnXZalY/R+TpatmwpS5YsqXD/hg0bzApL+ruqNGnSxNx22mkn2WeffWSrrbaSp556yqyilPbRRx9Jr169zFwil112WbXbDSA34giCQBwB4CoSUkhsYgrRYhgGEAxdeltv1enZs6csW7bMrH6kKySpyZMny6ZNm6RHjx55v5+upqS3zLlKdBWlQw89VPr37y9XXXVVkXsCFIY4AgSDOALAJiSlkC+G8gGAYzp16iRHHHGEDBo0SGbMmCGvv/66We3oxBNPLF9JaeHChdKxY0fze/XZZ5/J6NGjTSNk/vz5Zt6P448/XurXr29WYUoPuzjkkEPMkIvzzz/fzDOit2+++SbW/QWQG0MtUQziCICwkZRCIUhMIRA+DlWIYp9oUKBYDz30kGkw6FAJbRDsv//+ctddd5X//scff5S5c+fKmjVrzM/16tWT//znP+axO+64o5xwwgnSqFEj07BIT4D7xBNPmMbDgw8+KK1atSq//eIXv4htPwGXuRIbWb0xmYgjAMJCUgqFYihfAmjyo9HnP8S9GQgJDYpk0pWTHn744Zy/b9++vRlekaY94M8//3yVr6krPJWyyhNQCobzAdEijgAIA0kpFIMWreVIOgAA4kZ1J4JS1vqn6hsAiBsr8gWPpBSKRdYDiRuykLR9AQBELwlxZE3bTXFvAgAA8ACJKQAAPOZS5W0SkjkAAPiIaimUwp2rVQCx93QzBAMA7EmwJWGIZaeWi+PeBABANUhKoVQkpiKSlHJ3H3q7aVAAAAAAQPUJKZJSCAKJqSK4WDVCEgQA4AIfOjgAAHZj4vPSkZBCkEhMAQBQyaHN5oht4q68pYMjPyTWAAC+IymFoJGYQuBcvih3edttxNwgAIrBuTg6Lk2ODwCIH0kphIGrEQAAIubikHAAAJBsJKUQFhJTDlR1uNib6WJvt4vb7OJ3AwB8PSdHuc0MrQQARImkFMJUVKv29ttvl/bt20u9evWkR48eMmPGjLye98gjj0iNGjXk2GOPLeZtUSIuYu1pAPFZIOmII9EiiR0+FxNpgMuII0B0SEohbAVfqT766KNy/vnny4gRI2TWrFmy++67S58+fWTJkiVVPu+LL76Q//u//5MDDjiglO2FQ7hIB5ANccRdUSfVXYkjrmwn4AviCEpFoiX/48SxgpWJqRtvvFEGDRokAwYMkM6dO8u4ceOkQYMGMn78+JzP2bhxo/Tr10+uuOIK2X777UvdZjjEhYt1F7YR8AlxBIXgHG2nuFeJRLIRR4BwkZCC1Ymp9evXy8yZM6V3794/v0DNmubn6dOn53zelVdeKc2bN5eBAwdKkiX1Is7mRkUc22bDML6kfhcRP1/jiO1zFbqOOBJOHGGIJ1zkaxwBbElGkZBCHMoKefDSpUtNb0OLFi0q3K8/z5kzJ+tzXnvtNbn33nvl3Xffzft91q1bZ25pK1askKTTi8eGCzcFcjHb6PMfJI4L97JP7TrJ2dzQyQcNCriIOOI+4og/cQRwURRxhBiCJCERBRuE2rJduXKlnHLKKXL33XdLs2bN8n7e6NGjpUmTJuW3du3ahbmZSOAFvE3bAiA34khw1Y4ks4NFHAH8jSO+xhBUlPSEDNVRcLZiSk/mtWrVksWLKw5Z0J9btmy52eM//fRTM8ng0UcfXX7fpk0/XVSXlZXJ3LlzZYcddtjsecOHDzcTGmb2UhAQAMB9xBH4UDUVZ1LKhuHgxSprvSbuTYAHoogjxJDkqJyYWdnN304HklDwJjFVp04d6datm7z88svlS6zqiV1/Hjp06GaP79ixo7z//vsV7rvssstMz8Utt9yS8wRft25dc4NfwzBsaVTQoADiQxyBD3EEgN9xhBiSXJq88SU5RSIK3iamlPYe9O/fX7p37y5777233HzzzbJ69WqzKoY69dRTpU2bNqYEtl69etKlS5cKz99yyy3Nv5XvR3LE2ahg6EXx6OlGUIgj7ouzg0MRR4BkI44gTK4mp0hEIVGJqRNOOEG++eYbufzyy2XRokWyxx57yMSJE8snIJw/f75ZGSMJjfQNXzWIezOcFUejwqfGBHPFwGXEEQSBOFIa4ghcRhxBVEkeWxNUJKEgSU9MKS2TzVYqq6ZMmVLlcydMmFDMWyLAlfmS2KiwoTHBMD7gZ8QRuIY4Eswk/EBQiCOIAgkgIBp0JSSULRe3UVzo29CYsAkNCiC5w2ODrJKxIY7o+Z04AgAAkMCKKZSWFGjwJfnAbBf8QVdP0ZAAgGQgjgAAALiLxBS8a1jY2JCwobIAgB/o4Ah/iDhxBAAAIDpc2SaYrRe5pTQIbGxMAICvbIwjpQ7vS0IccWXi804tF8e9CQAAIAJUTDnGtwnQg6qeSkJDwrUGBQC4EkeSFEMAAABsQ2Iq4bS3u9HnP4itfGgs2FhREAV6ugE7JaWDI404AgAAYDdKL/4/GtEAABSOpAlsXYUSAAC4gcRUCbjIQlLwXQcAPxJ/pQ4H18n3AQAAgkRiKgalXtQFPccQvd3hoUEBJIdLlbfEEfjo0GZz4t4EAABQBBJTAAA4Vo1IkhkAAAC+IDEFg97u4HFMAQClII4AAIAkIDEFJHToDQAEiSSK/YgjAADARlyhOIqLS7vRQANgO+KI3WyMIwwhBQAAYeCqFFZfBCM4NCgAhI04AgAAgEKRmIoJSQKgIlZTAoDwEnxxVsgVO8k/AABIBhJTDgvjIpPe7tL51qAAYCdbOziIIwhCp5aL494EAAAQEW9bu1FVX9ALCN/xHQf8RdIbAAAAceOKFJuht7t4HDsAhfIx+cu5sHgcOwAAkDQkpgAEjiEYAOBXZZytQ0cBAID7SEzF2JgO4iIvrGEY9Njac8xoUADxYEL+0hBHCscxAwAASURiCgAAR9ncwYHCkJQCAABJxdUocuIiOX8cKwCKYawVcW6Ej3OoAQCAYJGYQpVoVCT7GNGgsNd3330n/fr1k8aNG8uWW24pAwcOlFWrVuX13FQqJUceeaTUqFFDnn766Qq/mz9/vhx11FHSoEEDad68uVx44YWyYcOGkPYCQNhxhIo45EIcAQBUduedd0rXrl1NbNBbz5495d///rdU5fHHH5eOHTtKvXr1ZLfddpPnn39eCsXVigeNdy46/cX8UshFGxMffvihTJo0SZ599lmZOnWqDB48OK/n3nzzzaYxUdnGjRtNY2L9+vUybdo0uf/++2XChAly+eWXh7AHsEmYccTn5H1Sjg9xxE/EEQBAZW3btpVrrrlGZs6cKW+//bYceuihcswxx5h4kY2e60866STTufHOO+/Isccea24ffPCBFIKMRsxcuNhz4aI5LhwbxGH27NkyceJEueeee6RHjx6y//77y9ixY+WRRx6Rr776qsrnvvvuu3LDDTfI+PHjN/vdiy++KB999JE8+OCDsscee5je8FGjRsntt99uGhmws4ODOAKgUMQRAEA2Rx99tPTt21d22mkn2XnnneWqq66Shg0byhtvvJH18bfccoscccQRpjq2U6dO5py/1157yW233SaFIDHlibCrpmhURH9MqIRDLtOnTzfDLrp3715+X+/evaVmzZry5ptv5nzemjVr5OSTTzYNhJYtW2Z9XS2/bdGiRfl9ffr0kRUrVuTsJQFQPOII4kIcAQBUR6tgtcNi9erVZkhfNnre1/iRSc/7en8hygp6NACDRB0KoRfkmerWrWtuxVq0aJGZtyNTWVmZNG3a1Pwul/POO0/23XdfU46b63UzGxMq/XNVrws/aBKj4cJNoZ43G33+Q2iv7xriCApBHAEARBVH3n//fZOIWrt2ramWeuqpp6Rz584FnfcLPeeTmELeaFS4x4UhPjbY4utNUlY7+GO14cefXrNdu3YV7h8xYoSMHDlys8cPGzZMrr322mqHXxTjmWeekcmTJ5ux30BciCPRCapaqpQ4UuwQVddWtwwrhijiCAD4r+mcjVJWe2Mor73hx40FxRG1yy67mGHby5cvlyeeeEL69+8vr776as7kVBBITAVEL742fNWg6Iu+Bl/WtL63W9GoiKaX24bhF3FP6u+TBQsWmFUt0nL1TlxwwQVy2mmnVfla22+/vRk+sWTJkgr364pHusJStqEVShsTn376qRm6kem4446TAw44QKZMmWKeO2PGjAq/X7z4pwZirteFHYgjbqFaKniHNpsjPiOOAACiiCOqTp06suOOO5r/79atm7z11ltmLqm//vWvmz1Wz+3p83ya/lzoOZ/EVJZeutmLKpaioaIkNypoTPjX0x2F9HKr1dlmm23MrTpaWrts2TKzWoYGi3SDYdOmTWYS22y0F/2MM86ocJ/OA3LTTTeZSQ7Tr6sTHGpjJT3EQ1dr0m0Ps4cEpXdwwB3EERSDOAIAiCKOZKOxYd26dVl/p+f9l19+Wf70pz+V36fn/VxzUuVCYsozUfR2w/9qKdhNV7zQ1S8GDRok48aNkx9//FGGDh0qJ554orRu3do8ZuHChdKrVy954IEHZO+99za9Ftl6Lrbddlvp0KGD+f/DDz/cNBxOOeUUue6668zY8Msuu0yGDBlS0lwmQGVJ7eCIKillwzA+2I04AgDIZvjw4WZFVT23r1y5Uh5++GFTEfvCCy+Y35966qnSpk0bGT16tPn53HPPlYMOOsis1nrUUUeZydLffvttueuuu6QQtIBRlCT2+Lq2zzQo/PbQQw9Jx44dTaNBl3TVpb4zA4A2MubOnWtWUMpXrVq15NlnnzX/ai/H73//exN8rrzyypD2wk+uVw1GlRx37ZxaqqTtL+xHHAEAVKYVr3re1nmmND7oMD5NSh122GHm9/Pnz5evv/66/PG6IIYmrzR+7L777mZOqqefflq6dOkihaBiyrP5QaKUpB5v13q5XeP73CBh0JWTNAjk0r59e0mlUlW+Rrbfb7fddvL8888Hso2IFnEESY4jKBxxBABQ2b333itV0eqpyo4//nhzKwVXLx6K8qI0CT3ASdhHAMhEHAlWEvaxMhbQAAAA+fI6MUUVRjR8vuD2ed+qQoMCQJR8PtdGuW9BJhQZDg4AAKLidWLKtcZ8kBeBUZfy+9ioiHqfaFAAsAlxpHQ+7hMAAEDQSEwhMD5dgPu0LwDcYVMHRxx8Ovf6tC8AAABhIjHlsTgmQPXhQjyOfWCyWgD4CXEk2XHE9VUtAQBA4fy4ioFVXG5UuLztNlRM0KAA/BJXssPVc7Fut6vb7lPlHQAAcAuJKcsa10FfDMbZqHDp4jzO7fWllxvwkYuLaPiSVHAxjsTFtjjCAhoAAKAQdl3JeICLMbcaFrZvXxz4DgOl86l6MO6kh+3nadu3DwAAwHYkpizkS9WUzRfutmyPDZ8NALvYmBy24Vxlwzk7CXHEl4o7AADgjrK4NwDJohfxjT7/Idb39xkNCgDpc0GDL+NPJoV1DieOAAAA+MO/q1ZY29sddy+zbY0Jmz4TAHDpnBVHHLGlQsrWzwQAAKBYVExZytfe7kyVL/DD6AG3rREBAAjnHJ+0GBJGUiqIqttShqL6NDcbAADIH4mpKi6OZi9qUfRF2YavGoiNF7ENF27yuoFhcyMiEw0KAC52cNgcR4Lq7HAljgAAAPiCxFTC2NyoSErDgKEXAFzt4HAJcQQAAMANXNlYjImskS++K4B9fK0iJCniJ+IIAACIC1eXCUSjIj4cewA+JBs4l8XH5mNfynBwAACQXPZe3TjO9oszmy9sfWX7Mbf9OwsASRdWHHG9WurQZnPi3gQAAFACu1vKcP5iEeHjOwL4KahkMVVTfuB4AwAAX3l/lUMvWm5c5EaHY50bf6OAuzi3ISi+zskGAACqxxVlwi+SaFSELwnHOAl/KwDgYxwJqqKO4eAAAKBY/reYY2T7MAy4jwYFYHdVny1J2zDjSBKS73Hi+AIAAN9xtQMuekPEsQWQhKQx5zo30fEFAABswJWkI8K+eKRR4d4xpUEBoBDEEfdwTAEAEFnZrU35DX4qi3sDktDbveGrBuLKBXDDhSQ7ktaYcKUiAwCSxKXODeIIACAo1SWf9PeNZi6MbHsQDRJTDtGLyAZfupPwSKooklJUSwHJEWQHR9hxhA6O5HVuBMGWudgAANErpgoq13NIWLmLxFQeF0uzF7WQpKBRUZqkNSYUDQqgasQRFILODQCAz8Icjpd+bRJU7iExFQGXersVjQq7k1I0KACUgjhiLxc7N+IexlfKqpkAgHDFNScUw/3cQ2IKWdGoSEZSKu4GBQD/5itMI44kI44AAJDG5OQolntdc4jsolIvkl3svY0ax6h49HQD/scRVI/jBABwla0r5tm2PagaV0IRzaETdGVKlD2eXDDbcWzo5QbgImKIv3GEqlsASDaSPwgKV4vICw0L/44JDQogOnRwUIGbDceEBTQAwEU2VkjBbYm4IvJ1uFDUFTRcQMd3LGytlqJBAaAQxJGfEUcAAC5yKSHl0rYmHVeIEfKhQoVGBccASCI6OILDOdSPY+DDNQ0AoDAkehAWVuVzXBTLfleW1JWW4mpIhNFwpEEBuCmM1fmII9HxKY4AAJIliqTUt51r5fzd1h9tLHq7G81cWMJWIQokplDSxXVSGhY+9G4DgE2IIwAA2J2E0oROGAmpqhJQ+Tyn2CQV7EViqoC5dGYvalHy6/jS252UhkXcDQmbq6WYXwrwR9xxxNcYoogjuRFHgOJlqwLJlUCgWgTFKjUpVUwCKt/XJTnlFxJTCISPDYu4GxMA/GJzB0fcfO3kiDuOMIQP8Fu+SQOGMiEqYSWiSk1O8TdgPxJTnoizt9u3hkXcDYk0GhQAokQc8S+O+M7XRQmA6jABNZKajMr23lRO+YErpxiENfG0LYkMly/Ibdn2sD5LJj0H4AJbzsXFbLct204cAZCJZBbCSArFmZQqFH8DdrPj6ikC9KrFc3FuywW6L9vqE/4mgeL53sHh2rnZtu206XPMhfmlgOjRMEc+35F8vic2JaRs2hYUj6F8McwP4vtQDFeGZtjUiHCtl5sGBeAv4ojbMSRMVEsBgL9cS0jBL8m7qkrAxZ2tPaU29H7bsA0ufnYAgqnyCzKpSxyJdxtsZetnB8DO5ANVVPAhKWXztiE/VEx5ysYe70yVL+rD6gW3ufFQGY0JADYhjrgVQ8KOI1RLAf4mI1ixLLlcT0jBHySmYhT2kt+2NyryufjPt6HhWuMhajQoAD+FHUdckvQ44lLnBsPBgeIFVeGUrXKqcnIq8zEkrpL3fXEtIZXPCn0kYe3l9lVYDLiYim/YRlU319GgQKG+++476devnzRu3Fi23HJLGThwoKxatSqv56ZSKTnyyCOlRo0a8vTTT1f43VtvvSW9evUyr7nVVltJnz595L333gtpL2A7l85NSY8jQKGII8mVK1mRWUHFkD/3JSkpBfdxJeZ5JYsPjQrfhf0ZUS3lJ21MfPjhhzJp0iR59tlnZerUqTJ48OC8nnvzzTebxkRl2iA54ogjZNttt5U333xTXnvtNWnUqJFpVPz4448h7EUyBZ3cJY6AOIJiEEeQDQkoP9iWlFrbcW21NyQbQ/kSwKUhfUlDgw/FmD17tkycONH0Snfv3t3cN3bsWOnbt6+MGTNGWrdunfO57777rtxwww3y9ttvS6tWrSr8bs6cOaYH/corr5R27dqZ+0aMGCFdu3aV//3vf7LjjjuGvGewFXHEXkmNI0EsRpBkxBHAH0ElEwtNSFVOJtWbU6/K3xf6erlet9ThfLBToq4yg7qIca23O8kXrjaL4jMJ+rsV1Hc/aQ2KFStWVLitW7eupNebPn26GSKRbkyo3r17S82aNU0PdS5r1qyRk08+WW6//XZp2bLlZr/fZZddZOutt5Z7771X1q9fLz/88IP5/06dOkn79u1L2maEiziSTEmOI0lDHAEQFk3m5JOUqq7CKawKKKqpkoGKqQShx9seNPDs0uiLH6SsLBX4627Y8FMgTfcap2nv8ciRI4t+3UWLFknz5s0r3FdWViZNmzY1v8vlvPPOk3333VeOOeaYrL/X4RZTpkyRY489VkaNGmXu22mnneSFF14wr4+fkqqTl3YMpHE+e1ELcQ1xxB7EEf9jiCKOwCZMHO22UoboxZ0cSr9/PtVT1VVN8T22E1eXlohq/gYuZOMX1WfAnCD2WLBggSxfvrz8Nnz48KyPGzZsmJmzo6qbDpMoxjPPPCOTJ08284Lkoj3bOvntfvvtJ2+88Ya8/vrr0qVLFznqqKPM72A3/uaTgziSPMQRAHEkpWyb/8mmbUGw6LpIIHq84+NyYpDhF8XTFY/0Vp0LLrhATjvttCofs/3225vhE0uWLKlw/4YNG8y8HtmGVihtTHz66adm6Eam4447Tg444ADTw/3www/LF198YYZ46HAOpffpqkr//Oc/5cQTT8xjb+E7Yki8iCPJRBwBEDWSQIgSiSmLhmFoz+SGrxpIFGhY+N2YoJfbTdtss425Vadnz56ybNkymTlzpnTr1q28wbBp0ybp0aNHzl70M844o8J9u+22m9x0001y9NFHl88dog2JzJWW0j/rayNYLscRYkg8iCOoDnHEb6yYhzgmNbeNbl91Q/qYBN09XFUmmMu9rq7hWCNIOomsLsc9aNAgmTFjhhkqMXToUNMTnV5JaeHChdKxY0fze6U94DqcIvOmdEnvDh06mP8/7LDD5Pvvv5chQ4aYFZt0GfEBAwaYeUEOOeSQGPcYtp7XOLdFh2ONIBFHgGTINykVxqTlNiPJax8SU5aJuoeSC13/jjG93Mnw0EMPmQZDr169zPLe+++/v9x1113lv//xxx9l7ty5pvc6X/p6//rXv+S///2v6U3XoRlfffWVWVK88pLgsBdxxD8+xJEgh/ElbWXXsBBHAL8VstKea1zcZlSNoXwWrqoU5ZA+xZCM8PjQmFA0KOyjKyfpvB256LLcqVTVq0Rl+732dusN0XB1db7KiCPh8SWOwD7EERSDFc38kITEDsP53FLUVeTtt99uglW9evXMOPR0iW82d999t+kt0UkP9da7d+8qHx82GsXZ0eMdPI4p4GcccUEcyQXOecFiqCRQNeIIUFy1lC9JKV/2A0Umph599FE5//zzZcSIETJr1izZfffdpU+fPput7JGmK3ScdNJJ8sorr5gVOtq1ayeHH364GbcO+xoVXAS7exzp5YYrXI8jdHDkRgxx+zi6UHUL+BBHgDDnSgo7KXXgTvOqvAGRJKZuvPFGM1GiTmTYuXNnGTdunDRo0EDGjx+fc/z6OeecI3vssYcZd37PPfeYVTlefvll8YGPF1s0LIrn47Hz8TuOeBFHovkbiytZTSdHaXxLSgFhII4A4SalSkk8uZKsYgJ0hxNT69evN8vKavlr+QvUrGl+1t6HfOgEijqZoo5rz2XdunWyYsWKCrckivMikYaFW8eLBgVcQRxJVhyBO3EEcEUUcYQYUjUa9O59BoUmpYJmc4IKDiamli5dKhs3bpQWLSpO1Ko/L1q0KK/XuPjii80ytJnBpLLRo0dLkyZNym9abpvUipK4Ew5cJNt/jML8jlAthaARR5L1t0ayJT9xHyOX4ghDaRFFHHEthiBZciWlclVL5ZuUiiJ5FPTrM8+UPyJdQueaa66RRx55RJ566ikzUWEuw4cPl+XLl5ffFixYEOVmohIaFtlxXApHgwKlIo6418GhOF/ae1xs+H4AtsURYghsSD7luoWVlIpKlO9V1dBGRfWfPcoKeXCzZs2kVq1asnhxxd4v/blly5ZVPnfMmDEmELz00kvStWvXKh9bt25dcwuzcTx5aUdnlvzWi8YNXzWQuKUvnpO+JHjcjYhMNCjgGl/iiGtsiiNJjyG2xRHANVHEEWIIXFJKUiqu4XXp9536yY6xvD/sU9DVYZ06daRbt24VJgpMTxzYs2fPnM+77rrrZNSoUTJx4kTp3r17aVucUDYlIGzo4Y2Dbfsd9nfC16FFiJcvcSSM6r+w/+ZsiSO2nUuTvO/EEbjIlzjiOipN3E5K2TLnUxDbwXC+BFZMKV2atX///uaEvvfee8vNN98sq1evNqtiqFNPPVXatGljxmara6+9Vi6//HJ5+OGHpX379uVjvxs2bGhucK/HO2kVVDY1ImxrYALFII7Ex6Y4kpQYoogjQLCII0Bu+SSlbBNnBZUmWRvNXBj5+6LExNQJJ5wg33zzjTm560ldl13Vnof0BITz5883K2Ok3XnnnWb1jN/+9rcVXmfEiBEycuRI8UmYw/lsbFT43riwsSEB+IA4klsS40jmuZY44k9SimophIk4AmSvlqoqKWVjQirbNjK8L5kKTkypoUOHmls2U6ZMqfDzF198UdyWwZlGhU+NC1sbEZloUMAHxBH42tHhQhwBfEAciR+VJm4kpVxISIV9rLb+aGPcm4FquHvlZyka9BXn0XDhAt2lbWXoBeC/KOKI7eeSzPOyC+dml7bV5c4NVnYFkATVzd9V3UpzrielXNxmxFQx5YMwVuaLkq1VU9lUvlCPuyfchYZDnA1JGhRAMrgaR+KOIYo4AgCIUnXJqGzVUi4neAod0qf7X29OvVC3CeGK/+rOQ1FVTbl6gRlHz7JLvdk+fdaAz3xItrp4bomjmsq1Ci4fOzcAAMlISsWxD6wyGb/EVkz5wqUe72yqu8DPt2fc1YaCbQ1HGhRAciZBTyOO5Pc6LnMxAQkASG5SCslDYsoDrjcqktpQyAeNCQBRII74y5fODR8qFAEfMQF6fMP4qlqBzxes0pcciR7KF+ZFTtSVJyQw/BP1Z0qDAigccQQ24zMFAD9FsQLfOS0mm1vcgtinQiaMT7qpU6fK0UcfLa1bt5YaNWrI008/XeXjdRVUfVzl26JFiwp6XyqmPOJzj3fS0JgAEAfiiD986twAAASbwCkk4ZTtsXcsPlRswwTowVi9erXsvvvucvrpp8tvfvObvJ83d+5cady4cfnPzZs3L+h9SUx5MkdIGo0K98WRlKJBAdiJOIJi0LkBIEoM5wtvEu5slT42zCuVmayyMUmF4h155JHmVihNRG255ZZFv2+ih/JFMcQojgY/F6TuIikFoDLiCAr53HyMIwwHB5AE+awMF8S8UkEPz4tiuB8TupduxYoVFW7r1q2TIO2xxx7SqlUrOeyww+T1118v+PlUTHmKHm+3+NwIpEEB3+l3fPLSjuIb4ohbfI4jAFjOPomfb77zIoU1hK8Q+rpUThWv0btfSVnNuqG89oZNPyWg2rVrV+H+ESNGyMiRI0t+fU1GjRs3Trp3726SXffcc48cfPDB8uabb8pee+2V9+uQmPJ0KIaiUeGGOBsTVEsBbiCOoCrEEQBxYzhf+ElHG4bwVYXklN0WLFhQYQ6ounWDSYTtsssu5pa27777yqeffio33XST/O1vf8v7dRI/lM/3C7e4yvqRHz4bAPkijiAbPhsAtqCqK7hjFsYqclEMubNhFT9kp0mpzFtQials9t57b5k3r7CkKYmphAw14sLVPnF/JvRyA8EhjiCJCUPiCIDKSE6Fk5QqtVoqyoQRySm8++67ZohfIRjKl4ChGGnpC1iGZcQr7oYEAHcRR2BLHIkqKZWEpC/gW7KHYX3Ff55hzCsVB4b1uWvVqlUVqp0+//xzk2hq2rSpbLvttjJ8+HBZuHChPPDAA+b3N998s3To0EF23XVXWbt2rZljavLkyfLiiy8W9L4kphLWqFDMGZLsxoSiQQG4iziSXLbEEACoDsmp4JKMha7EZ0vFEskpN7399ttyyCGHlP98/vnnm3/79+8vEyZMkK+//lrmz59f/vv169fLBRdcYJJVDRo0kK5du8pLL71U4TXyQWLK81WVcqHXO7mNCYZeAOEgjiBMxBEAriE5VVhCKoghfLYkpcKg+z31kx3j3gzvHXzwwZJKpXL+XpNTmS666CJzKxVzTCX8gs6GOSp8ZtvxjfK7R7UUEB7iSHLYdnyJIwAKTci4NhTRlqSUi0P4kpQoQ7BITMXEpkaFjRe+ruN4AkhiHIHfccS27xwAdyQ1OVVKUsrVIXyubBfsQmIqAz1zdl4Iu8Tm40eDAkBU50Bbz4Mu4PgB8FXSqqdK2VeG8CFpSEzFyOZEARfGfh2vqL9rJHmRVFF/94kj/rD9eBFHgPj4Nk9TEpJT+e5jvvNKVcWFpJQL24h4kZiKmc2NCkXvt/vHxvbvGAC//8ZdOE/GhTgCIKl8Tk6FkZTKVS1Fwge+IDFVCT10ublw8RwFl44DjQkAtnAlCRMFl45DHHGEazEAriIplZtr24tokZiygGvJgyQ2Llzc57i+VzQokHRx/A24GkeShDgCAH4rJSnlyyp8YeN4+Kss7g1AxYu/2YtaiEsyL7A3fNVAfOJS46EyGhNA8ujfvcsxRBFH7EEcARBFIseX+bNKHZro47xSQCFITFnGxYaFLw0MlxsQtjQmqJYC4uVyDPGhs4M4UjriCACfk1JJHMIXpKmf7Bj3JiAkJKZyXBRNXtoxtvd3vWFR1QW6LQ0NXxoPldHDDdghzjjicwxRxJFwEUcARMX1aimfJ3APwx2LDw319evNqRfq6yNcJKYs5UvDotAL+SAbHL42GnKhMQHA9eHhNsWRpMUQG+II1VIAfE5KBVEtleSkVCnVUlt/tLHk90e4SExZWjXlc3KqKklsCPjQmFA0KICKiCPxII64G0cAIElJqWK4NowvqCophvD5j1X5LMeFIqrDdwRAVThHoDp8RwBEzdVhfEEmpXyvliIphUKQmHKgAoQLRtj+3bDlbwWA3ecK2MeW7wZxBEiOpCSlNCEVVKWUS9VSmpCKOilV1fxSDONzA4kpR9hy4Qh78J0A7GdTY5tzBirjOwEA+SWkiklKVSVXtZTrwp7gHP5ijimHJHGuEGRHYwJAMYgjsDGG2JTABWyuMvJhFTjbq6WCOMalVEm5PIwv6KQUQ/iShYopxy6W9GLStgtKJPvzt+1vJCm+++476devnzRu3Fi23HJLGThwoKxatarK5xx88MFSo0aNCrezzjprs8dNmDBBunbtKvXq1ZPmzZvLkCFDQtwT/9n2N2LbOQTR4vNHGnEEUSMpVXy1lM3D+IIcuhdXUsr272YSUDHlKHq9k4fGBDJpY+Lrr7+WSZMmyY8//igDBgyQwYMHy8MPP1zl8wYNGiRXXnll+c8NGjSo8Psbb7xRbrjhBrn++uulR48esnr1avniiy9C2w/Eez4hjiQLcQSZiCPucbVqika/n9VSYQ3bKzQpxfxSfiAx5ciS39nQsEgGmxsStlWCJMXs2bNl4sSJ8tZbb0n37t3NfWPHjpW+ffvKmDFjpHXr1jmfqw2Ili1bZv3d999/L5dddpn861//kl69epXfr73eKA1xBHEijqAy4gii4kpSKu5qqaqSUjZWS9mSlII/GMrnwYWTjcO74P/navPfhO+mT59uhl2kGxOqd+/eUrNmTXnzzTerfO5DDz0kzZo1ky5dusjw4cNlzZo15b/TXvNNmzbJwoULpVOnTtK2bVv53e9+JwsWLAh1fxA/28838PNzJY7EhziCKLiSlApCWEP4bGRTUqqqaim4hYopj9Dz7Q+bGxIo3IoVKyr8XLduXXMr1qJFi8ycHZnKysqkadOm5ne5nHzyybLddtuZnvD//ve/cvHFF8vcuXPlySefNL//7LPPTIPi6quvlltuuUWaNGlier4PO+ww8/g6deoUvc2wt2oqE8PE/WF7HCEpVRjiCFwbzudSUiru4+nSED6bklJJ/K76jMSUZ40KRYLKXbY3JHxtUJR9/rWU1QzhYnnTevNPu3btKtw9YsQIGTly5GYPHzZsmFx77bXVDr8ols4dkrbbbrtJq1atzFCLTz/9VHbYYQfTmNB5Rm699VY5/PDDzeP+/ve/myEbr7zyivTp06fo94Y7cYQY4jZX4ohPQoshijgCWC3fFfiKrZayaRifbUmp6qqlmF/KLSSmPGxUpNG4cIdLDQnfklJR0CEMuupRWq5e7gsuuEBOO+20Kl9r++23Nxf4S5YsqXD/hg0bzApLueb9yEYnpVXz5s0zDQptYKjOnTuXP2abbbYxQzbmz5+f9+vCjzhCDHGHSzFEEUcKRxyBS1VTLlWglHIcg0hKuVItZVtSCv4hMZUANC7s5FpDQtGYKI42JjIbFLnoxbveqtOzZ09ZtmyZzJw5U7p162bumzx5sumpTjcS8vHuu++af9MNif3228/8q8MydF4QpY2UpUuXmqEbSCZiiL2II8lBHIErXEpKhZ2Q8mFeqbASUoqkFDIx+XmCLqpsnwQ1KfgcUCqdUPaII44wS3bPmDFDXn/9dRk6dKiceOKJ5Ssp6cSzHTt2NL9XOsxi1KhRphGiy3Y/88wzcuqpp8qBBx5YvlrSzjvvLMccc4yce+65Mm3aNPnggw+kf//+5nUOOeSQWPfZNy7GkfS5i/NX/PgcUCriCMJISCUhKRWk6qql4h7GZ3NSKqhJz/nO2oPEVIIaFWlc0EbPhwady995H+mqSHqhr3N76PLe+++/v9x1113lv9c5PrTHOr1akk44+9JLL5k5P/R5OtzjuOOOM0t6Z3rggQdMb/lRRx0lBx10kNSuXdssKa7/Ilgu/025fj5zEXEEQSOO+JEIirNhbcM2uKy65EyYiaHq3tfmpFQ+mF/KPQzlS8A8IblkXtwyRCMcLjcgMtGYsI+unPTwww/n/H379u0llUqV/6wT57766qvVvq4OFbn33nvNDeEjjiAJMUQRR+xDHPFHOjEU5rxTJJ/8FlUSLIikVFDVUrALiamENypyXfzSwCieTw0JRWMCCJePcYQYUjzfYogijgDRyEwe5UpSkWBCHNVYQVVJkZTyF4mpAPjSqMhEAyPZjYg0GhNANHyLI3R2FIY4AiBoJKDCHSZWyATo1SVsqpprShNIQcw1FdewwDiSUvkO4+NvxC4kpgLiW6MiEw2M5DQgMtGYAKJFHEkO4ggAIExxJqLCmEuKSin/kZgKkM+NiuouqH1sZCSl4ZANjQkgHkmNIz7GEEUcAQB7hTknV5ByVU3ZkHyKYnLzQpJSTHruLhJTAUtKo6KQi28XGhxJbjxURmMCiFcS44jrMUQRR35GHAGA4Ibz2ZyECnu1PSqlkoPEVAiS2Kgo9WI9jIYHjYTC0ZgA7EAcKfxcThyxA3EEAJIjjGRUsQmpQqqlmF/KPiSmQkKjojBc/MePxgRgF+JIYYgj8SOOAID/wkpGpVEllUw1494An3GBBlfwXQUAlII4AgDFJ1nCTvYEQbfR1qQUc0u5j4qpkNHjDdvRmADsRQyBC4gjAOCnKBNmVEolG4mpCNCwgK1oTAD2I4bAZsQ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"text/plain": [ - "
" + "
" ] }, "metadata": {}, "output_type": "display_data" }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Plotting at t=1\n" - ] - }, { "data": { - "image/png": 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ee26Nr1NWVqZt27ZF/Rd2tiQWAL9w/sWH5AL8FtZYgt9efLiWw082nH829AkAL3gRT5gWSwAwj2mzKADbZSRy502bNqmiokJt2rSJur1NmzZatWpVzMdcfPHF2rRpk3r16iXHcbR3715dddVVtU6vvPfeezVx4sREDg0+s6Fjh+DLWpepso7mBglrv81VfvuNfh8G4CtiCf+YnuAkloAJTI8lAOzjRTxBLFF/7E+BIKNAAaReUptpJ2LhwoW655579Nhjj2nZsmX661//qjlz5ujOO++s8THjxo3T1q1bI/+tX7/e7cOskQkjIE1OLLA8A0xj+vlo8u8ZMJXtsYQJTL/2mH7tRriYHt+a/nsGTJVoPEEsASAWm2ZR2HKcQJWEZlS0atVKDRo0UGlpadTtpaWlysvLi/mY22+/XZdccomuuOIKSdLxxx+vnTt36sorr9Stt96q9PTqtZKsrCxlZVF5l8zuiJjcgUO4MRoSMBexBA5GPAFTEU/UbH5JF/XNiz1qHfCCF/EEsQSAg5H4T872w9PYqwVxSWhGRWZmprp3764FCxZEbqusrNSCBQvUs2fPmI/ZtWtXtQa/QYMGkiTH4SS1FUmF+mn2jRP3f0iOyeeo3wVIE2aKIbzCGEv4/Zvz+5pTG5Ov1aYjlvCGqeeoyb9rwAthjCdgN9pj+1GkANyX0IwKSRozZoyGDRumHj166OSTT9bkyZO1c+dODR8+XJJ06aWXql27drr33nslSQMGDNADDzygk046SQUFBfryyy91++23a8CAAZGgALGZ2gExtcNmAjeCj3ifc/vhaSl/bdtVnauMhgTMQiwBYomaEUuYx9SZFex9hbAjnjAb+1MgSChSAN5IuFAxePBgbdy4UePHj1dJSYm6deumefPmRTaxWrduXdQohdtuu01paWm67bbbtGHDBuXm5mrAgAG6++67U/cu4BkSC/uYOBqipmMi6WBmgoHkAsKMWMI7Jg56IJbYh1jCLibGEkDYEU/ANs2+cWhTLROEAkVZh3JlrSf+hh3SHAvmOG7btk3NmzfXbYvPVnbThp69rp9LNZBYMIuJyYRkhTkwMi3B4Gehwo91pXfv2KO7TnlTW7duVU5OTkqf26t2ws33AHf5FUtIxBMHIpYIBmIJs4QpnghCLCERT9iq6hw5o9d4ZWRk+304xmNGRe3C3JbaIghFiiomFCpMioUryndr+TO3Wh1PBDWWSHhGBcIpbIkFky6gqXbweyNA8o+fsyrYBBPwBkWK/YglgiPMsQQzKwAAqVDVloapDbVJkIoUgE0oVBiIxII/gpxQqM2B7zvoQRLJBQCAm4glgh9LSObFEywnCcA0zKaIX9jaUBtQpAD8Q6ECtQp6kSKsCYWahCFIIrkAIAwY9OAdYoloYYglJPPiCQCA/cLShpqMIgXgLwoVNfBrqQaTEgtBTSqQUIhPkIMkkgsA4J0gxhPEEvEJciwhmRVP+DXwgaUkAcAdQW9DTUSRAvBfut8HAHil2TcOiYUkBfGzMylxZlKBEoD9TLqmmHStTYUgtode4bMDgHBg2afUq2pDaUfdE/QiRdDfH4KDGRWIKSiJBRry1Arahl8mjYT0A6MgAXf5uZG2CYISS0jEE6kUtBGiYY8lAADeOjgmCUJb6icS+IBZmFFhEFNGQAYhscBoA3cF6fM15Xw35fcPwG6mXEtMubbWV5DaOxMF5fM15Xw35fcPAPDOgbMtgtCmeokiBWAeZlQgiikdrWTRMHsraDMsAAD1Z3ssIRFPeC0I8QQzKwAAJogVw9jcvrqFIgVgJmZUxODHUg0mjICyObHA6AF/2f7523zuAwBSx/b2zHa2f/4mxBN+9CnCvswdgP3Yn8JMzLqIRpECMBczKmA9Glpz2Dwi0oSRkGu/zVV++42+HgMAezHoIXnEEmZp9o1jZSwBAIANaop7wtD2UqQAzMaMCgOQWEgOowHMZet3Y+PvoL4YBQm4I4y/LRuvoba2V2Fg63djwu/AhL4FAMA+B8+8sLEdrg1FCn+FoRCG+mNGBYzoUCUiaI1lkDEiMnHMqgCQDBKTiSOesIONszVNmKUJAF5j2adgCsrsC4oUgB0oVIQcRQq4zbYEA8kFAEicTfEEsYSdGPwAAIA56oqnTGqzKVIA9mDpJ58xAjI+QZx2GDY2fX82JdwAwG82XTNtaotQnU3xoN+/C/oYAAA/2dJehwkFG9iAQsVBwrSmtN8dqHjRwAUHCYb4kFwAkAg/rxk2xRK2tD+omy3fpS2/j1QIUx8KAFA3U2ZUkJwH7EKhIqRs6DiRVAguvlcgts2bN2vo0KHKyclRixYtNGLECO3YsaPWx+zevVujRo3SoYceqqZNm2rQoEEqLS2Ned///ve/at++vdLS0rRlyxYX3gFgHtqcYLIlTrQh5kawEEvAD+xPgQNRpADMNWXKFOXn5ys7O1sFBQVasmRJrfefPHmyjj76aDVq1EgdOnTQ9ddfr927d7t2fBQqfMSo6ZrZ0PFE/djwHYclucAoSHMMHTpUK1as0Pz58/X666/r3Xff1ZVXXlnrY66//nq99tpreuGFF/SPf/xD3333nX7xi1/EvO+IESN0wgknuHHoOICXvylmU9TMlkQ26ofvuGb0NcKJWAJIjeZryqL+Q3woUgDmmj17tsaMGaPi4mItW7ZMJ554ooqKivT999/HvP+sWbM0duxYFRcXa+XKlXrqqac0e/Zs3XLLLa4dI4WKELIhsYBwIIlUM5IL4bNy5UrNmzdPTz75pAoKCtSrVy898sgjev755/Xdd9/FfMzWrVv11FNP6YEHHtBPf/pTde/eXTNmzNAHH3ygxYsXR9338ccf15YtW3TjjTd68XYAX9G2hIvp37fpsTeCg1gCSM7BRYlYhQmKFXWjSAGY7YEHHtDIkSM1fPhwHXPMMZo6daoaN26s6dOnx7z/Bx98oNNOO00XX3yx8vPzdfbZZ+uiiy6qcxZGfVCo8AlJyNhM72jCHSZ/7yQXEMu2bdui/isrq3/HZdGiRWrRooV69OgRua2wsFDp6en68MMPYz5m6dKl2rNnjwoLCyO3denSRR07dtSiRYsit/3rX//SHXfcoT/96U9KT6fpR/2ZfG00uU2Be/jeY6PPYbZUxxPEEkDd4ilKIDHbD0+jSAH4JN5Yory8XEuXLo1q79PT01VYWBjV3h/o1FNP1dKlSyOFia+++kpz587Vueeem/o38v9kuPbMMBKJBZiq2TeOMcHNwbLWZaqso/cBz9pvc5XffqPnr2uzd0p/pIwd7q2Ru3dnmaQ31aFDh6jbi4uLNWHChHo9d0lJiVq3bh11W0ZGhlq2bKmSkpIaH5OZmakWLVpE3d6mTZvIY8rKynTRRRfpD3/4gzp27KivvvqqXscJc/iVgCSWgKmIJZAKbscSknvxBLEE/GDy/hSpLkI0X1Nm9Pv1g0ntrolFivwO1fvza9cziCAMTMtNbNq0SRUVFWrTpk3U7W3atNGqVativsbFF1+sTZs2qVevXnIcR3v37tVVV13l6tJPFCoOEPR12k1NLJBUQBWTEwzAgdavX6+cnJzIv7Oyag5Axo4dq0mTJtX6fCtXrkzZsR1s3Lhx6tq1q375y1+69hqACYgnIO0/D0yMJ4JerJhf0kV982J3dBFbvPEEsQRQN2ZGeM+kttaWIgWQaonkJhK1cOFC3XPPPXrsscdUUFCgL7/8Utdee63uvPNO3X777Sl7nQNRqPABU7D3I6mAg5larAh6cgGJycnJiQoGanPDDTfosssuq/U+nTp1Ul5eXrVNrPbu3avNmzcrLy8v5uPy8vJUXl6uLVu2RI2ELC0tjTzm7bff1meffaYXX3xRkuQ4+667rVq10q233qqJEyfG9T4AiUEPsIep8QRwoHjjCWIJIJqfRQlmVexDG1szChTwUryxRKtWrdSgQQOVlpZG3X5ge3+w22+/XZdccomuuOIKSdLxxx+vnTt36sorr9Stt97qylKQFCpCwsTEAkkF1ITkgvcYBeme3Nxc5ebWXaDu2bOntmzZoqVLl6p79+6S9iUGKisrVVBQEPMx3bt3V8OGDbVgwQINGjRIkrR69WqtW7dOPXv2lCS99NJL+uGHHyKP+eijj3T55ZfrvffeU+fOnev79nAQr2ZnMuhhP+IJ1MTEeMKPgQ8sJWk/YgmYyouEPTMlzGNa22rKbAoKFDBZZmamunfvrgULFmjgwIGSpMrKSi1YsECjR4+O+Zhdu3ZVK0Y0aNBA0v5BC6lGoQK+IKmAupBc2IfkQnh07dpV/fr108iRIzV16lTt2bNHo0eP1pAhQ9S2bVtJ0oYNG3TWWWfpT3/6k04++WQ1b95cI0aM0JgxY9SyZUvl5OTo17/+tXr27KlTTjlFkqolEDZt2hR5vYPXowZqw6AH2MjEeAJwC7EEgsL04kSYZ1WY1qZSpADiN2bMGA0bNkw9evTQySefrMmTJ2vnzp0aPny4JOnSSy9Vu3btdO+990qSBgwYoAceeEAnnXRSZOmn22+/XQMGDIgULFKNQkUImJZYIKmAeJFcQNg899xzGj16tM466yylp6dr0KBBevjhhyN/37Nnj1avXq1du3ZFbnvwwQcj9y0rK1NRUZEee+wxPw4f8BTxBOJlWjzBcpJwE7EEbGV6cQIUKWKhQAGbDB48WBs3btT48eNVUlKibt26ad68eZENttetWxc1g+K2225TWlqabrvtNm3YsEG5ubkaMGCA7r77bteOkUKFx8K+VANJBSSK5ALCpGXLlpo1a1aNf8/Pz682xTI7O1tTpkzRlClT4nqNM88807VpmgguBj3AdqbFE15jhmZ4EEvAJjYXJ8I2q8K0NtTvIgUFCthq9OjRNS71tHDhwqh/Z2RkqLi4WMXFxR4c2T6p3/UCRjEpsUBSAckK+7kT9gIngGhhvyaEvU1A8kw6d0yK0QEgGckm6ZuvKYv8BztQpIhGkQJwD4WK/8erzS/DyqSOIexk0jlEcgFAmJh0zTOpLYCdOIfcRZ8KQE2CWJwI2vuJxbQihd9SUaSg0AHUjKWfPOT1CEhTEgt0CJEqYV+2AQDCjHgCqWJKPOH1cpIs/wTAS2FI4gedCW3lwfyaTUFxAfAGMyoAWMWURJXXhUAvCp2MggTqx4vfEIMeAACAyYI4c6I2QX2vFCn2o0gBeIdCRUCRWECQcV4BQHhwzYcbTDmvTInZAaA+2HciWChS7EeRAvAWSz/BNaZ0AP3kZqCW7OZlQWHCsg1eL9kAAF4yIYFKLEEs4SYTYgkAsBVFiWAysV2kSAGEB4UKj3i5VAOJBX94HajFer2wJRxIMABAcBFL+PN6xBLe83LgA/tUAEDqNV9TFoj20+/28GAUKIDwoVCBlAtLYsHEESQHH1MQgiXTkVwA4JWwDXoIA2IJM5hQrAAAwE+mtYMUKYBwolChYG0gS2LBXSYmFGpz4PEGNdFAcgEAgifIgx6IJRALy0kCgN1snlVhWn+aIkUwBTm+R+pQqEBKBfHCY1tCoSZV78PW4Kk2fhcrSC4ACBK/Bz0EMZaQghFPBLlo4XcsESTzS7qob94qvw8DABAHk9o+vwoUEkUKwBQUKjzg5VINfgpaYiEICYVYgpxkAADYjVjCHkEcABGWYgVLSQKAO2ybVWFSm0eRAoAkpft9AEgdP0dABimx0HxNWaATCwcK0nsN0jlYm7AUPgHbuL2MJL99uwSpfa1L0N6rn/GE37OZAADhQZFiH4oUgFkoVAD/T9A62okIynsnuVB/QdqzB0DiGPRQP0FpT5MR5vcOAEAVG9pCihT7UKQAzMPSTwFBYiF5NgQSXgnCMg5hWbYBAIKEWCI4iCXqh32vAABuMqWv7GeBQqJIAZiKGRUuC/pSDTYnFhj5VzM+m+QEZVYFgHDiGpY42sua2f7Z2BzjxiPofRQA8JOp7R9Fin0oUgDmolCB0LG94+wlWz8nkgsAgiTov3lbr9m2tpFeI+4CAMB/FCn2oUgBmI1CRQD4NQLSxsQCHeXEkWAAALjF1liCdjFxNn5mfp2fzG4CAKQSRYp9KFIA5qNQgVAgqVB/tn1+JBcAIH5cu+JjW1toGuIxAEBYmNLeUaTYhyIFYIfQFyrml3Rx7bmDvFSDTSMgTQkQgsC2BINN5ykAhI1N12jb2j/T2fRZ2nSeJsrNvoqbfSwAQHxMKFKUdSinSAEgbhl+HwDqx48RkLZ02GzqBNum+Zoybe2c5fdhAABSgNkUtSOecEfV52pDPNHsG8fzZE/WukyVdfQ3sQMAQZa5cn3Uv8u7dkj5a/jZbzalSOE3ihSAXUI/owLBRFLBfbZ8xn4U1rxI+gV5xhaA/YL6W7dh0AOzKLzBZwwA8ELmyvVR/8X6O1KHIgWAZFCosBizKWKjw+sdkjjBxHINQGL4zQQPbZu3bPi8bYiBAQDRaitMBJnfsykoUgBIFoUKBIoNHd0gMv1zJ7kAALEx6KE609u0oOJzr45l2QAgcfUpTgShoEGRgiIFYDMKFS4J4lINJBZQGz7/aCQXAMA+tGX+Mv3zNz0WTkYQ+ywAwiesMycO5meRwoRNsyWKFIDtKFQgEEzv2IaFyd9DEJMLAGAbk6/FJrdhYcL3AACIh1vFCVuLHX4XKUxAkQKwH4UKS3k9WtvUxAJ7JJjH5O/D1PM4WYyCBIItaL9xk6/BJrddYWRyfOf1ecwMTQCI5sXMiVQ9/9bOWSl5nrpQpAAQFBQqYC1TO7Dgu6lCcgGAybhG7UObZS6+GwCAxNJOtaFIsY9NsynWrvdnMFLWemJ/mI9CBepk4ghIOq7m4zsCAFQxMZaQaKtsYOJ3ZOr5DABB4ndxwoaiCEWKfWwqUoQVsRPiFepCxfySLn4fQlIYAQlbkFwAAJjKxDYKiIXYH0CYBGXmhNvLPlGk2IciBRAsoS5UuCVIa0qbmNQlsWCXsH9fJBcAmCjs16awt022MfH7MjFGTpZbfRdbB4UB8IeJBQrTjqcKRYp9KFIAwUOhAlYxsaOKupn2vQUpuQAApjPtmmtam4T48L0BQDCZWKAwGUWKfShSAMFEocIyXo6AJLGAVOL7c0eQZnAB2I/ftjtoi+xm2vfnZawc9llQAIInyAUKt5Z9okixD0UKILgoVMAKpnVMkRyTvkfTCnEA4JWwDnowqQ1C8vgeAcBuQS5QuIkixT4UKYBgo1CBmEgsAKlj4yhI1pUG4sNvBfCeSbGhSTEzAJjMxgJFedcOfh+C7yhSAPAShQoYzaSOKFLDpO+U5AIAuMeka6xJbQ9Sg+8UAOxhW4GiPtxY9smv2RSmFCnyO2ykSAGEBIWKFHNzTWkbR2UDsZBcAAB4hTYHQeFmX4B9cQC4wcZZFKahSEGBAggTChWoxpQRkCQWgi1s3y+FRgAmCNu1KGxtTdiY8v2aEjsDgCmCUKAwYdmnMBcpgjqLYu16fwYGZK0PVx8A9qJQASOZ0vFE8AUhucAoSCBYgvCbDsK1FXYgZgQAs9heoKiPVC77FPYiBYBwolCBKCYkFuhwhgffNQDALbQx8JIJMTQA+CkIsyhMQZECQUKMhERQqLBE2JZqQHiYkEii4QSA1DHhmmpC2wLvhOn7pk8AwERBK1D4ueyTH0WKsg7lvhcpgrrUE4DEUKiAUcLU0cR+YfneSS4A8FNYrkFhaVMQje8dALzHLIr9UrHsk19FCr9RoABQhUJFCtm+prTfIyDpYAIAAMBWfsfS9WV7XwaAtyhQpJZfyz35jSIFgAOFtlAxv6SL34cA4AB+F6psTy4AgAn8vpb63ZbAX3z/5qHPBQRP0GdR+Lnsk9f8nE3BUk8AYgltocImYViqgY4lAAAA6isMMWUY+gYAzBTkAkV91HfZp7At+USBwltZ64kbYA8KFZDk/whIQApHcsEtLNcABAO/5eTRhsAExNQAgooihTsoUoTH2vXE+UBdKFTAdyQWcCA/zwcvkguMggTgBy+uPX4maIklcCDOBwBInaAv9XQgr5d9ClORgqWeAMSDQgUAAAAAAACihKVAUR/JLvsUtiIFwomZpkgUhQowAhLG4bwwAxtgArXjN2IG2gzEEvQZmgDgNooU7qFIAQCxUagwXJCXiSGxABORXACAxHHthImCHGsGuY8AwH8UKdxDkcJbffNWRf4DYD4KFSnC5pdAagU5uQAASA3aCiC16NMACGuRIpn9KRJd9okihbdMKk74tZF21noGNsAuGX4fAPzl1wjIsCcWMld9G/d9y7u0d/FI4IesdZkq6+hPwAggfBh5HUzEEvFpvqYs6fXD66PZN44vCSkAqI+wFimCyo8ihQkFCgD2olABuCyRREI8jw9TsoHkAgCYj0EP3qhPPBHrsWGKJwAAtaNA4T6v+5dhL1KYNJsCQPwoVMBzYUgs1Lc4kchzk2hAlbXf5iq/vTnBIYDEsOQKDuRmLHHw8wc9lvBr4AMA2IAihTfLPnmJIgVFCsBWFCoM5vZSDWx8mVpuJxTqet2gJhlILgAADhbkQQ9+xBNhKlp4ye0ZmiwlCaC+KFIED0WK2EWK+SVdPD4SkHNEMthMG54KYmIhc9W3vhUpTDyOoKBRhR82b96soUOHKicnRy1atNCIESO0Y8eOWh+ze/dujRo1SoceeqiaNm2qQYMGqbS0NOo+H330kc466yy1aNFChxxyiIqKivTpp5+6+VYQElwrU8eUdtyU40i1IMagQCzEEogXRQrveLXsE0UKM2dSsJE2TDJlyhTl5+crOztbBQUFWrJkSa3337Jli0aNGqXDDjtMWVlZ+tGPfqS5c+e6dnwUKoAkmdqRN/W46iOIyQU2t0UsQ4cO1YoVKzR//ny9/vrrevfdd3XllVfW+pjrr79er732ml544QX94x//0Hfffadf/OIXkb/v2LFD/fr1U8eOHfXhhx/qn//8p5o1a6aioiLt2bPH7bcEnwXxWhO0NsHUdtvU4wJQO2IJxIMixX5BWfaJIoWZRQrAJLNnz9aYMWNUXFysZcuW6cQTT1RRUZG+//77mPcvLy9X3759tXbtWr344otavXq1pk2bpnbt2rl2jCz9BCTBho570JeEAoJm5cqVmjdvnj766CP16NFDkvTII4/o3HPP1X333ae2bdtWe8zWrVv11FNPadasWfrpT38qSZoxY4a6du2qxYsX65RTTtGqVau0efNm3XHHHerQYV9HrLi4WCeccIK++eYbHXnkkd69SQARNsQS0r7jJJYA7EAsgXhQpAgeihQUKYB4PPDAAxo5cqSGDx8uSZo6darmzJmj6dOna+zYsdXuP336dG3evFkffPCBGjZsKEnKz8939RiZURFSfizVEIQRkDaOLrTxmGMJwvkD1GbRokVq0aJFJLEgSYWFhUpPT9eHH34Y8zFLly7Vnj17VFhYGLmtS5cu6tixoxYtWiRJOvroo3XooYfqqaeeUnl5uX744Qc99dRT6tq1q+tBBpBqQWgLbGyXbTzmWPw4f1geDV4ilkBdKFJ4z+1lnyhSUKQA4lFeXq6lS5dGtffp6ekqLCyMtPcHe/XVV9WzZ0+NGjVKbdq00XHHHad77rlHFRUVrh0nhYoUWPutP+vNwVu2d9BtP34/kFxATbZt2xb1X1lZ/ZNfJSUlat26ddRtGRkZatmypUpKSmp8TGZmplq0aBF1e5s2bSKPadasmRYuXKhnn31WjRo1UtOmTTVv3jz9/e9/V0YGEyuRPK6RibO9Lbb9+BEf+jbeSXU8QSyB2lCkqM72ZZ8oUsRXpGAjbQRZvLHEpk2bVFFRoTZt2kTdfmB7f7CvvvpKL774oioqKjR37lzdfvvtuv/++3XXXXel/H1UCWVUYcNFKmhrSts8AjJInXKWb0DQrfuuldIbZbv2/JU/7JakyLIHVYqLizVhwoSYjxk7dqwmTZpU6/OuXLkyJccXyw8//KARI0botNNO05///GdVVFTovvvuU//+/fXRRx+pUaNGrr02gP2CEk/YvrRk8zVlRiWZ6itrXabKOnqfqAoyt2MJKfF4glgC9UWRAqlgSpHCplkUbKQdXus2mJebSPg1KivVunVrPfHEE2rQoIG6d++uDRs26A9/+IOKi4tT8hoHC2WhAohXUJIKB7I5wRC05ALstX79euXk5ET+nZVV83l5ww036LLLLqv1+Tp16qS8vLxqm1jt3btXmzdvVl5eXszH5eXlqby8XFu2bIkaCVlaWhp5zKxZs7R27VotWrRI6enpkdsOOeQQ/e1vf9OQIUNqPTbAFLYOeghiLCEx+AHxm1/SxaqkkpfijSeIJQA7ubnsk9ezKShSIBHMvPZOvLFEq1at1KBBA5WWlkbdfmB7f7DDDjtMDRs2VIMGDSK3de3aVSUlJSovL1dmZuoLYhQqQsjrCwaJBTORYPCfG6Mg136bq/z2ZgSRQZaTkxMVDNQmNzdXubl1j6Tp2bOntmzZoqVLl6p79+6SpLfffluVlZUqKCiI+Zju3burYcOGWrBggQYNGiRJWr16tdatW6eePXtKknbt2qX09HSlpe3vKFX9u7KyMq73APe5sdRK0GZn2ohYwkxeD3xo9o3j+hrlsFO88QSxBOqD2RSx2bzsk5dFCgoUgNnijSUyMzPVvXt3LViwQAMHDpS0b8bEggULNHr06JiPOe200zRr1ixVVlZGBir8+9//1mGHHeZKkUJijwogpqAnFqrY+D69LnwxEgBe6dq1q/r166eRI0dqyZIlev/99zV69GgNGTJEbdu2lSRt2LBBXbp00ZIlSyRJzZs314gRIzRmzBi98847Wrp0qYYPH66ePXvqlFNOkST17dtX//vf/zRq1CitXLlSK1as0PDhw5WRkaE+ffr49n5hNwY91M3GNjYZYXmfgA2IJXAwihTBQ5EiMX4u/e7Xsk9ATcaMGaNp06bp6aef1sqVK3X11Vdr586dGj58uCTp0ksv1bhx4yL3v/rqq7V582Zde+21+ve//605c+bonnvu0ahRo1w7RmZUAAcJW4fb1tGQQBA999xzGj16tM466yylp6dr0KBBevjhhyN/37Nnj1avXq1du3ZFbnvwwQcj9y0rK1NRUZEee+yxyN+7dOmi1157TRMnTlTPnj2Vnp6uk046SfPmzdNhhx3m6fsDwoJYAoBfiCUAM7gxky5sRQpmUQCpNXjwYG3cuFHjx49XSUmJunXrpnnz5kU22F63bl1k5oS0b/+LN954Q9dff71OOOEEtWvXTtdee61uvvlm146RQgVcZdsIyLAlFqqQYADM0LJlS82aNavGv+fn58txokeyZ2dna8qUKZoyZUqNj+vbt6/69u2bsuMEUDNiCTuw7xWCilgCVZhNUTMbl32iSIF4sZE2ajN69Ogal3pauHBhtdt69uypxYsXu3xU+7H0k4HcXFOaZWxqFtbEQhWb3r9tBTAAQPJsuubb1Ja6IezvvzZuxuDsRwMAwRamIkXfvFUpK1L4uewTgORQqABEx7oKnwMAAMmhDd2HzwEA/MdsCn+lctknL4sUfgvSLIow70/BAGnUR1KFiilTpig/P1/Z2dkqKCiIbMRVky1btmjUqFE67LDDlJWVpR/96EeaO3duUgcMe9gyApIOdTQ+j+rC3NAyCgVusT2WMP23wexMb9F2RrPl87AlVgVQM9vjCXjPpmWfvJ5J4edsiiAVKQAkL+E9KmbPnq0xY8Zo6tSpKigo0OTJk1VUVKTVq1erdevW1e5fXl6uvn37qnXr1nrxxRfVrl07ffPNN2rRokUqjt93a78Nb5U0CGzpSKO6oKwtnbUuU2UdwzNKBpCIJRA/Esn2sm3PCgD28Sue2HZElhpk7uuHmNhOMZsCiQpigcL0QUVuYX8K2C7hQsUDDzygkSNHavjw4ZKkqVOnas6cOZo+fbrGjh1b7f7Tp0/X5s2b9cEHH6hhw4aS9m3gBcBcJBcAuIlYAkHCoAcE0dpvc5Xf3v/NVIHamBBPbO2cZWSxAv5J1bJPXs2mCGKRAoC9Elr6qby8XEuXLlVhYeH+J0hPV2FhoRYtWhTzMa+++qp69uypUaNGqU2bNjruuON0zz33qKKion5HjoR5uVSDDcEaiYXa8fkAcAOxBIKEtrJ2Nnw+XsasLJsGpI5J8cTWzlnGzPRmNkXtbFn2KQz7UgS5SBHm/SmA+kpoRsWmTZtUUVGhNm3aRN3epk0brVoV+yLz1Vdf6e2339bQoUM1d+5cffnll7rmmmu0Z88eFRcXx3xMWVmZysr2dxq2bduWyGECdbKh42wC02dWBGX5JyBMiCUQL9MHPRBLxMf0WAKAnbyIJxKNJZhdgVTwel8KP7hdpAjrsk8mYFAG6iupzbQTUVlZqdatW+uJJ55Q9+7dNXjwYN16662aOnVqjY+599571bx588h/HTokXvUGakJiAcmgwQX8QyxhDq6FSAaxFwATJBpPJBNLMIgq3Oq77FPQixR981YFeiaF39ifAkGQUKGiVatWatCggUpLS6NuLy0tVV5eXszHHHbYYfrRj36kBg0aRG7r2rWrSkpKVF4e+yI8btw4bd26NfLf+vXhmb6Ytc7+CwujSIKF5IJ91n7LVFOYi1iiOn6z9qFtDJYgxK5B6EMAifAinkg2lvBrKSiWfaqd6cs+haFIEQYs+wTUT0KFiszMTHXv3l0LFiyI3FZZWakFCxaoZ8+eMR9z2mmn6csvv1RlZWXktn//+9867LDDlJkZO6DOyspSTk5O1H9AKpBYSA6fG4BUIZZAPExOHNMmJofPDUAqeRFP1DeWYHYF4kWRInVY9gmwW8JLP40ZM0bTpk3T008/rZUrV+rqq6/Wzp07NXz4cEnSpZdeqnHjxkXuf/XVV2vz5s269tpr9e9//1tz5szRPffco1GjRqXuXaBOLNVABzmoTE5mAYiNWMJdjKyGqYjFiMmBVLIhnvCqWMFsCv/Vd9knLwS9SAHAfgltpi1JgwcP1saNGzV+/HiVlJSoW7dumjdvXmQTq3Xr1ik9fX/9o0OHDnrjjTd0/fXX64QTTlC7du107bXX6uabb07duwDgOjbDdE/WukyVdfRuFA3gN2IJ2IpEOwCYw5Z4oqpYwQAr/5i87JNXsykoUgSbCftTMBgDqZBwoUKSRo8erdGjR8f828KFC6vd1rNnTy1evDiZl4JlTA2+SCwgFZp941gxUgawAbGEfeh8IBVMHfjQfE0Zy7QAFrIpntjaOcuV/jKzKexFkSJY2J8CqL+El34CEF6mFnxMLZABABJn6jXd1DYQAGAPCqLBlMxgtqAWKfrmrfKtSMH+FID9KFQg8EgsAACA+iCWSC0+TwCAV0xc9inIRQp4z4Rln4BUoVABICEkFwAAAAAgOcyqCDeKFMEU9mWfWCIWqUKhIgS8umCYuFQDSXUAAFAfxBLuMPFzNTGWBRA8XGuCxcQ9DClSALAVhYp6WPttaiumWeuYrgU7kFwAALiBaznCwK1BRPQlgPBhI+3ambbskxezKcJapGB/CiAYKFQgsExMpgMAAGAfYjWzpXpQFgDAXWEtUvjNz2Wf2J8CQUOhAkBSwppcYO1FAGEU1mtfWNs6AABQt0SWffJqbwqvUKQA4AYKFUgJ05ZqILEAAAgjln6BbYjZAISJaf3mIDNt2Se3eTmbgiLFfmHfRFsK74AmuINCBYCkkVxIHZKLAGAW2rhwIokIAEg1t2dTUKQIJ5Z9QhCFrlDBBjtAcJFcAAB7cQ0HAAC2SGTZJzdRpAAQJKErVCD4GAEJALAFm9WaiVjCW3zewcdgMYCCvJfCsuwTRYp9/GpjWPaJZZ+QehQqAo6LBtxGcsFMJD8BADAXMToAhINbyz5RpAg3ln1CUFGoQKCQNAcAALAPMRwAwBamLPvkBYoUALxEoQL1xhRWkFwAAAQFbRqIbQHYIHPler8PwUhhWPbJq9kUFClqxrJPgDsoVABAgliuAUCYcM0DAKB+KICGkxvLPlGkiBbGPZBMWfaJPgLcQKECgcEISEh0AgDARly7IRHLAQDM5+eyTxQpzMBsCsA9FCoMkbXOjIookCySCwAA29GWwXb0KQCEWZCXfaJIASAMKFQAAAAAAAAAKZLKZZ8oUqCKKcs+AW6hUAEAAAAAAOACljcMDj+XfXKbjUUKP/anYNmnfdifAm6hUIF6MSXoYqkGAAAA+5kS05kS4wIA4hPUZZ+8mE1hY5ECQDBRqAgwKpzwminJBQAAEkUbBgAAUiFVyz5RpDCL37MpWPYJYUChAgAMwQaYAOqDawhgFwYVAUBqJDObIlFeL/tEkQKmIn6BmyhUAAgclmsAAHtwzQYABJVbbVzmyvWuPG+YmLzsE0WKunm9PwWzKQBvUKiA9ViqAQAAIDiI7QAAcI/tRQoAwUWhAkBKkVwAANiGtgsAADuZtuxTffen8GI2BQCYikIFACSBdRkBhAHXOgAAksPShuYyddknlnwyE8s+7UffAG6jUAEAAJAAr9fEBQAAQLBRpIgfsTgQXBQqAAAAAAAAYJWgLfvkpqAUKbzGbArAWxl+H4Ct1n7r78XKBCZMZWVNaQAAgODJXPWtyru09/UYmq8pM3Z5EABAcky8rrs9m8LrIsX5Ocsi///FbT/29LXhHpZ9gheYUQEAqBVTawEACCcGZwHJMWFQH+wQ5CKF7fyeTQGEEYUKACnHTBcAgC1oswAAsE8Qln3yYl8KL3lRpAjTIDqWfUIYUagAAAAAAABAoJm07FPQNs+OVaSwedknZlNEY9kneIVCBYBAYro1AJiPazUAAEDq+V2kQP0wmwJhRaECAAAAAAAgBSjCu8/2ZZ+CtC9FEIsUzKYA/EOhAgAAAAAAAIFlyrJPYSlSuLHsU5j2pzAJyz7BSxQqYC02vwQAAAguYj0AAOJnSpHCZibMpmDZJ4QZhQoAAAAAAAAYz+Zln7zYQNsLQS1SAPAfhQoAAAAAAIB6Yn8KM5my7JObvJpNEU+RwtZln5hNUR3LPsFrFCoCiosJAAAAAAAICi9mU7jFzdkUJhUpAKA+KFQAcAXrSgMATEdbBb8xuAgA7JXIsk9uoUiRGibMpgBAocIIWevMmtoFAAAAAABgu0SXfUpkf4p42b43RdCLFKZg2SeAQgUAAAAAAEC9sD+Fu2xe9sktXs2mSISN+1MwmwIwB4UKAAAMsXnzZg0dOlQ5OTlq0aKFRowYoR07dtT6mCeeeEJnnnmmcnJylJaWpi1btkT9fe3atRoxYoSOOOIINWrUSJ07d1ZxcbHKy/2fqg4AAFKLWCIcSNr7z+9ln1jyKTVMKVIwmwJemTJlivLz85Wdna2CggItWbIkrsc9//zzSktL08CBA109PgoVAAAYYujQoVqxYoXmz5+v119/Xe+++66uvPLKWh+za9cu9evXT7fcckvMv69atUqVlZX64x//qBUrVujBBx/U1KlTa7w/AMBuLCsbbsQSwH5BXfbJ1CKFG7MpAKTO7NmzNWbMGBUXF2vZsmU68cQTVVRUpO+//77Wx61du1Y33nijTj/9dNePMcP1VwAAAHVauXKl5s2bp48++kg9evSQJD3yyCM699xzdd9996lt27YxH3fddddJkhYuXBjz7/369VO/fv0i/+7UqZNWr16txx9/XPfdd19K3wMAAPAPsQSCihkk3jNlJoWbyz4xmwJh88ADD2jkyJEaPny4JGnq1KmaM2eOpk+frrFjx8Z8TEVFhYYOHaqJEyfqvffeqzbrMtWYUQEAQIK2bdsW9V9ZWf3XJF60aJFatGgRSSxIUmFhodLT0/Xhhx/W+/kPtHXrVrVs2TKlzwkAABKT6niCWMI/7E8Br3gxm8KUIgX8wbJPdok3ligvL9fSpUtVWFgYuS09PV2FhYVatGhRjc9/xx13qHXr1hoxYkTKjz0WZlQAAAIjc32mGmS7NyKlYnelJKlDh+hRXcXFxZowYUK9nrukpEStW7eOui0jI0MtW7ZUSUlJvZ77QF9++aUeeeQRRkACABCD27GE5F48QSwB7Ofmsk9+7U9hcpHCtmWfmE0Bt2V+a1ZuYtOmTaqoqFCbNm2ibm/Tpo1WrYp9bfnnP/+pp556SsuXL0/JMceDQgUAAAlav369cnJyIv/Oyqq5IzR27FhNmjSp1udbuXJlyo6tNhs2bFC/fv10wQUXaOTIkZ68JgAAiC3eeIJYAmFm87JPqdyfwuQihVvcXPYJsTGbwj6J5CYSsX37dl1yySWaNm2aWrVqlZLnjAeFCgAAEpSTkxMVDNTmhhtu0GWXXVbrfTp16qS8vLxqm1jt3btXmzdvVl5eXrKHGvHdd9+pT58+OvXUU/XEE0/U+/kAAED9xBtPEEsAcJtpRQo3MZsCQRJvLNGqVSs1aNBApaWlUbeXlpbGjBHWrFmjtWvXasCAAZHbKiv3zeLIyMjQ6tWr1blz53oefXUUKgAAcFFubq5yc+sOhnv27KktW7Zo6dKl6t69uyTp7bffVmVlpQoKCup1DBs2bFCfPn3UvXt3zZgxQ+npbFEFAIAtiCXMxv4U7kl2NkXQln3yYjZFfdi07JMpRQoTMZsi2DIzM9W9e3ctWLBAAwcOlLSv8LBgwQKNHj262v27dOmizz77LOq22267Tdu3b9dDDz1UbcmpVCG6AADAAF27dlW/fv00cuRILVmyRO+//75Gjx6tIUOGqG3btpL2JQm6dOmiJUuWRB5XUlKi5cuX68svv5QkffbZZ1q+fLk2b94cecyZZ56pjh076r777tPGjRtVUlKS0rWqAQDmKOvoz9rp8B+xRLjYvCRSGKRq2acwLvkkBX/ZJ2ZTwA9jxozRtGnT9PTTT2vlypW6+uqrtXPnTg0fPlySdOmll2rcuHGSpOzsbB133HFR/7Vo0ULNmjXTcccdp8xMd85hZlQAAGCI5557TqNHj9ZZZ52l9PR0DRo0SA8//HDk73v27NHq1au1a9euyG1Tp07VxIkTI/8+44wzJEkzZszQZZddpvnz5+vLL7/Ul19+qfbt20e9nuMwagYAgCAhlgCQCBOLFG5hNgXCbvDgwdq4caPGjx+vkpISdevWTfPmzYtssL1u3TrfZ0xSqDBAWcdyZa2jmgoAYdeyZUvNmjWrxr/n5+dXSwhMmDBBEyZMqPExl112WZ3rWgMAgGAglvAWyz65h2Wf3J9NkYoihU3LPpnCxNkULPsUHqNHj4651JMkLVy4sNbHzpw5M/UHdBCWfgLgivIu7eu+EwAAQIglkiADAISHDUUKt7ix7JMpsylMLFIAJqFQEVB0egAAAGpHUR0AAKRaffenMH3zbAQLsylgEgoVAAAAAAAAMIJXyz6FVapmU9iy7BOzKQB7UKgAAAAAAABAqJi4P0WYl3yS3Fn2CTVjNgVMQ6ECAAAAgHFYmguAyUzYSDvZmQcms/091WfZJ5uKFLbMpjAFsymA+FCogLXovAIAAAAAAJZ9QiymLPsEID4UKgAAAAAAABAapi37xGyK4C77ZOpsCpZ9gokoVAAIJEbUAAAAAHCDCcs+BVFYl32yqUgBAG6iUAEAAABfUFQGAAD1RTxhPzdmU5iw7BOzKYDEUKgAAAAAAACIQ87XZs2msH0Wgh9MWvbJttkUbKINwE0UKgCkHBudAwBsQZsF1Cy/fXLLmABAosJYcLGtSIHEMJsCSByFiiQRtAMIC7cDaAAAAABIhinLPiW7P4Vb3ChS2LSJtgnLPgFIHIUKJM2EgIBRkAAAAMFjQoxnQqwLAGHg5WyKRJZ9chODwYKN2RRAcihUAAAAJICOJQAAQPC5vT+FW1jyyV+mFikAG1CoAAAAAAAAgFVMmfmW6LJPbg56catIwbJP9mM2BWxAoQJASpmwVIMXTJkyDABuCsu1LixtFwAgmGzeiDpsyz4xMzfYmE0B1A+FCgAAAAAAAOD/sXHZJ2ZToCbMpoAtKFTAeoyCBAAACA5iOwAIh/rMpjBl2adE2Ljkk238XPaJ2RRA/VGoABA4NgatklTW0b5ROwBQX7ZeswEAABLdn8INbhYp3JpNETQmFymYTQGbUKgIMBPWX0S4MAISAPxDsROwC7E6AHjH72sue1NEYxNtALFQqAAAAEDoUWzHgZjpA8A2tm2obfKyTzbtT8FsCv8xmwJIHQoVqBdTOnEkFwAAAOxHTAcAQLCxiTaAmlCoAAAAAAAAgCe8nv3h1rJP8e5P4dayT8ym2M+vZZ+YTQGkFoUKACnBCEgAgO1oy2A79qoBEHQs+7SPm0UKxMfkIgVgKwoVhqBTUX8kFwAAAOxFLAcAgP/cnE3Bsk/eYDYFbEWhAkCgmLJvCgAgfly7AQCoPxs21A7bsk9uYDZFND+WfWI2BeAOChUA6i1sIyDdCnYBwERhu+aFrU0DAMAWNg5scGt/Crcwm6JuphcpmE0Bm1GoQL2ZFCyQXAAAALCPSTGcSbEtAASJ6TM+bNifgtkUqA1FCtiOQgUAAAAAAEAAmF4MQPLcLlLYOJvC62WfTJ9NAdiOQkXAhW25BnjPpBGQ2C+/vX9rpgKIH79VM9G2wW3E6ACQmGRmuwVxfwr4x/QiBbMpEAQUKhA4JBfCi6UaAMBeXMPDi9gt+Gxbox1A6pk+08ONZZ9See2zeTaFW/zYRBuAu0JXqCBIBgAAAAAAANwXhE20mU0BeCN0hQq4w7RRkIzM8wafc+qUdTR/4zYA5uNakjq0cd4w7XM2LaYFgGSYNnuhvsdj0rJPfmA2hb9ML1IgcTlfl/l9CKgBhQoAAAAAAACgDuxPkZigbKJtMmZTIEgoVABIimkjIL0SpJE5ABCvsF77wtrWAQAQJibvT8FsCn+ZPpuCIkXimq9hNoXJKFTUQ357KukmI7kQLizVAAD241oeLsRqZqOvA9jNlOWfWPYpeW4XKdxm+2wK04sUSBxFCvNRqDCIW+tKB6WRhjlILAAAwoI2D6nmVmzOHjUAgEQwm8JuzKZIDEUKO1CoQMqYOAqS5AIAAIB5TIzRTIxlAcBmpszqSBUv96dgyafYmE2BZFCksAeFCgAJMTGxAACAm2j7AADwnhcFZJP3p7CZW8s+ecGGIgWzKeJHkcIuFCoQeCQXgo8RkAAQHFzTg4/YDAC8EbQZDfEIwtLXzKbwD0WKYKFIYR8KFQDiFvbEQhCCXgBAcsLeBgIAkIgwFkmwj62baNtQpED8KFLYKalCxZQpU5Sfn6/s7GwVFBRoyZIlcT3u+eefV1pamgYOHJjMy8ICpo6CJLkAAGYhlrAPxVqkgqkxmakxLIDaEU8El2nLPnm1P4XtsylsXvLJBsymiA9FCnslXKiYPXu2xowZo+LiYi1btkwnnniiioqK9P3339f6uLVr1+rGG2/U6aefnvTBInkkF8ztGNuCz889ZR1Tvy4qYDJiCXdxTXEPbWH98PkRkwOpRDwRHz9mNvg1m8KEa6zJ+1PYvOQTsykoUsSLIoXdEi5UPPDAAxo5cqSGDx+uY445RlOnTlXjxo01ffr0Gh9TUVGhoUOHauLEierUqVO9DhiA90xOLDACErAPsQTqYvK13eQ2EQDChHgCQeP2bAq32TqbwoYiBeJDkcJ+CRUqysvLtXTpUhUWFu5/gvR0FRYWatGiRTU+7o477lDr1q01YsSI5I8U1iC5AACoCbEEEE4mx2Amx64AYiOeSIyXMxxS8Vpcl1OP2RR2YzZF3ShSBENGInfetGmTKioq1KZNm6jb27Rpo1WrYk9v++c//6mnnnpKy5cvj/t1ysrKVFa2/wTbtm1bIodptbKO5cpaRzXXTeVd2itz1bd+H4Y1TE4sILb89t6snwokg1iiuvz2G7X2WzpgNiGWSAyxhPtY8g1h40U8YVMsgcSZtj+F7bMpbMVsCsAsSW2mHa/t27frkksu0bRp09SqVau4H3fvvfeqefPmkf86dPBnfUMg7ExPLHg50saE9U6BMCKWMAvXwv1MbyMBAPslE08QSyTOr70pJGKU2ngxm8LGZZ9sKVIwm6JuzKYIjoRmVLRq1UoNGjRQaWlp1O2lpaXKy8urdv81a9Zo7dq1GjBgQOS2ysrKfS+ckaHVq1erc+fO1R43btw4jRkzJvLvbdu2ERQgpRgJCQD+IJZAvLZ2zqLTEQAUdAC4wYt4ImixRHnXDspcud7vw6iTzcs+mbiRts1LPkks+0SRom70F4IloRkVmZmZ6t69uxYsWBC5rbKyUgsWLFDPnj2r3b9Lly767LPPtHz58sh/5513nvr06aPly5fX2MhnZWUpJycn6j/Un5cjDGwILug4147PB4AbiCUQJLSVtbPh82F2JmAnL+IJYgl4xfZln5hN4Q6KFHWjSBE8Cc2okKQxY8Zo2LBh6tGjh04++WRNnjxZO3fu1PDhwyVJl156qdq1a6d7771X2dnZOu6446Ie36JFC0mqdrutWFfabsysiM2GxAIAexFLIEiIJWIjlrAb+13BBsQTiXNzVoVNyz6Ztj+FW5hNgSCjSBFMCRcqBg8erI0bN2r8+PEqKSlRt27dNG/evMgmVuvWrVN6uqtbXwBwkS2JBRtm7cTD9M0vTZy+DPsRS7ivrGO5staZP1KsLrYs/0SxAgC8RzwRPEHp4yXCrdkUXhUpmE3hDmZT1M6G/gGSk3ChQpJGjx6t0aNHx/zbwoULa33szJkzk3lJWIjkgn1sKVJ4jaUagNSzPZbom7fKyo5ZKmw/PI3OUwzEE/vZEk+EMSEGBI3t8URQ+DmbwhRhHODlZizMbArUxIY8I5LH8AIDmT7COohs6VC7ic8AAID6oS3lM6iJm4Me6DsASARFBXPYPpsC7mBAUM0oUgQfhYqQYWR4zcLcsbbtvTMCEgDCw7Zrvm1tairZ9N5tO68AIJVSWaxI1XMle112c38KG7HkU+1MX/aJIkXNKFKEA4UKuMq2TqBNHexUCeN7Bky1efNmDR06VDk5OWrRooVGjBihHTt21PqYJ554QmeeeaZycnKUlpamLVu2xLzfnDlzVFBQoEaNGumQQw7RwIEDU/8GAEgKZ9saxvcMmIhYAkgdNzfSdmM2RVBmUrDsEw5GkSJ1pkyZovz8fGVnZ6ugoEBLliyp8b7Tpk3T6aefrkMOOUSHHHKICgsLa71/KlCoAA4Spo62je/V6+IXs5DgpaFDh2rFihWaP3++Xn/9db377ru68sora33Mrl271K9fP91yyy013uell17SJZdcouHDh+vTTz/V+++/r4svvjjVh48Q8fraaNvAB8nONjZZYXqvgOmIJRCvVMyEYBmpcGI2hTuYTREbRYrUmT17tsaMGaPi4mItW7ZMJ554ooqKivT999/HvP/ChQt10UUX6Z133tGiRYvUoUMHnX322dqwYYNrx5jUZtpAImzZVPtAYdgQk6RCMOW3d2/UD9y1cuVKzZs3Tx999JF69OghSXrkkUd07rnn6r777lPbtm1jPu66666TVPOGkXv37tW1116rP/zhDxoxYkTk9mOOOSalxw+gOuIJM9lY+ALiQSwBW3m17JNbTNhIm9kUdqNIEZttuUTTPfDAAxo5cqSGDx8uSZo6darmzJmj6dOna+zYsdXu/9xzz0X9+8knn9RLL72kBQsW6NJLL3XlGJlREUKmNOamK+/S3srOdzyC+r5sw+aXONCiRYvUokWLSGJBkgoLC5Wenq4PP/ww6eddtmyZNmzYoPT0dJ100kk67LDDdM455+jzzz9PxWEjRdwoMnKNMUNQ29wgx0mpRuwNrxBLIFH1mRFh42wKU/anSPWyT14WKWydTWEyihTwQnl5uZYuXarCwsLIbenp6SosLNSiRYvieo5du3Zpz549atmypVuHGc5ChQnVbtgjaJ1wm98PIyBhim3btkX9V1ZW/5EeJSUlat26ddRtGRkZatmypUpKSpJ+3q+++kqSNGHCBN122216/fXXdcghh+jMM8/U5s2b63XMgJdsbgNsbntjCdr7gbvoe9Us1fEEsQSQOm7uT2Ert4sUbs6mMH3ZJ1THbIr4xBtLbNq0SRUVFWrTpk3U7W3atIk7Rrj55pvVtm3bqGJHqrH0k6HKOpYra11wLqQ2Lv90oCAs3UBSIXGMgLRPs/WOGmS6NyKlonzfc3foED2CrLi4WBMmTIj5mLFjx2rSpEm1Pu/KlStTcnyxVFZWSpJuvfVWDRo0SJI0Y8YMtW/fXi+88IJ+9atfufbaAParaoeJJ/xlc8ErFmZOpZ7bsYSUeDxBLAE3lXftoMyV6xN+TKoE7bpcF5tnUyD1mE0Rm835wyom5ibq43e/+52ef/55LVy4UNnZ2Sl//ioUKlIgv/1Grf02nOvohYnNCQbbkwpS+AJYmG39+vXKycmJ/Dsrq+bz84YbbtBll11W6/N16tRJeXl51Tax2rt3rzZv3qy8vLykj/Wwww6TFL2OdFZWljp16qR169Yl/bzA9sPTPO9c2T7wQbJ38EMQYgnEh/2uvBNvPEEsAbdVFR4SLVj4iUFlwVryKYyzKShSxGZ7rO+1eGOJVq1aqUGDBiotLY26vbS0tM4Y4b777tPvfvc7vfXWWzrhhBPqf9C1oFARUiQXkmdTgoGkAuCOnJycqGCgNrm5ucrNrTvw7tmzp7Zs2aKlS5eqe/fukqS3335blZWVKigoSPpYu3fvrqysLK1evVq9evWSJO3Zs0dr167V4YcfnvTzAkiebYMfghJP+DHogUQaahNvPEEsAa8cOFOipqKFjXtTSO7tT+HX8nbMpEAQBSFn6LV4Y4nMzEx1795dCxYs0MCBAyXtmzG5YMECjR49usbH/f73v9fdd9+tN954I2oPLLeEco8KoL5s2EDS9ONLBLMpEAZdu3ZVv379NHLkSC1ZskTvv/++Ro8erSFDhqht27aSpA0bNqhLly5asmRJ5HElJSVavny5vvzyS0nSZ599puXLl0fWjM7JydFVV12l4uJivfnmm1q9erWuvvpqSdIFF1zg8bsEcCDT22ob4h0A+xFLIJXKu3aI+q/qtlQKWz8vVcs+eV2ksHk2hamYTVEdRQr3jRkzRtOmTdPTTz+tlStX6uqrr9bOnTs1fPhwSdKll16qcePGRe4/adIk3X777Zo+fbry8/NVUlKikpIS7dixw7VjZEYFPBWUWRVVTBwRSUIhNdweAcma0ojlueee0+jRo3XWWWcpPT1dgwYN0sMPPxz5+549e7R69Wrt2rUrctvUqVM1ceLEyL/POOMMSfvWjq5aJuIPf/iDMjIydMkll+iHH35QQUGB3n77bR1yyCHevDH4xu09r5ihWX/EEt4IWzIM4UUsAbfYOpMiGSZvpM1MisSYuOwTRYrqghTbm2zw4MHauHGjxo8fr5KSEnXr1k3z5s2LbLC9bt06pafvn9Pw+OOPq7y8XOeff37U87i1D4ZEoSLU/EguBJXfSYYgJhSAMGrZsqVmzZpV49/z8/PlONHX7QkTJtQZJDRs2FD33Xef7rvvvlQcJgAXHNiWE08EA4Me4AdiCYQBy+p5h9kUqUUOrjqKFN4aPXp0jUs9LVy4MOrfa9eudf+ADkKhwmBuj4L0S9BGQh7I6yRDGBIKjIAEABwsyLGE5O0ACGIJAIAfvLw2u7U/RSJSsexT0JZ8cptpsykoUlQX5HgeyaFQAbjk4I5/KpINYUgmIHn57c2dogygbvntN2rtt+EaVYbaEUsAAGAuLzfSDuKST2GbTYFoFCkQC4UK+CLoIyFjITGQOL9GQDKVGADi59dSksQSiAezKQDAPPW5NrvdVzNxfwo/ihTMpkgtZlNEC1sMj/il130XBBkJWcB7rCkNwEtccwDvEWMDAFIhiDMppHDNpqBIEY0iBWpDoSJFWHIlcYxwQ204PwAAdaGtQG04PxJHnwYAUi/Z/Sn8KlIwmwJuoUiBulCoMByjIBFGfiYWGAG5n5drrgKwG9dOwFv0EQDYyutln0zYSDsZQS5SMJsinChSIB4UKuBrcoGRbgAAW1HMMwOxBGJh0AMAwFZBXe4pbChSAImjUAHAKCScAACJou0AAMB8ps+mSGQjbbcGrPhZpAjCbApTln2iSBGN2RSIF4UK+I7kAkzhxQhIlmoA4Acvrj2MIocpiC0BADYKepEiLChSRKNIgURQqLAAyQWEBYmF5LH5JRAM/JaTRxsCyf/zgEEPABCb39dn04VhuaewzKbAfhQpkCgKFTACQQsAAAAQLOzlAyAVTFv2KRHn5yyr8z5+FymCsOSTKZhNsR9FCiQjtIUKN4JmRkHWD8WKcPP7+2dWEQDUn9/XUr/bEviL779+6MsAcEvQrs+pzCeFoUjhBRNmU1Ck2I8iBZIV2kIFqvM7uYDwClrgWhOWagDgp7Bcg8LSpsA8xNIAgET4XaTwShhmU1Ck2I8iBeqDQgWMQnIBfiCxAACA3YghAcBM9b0+27zsk+mYTZEaFCn2o0iB+qJQYQmvRkGakLCloxkufN8AECzEEvCaCd+3V+d9WGZGAUDQ+T2bwqsiRdBnU1Ck2I8iBVKBQgUA35iQWAAABBNtDAAA/vJjNoXbUrE/RViKFF7wezYF9qFIgVShUAEjkVwIPlO+4yCMgGTzSyBY3PxNh2mGpmROWwP38B0DAA6U6LJPcA+zKcKBIgVSiUIFqiG5AAAAANOZEiuaEjsDgElMuUbXxe39KQ6ePcFsitTxczYFRYp9KFIg1TL8PoCgyW+/UWu/dadqXNaxXFnrwjWtbWvnLC58AWRK0EpiAQCCj1gimEyJJbzE7EwAYWJiXy3ZZZ/8Lk74IcizKShS7EN8DTcwowIxmRQUhLEjGmR8nwAQDsQScItJ36dJ5zkAmMKk6zT2C9IG2n7NpqBIsQ9FCriFQgUAz4Q1YPVqTfhUSsUmcUAY2PhbsfGalAphbYMAAAiLRPencHvZJwQLRYp9KFLATRQqLENyAbYy7TtkBCQAuM+0a61pbRESF9bvMKx9AADhZFr8EATMpqgfihT7UKSA2yhUoEamBQdh7ZgCtWFNaSCY+G0D1ZkWC5oWKwOACUy7VqeKjbNoAcA2FCpglaAGPUFn2vdGYgEAvGPaNde0Ngnx4XsDgHBINm5IdNmnMPFqNoUXmE3hH2ZTwAuhLlS4VREP0ihI05ILEh1V24T9+2KpBgAmCvu1Kextk21M/L5MjJGT5VbfhdHHQPiYeL2uCftTpJ4Xyz55jSLFPhQp4JVQFypsFfbkgmRXABRmJn5PQUosAIAtTLz2mthGoTq+J2J/AAD8QJFiH4oU8BKFCtTJxOSCRMfVdHw/AADT0VaZzdTvx9TYGAD8lIprNss+pR7LPiWHIsU+FCngNQoVsJqpHdiwM/V78Tqx4PYIyCAtMwfAe16P0jY1uWtqmxV2fC8AAFOwlF3dgrTsE0WKfShSwA8UKixFcmE/OrJm4fsAgNSgGOkd2i6zmPx9BG3QAwCkgsnX7VjCsj9FkGZTeIUixT4UKeAXChUuIbngLdsCo6Ay+XswudgGAGFh8rXY5DYsTPgevEWfBYApWPYJMANFCviJQgXiZnJyQaJj66etnbP4/A/CCEgANuBaFY32zF+mf/amx8IA4AfTr91h5eVsCi+WffJyf4owo0gBv1GosBjJheoIkrxnw2dOYiExrMEKwE02XJNtaNuChs+8OmJ9APCfjX0jlnxCMihSwAQUKpAQW5ILdHa9wecMAHZ2YFE32jhv2BK32RADA4DXUnX99vIaG5b9KYBEUKSAKShUIGG2dNRs6PTazJbP14/z1YsRkKwpDYSDF791P0ZtE0tAsufzteV8BYCwYX+K6ryeTeHFsk9eCetG2hQpYBIKFS4KanLBJrZ0gG1iy8hHicQCAJjMlmu0Te2eTfhMa8egBwCms3E2BezE/hRAeIS+UMFyDcmxKZggwZA6fI4AgLCiDUwN2+Iym2Jek9DHAmCioC/7xN4USBSzKWCa0BcqEB42dYpNY1tSQfIvscAsJwA28uvaZVsS2Mb20CR8dgCAVPFi2SeKrrUL0rJPYUSRAiaiUBEAJBfiR4IhcXxe5mGpBiBc+M2bh7YxMbbGXwx6AIDYWPbJPEGdTcGyT+6gSAFTZfh9ALDb9sPTrNxwqCqw4uJcMxsTClVILACAPYglgotYAgCAYArabAobY9FkEbvCZMyocJlXoyBJkCbH5g60W2wd9ViFxAIAJMfPWMLma7ft7aZb+EyS49XvkJlaAJLF9d08QZ1NgdSjSAHTMaMC9WbrSMgqjIjch4ATAIDkEU/sE4R4wubCGQDYoj7X2mT2pwjqRtoUKQAECTMqkBJB6NCFdURkkN63n+dhUGY1sWEckJyg/HaYVVF/QWpXExGU9x2U8xAA3BCE63y8ghLbucGrZZ/YnwIIH2ZUBEhZx3JlreNCXl8HBl9BHhUZtCAzLIkFlmoAwim//Uat/TZYawHHYvsszQOFYYZF0GIJvwVl0AMAwBvMpkAighyTIjgoVHiA5IK9gpZkCGpCwe8iBYkFAEHCwIfUCuIACOIJuzHoAUAyUnnt93rZpyDyq0gRtE20AZiFpZ8UrCl9fidMg9rBq1rOwNaOuc3HDgAIl6DGEpLd7bHtsVBd/D7v/I7hUylIfSsAwZDM/hTMVvCXl8s+BW3ALWAzZlQg5YI4s+JANoyMDGoSIRa/EwsAgNQLUywhmRlPEEsAAKqEqU2wAbMpkCgTY00gFgoVQD3ECtj8aADCGjiakFjwcgQkSzUA4eblUpImLP8U9GLFgUwoXIQ1ljBBkGZTAEBdTOjDxWt+SRfjZokx0wNAkFGo8AjJhfCoqaOfiqQDSYT9bApwAQDJCWs8QSzhnbDFEwx6AJAok9oNP/anMLFYAQBBRaECrglrcqEmJgV4SA1GQAIIMhMGPiAasURqha1IAQBIjinFCj9nU3i57JOX+1MAMAubaQeUKQlUOoBwQxjPKy9GQJoQfAM28+I3FMbR0GG85sN9ppxXpsTsABBLqgvkflx7k9lI20Qs+eSOMAyuZX8K2IRCBVxnSkcQwWDK+URiAUAYmHKtM+Xaj2AI6/kUxkInAKSKn4UCv4sUbKINwCsUKjzkdefAlOSCFN4OIVKL8wgAwos2AKlg0nlkUqwOAAczbblBP/anOJgfBQO/ixReY9knINwoVPw/LHniPpM6hrCPSecPiQUAYWLSNc+ktgD24fxxH30qADUJyjU4bIUDZlMA8BKFioAzKbkgBSc4gbfCft6wVAOAA4X9mhD2NgHJMe28MS1GB4ADmTabwjReFSvCVhRB6rE/BWxDocJjYU8uSOZ1FGE2084XEgsAwsi0a59pbQPMxvlCHwQAUs3tIoIJRQqvZ1N4vexTGDbSBmxDoSIETEsuSHQYER/OE4TN5s2bNXToUOXk5KhFixYaMWKEduzYUev9f/3rX+voo49Wo0aN1LFjR/3mN7/R1q1bo+63bt069e/fX40bN1br1q110003ae/evW6/HcBVtBGIh4nniYmxOYKDWAJBlOz+FPkd7CzSmlCkABBMU6ZMUX5+vrKzs1VQUKAlS5bUev8XXnhBXbp0UXZ2to4//njNnTvX1eOjUBESJnaITOw4whwmnh9+/I68GgHJmtJmGDp0qFasWKH58+fr9ddf17vvvqsrr7yyxvt/9913+u6773Tffffp888/18yZMzVv3jyNGDEicp+Kigr1799f5eXl+uCDD/T0009r5syZGj9+vBdvCS7xY3Q0sQRsY+L5YeLvCMFCLIH6cmPZJxOvx6ngRkHBlCIFe1PYj2WfcLDZs2drzJgxKi4u1rJly3TiiSeqqKhI33//fcz7f/DBB7rooos0YsQIffLJJxo4cKAGDhyozz//3LVjpFDhA6Ze7xfUgAX1w3mBMFq5cqXmzZunJ598UgUFBerVq5ceeeQRPf/88/ruu+9iPua4447TSy+9pAEDBqhz58766U9/qrvvvluvvfZaZJTjm2++qX/961969tln1a1bN51zzjm68847NWXKFJWXkzBLNYp+3qPNQCycF/vR9wgPYgnAe6ksLJhSpPCD18s+AWH0wAMPaOTIkRo+fLiOOeYYTZ06VY0bN9b06dNj3v+hhx5Sv379dNNNN6lr166688479eMf/1iPPvqoa8dIoSJETB3BRUcSBzL1fDD19wN/bNu2Leq/srL6j1ZZtGiRWrRooR49ekRuKywsVHp6uj788MO4n2fr1q3KyclRRkZG5HmPP/54tWnTJnKfoqIibdu2TStWrKj3cSNcTL0Wmtp2wB+mng+m/n7gn1THE8QSqC820U5OKgoMJhUpwjCbgv0pEBTxxhLl5eVaunSpCgsLI7elp6ersLBQixYtivmYRYsWRd1f2tf+13T/VMhw7Zkt1DdvlVGNQ5hsPzyNhgIkFg7CCMjE5XxdpowM986jvXv3NfodOnSIur24uFgTJkyo13OXlJSodevWUbdlZGSoZcuWKikpies5Nm3apDvvvDNqiYeSkpKoxIKkyL/jfV6YKb/9Rq391vuOZFnHcmWtM2/UG7EEJHNjiTAIyowyt2MJyb14glgCJgrLdXl+SZekr4Nhz0MxmwJBZFpuYtOmTaqoqIjZnq9aFfvaVVP772bbT6HCJyQXqiPBEF5hCV4RHOvXr1dOTk7k31lZNY8+Gzt2rCZNmlTr861cubLex7Rt2zb1799fxxxzTL2LJoCtiCXCzeR4gkEPiCXeeIJYIpyqZjd4tc68qbMpbNpIO5lihWlFijDMpggD9qcIj0RyEzagUAGjkGAIH5OTChLLNCC2nJycqGCgNjfccIMuu+yyWu/TqVMn5eXlVdvEau/evdq8ebPy8vJqffz27dvVr18/NWvWTC+//LIaNmwY+VteXp6WLFkSdf/S0tLI34BkmD7wQWJKf9iYHk8AscQbTxBLhNvWzlkkHS2SSLGCIgWA+oo3lmjVqpUaNGgQab+rlJaW1tiW5+XlJXT/VGCPihAyPfG6/fA0OpshwfdcM0ZABkdubq66dOlS63+ZmZnq2bOntmzZoqVLl0Ye+/bbb6uyslIFBQU1Pv+2bdt09tlnKzMzU6+++qqys7Oj/t6zZ0999tlnUYmL+fPnKycnR8ccc0zq3zA8xbWiZrQx4WBD3Gh67A3zEUvA7dkObj2/6ddnt8RTgDCtSOEXP5Z9YjALwigzM1Pdu3fXggULIrdVVlZqwYIF6tmzZ8zH9OzZM+r+0r72v6b7pwKFCh/5mVywocMU1qAmLGz4fm34naRCUNaUtl3Xrl3Vr18/jRw5UkuWLNH777+v0aNHa8iQIWrbtq0kacOGDerSpUtkVGNVYmHnzp166qmntG3bNpWUlKikpEQVFRWSpLPPPlvHHHOMLrnkEn366ad64403dNttt2nUqFHWTws1VVh+UzZcI21oa5A8G75fP38nFDLDh1gi2Exdmgmx1VaIMLFIwWyK4GAGFmoyZswYTZs2TU8//bRWrlypq6++Wjt37tTw4cMlSZdeeqnGjRsXuf+1116refPm6f7779eqVas0YcIEffzxxxo9erRrx8jSTzAaS0EFjw1JBcmOBByC57nnntPo0aN11llnKT09XYMGDdLDDz8c+fuePXu0evVq7dq1S5K0bNkyffjhh5KkI488Muq5vv76a+Xn56tBgwZ6/fXXdfXVV6tnz55q0qSJhg0bpjvuuMO7N4bAMnkJqCosBRVMtsQTgNeIJYKj+ZqyasUJN5aBMrkAkuz+FKaItQyUiUUKAOEwePBgbdy4UePHj1dJSYm6deumefPmRTbMXrdundLT989pOPXUUzVr1izddtttuuWWW3TUUUfplVde0XHHHefaMVKoOEjfvFWhaThsSC5IJBiChKRCfBgBGV4tW7bUrFmzavx7fn6+HGf/tfDMM8+M+ndNDj/8cM2dOzclxwjz5LffqLXfMgquLgx+CAabYokwDXoIy0wyGxBLAGaxIb/k12wKP5Z9AsJu9OjRNc6IWLhwYbXbLrjgAl1wwQUuH9V+LP3kM78TkjZ1oGzqmCKaDetHH8im3wUA+M2ma6Zt7RGi2fTd+f278LuPAcA9qZwB4eZsCpuu2WHGkk/BwrJPsB2FCliFBIN9bPu+SCwAsJHf1w6/r52Jsq1tCjviPwCIZvJyTUA8/JpNwexawGwUKgxAciFxdFbNZ2NSwcbfAgCYwrZrqI3tVNjY+h35/Vvwu28BIHVqGx1d32JFkIsd+R24DsaD2RQATEOhApL871Alw9bOa9DxvdiFNaUBd/Dbsgftlpls/U5sjKkB2MvUYoOt1/AwoUgBwEQUKhBha8eKBIM5bP4eTDj/GQEJoD5MuIaYcC1Nls1tWJDYHNfZfP7XB4VZAG4o6xDOa2oYsIm2O9ifAkFAoSIGP4JtE5ILtrO5Y2s72z/7sCYWAMANNl9TbW/PbMZnnxr0KYDwSWZWhakzMeCNsM6mYH8KwHwUKhDF5uRCFTq63gnCZ23KOU9iAUAqmHItMeXamqwgtG+2CMpnbfs5D8Bc8YySpvAQLayJ+Hj4/dkwmwJAbShUGITkQmoFpeNrmqrPlc8WABB0tHnuCdLnakrsbEpfAkB1mSvXK3PleldfI95ihdtFjaBc2wGbsOwTgoJCBWIypcOVCkHqCPspiJ+jKee5X4kF1pQG3BX235gp19hUCWI76LUgFn6Cdp4DcJcpxYow8HvmgIn8/kz8nE3Bsk+AHShU1MCv5IJJI6GC1vEKYufYC0H9zIJ2fgNAFWIJdwW1XXRTUD8zk85vBj0A5nK7OHGw2ooVYStk+J2YNwmfBQAbUKhArUzqgKUSRYvaBf3zCep5DQAmCuo1N+htZX0F/fMJ6nkNwH3JFC4SXdbF9oJEWYfUXWNJ0JvxGbA3BYB4UKgwkEkjIaXgd8SC3pGOV1g+B9POZ9N+7wCCwbRri2nX3lQLSxtal7B8Dqadz6b93gGY4eBihRfFC1Ov/yYk6gG3sD8FgiTD7wOAHco6litrXfAr4AcGVkFfw9DUINJNpiUW/MRSDQC8FsZYQiKeCBpiCQCpkLlyvcq7dnD9dbZ2ziKJ+f+sXZ+r/A7hK+yaUKTxezZF0GMxIEiYUWEoE0dGha1jduCowKB0woP2fhJh4vlr4u8cQGr5WRQ08Rpj4rXYbUFre4MYH8XLxPPXxN85gP283p8ilq2ds6xfCipVTEjaeyls7xeA/ZhRUYu+eas0v6SL34dhlLCMhowlVmfc5Mp82JIHtTExsQAAYUUsEY1Ywg7EEtUxOxOon0RnVTRfU1ZrwaHqmm1yuwLvmFKk8Hs2RdAxYwpBk9SMiilTpig/P1/Z2dkqKCjQkiVLarzvtGnTdPrpp+uQQw7RIYccosLCwlrvj/1MHSFFR22/g0cVej3C0O/Xt4Gp56upv2/AK8QS3jD1WmPqtdkPfrflfr++DUw9X039fQNeIp7Y58BrNtfv2pmSwHdTGN5jvCjcAXZJeEbF7NmzNWbMGE2dOlUFBQWaPHmyioqKtHr1arVu3bra/RcuXKiLLrpIp556qrKzszVp0iSdffbZWrFihdq1a5eSNwHvhXk0ZLwIEP1HYgEwE7EEJGKJeBBL+M/UWAJAMOKJVO1V0ewbp1qxwusErU1tVpD3qzCpSMFsCgCJSnhGxQMPPKCRI0dq+PDhOuaYYzR16lQ1btxY06dPj3n/5557Ttdcc426deumLl266Mknn1RlZaUWLFhQ74MPA5MTmnTcYDLOz5qxVAP8FrZYwu/fHLEEkByTz0+Tf9eAV8IWTyTKpsLBgco6eHPtNSmhnypBfE+oGcs+IYgSKlSUl5dr6dKlKiws3P8E6ekqLCzUokWL4nqOXbt2ac+ePWrZsmWN9ykrK9O2bdui/vOL38kF05ncgUN4mXxeklhA2IUxljCBydcek6/ZCC/Oy9rRR4LfvIgnTIwlEk1M2lqs8EqQEvumvRcTZlOw7BNgn4QKFZs2bVJFRYXatGkTdXubNm1UUlIS13PcfPPNatu2bVRAcbB7771XzZs3j/zXoUP9p0LazOTkgkRHDmbhfATMRiyBWLh2wySmn4+m9w0AL3gRT3gVS2SuXO/K81ahWFE70xL8yQjCewAAKcnNtJP1u9/9Ts8//7xefvllZWdn13i/cePGaevWrZH/1q93t+FG/ZneoUPwlXUsN/48JLEA1B+xRPJMvwbZcB1H8HEOAuEQTzwRpFjC7WKF7cUQmxP9Jh67CbMpANgpoc20W7VqpQYNGqi0tDTq9tLSUuXl5dX62Pvuu0+/+93v9NZbb+mEE06o9b5ZWVnKyspK5NACL7/9Rq391rwG6EBsigm/kFSIH0s1wG9hjSX65q3S/JIufh+GFYgn4Bcb4gnTC46AV7yIJ0yLJerLjw22bVKV8Ldpk20TixTwBvtTIKgSmlGRmZmp7t27R202VbX5VM+ePWt83O9//3vdeeedmjdvnnr06JH80frElMSeDR0TRkPCa7acbzb8fgEvhDWWMIUt1yJbru0IBlviV1N+v6b0jRBuQYsnEln+qT4JSttnPnjBluS/qcdpymwKinKAnRJe+mnMmDGaNm2ann76aa1cuVJXX321du7cqeHDh0uSLr30Uo0bNy5y/0mTJun222/X9OnTlZ+fr5KSEpWUlGjHjh2pexcwjg2dPdjPlvPMlMQCYApiCX/Zck2y5RoPu3GeAfYinogWb2KWYkXdTC0CVDH1+EwpUgCwV0JLP0nS4MGDtXHjRo0fP14lJSXq1q2b5s2bF9nEat26dUpP31//ePzxx1VeXq7zzz8/6nmKi4s1YcKE+h19CNmwBFQVlm6AW0gqJIcRkDBFWGMJln9KXNX1nngCbrApnrClwAh4yYZ4orxrB9c3y04Gy0DVbe36XCOXgTK1SGGSoJ/bLPuEIEu4UCFJo0eP1ujRo2P+beHChVH/Xrt2bTIvYRyTkgu2FSskEgxIHZuSChKJBaAmYYwlTGJTLCEx+AGpRSyRPAY9wDTEE8lLVbEiyDM0TCtWmFykYDYFgFRIeOknIBm2dQhhHlvWjz6QSYkFADiYbdcoG9sBmIdzCICp3NqnoqxDuco6xL72BbnIkCpr1+caUSAw4RhqQpECQKpQqLCUbckFiQQDksd5AwCoQpuAZNgah9oY8wMwx4EFCooV9eNnocDkIoVpgr7sExB0FCosZmvHxdaOIrxn87li2u+TpRoAM5j2WzTtWhUvm9sHeMvmc8XW3ycAc1GsqB+vCwamzOaoDbMpvMX+FAg6ChUJMC25INndgbG10wj32ZxUkOz+XQIIH5uvWba3F3CXzeeGib9LE/tCgA3Ku3aI+75ebLxtcrGipmMziVfFA9MLFADgBgoV8BUJBhzM9vPBxMQCANTF9muX7W0HUov4EgBqZ0NBwHRuFhJsKVKYNpuCZZ8A+1GoCADbkwsSHUpwDriJEZCAWfhNuoN2BEE5B4IQ2wNwXzxLwNSWSI5VrEh0VoUJszD8lOrZFTYs9VTFtCJFGLDsE8KAQkWCTE0uBKVDE5QOJuIXpO88KL9DAOEUlGtYkNoVxCdI37mpv0NT+0AAoiU6ojwVxQqkZgaELQUKiSIFAPdQqAgQUzs2yQhShxOxBe07DtLvD0B4BelaFrR2BtUF7TsO0u8PQDTT9qk4EMWK1Eh2NoRNsyhMxrJPQDBQqAiYoHVwgtYBRTC/U5N/d4yABMxk8m/T5GtaMoLY7oRdEL9Tk393Jl+vAKQGxYrUibfoYGuBgtkUANxEoSIJBOveC2KHNEyqvr8gfocmJxYAIFlBvLYFuS0KC74/AHCPCcWKoCTB6ypC2FigkMz9fsIwm4L9KRAWFCoCKIjJhSokGewS9O/K9N8aRVXAbKb/Rk2/xtVH0NunIAlD7Bfk3xoAu1CsSK2DCxa2zqKQgvW9ADAXhYokkVzwXxg6rjYKy/cSht8YAAT9WheWNss2YfpeTP+Nmd7nAWxiyj4V+R02Rv6LhWJF6tlcoJDM/j7CMJsCCBMKFQFmescnlcLSmTVVmBIKUrh+WwAQlmte2NoyE4Xt8w/LbwuAv2pLMtenWMEeFgCAVKNQEXBh6wCRZPBOWD9rW35TjIAE7GDLb9WWa1+qhLWN89qBn3PYPuuw/aYAmOPgkf2mzqwweRR/mJj8PYRlNgX7UyBMKFTUA8kFs4W58+uWsH+eYf0tAYAU3mtg2Nu+VOPztOe3ZEtfB7BJIss/JSuZ5G2ixQqvChYmJ8nDgM8fgNcoVISELR0iN1G4SByf2X42/YZILAB2sek3a9O10A20i4njM4sW9t8QgPi5uU9FLIkUK7xEstwffO4A/EChop5ILtiLjnO0gz8PPpP9+O0AwH5cE/ej3ayOz6RmNv12bOrjAGGW6iVhTC5WkDj3jg2fNcs+AcGU4fcBwFtVHaS13+bWcc/widWZzlpnfgOdDBIH8bMpqSCRWABs1TdvleaXdPH7MOKW334jsUQMNbWvxBPhZlssAcBd5V07eD5bIhH5HTZW28dC2les8DuBnbU+0/eiSdD5/R0DCDcKFSlgW3JBIsEQr9o64DYkHUgg1A+JBQCoGbFE/GwuYBBL1I+NsQSDHgBQrAgnv7/beIVlNgUQRhQqQowEQ/3E23FPdRKChIE3SCwA8BoDH8InkTY9lfEEsYQ3bIwlAJglc+X6lGzAfXByf+363BqXeapCsSJc/P5OUR3LPiGMKFSkiI3JBYkEgxdIBtiFpAIAJIZlJb1BPGEXW+MJBj0A3jB9+acqFCvCwe/vMhHMpgCCjc20ofz2G63tTAGpZPPvgMQCEAw2/5ZtvoYCqUJcDSBo6pp54SebEuymsukzpEiB+traOUtbO2dp2xFZfh8KakChIoVsTi5IJBgQbpz/AFB/XEsRZraf/7b3ZQDbpGJJp5qkOqEbq1hhymyGrPWZViXbTcLnBsA0FCoQxfYOFpCoIIx8JLEABIvtv+kgXFeBRHHOAwg6k4sVEkn3RNn2eYVtNgX7UyCsKFSkmO3JBYkEA8KD8xwA3MM1FmEQlLg5CH0YwEZuzqqoS6y9J+pCsSIY+JwQRls7s9yTDShUoEZB6HQBsQQlqaD/v717D46yuv84/kkISfDXhEC5BNoABRRQsbagaVB/VkFDpVQ6zoiKFG2UtoK2Yi8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" ] }, "metadata": {}, @@ -482,19 +579,14 @@ } ], "source": [ - "plotter = Plotter()\n", - "\n", - "# plotting at fixed time t = 0.0\n", - "print('Plotting at t=0')\n", - "plotter.plot(pinn, fixed_variables={'t': 0.0})\n", + "plt.figure(figsize=(12, 6))\n", + "plot_solution(solver=pinn, time=0)\n", "\n", - "# plotting at fixed time t = 0.5\n", - "print('Plotting at t=0.5')\n", - "plotter.plot(pinn, fixed_variables={'t': 0.5})\n", + "plt.figure(figsize=(12, 6))\n", + "plot_solution(solver=pinn, time=0.5)\n", "\n", - "# plotting at fixed time t = 1.\n", - "print('Plotting at t=1')\n", - "plotter.plot(pinn, fixed_variables={'t': 1.0})" + "plt.figure(figsize=(12, 6))\n", + "plot_solution(solver=pinn, time=1)" ] }, { @@ -525,11 +617,8 @@ } ], "metadata": { - "interpreter": { - "hash": "56be7540488f3dc66429ddf54a0fa9de50124d45fcfccfaf04c4c3886d735a3a" - }, "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "pina", "language": "python", "name": "python3" }, @@ -543,7 +632,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.9.21" } }, "nbformat": 4, diff --git a/tutorials/tutorial3/tutorial.py b/tutorials/tutorial3/tutorial.py index bc2a8f697..97ad5ed69 100644 --- a/tutorials/tutorial3/tutorial.py +++ b/tutorials/tutorial3/tutorial.py @@ -2,41 +2,45 @@ # coding: utf-8 # # Tutorial: Two dimensional Wave problem with hard constraint -# +# # [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial3/tutorial.ipynb) -# +# # In this tutorial we present how to solve the wave equation using hard constraint PINNs. For doing so we will build a costum `torch` model and pass it to the `PINN` solver. -# +# # First of all, some useful imports. -# In[1]: +# In[ ]: ## routine needed to run the notebook on Google Colab try: - import google.colab - IN_COLAB = True + import google.colab + + IN_COLAB = True except: - IN_COLAB = False + IN_COLAB = False if IN_COLAB: - get_ipython().system('pip install "pina-mathlab"') - + get_ipython().system('pip install "pina-mathlab"') + import torch +import matplotlib.pyplot as plt +import warnings +from pina import Condition, LabelTensor, Trainer from pina.problem import SpatialProblem, TimeDependentProblem -from pina.operators import laplacian, grad -from pina.geometry import CartesianDomain -from pina.solvers import PINN -from pina.trainer import Trainer -from pina.equation import Equation -from pina.equation.equation_factory import FixedValue -from pina import Condition, Plotter +from pina.operator import laplacian, grad +from pina.domain import CartesianDomain +from pina.solver import PINN +from pina.equation import Equation, FixedValue +from pina.callback import MetricTracker +warnings.filterwarnings("ignore") -# ## The problem definition + +# ## The problem definition # The problem is written in the following form: -# +# # \begin{equation} # \begin{cases} # \Delta u(x,y,t) = \frac{\partial^2}{\partial t^2} u(x,y,t) \quad \text{in } D, \\\\ @@ -44,55 +48,72 @@ # u(x, y, t) = 0 \quad \text{on } \Gamma_1 \cup \Gamma_2 \cup \Gamma_3 \cup \Gamma_4, # \end{cases} # \end{equation} -# +# # where $D$ is a squared domain $[0,1]^2$, and $\Gamma_i$, with $i=1,...,4$, are the boundaries of the square, and the velocity in the standard wave equation is fixed to one. -# Now, the wave problem is written in PINA code as a class, inheriting from `SpatialProblem` and `TimeDependentProblem` since we deal with spatial, and time dependent variables. The equations are written as `conditions` that should be satisfied in the corresponding domains. `truth_solution` is the exact solution which will be compared with the predicted one. +# Now, the wave problem is written in PINA code as a class, inheriting from `SpatialProblem` and `TimeDependentProblem` since we deal with spatial, and time dependent variables. The equations are written as `conditions` that should be satisfied in the corresponding domains. `solution` is the exact solution which will be compared with the predicted one. # In[2]: -class Wave(TimeDependentProblem, SpatialProblem): - output_variables = ['u'] - spatial_domain = CartesianDomain({'x': [0, 1], 'y': [0, 1]}) - temporal_domain = CartesianDomain({'t': [0, 1]}) +def wave_equation(input_, output_): + u_t = grad(output_, input_, components=["u"], d=["t"]) + u_tt = grad(u_t, input_, components=["dudt"], d=["t"]) + nabla_u = laplacian(output_, input_, components=["u"], d=["x", "y"]) + return nabla_u - u_tt - def wave_equation(input_, output_): - u_t = grad(output_, input_, components=['u'], d=['t']) - u_tt = grad(u_t, input_, components=['dudt'], d=['t']) - nabla_u = laplacian(output_, input_, components=['u'], d=['x', 'y']) - return nabla_u - u_tt - def initial_condition(input_, output_): - u_expected = (torch.sin(torch.pi*input_.extract(['x'])) * - torch.sin(torch.pi*input_.extract(['y']))) - return output_.extract(['u']) - u_expected +def initial_condition(input_, output_): + u_expected = torch.sin(torch.pi * input_.extract(["x"])) * torch.sin( + torch.pi * input_.extract(["y"]) + ) + return output_.extract(["u"]) - u_expected + +class Wave(TimeDependentProblem, SpatialProblem): + output_variables = ["u"] + spatial_domain = CartesianDomain({"x": [0, 1], "y": [0, 1]}) + temporal_domain = CartesianDomain({"t": [0, 1]}) + domains = { + "g1": CartesianDomain({"x": 1, "y": [0, 1], "t": [0, 1]}), + "g2": CartesianDomain({"x": 0, "y": [0, 1], "t": [0, 1]}), + "g3": CartesianDomain({"x": [0, 1], "y": 0, "t": [0, 1]}), + "g4": CartesianDomain({"x": [0, 1], "y": 1, "t": [0, 1]}), + "initial": CartesianDomain({"x": [0, 1], "y": [0, 1], "t": 0}), + "D": CartesianDomain({"x": [0, 1], "y": [0, 1], "t": [0, 1]}), + } conditions = { - 'gamma1': Condition(location=CartesianDomain({'x': [0, 1], 'y': 1, 't': [0, 1]}), equation=FixedValue(0.)), - 'gamma2': Condition(location=CartesianDomain({'x': [0, 1], 'y': 0, 't': [0, 1]}), equation=FixedValue(0.)), - 'gamma3': Condition(location=CartesianDomain({'x': 1, 'y': [0, 1], 't': [0, 1]}), equation=FixedValue(0.)), - 'gamma4': Condition(location=CartesianDomain({'x': 0, 'y': [0, 1], 't': [0, 1]}), equation=FixedValue(0.)), - 't0': Condition(location=CartesianDomain({'x': [0, 1], 'y': [0, 1], 't': 0}), equation=Equation(initial_condition)), - 'D': Condition(location=CartesianDomain({'x': [0, 1], 'y': [0, 1], 't': [0, 1]}), equation=Equation(wave_equation)), + "g1": Condition(domain="g1", equation=FixedValue(0.0)), + "g2": Condition(domain="g2", equation=FixedValue(0.0)), + "g3": Condition(domain="g3", equation=FixedValue(0.0)), + "g4": Condition(domain="g4", equation=FixedValue(0.0)), + "initial": Condition( + domain="initial", equation=Equation(initial_condition) + ), + "D": Condition(domain="D", equation=Equation(wave_equation)), } - def wave_sol(self, pts): - return (torch.sin(torch.pi*pts.extract(['x'])) * - torch.sin(torch.pi*pts.extract(['y'])) * - torch.cos(torch.sqrt(torch.tensor(2.))*torch.pi*pts.extract(['t']))) + def solution(self, pts): + f = ( + torch.sin(torch.pi * pts.extract(["x"])) + * torch.sin(torch.pi * pts.extract(["y"])) + * torch.cos( + torch.sqrt(torch.tensor(2.0)) * torch.pi * pts.extract(["t"]) + ) + ) + return LabelTensor(f, self.output_variables) - truth_solution = wave_sol +# define problem problem = Wave() # ## Hard Constraint Model # After the problem, a **torch** model is needed to solve the PINN. Usually, many models are already implemented in **PINA**, but the user has the possibility to build his/her own model in `torch`. The hard constraint we impose is on the boundary of the spatial domain. Specifically, our solution is written as: -# +# # $$ u_{\rm{pinn}} = xy(1-x)(1-y)\cdot NN(x, y, t), $$ -# +# # where $NN$ is the neural net output. This neural network takes as input the coordinates (in this case $x$, $y$ and $t$) and provides the unknown field $u$. By construction, it is zero on the boundaries. The residuals of the equations are evaluated at several sampling points (which the user can manipulate using the method `discretise_domain`) and the loss minimized by the neural network is the sum of the residuals. # In[3]: @@ -103,65 +124,130 @@ class HardMLP(torch.nn.Module): def __init__(self, input_dim, output_dim): super().__init__() - self.layers = torch.nn.Sequential(torch.nn.Linear(input_dim, 40), - torch.nn.ReLU(), - torch.nn.Linear(40, 40), - torch.nn.ReLU(), - torch.nn.Linear(40, output_dim)) - + self.layers = torch.nn.Sequential( + torch.nn.Linear(input_dim, 40), + torch.nn.ReLU(), + torch.nn.Linear(40, 40), + torch.nn.ReLU(), + torch.nn.Linear(40, output_dim), + ) + # here in the foward we implement the hard constraints def forward(self, x): - hard = x.extract(['x'])*(1-x.extract(['x']))*x.extract(['y'])*(1-x.extract(['y'])) - return hard*self.layers(x) + hard = ( + x.extract(["x"]) + * (1 - x.extract(["x"])) + * x.extract(["y"]) + * (1 - x.extract(["y"])) + ) + return hard * self.layers(x) # ## Train and Inference -# In this tutorial, the neural network is trained for 1000 epochs with a learning rate of 0.001 (default in `PINN`). Training takes approximately 3 minutes. +# In this tutorial, the neural network is trained for 1000 epochs with a learning rate of 0.001 (default in `PINN`). As always, we will log using `Tensorboard`. # In[4]: # generate the data -problem.discretise_domain(1000, 'random', locations=['D', 't0', 'gamma1', 'gamma2', 'gamma3', 'gamma4']) +problem.discretise_domain(1000, "random", domains="all") + +# define model +model = HardMLP(len(problem.input_variables), len(problem.output_variables)) # crete the solver -pinn = PINN(problem, HardMLP(len(problem.input_variables), len(problem.output_variables))) +pinn = PINN(problem=problem, model=model) # create trainer and train -trainer = Trainer(pinn, max_epochs=1000, accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional) +trainer = Trainer( + solver=pinn, + max_epochs=1000, + accelerator="cpu", + enable_model_summary=False, + train_size=1.0, + val_size=0.0, + test_size=0.0, + callbacks=[MetricTracker(["train_loss", "initial_loss", "D_loss"])], +) trainer.train() -# Notice that the loss on the boundaries of the spatial domain is exactly zero, as expected! After the training is completed one can now plot some results using the `Plotter` class of **PINA**. +# Let's now plot the losses inside `MetricTracker` to see how they vary during training. # In[5]: -plotter = Plotter() +trainer_metrics = trainer.callbacks[0].metrics +for metric, loss in trainer_metrics.items(): + plt.plot(range(len(loss)), loss, label=metric) +# plotting +plt.xlabel("epoch") +plt.ylabel("loss") +plt.yscale("log") +plt.legend() + + +# Notice that the loss on the boundaries of the spatial domain is exactly zero, as expected! After the training is completed one can now plot some results using the `matplotlib`. We plot the predicted output on the left side, the true solution at the center and the difference on the right side using the `plot_solution` function. + +# In[6]: + + +@torch.no_grad() +def plot_solution(solver, time): + # get the problem + problem = solver.problem + # get spatial points + spatial_samples = problem.spatial_domain.sample(30, "grid") + # get temporal value + time = LabelTensor(torch.tensor([[time]]), "t") + # cross data + points = spatial_samples.append(time, mode="cross") + # compute pinn solution, true solution and absolute difference + data = { + "PINN solution": solver(points), + "True solution": problem.solution(points), + "Absolute Difference": torch.abs( + solver(points) - problem.solution(points) + ), + } + # plot the solution + plt.suptitle(f"Solution for time {time.item()}") + for idx, (title, field) in enumerate(data.items()): + plt.subplot(1, 3, idx + 1) + plt.title(title) + plt.tricontourf( # convert to torch tensor + flatten + points.extract("x").tensor.flatten(), + points.extract("y").tensor.flatten(), + field.tensor.flatten(), + ) + plt.colorbar(), plt.tight_layout() + + +# Let's take a look at the results at different times, for example `0.0`, `0.5` and `1.0`: + +# In[7]: + -# plotting at fixed time t = 0.0 -print('Plotting at t=0') -plotter.plot(pinn, fixed_variables={'t': 0.0}) +plt.figure(figsize=(12, 6)) +plot_solution(solver=pinn, time=0) -# plotting at fixed time t = 0.5 -print('Plotting at t=0.5') -plotter.plot(pinn, fixed_variables={'t': 0.5}) +plt.figure(figsize=(12, 6)) +plot_solution(solver=pinn, time=0.5) -# plotting at fixed time t = 1. -print('Plotting at t=1') -plotter.plot(pinn, fixed_variables={'t': 1.0}) +plt.figure(figsize=(12, 6)) +plot_solution(solver=pinn, time=1) -# The results are not so great, and we can clearly see that as time progress the solution gets worse.... Can we do better? -# +# The results are not so great, and we can clearly see that as time progresses the solution gets worse.... Can we do better? +# # A valid option is to impose the initial condition as hard constraint as well. Specifically, our solution is written as: -# +# # $$ u_{\rm{pinn}} = xy(1-x)(1-y)\cdot NN(x, y, t)\cdot t + \cos(\sqrt{2}\pi t)\sin(\pi x)\sin(\pi y), $$ -# +# # Let us build the network first -# In[6]: +# In[8]: class HardMLPtime(torch.nn.Module): @@ -169,65 +255,82 @@ class HardMLPtime(torch.nn.Module): def __init__(self, input_dim, output_dim): super().__init__() - self.layers = torch.nn.Sequential(torch.nn.Linear(input_dim, 40), - torch.nn.ReLU(), - torch.nn.Linear(40, 40), - torch.nn.ReLU(), - torch.nn.Linear(40, output_dim)) - + self.layers = torch.nn.Sequential( + torch.nn.Linear(input_dim, 40), + torch.nn.ReLU(), + torch.nn.Linear(40, 40), + torch.nn.ReLU(), + torch.nn.Linear(40, output_dim), + ) + # here in the foward we implement the hard constraints def forward(self, x): - hard_space = x.extract(['x'])*(1-x.extract(['x']))*x.extract(['y'])*(1-x.extract(['y'])) - hard_t = torch.sin(torch.pi*x.extract(['x'])) * torch.sin(torch.pi*x.extract(['y'])) * torch.cos(torch.sqrt(torch.tensor(2.))*torch.pi*x.extract(['t'])) - return hard_space * self.layers(x) * x.extract(['t']) + hard_t + hard_space = ( + x.extract(["x"]) + * (1 - x.extract(["x"])) + * x.extract(["y"]) + * (1 - x.extract(["y"])) + ) + hard_t = ( + torch.sin(torch.pi * x.extract(["x"])) + * torch.sin(torch.pi * x.extract(["y"])) + * torch.cos( + torch.sqrt(torch.tensor(2.0)) * torch.pi * x.extract(["t"]) + ) + ) + return hard_space * self.layers(x) * x.extract(["t"]) + hard_t -# Now let's train with the same configuration as thre previous test +# Now let's train with the same configuration as the previous test -# In[7]: +# In[9]: -# generate the data -problem.discretise_domain(1000, 'random', locations=['D', 't0', 'gamma1', 'gamma2', 'gamma3', 'gamma4']) +# define model +model = HardMLPtime(len(problem.input_variables), len(problem.output_variables)) # crete the solver -pinn = PINN(problem, HardMLPtime(len(problem.input_variables), len(problem.output_variables))) +pinn = PINN(problem=problem, model=model) # create trainer and train -trainer = Trainer(pinn, max_epochs=1000, accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional) +trainer = Trainer( + solver=pinn, + max_epochs=1000, + accelerator="cpu", + enable_model_summary=False, + train_size=1.0, + val_size=0.0, + test_size=0.0, + callbacks=[MetricTracker(["train_loss", "initial_loss", "D_loss"])], +) trainer.train() # We can clearly see that the loss is way lower now. Let's plot the results -# In[8]: - +# In[10]: -plotter = Plotter() -# plotting at fixed time t = 0.0 -print('Plotting at t=0') -plotter.plot(pinn, fixed_variables={'t': 0.0}) +plt.figure(figsize=(12, 6)) +plot_solution(solver=pinn, time=0) -# plotting at fixed time t = 0.5 -print('Plotting at t=0.5') -plotter.plot(pinn, fixed_variables={'t': 0.5}) +plt.figure(figsize=(12, 6)) +plot_solution(solver=pinn, time=0.5) -# plotting at fixed time t = 1. -print('Plotting at t=1') -plotter.plot(pinn, fixed_variables={'t': 1.0}) +plt.figure(figsize=(12, 6)) +plot_solution(solver=pinn, time=1) # We can see now that the results are way better! This is due to the fact that previously the network was not learning correctly the initial conditon, leading to a poor solution when time evolved. By imposing the initial condition the network is able to correctly solve the problem. # ## What's next? -# +# # Congratulations on completing the two dimensional Wave tutorial of **PINA**! There are multiple directions you can go now: -# +# # 1. Train the network for longer or with different layer sizes and assert the finaly accuracy -# +# # 2. Propose new types of hard constraints in time, e.g. $$ u_{\rm{pinn}} = xy(1-x)(1-y)\cdot NN(x, y, t)(1-\exp(-t)) + \cos(\sqrt{2}\pi t)sin(\pi x)\sin(\pi y), $$ -# +# # 3. Exploit extrafeature training for model 1 and 2 -# +# # 4. Many more... diff --git a/tutorials/tutorial4/tutorial.ipynb b/tutorials/tutorial4/tutorial.ipynb index 022331512..f1df1b224 100644 --- a/tutorials/tutorial4/tutorial.ipynb +++ b/tutorials/tutorial4/tutorial.ipynb @@ -35,23 +35,27 @@ "source": [ "## routine needed to run the notebook on Google Colab\n", "try:\n", - " import google.colab\n", - " IN_COLAB = True\n", + " import google.colab\n", + "\n", + " IN_COLAB = True\n", "except:\n", - " IN_COLAB = False\n", + " IN_COLAB = False\n", "if IN_COLAB:\n", - " !pip install \"pina-mathlab\"\n", + " !pip install \"pina-mathlab\"\n", + "\n", + "import torch\n", + "import matplotlib.pyplot as plt\n", + "import torchvision # for MNIST dataset\n", + "import warnings\n", "\n", - "import torch \n", - "import matplotlib.pyplot as plt \n", - "plt.style.use('tableau-colorblind10')\n", - "from pina.problem import AbstractProblem\n", - "from pina.solvers import SupervisedSolver\n", + "from pina import Trainer\n", + "from pina.problem.zoo import SupervisedProblem\n", + "from pina.solver import SupervisedSolver\n", "from pina.trainer import Trainer\n", - "from pina import Condition, LabelTensor\n", - "from pina.model.layers import ContinuousConvBlock \n", - "import torchvision # for MNIST dataset\n", - "from pina.model import FeedForward # for building AE and MNIST classification" + "from pina.model.block import ContinuousConvBlock\n", + "from pina.model import FeedForward # for building AE and MNIST classification\n", + "\n", + "warnings.filterwarnings(\"ignore\")" ] }, { @@ -149,18 +153,18 @@ "D = 3\n", "\n", "# create the function f domain as random 2d points in [0, 1]\n", - "domain = torch.rand(size=(batch_size, number_input_fields, N, D-1))\n", + "domain = torch.rand(size=(batch_size, number_input_fields, N, D - 1))\n", "print(f\"Domain has shape: {domain.shape}\")\n", "\n", "# create the functions\n", - "pi = torch.acos(torch.tensor([-1.])) # pi value\n", + "pi = torch.acos(torch.tensor([-1.0])) # pi value\n", "f1 = torch.sin(pi * domain[:, 0, :, 0]) * torch.sin(pi * domain[:, 0, :, 1])\n", - "f2 = - torch.sin(pi * domain[:, 1, :, 0]) * torch.sin(pi * domain[:, 1, :, 1])\n", + "f2 = -torch.sin(pi * domain[:, 1, :, 0]) * torch.sin(pi * domain[:, 1, :, 1])\n", "\n", "# stacking the input domain and field values\n", "data = torch.empty(size=(batch_size, number_input_fields, N, D))\n", - "data[..., :-1] = domain # copy the domain\n", - "data[:, 0, :, -1] = f1 # copy first field value\n", + "data[..., :-1] = domain # copy the domain\n", + "data[:, 0, :, -1] = f1 # copy first field value\n", "data[:, 1, :, -1] = f1 # copy second field value\n", "print(f\"Filter input data has shape: {data.shape}\")" ] @@ -210,32 +214,26 @@ "execution_count": 3, "id": "b78c08b8", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/u/d/dcoscia/.local/lib/python3.9/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3483.)\n", - " return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]\n" - ] - } - ], + "outputs": [], "source": [ "# filter dim\n", "filter_dim = [0.1, 0.1]\n", "\n", "# stride\n", - "stride = {\"domain\": [1, 1],\n", - " \"start\": [0, 0],\n", - " \"jump\": [0.08, 0.08],\n", - " \"direction\": [1, 1],\n", - " }\n", - "\n", - "# creating the filter \n", - "cConv = ContinuousConvBlock(input_numb_field=number_input_fields,\n", - " output_numb_field=1,\n", - " filter_dim=filter_dim,\n", - " stride=stride)" + "stride = {\n", + " \"domain\": [1, 1],\n", + " \"start\": [0, 0],\n", + " \"jump\": [0.08, 0.08],\n", + " \"direction\": [1, 1],\n", + "}\n", + "\n", + "# creating the filter\n", + "cConv = ContinuousConvBlock(\n", + " input_numb_field=number_input_fields,\n", + " output_numb_field=1,\n", + " filter_dim=filter_dim,\n", + " stride=stride,\n", + ")" ] }, { @@ -254,11 +252,13 @@ "outputs": [], "source": [ "# creating the filter + optimization\n", - "cConv = ContinuousConvBlock(input_numb_field=number_input_fields,\n", - " output_numb_field=1,\n", - " filter_dim=filter_dim,\n", - " stride=stride,\n", - " optimize=True)\n" + "cConv = ContinuousConvBlock(\n", + " input_numb_field=number_input_fields,\n", + " output_numb_field=1,\n", + " filter_dim=filter_dim,\n", + " stride=stride,\n", + " optimize=True,\n", + ")" ] }, { @@ -287,7 +287,7 @@ "source": [ "print(f\"Filter input data has shape: {data.shape}\")\n", "\n", - "#input to the filter\n", + "# input to the filter\n", "output = cConv(data)\n", "\n", "print(f\"Filter output data has shape: {output.shape}\")" @@ -311,23 +311,26 @@ "class SimpleKernel(torch.nn.Module):\n", " def __init__(self) -> None:\n", " super().__init__()\n", - " self. model = torch.nn.Sequential(\n", + " self.model = torch.nn.Sequential(\n", " torch.nn.Linear(2, 20),\n", " torch.nn.ReLU(),\n", " torch.nn.Linear(20, 20),\n", " torch.nn.ReLU(),\n", - " torch.nn.Linear(20, 1))\n", + " torch.nn.Linear(20, 1),\n", + " )\n", "\n", " def forward(self, x):\n", " return self.model(x)\n", "\n", "\n", - "cConv = ContinuousConvBlock(input_numb_field=number_input_fields,\n", - " output_numb_field=1,\n", - " filter_dim=filter_dim,\n", - " stride=stride,\n", - " optimize=True,\n", - " model=SimpleKernel)\n" + "cConv = ContinuousConvBlock(\n", + " input_numb_field=number_input_fields,\n", + " output_numb_field=1,\n", + " filter_dim=filter_dim,\n", + " stride=stride,\n", + " optimize=True,\n", + " model=SimpleKernel,\n", + ")" ] }, { @@ -358,33 +361,25 @@ "from torch.utils.data import DataLoader, SubsetRandomSampler\n", "\n", "numb_training = 6000 # get just 6000 images for training\n", - "numb_testing= 1000 # get just 1000 images for training\n", - "seed = 111 # for reproducibility\n", - "batch_size = 8 # setting batch size\n", + "numb_testing = 1000 # get just 1000 images for training\n", + "seed = 111 # for reproducibility\n", + "batch_size = 8 # setting batch size\n", "\n", "# setting the seed\n", "torch.manual_seed(seed)\n", "\n", "# downloading the dataset\n", - "train_data = torchvision.datasets.MNIST('./data/', train=True, download=True,\n", - " transform=torchvision.transforms.Compose([\n", - " torchvision.transforms.ToTensor(),\n", - " torchvision.transforms.Normalize(\n", - " (0.1307,), (0.3081,))\n", - " ]))\n", - "subsample_train_indices = torch.randperm(len(train_data))[:numb_training]\n", - "train_loader = DataLoader(train_data, batch_size=batch_size,\n", - " sampler=SubsetRandomSampler(subsample_train_indices))\n", - "\n", - "test_data = torchvision.datasets.MNIST('./data/', train=False, download=True,\n", - " transform=torchvision.transforms.Compose([\n", - " torchvision.transforms.ToTensor(),\n", - " torchvision.transforms.Normalize(\n", - " (0.1307,), (0.3081,))\n", - " ]))\n", - "subsample_test_indices = torch.randperm(len(train_data))[:numb_testing]\n", - "test_loader = DataLoader(train_data, batch_size=batch_size,\n", - " sampler=SubsetRandomSampler(subsample_train_indices))" + "train_data = torchvision.datasets.MNIST(\n", + " \"./data/\",\n", + " download=True,\n", + " train=False,\n", + " transform=torchvision.transforms.Compose(\n", + " [\n", + " torchvision.transforms.ToTensor(),\n", + " torchvision.transforms.Normalize((0.1307,), (0.3081,)),\n", + " ]\n", + " ),\n", + ")" ] }, { @@ -400,16 +395,7 @@ "execution_count": 8, "id": "a872fb2d", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Original MNIST image shape: torch.Size([8, 1, 28, 28])\n", - "Transformed MNIST image shape: torch.Size([8, 1, 784, 3])\n" - ] - } - ], + "outputs": [], "source": [ "def transform_input(x):\n", " batch_size = x.shape[0]\n", @@ -418,19 +404,15 @@ " # creating the n dimensional mesh grid for a single channel image\n", " values_mesh = [torch.arange(0, dim).float() for dim in dim_grid]\n", " mesh = torch.meshgrid(values_mesh)\n", - " coordinates_mesh = [x.reshape(-1, 1) for x in mesh]\n", - " coordinates = torch.cat(coordinates_mesh, dim=1).unsqueeze(\n", - " 0).repeat((batch_size, 1, 1)).unsqueeze(1)\n", - "\n", - " return torch.cat((coordinates, x.flatten(2).unsqueeze(-1)), dim=-1)\n", - "\n", - "\n", - "# let's try it out\n", - "image, s = next(iter(train_loader))\n", - "print(f\"Original MNIST image shape: {image.shape}\")\n", - "\n", - "image_transformed = transform_input(image)\n", - "print(f\"Transformed MNIST image shape: {image_transformed.shape}\")\n" + " coordinates_mesh = [m.reshape(-1, 1).to(x.device) for m in mesh]\n", + " coordinates = (\n", + " torch.cat(coordinates_mesh, dim=1)\n", + " .unsqueeze(0)\n", + " .repeat((batch_size, 1, 1))\n", + " .unsqueeze(1)\n", + " )\n", + "\n", + " return torch.cat((coordinates, x.flatten(2).unsqueeze(-1)), dim=-1)" ] }, { @@ -451,6 +433,7 @@ "# setting the seed\n", "torch.manual_seed(seed)\n", "\n", + "\n", "class ContinuousClassifier(torch.nn.Module):\n", " def __init__(self):\n", " super().__init__()\n", @@ -459,30 +442,32 @@ " numb_class = 10\n", "\n", " # convolutional block\n", - " self.convolution = ContinuousConvBlock(input_numb_field=1,\n", - " output_numb_field=4,\n", - " stride={\"domain\": [27, 27],\n", - " \"start\": [0, 0],\n", - " \"jumps\": [4, 4],\n", - " \"direction\": [1, 1.],\n", - " },\n", - " filter_dim=[4, 4],\n", - " optimize=True)\n", + " self.convolution = ContinuousConvBlock(\n", + " input_numb_field=1,\n", + " output_numb_field=4,\n", + " stride={\n", + " \"domain\": [27, 27],\n", + " \"start\": [0, 0],\n", + " \"jumps\": [4, 4],\n", + " \"direction\": [1, 1.0],\n", + " },\n", + " filter_dim=[4, 4],\n", + " optimize=True,\n", + " )\n", " # feedforward net\n", - " self.nn = FeedForward(input_dimensions=196,\n", - " output_dimensions=numb_class,\n", - " layers=[120, 64],\n", - " func=torch.nn.ReLU)\n", + " self.nn = FeedForward(\n", + " input_dimensions=196,\n", + " output_dimensions=numb_class,\n", + " layers=[120, 64],\n", + " func=torch.nn.ReLU,\n", + " )\n", "\n", " def forward(self, x):\n", " # transform input + convolution\n", " x = transform_input(x)\n", " x = self.convolution(x)\n", " # feed forward classification\n", - " return self.nn(x[..., -1].flatten(1))\n", - "\n", - "\n", - "net = ContinuousClassifier()" + " return self.nn(x[..., -1].flatten(1))" ] }, { @@ -490,7 +475,7 @@ "id": "4374c15c", "metadata": {}, "source": [ - "Let's try to train it using a simple pytorch training loop. We train for just 1 epoch using Adam optimizer with a $0.001$ learning rate." + "We now aim to solve the classification problem. For this we will use the `SupervisedSolver` and the `SupervisedProblem`. The input of the supervised problems are the images, while the output the corresponding class." ] }, { @@ -503,64 +488,60 @@ "name": "stderr", "output_type": "stream", "text": [ - "/u/d/dcoscia/.local/lib/python3.9/site-packages/torch/autograd/__init__.py:200: UserWarning: CUDA initialization: CUDA unknown error - this may be due to an incorrectly set up environment, e.g. changing env variable CUDA_VISIBLE_DEVICES after program start. Setting the available devices to be zero. (Triggered internally at ../c10/cuda/CUDAFunctions.cpp:109.)\n", - " Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass\n", - "/u/d/dcoscia/.local/lib/python3.9/site-packages/torch/cuda/__init__.py:546: UserWarning: Can't initialize NVML\n", - " warnings.warn(\"Can't initialize NVML\")\n" + "GPU available: True (mps), used: False\n", + "TPU available: False, using: 0 TPU cores\n", + "HPU available: False, using: 0 HPUs\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "batch [50/750] loss[0.161]\n", - "batch [100/750] loss[0.073]\n", - "batch [150/750] loss[0.063]\n", - "batch [200/750] loss[0.051]\n", - "batch [250/750] loss[0.044]\n", - "batch [300/750] loss[0.050]\n", - "batch [350/750] loss[0.053]\n", - "batch [400/750] loss[0.049]\n", - "batch [450/750] loss[0.046]\n", - "batch [500/750] loss[0.034]\n", - "batch [550/750] loss[0.036]\n", - "batch [600/750] loss[0.040]\n", - "batch [650/750] loss[0.028]\n", - "batch [700/750] loss[0.040]\n", - "batch [750/750] loss[0.040]\n" + "Epoch 0: 100%|██████████| 110/110 [00:19<00:00, 5.61it/s, v_num=21, data_loss_step=0.723, train_loss_step=0.731, val_loss_step=0.723, data_loss_epoch=3.200, val_loss_epoch=0.635, train_loss_epoch=3.200]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "`Trainer.fit` stopped: `max_epochs=1` reached.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 0: 100%|██████████| 110/110 [00:19<00:00, 5.61it/s, v_num=21, data_loss_step=0.723, train_loss_step=0.731, val_loss_step=0.723, data_loss_epoch=3.200, val_loss_epoch=0.635, train_loss_epoch=3.200]\n" ] } ], "source": [ - "# setting the seed\n", - "torch.manual_seed(seed)\n", - "\n", - "# optimizer and loss function\n", - "optimizer = torch.optim.Adam(net.parameters(), lr=0.001)\n", - "criterion = torch.nn.CrossEntropyLoss()\n", - "\n", - "for epoch in range(1): # loop over the dataset multiple times\n", - "\n", - " running_loss = 0.0\n", - " for i, data in enumerate(train_loader, 0):\n", - " # get the inputs; data is a list of [inputs, labels]\n", - " inputs, labels = data\n", - "\n", - " # zero the parameter gradients\n", - " optimizer.zero_grad()\n", - "\n", - " # forward + backward + optimize\n", - " outputs = net(inputs)\n", - " loss = criterion(outputs, labels)\n", - " loss.backward()\n", - " optimizer.step()\n", - "\n", - " # print statistics\n", - " running_loss += loss.item()\n", - " if i % 50 == 49: \n", - " print(\n", - " f'batch [{i + 1}/{numb_training//batch_size}] loss[{running_loss / 500:.3f}]')\n", - " running_loss = 0.0\n" + "# setting the problem\n", + "problem = SupervisedProblem(\n", + " input_=train_data.train_data.unsqueeze(1), # adding channel dimension\n", + " output_=train_data.train_labels,\n", + ")\n", + "\n", + "# setting the solver\n", + "solver = SupervisedSolver(\n", + " problem=problem,\n", + " model=ContinuousClassifier(),\n", + " loss=torch.nn.CrossEntropyLoss(),\n", + " use_lt=False,\n", + ")\n", + "\n", + "# setting the trainer\n", + "trainer = Trainer(\n", + " solver=solver,\n", + " max_epochs=1,\n", + " accelerator=\"cpu\",\n", + " enable_model_summary=False,\n", + " train_size=0.7,\n", + " val_size=0.1,\n", + " test_size=0.2,\n", + " batch_size=64,\n", + ")\n", + "trainer.train()" ] }, { @@ -581,25 +562,26 @@ "name": "stdout", "output_type": "stream", "text": [ - "Accuracy of the network on the 1000 test images: 92.733%\n" + "Accuracy of the network on the test images: 82.600%\n" ] } ], "source": [ "correct = 0\n", "total = 0\n", + "trainer.data_module.setup(\"test\")\n", "with torch.no_grad():\n", - " for data in test_loader:\n", - " images, labels = data\n", + " for data in trainer.data_module.test_dataloader():\n", + " test_data = data[\"data\"]\n", + " images, labels = test_data[\"input\"], test_data[\"target\"]\n", " # calculate outputs by running images through the network\n", - " outputs = net(images)\n", + " outputs = solver(images)\n", " # the class with the highest energy is what we choose as prediction\n", " _, predicted = torch.max(outputs.data, 1)\n", " total += labels.size(0)\n", " correct += (predicted == labels).sum().item()\n", "\n", - "print(\n", - " f'Accuracy of the network on the 1000 test images: {(correct / total):.3%}')\n" + "print(f\"Accuracy of the network on the test images: {(correct / total):.3%}\")" ] }, { @@ -628,7 +610,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -642,11 +624,11 @@ "def circle_grid(N=100):\n", " \"\"\"Generate points withing a unit 2D circle centered in (0.5, 0.5)\n", "\n", - " :param N: number of points\n", - " :type N: float\n", - " :return: [x, y] array of points\n", - " :rtype: torch.tensor\n", - " \"\"\"\n", + " :param N: number of points\n", + " :type N: float\n", + " :return: [x, y] array of points\n", + " :rtype: torch.tensor\n", + " \"\"\"\n", "\n", " PI = torch.acos(torch.zeros(1)).item() * 2\n", " R = 0.5\n", @@ -661,19 +643,22 @@ "\n", " return torch.stack([x, y]).T\n", "\n", + "\n", "# create the grid\n", "grid = circle_grid(500)\n", "\n", "# create input\n", "input_data = torch.empty(size=(1, 1, grid.shape[0], 3))\n", "input_data[0, 0, :, :-1] = grid\n", - "input_data[0, 0, :, -1] = torch.sin(pi * grid[:, 0]) * torch.sin(pi * grid[:, 1])\n", + "input_data[0, 0, :, -1] = torch.sin(pi * grid[:, 0]) * torch.sin(\n", + " pi * grid[:, 1]\n", + ")\n", "\n", "# visualize data\n", "plt.title(\"Training sample with 500 points\")\n", "plt.scatter(grid[:, 0], grid[:, 1], c=input_data[0, 0, :, -1])\n", "plt.colorbar()\n", - "plt.show()\n" + "plt.show()" ] }, { @@ -696,19 +681,24 @@ " super().__init__()\n", "\n", " # convolutional block\n", - " self.convolution = ContinuousConvBlock(input_numb_field=1,\n", - " output_numb_field=2,\n", - " stride={\"domain\": [1, 1],\n", - " \"start\": [0, 0],\n", - " \"jumps\": [0.05, 0.05],\n", - " \"direction\": [1, 1.],\n", - " },\n", - " filter_dim=[0.15, 0.15],\n", - " optimize=True)\n", + " self.convolution = ContinuousConvBlock(\n", + " input_numb_field=1,\n", + " output_numb_field=2,\n", + " stride={\n", + " \"domain\": [1, 1],\n", + " \"start\": [0, 0],\n", + " \"jumps\": [0.05, 0.05],\n", + " \"direction\": [1, 1.0],\n", + " },\n", + " filter_dim=[0.15, 0.15],\n", + " optimize=True,\n", + " )\n", " # feedforward net\n", - " self.nn = FeedForward(input_dimensions=400,\n", - " output_dimensions=hidden_dimension,\n", - " layers=[240, 120])\n", + " self.nn = FeedForward(\n", + " input_dimensions=400,\n", + " output_dimensions=hidden_dimension,\n", + " layers=[240, 120],\n", + " )\n", "\n", " def forward(self, x):\n", " # convolution\n", @@ -722,25 +712,30 @@ " super().__init__()\n", "\n", " # convolutional block\n", - " self.convolution = ContinuousConvBlock(input_numb_field=2,\n", - " output_numb_field=1,\n", - " stride={\"domain\": [1, 1],\n", - " \"start\": [0, 0],\n", - " \"jumps\": [0.05, 0.05],\n", - " \"direction\": [1, 1.],\n", - " },\n", - " filter_dim=[0.15, 0.15],\n", - " optimize=True)\n", + " self.convolution = ContinuousConvBlock(\n", + " input_numb_field=2,\n", + " output_numb_field=1,\n", + " stride={\n", + " \"domain\": [1, 1],\n", + " \"start\": [0, 0],\n", + " \"jumps\": [0.05, 0.05],\n", + " \"direction\": [1, 1.0],\n", + " },\n", + " filter_dim=[0.15, 0.15],\n", + " optimize=True,\n", + " )\n", " # feedforward net\n", - " self.nn = FeedForward(input_dimensions=hidden_dimension,\n", - " output_dimensions=400,\n", - " layers=[120, 240])\n", + " self.nn = FeedForward(\n", + " input_dimensions=hidden_dimension,\n", + " output_dimensions=400,\n", + " layers=[120, 240],\n", + " )\n", "\n", " def forward(self, weights, grid):\n", " # feed forward pass\n", " x = self.nn(weights)\n", " # transpose convolution\n", - " return torch.sigmoid(self.convolution.transpose(x, grid))\n" + " return torch.sigmoid(self.convolution.transpose(x, grid))" ] }, { @@ -753,7 +748,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 14, "id": "a4db89a7", "metadata": {}, "outputs": [], @@ -772,9 +767,7 @@ " weights = self.encoder(x)\n", " # decoder\n", " out = self.decoder(weights, grid)\n", - " return out\n", - "\n", - "net = Autoencoder()" + " return out" ] }, { @@ -787,7 +780,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 15, "id": "700a7cf3", "metadata": {}, "outputs": [ @@ -795,48 +788,57 @@ "name": "stderr", "output_type": "stream", "text": [ - "GPU available: False, used: False\n", + "GPU available: True (mps), used: False\n", "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", "HPU available: False, using: 0 HPUs\n" ] }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "fca56b2f81fc4374af4c2ff6fbfc4eb0", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Training: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 99: 100%|██████████| 1/1 [00:00<00:00, 6.65it/s, v_num=22, data_loss=0.0318, train_loss=0.0318]" + ] }, { "name": "stderr", "output_type": "stream", "text": [ - "`Trainer.fit` stopped: `max_epochs=150` reached.\n" + "`Trainer.fit` stopped: `max_epochs=100` reached.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 99: 100%|██████████| 1/1 [00:00<00:00, 6.35it/s, v_num=22, data_loss=0.0318, train_loss=0.0318]\n" ] } ], "source": [ "# define the problem\n", - "class CircleProblem(AbstractProblem):\n", - " input_variables = ['x', 'y', 'f']\n", - " output_variables = input_variables\n", - " conditions = {'data' : Condition(input_points=LabelTensor(input_data, input_variables), output_points=LabelTensor(input_data, output_variables))}\n", + "problem = SupervisedProblem(input_data, input_data)\n", + "\n", "\n", "# define the solver\n", - "solver = SupervisedSolver(problem=CircleProblem(), model=net, loss=torch.nn.MSELoss()) \n", + "solver = SupervisedSolver(\n", + " problem=problem,\n", + " model=Autoencoder(),\n", + " loss=torch.nn.MSELoss(),\n", + " use_lt=False,\n", + ")\n", "\n", "# train\n", - "trainer = Trainer(solver, max_epochs=150, accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional)\n", - "trainer.train()\n", - " " + "trainer = Trainer(\n", + " solver,\n", + " max_epochs=100,\n", + " accelerator=\"cpu\",\n", + " enable_model_summary=False, # we train on CPU and avoid model summary at beginning of training (optional)\n", + " train_size=1.0,\n", + " val_size=0.0,\n", + " test_size=0.0,\n", + ")\n", + "trainer.train()" ] }, { @@ -849,13 +851,13 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 16, "id": "0269fedf", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -865,10 +867,10 @@ } ], "source": [ - "net.eval()\n", + "solver.eval()\n", "\n", "# get output and detach from computational graph for plotting\n", - "output = net(input_data).detach()\n", + "output = solver(input_data).detach()\n", "\n", "# visualize data\n", "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 3))\n", @@ -880,7 +882,7 @@ "axes[1].set_title(\"Autoencoder\")\n", "fig.colorbar(pic2)\n", "plt.tight_layout()\n", - "plt.show()\n" + "plt.show()" ] }, { @@ -893,7 +895,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 17, "id": "ded8f91b", "metadata": {}, "outputs": [ @@ -901,16 +903,18 @@ "name": "stdout", "output_type": "stream", "text": [ - "l2 error: 4.32%\n" + "l2 error: 4.73%\n" ] } ], "source": [ "def l2_error(input_, target):\n", - " return torch.linalg.norm(input_-target, ord=2)/torch.linalg.norm(input_, ord=2)\n", + " return torch.linalg.norm(input_ - target, ord=2) / torch.linalg.norm(\n", + " input_, ord=2\n", + " )\n", "\n", "\n", - "print(f'l2 error: {l2_error(input_data[0, 0, :, -1], output[0, 0, :, -1]):.2%}')" + "print(f\"l2 error: {l2_error(input_data[0, 0, :, -1], output[0, 0, :, -1]):.2%}\")" ] }, { @@ -933,13 +937,13 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 18, "id": "fcbbaec6", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -952,17 +956,18 @@ "# setting the seed\n", "torch.manual_seed(seed)\n", "\n", - "grid2 = circle_grid(1500) # triple number of points\n", + "grid2 = circle_grid(1500) # triple number of points\n", "input_data2 = torch.zeros(size=(1, 1, grid2.shape[0], 3))\n", "input_data2[0, 0, :, :-1] = grid2\n", - "input_data2[0, 0, :, -1] = torch.sin(pi *\n", - " grid2[:, 0]) * torch.sin(pi * grid2[:, 1])\n", + "input_data2[0, 0, :, -1] = torch.sin(pi * grid2[:, 0]) * torch.sin(\n", + " pi * grid2[:, 1]\n", + ")\n", "\n", "# get the hidden representation from original input\n", - "latent = net.encoder(input_data)\n", + "latent = solver.model.encoder(input_data)\n", "\n", "# upsample on the second input_data2\n", - "output = net.decoder(latent, input_data2).detach()\n", + "output = solver.model.decoder(latent, input_data2).detach()\n", "\n", "# show the picture\n", "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 3))\n", @@ -974,7 +979,7 @@ "axes[1].set_title(\"Up-sampling\")\n", "fig.colorbar(pic2)\n", "plt.tight_layout()\n", - "plt.show()\n" + "plt.show()" ] }, { @@ -987,7 +992,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 19, "id": "ab505b75", "metadata": {}, "outputs": [ @@ -995,12 +1000,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "l2 error: 8.49%\n" + "l2 error: 9.68%\n" ] } ], "source": [ - "print(f'l2 error: {l2_error(input_data2[0, 0, :, -1], output[0, 0, :, -1]):.2%}')" + "print(\n", + " f\"l2 error: {l2_error(input_data2[0, 0, :, -1], output[0, 0, :, -1]):.2%}\"\n", + ")" ] }, { @@ -1014,13 +1021,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "75ed28f5", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -1032,7 +1039,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "l2 error: 8.59%\n" + "l2 error: 9.53%\n" ] } ], @@ -1043,14 +1050,15 @@ "grid2 = circle_grid(3500) # very fine mesh\n", "input_data2 = torch.zeros(size=(1, 1, grid2.shape[0], 3))\n", "input_data2[0, 0, :, :-1] = grid2\n", - "input_data2[0, 0, :, -1] = torch.sin(pi *\n", - " grid2[:, 0]) * torch.sin(pi * grid2[:, 1])\n", + "input_data2[0, 0, :, -1] = torch.sin(pi * grid2[:, 0]) * torch.sin(\n", + " pi * grid2[:, 1]\n", + ")\n", "\n", "# get the hidden representation from finer mesh input\n", - "latent = net.encoder(input_data2)\n", + "latent = solver.model.encoder(input_data2)\n", "\n", "# upsample on the second input_data2\n", - "output = net.decoder(latent, input_data2).detach()\n", + "output = solver.model.decoder(latent, input_data2).detach()\n", "\n", "# show the picture\n", "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 3))\n", @@ -1066,7 +1074,8 @@ "\n", "# calculate l2 error\n", "print(\n", - " f'l2 error: {l2_error(input_data2[0, 0, :, -1], output[0, 0, :, -1]):.2%}')\n" + " f\"l2 error: {l2_error(input_data2[0, 0, :, -1], output[0, 0, :, -1]):.2%}\"\n", + ")" ] }, { @@ -1087,11 +1096,8 @@ } ], "metadata": { - "interpreter": { - "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49" - }, "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "pina", "language": "python", "name": "python3" }, @@ -1105,7 +1111,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.9.21" } }, "nbformat": 4, diff --git a/tutorials/tutorial4/tutorial.py b/tutorials/tutorial4/tutorial.py index 46abe100f..d4db53c89 100644 --- a/tutorials/tutorial4/tutorial.py +++ b/tutorials/tutorial4/tutorial.py @@ -2,7 +2,7 @@ # coding: utf-8 # # Tutorial: Unstructured convolutional autoencoder via continuous convolution -# +# # [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial4/tutorial.ipynb) # In this tutorial, we will show how to use the Continuous Convolutional Filter, and how to build common Deep Learning architectures with it. The implementation of the filter follows the original work [*A Continuous Convolutional Trainable Filter for Modelling Unstructured Data*](https://arxiv.org/abs/2210.13416). @@ -14,34 +14,38 @@ ## routine needed to run the notebook on Google Colab try: - import google.colab - IN_COLAB = True + import google.colab + + IN_COLAB = True except: - IN_COLAB = False + IN_COLAB = False if IN_COLAB: - get_ipython().system('pip install "pina-mathlab"') + get_ipython().system('pip install "pina-mathlab"') + +import torch +import matplotlib.pyplot as plt +import torchvision # for MNIST dataset +import warnings -import torch -import matplotlib.pyplot as plt -plt.style.use('tableau-colorblind10') -from pina.problem import AbstractProblem -from pina.solvers import SupervisedSolver +from pina import Trainer +from pina.problem.zoo import SupervisedProblem +from pina.solver import SupervisedSolver from pina.trainer import Trainer -from pina import Condition, LabelTensor -from pina.model.layers import ContinuousConvBlock -import torchvision # for MNIST dataset -from pina.model import FeedForward # for building AE and MNIST classification +from pina.model.block import ContinuousConvBlock +from pina.model import FeedForward # for building AE and MNIST classification +warnings.filterwarnings("ignore") -# The tutorial is structured as follow: + +# The tutorial is structured as follow: # * [Continuous filter background](#continuous-filter-background): understand how the convolutional filter works and how to use it. -# * [Building a MNIST Classifier](#building-a-mnist-classifier): show how to build a simple classifier using the MNIST dataset and how to combine a continuous convolutional layer with a feedforward neural network. +# * [Building a MNIST Classifier](#building-a-mnist-classifier): show how to build a simple classifier using the MNIST dataset and how to combine a continuous convolutional layer with a feedforward neural network. # * [Building a Continuous Convolutional Autoencoder](#building-a-continuous-convolutional-autoencoder): show how to use the continuous filter to work with unstructured data for autoencoding and up-sampling. # ## Continuous filter background # As reported by the authors in the original paper: in contrast to discrete convolution, continuous convolution is mathematically defined as: -# +# # $$ # \mathcal{I}_{\rm{out}}(\mathbf{x}) = \int_{\mathcal{X}} \mathcal{I}(\mathbf{x} + \mathbf{\tau}) \cdot \mathcal{K}(\mathbf{\tau}) d\mathbf{\tau}, # $$ @@ -49,7 +53,7 @@ # $$ # \mathcal{I}_{\rm{out}}(\mathbf{\tilde{x}}_i) = \sum_{{\mathbf{x}_i}\in\mathcal{X}} \mathcal{I}(\mathbf{x}_i + \mathbf{\tau}) \cdot \mathcal{K}(\mathbf{x}_i), # $$ -# where $\mathbf{\tau} \in \mathcal{S}$, with $\mathcal{S}$ the set of available strides, corresponds to the current stride position of the filter, and $\mathbf{\tilde{x}}_i$ points are obtained by taking the centroid of the filter position mapped on the $\Omega$ domain. +# where $\mathbf{\tau} \in \mathcal{S}$, with $\mathcal{S}$ the set of available strides, corresponds to the current stride position of the filter, and $\mathbf{\tilde{x}}_i$ points are obtained by taking the centroid of the filter position mapped on the $\Omega$ domain. # We will now try to pratically see how to work with the filter. From the above definition we see that what is needed is: # 1. A domain and a function defined on that domain (the input) @@ -57,16 +61,16 @@ # 3. The filter rectangular domain $\rightarrow$ `filter_dim` variable in `ContinuousConv` # ### Input function -# +# # The input function for the continuous filter is defined as a tensor of shape: $$[B \times N_{in} \times N \times D]$$ where $B$ is the batch_size, $N_{in}$ is the number of input fields, $N$ the number of points in the mesh, $D$ the dimension of the problem. In particular: # * $D$ is the number of spatial variables + 1. The last column must contain the field value. For example for 2D problems $D=3$ and the tensor will be something like `[first coordinate, second coordinate, field value]` -# * $N_{in}$ represents the number of vectorial function presented. For example a vectorial function $f = [f_1, f_2]$ will have $N_{in}=2$ -# +# * $N_{in}$ represents the number of vectorial function presented. For example a vectorial function $f = [f_1, f_2]$ will have $N_{in}=2$ +# # Let's see an example to clear the ideas. We will be verbose to explain in details the input form. We wish to create the function: # $$ # f(x, y) = [\sin(\pi x) \sin(\pi y), -\sin(\pi x) \sin(\pi y)] \quad (x,y)\in[0,1]\times[0,1] # $$ -# +# # using a batch size equal to 1. # In[2]: @@ -85,26 +89,26 @@ D = 3 # create the function f domain as random 2d points in [0, 1] -domain = torch.rand(size=(batch_size, number_input_fields, N, D-1)) +domain = torch.rand(size=(batch_size, number_input_fields, N, D - 1)) print(f"Domain has shape: {domain.shape}") # create the functions -pi = torch.acos(torch.tensor([-1.])) # pi value +pi = torch.acos(torch.tensor([-1.0])) # pi value f1 = torch.sin(pi * domain[:, 0, :, 0]) * torch.sin(pi * domain[:, 0, :, 1]) -f2 = - torch.sin(pi * domain[:, 1, :, 0]) * torch.sin(pi * domain[:, 1, :, 1]) +f2 = -torch.sin(pi * domain[:, 1, :, 0]) * torch.sin(pi * domain[:, 1, :, 1]) # stacking the input domain and field values data = torch.empty(size=(batch_size, number_input_fields, N, D)) -data[..., :-1] = domain # copy the domain -data[:, 0, :, -1] = f1 # copy first field value +data[..., :-1] = domain # copy the domain +data[:, 0, :, -1] = f1 # copy first field value data[:, 1, :, -1] = f1 # copy second field value print(f"Filter input data has shape: {data.shape}") # ### Stride -# +# # The stride is passed as a dictionary `stride` which tells the filter where to go. Here is an example for the $[0,1]\times[0,5]$ domain: -# +# # ```python # # stride definition # stride = {"domain": [1, 5], @@ -118,15 +122,15 @@ # 2. `start`: start position of the filter, coordinate $(0, 0)$ # 3. `jump`: the jumps of the centroid of the filter to the next position $(0.1, 0.3)$ # 4. `direction`: the directions of the jump, with `1 = right`, `0 = no jump`, `-1 = left` with respect to the current position -# +# # **Note** -# +# # We are planning to release the possibility to directly pass a list of possible strides! # ### Filter definition -# +# # Having defined all the previous blocks, we are now able to construct the continuous filter. -# +# # Suppose we would like to get an output with only one field, and let us fix the filter dimension to be $[0.1, 0.1]$. # In[3]: @@ -136,17 +140,20 @@ filter_dim = [0.1, 0.1] # stride -stride = {"domain": [1, 1], - "start": [0, 0], - "jump": [0.08, 0.08], - "direction": [1, 1], - } - -# creating the filter -cConv = ContinuousConvBlock(input_numb_field=number_input_fields, - output_numb_field=1, - filter_dim=filter_dim, - stride=stride) +stride = { + "domain": [1, 1], + "start": [0, 0], + "jump": [0.08, 0.08], + "direction": [1, 1], +} + +# creating the filter +cConv = ContinuousConvBlock( + input_numb_field=number_input_fields, + output_numb_field=1, + filter_dim=filter_dim, + stride=stride, +) # That's it! In just one line of code we have created the continuous convolutional filter. By default the `pina.model.FeedForward` neural network is intitialised, more on the [documentation](https://mathlab.github.io/PINA/_rst/fnn.html). In case the mesh doesn't change during training we can set the `optimize` flag equals to `True`, to exploit optimizations for finding the points to convolve. @@ -155,11 +162,13 @@ # creating the filter + optimization -cConv = ContinuousConvBlock(input_numb_field=number_input_fields, - output_numb_field=1, - filter_dim=filter_dim, - stride=stride, - optimize=True) +cConv = ContinuousConvBlock( + input_numb_field=number_input_fields, + output_numb_field=1, + filter_dim=filter_dim, + stride=stride, + optimize=True, +) # Let's try to do a forward pass: @@ -169,14 +178,14 @@ print(f"Filter input data has shape: {data.shape}") -#input to the filter +# input to the filter output = cConv(data) print(f"Filter output data has shape: {output.shape}") -# If we don't want to use the default `FeedForward` neural network, we can pass a specified torch model in the `model` keyword as follow: -# +# If we don't want to use the default `FeedForward` neural network, we can pass a specified torch model in the `model` keyword as follow: +# # In[6]: @@ -184,29 +193,32 @@ class SimpleKernel(torch.nn.Module): def __init__(self) -> None: super().__init__() - self. model = torch.nn.Sequential( + self.model = torch.nn.Sequential( torch.nn.Linear(2, 20), torch.nn.ReLU(), torch.nn.Linear(20, 20), torch.nn.ReLU(), - torch.nn.Linear(20, 1)) + torch.nn.Linear(20, 1), + ) def forward(self, x): return self.model(x) -cConv = ContinuousConvBlock(input_numb_field=number_input_fields, - output_numb_field=1, - filter_dim=filter_dim, - stride=stride, - optimize=True, - model=SimpleKernel) +cConv = ContinuousConvBlock( + input_numb_field=number_input_fields, + output_numb_field=1, + filter_dim=filter_dim, + stride=stride, + optimize=True, + model=SimpleKernel, +) # Notice that we pass the class and not an already built object! # ## Building a MNIST Classifier -# +# # Let's see how we can build a MNIST classifier using a continuous convolutional filter. We will use the MNIST dataset from PyTorch. In order to keep small training times we use only 6000 samples for training and 1000 samples for testing. # In[7]: @@ -215,33 +227,25 @@ def forward(self, x): from torch.utils.data import DataLoader, SubsetRandomSampler numb_training = 6000 # get just 6000 images for training -numb_testing= 1000 # get just 1000 images for training -seed = 111 # for reproducibility -batch_size = 8 # setting batch size +numb_testing = 1000 # get just 1000 images for training +seed = 111 # for reproducibility +batch_size = 8 # setting batch size # setting the seed torch.manual_seed(seed) # downloading the dataset -train_data = torchvision.datasets.MNIST('./data/', train=True, download=True, - transform=torchvision.transforms.Compose([ - torchvision.transforms.ToTensor(), - torchvision.transforms.Normalize( - (0.1307,), (0.3081,)) - ])) -subsample_train_indices = torch.randperm(len(train_data))[:numb_training] -train_loader = DataLoader(train_data, batch_size=batch_size, - sampler=SubsetRandomSampler(subsample_train_indices)) - -test_data = torchvision.datasets.MNIST('./data/', train=False, download=True, - transform=torchvision.transforms.Compose([ - torchvision.transforms.ToTensor(), - torchvision.transforms.Normalize( - (0.1307,), (0.3081,)) - ])) -subsample_test_indices = torch.randperm(len(train_data))[:numb_testing] -test_loader = DataLoader(train_data, batch_size=batch_size, - sampler=SubsetRandomSampler(subsample_train_indices)) +train_data = torchvision.datasets.MNIST( + "./data/", + download=True, + train=False, + transform=torchvision.transforms.Compose( + [ + torchvision.transforms.ToTensor(), + torchvision.transforms.Normalize((0.1307,), (0.3081,)), + ] + ), +) # Let's now build a simple classifier. The MNIST dataset is composed by vectors of shape `[batch, 1, 28, 28]`, but we can image them as one field functions where the pixels $ij$ are the coordinate $x=i, y=j$ in a $[0, 27]\times[0,27]$ domain, and the pixels values are the field values. We just need a function to transform the regular tensor in a tensor compatible for the continuous filter: @@ -256,21 +260,17 @@ def transform_input(x): # creating the n dimensional mesh grid for a single channel image values_mesh = [torch.arange(0, dim).float() for dim in dim_grid] mesh = torch.meshgrid(values_mesh) - coordinates_mesh = [x.reshape(-1, 1) for x in mesh] - coordinates = torch.cat(coordinates_mesh, dim=1).unsqueeze( - 0).repeat((batch_size, 1, 1)).unsqueeze(1) + coordinates_mesh = [m.reshape(-1, 1).to(x.device) for m in mesh] + coordinates = ( + torch.cat(coordinates_mesh, dim=1) + .unsqueeze(0) + .repeat((batch_size, 1, 1)) + .unsqueeze(1) + ) return torch.cat((coordinates, x.flatten(2).unsqueeze(-1)), dim=-1) -# let's try it out -image, s = next(iter(train_loader)) -print(f"Original MNIST image shape: {image.shape}") - -image_transformed = transform_input(image) -print(f"Transformed MNIST image shape: {image_transformed.shape}") - - # We can now build a simple classifier! We will use just one convolutional filter followed by a feedforward neural network # In[9]: @@ -279,6 +279,7 @@ def transform_input(x): # setting the seed torch.manual_seed(seed) + class ContinuousClassifier(torch.nn.Module): def __init__(self): super().__init__() @@ -287,20 +288,25 @@ def __init__(self): numb_class = 10 # convolutional block - self.convolution = ContinuousConvBlock(input_numb_field=1, - output_numb_field=4, - stride={"domain": [27, 27], - "start": [0, 0], - "jumps": [4, 4], - "direction": [1, 1.], - }, - filter_dim=[4, 4], - optimize=True) + self.convolution = ContinuousConvBlock( + input_numb_field=1, + output_numb_field=4, + stride={ + "domain": [27, 27], + "start": [0, 0], + "jumps": [4, 4], + "direction": [1, 1.0], + }, + filter_dim=[4, 4], + optimize=True, + ) # feedforward net - self.nn = FeedForward(input_dimensions=196, - output_dimensions=numb_class, - layers=[120, 64], - func=torch.nn.ReLU) + self.nn = FeedForward( + input_dimensions=196, + output_dimensions=numb_class, + layers=[120, 64], + func=torch.nn.ReLU, + ) def forward(self, x): # transform input + convolution @@ -310,43 +316,37 @@ def forward(self, x): return self.nn(x[..., -1].flatten(1)) -net = ContinuousClassifier() - - -# Let's try to train it using a simple pytorch training loop. We train for just 1 epoch using Adam optimizer with a $0.001$ learning rate. +# We now aim to solve the classification problem. For this we will use the `SupervisedSolver` and the `SupervisedProblem`. The input of the supervised problems are the images, while the output the corresponding class. # In[10]: -# setting the seed -torch.manual_seed(seed) - -# optimizer and loss function -optimizer = torch.optim.Adam(net.parameters(), lr=0.001) -criterion = torch.nn.CrossEntropyLoss() - -for epoch in range(1): # loop over the dataset multiple times - - running_loss = 0.0 - for i, data in enumerate(train_loader, 0): - # get the inputs; data is a list of [inputs, labels] - inputs, labels = data - - # zero the parameter gradients - optimizer.zero_grad() - - # forward + backward + optimize - outputs = net(inputs) - loss = criterion(outputs, labels) - loss.backward() - optimizer.step() - - # print statistics - running_loss += loss.item() - if i % 50 == 49: - print( - f'batch [{i + 1}/{numb_training//batch_size}] loss[{running_loss / 500:.3f}]') - running_loss = 0.0 +# setting the problem +problem = SupervisedProblem( + input_=train_data.train_data.unsqueeze(1), # adding channel dimension + output_=train_data.train_labels, +) + +# setting the solver +solver = SupervisedSolver( + problem=problem, + model=ContinuousClassifier(), + loss=torch.nn.CrossEntropyLoss(), + use_lt=False, +) + +# setting the trainer +trainer = Trainer( + solver=solver, + max_epochs=1, + accelerator="cpu", + enable_model_summary=False, + train_size=0.7, + val_size=0.1, + test_size=0.2, + batch_size=64, +) +trainer.train() # Let's see the performance on the test set! @@ -356,24 +356,25 @@ def forward(self, x): correct = 0 total = 0 +trainer.data_module.setup("test") with torch.no_grad(): - for data in test_loader: - images, labels = data + for data in trainer.data_module.test_dataloader(): + test_data = data["data"] + images, labels = test_data["input"], test_data["target"] # calculate outputs by running images through the network - outputs = net(images) + outputs = solver(images) # the class with the highest energy is what we choose as prediction _, predicted = torch.max(outputs.data, 1) total += labels.size(0) correct += (predicted == labels).sum().item() -print( - f'Accuracy of the network on the 1000 test images: {(correct / total):.3%}') +print(f"Accuracy of the network on the test images: {(correct / total):.3%}") # As we can see we have very good performance for having trained only for 1 epoch! Nevertheless, we are still using structured data... Let's see how we can build an autoencoder for unstructured data now. # ## Building a Continuous Convolutional Autoencoder -# +# # Just as toy problem, we will now build an autoencoder for the following function $f(x,y)=\sin(\pi x)\sin(\pi y)$ on the unit circle domain centered in $(0.5, 0.5)$. We will also see the ability to up-sample (once trained) the results without retraining. Let's first create the input and visualize it, we will use firstly a mesh of $100$ points. # In[12]: @@ -383,11 +384,11 @@ def forward(self, x): def circle_grid(N=100): """Generate points withing a unit 2D circle centered in (0.5, 0.5) - :param N: number of points - :type N: float - :return: [x, y] array of points - :rtype: torch.tensor - """ + :param N: number of points + :type N: float + :return: [x, y] array of points + :rtype: torch.tensor + """ PI = torch.acos(torch.zeros(1)).item() * 2 R = 0.5 @@ -402,13 +403,16 @@ def circle_grid(N=100): return torch.stack([x, y]).T + # create the grid grid = circle_grid(500) # create input input_data = torch.empty(size=(1, 1, grid.shape[0], 3)) input_data[0, 0, :, :-1] = grid -input_data[0, 0, :, -1] = torch.sin(pi * grid[:, 0]) * torch.sin(pi * grid[:, 1]) +input_data[0, 0, :, -1] = torch.sin(pi * grid[:, 0]) * torch.sin( + pi * grid[:, 1] +) # visualize data plt.title("Training sample with 500 points") @@ -427,19 +431,24 @@ def __init__(self, hidden_dimension): super().__init__() # convolutional block - self.convolution = ContinuousConvBlock(input_numb_field=1, - output_numb_field=2, - stride={"domain": [1, 1], - "start": [0, 0], - "jumps": [0.05, 0.05], - "direction": [1, 1.], - }, - filter_dim=[0.15, 0.15], - optimize=True) + self.convolution = ContinuousConvBlock( + input_numb_field=1, + output_numb_field=2, + stride={ + "domain": [1, 1], + "start": [0, 0], + "jumps": [0.05, 0.05], + "direction": [1, 1.0], + }, + filter_dim=[0.15, 0.15], + optimize=True, + ) # feedforward net - self.nn = FeedForward(input_dimensions=400, - output_dimensions=hidden_dimension, - layers=[240, 120]) + self.nn = FeedForward( + input_dimensions=400, + output_dimensions=hidden_dimension, + layers=[240, 120], + ) def forward(self, x): # convolution @@ -453,19 +462,24 @@ def __init__(self, hidden_dimension): super().__init__() # convolutional block - self.convolution = ContinuousConvBlock(input_numb_field=2, - output_numb_field=1, - stride={"domain": [1, 1], - "start": [0, 0], - "jumps": [0.05, 0.05], - "direction": [1, 1.], - }, - filter_dim=[0.15, 0.15], - optimize=True) + self.convolution = ContinuousConvBlock( + input_numb_field=2, + output_numb_field=1, + stride={ + "domain": [1, 1], + "start": [0, 0], + "jumps": [0.05, 0.05], + "direction": [1, 1.0], + }, + filter_dim=[0.15, 0.15], + optimize=True, + ) # feedforward net - self.nn = FeedForward(input_dimensions=hidden_dimension, - output_dimensions=400, - layers=[120, 240]) + self.nn = FeedForward( + input_dimensions=hidden_dimension, + output_dimensions=400, + layers=[120, 240], + ) def forward(self, weights, grid): # feed forward pass @@ -474,9 +488,9 @@ def forward(self, weights, grid): return torch.sigmoid(self.convolution.transpose(x, grid)) -# Very good! Notice that in the `Decoder` class in the `forward` pass we have used the `.transpose()` method of the `ContinuousConvolution` class. This method accepts the `weights` for upsampling and the `grid` on where to upsample. Let's now build the autoencoder! We set the hidden dimension in the `hidden_dimension` variable. We apply the sigmoid on the output since the field value is between $[0, 1]$. +# Very good! Notice that in the `Decoder` class in the `forward` pass we have used the `.transpose()` method of the `ContinuousConvolution` class. This method accepts the `weights` for upsampling and the `grid` on where to upsample. Let's now build the autoencoder! We set the hidden dimension in the `hidden_dimension` variable. We apply the sigmoid on the output since the field value is between $[0, 1]$. -# In[17]: +# In[14]: class Autoencoder(torch.nn.Module): @@ -495,38 +509,46 @@ def forward(self, x): out = self.decoder(weights, grid) return out -net = Autoencoder() - # Let's now train the autoencoder, minimizing the mean square error loss and optimizing using Adam. We use the `SupervisedSolver` as solver, and the problem is a simple problem created by inheriting from `AbstractProblem`. It takes approximately two minutes to train on CPU. -# In[19]: +# In[15]: # define the problem -class CircleProblem(AbstractProblem): - input_variables = ['x', 'y', 'f'] - output_variables = input_variables - conditions = {'data' : Condition(input_points=LabelTensor(input_data, input_variables), output_points=LabelTensor(input_data, output_variables))} +problem = SupervisedProblem(input_data, input_data) + # define the solver -solver = SupervisedSolver(problem=CircleProblem(), model=net, loss=torch.nn.MSELoss()) +solver = SupervisedSolver( + problem=problem, + model=Autoencoder(), + loss=torch.nn.MSELoss(), + use_lt=False, +) # train -trainer = Trainer(solver, max_epochs=150, accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional) +trainer = Trainer( + solver, + max_epochs=100, + accelerator="cpu", + enable_model_summary=False, # we train on CPU and avoid model summary at beginning of training (optional) + train_size=1.0, + val_size=0.0, + test_size=0.0, +) trainer.train() - # Let's visualize the two solutions side by side! -# In[20]: +# In[16]: -net.eval() +solver.eval() # get output and detach from computational graph for plotting -output = net(input_data).detach() +output = solver(input_data).detach() # visualize data fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 3)) @@ -543,39 +565,42 @@ class CircleProblem(AbstractProblem): # As we can see, the two solutions are really similar! We can compute the $l_2$ error quite easily as well: -# In[21]: +# In[17]: def l2_error(input_, target): - return torch.linalg.norm(input_-target, ord=2)/torch.linalg.norm(input_, ord=2) + return torch.linalg.norm(input_ - target, ord=2) / torch.linalg.norm( + input_, ord=2 + ) -print(f'l2 error: {l2_error(input_data[0, 0, :, -1], output[0, 0, :, -1]):.2%}') +print(f"l2 error: {l2_error(input_data[0, 0, :, -1], output[0, 0, :, -1]):.2%}") # More or less $4\%$ in $l_2$ error, which is really low considering the fact that we use just **one** convolutional layer and a simple feedforward to decrease the dimension. Let's see now some peculiarity of the filter. # ### Filter for upsampling -# +# # Suppose we have already the hidden representation and we want to upsample on a differen grid with more points. Let's see how to do it: -# In[22]: +# In[18]: # setting the seed torch.manual_seed(seed) -grid2 = circle_grid(1500) # triple number of points +grid2 = circle_grid(1500) # triple number of points input_data2 = torch.zeros(size=(1, 1, grid2.shape[0], 3)) input_data2[0, 0, :, :-1] = grid2 -input_data2[0, 0, :, -1] = torch.sin(pi * - grid2[:, 0]) * torch.sin(pi * grid2[:, 1]) +input_data2[0, 0, :, -1] = torch.sin(pi * grid2[:, 0]) * torch.sin( + pi * grid2[:, 1] +) # get the hidden representation from original input -latent = net.encoder(input_data) +latent = solver.model.encoder(input_data) # upsample on the second input_data2 -output = net.decoder(latent, input_data2).detach() +output = solver.model.decoder(latent, input_data2).detach() # show the picture fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 3)) @@ -592,16 +617,18 @@ def l2_error(input_, target): # As we can see we have a very good approximation of the original function, even thought some noise is present. Let's calculate the error now: -# In[23]: +# In[19]: -print(f'l2 error: {l2_error(input_data2[0, 0, :, -1], output[0, 0, :, -1]):.2%}') +print( + f"l2 error: {l2_error(input_data2[0, 0, :, -1], output[0, 0, :, -1]):.2%}" +) # ### Autoencoding at different resolutions # In the previous example we already had the hidden representation (of the original input) and we used it to upsample. Sometimes however we could have a finer mesh solution and we would simply want to encode it. This can be done without retraining! This procedure can be useful in case we have many points in the mesh and just a smaller part of them are needed for training. Let's see the results of this: -# In[ ]: +# In[20]: # setting the seed @@ -610,14 +637,15 @@ def l2_error(input_, target): grid2 = circle_grid(3500) # very fine mesh input_data2 = torch.zeros(size=(1, 1, grid2.shape[0], 3)) input_data2[0, 0, :, :-1] = grid2 -input_data2[0, 0, :, -1] = torch.sin(pi * - grid2[:, 0]) * torch.sin(pi * grid2[:, 1]) +input_data2[0, 0, :, -1] = torch.sin(pi * grid2[:, 0]) * torch.sin( + pi * grid2[:, 1] +) # get the hidden representation from finer mesh input -latent = net.encoder(input_data2) +latent = solver.model.encoder(input_data2) # upsample on the second input_data2 -output = net.decoder(latent, input_data2).detach() +output = solver.model.decoder(latent, input_data2).detach() # show the picture fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 3)) @@ -633,15 +661,16 @@ def l2_error(input_, target): # calculate l2 error print( - f'l2 error: {l2_error(input_data2[0, 0, :, -1], output[0, 0, :, -1]):.2%}') + f"l2 error: {l2_error(input_data2[0, 0, :, -1], output[0, 0, :, -1]):.2%}" +) # ## What's next? -# +# # We have shown the basic usage of a convolutional filter. There are additional extensions possible: -# +# # 1. Train using Physics Informed strategies -# +# # 2. Use the filter to build an unstructured convolutional autoencoder for reduced order modelling -# +# # 3. Many more... diff --git a/tutorials/tutorial5/tutorial.ipynb b/tutorials/tutorial5/tutorial.ipynb index 288dbe87e..688046c91 100644 --- a/tutorials/tutorial5/tutorial.ipynb +++ b/tutorials/tutorial5/tutorial.ipynb @@ -21,34 +21,41 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "5f2744dc", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2024-09-19T13:35:28.837348Z", + "start_time": "2024-09-19T13:35:27.611334Z" + } + }, "outputs": [], "source": [ "## routine needed to run the notebook on Google Colab\n", "try:\n", - " import google.colab\n", - " IN_COLAB = True\n", + " import google.colab\n", + "\n", + " IN_COLAB = True\n", "except:\n", - " IN_COLAB = False\n", + " IN_COLAB = False\n", "if IN_COLAB:\n", - " !pip install \"pina-mathlab\"\n", - " !pip install scipy\n", - " # get the data\n", - " !wget https://github.com/mathLab/PINA/raw/refs/heads/master/tutorials/tutorial5/Data_Darcy.mat\n", + " !pip install \"pina-mathlab\"\n", + " !pip install scipy\n", + " # get the data\n", + " !wget https://github.com/mathLab/PINA/raw/refs/heads/master/tutorials/tutorial5/Data_Darcy.mat\n", + "\n", + "import torch\n", + "import matplotlib.pyplot as plt\n", + "import warnings\n", "\n", - " \n", "# !pip install scipy # install scipy\n", "from scipy import io\n", - "import torch\n", "from pina.model import FNO, FeedForward # let's import some models\n", - "from pina import Condition, LabelTensor\n", - "from pina.solvers import SupervisedSolver\n", - "from pina.trainer import Trainer\n", - "from pina.problem import AbstractProblem\n", - "import matplotlib.pyplot as plt\n", - "plt.style.use('tableau-colorblind10')" + "from pina import Condition, Trainer\n", + "from pina.solver import SupervisedSolver\n", + "from pina.problem.zoo import SupervisedProblem\n", + "\n", + "warnings.filterwarnings(\"ignore\")" ] }, { @@ -69,21 +76,26 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 2, "id": "2ffb8a4c", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2024-09-19T13:35:28.989631Z", + "start_time": "2024-09-19T13:35:28.952744Z" + } + }, "outputs": [], "source": [ "# download the dataset\n", "data = io.loadmat(\"Data_Darcy.mat\")\n", "\n", "# extract data (we use only 100 data for train)\n", - "k_train = LabelTensor(torch.tensor(data['k_train'], dtype=torch.float).unsqueeze(-1), ['u0'])\n", - "u_train = LabelTensor(torch.tensor(data['u_train'], dtype=torch.float).unsqueeze(-1), ['u'])\n", - "k_test = LabelTensor(torch.tensor(data['k_test'], dtype=torch.float).unsqueeze(-1), ['u0'])\n", - "u_test= LabelTensor(torch.tensor(data['u_test'], dtype=torch.float).unsqueeze(-1), ['u'])\n", - "x = torch.tensor(data['x'], dtype=torch.float)[0]\n", - "y = torch.tensor(data['y'], dtype=torch.float)[0]" + "k_train = torch.tensor(data[\"k_train\"], dtype=torch.float)\n", + "u_train = torch.tensor(data[\"u_train\"], dtype=torch.float)\n", + "k_test = torch.tensor(data[\"k_test\"], dtype=torch.float)\n", + "u_test = torch.tensor(data[\"u_test\"], dtype=torch.float)\n", + "x = torch.tensor(data[\"x\"], dtype=torch.float)[0]\n", + "y = torch.tensor(data[\"y\"], dtype=torch.float)[0]" ] }, { @@ -96,13 +108,18 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 3, "id": "c8501b6f", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2024-09-19T13:35:29.108381Z", + "start_time": "2024-09-19T13:35:29.031076Z" + } + }, "outputs": [ { "data": { - "image/png": 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", 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wvRMkZQHB++y0u0T9sNk0v3v//+Vd6aGVWMeN5n3lZCUtNfp23ihIQKTtmwKCtn79z9vjl4X5Bp/gj65XPy65PLLvXZJTv73T7tZu6/DrJyDM7tFvb2/Uf+8cbpt5cTdJP2FxOvTbOoSxg3RihyBumHKg+fnnn6eFCxeqacF5EisOQDzz7YYNG1pt/4tf/EJNXPfII4/QDTfcQE8++aSaII8nwwOAzgFxAwBimrB4PB41U+WUKVO++iV2u7rNM+m2hu9v3p7xnlWk9jzFeHV1ddgCAPErFnGDIXYAxC+7GZPB8dwqffr0Cbufb/Nx6dbw/ZL2PATMU4gHF5w0BxDfYhE3GGIHQPyKj2sPW8jLy6OqqqrQwmcXAwBEg9gBEL/a/aTbXr16kcPhoHPnws8O5tuZmZmtPofvl7R3u91qAYDEEIu4wRA7AOJXu4+wuFwuGj16NO3atSusOBPfzsnJafU5fH/z9mznzp0R2wNAYkHcAIAOuayZL03Mzc2lW2+9VdVQ4MsT6+rq1Nn/bN68eXT11Ver48nsoYceookTJ9Jzzz1Hd955J23evJkOHTpEv/rVr8zoHgBYEOIGAMQ8YZkzZw6VlZXRihUr1Alwo0aNoh07doROkCssLAwrwTtu3DhVQ2HZsmX02GOPqQJQ27Zt066lEORssJHdiH4tt7NWVieiZpB+rY3kvjX6K67QL2DUmO2TFYMrFNQd0a8bRIEk4XvXT/+6fY2P7qu2gvIu0to7Vdfqr9wm6Ei3Yv16B6ypu35RrtST+t874+oMUT9smlfS2Aw7kX6tO8vEDYA2mTVjg1VmgrBJiod17GmvNoML+cc5vjSRz/gfuOJnWoXjpAlLYx9zEpZ6QcJCAZtpCUu3IsO0RIH/hsVbwuJJ0++ITbD1SBMWSSCRJCw2v2yTN744qdXOZ3jpw6a31MmsaWlpFE+x4+L/DdQqHHdeWOm2TFA4rkJQkfaCX78KbKVfVtn1gqDSbblXv22ZR1YBuaxRsO4G/eS+olq/LWuSVLq9IKioLal0WybbZruUCSrdlppY6fbLs9HbGF7aTdu14kZcXiUEAAAAnQsSFgAAALA8JCwAAABgeUhYAAAAwPKQsAAAAIDlIWEBAAAAy0PCAgAAAJaHhAUAAAAsDwkLAAAAWB4SFgAAAOiccwl1FE+Gj+wp0efc8fSRlTm+YVD08sJBxVXp2m3Tetdqt236c3cyqyR+bV/9EtF22ZRGIgFBuf2A/hRFij9ZMP2AoN6+ZIqAgFM2n0CXc/r9aOyjX369y8mLon7Yuut9p+0BD9F50aohBvySYMDtBZPc+AX7vAHJ3BsWmTrn0hMEfy9sJk1bIh1asEnaWuNz0YERFgAAALA8JCwAAABgeUhYAAAAwPKQsAAAAIDlIWEBAAAAy0PCAgAAAJaHhAUAAAA6X8KSn59P3/jGNyg1NZV69+5NM2fOpOPHj7f5nI0bN5LNZgtbkpOT27trAGBRiBsAEPOEZc+ePbRo0SI6ePAg7dy5k7xeL02dOpXq6urafF5aWhqVlJSEljNnzrR31wDAohA3ACDmlW537Nhx2V4Q7zEdPnyYvvWtb0V8Hu8dZWZmtnd3ACAOIG4AQIeX5q+qqlI/e/bs2Wa72tpa6t+/PwUCAbrlllvo6aefpmHDhrXatqmpSS1B1dXV6qfN5VdLNH16X+qTria//ts0oId+2fNjRwdot7V1k00nQKTf3u7RL81sM7PcfkpAu62REv1zbs6hMWVDkNOpv25Pvf4cAXXJss3N7tF/85IvCsqpp6eI+uGo0PxOB2SfSazjRluxI5FJyuezgGGNcvsBcbTRY5OU2ldP0G9q2AXTethtph0LMQRx14ijMv6mnnTLQeThhx+m2267jW666aaI7YYMGUIbNmyg7du30xtvvKGeN27cOCouLo54vDs9PT209O3b18RXAQCxZFbcYIgdAPHL1ISFj0kfO3aMNm/e3Ga7nJwcmjdvHo0aNYomTpxIb7/9NmVkZNDLL7/cavu8vDy1BxZcioqKTHoFABBrZsUNhtgBEL9MOyS0ePFi+s///E/au3cvXXPNNaLnJiUl0c0330wFBQWtPu52u9UCAInFzLjBEDsA4le7j7AYhqGCzjvvvEMffPABXXvtteJ1+P1++uyzzygrK6u9uwcAFoS4AQAxH2Hh4dw333xTHVfmmgqlpaXqfj5enJJy6WQ/Hsa9+uqr1fFk9sQTT9A3v/lNuu6666iyspJWr16tLk984IEH2rt7AGBBiBsAEPOEZd26dern7bffHnb/q6++SvPnz1f/LywsJLv9q8Gdixcv0sKFC1WQ6tGjB40ePZr2799PN954Y3t3DwAsCHEDAGKesPDQbjS7d+8Ou/3CCy+oBQA6J8QNAIgGcwkBAACA5SFhAQAAAMtDwgIAAACWZ3pp/lhypXjJ0SV6TeK+qZWi9doFpZw/+uw6/RW7BaXoHcJy0j79EsoBSaV2p6wfdkFJ/G7dGrXbZnRre1K8y9qn1JIZSuvStNueKeolWrenu3597UCSYHoFr/73DmJHUhLfI6i9Li2f7zepNL9ZpfaZrLq8LIbZBLFXVBJfMm2J8C91wKH/jhgOwbhFIpfmBwAAAGgPSFgAAADA8pCwAAAAgOUhYQEAAADLQ8ICAAAAloeEBQAAACwPCQsAAABYHhIWAAAAsDwkLAAAAGB5SFgAAADA8hKqNL8RsFEgEL108PHy3qL11hSnmVO6PklQIl3jdYVx6a/b7vJrt03u4hF1IyNVv4T+oLRy7bZDu5WI+pGdpD8dQ13Ard32I8dA7balqakkYQu4tNv6BaX57XWNsn449cKELYCS/y35BUXjJW0lZfy9hizMewU14/2CfV6/cIoAs4iry9sFpfkF8V9Sbj/glHXaEJXmF6zbjtL8AAAAAG1CwgIAAACdL2FZtWoV2Wy2sGXo0KFtPmfr1q2qTXJyMg0fPpzee++99u4WAFgY4gYAdMgIy7Bhw6ikpCS07Nu3L2Lb/fv309y5c+n++++nTz/9lGbOnKmWY8eOmdE1ALAoxA0AiHnC4nQ6KTMzM7T06tUrYttf/OIXdMcdd9AjjzxCN9xwAz355JN0yy230Jo1a8zoGgBYFOIGAMQ8YTlx4gRlZ2fTwIED6b777qPCwsKIbQ8cOEBTpkwJu2/atGnq/kiampqouro6bAGA+GZ23GCIHQDxq90TlrFjx9LGjRtpx44dtG7dOjp16hRNmDCBampqWm1fWlpKffr0CbuPb/P9keTn51N6enpo6du3b3u/DACIoVjEDYbYARC/2j1hmT59Os2ePZtGjBih9nj4RLjKykp666232u135OXlUVVVVWgpKipqt3UDQOzFIm4wxA6A+GV64bju3bvT4MGDqaCgoNXH+Vj1uXPnwu7j23x/JG63Wy0AkJjMiBsMsQMgfpleh6W2tpZOnjxJWVlZrT6ek5NDu3btCrtv586d6n4A6JwQNwDA9IRl6dKltGfPHjp9+rS69HDWrFnkcDjUJYhs3rx5alg26KGHHlLHrZ977jn6y1/+ouoxHDp0iBYvXtzeXQMAi0LcAICYHxIqLi5WQaaiooIyMjJo/PjxdPDgQfV/xmf+2+1f5Unjxo2jN998k5YtW0aPPfYYXX/99bRt2za66aabxL/bMC4t0dScSRet1+7VbxtIFqzY5zBljgrFod/eKZhLqEfXBlE3hnQPH7ZvS07aSe22tyRHvoKkNRl2n3bbIr/+IYPCpqu029oFc5IwydQrDo/w+yEQqKzSa2fI5pmyStwwU0DwIUrm/PGI5vuRzf8SELT3BRymzH90qb0589bY7bI5r2yC7TYgiLuSmG4I5xIKCP6yG07B59JsG0yIhGXz5s1tPr579+7L7uOT7XgBgM4JcQMAosFcQgAAAGB5SFgAAADA8pCwAAAAgOUhYQEAAADLQ8ICAAAAloeEBQAAACwPCQsAAABYHhIWAAAAsDwkLAAAAGB5ps/WHEuuT7uRwx29Nn5Skmy9AcHkrka9oIyzoB8Bl6z0ut9mTqn2rkmy8uuZ7mrttgNd5/XbCr+53ezdtNvWG7XabR02/TLffr9s/8ApmBLCFhCU+U6WbQABj15HAob+9Afxyq8z90czHsE+oaTcvqSMv1ewXtVeUm5fUMZfWmrfrNL8UpLS/JIhAMHbLCq1zwKCUv6GQ/A+2zr2M8EICwAAAFgeEhYAAACwPCQsAAAAYHlIWAAAAMDykLAAAACA5SFhAQAAAMtDwgIAAACdL2EZMGAA2Wy2y5ZFixa12n7jxo2XtU1Ojl5LBQASC2IHAMS0cNwnn3xCfr8/dPvYsWP0ne98h2bPnh3xOWlpaXT8+PHQbQ48ANC5IHYAQEwTloyMjLDbP//5z2nQoEE0ceLEiM/hIJOZmdneXQGAOILYAQAdVprf4/HQG2+8QUuWLGlzz6e2tpb69+9PgUCAbrnlFnr66adp2LBhEds3NTWpJai6+lL5d65ArVOF2l0pK69t2G2mlESWlPz3pcj2HCVVrb3J+l+DWq9L1I96v377esEbUm/UifpBgUbtpmX+FO22FR79kv8+j6xEuktQ6d7u1f9O2xpk0yvYXXql/O1ctl7/bbZU7ND11fiPnoChf9Q9IDhC7xe09QrruktK+fsFgUZSxt9MNjPbC8r4G6aW5ieTyvh37Gmvpv72bdu2UWVlJc2fPz9imyFDhtCGDRto+/btKkBx4Bk3bhwVFxdHfE5+fj6lp6eHlr59+5r0CgCgIyB2AEBME5Zf//rXNH36dMrOzo7YJicnh+bNm0ejRo1SQ79vv/22Ghp++eWXIz4nLy+PqqqqQktRUZFJrwAAOgJiBwDE7JDQmTNn6P3331dBRCIpKYluvvlmKigoiNjG7XarBQASD2IHAMR0hOXVV1+l3r1705133il6Hl8l8Nlnn1FWVpZZXQMAC0PsAICYJSx8LJmDTm5uLjmd4YM4PITLw7JBTzzxBP3hD3+gv/71r3TkyBH6wQ9+oPawHnjgATO6BgAWhtgBADE9JMTDuYWFhfSjH/3ossf4frv9qzzp4sWLtHDhQiotLaUePXrQ6NGjaf/+/XTjjTea0TUAsDDEDgCIacIydepUMvgSx1bs3r077PYLL7ygFgAAxA4AiARzCQEAAIDlIWEBAAAAy0PCAgAAAJaHhAUAAAA691xCsZZ22k/OJOlsH+08l5DetCuKz62/Xns32QwYtoB+Ltro0J/vp8ytP3cO+yJZf2K6dGeDdluPZCIOIkq2ebXbft50tXbbgppe2m0DdYIvB3/mkil/JNNjNbvSRoctWa/Qmo3nlWmnuYSsSjBlk+IXzEQj+U5L5vuRzuETEMwP5AsI+iGZ4EzYXvixiNhsgrVLXqJovcK55Ozm/H2T9qO9YYQFAAAALA8JCwAAAFgeEhYAAACwPCQsAAAAYHlIWAAAAMDykLAAAACA5SFhAQAAAMtDwgIAAACWh4QFAAAALA8JCwAAAFheQpXmT77oI6fTF7VdwCkscy+ooCxZtz9ZP190Nsn6bPcKclFBGf8mo4uoH38J9NFuW92UrN32825Zon6kOPRL859rTNVue7q8p3ZbZ7VsOgGHxzDlO0qSUtwsSXPqhoBstZ2BX7BPGDDMaeuX1GmX9llQi15amt8Qttder7S9Sf2QlPGXdsGQfOSi6QRQmh8AAACgfROWvXv30owZMyg7O5tsNhtt27Yt7HHDMGjFihWUlZVFKSkpNGXKFDpx4kTU9a5du5YGDBhAycnJNHbsWPr444+lXQMAi0LcAICYJyx1dXU0cuRIFSha8+yzz9JLL71E69evp48++oi6du1K06ZNo8bGyNO4btmyhZYsWUIrV66kI0eOqPXzc86fPy/tHgBYEOIGAMQ8YZk+fTo99dRTNGvWrMse472kF198kZYtW0Z33303jRgxgl5//XU6e/bsZXtUzT3//PO0cOFCWrBgAd14440qaHXp0oU2bNggf0UAYDmIGwBgqXNYTp06RaWlpWo4Nyg9PV0N1R44cKDV53g8Hjp8+HDYc+x2u7od6TlNTU1UXV0dtgBAfIpV3GCIHQDxq10TFg46rE+f8CtD+HbwsZbKy8vJ7/eLnpOfn68CWnDp27dvu70GAIitWMUNhtgBEL/i8iqhvLw8qqqqCi1FRUUd3SUAiAOIHQDxq10TlszMTPXz3LlzYffz7eBjLfXq1YscDofoOW63m9LS0sIWAIhPsYobDLEDIH61a8Jy7bXXqmCxa9eu0H18jJjP+s/JyWn1OS6Xi0aPHh32nEAgoG5Heg4AJA7EDQAwpdJtbW0tFRQUhJ0wd/ToUerZsyf169ePHn74YXU1wPXXX68C0fLly1XthZkzZ4aeM3nyZHW1wOLFi9VtvjQxNzeXbr31VhozZoy6YoAvg+Sz/wEg/iFuAEDME5ZDhw7RpEmTQrc5aDAOHBs3bqSf/OQnKmj8+Mc/psrKSho/fjzt2LFDFXYKOnnypDppLmjOnDlUVlamCkfxCXOjRo1Sz2l5Ql00SVVN5NSofh5IThKt1+bXrzluOARlrRv02zoapWXdJW31yy07PLJBuab6FO22RZWaJeCJqLhrD1E/7En6n2HAI3ivq/U3oeRq4fQKgtL8ktLdRpLsu2Tvpjcdgy3gIPpqs46buCEhnX1AVEJfMODtl5TEF9Vel5fQN2u9ZpX9l5baN8xqLJ0jQEA0VUccsRlcBCHO8fAxn/E/6eafktPhjp+ExaXf1pci+yPj7abfvilNfwNu6iHb2Jt66H+9vN392m1tXaPPGWW5hOW87DPsUqL/3nU7q/9+pHxZI+qHvaZBq50v0ETvn16jTmaNl3NDgrHj4v8NpLTU6NvjSW+taP1FPv334UuffhJe5tOf7+qir6t2W9Xeqz9fWI1Pf/6vKo9+W7Vur377Wo/+zk5tY/S/Ec01NOiv21+r/7fFIZhbzHVRtqPY5bwgdnypHzu6nLwo6of/+FejqpH4DC/tpu1acSMurxICAACAzgUJCwAAAFgeEhYAAACwPCQsAAAAYHlIWAAAAMDykLAAAACA5SFhAQAAAMtDwgIAAACWh4QFAAAALA8JCwAAACTeXEKJwFHVKHyCOXNr2AVzutibhHMJNel/tM4G/XUn1clyXHel/nvn7abfZ1+K7KsbcJkzx4ddMGdTkqyqu2g+KMncIf5ustLkZNf7zAP+ThlOLM8vmM9IPP+RYF4eyXrNnB9IOpeQaKKugH5bm6itfhesNKdRe8MICwAAAFgeEhYAAACwPCQsAAAAYHlIWAAAAMDykLAAAACA5SFhAQAAgMRLWPbu3UszZsyg7OxsstlstG3bttBjXq+XHn30URo+fDh17dpVtZk3bx6dPXu2zXWuWrVKrav5MnTo0Ct7RQBgOYgbABDzhKWuro5GjhxJa9euveyx+vp6OnLkCC1fvlz9fPvtt+n48eN01113RV3vsGHDqKSkJLTs27dP2jUAsCjEDQD4usSVnqZPn66W1qSnp9POnTvD7luzZg2NGTOGCgsLqV+/fpE74nRSZmamtDsAEAcQNwDA8uewVFVVqaHa7t27t9nuxIkTaih44MCBdN9996lAFUlTUxNVV1eHLQCQOMyIGwyxAyB+mVpLu7GxUR2bnjt3LqWlpUVsN3bsWNq4cSMNGTJEDes+/vjjNGHCBDp27BilpqZe1j4/P1+1acle7yG7Thl9m7A0s7Qssiabx6fftlFWmt/eoL9uZ52gNH+N7CvjT9Zfty9FUBLcLfsM/S5BCXHZW63Nrv+RKEn1+l88b1f99y7glJXmd9n13jufL2DpuNFW7IArIym3HyCbaaX5/QFBPwQl8VV7v6Q0P5lSbt/u12+r1u03Z1oP0owFcTfCwifSff/73yfDMGjdunVttuWh4tmzZ9OIESNo2rRp9N5771FlZSW99dZbrbbPy8tTe2DBpaioyKRXAQCxZGbcYIgdAPHLaWbQOXPmDH3wwQdt7iW1hoeBBw8eTAUFBa0+7na71QIAicPsuMEQOwDil92soMPHlt9//3266qqrxOuora2lkydPUlZWVnt3DwAsCHEDANo9YeGgcPToUbWwU6dOqf/zyW4cdL73ve/RoUOH6De/+Q35/X4qLS1Vi8fjCa1j8uTJ6iqAoKVLl9KePXvo9OnTtH//fpo1axY5HA51DBsA4h/iBgDE/JAQB5VJkyaFbi9ZskT9zM3NVYWc/uM//kPdHjVqVNjzPvzwQ7r99tvV/3kvqLy8PPRYcXGxCjIVFRWUkZFB48ePp4MHD6r/A0D8Q9wAgJgnLBw8+IS4SNp6LIj3iJrbvHmztBsAEEcQNwDg68JcQgAAAGB5SFgAAADA8pCwAAAAgOUhYQEAAIDOXZo/1mweL9ns0XMwI0VYOMrjFXRCULpY40TDIHuToA+8akkJ5Tr9vNXhlNWtN5IcprQNuGVf3YBLUPZf0NZwmleq2ttV//3wJ+mv19koLKGv+52WTnkBnYqk1D4zRKX5BW39sv10Q1Ca3+YTtJX8WfGZNw2ILSCoze83aZ4aTRhhAQAAAMtDwgIAAACWh4QFAAAALA8JCwAAAFgeEhYAAACwPCQsAAAAYHlIWAAAAMDykLAAAACA5SFhAQAAAMtDwgIAAACWl1Cl+Rv79SSnMzlqO/eXVbIVu/TrntsamsgUfr+oua1BVsrfLDpTJYQIyv7bk5ymfYaGS3/dAZd+nz09hFNCCKZucFcJymsL6ZbuFpX4hk5Xbl9amt8XsJvSNiAszU9e/fZ2r82kttpNL7X362+LNp9guw2gND8AAABAm5CwAAAAQOIlLHv37qUZM2ZQdnY22Ww22rZtW9jj8+fPV/c3X+64446o6127di0NGDCAkpOTaezYsfTxxx9LuwYAFoW4AQAxT1jq6upo5MiRKlBEwoGmpKQktGzatKnNdW7ZsoWWLFlCK1eupCNHjqj1T5s2jc6fPy/tHgBYEOIGAMT8pNvp06erpS1ut5syMzO11/n888/TwoULacGCBer2+vXr6d1336UNGzbQT3/6U2kXAcBiEDcAwJLnsOzevZt69+5NQ4YMoQcffJAqKioitvV4PHT48GGaMmXKV52y29XtAwcOtPqcpqYmqq6uDlsAIL6ZHTcYYgdA/Gr3hIWHdV9//XXatWsXPfPMM7Rnzx61Z+WPcFlueXm5eqxPnz5h9/Pt0tLSVp+Tn59P6enpoaVv377t/TIAIIZiETcYYgdA/Gr3Oiz33ntv6P/Dhw+nESNG0KBBg9Te0+TJk9vld+Tl5alj10G8l4TAAxC/YhE3GGIHQPwy/bLmgQMHUq9evaigoKDVx/kxh8NB586dC7ufb0c6ns3HutPS0sIWAEgcZsQNhtgBEL9MT1iKi4vVseisrKxWH3e5XDR69Gg1FBwUCATU7ZycHLO7BwAWhLgBAF87YamtraWjR4+qhZ06dUr9v7CwUD32yCOP0MGDB+n06dMqeNx999103XXXqcsNg3iId82aNaHbPET7yiuv0GuvvUZffPGFOuGOL4MMnv0PAPENcQMAYn4Oy6FDh2jSpEmh28Hjwbm5ubRu3Tr685//rAJIZWWlKhI1depUevLJJ9VQbNDJkyfVSXNBc+bMobKyMlqxYoU6YW7UqFG0Y8eOy06oi8ZVUU9OR/Q5d/w9uojW67hYr93WSHZpt7XVNeh3wi+bw8HwCeYe8nr025o5X4xdMNeIQ38OH2ZzCT4XwbxD1L2bdlOHR7BeIkopE3zmgrfOlyx772r7Juut1xufccPMPTy7Tf8zdJCkrf526BD0wUzSuYT8knmKRHMJyfpBgvY2k+YHskvm+yFuL2jr1f9+2CR/V0xgMwzBDGsWxSfO8Rn/377pEXI6ok8wF+ii/8dLmrCQw26NhKVJkIQgYQknSFgCgoTF2zOFRCRvtYkJiyddr73P20iHf7uMqqqq4ubckGDsuPh/AyktNfq2e8pbK1p/kV//+3HW20O7bZlP//0t9+n3gVV49Ntf8Ojv/F1oku0oVjXpJcqspkG/bUOdLP4HavXjgbNKf9tyVelvtO5KWdxNqdBv36VEf8JeV1HkUgOt8Z0ujN7G8NJu2q4VNzCXEAAAAFgeEhYAAACwPCQsAAAAYHlIWAAAAMDykLAAAACA5SFhAQAAAMtDwgIAAACWh4QFAAAALA8JCwAAACReaX4rM5IcZGhUQLX5ZFVjJaX8HRfqtNsakhLw1bIKmxTQL6FsePXrOBs+n7Af5lTGtQkqCjOjUb+aoz0tVb8fHv33w3Ve/7vBAi79zdNzlX6lTylvil5FTr9DWPI8Dpn5EiVl/K0iICixbEhL8wvK7fv9gtL8PlnssPkE5fYFletFbYVh1y4o5S/6eyisuN7eMMICAAAAloeEBQAAACwPCQsAAABYHhIWAAAAsDwkLAAAAGB5SFgAAADA8pCwAAAAQOIlLHv37qUZM2ZQdnY22Ww22rZtW9jjfF9ry+rVqyOuc9WqVZe1Hzp06JW9IgCwHMQNAIh5wlJXV0cjR46ktWvXtvp4SUlJ2LJhwwYVSO6555421zts2LCw5+3bt0/aNQCwKMQNAIh5pdvp06erJZLMzMyw29u3b6dJkybRwIED2+6I03nZcwEgMSBuAIClS/OfO3eO3n33XXrttdeitj1x4oQaLk5OTqacnBzKz8+nfv36tdq2qalJLUHV1dXqZ2NGCjmTopcod1/QL9PO7PUe/cbO6FMDXAmbyyVqbzQ06q9bYzqD0Hqbve9a7SWl+QXTCRDJ3g+74P0zvF79FZdd0G5qc7tJJPsqU0pxV/eXbfZJDXrrtnkNS8eNtmKHWRwUiLsy/pJy+wFBuX3Jepk/oN/e7xesW9JWWJrfJghhZrVldsG2aJeU5jfMmWrFEifdcsBJTU2l7373u222Gzt2LG3cuJF27NhB69ato1OnTtGECROopqam1fYclNLT00NL3759TXoFABBrZsUNhtgBEL9MTVj4OPR9992n9n7awkPFs2fPphEjRtC0adPovffeo8rKSnrrrbdabZ+Xl0dVVVWhpaioyKRXAACxZlbcYIgdAPHLtENCf/zjH+n48eO0ZcsW8XO7d+9OgwcPpoKCglYfd7vdagGAxGJm3GCIHQDxy7QRll//+tc0evRodWWAVG1tLZ08eZKysrJM6RsAWBPiBgC0W8LCQeHo0aNqYXzcmP9fWFgYdiLb1q1b6YEHHmh1HZMnT6Y1a9aEbi9dupT27NlDp0+fpv3799OsWbPI4XDQ3Llzpd0DAAtC3ACAmB8SOnTokLrcMGjJkiXqZ25urjoBjm3evJkMw4gYOHgvqLy8PHS7uLhYta2oqKCMjAwaP348HTx4UP0fAOIf4gYAfF02gyNEnOM9Mz7jP2fq4x1+WbNNcomYgK2mXtTeqDLncs1Afb0lLmu2JQkva06J/r0IcSWRGaSXNfsFlzX7uur3uXKQ25TLmv3eRjr81jJ1MmtaWhrFU+y4+H8DKS01+oBzoa9WtP4iXxfttl/6emi3LfPpv7/nvbLPosyTqt22vKmrdtuLTfrvBbtQn6LdtqZOf/v21gpjR43+fn1Sjf4l0K4q/bbui7I/013K9GNpSmmDdltHiX4ZB+Yr/jJ6G8NLu2m7VtzAXEIAAABgeUhYAAAAwPKQsAAAAIDlIWEBAACAzj2XUKzZvQGyG9FPevV2k51UaU/Rf5tcpZHLgrdkaxTMUSSZ34bX3SNdu23gXJl2W8Mvm9TCLjjR1PDrz2lkc8hybX9tHZnB0U3/pEOjZ3fRum1e/ffaL3if7T5RN4h0z/eL+9P3O5bDpDfQLlyvtL0Z8w4xQ9LerLaqPZnUD/2m4qmjDEpIGGEBAAAAy0PCAgAAAJaHhAUAAAAsDwkLAAAAWB4SFgAAALA8JCwAAABgeUhYAAAAwPKQsAAAAIDlIWEBAAAAy0uISreGcamsn8/XZMr67X79soF2v34fbAFBpVtJW9Vev88BQ3/dAUNWcdcuqPwY/Bx12ITVKgOGtLyrHkPw3pHgu6HWLSgq7PPp73v4PbJqxeTV+1z83kbx59jRgn2trtUrJVrjk5UcrRO0r/fpfy4Nfv3vc5OwSrbHo9/e69H//vsaZX9u/A363+lAvf56Aw3CsrGN+hW4/Y12U8KB3yPbpnyCKtk+/6XtVocRkMUwn8bfCx95teNGQiQsNTWXyuF/9Mefd3RXoCX9bUFGFoPNU21S2zjfHtPT9aeGsELs6H/L6Y7uCkCnVqMRN2xGPO0ORRAIBOjs2bOUmppKNttXe97V1dXUt29fKioqorS0NEo0if76OsNrTKTXx6GEg052djbZ7fFxtBmxA68vXlUnyGuUxI2EGGHhF3nNNddEfJw/zHj+QKNJ9NfXGV5jory+eBlZCULswOuLd2kJ8Bp140Z87AYBAABAp4aEBQAAACwvoRMWt9tNK1euVD8TUaK/vs7wGhP99cWrRP9c8Prin7sTvMaEPOkWAAAAEltCj7AAAABAYkDCAgAAAJaHhAUAAAAsDwkLAAAAWB4SFgAAALC8hE5Y1q5dSwMGDKDk5GQaO3Ysffzxx5QIVq1apcqIN1+GDh1K8Wzv3r00Y8YMVZ6ZX8+2bdvCHueL2VasWEFZWVmUkpJCU6ZMoRMnTlCivL758+df9pnecccdHdbfzixR40Yixg7EjfmdKm4kbMKyZcsWWrJkibpO/ciRIzRy5EiaNm0anT9/nhLBsGHDqKSkJLTs27eP4lldXZ36jPiPRWueffZZeumll2j9+vX00UcfUdeuXdXn2dho1uyKsX19jANN889006ZNMe0jJH7cSLTYgbhBnStuGAlqzJgxxqJFi0K3/X6/kZ2dbeTn5xvxbuXKlcbIkSONRMVfy3feeSd0OxAIGJmZmcbq1atD91VWVhput9vYtGmTEe+vj+Xm5hp33313h/UJEj9uJHrsQNxIfAk5wuLxeOjw4cNq+K/5JGd8+8CBA5QIeFiThwkHDhxI9913HxUWFlKiOnXqFJWWloZ9njxZFg/XJ8rnyXbv3k29e/emIUOG0IMPPkgVFRUd3aVOpTPEjc4UOxA3Ek9CJizl5eXk9/upT58+Yffzbf4Cxzve4DZu3Eg7duygdevWqQ1zwoQJaoruRBT8zBL18wwO677++uu0a9cueuaZZ2jPnj00ffp09T2G2Ej0uNHZYgfiRuJxdnQHQI6/kEEjRoxQQah///701ltv0f3339+hfYMrc++994b+P3z4cPW5Dho0SO09TZ48uUP7BokDsSOx3NvJ4kZCjrD06tWLHA4HnTt3Lux+vp2ZmUmJpnv37jR48GAqKCigRBT8zDrL58l4uJ6/x4n6mVpRZ4sbiR47EDcST0ImLC6Xi0aPHq2GyYICgYC6nZOTQ4mmtraWTp48qS7dS0TXXnutCjDNP8/q6mp11n8ifp6suLhYHYtO1M/Uijpb3Ej02IG4kXgS9pAQX5qYm5tLt956K40ZM4ZefPFFdYnYggULKN4tXbpUXZvPQ7lnz55Vl2DynuHcuXMpngNn870CPrZ+9OhR6tmzJ/Xr148efvhheuqpp+j6669XgWj58uXqxMGZM2dSvL8+Xh5//HG65557VIDlPyA/+clP6LrrrlOXYELsJHLcSMTYgbjxeOeKG0YC++Uvf2n069fPcLlc6nLFgwcPGolgzpw5RlZWlnpdV199tbpdUFBgxLMPP/xQXbbXcuHL9oKXKC5fvtzo06ePuixx8uTJxvHjx41EeH319fXG1KlTjYyMDCMpKcno37+/sXDhQqO0tLSju90pJWrcSMTYgbgxtVPFDRv/09FJEwAAAECnO4cFAAAAEgsSFgAAALA8JCwAAABgeUhYAAAAwPKQsAAAAIDlIWEBAAAAy0PCAgAAAJaHhAUAAAAsDwkLAAAAWB4SFgAAALA8JCwAAABAVvf/AFzg6Qh9JoIaAAAAAElFTkSuQmCC", "text/plain": [ "
" ] @@ -113,11 +130,11 @@ ], "source": [ "plt.subplot(1, 2, 1)\n", - "plt.title('permeability')\n", - "plt.imshow(k_train.squeeze(-1)[0])\n", + "plt.title(\"permeability\")\n", + "plt.imshow(k_train[0])\n", "plt.subplot(1, 2, 2)\n", - "plt.title('field solution')\n", - "plt.imshow(u_train.squeeze(-1)[0])\n", + "plt.title(\"field solution\")\n", + "plt.imshow(u_train[0])\n", "plt.show()" ] }, @@ -126,24 +143,25 @@ "id": "89a77ff1", "metadata": {}, "source": [ - "We now create the neural operator class. It is a very simple class, inheriting from `AbstractProblem`." + "We now create the Neural Operators problem class. Learning Neural Operators is similar as learning in a supervised manner, therefore we will use `SupervisedProblem`." ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 4, "id": "8b27d283", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2024-09-19T13:35:29.136572Z", + "start_time": "2024-09-19T13:35:29.134124Z" + } + }, "outputs": [], "source": [ - "class NeuralOperatorSolver(AbstractProblem):\n", - " input_variables = k_train.labels\n", - " output_variables = u_train.labels\n", - " conditions = {'data' : Condition(input_points=k_train, \n", - " output_points=u_train)}\n", - "\n", "# make problem\n", - "problem = NeuralOperatorSolver()" + "problem = SupervisedProblem(\n", + " input_=k_train.unsqueeze(-1), output_=u_train.unsqueeze(-1)\n", + ")" ] }, { @@ -158,17 +176,21 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 5, "id": "e34f18b0", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2024-09-19T13:35:31.245429Z", + "start_time": "2024-09-19T13:35:29.154937Z" + } + }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "GPU available: False, used: False\n", + "GPU available: True (mps), used: False\n", "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", "HPU available: False, using: 0 HPUs\n" ] }, @@ -176,7 +198,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 9: : 100it [00:00, 357.28it/s, v_num=1, mean_loss=0.108]" + "Epoch 9: 100%|██████████| 100/100 [00:00<00:00, 289.72it/s, v_num=3, data_loss_step=0.102, train_loss_step=0.102, data_loss_epoch=0.105, train_loss_epoch=0.105] " ] }, { @@ -190,7 +212,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 9: : 100it [00:00, 354.81it/s, v_num=1, mean_loss=0.108]\n" + "Epoch 9: 100%|██████████| 100/100 [00:00<00:00, 286.77it/s, v_num=3, data_loss_step=0.102, train_loss_step=0.102, data_loss_epoch=0.105, train_loss_epoch=0.105]\n" ] } ], @@ -200,11 +222,20 @@ "\n", "\n", "# make solver\n", - "solver = SupervisedSolver(problem=problem, model=model)\n", + "solver = SupervisedSolver(problem=problem, model=model, use_lt=False)\n", "\n", "# make the trainer and train\n", - "trainer = Trainer(solver=solver, max_epochs=10, accelerator='cpu', enable_model_summary=False, batch_size=10) # we train on CPU and avoid model summary at beginning of training (optional)\n", - "trainer.train()\n" + "trainer = Trainer(\n", + " solver=solver,\n", + " max_epochs=10,\n", + " accelerator=\"cpu\",\n", + " enable_model_summary=False,\n", + " batch_size=10,\n", + " train_size=1.0,\n", + " val_size=0.0,\n", + " test_size=0.0,\n", + ")\n", + "trainer.train()" ] }, { @@ -217,16 +248,21 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 6, "id": "0e2a6aa4", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2024-09-19T13:35:31.295336Z", + "start_time": "2024-09-19T13:35:31.256308Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Final error training 56.04%\n", - "Final error testing 56.01%\n" + "Final error training 28.57%\n", + "Final error testing 28.59%\n" ] } ], @@ -234,14 +270,22 @@ "from pina.loss import LpLoss\n", "\n", "# make the metric\n", - "metric_err = LpLoss(relative=True)\n", - "\n", + "metric_err = LpLoss(relative=False)\n", "\n", - "err = float(metric_err(u_train.squeeze(-1), solver.neural_net(k_train).squeeze(-1)).mean())*100\n", - "print(f'Final error training {err:.2f}%')\n", + "model = solver.model\n", + "err = (\n", + " float(\n", + " metric_err(u_train.unsqueeze(-1), model(k_train.unsqueeze(-1))).mean()\n", + " )\n", + " * 100\n", + ")\n", + "print(f\"Final error training {err:.2f}%\")\n", "\n", - "err = float(metric_err(u_test.squeeze(-1), solver.neural_net(k_test).squeeze(-1)).mean())*100\n", - "print(f'Final error testing {err:.2f}%')" + "err = (\n", + " float(metric_err(u_test.unsqueeze(-1), model(k_test.unsqueeze(-1))).mean())\n", + " * 100\n", + ")\n", + "print(f\"Final error testing {err:.2f}%\")" ] }, { @@ -256,17 +300,21 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 7, "id": "9af523a5", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2024-09-19T13:35:44.717807Z", + "start_time": "2024-09-19T13:35:31.306689Z" + } + }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "GPU available: False, used: False\n", + "GPU available: True (mps), used: False\n", "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", "HPU available: False, using: 0 HPUs\n" ] }, @@ -274,14 +322,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 0: : 0it [00:00, ?it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 9: : 100it [00:02, 47.76it/s, v_num=4, mean_loss=0.00106] " + "Epoch 9: 100%|██████████| 100/100 [00:02<00:00, 36.66it/s, v_num=4, data_loss_step=0.00164, train_loss_step=0.00164, data_loss_epoch=0.00229, train_loss_epoch=0.00229]" ] }, { @@ -295,7 +336,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 9: : 100it [00:02, 47.65it/s, v_num=4, mean_loss=0.00106]\n" + "Epoch 9: 100%|██████████| 100/100 [00:02<00:00, 36.56it/s, v_num=4, data_loss_step=0.00164, train_loss_step=0.00164, data_loss_epoch=0.00229, train_loss_epoch=0.00229]\n" ] } ], @@ -303,20 +344,31 @@ "# make model\n", "lifting_net = torch.nn.Linear(1, 24)\n", "projecting_net = torch.nn.Linear(24, 1)\n", - "model = FNO(lifting_net=lifting_net,\n", - " projecting_net=projecting_net,\n", - " n_modes=8,\n", - " dimensions=2,\n", - " inner_size=24,\n", - " padding=8)\n", + "model = FNO(\n", + " lifting_net=lifting_net,\n", + " projecting_net=projecting_net,\n", + " n_modes=8,\n", + " dimensions=2,\n", + " inner_size=24,\n", + " padding=8,\n", + ")\n", "\n", "\n", "# make solver\n", - "solver = SupervisedSolver(problem=problem, model=model)\n", + "solver = SupervisedSolver(problem=problem, model=model, use_lt=False)\n", "\n", "# make the trainer and train\n", - "trainer = Trainer(solver=solver, max_epochs=10, accelerator='cpu', enable_model_summary=False, batch_size=10) # we train on CPU and avoid model summary at beginning of training (optional)\n", - "trainer.train()\n" + "trainer = Trainer(\n", + " solver=solver,\n", + " max_epochs=10,\n", + " accelerator=\"cpu\",\n", + " enable_model_summary=False,\n", + " batch_size=10,\n", + " train_size=1.0,\n", + " val_size=0.0,\n", + " test_size=0.0,\n", + ")\n", + "trainer.train()" ] }, { @@ -329,25 +381,39 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 8, "id": "58e2db89", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2024-09-19T13:35:45.259819Z", + "start_time": "2024-09-19T13:35:44.729042Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Final error training 4.83%\n", - "Final error testing 5.16%\n" + "Final error training 3.36%\n", + "Final error testing 3.54%\n" ] } ], "source": [ - "err = float(metric_err(u_train.squeeze(-1), solver.neural_net(k_train).squeeze(-1)).mean())*100\n", - "print(f'Final error training {err:.2f}%')\n", + "model = solver.model\n", + "err = (\n", + " float(\n", + " metric_err(u_train.unsqueeze(-1), model(k_train.unsqueeze(-1))).mean()\n", + " )\n", + " * 100\n", + ")\n", + "print(f\"Final error training {err:.2f}%\")\n", "\n", - "err = float(metric_err(u_test.squeeze(-1), solver.neural_net(k_test).squeeze(-1)).mean())*100\n", - "print(f'Final error testing {err:.2f}%')" + "err = (\n", + " float(metric_err(u_test.unsqueeze(-1), model(k_test.unsqueeze(-1))).mean())\n", + " * 100\n", + ")\n", + "print(f\"Final error testing {err:.2f}%\")" ] }, { @@ -370,11 +436,8 @@ } ], "metadata": { - "interpreter": { - "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49" - }, "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "pina", "language": "python", "name": "python3" }, @@ -388,7 +451,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.9.21" } }, "nbformat": 4, diff --git a/tutorials/tutorial5/tutorial.py b/tutorials/tutorial5/tutorial.py index 9386bc16f..7a835c757 100644 --- a/tutorials/tutorial5/tutorial.py +++ b/tutorials/tutorial5/tutorial.py @@ -2,101 +2,101 @@ # coding: utf-8 # # Tutorial: Two dimensional Darcy flow using the Fourier Neural Operator -# +# # [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial5/tutorial.ipynb) -# +# # In this tutorial we are going to solve the Darcy flow problem in two dimensions, presented in [*Fourier Neural Operator for # Parametric Partial Differential Equation*](https://openreview.net/pdf?id=c8P9NQVtmnO). First of all we import the modules needed for the tutorial. Importing `scipy` is needed for input-output operations. -# In[1]: +# In[ ]: ## routine needed to run the notebook on Google Colab try: - import google.colab - IN_COLAB = True + import google.colab + + IN_COLAB = True except: - IN_COLAB = False + IN_COLAB = False if IN_COLAB: - get_ipython().system('pip install "pina-mathlab"') - get_ipython().system('pip install scipy') - # get the data - get_ipython().system('wget https://github.com/mathLab/PINA/raw/refs/heads/master/tutorials/tutorial5/Data_Darcy.mat') + get_ipython().system('pip install "pina-mathlab"') + get_ipython().system("pip install scipy") + # get the data + get_ipython().system( + "wget https://github.com/mathLab/PINA/raw/refs/heads/master/tutorials/tutorial5/Data_Darcy.mat" + ) + +import torch +import matplotlib.pyplot as plt +import warnings - # !pip install scipy # install scipy from scipy import io -import torch from pina.model import FNO, FeedForward # let's import some models -from pina import Condition, LabelTensor -from pina.solvers import SupervisedSolver -from pina.trainer import Trainer -from pina.problem import AbstractProblem -import matplotlib.pyplot as plt -plt.style.use('tableau-colorblind10') +from pina import Condition, Trainer +from pina.solver import SupervisedSolver +from pina.problem.zoo import SupervisedProblem + +warnings.filterwarnings("ignore") # ## Data Generation -# +# # We will focus on solving a specific PDE, the **Darcy Flow** equation. The Darcy PDE is a second-order elliptic PDE with the following form: -# +# # $$ # -\nabla\cdot(k(x, y)\nabla u(x, y)) = f(x) \quad (x, y) \in D. # $$ -# +# # Specifically, $u$ is the flow pressure, $k$ is the permeability field and $f$ is the forcing function. The Darcy flow can parameterize a variety of systems including flow through porous media, elastic materials and heat conduction. Here you will define the domain as a 2D unit square Dirichlet boundary conditions. The dataset is taken from the authors original reference. -# +# -# In[12]: +# In[2]: # download the dataset data = io.loadmat("Data_Darcy.mat") # extract data (we use only 100 data for train) -k_train = LabelTensor(torch.tensor(data['k_train'], dtype=torch.float).unsqueeze(-1), ['u0']) -u_train = LabelTensor(torch.tensor(data['u_train'], dtype=torch.float).unsqueeze(-1), ['u']) -k_test = LabelTensor(torch.tensor(data['k_test'], dtype=torch.float).unsqueeze(-1), ['u0']) -u_test= LabelTensor(torch.tensor(data['u_test'], dtype=torch.float).unsqueeze(-1), ['u']) -x = torch.tensor(data['x'], dtype=torch.float)[0] -y = torch.tensor(data['y'], dtype=torch.float)[0] +k_train = torch.tensor(data["k_train"], dtype=torch.float) +u_train = torch.tensor(data["u_train"], dtype=torch.float) +k_test = torch.tensor(data["k_test"], dtype=torch.float) +u_test = torch.tensor(data["u_test"], dtype=torch.float) +x = torch.tensor(data["x"], dtype=torch.float)[0] +y = torch.tensor(data["y"], dtype=torch.float)[0] # Let's visualize some data -# In[13]: +# In[3]: plt.subplot(1, 2, 1) -plt.title('permeability') -plt.imshow(k_train.squeeze(-1)[0]) +plt.title("permeability") +plt.imshow(k_train[0]) plt.subplot(1, 2, 2) -plt.title('field solution') -plt.imshow(u_train.squeeze(-1)[0]) +plt.title("field solution") +plt.imshow(u_train[0]) plt.show() -# We now create the neural operator class. It is a very simple class, inheriting from `AbstractProblem`. +# We now create the Neural Operators problem class. Learning Neural Operators is similar as learning in a supervised manner, therefore we will use `SupervisedProblem`. -# In[17]: +# In[4]: -class NeuralOperatorSolver(AbstractProblem): - input_variables = k_train.labels - output_variables = u_train.labels - conditions = {'data' : Condition(input_points=k_train, - output_points=u_train)} - # make problem -problem = NeuralOperatorSolver() +problem = SupervisedProblem( + input_=k_train.unsqueeze(-1), output_=u_train.unsqueeze(-1) +) # ## Solving the problem with a FeedForward Neural Network -# +# # We will first solve the problem using a Feedforward neural network. We will use the `SupervisedSolver` for solving the problem, since we are training using supervised learning. -# In[18]: +# In[5]: # make model @@ -104,71 +104,108 @@ class NeuralOperatorSolver(AbstractProblem): # make solver -solver = SupervisedSolver(problem=problem, model=model) +solver = SupervisedSolver(problem=problem, model=model, use_lt=False) # make the trainer and train -trainer = Trainer(solver=solver, max_epochs=10, accelerator='cpu', enable_model_summary=False, batch_size=10) # we train on CPU and avoid model summary at beginning of training (optional) +trainer = Trainer( + solver=solver, + max_epochs=10, + accelerator="cpu", + enable_model_summary=False, + batch_size=10, + train_size=1.0, + val_size=0.0, + test_size=0.0, +) trainer.train() # The final loss is pretty high... We can calculate the error by importing `LpLoss`. -# In[19]: +# In[6]: from pina.loss import LpLoss # make the metric -metric_err = LpLoss(relative=True) - +metric_err = LpLoss(relative=False) -err = float(metric_err(u_train.squeeze(-1), solver.neural_net(k_train).squeeze(-1)).mean())*100 -print(f'Final error training {err:.2f}%') +model = solver.model +err = ( + float( + metric_err(u_train.unsqueeze(-1), model(k_train.unsqueeze(-1))).mean() + ) + * 100 +) +print(f"Final error training {err:.2f}%") -err = float(metric_err(u_test.squeeze(-1), solver.neural_net(k_test).squeeze(-1)).mean())*100 -print(f'Final error testing {err:.2f}%') +err = ( + float(metric_err(u_test.unsqueeze(-1), model(k_test.unsqueeze(-1))).mean()) + * 100 +) +print(f"Final error testing {err:.2f}%") # ## Solving the problem with a Fourier Neural Operator (FNO) -# +# # We will now move to solve the problem using a FNO. Since we are learning operator this approach is better suited, as we shall see. -# In[24]: +# In[7]: # make model lifting_net = torch.nn.Linear(1, 24) projecting_net = torch.nn.Linear(24, 1) -model = FNO(lifting_net=lifting_net, - projecting_net=projecting_net, - n_modes=8, - dimensions=2, - inner_size=24, - padding=8) +model = FNO( + lifting_net=lifting_net, + projecting_net=projecting_net, + n_modes=8, + dimensions=2, + inner_size=24, + padding=8, +) # make solver -solver = SupervisedSolver(problem=problem, model=model) +solver = SupervisedSolver(problem=problem, model=model, use_lt=False) # make the trainer and train -trainer = Trainer(solver=solver, max_epochs=10, accelerator='cpu', enable_model_summary=False, batch_size=10) # we train on CPU and avoid model summary at beginning of training (optional) +trainer = Trainer( + solver=solver, + max_epochs=10, + accelerator="cpu", + enable_model_summary=False, + batch_size=10, + train_size=1.0, + val_size=0.0, + test_size=0.0, +) trainer.train() # We can clearly see that the final loss is lower. Let's see in testing.. Notice that the number of parameters is way higher than a `FeedForward` network. We suggest to use GPU or TPU for a speed up in training, when many data samples are used. -# In[25]: +# In[8]: -err = float(metric_err(u_train.squeeze(-1), solver.neural_net(k_train).squeeze(-1)).mean())*100 -print(f'Final error training {err:.2f}%') +model = solver.model +err = ( + float( + metric_err(u_train.unsqueeze(-1), model(k_train.unsqueeze(-1))).mean() + ) + * 100 +) +print(f"Final error training {err:.2f}%") -err = float(metric_err(u_test.squeeze(-1), solver.neural_net(k_test).squeeze(-1)).mean())*100 -print(f'Final error testing {err:.2f}%') +err = ( + float(metric_err(u_test.unsqueeze(-1), model(k_test.unsqueeze(-1))).mean()) + * 100 +) +print(f"Final error testing {err:.2f}%") # As we can see the loss is way lower! # ## What's next? -# +# # We have made a very simple example on how to use the `FNO` for learning neural operator. Currently in **PINA** we implement 1D/2D/3D cases. We suggest to extend the tutorial using more complex problems and train for longer, to see the full potential of neural operators. diff --git a/tutorials/tutorial6/tutorial.ipynb b/tutorials/tutorial6/tutorial.ipynb index f294906bf..522a9087f 100644 --- a/tutorials/tutorial6/tutorial.ipynb +++ b/tutorials/tutorial6/tutorial.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Tutorial: Building custom geometries with PINA `Location` class\n", + "# Tutorial: Building custom geometries with PINA `DomainInterface` class\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial6/tutorial.ipynb)\n", "\n", @@ -20,27 +20,36 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "## routine needed to run the notebook on Google Colab\n", "try:\n", - " import google.colab\n", - " IN_COLAB = True\n", + " import google.colab\n", + "\n", + " IN_COLAB = True\n", "except:\n", - " IN_COLAB = False\n", + " IN_COLAB = False\n", "if IN_COLAB:\n", - " !pip install \"pina-mathlab\"\n", + " !pip install \"pina-mathlab\"\n", "\n", "import matplotlib.pyplot as plt\n", - "plt.style.use('tableau-colorblind10')\n", - "from pina.geometry import EllipsoidDomain, Difference, CartesianDomain, Union, SimplexDomain\n", + "\n", + "from pina.domain import (\n", + " EllipsoidDomain,\n", + " Difference,\n", + " CartesianDomain,\n", + " Union,\n", + " SimplexDomain,\n", + " DomainInterface,\n", + ")\n", "from pina.label_tensor import LabelTensor\n", "\n", + "\n", "def plot_scatter(ax, pts, title):\n", " ax.title.set_text(title)\n", - " ax.scatter(pts.extract('x'), pts.extract('y'), color='blue', alpha=0.5)" + " ax.scatter(pts.extract(\"x\"), pts.extract(\"y\"), color=\"blue\", alpha=0.5)" ] }, { @@ -61,13 +70,15 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "cartesian = CartesianDomain({'x': [0, 2], 'y': [0, 2]})\n", - "ellipsoid_no_border = EllipsoidDomain({'x': [1, 3], 'y': [1, 3]})\n", - "ellipsoid_border = EllipsoidDomain({'x': [2, 4], 'y': [2, 4]}, sample_surface=True)" + "cartesian = CartesianDomain({\"x\": [0, 2], \"y\": [0, 2]})\n", + "ellipsoid_no_border = EllipsoidDomain({\"x\": [1, 3], \"y\": [1, 3]})\n", + "ellipsoid_border = EllipsoidDomain(\n", + " {\"x\": [2, 4], \"y\": [2, 4]}, sample_surface=True\n", + ")" ] }, { @@ -82,13 +93,13 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "cartesian_samples = cartesian.sample(n=1000, mode='random')\n", - "ellipsoid_no_border_samples = ellipsoid_no_border.sample(n=1000, mode='random')\n", - "ellipsoid_border_samples = ellipsoid_border.sample(n=1000, mode='random')" + "cartesian_samples = cartesian.sample(n=1000, mode=\"random\")\n", + "ellipsoid_no_border_samples = ellipsoid_no_border.sample(n=1000, mode=\"random\")\n", + "ellipsoid_border_samples = ellipsoid_border.sample(n=1000, mode=\"random\")" ] }, { @@ -108,30 +119,33 @@ "name": "stdout", "output_type": "stream", "text": [ - "Cartesian Samples: labels(['x', 'y'])\n", - "LabelTensor([[[0.2300, 1.6698]],\n", - " [[1.7785, 0.4063]],\n", - " [[1.5143, 1.8979]],\n", - " ...,\n", - " [[0.0905, 1.4660]],\n", - " [[0.8176, 1.7357]],\n", - " [[0.0475, 0.0170]]])\n", - "Ellipsoid No Border Samples: labels(['x', 'y'])\n", - "LabelTensor([[[1.9341, 2.0182]],\n", - " [[1.5503, 1.8426]],\n", - " [[2.0392, 1.7597]],\n", - " ...,\n", - " [[1.8976, 2.2859]],\n", - " [[1.8015, 2.0012]],\n", - " [[2.2713, 2.2355]]])\n", - "Ellipsoid Border Samples: labels(['x', 'y'])\n", - "LabelTensor([[[3.3413, 3.9400]],\n", - " [[3.9573, 2.7108]],\n", - " [[3.8341, 2.4484]],\n", - " ...,\n", - " [[2.7251, 2.0385]],\n", - " [[3.8654, 2.4990]],\n", - " [[3.2292, 3.9734]]])\n" + "Cartesian Samples: 1: {'dof': ['x', 'y'], 'name': 1}\n", + "\n", + "tensor([[0.1086, 1.7192],\n", + " [0.0194, 1.5690],\n", + " [0.7047, 1.3665],\n", + " ...,\n", + " [0.5924, 0.8842],\n", + " [0.1326, 1.2767],\n", + " [0.6012, 0.9822]])\n", + "Ellipsoid No Border Samples: 1: {'dof': ['x', 'y'], 'name': 1}\n", + "\n", + "tensor([[2.0577, 2.0362],\n", + " [1.5990, 2.4981],\n", + " [1.9673, 2.9884],\n", + " ...,\n", + " [1.4765, 2.1523],\n", + " [1.9655, 2.0474],\n", + " [2.9667, 2.0016]])\n", + "Ellipsoid Border Samples: 1: {'dof': ['x', 'y'], 'name': 1}\n", + "\n", + "tensor([[2.0763, 2.6168],\n", + " [3.8528, 2.4777],\n", + " [3.1241, 2.0077],\n", + " ...,\n", + " [2.3080, 3.7219],\n", + " [2.5890, 2.0884],\n", + " [2.5648, 3.9003]])\n" ] } ], @@ -153,12 +167,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -169,8 +183,12 @@ ], "source": [ "fig, axs = plt.subplots(1, 3, figsize=(16, 4))\n", - "pts_list = [cartesian_samples, ellipsoid_no_border_samples, ellipsoid_border_samples]\n", - "title_list = ['Cartesian Domain', 'Ellipsoid Domain', 'Ellipsoid Border Domain']\n", + "pts_list = [\n", + " cartesian_samples,\n", + " ellipsoid_no_border_samples,\n", + " ellipsoid_border_samples,\n", + "]\n", + "title_list = [\"Cartesian Domain\", \"Ellipsoid Domain\", \"Ellipsoid Border Domain\"]\n", "for ax, pts, title in zip(axs, pts_list, title_list):\n", " plot_scatter(ax, pts, title)" ] @@ -194,12 +212,12 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -210,27 +228,28 @@ ], "source": [ "import torch\n", + "\n", "spatial_domain = SimplexDomain(\n", - " [\n", - " LabelTensor(torch.tensor([[0, 0]]), labels=[\"x\", \"y\"]),\n", - " LabelTensor(torch.tensor([[1, 1]]), labels=[\"x\", \"y\"]),\n", - " LabelTensor(torch.tensor([[0, 2]]), labels=[\"x\", \"y\"]),\n", - " ]\n", - " )\n", + " [\n", + " LabelTensor(torch.tensor([[0, 0]]), labels=[\"x\", \"y\"]),\n", + " LabelTensor(torch.tensor([[1, 1]]), labels=[\"x\", \"y\"]),\n", + " LabelTensor(torch.tensor([[0, 2]]), labels=[\"x\", \"y\"]),\n", + " ]\n", + ")\n", "\n", "spatial_domain2 = SimplexDomain(\n", - " [\n", - " LabelTensor(torch.tensor([[ 0., -2.]]), labels=[\"x\", \"y\"]),\n", - " LabelTensor(torch.tensor([[-.5, -.5]]), labels=[\"x\", \"y\"]),\n", - " LabelTensor(torch.tensor([[-2., 0.]]), labels=[\"x\", \"y\"]),\n", - " ]\n", - " )\n", + " [\n", + " LabelTensor(torch.tensor([[0.0, -2.0]]), labels=[\"x\", \"y\"]),\n", + " LabelTensor(torch.tensor([[-0.5, -0.5]]), labels=[\"x\", \"y\"]),\n", + " LabelTensor(torch.tensor([[-2.0, 0.0]]), labels=[\"x\", \"y\"]),\n", + " ]\n", + ")\n", "\n", "pts = spatial_domain2.sample(100)\n", "fig, axs = plt.subplots(1, 2, figsize=(16, 6))\n", "for domain, ax in zip([spatial_domain, spatial_domain2], axs):\n", " pts = domain.sample(1000)\n", - " plot_scatter(ax, pts, 'Simplex Domain')" + " plot_scatter(ax, pts, \"Simplex Domain\")" ] }, { @@ -272,13 +291,13 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "c_e_nb_u_points = cart_ellipse_nb_union.sample(n=2000, mode='random')\n", - "c_e_b_u_points = cart_ellipse_b_union.sample(n=2000, mode='random')\n", - "three_domain_union_points = three_domain_union.sample(n=3000, mode='random')" + "c_e_nb_u_points = cart_ellipse_nb_union.sample(n=2000, mode=\"random\")\n", + "c_e_b_u_points = cart_ellipse_b_union.sample(n=2000, mode=\"random\")\n", + "three_domain_union_points = three_domain_union.sample(n=3000, mode=\"random\")" ] }, { @@ -291,12 +310,12 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -308,7 +327,11 @@ "source": [ "fig, axs = plt.subplots(1, 3, figsize=(16, 4))\n", "pts_list = [c_e_nb_u_points, c_e_b_u_points, three_domain_union_points]\n", - "title_list = ['Cartesian with Ellipsoid No Border Union', 'Cartesian with Ellipsoid Border Union', 'Three Domain Union']\n", + "title_list = [\n", + " \"Cartesian with Ellipsoid No Border Union\",\n", + " \"Cartesian with Ellipsoid Border Union\",\n", + " \"Three Domain Union\",\n", + "]\n", "for ax, pts, title in zip(axs, pts_list, title_list):\n", " plot_scatter(ax, pts, title)" ] @@ -323,12 +346,12 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -339,17 +362,17 @@ ], "source": [ "cart_ellipse_nb_difference = Difference([cartesian, ellipsoid_no_border])\n", - "c_e_nb_d_points = cart_ellipse_nb_difference.sample(n=2000, mode='random')\n", + "c_e_nb_d_points = cart_ellipse_nb_difference.sample(n=2000, mode=\"random\")\n", "\n", "fig, ax = plt.subplots(1, 1, figsize=(8, 6))\n", - "plot_scatter(ax, c_e_nb_d_points, 'Difference')" + "plot_scatter(ax, c_e_nb_d_points, \"Difference\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Create Custom Location" + "## Create Custom DomainInterface" ] }, { @@ -375,9 +398,7 @@ "outputs": [], "source": [ "import torch\n", - "from pina import Location\n", - "from pina import LabelTensor\n", - "import random" + "from pina import LabelTensor" ] }, { @@ -385,21 +406,20 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Next, we will create the `Heart(Location)` class and initialize it." + "Next, we will create the `Heart(DomainInterface)` class and initialize it." ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "class Heart(Location):\n", + "class Heart(DomainInterface):\n", " \"\"\"Implementation of the Heart Domain.\"\"\"\n", "\n", " def __init__(self, sample_border=False):\n", - " super().__init__()\n", - " " + " super().__init__()" ] }, { @@ -407,16 +427,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Because the `Location` class we are inheriting from requires both a `sample` method and `is_inside` method, we will create them and just add in \"pass\" for the moment." + "Because the `DomainInterface` class we are inheriting from requires both a `sample` method and `is_inside` method, we will create them and just add in \"pass\" for the moment. We also observe that the methods `sample_modes` and `variables` of the `DomainInterface` class are initialized as `abstractmethod`, so we need to redefine them both in the subclass `Heart` ." ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "class Heart(Location):\n", + "class Heart(DomainInterface):\n", " \"\"\"Implementation of the Heart Domain.\"\"\"\n", "\n", " def __init__(self, sample_border=False):\n", @@ -426,6 +446,14 @@ " pass\n", "\n", " def sample(self):\n", + " pass\n", + "\n", + " @property\n", + " def sample_modes(self):\n", + " pass\n", + "\n", + " @property\n", + " def variables(self):\n", " pass" ] }, @@ -434,17 +462,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now we have the skeleton for our `Heart` class. The `sample` method is where most of the work is done so let's fill it out." + "Now we have the skeleton for our `Heart` class. Also the `sample` method is where most of the work is done so let's fill it out. " ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "\n", - "class Heart(Location):\n", + "class Heart(DomainInterface):\n", " \"\"\"Implementation of the Heart Domain.\"\"\"\n", "\n", " def __init__(self, sample_border=False):\n", @@ -453,16 +480,24 @@ " def is_inside(self):\n", " pass\n", "\n", - " def sample(self, n, mode='random', variables='all'):\n", + " def sample(self, n):\n", " sampled_points = []\n", "\n", " while len(sampled_points) < n:\n", - " x = torch.rand(1)*3.-1.5\n", - " y = torch.rand(1)*3.-1.5\n", - " if ((x**2 + y**2 - 1)**3 - (x**2)*(y**3)) <= 0:\n", + " x = torch.rand(1) * 3.0 - 1.5\n", + " y = torch.rand(1) * 3.0 - 1.5\n", + " if ((x**2 + y**2 - 1) ** 3 - (x**2) * (y**3)) <= 0:\n", " sampled_points.append([x.item(), y.item()])\n", "\n", - " return LabelTensor(torch.tensor(sampled_points), labels=['x','y'])" + " return LabelTensor(torch.tensor(sampled_points), labels=[\"x\", \"y\"])\n", + "\n", + " @property\n", + " def sample_modes(self):\n", + " pass\n", + "\n", + " @property\n", + " def variables(self):\n", + " pass" ] }, { @@ -492,12 +527,12 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { - "image/png": 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lZYfrHmI6/9EfHU5uUQwwQwKf0PCZn5wKfR4GS+BTYAQTUmo852532559RFjU7CYWke/7vuF0zMLiXU7ExoyJU5gINAw81qIl2SLGzlXENeY6wgR4BuUNLih0hOQeO3A+go3y+NSnXrbkWbO//JcHBVUQqh+WBEJZ0DiaQ5cUAsqq+5797MPhIz/yOBdSS3PHZtBtcUd51z/5J4fDS14y0J3rKCc+hz9z5zrzrOyBZCChfXsADszhXd7lssXoSigVe2HJiwe78rLDdQ8xnf/kTw4ChBDPqS8/b/d2Nw7kxSQx0pisc0L0OdP+nLCfY6j+dj+m+B3fMSgS3FLGh3HHTcQig8GzvPz1vz4oWBWmfP+JS6CsUMAqpBAYhcw799iB0wu2v/23D4ev/MrhcwqyNWRNIKT9Tyi71ufWx3r7DK1QXNAIJfO//tfBpfkP/+ERgyw0V2lUWv4Wob/VHfWP//GgwHzbtw3XJjg3LiKfAfPPIcNeAPYBfAmiv/vdB4XsSigVF7HI3w678rLDdQZjSgPm8imfMggC32E+FBGBuU6BhIZ7o8D4333+5mLxOebqmb6bEvZLGWrN2BAULEiTa4FC4T1+U5SMT5aFZ7TBkVPZIsboHb7H+KvVBhCYFBfz3GMHTivYfviHD4fnPW/429qhA/RjLV2L9rLGqXMC0JYYK0qrZ/lxPevcKaGlUbTB8kNBut3tlikyW9xRnvmpnzpYAwXDA3OkvMBNXLToNjFacAFflPtP/uRBAfKcrUrFGkvNXljy4sJuednhuoE5pQHT+/iPH3zuGJbvne4wP5k+TpBOgUzUFWJ1wcgAJlmFfWWGGO63f/vgk59jqG3GhlP2B33Q8JkxGQflwmk5ZvR2XNU83zJl73bS56KggHk+ENtjnn7gyXiuZOzA9QJjgg3uKYtRTAhgvymLhKTP4+6wFjXuI1YZ62hNYv2j2J4KWqHvfWodSUX+zu8cLEbocIn1Yos7yj1PfOLh8LVfOyh5XLVwQMmj9NinP/IjN86Mg9+qEG1VKtZaas67yN8O22FXXna4LmDpKYy5mcB3HcZDQXDiw0AxPgKGcAGYXqwSydDxzOrLr8yQEEi2yId+6OVAxjGGmowNJ3LPJsi8h1ncGJzCnWYf8pDD4RWvmDbPy1ZqmfJ7vufgDjM3z3a9U22UF9f53mn7SrVIuJ5gTLBx/1Be4ZbLA77hN9k0/kYj1j+uSN/5O9dZJ65E11EmpE0vhSnLgnX/hm8YrDv2Axr4j/9xoDWWDWO3N/6f/2e5S6R1Ry0Bz2RFoaxx55onfKHn93iPIUB5SiHaolSssdQEhw411nJMud9Ttq8e7MrLDtc8rDmFtaZuAp3CQYG4/e0HRu55mHmsLZQSzDWBhARJz2ztGgGafjs5cgUxxcd33zJUTJlg+8EfvFxRlCBLsC7GiKH/nb8z/F/N85Qc2SoUHf/3rD2yoggHJ1rP95m5xDUGP5jyy18+CIzdb3+adOEoh/CONlKtGf5jfYnli5KZDBoKS9KH/VA63+mdDofP+IzlwbpTlgVAcRFj5XlcN4kxQZvZN2jFe4+tazQ3TvFA8MNNVWkWPc8pTWtTtZfwCFan+9zncPjpnx6C8uGH5dNYHS7e//0vWy+tWZRU6zg2jh3OD3blZYcLBWOnxqnT5NpTWDV1u+9f/sshewEzxNQpBdw/GBZFhPCRIcL3zoTNhN9jhu4hgDAzgsf/lBcMjwAhzCpDxawxSJ/VNGande+mxHz4h1+ea8aMwWOurvumb5q39gj09Q5gHhQfzw4z3htCLqfDJenC8IuOKMDWPB2R3R83EesKRcVnFGd0gpYoDqF3z/zMz1yeJj3XAgMQxsYmJdt3rjUHY3U9GjRn4/R+1ab9UOxPpcCcIo5kbar2HI+wBg4AP/RDQ4aXPWxPvu/7Di5Xliq/FZAUTMzSyTIGnw4nz3jG4fBP/+nleJwdzh925WWHCwNjp8b3e7+B+Y75qbcUzIqp22eYOYZEqPgbA/+bf3N4JibIhfOP/tEg6DErDLHHDAkeDM5znLST/pnMIYw5DNV33/3dgzJBkHlH3Fa+N15jkZkRZpi0bmnVPWuPtFuxCjkdxlL0yldeDoYUPxHFhQADu99+GR22cRFj6cKEGYvZL/zC4LpzDWHHyhf6sLZZH8ql/1Mmn/KMFig9SxWXKYXAmFkVAOFKqY7yhN78nXT8KN/2m71BiX7a04biiafK4BlTJGLNgG+0TEGn/PWUx7Wp2lM8ghLC2uI3gBN7yFoZAzzBi8wse9n4kr0Xyyxl6/u//3C4610Ph4c+dLdiXgnYlZcdLnzWxjd/8+Wusj0/9TH9W+q9hA7BjkFhXgRLAil9TqEIQ6QwVGaIYfretQnOxNT871ksMeII7nvf4f4wcHNyyk2QbpQpliDvrMx2zNrjt5M7E7bnuC9tEH7mZ4bPzZErgKBKjZEoOrvffp4OxzJY/v7fH2I2uBspKpRg11MS0ZPflBCuRnTlGZ7JosZ986u/Olg9XEeJIZCtH+uDNV4aSF0VAlBjqGLpAegiNI72E9dlDOjZvP1Gt2gmvYROmRbcUyTSsiPWJzRrLRwwvL9VHtemao/xiBSBNKZ0so51khLH4mJtPN/BJXVnPMuPe4zPnrUXX/WqYS73vOfyrK0dtsGuvOxw4bM2nMYIZ/+nPkk1Lz/qUdv7t7QnOH97Hwbkff7GoLyLuyiWkJYZxv9NAFFUYv6PqyBZTIRdDdbFcN2P2XpGBI7Pk9Y9dWKNi8LzPQcOPYfgDFMmgFyfIncpUBdFZ2tzvusN1rgzWFJYMwR0xsUoRgSdEmaUEy6HWAyTRszykayZO995KGCHXqype1NZd23Z/tAThZlb0RwSQ+W96YfFghAaZ+2jZHunfYImcl0KxAniJbjBqdyL7d7xXnVsjMlz4TIB5b6z73rK05pU7TFLTfY6sGfgMbWd7OfEH7FK2TvhRRQZ19RYIYcdtMD1ZN1ZN/f+YecHu/Kyw1WHMTMyJpYTIsHgf4K4jWWhnGzt39I7wfFrMyNrBxDXEaaOgXH1JP6kMsMoKNwFmJ8TtetST8ZzMb+kvLYMPJVXAzGfV4Wid2Kt1iLvTrxCmLLnUqiA+Jq4lIzFnBPX8QEfsKdLL42d+oEfOBye+9zBqpUsodQGAg94wOXYB26EsdgZVrfHPe6y8KWsbi3bn+BvZQDQYaw+lH9KCBdI4m/QWVLy7Q9jc13qrXhW3Ei1ttCp3ItVkTBHCiBaBPZDWmGgazg1RoHp3t0qT0tTtccsNRQUigiF03MofmlJ4N2pOwOnfidGKe6/KHvGTQl1DwsnnFEM90J25we78rLDVYdYLVL5NrVXfvzHB6UFE07HXYpFBHB1dzjlrimY1QZkPuxhg2KSOAeKAIaEUbnXCRRjqsyoMsOY4KM8EICUGkw4xeESL7O1vHrP9O2+nKQpJxQlAgg+MVhMOXN30q7p0uYjfsY7PvETd/P2ktgp+PvGbxzcblEEoiRQrsW5fO/3DsrLkjTiU5XtJ4xZXdCYwou53xjRLosAugn92UOUA2Pm8kpLAsKbwHYPuspey/xP0cm5KhKsFKxYcc/CYzKr7H3vxwvQ7pjytDRVu2epwVe8A//wDJ85CNj3qesUC6oxWGt7J+B++9UYfQ53cJzMsrkA5L1f0nbYlZcdripgRjJm/HbytPn9cLtgBphFghsJZBVoE6vRujuWCoKxgEzZAmJSKALGhPk4HY+5DzTNCzP0TEB4CPY1lppW6Z6qjIydBM2bAKT0/L2/d+Nxjyk83sMdpcgYfCUgk3BSIyTjkA7tmri04NUpkQAmcDPumyssiZ0irMQuEbQUw+q+8z+BSyAL7qRELIEtdVJaoFTZN7LhCNIEf1NUCV3KiP3EymGPxDJpvGIzKO+UXcG55lHdIXX+p3IvojPvVMMoVk2CvMYKUa7tRd+5xoFirfLUKgfm/pjHXP4MHr71W4e4JZCDgJ8E48IjK2xaisBZWhXYQ2noGvdSavegialCdnu/pONgV152OBrCIGz4MAkMaO4EmeDI2tcHQ5CqiDkQpn77HOPi/sCYKR3+H2v+NiUIlgRkGjsFwHPnUq+rwuTU+uIXDwoRhkfQTbmu2pOgZ6YMPPfSC14wnPATqDgVpAiHis2pIOxen3/LtwxjwnABs7a5UVgognz3d7zjMKdTxTNcyyfJJdYwih7lD+56tME1Yw0poGPKy3ngyLMISynNDgE1+NveorhSqKT7W/ueZdK4ZBWZfwtzsWNbAI3CI9ol9OHAOyje/raHkiWXdPM1ytOccpB1oOC7VhA+nsC6K2bFuCh/iRXzXRpDJk4oLqVY7OxzeIfz9CHrWaz2fknHw6687HAUhEGwiFA6MBibFeOeKjE+1teHopIeQ4QABoZxYGYYReJfuJRqZ9kl0FYXHQsAFqewJvU6CpMfGSdrer1E+RFHod5M4mmStdL6zNcEKX7cxw3fx61FeYHL9EoSUBjcnSKe4Vo+SUaQoUX4iiBrY6cUDLRWY9AqNFcKR7EaUQIosTX4mxBlfWFRefjDL7s3Q9/mR7GhlGyNHdsCUa7gGe4pCXAS11ENRPcDd0uVpznl4KM/+sblF+CKlQUPgzMHJ+ui2KRxpp1IeBCwj/ASlk14t6fiak6sEAWHG8qzY80Bx9a52WFXXnY4AsIgbHgbFKNxMrVRE+w6ll7Z6+vD9aEgltiNNGqzmX1O8NrsmIdTj5OkBm9LGb6x1uqiFKNUsvXualFR02Vr6vXWGAYWFoyOq2iOmS19R1V0NMET32B9ziOeYUxYcLFQBK5U6ugWq0arULSCrCqH/v+arxlcLD5vrTOUb64bSuyV7E7cWo1q8He1mlBMUx1aZ+eeErW22eJWsD6sKlGuEvwKaoBsFJelytNc1pjYuac8ZVgDeMs6oBvrG1r1nEc84nL6doLv4dD+w6fsTc9woEJzLFvZWyw3xk3B8W69nLht8bOlRTVTVuFatGSeN+yWlx02QRhE+q9gNokBsMl87jO/e6cImzFl9xNMiMFipAHX28juJQwS2OtalUeXxhREaGAiqS7qlJficW29E+PHgAheLhZMNCdYMGc+r66rJcJ0S5+WNUGK8EcpXBrPUMdcT+cZf8acOXleT1gQRoQ5vF+J1NEtVo0xhaIVZLXS84d92OHwkpcMtJ24hmSe+F6adLs2592deE3dkyVKVI0LqevOQnMqQep+8xVz4nmsjal5kywuhxgHFzjVl2wJ3UztJ0ARoXiovVMrUsfq5ududxuUbpZf1jaKvzWGR4qh51JSje/BDx7W/kUvGn67x/Ptubi8ufMoanAsvoayxnLcg/AhLl+u32vRknklYFdedtgEYRAEOh97+veAuGIwRxu0546gsChrL92UAuMkixGwCtjsqfWQmheUC++wqVknlro2qtCw4RPMmkq2BBCGcqc7XRbg3qOqr+wjmRgElB9zwmApTUtOgEuF6ZYKwWvAODHPJfEMcPDFXzyMOS0OALcexQfDBphy5uQ7MR5M7RHkrBaUP8ycOT04P6/U0S1WjSmFohVktcqxCqrmK/gVDjwnwabW90EPuiltEPpcnegndXjqfjmF226JS3GpEiUY/byDS+HIwUH2lkMFOqG85OCTPepve37pe6b2U61v4yBUDybtOoRHUWIShFutteiLQuL/6jJ2fxSUlFSIlROO8RTPpkTlQFQhpRkoQ8Z3aivd9QK78rLDJgiDwMQwF4y7Qnqk2PA2aRuspo9ILDZ+A4wV4yIgMRnPZJ3BUJ1+MeO1fvd6CsMI3U+QpPliFCfgeyc8TEL2jROW8cekTcALMOQvn2McNRgZg8LkkqnSMp81FYK3BnsuOZlLF02zPH77FNtLtV5MXh0Ryom4CkoLJZNLihVLQDU8mbt3pE5GiqUtSR3dAlutGlssXoEUFqTEoR9zNHf1gSg3n/u5l0v7e8ZznjMoO3CeFg1VqJ0qDXnOpXhMN+YltLwW0JDDANxxvXgHRdfeS+Van6urY8xL3jO1n7wnVl/rkay7rEf6j7F6aFiKP/ix78051lquH2OtFtg5K2faH+BveJB18JzW9QineCs6mcp2fK+beUzMrrzssAnCIDAam98GTz0T4MSQgLsaHxJB49QjtkRHZX/XFGlCkyBMhVuneEzF6av63ZcI8noKwwQwFC4MTMxnSU/m2sDAMAsWF4KQWRkk+JHwxdgIKEG9YKyJpDkma4plKgI8heQq81la86VaRdacfoMnYxDEy+fPclBP5ioHZ96eRUnxjhS4syasZNbY576HO+vvxxpq5UDpgS/vSkA05c/vpJOeupfSViVki8Ura2vOlL1XvOJyYbcEQzupP/rRw/WsUQS/ooVwk3pArcvylGnIUy7FqTmjtbhx4TNCeQ0trwXzjUXPHvMO+Eg8kbGmK/rS90ztJ+NFq/YyRaw2Q03/Meuop5HrNDsVj+ZvyoM5cwkJ5FU4rz1I+TsKnr2Td9f2B3ijd6Eb9OSd9SBh7sa0JNvxXU+wf65V2JWXHTZBGITTVzZ0Al8xwdSW8LtWb42gwcgJ0GQXYIhO6pgJBuAeDeowDQzzUz7lcLjLXS4ziqVm7HoKS8YNJkT4OH2lKioh4/t0a66CsAY/JmZAxgmm1ns/xiVzKkHMtdppCslRIMJ8pqwiPotQU0reuxJkiKEbLxM1/PQ62vbwRACoAuvUG6WrKgDWDJOtrg33GbsTqHF5FjzCDWYMrxRAz0QHLDIpNpYiXxQBa3rqXkpb3W5bemIFTwQTvBK4sdDBFbcQfLDIUFoIP7iUfuva9M2qLRrG0v7PA3pztl8F2CsWZ7+hU5lv9jbLwBpa3spH0DF6Rj85BFU+wiWzVGBP1VCi0Bhz+hdZs7TMSP+xj/qoYW72AhylGnFNP/ejLEHv0NBrfwCHeA38ZX6UMla7NjCcMqOf23m5ka8X2JWXmwGcR12JyiCyoZ3YCO6YPH2GKdTTiWsxCRua8pD0QpATXSwiGIvnYGgYXFVclsY31FNY6sQ46VGS0ogu9U6MzbNjtjaf6g8H3uUazB1z7b2fkoUh1SBmUJmk752+puIVCDtjJAxlKmDklCz4SDNHeMQAKXisKn4y9zE8UXYIS++LIKgKQPriVFcg3MOLMSd+xcnR55gu3BqPMRLG5myMuTaVUt3vu1P2Uppqupcgb/NphcGWKsehX9Y3Ai69eOCKQp6+OOgH/n3v2X7XvlnGeUza/1Zo52xNuE/QDzpL40ZKGIVCwb20LVhKy1v4CJpMnSf8KsX1knZsPMnoWQJj1XTRIoXS/1mHFPNL/zEZQ5SedIaHD1YW9JC6MxSRtPqYwrFxeL/r4yL0XvvFM72H2+x+97tcGwuvlhW5Jdvx5gS78nKdw3nW3qgMInVeMDFCwgnbicXJDVNIHQnfJbvIiQpgTJiCzZiURAylJ0DWxjdUJQsuKEzpA5Omhal3YmwYpvE5ibb+cMzH+Hzvu7G0ZiX3zSFZCRUiuFPQr8VnLXgnYC8VR40HnuARTiIkY6L2LL52TNmaeM4aPFUFIM0eqyswtXfgL52KY1kBnpH7XAP85g6AP2OkRKBBv09pZegpIbFqRLkzBpkbVbnbUuXY2qPzrB06BSlmlmJr7qXUoKcoTeinnuK3pv0fA3XOLCYsKn5SiC0l+l/96mHccIdWKaTmVBXAKVpeA+bNcphGpElJrin9vV5fS56b/eS59sbXf/3wTGvdWlN8Zu7w0lb8Dj3jZfaCsaAFrsMxHHuv1GguQ/sU7XtXFDLXOVRQgn2fg8QWpfrmCLvych3DedaVqHEU97738GNjxrqTTrGqxFalifkcpOprTnqprZEiTp7jpNUG6G6Jb4iSpc6L7AGnxV7/Fvd4Z+JWvLsN0qM8AExx7P2YXoKNE/cRMG+fW4seI04MDEFLuInBYc5P1o/74YaQiX/eWAlc91lrSsm97rUOT+1pMc0eg5u4ieABg/d3Ne8TwubjxxonU8xvY00asfed2srQKiHWVnwOPCXOSRVVCmGUu7HOxFNVjq05mk7hRM82J4LPelsv6+B9sVzBUz1Bp6fQ1rT/U4A5U5aU5icIU1/F2qVZoXGlBQIFLH2xouAtoeU1wOXJDSOWKgXhYvE8RmDX+J/UlTG3ug4p5sfyY31dl4rf3m3/Ae+mjKMP6yvpQCmFMR4aV6vrPcu7jYVLfCpQe03a+80ZduXlOnX7nGddiSlrjiwU3zN79pQm5uG4hGK2taltZsIyY/Gd9N62MNZYfENcBIQo5tuasT1DzAggxPxfLSPwxd9tLuYgnbEN0nOKch+h5hTaA+MyFmZh97em6aRpskz10iRbBQ0OrVX6v8CPMWOeKWJFyBCgxh6lJPFEa6oEVwsVpuzUTZkD5uDZmHuqnqa2iTmZC9pMXQsWLb9b6wdT+dJ6HWsgSsh3fucgVJK1hq6ioKbH1Fhn4rkqx6w28Er4UI5iPan7Bx25L/sOLaG3ugf93pL2fyp46UuHbJi4SFPRFn2lf1Osk9YRvVLEfGcfGK97ltDyUoBDdEE4wx1aM7ZTCuyeRSPxbNbtVa8a5mNN0kfNHgP2GNqO4p5sox4PrYdGPAxkz/pZ4gIaS3tnJWa9+7Nizb65KjHnqrz8mzNu//SnP/3Mp/qfzgTT/zhbiO86I8AzCpyAHzpzsj7ykY88/OzP/uwZ8/4bh8/7vM87fKKWtzdT2Or2OSYN9BhrTrozjylNlALXM7fakNVs63SMWRII6kzY+O3G7MU31Ej+mHbTWLHiyDvU4DD+WFgyfqcr71ULhXLSC9LDwDVvlGEy5Y8mNM1VVhNmhxHmGeJrPEcm09gpMgoaYULwmU9igxIYmnYJxpeYEwIEw4dXsDYYtWWYTt2xkCXIVP0bliufm1fM+xi4gN1kZBmfMVtnAt+zP/mT+0HFp1LYveM+9xliOLzXOqyppzJX5VhhOvSVGAzrkniGNOIzbvghbOFSjFJaNFAI3ROrizldiRN0xaU5ftmXDe9Hh+ZBsTQH62cO8E1Yoy/32iM+R1sp8IY2l9DyGljT+mILTFk06v53HVq37hQna1utmxQI38NJS0vtoRFYdwcz+yS92dAHmLIotW4vFhwKVWvN/tibadG6c1Ve/vCMKm53u9udCYwHnWnVZ2r1DPzymWP/oz/6o89Mmp96Fm39zWd+11efmTYfcrbof+3wER/xEec51OvO7XMehc+WWHMUnGqzdQL+lzXge89wCk8NlRSBM2dKS09xGQs4TCS/+zEgQleMCNy1OBpjkOIOzC9MpDUrY+bxcWMmc/5oqcdqplCsnEzdn4DXNoi5hShowRMBbL3MLUXREo/jNBicJB3ZvfC8xW/eMsy2wq7fz3728D3LirEZBzxyfdSeMcGtlPg1wmeLwh4Bbb7Wy5xrTM5YKvAad6TYheAia8hax5qY9HDXwp9U6dR5QW8CrqWSWy/QthDYGlQ/d1/FpT3CLWP8XHcUeALZM4w9VpjEeHgOZY1g9r1r4ZQCy2KxhJbXwtb2Gmuev2T/A7RvnSh4cINX2f9TLp8eHSVQ217G5yi3SXSYsyjF7TVlzf61m2nRunNVXj7qoz7q0s9SeM5znnPG6N/t7GRwdjQ4g9uercaP/uiPHp7xjGfc7JSXY90+W9JA52CpNcfYx6w5GEHcLq51DyFoPEv6l9TTk3oaBHwqVSY4UkE7zxnDUY9BGvMTn3hjfFWzMuaTPixL/NEtk/Qe+OZymBPkUUZkehAmTreYnhMceiCEUj7d5+Ji4hapPWyWjLOH57nWA2I0Mi+MuD0dq4GzVfgsUdjbtfM9a5/xsCh4hmDZnJCrdY7Ahr+kAkchWqLspwK0cWRPWk/l433m+QQ75S6BvAHPdi/hF4XPOphr2yRw6Yl6TslrcQk35m4ehK59E4ULoB8/6duUTDtzBJQ0+y3lDZbQ8hZY2vpiKyzd/+Zv7yWrjrXJZ2iLgpxDV+WhPTqqgdr2i/vH3OI9OO/WEtcqXKiYlx/7sR873Emd9gKUlodrhToC//uMgvwEfp8EuwahPUH5/xi3z3lErC9h8LEKTClNLCPqjKROylrzcA3AJYAIggiHGoA7haOWQRr3Uny5d4l5e+spMgoa5Yw5u5ryKVCekdOy/2MhapWS8zLDz81rq/BZwqQpHZTdxPSkdgoXFSXOOAgHVhLfERog1jk4YyFieagK0RJlH65Z1JyAW4WQIkBZ8qyquNSijNUdlXmNNQmcO1HPNcIU/Cr7jJBMlVb0gZbRk/v8jaayZnADWPh8ngD7KMb2F2tpTeu9VoXlkv1vjvYThTctT6TIpxYOfLsnpR7AGB3FkmsNKDBjbvHzkAXXK1wo5eU3z5yp79Akz/ufQvLHZ/bLt4g9s8BTz6Iwn/SkJ12pIZ4L9E5QmDEin2veNeb2OY+I9SUMnpmVL1ww2ZQSIPbBz9YTOoZ+//tfnlusIvV95ut9sXxMvWMtvpYqJlsFuedrjvfpnz5kSCXFMgoaXAogdjJMfZ2eUmKMMsEIe8BVkcJ4F+10PGfZM0dtG9K5Fz5UaCZcWKIoK+iSEmEfic1gLYAjyh0lF42gS79dm1Prox61THlFsyxgcwphBJBrUsdlTZPAqRP1mJJXG2F6JwGLXlI4kLWTUuJ8Z07uo2ilSnKy7HzmWkoPPhTF2DM+6ZNuPMdTNmq8mjC2/601FzRcxu1rvVOmAHAPR8mcOjQCuJ9yi5+HLLhe4UIpL1vgsY997KUA3wBFR6DvtQJjJyjEq54ExiNmYovbZ+3Je85/PmfNsfEJtBR5YlmpWRs9JeAYAYh5OIXDWU+ZMh6C32k92ThT5vi1+FoqwLfEM6ALAaLJ9oA/84Vb/8OlTBYn7FopdyzeYUuA39i4z6Po4ZhlL1lkPqeAwYP3WW8WFsKfoCEUEghJyFIGKC5ogNBxvaDmapmrp1b4XKq89hRCY3KdkzllMi0YEl/DFWPP5N21SSBLCGvH0hN1T8mrVVwdIOAF7igzPmeB8jzr1abl2htwZAzmbIw+80zP6e2BU9ePOi+aWgPt/ree8ELRQBN4NF5mrKGlnnt666HxPGXB9QgXSnl5x7Mj+/9MkYUbwP9vdcZ5elYXcMuzne/nWoQpMzlzrc3MDEzjrsS+xu2z1EKwhBlNbUz/M98zWafhoXE6mRk/V9GpsgaWKFNOKiqEIhvvb9Nex8zxpw4Y3MLkKxOjrDj9iYmw5tI5ZWURzlO4PLbGz9i4xXRsic9YAr2y6sn4QlPW1FZPSweWBq4Oyly6mFMIxCkRwJps6kGDFu0nQr09CddTq3iUOeW1h5faaTsVZ41JTAjFBr6tnXekl1HGn7iJHgsbO1G3Sp59Vqu4+p/SkQKHUewou2iHpScuItcmUNf1aMXfxs21kRT4ugdOXT9qao+cZ/BuD2ravMOD+VhXijIeZj+yLFcrb6tkrjkERWlDtzIkq5vv1LLgeoMLpbx8yId8yOFlL3vZjT575Stfeenz6xGmzOQIFEE6wTEB21Bb3T5zFoI1zGisjD3FJYF+Njew6eMPFuOyNE126alsqkIql4qNrRR4zLtLA9ym8LXmhLiFyfcUWuMWCoaBGjv8fuRHXi5f3r7/2AC/sXHLltFzBZ4x2FNkPFR8ep4xsZgQwix36QfD3YHGUquFMppGfu71v98l/O3SvUlJJZh7bpv21DqlvPbw4rrv+Z7h2ZQD7zdG86LIqwfCdWPsOUUbU1w2rjfXXp2UsRN1q+QlkyUC1TPRRqr8uj6KHaXEuOxb40ydGi44SrI5UW7goefaOHXw6NQeoSSjdXzkSqYGizNLZg8+gJ+w4BqX32l/MaVkLjkEVaUtgebVzXcesuB6gnNVXv7gbNV/0WqXVOifPONMb3t2VLnN2Upy+fzG2aq/QOL6GUiR/sozB+KjH/3oS+nVP3Cm/r7oRS8683OfObqvQ5gLgEXsTnK0fcR96roHW5lR3ZgUlq/+6sFcHkGbLBimVScJ42a6prxsUQB6jQWdYOMuSW2Z2sOEsJKi67R0yho3S60oW5n8mEKbIlcE8pk+f4lpYaK99x9T42ds3PBMUSAMCUnjcG1iSLZkPIxZMdxP+SSECa8oKGgKc0dz3//9g8A1HsbaVIetVX/hyKnVONtCcVOn1p7y2sNL7k+13cQmESYUE7RPEMIJxcGP+JzUWEGr5tcreDh1om4tjrFApWVEmhl6LwXQe43fmqWztb2TjspoEt79bUyUmAc+sL+Op6wfNbVHCG9rTAm7852XW06Phd6YfJY2AdU9mfGOKZlTh6BWafNslbmrm68qMFdCFlxrcK7Ky4+fqYl3uMMd3vB/YlMeeLYznv/8518qXPealDA8A2nSFJVHPOIRh2c961lnG+GdD8997nOv2zTpJQGwhK9kKxvhPEynW5lRakAoY69iZ+pX2FjmZD42eE6GCod5hnuWKgBjJ91v/dahnoysEfhxv+Jx973vjXuYjJlRtwS4rbGiYHYyPX7sx26qPM3hdUyhTUxDCtURMBh67/3H1PgZowdrmNO6cRsPQZi+T8ajAKG+Tmk6OJWRMoZPczF2rozUsfHbcyhricNwjfX3uXvSfBF9JbPG2NAVSKG4rYHrPbzE4sFNBS/GlqaMUeR9zx1FGHk/lxIhRXngVlJ/09lszdhaiyNchz+kEnNie7w3Kc5wAIeUgVhK4ZJAJhjdax0f//jBstY7ZJyyftQYraUkf6yryYq6EqnBvTEl6wjOqhUr677WbbNVQWplwVuWGkw3x2q756q8fPiZ0/n1KdHZAQpM756fwJFvBrA0nfkU2SFjsIYZVWbmtKsMO9Ou/403J78UUsMMWV9SPM3pl3BbqgC0G5xwYPIm2ACTN2Zc7yco0sMkQYuJK4hZfSrArcewwVIrCqbrWooLoYHBYSw1ULTF65xCW2MafJbqu0uaLK6t8TNGD1wJ8G/uftwLpxg6WjCeBKxaH/dTLgjPVjGdOnG7TgYRKwC3heemz433s7DBA1pLsGmyaCgvYhXEdUhLru89NmW8h5dq8cg6mUsaWrJsRKGgrKg/JABbE0L0EGGjCu/asVX3LUUQUPQ8y3ehNQKwTXGmJKkhJKYDrcct5z4WF4rLmJWRG+xU9aPGaC1KYQKPqyvwvFODe2PyzhSaq1asqiSvcdscoyBFFvzc2fy/7dsuRpzQ1YILFfNyc4PzSGdeC0sFHQGF2dksTrdM5JSHxBJgfn7CvONPd585EUICaJe6UdoNXgW4EwiG5rTt8/b+NFDjXvGsnOLh0/eEYO+kNMWwl1inCM74yo2RoPLeNHaspuAxJt9TaMPM4SougcRIzDVZXFvjx1qlb0oq6ALPhkdKoWekOaF1lZqbM4rvMVw0IsUUHlvFdOzEnQDddDpmvRKgmOrBSeElPDwXLrwfTn2G1riRYoU5ZSB2b5/U7tvmX+NMjAn9p22A79PL6C53mS+auLQdQu1C/uIXD3iBJ/MPH6kpzujkS77k8j6GV64I9WsSkzZlZfQue6sW6gustUJUnPo71azhIApg8FdhTPE/BUzVaelZsba4beYUJHRtr40pSGvihG55y+F/8ViU5+tJkdmVl6sMY5HpqnYSmksbcG1NNVwi6AhHVhZCIdkILCCYX6raem9OoWHoNk4EDAbp9L7UPdVu8DYosZ5q2/uNy7v8plBRIoDvCFR4bZXCKYaAYZnHVJ0FeBK4WXuaWDeMDuNZ2tOkp9BiYuaSVNYI8yVNFtcoxXBA+YIj44U3TJv1zAk4LkBM29qmvxEwPoKb2yHWH8oIOmgb2PWYd5vqm+7VtaO3NfCZtWfJYCHwPFkY7rcH4Mgcz0LlLs398z//xtlyW0/qvX3SFjGrcSbcMPYD3ANjmatq7B3Zw2m6uaSgoTn5EYw7lyk15qqz7tbO+OesjKmKfeyBKzgVCJ4eYHBmHbm0PBPfaIOZj0kNPqYcRM+KtUUZOEZBWhMn9Cd/Migz4sc0LGWR7FlCr1XYlZcLAPUEhVEzvWPkAmFTVXUqyn7MYrCESOcEnc0TIZXN4oTrN+GWGBcKCoaQ6pw2GYHmBKE2Ac1fpsoS91Rvg7cm+rzH58bjb++DPzEE3itUihuHYMsp0/1OIvC9NLhWLId3pA1BC8boffBFiMz1NKFYEfRO4UsU2jB1zKmWvF/TZHHOHVEFm3RoXZMjRFNSP20K0Ay8w1vciWgEcw8jT8yHsVv/qpi2a9um+no2Bca7ovxwN/ob7jzbe62pe+DYtZRm77dGnmHd1PjRbeQURfmmiphFgU5TTgoVmjCu4IDQG8sQO2YPRyCjEfVnQhNtbZ4lrs973Wveymg+x1TIrjhFa/hC+iThKfZZ1pYF8xTVwc2fZdQBw/1jfHWOH7aF+rbAMQrS0jih1752GD8cel74ELq8Xnoh7crLBYEEwD7veYeDbPHENzhhIt6xKPspi8FSIp0SdASsZLC6WSIo4j6gSNggPmcy93/M6Jj7Z3/2cIJyulvqK283eN6ZkzeBBqQOEhKYk/spftnceQ+8EgjGBM9OehhZOlHMBS2nmSTm0JZ4DzN1LyE/1dPEmLlC4MW6wisB0IsJcY2eN1K9PVNgNCEZy82WJotjFrmeYKNcGbcxG7s1tR5ObxS5mPjTyC+ZRxViHSMo/I5i2q5ttaoBFhZCw9gx2/TT8Tw/xm+MGLRn+jvuLuPOulsPsR0sYOJvjoWxfSJYPAXhFKbzmeBx1iJ0zV1qDoS0PdAKzGP28JTSM1fYrhYBdB9lL9WJ5w4Z6FcF6GNiK9AMy4C19jzryeJin5uD9bOf0wttqzsdjjTGXMpXr2Z36zkFaUmc0OteN/CqHAay3pRC/Bher4deSLvyckHABnvWswZGh6E4qWHGBAVixAhb8/spay6MCTruoXaz1OAyn2M4hIpgwbhs0iaA8OVLN1YMg2/eu5KRMSaA2w1u03mm6zyLgDNP78fsvJMgFdBp/Fw88JVaISxIrnOfZz75ycNzKDCun2LYtZnkmKn8bncblJGxniaYsHfDCZfgWOonOmDilZ2F2bhOVsqHfdjw/6mbLI4JttpVG30JQE6Xa+sFl7GaJN6lfX9iPiiXVTFt19Z3rqWgJosp1Uv9be3QmDlbB8zXWluzKLNwG7rMHNALuqBQnEJ5mdonwWP9jABZ0mRy6x5eo/T0aLwWAYT/CHZK6ZJDxho3XM9dE7rj/qsxL+jAWlI6KTeuS4XstQrEFr46tc6nEvZbFaQxl9P/vsEynf1oP1Qem4OEdb5eeiHtyssFgCghiMnmwIATAEuIpF6ETJpKdKesuQB6zKi3WWpwmc/dx+JCSGGQNk1KWRNSycAhRAR3+t/YzMepfUwAtxscU/NuQpxQE4sRXzlmhxFheGFM1RXhd+rSuMcpU2bFPe4xBCv2GEJOpZ7vfQ95yGDp6TGbxDqMxQ6ZO1xS5jLHVkChA0qVZxij9eeqcS/lRy+nVLc9xWkwAoWiZI6t5cYcrGWUFmMCxgQffmctKIspdZ8A6yhrcEgBq8+vawunqZpbS/gn+Da9ZNBI4mqMB8MOs/ZO91ThPJHoOIqLJYJqTGi3jf6OcdMYu/kTSKoqcyNwvWZMaw8uverFiTGyd3wXl3CCPVVxPoXLZsw6RGmJQhVaqwAnxvfgBw9rvlaBaPkqJRgNw2naJPT46tw6n6qVwRYFaczldMtbDvwCf4v1JS52UIPHzzPg+UrCrrxcAIgSYnPphluJrtaLsOls9hDdnMXgFEQ6tlkICsqCYDDCy8YhpLyT0pLy9SCnQ5YZ7iWCGBN0Kp4rdd9ucJabL/3Sy03obMjaTdoJg7AV9Ob7FFSDV8I0LhljxsgxeAzbM2oGRU6lfmMIBGdcPKkn0zKbMVMwBol5KBTdMqYome6hUHFz1MBn8/G3dxvj059+udT8MYyzChQKgv8JyzamhsuImw2+KQ7ozQ+cGh9G6QeeWd1cTzj5O2P3vJ5lKGtL4XQ6Vv9EUblc552xsCTTCCR7zfyN3zu8s+4bgjaMnNtvKS7WxpuMwdKDRbpjtxYRCiW8pBM2N4LUcQp3GpquObjUfWxOVbHPO9EXtyilBi2wuno+XNtTcWlQ9te4bMasQ2jeGs918jbuLRaCylfh2f5Hl4k5SlNK+Kh89UrSytog8jGXEzAv83mP9xh4bJImauHCtIu4Hnoh7crLBYAoIawVNVMngCkTMJiZDRdiPaaexyn8sxhemgNiahGoCTIDX/zFNy11b56YI0ZAeOnoa95LN7jN6USPqcNT7TMSF08yFsw/hc185nrXwKn7vTcl95NBgWEmYNVzBQ1SsihOlMHUk1lqCjZe754qmie2w/ONyQmUMhhLlt/GS7HBkJe6QMZOiK1A8bn1oODlxJ0eOYSM75IGD9fGmIJwxgZv0jDhTRG2nhI7xtiNx3w+9VOHMRlbaIzCFwGW9TIOYCypdRMlJ7FP/o9lRpXlKSvBEtfLFvfB0oMFaC0ilEXfJcA8bg50pVcTfKr9ufTgku7P1sk8xX5Q7CnkcJSO29acYLNW4qusH6FvTY3PM60F16z5LwkknrIOxSUNt7Wfz6l69mQN4BAv8MwctNAK+sUX0L1A4Tk+eYr4wlPAGJ+58w3FB80Zj4vVEh7MO0kK10svpF15uQAQJSS1SBBgAq1sDhYKmwwR0p5VmL373QdirFYREL8xJQdRt+b6K+mfxTDHSt1jFhiWuSVLZymuIsySCVV95YCyIZaF4gSXGDUB4D4MJ3UwMDHC2GbHfD7904dTp7RwQsSp3TrEqpO+OlNxRD1TMCb+xCeOK5nWFg7MwTujyPltjE7I3pWA0CXKy9gJUXAp4dMKFBYX77cerFaaGsKdNQTw7cccUtcEEAqu+fIvH9bUWrRK7JJTeo/GrKcxe5f1Mrbsi5wmWfPEwFB0km3m/cZoDBi4uiZbWzjIVrInWc/WnLSXHixYhapFxLqxXsVlCL+pLmuOaIUbCX1Wq0VvH6Q+k2rTUmjThyyuNkqpe5Md6LTu/cn0YulBA3iOPew3WlwqrJdYh1JygdURLpYE5i5126SQIle1OaZSr3fHhQKf1lal4yk+eeqeTsfCbRs+EyXWGorvQfepmowOjMnhCH7xj+uhF9KuvFwAqCbd9EHBnDCvpOjaaJiH1ELCxebGPGIV0bQrpb4RrB/WBJvyFETabpaWaZy6lPgYg6q4IsgwnrjUwoQpbO6lpGCMyTJy+vBZqu7ayGnwh2Gy2NznPoPJnmUl7pAwqqVxRK2lyJh6rrcIEO8jbHvWJ9dae/PAhJbgaM5U73pCvwqUtsaE355F4UtLAvel4J974dz/aBTTlD57ahrz7q/8yuH0772tW4qVioXA2hGE6MHnBKE1tOZjwnZOuLpX2r1MOeu35qS9pnp29rDYnxQDtPft5Vi7kmVlP8E7RTbrT1kf2wdf93WDJYcS5LMIcX+bi98OAWjdO9GhsRiDFhwUJtazsTitKWE9tf/xt7RLSC0jdGcdpzrQr3HbWAM8kGvb73Qnt5+S3ZmsSS7wKSUJT/buU8UXngJucQOfaavt+rHe5m9+kQdoDh5kMY7R7bUEu/JyAQARhoHZ1JiDTYAZx2zO9YAZtRYAbesRo0BPQsTG9OM6ghnzTcnwYwPOsllamKpMu8WtNceg/Ba46jRpTBhvYhwwpH/7b4dTOlcGZswykAylVGjFqJ3sYkXAYBPEZ8MLQI2L4tg4op7rzTgT+5OKscaRDKMoXMbgN+ZjTRO/sdayEqGTrKGe28vzWVwoLg960CBIWK/cg+knNT7F4Gol5a/5moE+WdPWwhw9xiLDKjbmlrI/HvvYy+6MmmkxJmynhKt5peCh8YR+13QmX1oo0HjN8TnPuRysnTYM3gfnoFawdn8OKnUfwGWKAzrQWDcKckoXpI+Oz62bv+HKO6KQxoLDTenA5O/a3mKpsB6zPiVYOOtIUbHnKGBof6wDPX741KcOCmzcnVOWIPdza8vegw/j9057LNXB8UZ4Q+sV2v2FDvARz+vxsqsVBPtzzUGFwq29hv/NCQ9O9/JY4/Gdu951t7zscCIIA8uGiX85WTkYdc8CkHRCG1lAaFJOUzOjZbKnDjg7dSnxpTEIThDwY06YEeaX7ATPxawwVfebZ9wyxoDxY2Q2Okbq3rjXjPk84ojq+mLcYmoICmOmKIl5MUbPxyjNy0+sHH5knOSkNYYjDB4+WssK8L/rtXagwPi7haSVwwcak+VhrZzEjceYI/RAzO/wRrA87nHr6GgJPVaLzJhbKllmYpPadRsTtlOuHe9JMcHW4rVUeK9xt/r7Mz9zuI5rEE6tb323NYgCGXen77MPuAqsPXC//WKt8n+AEuQZseKYK5qzP8zH2iZ12X299hZLhHXP+pSChHBOeaAc4RG+EyycwPV0oA+g60c8YlBwvNtapylo4mfUyGoLuznAoAm4hJvwVeuNprybFavu597+4prEZx2MxFEtKRR53vAXjSsL5ABsfNYUvihc4QWRAdd6mjTYLS9XgMCWWjkqk+ZK+IZvGGqC9NwJYR4YHWZQT4cV2rL5pww4azfPsaXElzyPIia92XzEaaRwWdJpnRZtXIwcw2WCVmlXnAAG5BrMzTv9752Yphginx3TF2gO4DYWAmsRCwHgl/fsnIxj3idgAEbzyKEp+ySOpiwrgNJnDay39RibnzUSK+JZhCZlMG4F46txAwQAgeBEvMbvv0RRDT2OWf0CW1yUU2ud7BPr1auqvPSkPedurWB+Uuk91/sTWBrXBuXCfQR+6ufAl0BN9I5nmIP9RmmxHmli6nm+i+ISK04sZ9agtl+IMkOBi1undjpeIqx71ifPt++MC83WVhdjSqH/v+iLBkFsbhTK2joiRSQdACgxrqlNCu1vSkeeZw7Zd8ZV9/MYDzIuB4Lw21pP6BTBxVvgNcXtmfYjqeCdGDXrTnlLi5TrJU0a7MrLOcIWK0dl0umgO2UBAHNM28Yylpe/fIisr+moxwSc1c1zilLiS57HFI6JYdTmlsBd3yWFl8LCVI75Av9j8E50mA+mjkETutwkuvxWITnXLgEzxCTb8utLBJRntBaCBL8SSKxBsZ7F4mGOrGrGWHEEMs9Y24x3zrIiYG+q4B7FR5wJhijew9wof6mcnDYBBKDxRrh739JTnWeqNose4cr/8LWVHrdk3k2ttTmYm7G1tDj2vDGYU7zqdZRoZn37JVYuimHciqmDYx+xilF6WRSseb43XuscxQXAb34nFd91cTex7BL+9oV5R3H2bO+yh9Lp2HovFdat9YlgpSQZJ3oxRjSc2LJWuEaZYE3wHbo15rjGjUNMj3Wwf7KGUYC51I0bnuDVddwp9rF3eMbHfMx8bIu/zcWz4Jq11BymDmJL+MEx8LobFPYEPFvzKLw5ZLAqcSPFWnQ1LETnBbvyck6w5lTZg6UWAKeOKaZtA9lsz3zmZXfUVh/22OY5VSnxuefZpNwtmEZiVFzvs0TSY4xpIlhN5eYqewLDx2SMCe7SYn6JuR+O4F4l3aqMCqJO8bg5JbU3x5jt4cTfYneM33OM24kvMRi5H6My5wRpJlONsjFnWREnolYH5bhVKPN5PXlyIREe6COZT66Hb+9Ee6wBrDpqtRjjHKQDN1qkwGT8ocu19Lhkv7BiYugRYu4ZW2suOvTh/xTBa58HX8bJZTVF12vAeDSTVM5eLIt1hKPE+PjeWhBS8G7M8EQBsB6pgdNbg7gf4YBSlHGag+sJvcQKpUFmrDsOIcnegpM1JfpjfbLm3/RNw14Ru4Qvud9z7WmWVHu2CteqrFP62zISycBKLGBcQmjAQecpTxnupfx7FlpDI65NuYXP+qzhvgTGj8W2hIew4qT8Qu8gdmrX/Bjc+oZsKvi0XtY9MXTJrDI/68VVnWrC10OaNNiVl3OAOffHmH92S8Afxj7GtFkYpM25z8bzLAS/1YfdwprT7pLT59TzbEZCx2YkhOAwRbxyqkxPHnj3d/z9ud/8bOBP+IT17RIwztpZO2vh1KdnjfURrDqnpPbmmNLeToOJYcBgrA36IEBYjCIgXa9Hknt8H3eOdY2/20l5im6MKZYcz40y5T2yXpwsqzmftQ6uk/VD2BhnuoajQd/3TnXtKdRYpCBbJ8/BgKsbAF0yc6+hx7n9gpHbD1LWewKl59pJef8pCxXX2pyQyvyXppG7V+o5+n71qwdhmVos6NhY4CqFx9LVmuCFs5ywPTuuoeyBrGfSpY0jWVzpU5amp+jRZ7GWuN5zWR7XVnWGS8qqsVY3VpQP+4pigUewhES4Rln3f1tGIlk0xmp8lLvq4vNcY6esoyfr5z3BBUUNDbJOUNQoJuhwKrYFzVOyHvrQ4V2hlawjvsTFZR/OBRUfa525TcmmQgcpXgnSnTs0Y56uo5RdD2nSYFdezgGm3B+IaMw/2zKDpQF/PaZt4yJW77cJU+0U0ablwFofdm/znDI+ZOp5TpZpgIgRETYJ/MuYE5BHgSHwWSa4hRLEu7ahWxQuTOAlL7lxZ23gvZgdwYKJ+D8ZImOuj94cCW9MBiPHfAh0FpS8p65NMpUwXO6fPNczzI9ygZHJQjPmWjOl19m4TbWkpPnN+mN8Yd5+o6PUHQqTTHVj727jB3qnUOPwDn9bxwQmxw0QuqQYrKXHKYuZPef/KStoq1xP7T/jk8k3Z1nN/JMplWJv1o6SNnYahxPX+FEevyqZ6jyhZ8oyCyLBjR6ifMAvmg8t2hdxIdWWCZROQbDmQHFAEylIaD2ss2usC2FoL7Fk9KyVU1BdP3FZ+cxPqtymsaXvzSvPj7JvPmlJ4jmpexNXpj1f42dcl3YVuYYClSB59GfOng/HcMBqaH8siW2h6PSyoaaCiis/SMuUY6wztyjZVJTCHNpqOjxAN2nnoaDo9ZAmDXbl5RxgzP2RFMFkx7T+2Z4raUnAX4/Jhkkw+TuFpzdMTi5pObDWh11ha3zIlucRAnBGaNigKRdvozp92agUCUoEZgRfqVdiHMf0ABpTRtP3CD4xD2bwWEvGXHFjczQXzNT4KxNulUDPonw4KVq/pNJi0Ji1zyP046qjxI11NgbVvWnsxuadUdaMyefoCLP0LO43bhXfESzm0iqHY1kbGLfnYahwmJN0hK19QnjMVcddWrxLR27z2VJcrLf/rBmLy1xgOZpMjRq4T/VkyqcKtimOOOdCNq7QD2sM/IUWo1RyMRmP/eD5aS9hTHEVgfxGM5QE6wVHxkx4UyQ8A42FxikVFE0tCrY0uMz+sebWFSTAPv2y4MY7jBV9sb6Yd6vsp0t7arYkbRxdVitJAq6TJkwpS+fyWJ7gCU17T/hhKgwvjW0JoHXZdmNBxcYdfhCX6SkSJ253u2EdxbwkPs7cYsG13p4d93ObEn4tw668nAP0XANJEUwjNJur+menmGhlXmPQMlkKjMqaYf42bD25ZHNt8WEfEx8yd7IYex5TLXyFqWJIqcdiHmFkMRFjsqlXkjTotXObU0ZT3C7WFz72xIGY65jrozdH90S4pQJwj2F6ltOkEyJBgAH6LD2eZMcQNj5zPQb9fd83nsoOt1UIWzM0KcDRydHpm4KC+aEvz1NLJv1iCLyeNXDMdZr2FhFa1jMn6dSQMX7422reboW9cR5TXKzdf2OVo+sz7WfzSM0V60lQ+h5/iBWi19F4LS0mo47QTxsM7zBO/8MxZZNSYiwEtHvsnShjqfGCfvCEuGOT1eR+lpctkDEbR7KYjD/BpX58hn6Nt65HT9m3xykFPvMcc6mKC5xS2P32Tm7QFFuMWylZfEl6SNdl4yDgl8S2BELrXG+9oOJYE43bfmYRPVWl3tvcZrhWAoP3xsKeGlXWER9KT6c1lsyLDrvycg7Qcw0Qbjkp1yZZxwTM9nymtYZFNmwUqFRRtZGcgBH2Vh/2lviQpSeLqdNucBpXS/obJYOB8CeACV2bHyM6RnGZisXxP/dKTjwEOqaR05Zxjrk+xqrJ9oJo69pkLNbWabOWhG+brs3FXjmtGSuhlO+iDMErnCUt2jhZC8QMyc6aswaOWauMM8wVQxXsHJO3Z3kfOq0ZYMfA0qDyNbFeS55JYXLaJ1gpeG2l5pz0KRBr9v0YLXoPC0wqJBNYfscygV7SQsFYCDo00Frzkq2XbCP/+84z1rTx6I0ZTUVJSsG8lIGIxQB9oIG6HmMHGrVg4r6rxR/Fg1EkctjxgxfZq9YGj8i8QrO16/KS2JYKU0HFda1jfWOdScG8ShfAtcbvp+eamnId/emfXn53FEJ4tZ7GlRic6wV25eUcoHdasFlsLARmA1XXwBYmOhfR3lOggE1DyDqNOGUpjLXWh70lPmTtyaJnbao4dRLEbDFUGzRBo5gE3LAceKesDbE/x0T693AZd45xYhiEQaxCOVlzEUoTH2MYvTnWINoew2zHknoVYy6mKQsBOuDSw1Bzv+v9FnOTjtqJJfI5pTtrt6XuCmZNEKJ1Cgy8RQlLkz7/t0XKerAk4HFNUPlSWPJM+E1bj1hbK+SkT2Cmb9kxtAh/qZ0is0ZQuj2O/hNjZCyUmra/Tc+alww2ylW15m1Zg4xZnJ+1N564tVO/JlmQxmdft+sx5T7nDsEL7XvWT3RrfdI5ntLtEANH9gceHDqk7FsL33l3SuunL9wSBWIqqLiutb0FPNs8UuUbboDvKV2U3qc9bVjHJXzrdjcU4vNMP+6PWz0HGri4292uj0DdwK68nBO0pwXCPKZjxHZMhcaladhT8SiYkk6+a33Yc8yqrUOCESTCn7CkdGyt8NjiFMNNgBq/L+btxILRehfGQ6GYsvgsYb49ZTT+bEwh12AaKT+OmfmMEuWktSYIbwovc3FGrYtpykIQgWrM8GX8rCDpZAwfcJsu08BaLlm7MQEf9yUBE6uRdfO5Z6NL85tjspSu5z9/oANrSOCF5iuux5T4qaDyOZqYUiDg27oku43rBl59ngKBgACN5RCefL4k5bpdf/RnrAmiTvdgwkugr7Vr8WTMW615FZamBNcxe1ay1Mw5vZhSM6hahHpz79Fdij8++9lDPSLPwGeM31zsU3SdDsvwgF94H35hf1pDdCgeZW1fuKmg4igu+KA1p/ynzINnGxtlLnTnOZQocmJNWY0P+qBh35oXS1/6YsWapc7VkgPBtQS78nKOUE8LiFf/F6eApK9tqdA45gqwGbhNMBRp2NL11pQnb9/RY95LmFWtQ4IxYMgsTgDT8iwMdI2ZHoNzorLJnaA++7MHZhQXVQrX8VHbtOm2HYE7ZvFZU4+hxWUyOhIEZ10xjZhtKRHAulI011YvnoKl6zoWexXhlC7WaAnD5zdPnYhYY5Jaz8KDqQbvczClNMANJu4Hvpz2l9Bl4KUvHbJeCOzUBaEgOLX2KvMuVfaW0kT7THSdyqYUZ3j13MS2eR+cw6n3E5pxHftN0RdUDA9L4sOy/qwqL3vZsL+sHwEO7+ghNEeYOcWfypq3tY5VpVl44N4yHriDp/Ri29Lx2Fi4TdSQQQfmiBasAZr1/LjS4NbhMYHIronrF8z1hetBi78aVJy6U7UXFz4V64wf4wexHKMLdAKWWKpvUejR/Sw2afvgQGB83LCuC1yJInrnDbvyciIYIwY/mIvqtgSezANMKj2LbN41abw9V0ACwhCud6XapmDVNeXJW+btWTa1d4lNECMxF8eSOiSqOmIg0fxT04GSgYnAxVgJ+1ZQYZLmlBL1XBreJyPBM5woKEqYtBNLW5ytF1O0pYhgxSX8qFXCUkAxMB9Mn2BK5VI04R4Cf2314jlYsq4tU00Jcb/h0nzhxTgF9Vo3a5TMH2D9ZEFhwgRhTrMCIqdqlswpDYTUwx42fLaGgbK4aEIKp57pZGsuBFAqKre4XqrsraGJVoGgyJpDeuegU7hM2rHPCDFzT5dohxg/BFnKACyNDzM/gs9egDfzqZk0rdA7lTVvTRuPlt5Ds/e976Aoi4uL+5ClJNlPa+qgZM3QKDqw/zwPrtGD55oLyyK6NTfpzJ7jfwoPfuTzub5wa/BHSaHM+syYzMVaubZaZxKf4nt04N01pGBpLORtGxqPEiwOqj0QXKkieucNu/JyApgiBhCGSItnHVEECVNA3HyV6Yy7hHBaV0BiK+JXz6mDcKkn/iUumsq8bTi/jVGpcim2Njnz41SH3cT3YEpp5heBGDM5pYi1pNc5tlVc1CtJjyJjSlquz0FSKs2dgEh59BZqTNFW5gsiCDBSPnxCxjs9G/OE+1QwTdfqLcHYS2CNUIJvzNG40oKAshW8uLbWAoFL1xm/z2NJc3oUOM3SNFezZKvlbwzglQuEoginyRSpWR3+tn4trueUvS004e9UNfYsOBVnAieUE+PxHrSb/eDdaBUe4Qy9+G4tHZqHg4DvEjdSA0CX0FyrHFAm5wLG8+65bKveu/M+vAk9UjLiPmFtUN0571kiYOuawT8rbwLfk9kUXkaZNE9jToyf6/E3+6AqLmsVhylav8MdhrXEP8Oza+JEmmcaDyuoYpA1pGBNLORtFxxojq38fpFgV16OhLmuym0qKsZ0pzsN5k33IrZHParffLEH1RXg76RfJ0AsKYlrT/yVEXgWhcVzbWwmVIySmZWiVGMgWmYFkj6cEtV+PD9xIBgJwT/FFCg58Ioh12Js8Gc8LFh83B/5kQPu1gRmbmW+Y4oBixSBmqZ3hFQEKzxiSKk7MQdVoITZZa23mHbRAaHktAmXKeCXHjlwZ20J4dQFQZspYmc+TrGx1GDumO3SmiVLGOpSyLoRMsYFIrhr1tlYcOmUsreFJvzNigqPsb4l8yx7HX4IKpZL+GNB/PRPHzL84FjF3/rO6tZzf08RA+YXN1mKu9UWC3MViseUA2nwLCPHtPHoCdxarE87D/SEh9jb5omG1MRBP2CJgK1rBpfwHvqE97RQiOumDVqlRHFfgaSF1xYVa5Moxmjd/+q6tJmf3mU+rNTGhE78RkdRpNYGlN9igsaPObRdRNiVlyNgSyoq8DfmwkxK81+TgoiJirGw8ZhEne6quTi+dIIFAS49fWECTo02mzL8VSHCaGxszABjbStPtlYNY3J9ytYnRbLWaJnLsHAyw+hTRKqC/41NjInrBBkuCcyU+mh8rokvuAe95nA94RvFQOCzefostRTgB7OEC/jEpOcYUBUoBFMC+aw3PGw17ZqPZ2DyhH49YYZRmluyiXyXImCEcU6lfsxxbc2SKYa6BkJfaSfgnRlnUrr9hvu19Sy2COT2nqTvJ+4pwZrwB//GZw0oLvCBLloratx6SVUH9nqLv7igU3yytoiYS9NfcvqecumuzeDK+8zPO9OawP42B7SEl8EV+reGcwJWfE8aPaJX/DSZPp7lfuOAX7/boFVjetGLBn6ZonLWKcHPMo1qwcel0KP1Md4UpTiZVuihKqLGZV3Jj1OkOL/mBIe2iwS78nIEWGSLbWMRNlUo+EH8GI/NkD4ip0iPdtpLl1QbTtxFFJekDHvP3POrsExZeNYa8QPGnrHGN0+QxZ2EQSawrzIrSkLSMlPLIaf4VMN1KvL/FFNIvYRk87RgbsYRAb+ktw3m6aRLcUkzOwrNVOZXLwbIs5m4MULvsNEJUM/DQN1bA6m9XxrqFANqXXbGahwJ7Cb85rKmUjzNvQQCJcLYUnzL3yleFYhSQpmMAExzP/ekFlDm41lp3ndszZK1AJfoEp7tKWOLxSW0ZR7o15jXPnttSnV7T9JvE4yZjKJ0CG8DX+v9xl7dv9YiPX8IWdWMs+bWmtKeon5+p82E9+JFLJssKC3NneL0vSaDq76PUoFnma/PKQyhMesJV7J9jAX9jAlYc3/sYy9n8tkTLIlJh/Z8f9uvabdhvAlazZi8mys/NaFSnC/uJmt5l7scrzjM8SbPT8FH+xZdGIfPYi2yp44Nqn3dOdQ8upqwKy9HQMyOSbOrZkfgZIHRihXA8KtJ8pj0aBvOCZllh7CyiaX22RRLn9+evlIWPg3f0rG0Crj0CrGxEhzZMqv43W2SxFfEJVbLgWPGU0wB88HAvWtMmPg+5a4xJMoSN5JKoxjSWG8b77UugvyMs7rB6ny8gym7xgBRlriCBLje9a6DTxsunKZZrmz8tC8w/zTUo7yMMZ7a+wWjJcRiQfMsn0cBsm69rKl0IU5hMrhxv8qr4oLGBLO1SuHEBDXG4pY19zml1vOC91PULFkL1o0AtwYETNxp6Z6baqIEwPd8z3DtUma/JaW6d0+CMRMDZT0TQNsGvtb6J07c1dqZInHiadBXXXOCjGBjHRDAnNia3AM31kRBwXb+pzh9zwnjOk+WFfuCopK6Qejd9Tnk2SfGj9bsSfFAc53l/Wa59jzvdJ89jzeiQXvVd8bC4nL3u19W/oID+Mc/KHt4aKzDia1Dx/ZTLXWwNUtnLCZGk1nvJz9S8dy40bM1xKee9ayhQaf1/riP2x6TcutzqHl0NWFXXjaCDf7iF19m7pSTmG1tIhsB00mwmE1R+1ykYuXW9GjE99EfPQgYGy91TWwqzB3hp9DSktNXXAaYgI2UUte10BIG5HObNZaGtrUAYYiBYEiYlfHEXRTLBQHDclE3fcsUMF6MG3NMue16rWdjXhSPXvM/yhx3EvN3GtlVocTiYn2sFQW0beBofIIX2xgg84Nr96kpYRxZ55oiGWHuWs+TnjkG5k1hcR9hVGutuLf2XWmFi+v1MKLIYj6J/zEmz+FrR3fWlrBpBTOA3zSW9AxrjIlFCKbgFzpK1lcU217NklMwvzEhQWiiCZA2A6Er133ohw5zWWsB6glkc07GCIWopdnePSl3b70ikNAk+mqrBtf5ZA8nhqdaUa1BnU9O0JSZpLizuvkswdbop2d9OtXpe0wY1wBfY37Oc4bxxTWSg1FoMBazxIkR3r6b6yzv+faitabIpfkkvHPR4seu+eRPvmliQMWBscBh4pSSXACHUo4981TNFHsxMYl7wsv8Tz6gOQqta8KLzZtlhuL2+Z+/TYHZoqBf5LTqXXnZABH+CDhmR4ua+h6ppIgZ+d5my6b1m7AkTAjFrenRwH00dxsX85KyafOlhsdYoaXe8+qpkebvx6kk7eZtZpvdveCHfmgw4bdMOQWT0jWYMmR8nh/BRsCYuzG7foopYIpObwmgw9QS5/MZnzFs6J7/Pn1d/F8b2WWjJuXa370GjuZrPASS7LB6Ks7a+syJOZsbA6pFvqYUyNaChym5Pr1J4srxHKd3NORv741wobg8/OGDYgVvSdH2g/HH5+869ObesbRlirB5pvuxOaQ2BUUy1p8obtVCUGuWzM11CcxlmtzjHgPOQOZtPBRESgZc1RispYy3CuS2CzR6oMwm1mnsHuuYzshx06WEwdg7Mx/3Ed7GbP2SzUSAVYWinqBBKjzbr+mibP86XFV306lP31PB2DWFmRLmu9S3QidoPMHtcSmjd/vavoxlpKaA187ylBM0md5a7rcf/RgPGldlWHJECxUHcG1tUqzTGkQBjeXzlM0U25iYGvfkXeaaKsHwlbYOUdQdUpRo+LIvW69ArLGYXQtp1bvysgFas6PNmIqKKfCVIFfMHOREblMQllwAurQuIYCp0xKm7RmvfOVw0s7GxMRd3yu0NPa8pPDVQF8Kinklg8M1tHafYbQtU64bBE5c65o0qYvV5Au/cBin5xOGqSdTmYLNJCDW5nHih79UkaS4cNt88RdP+++1KvD+saBI93gm37ZMizDfMJUEhbbxSnGVpMS4uYQhECSuxRBaBbUVpq7n5vKeuK4wYvfHfM2SRhH0TmtsPoSd2kGxQrjHO9Bdnm1M5pCy+7JcKDpjJ2X4dB3Fpa3hQgFkvnYKjHLge89OB+OlyvgULAkmpaSwnEXZa1NcI4DhzfqvYbw+t0ZR+IzBb+uQDMKP//hh30dYu0cAqTgMa+P/GhdEAZyKJcl8YvlDT/BvjeFZrEhVKFp3k3e6JsokJcCeN+f2nVtP32sCVKtlV6YdpRvPi5XD+BIz57tU3I3VCI1RSijT7nEIMx7PSGd5kEzLFHSDBzyJNTYBsGi6xXfFAWU8LhrXGQMekcB1fOnUzRQrtMokBc3By3Ps/yh38AJf9r+D46/8yrYO30ssZtdKWvWuvGyAKvwRXHUXxHyJIAjZCCRCzoZNDZT73W/5wk+dlsJwMIU73nG4bq7QUqxENkLL/I2X+8n/GKIxYwLmljb2No75eGYvw6R3grUpI1SMVeqovyldcCJWpVc/xn3qvRC6qbCLOWFixjHnv3fyA2NBkdbSjxMNBabiyPhSGbX2YcnpjMLgOT5zeibw5oqgtacY86EYGGt89JhWUjutD8buc+uQnk1f+qWXLVoZc9LS02U7+CRMEsTIxTRmiZjLCkp5de+N1cm4nO7X1Coag6XBpEoLeE8rgK1PShDAJ8uDMa5hvMbAwuK0y21Qx2Bv6JOFhlgHCJQoQ/5Gn+o2tXt0LpbEGhCiKq/ai4RUgqftUWnEUazrAQG9oQcHgNbdZG6tu2nr6XstVMuu58Sim7HZP/Dr/yQDGDvawk8cZuCDwu4zBxd7SsG1NFpMo9uqtHoX3hKr4Bi+Kw7wB2vu/e5pkx7gxV537VQQ8dZA9VaZzIHQPIBx+TuFPnPY+4Vf2Ka8LC1fcC2kVe/KywZolYnk7KdqIuaG+TgxBCx+TmM2ZtuobQqmTksxpbrGT7u5ehvLuDFaQhPjqg3C0jSPn5iQ+LZvG054xm4+yYLAUM3VibG3cXOCReTuw0ijNBDSlIUEVBK46bTaphF6NgbOHbPFf+/ZTlHG1wuKTMZBe0oNzllFkiKOsSUt131wgvnBidNgsgJ6DGHsFON0TQDlhJrO46kLY4zeB2fwjXl5lzUhBMJcgWu9L+m6BATwWe2nspbB1rFzE3ovusPQ4VLn3bmCg1PQpuvLWpoSEui9FcAYPuXOd+bvO+vGRTlVVHFpQCvcU6ATn5G9EGWI8t2jxdRtQTcEk7/HIAGs7sm9+azFFwHOnQdfCfQ0b7SeoP3qPtt6+t4C7b6sRdmsT5RuOPZjD7knRetYje01/wuET/8mwpoVy56JxSTB49V9GYvJVOxOcKCtQIL57a2KvxwMjREPOo8snVaZNCfvTYmC8OaqoJ8CbjHDB66FtOpdedkAPWUiygkGGX/8mJVkjVl27rSEgGJKbYmst7Gc1p761Msn+wg8mzPm0tpfhJKBWSJQDCk9fWwuDNNzmMzbjZsTLGYPR0yd8JPMJe9yekixM+8nxNMTZkmq9xL/PSYv0FK/pzWn1ODc/0zYTNm1do35UYb4wq2loOCa0dDiApPEpJM+6vnG7HqKm3kSRv6mJKSLM/zEDUnYRil0P8YSixIIM6/ZKv6HI8rRlliUmgnFEpeAaDTiBz2ika1N36o1yonS/ClqaKZNYa/0gN57sSb2hvWj3GH+FA6Cc6yoYq/4W6uEpNs2uqe0pBYHARplKFaTSott2w73KEeP1iqdRLGmoOMPNeA7fW7SENNzarNXdGEfsAQZW8991mv6eMrigUv2ZT3gscTaiyx2rCrmRGE1H/uz8gjrDS/2MdrQpd1nSywmc7E7cMDVhz7EkeBlxsTSkQ7UxuxQ8oIXnF+WTlUm7SVr4Jne7Se0iA5TxuI9z8Y5BccG2V4LadW78rIBesqEjZMeJRiixY35+hRm2bHTEstHTKk9qBuL4qLSaiwuic/xO9YFzFKcifdheOZoMyeuJ5kmnolx+MymansVVc09jDunpGRLJbDVBvB8wsa7YgVKpssYU1jqvydY4YEPPUGRS06pcCC2xn0Ea0rlx2WUysHGaAze13NHUHD0ccEMMN2aUu83GoFn8VE14Be+KMIUJy6MnIJYPZLdI5g3fV1yakthwWRNeD7FaAvUTChKXFvJ9ZgT2Fi6ftahKh09ITEWa5KMOGO01lNFFZcI3uqiSCxbsq2iDNn7lAd7Jn2k4qL0HLRtLiyWbaPOmj0Eh21XZ/RG0UcLUvSDr5ryDyhT9YSOHo1tqunjeZyap4qymY95qUHDDfT0pw+WFrT+Iz9yUx6RmlL4gjXj+oS7ZNE59HAv1b1sT+JxaMPfUfbHBLxnSjoQKAtP6UAdS5R1oQTP8Rm4bpXEpXw+yqT7n/CEw+GHf/hy/aK0G4EHtMD6+a4T63aKINtrIa16V142QjU7Ku+MgWA4NqcTqc0EMM9TmWV7pyUbRtCqWjK9Bm21ZolO0zZ1KkpixDaEDSI+B1NPZDuIKdfmpqR5XhoPAsqF57rG5q6ug6q5p/R2TbFNHZTETxi3cXlWUsr9P1Vdcsoi1SqKS4M8283oeamr4z2ET6q8JubJ/6wSvXYMGIlTXSwXqQ5b0+YpfZQj11qH+Lsx0gRf155NtRhaUrGtSzo+UzDg0WcpJOjkaI3WZgkQmNwx3m1c5mxuGf+a1gdL0vXN0zzQRFU6xqyWPmtjTVLpFh6qAOwVVVwieBPn5Jn2M1pIcKXv4BoOKJ7o2SEBfsL4Kebwhv7Mp3VbtYKidnXOeM2JC7ONQfBM7+6l/GcvoVs04x2EH6vUeQZcLtmXrJRwmn5ksTS1PMLnsa5kzQjuz/3cIc6qtZjYn1Kz8ZakIfcEd0/AWw+WnRqMnX08Nx97WM+vYxQG7yI7Hve4ATesidXNGDeazMCf/dm+gnSqINtTB3afB+zKy5GAcTKBpqtsar5QWPw9thlO5atErAiV+bWNzjemWrOEAEXMUVJsEBseQ7bpCaJkOgDPwlzMA9OLL9YmSrM+DEbwrXFQoNIc0jvCkNvKoxEIESJpTObHycv9Nofx2UBT1SWX+u9jKl+7GcM83YNp+d8YKQapnAp/5tBaISKgMVem3syxNhI0ZoyP8MWA0ZNnpHIwYZb+J6kGXNPaCS3jcYplfaCcEgBxF7jOGm9hYHH9cWkQLGjD+M3denpmr/XBEpP1XLq+H0oM3JrfmNWyZ96u9Abv6NSzgs8pxuvZgmMJhx/7sWFN7QFzSil6+5mbKBlrsb4ZA9x+wzcMCgKcGVu1CoCWTpYICrEe5tPGIHgmIU5xqSn/KX7mPeihWs18D2/nGXC5ZF+yMkzxiNQPwmPaNTNmadAOBHmHHy4p17NUurZH92MCHu5iPW0tG1PzsX9ldZ4qK8e1ark4GFPEkq6fuTuI/ElHQTplkO2ag+HVgl152Qg2gNRRpyHMleDBHDAMwiq1MfgwdUBes8g95g/azwiqbEKb1fcYbKLzaejMoalZkuC4WIkwh8RvGDdB4dlxAaWPkjnGgkJAYqjJEiC4MBuKy9OeNrwrpxiCI4XRaht470jaOEGe9OKU866n3Oc+d2C2U6eYJf77rZuxnoqjZKVvFIDHuBFad0QEtLElXTSMOWbxxP5QPgRIiw1ymvR893lmLHsYVixk3ucUFlO79YN7qaGJFTiWgXF3JWA55dz9Tv2ZFFqsrQ+Wmqzn0vUpDwQuJQANiulI7EN1A/TM21GCYs1CT55D0TIXiuIY43U9hc0crZe9lBYVcM4VYb0SfO55cO0d4qpYBO5//wHPBKt759qCLKFNOK6diVucsbgQvg960OB+hCMuYnMwtmTX2e8pxc9Ncp4Bl3P7slXaWh5BWcZD4GosDb+6W/Bj3zuE5bpeT6StAn7M8s3icgqFoeX75Ib1T+FRrmc8cUxBeou3mA6yRY/k0SteMeB67jB93oHdx8KuvGyAaLgIzeLH3eEnJ2qKBQVgbTxAj/nbwMAJOJ8lTbluGu+w+V3jncbiOoLAZ0nBtRk8Iym/gGCOJSTxGSw5GDXB6T3mmnoIybhwInRCxWji3sgpJjgJQ8ZUkhGSLrA+N/4E82GsxptAVUwAfudOMXPR81s3Y2Ww5obpJ4snWTeuIRxS7yVWiAhoQr8yZnOCxwTgeW8YM9eOzykm1TXgPYS5dU1sQ9x9qojGutfrVLwlS8BzUiPHdYSgv9OzKsLdO9P6YI3Jes6nDsepvooGWQbRTtugcspqAeKGaFsaLGnBYf/G7WK9zNtJ2JrCt7GnRhGFlqL3yEceDp/1WZdds0vjBeZo07vbzsQVKFPeSXGxXtx9rBC1iSaI1S+NHdHkeVZQndqXPaWt8ojEVpnTlLCMRdNeQgvt2CvdU9iOyaJp57OkXMOS/Tal9Nvj9iLFZUpBuutdb3wgqB3K7de4vb/8ywfevcStdZ6B3cfCrrxsgJyobaykygZqoBnGsSYeoMf8vUufFs918kRIGBiztVNhrUWRjCcQLdzmsqk9kw+VQIjwiQKRMuYEBkGJqT3lKZcrWSa9OX2J3O85NofNQKliebJRM/9sKp9hqgkYZKkh7JykBR/KMkjaeKpLYqzGBH+UIErDWE2ZtbB2M1YGS6D4n5vQ3Gsqo7H5GWu8V9NFk02SDsiUj5jRxxgh8K64+9KMLyeqMKC2U/HWLAH4SbC2d6VTb1LFgy9CXjzRWpP1mNKRUgOp4hs8oz3f9RpU9oLn0Q3G7aRdrVXG0+sPNTZ+a5FmgBQZY0CbqaxsfRNsDKfW7xu/cRh7rx1Dz0UZ5cG63vvew2fp2xTadM2aGARj8YyUZ0iGXSytgEJMyeHyuFoVVFulrfIIP2hribBcmh2Dt5xif6x979Tz5pR+/YyWKEj/6B/duNFneI3/8W+ATtF9TfOfc2stORheDdiVlw0QgsVI2yCzGmiGeS6NyB4LYMSUUqAomUwYsA1NUI0VT0q2k8h1SktKnXtWUqRBhGF8zOpGOGGbU8z3viMAMNicGgkt4xFLkHiSOoZsKqcFGTs2QOsGM1cbyHxcb4ysCxGOLE42W4JD4eUUtQXWbsYwWM0Pg59kThijOWqKyFrSa7wXgVPTRWMdw6CTZjyVput3mhKKZ6DQ9QTxqbIEEqzthOadxm4+CV41f+tiXj5fWxdiLGOP0kFxSWNO1ixzBWMNKlsBSFE2Nopxr2t47zQ8NX50GsUlva0IJAcA+ynWyPSUSlfvsXYM1UU5deKuNLrW7Zm4s3RwR6Nw6R2JX8O7uDwoplHwrkYF1dtOHCiWWoWW0j262rI/xsaxdr/1KmzPKf1tlfAxBenWtx5oR6ZSOmQnqzNlF9AnpdWhxL5yn9ia+9znpgrzRYddedkAFhhBxqxZYxmqyRsB0YaXRGT3mGdSNJ2eEF/NmkilWoy+ZlK0z8vpkBBKSq3rEScLik2RSrFJs6Wl+82qkhRRTJow8D5Mm0DLOJnXWwFRN5VNUdOoA5UZY6DGa3MZL4EAt6kYTCC41me9mjLnbdbEXNMawTuMJdWU4dEYBGa6bk7gwBvagE9ZF1PxGxhNKiGnbg2mk/VeEvwZ83FVmOZo0li8y3WpfBp3FzB+yoYaGFFM155Ap5QOyjGrQI0XmWpQWQUg146gWUpeOprPjWVs/FEek8ZvLNba58bm83QdjyXOHvaZjt5iDOy7WDzbxoVrMkPWuD2T9Ziu87UsQublf/s5ZQ/gd8xStnaPrb2+d6BYk/K7NDtGcLMqybkOzPUimxoH/Cy1iI1V2Eb36H1JlfCqIL3+hn1tL1pLeBZsLmbLuuKpiVED+Ch+r6q5QxdahXcHVHvGXhnD8UVszrgrLxugbhTEm1iGWEiYlBMdvjQiu8c8c8qNWyqZLyAdVVkq3FcBoSFGhIpInQLdl6JWro/52LMTgOqz9KlJSex8BowNg7ExCAaETssfm9/cKb8yYxYNQjpM3tyCi7iivNdY07LAc73DZj2l2bu3Uf0vjsmzjSUxLKnHAVJIrDLhNQKnZcCEWvCSYEvKW2JulgR/pjop3LnG/1x85jKFnzqWWjwvFhlzvvOdL1uNeorXEloYUzrCkKtLtqbO1gaVPQGIJmqLhLmxjI0/B4iUtc+BIen/aNN74h6mwPpc7BLF0zOBz2X9pZzAWjfbEtdSu37WzXPSMiIp9MmSy0GhTUtvLWX4iHgbQc/JfEG/3Bk9GjpFnZG1it1Sy5S1yXVcgQQ7ZXisme2ScSx5b02u6FXYjiV1rEq476sb8rduyFT0O21F1POhnJmDeZqbnzTujNsfb/c/PuKAii7iXh3LzrqIzRl35WUD1I2CeGi4hClmmtOXzz7mY9YXBUJsYSwx8cZSEsYDUl7bOzEYAj8bwkZJf4z4vOPv9/z07PFs7zVeDMz1qQXhWd6fgmd5r78x8pjy4zZxyttSCyDCSwR8gomNLRkeAWOkFLL46KiaKr8YjyJVa83eYyeJsY3q+VW5jNsoCoy1SbbU2BznTi6VrhJjYZ4JkI4grFVjp4I/ubl0Gg8tYFDei2baQmlzNM5yhsnlpOc5MtnGXGRraKGndKRWS6W9pM6mku2aWi1t7yPupDT0m7oncSLendgrGV4pdIgu07wTHTpJ9wLY4ZECkPikNW62WlUXDZo/ASUGTkzI2PqhEfE3yRKzp3wet19cxZ7X1sKJUky4qh7N9RyrDWAt8JmU3kpDPWFvXwtm5hJEM3OtJLam/C49KPgtE1NcH0U0RTfbZraeX6tLWwdjs+/rOGQFTb3Xc8YayBoLfp1g9JYWYm1l4UQ/3un/n/mZgZe43n0y6KyVJAs0e4c7DHjPAchz0H+yOqtcAehM8oX1Nyb3POc5A564laaynK6WArMrLxuhFRCYAO2XME+PoV5H5zFwj3uZ9BAUBo740rY9LqqcuhEiIkRcCYjNpiGkECaNvFasdH9SpD0vqc8REFFuEqQZn65nxS2U/iGJt5Ci6RnH1ALwPcGBIXs2M2Z1UxhvBDkrBAFk/jYUZm5TCQCeMntXGFNQ1KsZq9dgTKlNkxTn2p067rm20vDaOBu0osKxNNcocdbG89GI//Nu6z6mFMTNBS+94oVj+GmVOmNpLVtcoa3V6BR1IaoC4dnVJdujvSllaKz3kc8itFXmdcqmmI6NP1l4mHpqtZi/GCdrn8B1tEuRgCdKNrz2AtiD86Vutraqrj1gHlwfhAqhRUnpnYIpUg4V9gleYI7GY139JFPKZ4mHCCQ+ztpzfaWoYyyAaNPn0vodJsasSdVCAC8sxbpyj7XSOLavzpKDgnES8p7PUhEFrm1me697La8uPfXeqUB8dJUK2/gaBbin9FP4KFDWXDbob93Q5ww91l5M1gQPCs2ak1jCZAYmntBv+ynxeywwKbIKrC+eF3m0tkfYlYBdeTkCtgqIHtC+MZNoxZiUzQJS6t3/TuSEYwrQOcm0mwZhfvZnX84QyskVQ7QBvCdWlaTcxtJBOGDCCBcB2xg2Xy3ylU1LeFKebBQnK8/dWgtgzk1BqUqBtJRM9z1mYmxcDmIMembvtqhfz3zLJG6tMA8MrT3tJd3c85x0WD9S68MYMAzrg7HU7J8tYEwEj/El1gUtpZqxNTZnAsxYBAq30Lq54h+Pm8sYW/yMKXX86Mq5z/m7l558x6xeVYEwFmNEwz3am1OG6ljS+yhWLAJYHAGmDoePf/xlK2k7friCA+CdAM7RGguM+jrW6cM+7HLBytQlqhafBO8TLvbTEjdbW1U3p2jzsE9jBRurmOsdsf7YP7W4IoBbAiyHo3weoYkuWERTmDJWAOPG96yLukTmQ0lqlQ4CtnZxT1dymZJjrTROkcEzd1DIONFdD/ehsVe9aqAbe7B2+h6rLj323qn5wFNbYXtM6YcrgbV43fu+7+WEhpptyiVsfPZ+yi0k3iY1uszZ81N00tzSRsF9wYH5GncqqK/pEXYlYFdejoBWQBxTU4NGjZgEi1FQ0tsGs/E7pegxBKeG9vRSn+95BAZrTKwmIXDPC6PCiEK8fhB+2qw7JWGQ3osBInqQ3iEYI4b41V89EHeCgmPKXhvQNeWmgD/MyhgzlwShsRL5GwOtJ5ceg5syR6eAUwJS27XEkAk7eCPw4h9PR1/PSGHCY08jieuwpnAL4DuWHvP1TmsC773S/5VhtlYi658GeMHPEt++NTk2c0ThO9kT6fqMBqv/vFUgWtrz91LFuPY+oqzEkmk/xQWLbrgPmMwJjt743fOVX3lji4x7KQX2EzpIg0F0cPvbX2byFfdO+AS5YmoOHWuq6oI0h6z72RjxjF5rihwIKDf2clwkOVyhdXMxNkHS/q4NCe0tylj6frkvMXLmjoY8E5/BNyrNeX47XusPl8blnWP7ZEyxawNUx5SbOcg4rV1aPNR2IZ4LBwpveo/xR7Gr1bF71aV7MKeo2gO1wvbUAdAz7N93eZfLvKGCNXPw8Tv0ai3xRbgzP/dTXJOoEaXMWvku7lXjcj36Xtsj7ErArrwcAafqvJnGfZif5yEcmwTjzsZAWDR9RIaAaeBRNCrkVEvQOPWwGGBs6aZscyBcRGtzpIIjZSVp0cbh2VwG5uD7zNGGZUp2IkxApM2CIREE6TOyRXBHcLXBgZg4s61NlI1jk0Ugpm6NDRblpRcTMWWOjumYAtnL3orAcsLGFNJVum3w6Jo1p5GeFaLH7JJmzbxLuTUeQpLS1fNB5xmebV1z+g2jsu5wgB4I7VOVFZ/KHImLNb2srJX5tpaDVoEI7W1J5TTPatV0fwLSY330brEh4hJiAWrH37MocZkKws0eQTfmiB5Ba3mIAETLlCFWy9ZNhY4pA4QPi671jlKRzL+sTwKYe60p2gNBegexFBl7Um/tKQIabRlXLXn/pCcNOI81E8CbeaEh+75CpVt03Y437mrXGK9515YiWdde/FGUwDZAdSxoeAq8i8KicjV8t+4geE1MiHF6X5Q+ENcbfofXTTV/XBoPxtqrwnb48RidL7HY/dW/OlR55vKBM3NxLWVNMLx3suB5jj3gB82GB/o+MVsJ9F7bI+xKwK68HAFbMywqpHFfTYdFTAnYpWFTPBB0TnuYm+t6z6pmf4SPOdl8mDeiDNORKYI4bc4wBYyNcnKPe/R96J7/8pdfrgJLqUDYBKDxncryUM3t9XeF1KuJObzCWIDoWB0VGzLBb8H92Fp6HqESpt82eFxzGply04wxO/MiDH0fJaynZBin34ItMS9rnRM3RuszeBNcmpTaY6uELmmlkeBmOEdvrFnw2fbaOVVhLO9JAHuC0wFhFVcIpg13U/NbUovEb3RB4bAe1fIA7A371yEETuy1GlNUe1rZo2JdPAudJ9up0noNYB6ju9Z9lqq6ORQQmlUBMy/rrVKwMfX2H9pxrd9ohgBvhTTXWR1vBCJF337Bj/AReKOo4XHGGr5T449qgCogPL2X4mM/fOZnrlNgUhkZvswV3407yMExSgyeTIkj6GsMXvpuoSm8eKz545ZMqDmaX6IIvfM7D1bkZMWh8zTnDZ9xn8ODMQH0ZT7GkD1qX6bGUfphhTcuTcg4T9iVlyPg2M6bcWMgspTDx4hyIsScUiMipdL93zOZjpn9MQ8bwinRswkI/ycwqxZOY7lwj1TM1qrTtkRwz6laIvTm4N7MwedwAU8pyFcDkDFFDC7ZP2MBoq2yWU36mBdLit82aT1V1rXEqL0LXm3oFpaeRubcNL1TOSbSi6kYUzKSbVLT7gnH+LoJDO5Kcz1l1dExukmafrpyx2JGQBP6a3vtLKk9kUqzPm9rvsSEbkyumZvfklok6WFFMYvAC40SEClfn7ViQX3MYwbra+1pFXco4UjpY2Wr2VetMtC2phhTvBxkuLjiDqC8tHih4LCQRYilqF09VESIOcmDlC6geKNbfMr6pCN0nT8Lj/nGamSulAY4qxY4v2uAKjpMYDHcAXMxbyXvlxyW0myUApRsxSglxo/O4cP++6ZvGsZcK2P7SbVa+wZPWFKtdk3JhCmYU4Te+IZO4v7GV9Brst18x1KLDijV7ktdKnwzWWj2pXmjrdq2JQr/FI+9krArL0fAsRkWiMgmTECcjWUTJcMmJn6MgKChTedkUE2mU7EcXALGRjHR+0I2DWZRwfU2s8/HrDpxuRCcp2yJEJiag8AzTMoc4DWF3jDRpKvCSZoTjjGEqmxSuNqg2wgwAYUxX7driRkc2yp+SSpoeyrH7BJToSWEOVNEWl99VTKSbYLZOykSADEhs8Qk2Nk7Mv+1FsQ5BaLSjROs67N+AJOMvz1WgSm85V3mTtlJ24mx2hPG5H2ElLlXQW294jJIt3WugKWFuMYUUOOB18QYWKu2s3S7Vr2eVtZVlgcLTCpRU7DTiiDKAJijO/NAPyyn1dInfqvFme9jAUJfeFF6oFUrjO/gj+WhrgHBny7X8OCEn/m7X5Av3NhTeKZnUAhSr6pWTk6AKvxSctGKeaBpYzI2SoZD04MfPL5WLT3ii7WEftxqLN3GxepQ97lYPrwXzQE82pzwZeu1xL26tGTCHIwpQu///vNNWe17fAXdUiLFhMEhXHAvxxVOyQlfYVFHJ+E5Uzz2SsKuvBwJx2jUiTVJcSuElxoXaZqYYnKYHaKqOf3ehRBtfOZ/37Uw1vtirZA6j5YIPUXOuCghVSjb3DafDWTzeHcUvlT+ffjDh002xRCibHpXL+iWYHHCpTj4wSR7G/XYlOClqaA5lYfZwa1y7sY25quvuE+2CQabeiXmGLOx9SRY0FcyqTD1MYXMuKpwX1IgMHQDj96fdwesYTpzT1k/qoUj7ibzFayafl+90296ucAdBabGvHhvLDLw5EAwpwwtUUBZMygjrmGZsD5tZ+m616bowbgITmuOztC/U7C1ILDhAB3O0d2aom9Rcq2NH+PMnkMPcT2m8zs8tc/Dl+CBNSlxcK6hiFEC4Mnea623rq0uvGTY4XF4oz0ZC7XP/Y2HPvOZw/vQ7xRU17ExxLKQoF2fE+re2+7zWGqS9Vitn0vdq6dyh962owgtacqKzqzbXe4yuBHtX/NOXSV48BzKi/nCg3VBg5JE0gB2r7B7nQAi4mrB+ICNmv4tU4AIXGMz2cgYUZrfZWMGbCpEWXP6nQL4pt3npEMQES71hNfrfZE6GqksmRovU6e382iJUIEyZg6g1rXJyTJmz9SqCGAixu5UT/FY0lnaJrRxx4JubVBMwUmu1zr+WBPwmkDvyuycjJKNMOar5x4M7mNp4nYgFMyrpsPG7YB+nWp9N6aQEZQUJ8wZztCo355JmR4ThqGbVHPO6TpCq9YZ8Yye0lsFr7F5fgSo90UJ9b621xMcEGro0hzhIeZv803V4lQ7XVqIa04B9X5KBhy1pd9bC13t+t7LfnENoY0efZeyBJ7d0l3PEgbWBGRTmNCWMSVuJa6quIz8hifxO9kb9XmaySreRghmn1AK0K95Bs8VZ+5HkznQAXNAI+abPmzur0qD57inBl2PQes6botNJrbQdfZd3efGju5Tu6ttiXKls29u0ShCS5uypnYQa7u9EDmQgp8sOIA82+LaulKwW16OhGNKJ0e4SH22MUN0iYHBWDFZnztRKKKWTZtAKpsOcw4h5rOak59Tnk1qXN7HlxxTMPAeQmjs9DbWEgEDwNwIhwS0rvWDwuGLXjRsHu6FlLLOXMwrZl24IGxyMnRPeh8tDRQmcKeCbr0/ptMtAZxbqinXMfQsYK2vPhYu7yNo4pMWfJkx+G29KYURdJ6D1moMgvl6/z3vOQjSViGjuHA3snaknHoCFuHdmsFFFV72RFKx4ZuinaKIqbeStPQEXwsgTSxWnXcVvInNMleC3njM3Vqi9/b0GxxQQtCOe+Pf92zXGLtx9aqfptKooNB6IJlTQOE0QdKsooS1MZp7a6Fbkv2SlHJj4JZJ93W4TdzKGC9ikVwTkA0flFo4M5YEBoNYgv3P6tXSe/u8uk+4f2RZWcs2yB6YJzr1zLjwrFW62qP7mvWTvQPXnrck1q7XgqPGvdl7vo/rPOM3FnV9lCWIa6mFY7NvquK5JcPu1itrB6GbXm8n83cYv+iNGnfl5QhY23+jBQShwicXBobGvE8YJBgujRMxCkQtUyFWFxvOZvNO2jJGSdjbVNmQ2WBtc7CU4U/gpI3fMrW5Oiw2vr/TqNFY73SnoX7F1JzHuqqasxON5/k89RQIyQTkgrg9AsZNIBF8SwOFPb8Nuk1tg/Q4yelrDidbTMBtNeXEYlgvjBJ+WwtY9dVbZ24JuEIDnoGBUwBaYWotrIlTb4JXW0tTFCH1eZQhb9eHxYXikk61MdkTKtYe/YYGE/D34hcPgt+7zCfWhVhcIij8DYcJHm0tdq2Fw5zRc0q1+yzuVXgh5IxjrAFkLEdw5nM0K2aiFewJ5kZ/xo7WWRlyKJkTFMZtnK5LkKy1EYNknvUkuyT7JRa1noIC/1PVoSmv9hCh24PWYmA9FOFTssD6pip3gna905jS2HDueXWfOBAkZqm6MEH2nvWTJScBwBwTD2MMcGMccfvZw+gw6cpLgq7bFhyem8aaSQOmZIV/G0doh8vSWrbWl2Ozb1q3aM06g48lB+LbLGjKin+gtUrvtSwE/BiD30vqOl1N2JWXjbC1sVp7SkdMKnw++cmDmS5m0XrCoLRg1lFIUvMhJ+mcsPK5exO573055dkgn/zJw9hidncthikIdKpwVM9dkuJHNgKhN9ezZK6rqr8x2ZqWSLgFZ2lbUCGxNrluicl27enr1PBfbqg7EvcgHFpr9OGEJ2iztV7lpI8OKCIgjD+dY5mDX/rSId6ggnXhKhOI3KsEXZluq5AZTxTepP2mT0paSOTdFAE4lNaaQoNoObVO4naItSvz9j/ls2exa4vtpWZN5h9LUgqt2QNxaSyxlEWpqtfX+ixxLaC9eiiZ6iZsfbUPMLbU/ol1y2/WsQihpdkv7vHcLdWhUzLeOrWWrZ7FwBo89KEDjcYVYV/AK8WUWxzdrml8mc/xpVjtUtyRcDZ3ioQ5WB8KV+2NhOZ9nwzKKOxx+bnPgWDuwNFrweE53o/PWlPPDP+2PrEWGY+O7FK08SyfcVXDyTHZN/UQnPYf2WPWEl9cciC+RZNAYlztAROOks3ZgyWur4vSYXpXXhZCu2D+39pYrXUtKU2OWdlMrsUYEFoERQINUyQowZeI24aLIEpKHwbhxGYT2Fw2FOCDJmQ83ybx2/OdqjEijG/OerHVXTJmpUpXVZYAp++aluj5EZLGk+JYNVA4Ju00LlzCvHqnrwSTel8YfD19LaGJJXiI0mu8H/mRl9sgEODW3PeJQ6kQaxSXX4IXo+hax2R4Pfe5Q1O2GrhoTKoxY7CJG0kwXpgu4dibSw0qD52lQGAsRt5vbdCctXMtJgkw+lQ+tlbuSyxXqkdbX8pVD8+xcGC+nh1LZFyeGY//406aWveWrlsLSlsZNsHEfpJpx+Wqam8vcBsOKS6xJFJK0gsIwL94EHM1nqXZL2jj275tW3VoGSWUUJYwSmqatYYe0aBrarE149N00Vy5e1IwkvVJOjSFa03Gnf1vP6XEgfWMVQXtxKKTmju1lw66+6iPGuYPl3CduJdkXVFAjKW+c00LjtZ17B3GgfYrzo1JTJ/54FsOBGJEtsaF1EOwez0PjeMBAG7gk9LUxnP1IAfMWhDSmK2vuXoefm/uDgxrXV8XqcP0rrwsgN6CYQCUhDlTbNtYbcy1hFhtkDYOw31OgUn1jNDyDN/XiPfUbEHsxkZZobwAihFBEmGfE2rM7qnD4VS1pd7FVitVzLJOeDaU8Yu9cW3STOEqLh6niAQKJ+gUzsyHD34p82pPX9bFOHunrzXNHec2cXWDmH/cNnFVAUK4VSDNyzy5CIyzBi7GigY/hEEvcHEqyJiFZCxrCBPzHJ/HvFwLBGL8KSlu3BFEocMUEYwVDY1hpMZJwSEM0WpbrbXOO0HHqfWRUgJpUhrXJ0FD6HjWUqtZa4WrlWyBdwKC3z6BZ8ouJZulqeLUfFlyXCOmKtWe4wI1T88moOxFe3Np9ouT/pbq0MaPjimQ3oWukqlESFJM3J9MlbZVAyVtrA/V0ow7tPoN3zAIe8+k1Nnz7k+bD7RLYWFxMfe2l4419T3coZ3MGY4pLtxxa6we8BR6aRUXYD5wDm/otX4XPkv4W1NWKmu5xfpQ+YH1a6sSpwSF75ZkMwH8KuneraWVvEr8kb/rmOdcX8eGSZwaduVlBsYWDMFVDbZtfAfaxmpTrqVeHEY2ik2cugmEDqbfNourgNBtpmwoDcac9qK4+CwMy98ph20jgnRGPpV5cCozI11VbRr4ZFKOJSK1byglcCNbpC1xbuxwBk9rTbZLTl9rmjsu2cRtoGdq7ATMGw5aBdK8pCu+8IWXu03n+vj+E+uxpuNu+vaMzYWCV4PK46pKgcAEF8Z6kZgT1gD3wKfPE2CdVEw0bv2Sjj520ouVDP1H4anWluDQ/rN+sXZMWeBauk5hNYpHXDeemcrR9qs5wnFq5kgBTgPO4BSNP/3pw5q07pl0iU/tk6c9bXDVCqZts18qPSQeCYwFCCfVta0OTXFhWaRU+l5cTE7yxms9CH7jsLd7NDx2UFmacYcWKS7iZ9LAFO04JCXt2bzDi6Z66eBJySzzvXvQA166xuphTOKc/KYYor8ER9ckB+NJmYoK4fXhT1Hwt0DlB+F7NZg5bnHzThf5WDdvPcKTrS3FC10mqDmBz67FTwWRU8hds6TUw9owiSsBu/IyAVML5sQYDZYgiQ+0Cl2MqQ2OGnMt9YKtAgiKZYbp8H73uywQ2mZxPQI0Do3wMIrUa0j8AWaSUvF5n89YerxvrobHUljSVRUOFK/ynrgckmFg82lZQBiOlTjfYrLtNUCssLa545JNjOFsrbUjoJZwhgfjSNZOfP9J/50KXKzCyHUsNFNzofQ+4AGXg8oJ7ATsJoi6QixnKWdvLauVxvvRWJiyZ3JzTVnMEnTMOkfwexa6SFxIFPhkUfUCf+csZgR7PoePuKDQgP1VU3Od9o2hrjOcmo/nZZ/lEGNN0ZHxJX6Ggmevo3s4ojBMuWAI+zG6wQ9S6iCuI/fiTZ6bAxXl37utT7ol28+h/bWCaM6FHCWfxSW1dBLbksQEh4dkK4YnTfXS8YwnPOGyS3NMgI9BxuT9eBzrCzquWZqxGppf4myC81qVO8oXRQgut1gdKj9IrZVYFFPrK/sangULU5zf5E3GeTK8JObR/NrsNfKIEsmiBO9LSj0srU11JTtM78rLBNQFA20NBhrsD//wEOXvs1boYrRgSXBUG2wVv25KPTsRPPCBN45lWHLy4etOYaJU7a1VMhMw6f3ux8SZbBGiuZ7CPDgnsH2HqSaAmGDwNyGRExrB9ehHD7hILyLPtQ5bLUJbFIljN/GUkjpntvUZpZkwQGvW1E+EJMa6JnBxyVzQIh//J3zCEE+DHuHEO52A03WcckkJVeiNq5TAwXStZegncTNo0N++W2oxI2gS2+PdiftCC3E9wIVxY9zcEi2NjlnM7OEoZlxBaM08U74+pdRrbRx02a4znMM9PoEH5BSfQPD0LktPMZC97lQ9dRCZquwM7B/rgQ/AJeEEB8lI9BxjErMD8CdC2/+1FchaQTRmmWljOYwnqc7wkgaElJdY44Cxwt9UL50lNbR6UMeEj3p3XPLWzm8WPusbfltje+LCTxwJ2rCWlEJ0tcVtUvmBe62JwxlImjpcoEdxTfDD3XOrW43zZLjOM9ybSu1R0MzduKX/L1UCT9WE+JSwKy8TUDM8MORYVqLF8ilGGUifFt9hjKkNECYI5up5VFNsLAwJlPN8GynBdEtPPmpU5JRp06VibQIuQU6xnuXHaZvwcUKLOfcY8+CcwE4xJB2bnQT8j5GYEyuDjcr8jXmdUqvfokgcu4l7SurSCr1JrSdsKcaErXWrvXPWBC5mLmPF0dA9Zs7Fgf48E42hcff0TPZaVpg/2sMoPTexLnH3pIcW3H/u584zfDT4dV93ueZPgjs9wxitlX2IKXuW97fCZMxiZj5Rggh/e9dcU8Ke8KAUpqZQrY3Tuvfgx/soT3DpPvhMSjj+4H0saNU1CR+sWw4NUweRKbqhgKj/Yr/APUFnDISwvesd5mnc+IB5JebFmHpFLY8RRFUxjnUMvowlSowxwVVS1+1D9AhPcZehH2P1/xbX8NiY4D8u+VhSYhVi4f6kT7rsNotL0XfJ2EIL8GQt5+LjpqDyAzwfLpIWnlIA6Ij1HO4E+ccl+VYdngw8J6EAfntGlEbPkp2mjssaJfAYi/F5wa68TICFwAAEicWPXbXY9JtRatmCtswf4dDKRX1b2CX1PFIlE0E6AdhofmM4PS177OSTU6aTVarQpmZKiDr1JIwJM2RJSm2LZAJUc2OvCNgSmBPYGGqaA3q2uVKegmtMjvuNAnlK5WWLItHbxDXeaUmNmC0VemvnYgGCsgngJPEOWwIXp4qjOZlh2OjF3wKZzdkYUtAO425Pa8ZOiEr9j8KC3owtMV3W1lwFg86VcwcOARQ296OT1JsBqRvjO+4iCpX1aIXJmJUpRe/MMW4KdG58Pocf+8DzE8xda+PUda70BFKyIO4911LQaxuPKArmVVtB9E7CS+hG/zLBwPYwPFjLtNRIgLdxGVOye9rYkjWCaCwurir5nmvOGXMOUMaQrtnol6KSvkW1ZYTvuTgoeNYhTSDXWlx7B4/amNZ4jI9rvh4Q4VzcDqFvTJ7R9qo6xm1SkwdiRQQJdE9WVa24PmYpAyxueLn9W8tOWGt04DmUtjW4O8ZifF6wKy8TgChsJtowoZDFtvkRPCJJgFmYaYWcIgLJPkBkNq2YmFbQpO6DzSuQbktgVD1lcjOk0605YGqYWVxInmEDMpe7LxlJNjglrJobEbw5bDmVTTFedU1kyGBKSVHNvKMMEtSUSPVkThkQtlaROFWNmGo1q26wpEvXOfbiNNCGezCnrYGLY8XR0EgahlIuEvNRG3366a2FsTj9w5MxojVzTJxKrA9LFRfAEmcP2mfwk0aLnutZxmAulI0E9LbCZMxilm7bnkNJiZvCXoHPKIyEQcr8TzHrtiAenOIhxm6/t/3HqqKwJItvztrqt/0CD8ZtnCksmD2VuBj/p+5HYkvWCKKpjLuq5MOpwNgE5AbnibVLI8u4syOko5RbB3P9wi8c8OjzXrzHXILBmPUgQdJ+c7O0wdaef//7Xz7gpIN9m510jLUq7ldKETqMMpmYP2uCL7Oq4JdvXjKI2nfDZzqTt6n3ya4ay+4bg2MsxucFu/IyARalFvapGizmieBtenUdbLh6ckU8hLHFlOJXs2g800bt1fM4RWBUfYb3p4AdQouv33goL4jYaZBQMQ+/3WMzmKO5xNzomeIaeqeyJZlJY4wXeLegUDhptXrXcpFggK94Rb/f0DGwpnbNqWrE5FkEm5ipscDosTgN78do+a23NEubKo6GuaV5Xk3FX0KDoT1jcT/hk3YSOVEnDmMrGEP6XKWJqc+mhMmY4EqQJAFf44c8C/4j2H3e1sYZY9atYiqg02Gl7YWz9cQ6p+RECfZMlp/Uxkll2lhdzKGWYDDnpYJoLuMuWWqsFRTPxAGm1YH3RYnx23oaYxQXvCmWV+uIL+FBvuc6MadqiQZVkUpMlgy9WAiPsR7Y1/ZbUtpP7TZBp3BkvrUHGYCXlCvAA+O+fPuRhqyh82pVqlmw1mPLOI/t6XZq2JWXCUiTLinHiKZqsIQsYpaGjOhlvdSy3k6ciMTnTnF+ltTzaE+IbQr2Eg2/fUbr28UobEDv9R3XEitSAnRT8CwBcxQH12MeTsFt48U1NU/GGC8moyBWinlVJTFBxRjhl3/5wJROXRhpTe0agsm7FfFKldA1NWLWCAAKxlQ2kNiGRz3qco2RpUrMVHE0NEzARJlpYYoGexaOmv5rHdHQmhOq4FgHCPsJjttCecmkqyUGWmGS+i9cj2gmJ+0IpaSW1lO3dbTP/aDFLY3qjFGp/W//9vUn1q2lCqqQjvsrdXGCOzj1HXzEBQ4PS+a2JONO3JwUdDQpQwZNJQMrNVrSuy1KTarppiktOjJfzwCUGXGA3Jy6deddsm9Ssdc+8jfrn8MQniJ2D/+zZ7daD87bbdKrpxSIguc3PGVf/o+RhqztOPO8uFN7HeKX0tYxPd1ODbvyMgE5rdFyCdeqRCCgV7/6MtNsy3ojDJvTxgmhL6nn0ZpbI1Ri1WEKRKxTmnPvlFm18ARM6lmD6J71rIFpuMd1Nn/8ozYTpYVJ08kA8dYNfqrCRU5HKU6X3kvma/z+9+7UwPGbInM1CiNhpM9//vB+GzhC3oamqM7ViFkjALjSMKgpKxzrDxcMZXlNSvtUcTQ/CfisdUOWnDKXBPbZP961NHYB/lgHCUSWhMSe2WuYNzpxTWXSbT8vuKZ8yNSiqKdQW+JwPNNzEohZA2EpkT5byqx7yjzlyyFgaWrqMZVMq3XQ3jUHeMPH7O1kSvZKMKxRfOesw/e5zxAbpZ4IvKa6Mh5mPmnrkeJ/LeA/oWtjS8o9Vyf3CYUETgVXmwd3iv2Edll2fJaig5VfWE972Bzicp1T2s7bbRLlqNZTAin+mErjIB3ab920j8i758aZDvFby2CsLVJ6TSsvX/VVX3V4+tOffkZ0v3kmpG53ePazn33m/z1zAHfg+WdU9UlCvQvc8ozb/QksX2Fote2qfBCmYW7Si1PmvfoWMYxekNWUAMg7BSgmMDHMGlGzkqTC7tJx1xoVnoXgbXRjx4jMhfDFZGLSxxQynwgIhK830lwGx5r4nAB8OQ2ar98JRCXgMGDMD6NzUowSY2xXsjCSvkECUdM0zfvNOZkK1jKugTkL2VIBkM7HPSCI1Ovwm+K3RnFslYz2hIaea92QgO88O5ZESnoVeHMnVDQBBBxTjJYwztprx3xjnYtrwbzRQM+tYx6Uc/j2maBeLhzXeB68ObVi6ATHmDm8WkFSl6lHc3NuPkGnc26+UxwIqok/SgC8RWmzrtYCvSazZimsybhzKBFsi3dxIcYtGQuLNUvPK38nPTifxZ2V+kUpSOnZ1grtsMhZR1BbO4T2vAs/S3HBVOpObBlLbu03dSq3yRrLWTIJ23pKac8Bp/5PFtFrX3vj9hF1LeaqaY818Lwah8ELrby88IUvPDzykY88POc5zznTlD/o8MxnPvPMjPoRZ0zlv5yd5pvotRvgrc64qe8Db9Ry9ysEU9o2BoN4EINp1DLvcR/pGuy0hxCXlmF2XXqH2GDujeJCgGN8hEztj3LMKSGMyDh87qRvLq7PSc1GcYrha9bk79SFi+rJmFnYb/e6h7Jmoxlr+jjBLdzAP8vDsYWRljAZFpenPGVggMbmnqTqJuW8Zm3M+cCXCIAw154VAw1R7DB7dJDvxxTHXjfvNXVDQj8pNW8NBFC2yscU7fkf/fksNLaUcY712kF7CVxGJ/UUbe5nrOdSlWvjQktoJqXoCXTXcLv5XKZOjw6WWkHaMvieAa9+o13PEbP1RV803oPplAeCauKn9MODfUMYHhOr0Cq+LVTaT/o4Wk1sBsDT/F2VmFRiRtuh2Sgx6eUWq00ODegTJP6wLa+fYoj4Jysbwa3EhfXDM7yTAr4kRq3F6RS/6NGMQxnleUx5rU16k0mYwn3mjbad+VMe4ZalfUR7SOqNMx3iL1KV3AutvHz5WZDCJ58d12NNocR87xkFfcPZLn+M3MAOUFbeEee8ADCmxWrGZTOkymisGhG+CCSpdxguMy1Gu8TMiCBNH9Ha3Kkfk/Q8xLukgeLUKSF1aHweS04Ce2t6XVLrbDTN/ep4T1G4qJ4yZXOYI0YHR4RNUlLNM++xaQm/FGPKSXwLLBFMmCgzM4aCASQ4F0NICmPqgaQM95wPfIkAwKzQAUsBJTaxAp5PgYgi0PrIlzYGZTFcUjck9INheq/3Wasx5aNHe8YNvKfX+XgJ42x77Vh/yith3DtFi0dTpqAt1mUd01lXPIbnJZuw3U9LrSBjZfAdbNLTyDp4H9p50IOuTCXTzMmP7LBTxCqsif8YsygASgseEwtLAq9jhUk9n9ThsR9yOIhS4mCVgo1ouy2vn5Yoro9VJsHkWwX3nNukRzPwrrHkC14w0Bq66CnAmvRyP8etlSw9fJEVqyoub31DvZmxQ1I7zhQsvUhVci+s8vKnZ5Tzn86OSY/FcW6AW5xh9E53utPhxzRXGIE/OFuxdzmzh/3FGUf6u2e74IvOjirvM5JX+b/PVtJP4Pet5olhSoutKbNOhLTgnBIIWJYZJzHTxdAQbe/EU0/GmD0iVTYd8bb1Y2zQXtn6ljGNnRIYtZSFT5dewpHC5TRaA3uhcqqY2BIBPBXf0Dtleo6TCSEpbdu4I1gruJbimDTjLVaVpYIpAsVcapopRmptUgDQ95iptZnzgS8VAMge4+fH93w/cJQTGfrqGSaXNgZtFZT2RB5rRLJmUgxuTvloac/9XEXVnL+FcYYhu5aikHnVU/SZV/qSok0AeLeTdkrg2zvWiquUlRENcSt96qf2zf5LrCCuc3Jvy+AbC9y63mfojyJjTcYqsp5nJdOe0N0SFLw2/qNnUfBDkbEG3o2u0lbFGtUq4CzAsWjnmlRoJtDh1ntZt+A/gcG1IjLwbmsR6895CO4ezTjUpGFneg1RpscsjvClMGTdd2jGHsVfEv/4djco5DVYdwouYpXcC6u8/PbZCv752S5+hyap3P8/byU68F5nHI9V5v3OjoW/d7ZyX/qlX3q4/e1vf7b4P3tGYDfU1C7w1Kc+9fCkJz3pXMY/t/HblFk/mBaCSiVQxCA7hxWB71dqa1vZsD39I84Ub1rStnzMesD91AYaUlxage09P/IjQ7qywGQnYxvd3DCGsZocx8Y3jJ0y0z+G8ItJNHEZ9fnZiGOumSmrimcvNc97P8aTU2BOjokNwpwwFULR2ognmjLHR2CkZgrGVhWLGljHzM3SkWDS1HyAH/7uNGjs0ciSxqC17cJY3RD0ShBTvn23VPmoe8Z7jP0UjHNKqaAcUfb8pPBY2ikQfNYq2VQ5paOxqkxkfdCNODB4nmqfwPLTlsEnaD3HnPO/v73XddyPvZP+GpfMsXBMUPDa+I/WopBAWfSLzzis5GCAbtF3ar+w8qX2icNWPVQ97nED/qyfMXCvWA+4Q0upiAz3lB082CFwKf2tVe5anmb8NQ7HOIzP50vcu3iAYHUWRPQcJfiP/mg4cJqLa5YWpbxStHWzzDb6kA/5kEs/AYrLbc92wteeScCnCDpogFVHTE21vPyNNCM6Z6iVEQmvnA4svo1m4yS9GIEhWjDX1dcmcw+FIsWLAr1MirF+LTaFUwdmEJ+r61qmj6m4D8N3UrQxMFmWn60R+EviG5IOOSbQCG049J62UiQGRvAYe48ZzVlVlLFfakLt9azJPZ5rfASiSsthprECtMyuFRhoBkPm/so6WVuuj6RJUyZBjamCW3QSJahnuYGbuWwl41nSduHYU9spGeeY0otGlNhPWillBS58nhL0ce+mtot9qpAjPMSKkoakFBG/rbn90tZpMWf7FF0Qitn3cB4B4xrrm/ID9iO6TixNe9K/UpVMTxUU3LNIG2PP0lotCtUa5xks06kBYx+klo9nGaPPrRM89g5Vta2Kg1dcvAlOdmjzjN5eGKO/Lcpdu0/aOJzaJXqJezeV2JPqT86k8vl7vdfwG67FI44dQC5yldwLq7y8/dlOfuMzCvyfSc+4Afy/NKblTc8k1N/5O3/nzFd85izugEwkP1cDMDqE5PWi3Q2RKS9xMClx7QSBaJiMz+KVLzG7FFKCGkRU4wAQOhdOCrepfptGXNUsC3onUO/1mWfbLMyKGCk3jDHWyr0BzMB7bKIHP3h5IbicwJjwpSOmnLfNMBffcK97TQs0uEtwW3ri1Do7fvc6CC8x96fT9hJhnJ451s+6+dwcY35OEC+m9qIXrS841yu53xPQNbbFulB4vGPMdC8F9pu/ebx30Rprx7HKxykZZ0+RqidcrCUm9wiMxG6hmfRIgkN0TxFmQWNBlc1EOYZ3eCIoIxS4VKsCY845LacMfo0bS+aeMVlz1oQU/RvD/VKXDNhSp+PUQcHVuoa+51Jwe9Y4li0uH0pf6u8A+KegJ/bF/hs7VM0FJ7MoO7zBOVqZo7+tyl27T6JIJw4nMTjVejvl3lVVPDwb3mq5jre+IeZlabmEi1gl98IqL292xjU+4CxS9dWvfvUZUoYdJ47F/w9jslgA3E4/fUbld+V8v0AQrdyJQfO6EAElw2ZIwG7S/9SESdElhJUqt04EiND1lTFiqjYcS4gNmRbo1SzbC8AKEyeYMWTE7T0xxRpTTnv1npjYbQyKwVq/b32Wd2M0tQFdvS6nDTAl0GxqipfnJcAyVTjNa6xR25Kgx3TXXSKMs+mtRQKECadkRWB4ghJf/vJtBed6JfeXWDqsqW7O3E490z1GL4i017uoV5lzCo5VPk7JOHuKVD3hpj5RFBTKG3yib5AaRu7lUkIvaXvBWhXlPlWwCVXvqtlkmbN9mO/jtqLkwJODTdqDUKgE+WePTyl7rUvGe/ARdIU+KB+JWVtqEajuCLiy/04ZuLlF2GcdrUsvWSDB8PigfI+5FPO54OS4zOfor1XuQl94IyULnYwpd+0+sUcTh2NOicFBp0vcu55HyYPDWstoa7mEi1Yl90K7jbh0HvjAB56Z/D7wUm0XqdJ/eIbdZB894AEPOBOy73QpdgU8+Syq64PPjtPvfhZl97/Ojovqw/zqmaR5yEMect5DXQx1o1IyMD4mTlq+WJVo1SFcYIPQvp0ywnBTVRJht83RACZgY7OEUETajdsTcJWJY9JOHzaGd6cMOB8/Ju35tTePDYBZCBAztiWEXHGRjCBKFYFqU1HmeuZ2m8Ymi0BzvTEnbTIN8j7lU4Z7stlSTE/2Vt1sbcDznFXFezAi764Mg1BipTAvwqYNs7Km1smaJZYBs3I9hcj69DpxLyk4VwXGUksHBbHHqIFsG8oWgeCZtQI0/LISLg32O4XysZVxLknzzgnXfkPzwWm1gMTllpJRKdzoezRr/8rAqnWRIlT9OASw/tlLmfMZa7uklNaxpFcYoByJ2bjjHS/jZomyF0vCD/zAUBbB9d4vGNn8CDFKbxVWcPTxH39TId+6P+wN86UU9JqL2huuWRq4OdWxOyniz3veTVPEW0E/lSywtA/WVIziUvqrh5/KH2uh0LESDe0+wbe5nK2fe61XrG9L3LvG533WPP2nAq9//XCgsqZorsd3ekrWWnffzVZ5udeZb+C3zjjIE57whEtF6t7/TCJ835l9LEG8rznDoAykwO+eHVekVrv2bc60Apabf3fm73jvqMBXGXpaOUYQ4kMwGADGZloxeyYWho+cwpPCS2mOWJujBWxep+fUJGihJ+DCxDFkxJggSQyJWdG1GAOm6DSIeG0C91N2jI0CNpYNMYWLbDxCkUKXQlKtUlZPneYl48X7uJ2MG07EELBm5P3tZmuzhtYGPMMroc/dFWGMYSdNG6NxDTN46u5QVmSxJHA2mT8qfIpPsnapJdLrxD1VcK51I6xNR63PTSaOHxYwOKWwGhMFMZU50UStzDkHpzi1rS0vPhZ30KZ5JwsFk7fXMHxz9XeKLbomrtzUyPBe1yQeJsGUwXcsKd5DEYRHrKvO2b09pS4xMEmpX6PsmYs9qqgaHgJn7mdFg/c07EN/NVBZzIfsqvANeBL0TYmDj9RIwWu4JCQTgCqgE5yOX4gZmYOepTO90KZSxHsK8dJkgSmYCrJdQn85FDroCdj2G+6Wdrlv90mN4bGX8Ub4b927vcOW9+LT6K6t0/pLvzTgzbjwCb97fCdFFdsmsJ4NtyxSx1Tcva4DdrmIxtxEP4TzF3jGM55x6eeiQm+j1tNZAkoxqfg6EZEN0HZwRTyIzYkqp8cxAdWDXr8WG8UmsbkwDYoEZo3h2XiINmNFxJig56csupP80t48Y+4Z87J5fJ/aJ1Ol2zFXDJgvO24h9/jcqTUCoscoWGwY7Qhp4/BMuKcksjhNBTwruOdklEA/liLv9pyUjscUvIMAMRbjqAqm+eVUZC3Mo9eJe6rgXM+NsNXSEUsYJQo9GRPcsCalV5fnMF1jimsbJJ6it8nS8uJzroia5p3+RnCv743PY3GxjvaYvWBd0ABAs5g12qfsgFSirWuMlu0L68gS2MaDjSl1Syr3js1bbSj9kNAXvpEeY+gQLRhzDgbwk0Bl+9m6poWGPexv604AJpA5tWiUd4CntA3I567zfu+cE2CtBdiY7acI/akU8THczSULTNHMXJDtHP0ZL3z1utzH1YpGZAChwV7BwXafwCmcUBTaHlno00HD2qLNNjYN7cNL2pGkbswP/dCwdr5Hmz2+k3iab/mW4f0U8BR4VG/G4Q7/ilvyWqq4e+GyjS469Fw1OZ3R0hFPahVgGpgiAkzvjjTZqmbpCHhKTwrGzZ3OskltBhvVRmOi5U7x3nQOxbAxt6SHRqHJyc24MBYnAkQaF88Sv/dYXEbmxefv2U4qcwHHbcZM0lCnFChKhUwvODCGFAbzbgHPAuBYRJjse+9PPRJWHic8jCot4/3AH7yIa7Ku7SkUo0naJ5z73zNTRK924k7BOVa6pTEjSy0dOWmmJoT3xsefTryEcZhiaNL/p6oXcmpYElTapnlbI+65VF9GfwSA7+At8QxpEkjBw+CtORpD770TrvVJSw0ZZT1anFLqxir39qAqn8aR6tzmZl7WzJzgAW4opTVQ2b633va4ewXpm7N5ooFYD+wFv+0h1xJcsSp4tkquaGdJ4G61APu7LdFvHlMp4qdq9neqPmveDZdtl3vPgpP0WVMtGd8YKzhY9wke4LDUm2PSxlVk9z/eHOstvODZlCTrRPFB77/4i8P8/NjPY3wHvUgigBM0FF6QDEf7yPtkKx1TuO9qwK68rITWVYOxERrpKmzBkyWTgliIxN8YCytATY5a2rm2mkIJaacirpGU13YSs3lTjdUzEHbKbhsTgrXpjDPt1zFCY0PoGFxgSSbKVFyGTSSAjICJJWRJwPHSwEGf86ET0Da1DdyePPj0nSyNIc3xeqdezMD4rJXTahvYahyYRVyDbXwRPFgH84yS4BnWuXbi5qKRFr/GkjLH2OtJk+DxP1oyBopj3CRoC53AEXzFPbLW8nKlYGml2aR5+9vhIY37CPW4SAkiAjQu3DSiTAwM/NqXvm9PuFkfOKO8THXuHlPqlip7VWHzfApM4pS4QOOCsZYJaE3pgwQq10yWNNZEi2Ltaiwe/HlO+q/BmTmnirf967olgbvVxYm31NRgc/I//AG8qffMtQpxLw7qlBlUbZf7uOHhH55iBYs1aUnzzrE54mHwj//Yo/iw8ZsfHonuWPzCB+yLf/kvLwfzpqWLe0Lf+I7nwlMChq1zQhbiOjYHf7u2WpiuhYq7u/KyEupGtdhpyIg4CISUcEfkKWiWSrCIMzUnqiVgrnNtFVCe4f80BfQ/IZSieN7vM75u7/e+mE/TkTpluf2kU6v71tbdmIvLgAPZMPe+9+VT2VzAcb0/DeXMu1eZ19zGTh6UlvROMTfzHGvAhgFxGcFHPZ1GEWLN8h6nNxu6pqSnmrLNbT7WJl1y/fjbM+HHM5J1tMaNMFYZNTERqR+SFHWnW8zavN0bs3ZioZIBc5FhTV2ZtsUERdza+dxc7Ycolqm0G1ciXKEVyq7r6gk362PN4VVp96kWElOWgyXFzqrCFutt3H+pzur9xpxSAam4nVICNZPF+qO/nOYrmGviwxLjYx8RkEubiwaqixPO7XtzTBXjvJubwwHCPj2mimvPNeSwaM2sf5tFmdgge2KpIK5d7o01Vi+0ER4aa5JxKDWGplJPCH2I2ZuL1wkvi/Uj8sS6eL7vrWmUrvCZN3mTAce9LC3zNg7jdD+6b3s++Z06ZHDXi7m86BV3d+VlJdS0WXVY/G9DAgwygi+EkOyZEDsCoi1Hg1/i/w5jRmyUHUwC80GYiDWBeLGyEPjejdDVNhCYhxBjHUGwKZntWSkMtSbeZmlchu8pZ2ssNzF7YtLmSUCziPQq85prrXprU6Zqqucbj6whn/UasMGh2BjMIqdNUBUhig1cWkMuJPOMpSxCke84jRJz2o0CI5X+6U8f8JDKx/e973YTeY2JSLwUBTKtGKL4BRcRbt6RDDQnccx2qjv5qWFNtdKl2VbWV8+Y9sSNNqyTWBDvVKUUPZq/z9BnFH34SEFFAqe6eWLlpBiMuSLAVJzF0mJnVWHzPopXcGX9UtQxRc4E5tr7CVT2dzJZQMrpm2dSx9GIOceK4DMnfe9grYKb1LNZUzgwLk6p+ayUceV5P7zGMuRz41saDLzUNeQAIoDVvozloQYhh87GgmxbQJvwgO9M8dAccrwnPMHakA8KhT7hCUN14TGovCyB17WWC0ALVemqe+Ov3BCykLnCeZrFWmfKhx/PNKfK51OnKnyqxlxeCxV3d+VlA9CCneIJ/WQUJaaEQHBaStXVnH4RJkYhmj7pz3OF4KoZGZFiLDTqmAdBFJZYUxCaa5x6PB8xu9dnMTVTgjAbQo5ATwDj0nibU2Wg9Cw3NdjPeCgFlAOnNiZ7aZPw0uuEnfTDxA8ZO4ZCufRdz3xs/pgtBg7PzN6Zc8yrXHHwaxzeFX+zsRm/d2LGCRoFmESUWWtEmfIOihJGpE3EMcw7TSurohJlhRBCa74zjlhcosS4BiN00r5SjGlttdIebWRt3Q+HTPtgyr2U4PPEfAAxTuIZ4nJNsTrf1yBxdKaYIeEx5oqgWKfz+lhQsWcuicOIUDIuJ3B/G0MsKImtyl5nBeD2SaCyOBX7IJC4lsT3JN4pmTRxK8ei4N4Iff+vrbhqHoLnrZM9Bud1Pxkz+jN+vEwMyBqlfSoOyrvhzAHCMz2/ZgnBqTUS/0EZmYt9qQczShFIuj1XJB6Kh3hnrHuJsYpF28FCUXh8YswC01oYzalaP+xZz6nWj3Zv/JUbUv7tDfxJnJP1811qjRm3ubi3vovMSvuVLQfYqwm78rIBME7KwJ3vfDk63A+fewSITcovmsh+gMkhTMzId/UE0DuVRit3cknZfoSaLroxgUfL9l6fI0Ljke0QZaBXMhtxYngJ9LNJttTdsIk1CN8ScOf9LBMaDxIqBEIqoKYmRzIiMArm2c/6rPHiVsaWyqoYdT2J9spxP+c5w+nK+wj9WkQqnaJT4M1JlzsOg8AIvE9VXAyRMEx36WQjpJJq+pmw2nh2Yiq+7Mu2M2+CEL35LAoIfHgeujAW4DdGCo9+MxH72xzE4XTahZ0ctgRStlY9+PI3GrYe/ieECKsp91JcKNYWQ7dfCXxrl/R4tCXA296syu1c3I09TKm2puISWuWGsq0ui/VZ0swyQomCi+4ShxMrRiy41pAVT7Cy+cU65J5kkxk7awG6tg/sJTiPi5ti4Tr8BI1k/xgrPpCeTmsrrnof94faNHCd+Jy09PBOc4fXtbEUU+th7awnGrEnasBw3G32r724NPaltSalwm/igvBbeIulA17hLpmF5m08gsgVFey9b0vl6jGLN0Bz3ouezZ/Saq9b7/TgSj80NAX/5lUTSq6Viru78rIBoi1jClEgbBCEkmq4YTSpuBszo+9CjFECajnr2sAQw4tfOj5LhIlBYQTZmLHyRHjaYN5tk7PwjJXMjpJSA0KzCZb25tlaE6A+x1wwYPjL6cUPxu27nJ7M2/WYgY2ZAnPVbJou3BQyJ3PXVWjLcXunzYxxWCsb3cZNmwPr6HPCKcwlLok0VdQnifKFCSTuKIoLHIUWEptkjE7jTuUpZraWeRPG1jx1OwD8JLMj8TixzKUeiNMnnBpj6jucZz2HY0rRp3cYJU+FanPChCm5SR998YuHz6daTLC8mTeBnyBrwi6WSNaBlKWvyu1c3A38WnP3eG4bU2A8FGNpv0sC0iOUKPMJqDbWlMdPHQ+fu8ec0LjDyDu9040r8sY6aG+w+sEP3JkPmk/WiXgukP0TVxoc9zptL4EoV1Haa0sPvMI4xKesjaWYWg84ggdzZymx5pQKn9cGjdZ8TRBqrEmglqSI66sW80vLl4QKRDHGG8bet7Vy9W07Fu9kuFrTxHTlcJfgdDQDF7HioVnjWnuAvQiwKy8boKct12qcCdytdSRiZkRkCMO9tPFaXyRWEdemvgiiI6hCmJ6ZBl0+8z+ml8wm47J5bF4n07mS2REYydgQP7C2N8/aVMT2OU54BLpNDlfqdFCy0gKgVuyEj5xKnaRrcSuKB0ZtY2Lqtb5LoC3HrdmbZ8b1ZDP7G/7MRVGwuK7GhA8LBmZvzTFma+EZMfdbjyia/sYouKK4JNaYzivzjqUtZcdTBAugH2Owfixb8Ihe0F4KETodh87Os57D1GkZ2D/M3H5uf/ubKsoUGwpAgi7TH8gc4NMeiRup16CytphAK3AS6xg6ilDLfTVIcepU7F6WjVRj9v5aIAwQ2NYn69JCLyDSGsgsQccpMkkZNU7PT6NH+8P7007CfW02in2FntFyjf8IH6nNJuN2QCfmy605Fqs2B/CW/dmrW+I9W2Ip5qwUsTz67ScHCLyNQI+VaW0QqnWUDo1nEe7eEYtqrLJZo9BR4otijUnW0pa4QUkGPb592yYT0by+/utvrOiYcw533pE6RvYRvoZvtgfYvcLudQxj2jJCISyiwCQYlrJBuGF4iJGCInDU/xgK4eNeVgfmWsSGMAnzFJSLdSU9inKa9O5kvNjYMQli2L3S1WOnjTnFZKo3z5LW7rWmQe8kjrFhMN7lVEbA1JMs8BnGa3zm/oAHDL7tGmtDKMNrrwGbd7M2WA8n05x2W9dT4l+46jxD1kFP8Eb4mJ/14sJIFldM5jVgNu7EFAs01zWm88q8/TZOwhOjRheJXUpMhM+cyCnLgstdC3eUrSh2U9aPNQG2a0/Lia1IAT3dhvUUahXlZNsQxN6doFL7LF2IUxxyjPnXFhOpmGo9a1pwz0w/ts+rK9Z+sxfhvKbpR1Am5qkHYwGRFFq1ib71Wy9nkaVKqzWGU/P0vko/bTZKrL6pWA2H/mZBgJu6Jp5tv6WezTGpsefVvXjquejIQSPWFrgIz6S8HxuE2rN0WBP7yZpnfeo8k+Xo95Sy1Ht2rB9kBb5rP/QymW5ReLo52Q9Vuavrj07RvnsdbK13e4A91b6/ErBbXjbAlLaMsSE6mwbB2TiYDAbuc0Wu+Mm5ZKKAIFSMLmXExXgg+jDDZAh4FqGT05jrEWdO2awDmBaixrCXEtwS0/6a3jy91u6x4Bj3nN9aXRz4bPt4JB4l5bIpIb1Ym14DNtc4wWMkKc1NEOb0mdOJzzGJBLp5L8sYvLYuqDBCTABDEPfifm6INGEDBJsfa5py9ebgszUnwMq8MTxzS1aTsSSuRcCpeRJefhNWFGMMTAponcdYPYdTuQd7p+UalB0lDF33FOXUO7EH0LOxwzFlLH3BUqDNM2Jh6pm+KWf2nc7ulFdWt7pHem0Xau8t47dm6MgJlmJIMMRC2Aa8+hzO0VtNsc+7PDeurOqi9UPBhH/zy/U1WJRQi0tmDOfW03xqXx7KnH3j3l49m7ZB4RYhNsUfj4mlGHsu64cCb+mHhubxIDTjb3wLT8V7jwlCbS0dcOK5n/zJl2u0JGTAWsXVvSQwvvds+HLIxQ+TeWYvOlT9mzNe8/jH3ziTqVXual+mFCb0ve/GAoiTyWhe3mnsDpXaoqzZ91cCduVlI4xpyzkRIWRWgNRqIFC5KpzOMc5E3ROgte4KBsfMaBMmSwYD5AfHMP0kmj2pejYLgYkpcbk4wXpvL2blmIJgS3rzjLV2j2CCkzm/tevck02cYL+Y+CP00vF5rgGbsWMA8Ob5GAo8JhAyJ+XEJiWl/SM+YsB7Tv+5bkzQYSZPfvLw7GT/xFVkvKmKah7u8d2aE2Bl3uZEeFJK4tMnFMzde40ztYMwIkGHaCPB4721iyCsVrgoSARDzZRayshahgpSryil7dFxAodbRbkW2YoSj6miIfhMdhelxTxY48a6D/ubsvBpnzbMzzznBKt51t5b3p/sDPhPOvZYwCulVnxTK8T9n7ilL/zCmyqHtc5IBFeNG5nqCG7eeAe3ZNw1tXYRWuZWxVdqPZuq7G1RXltlZ01No6WKUo/vpnaROacVS1yk8IR+xcGwZOMVxwSh9viNUgzW0xjC1801jXe5ZxI4vvTZ6RyOb6HtZE4l4ePXf/2mmUyVP2gcaa0TF4fXZAxtuYiANX/Sk4bDGpkS/oVGyK3P//yLpcDsyssR0GrLCFUPCYQVCwZzpvgGRIRppLJt8upjUrbBclr3PcaYzKUwxZjH0ysnJ05WFmb09M/A/NcUS1pSEGxJb56p1u6x4PDTJ4249xyKA2YAT/CaE0yYdlJfozSMMb2sTU7aORlRHMPs4N5PTsoUTOtgvpRB7zIWuOEioMhwx6VORivonIIwk+c//3LLBgIq9UhACk7FQrf2BNgy78R+AELbe1zTCghCxLjnMhra9HynvJzc4W9pplRdF7Tpb+vm/UnvjSJnXUFiIShbaCRuvZoO75508zYe62itWFEwXwLqcz5nemxTZvoWb7X3FuUvnc39oGUKABO8vTcW8MoSVt+VODaCyHe92DE0kjojeEGKX0YA1T0wBugiP+3/BD2rJTpu986W2LYxZWdJTaO1ilLLd+EIzaD/xLnAmfHHRRheeerYLvMRdE9pMRaKBvzaI+gW7xJXsnafm1stslhj/+Dnnd5p4EltJpO5URq1TbGfwrvtkco/Wzexa772awdFHJ3VYp1x1YqjWpsheZ6wKy9HQtWWCUpaaiwYmDTztk2EEOKKSOXEnMpd63MbN0pCanjY8JikTZGCUwgSw7RZMUPvcY/mXjExpvPxkmJJS9L1lvTmmWrtHguO7+eew3JkrOIg0nDRPfBn40VpoGxMMb2Y4FlOEvScUyjh53N4BZiBE3OusbHT9oHiZ0MbOwWGojN2gnStcSfD64UvHBSxdHTG5KzZMWmIPaU569QTEGtiEGKFo1S0tTIwMrQ8lynVE0boB46dgK0FBTwKKYjSGyubeWDYqaVjLVLvIgXbogD43nzXlDPvmel7VXGjyFES0/zQ/oUPf6f1BwWNVa8X8Frflf5T7p9Kn6aA5RRt3rHawE1Pce6VcuB6cm2Kl8EbIWYdjNN3vcrNa7PDlig7YzWNtiYBVL5rP7X8xnPQcIJq7UFlDU5tOajWDgcSOEq2UdxYW/a59YpbsI39A7e8of9aj97NnWyAz8S+1Wf09gl+TE4Ye1oI5D3+R4MJd9gayH1q2JWXI6GeMG0ivlbEg5DDkG2iWC0SyEUQJJgvykvKySOYuAJsuqS3ud4zbAoWhDRUw1AQOkbqfRgcBpuUPuOaKpa0VLi1vXkSl+N7m5TSIchwrqQ7kzWGMuUPx2Qe97jLQlCMSz0dgyVMD64iYOqmzMnCZoQ7zyeobUxCtZbpThVS75F1wAc85Y4LY02F48whFodTpCH2zNeniEFIhc6cWuuJD11G8RzLlBoTRt6NXu91r+E6a2EcrqsdiL0D2Ae1K3gan7JwJBDSGsBhXHlry5nP4bBXZ4nwNzb0m1g1NIZe/sE/GA94zbvgzhwS6Nlm4lTBssZC1LOkUjC8s32P8Y+lKlcXMqAg1nvr+ODffL7ma4aDQI0hogzClWuf97yhD1nrsjwmjb6CPUY5to+9s6arGzf+iYY85zygXac0AsUntu7zuMStY5vpCP70BkscHLbrmArMqQreQm+foAf4s797B0/8D926bldergNoT5gYGRcRxSFNr3LKT+BdNnCUlfqZaxBsCrSl2mbiYWq3UYTnvUySiA6TIRxzCvG/Z2HyhEbPxDhm2p9SKLJJ2/bq5izddarmRtwT/PlVqI8x5bHTMTCPJUzPfSnh3zud5RSqVovxC1BzfzJ4UuU0eE1dhKUwdcK/klH9SwWhcaAzjKp34kvsCb94m97cE0aUX5+jQXMl5CgirFK+s4cI1xQYjBsIfbBapis45knxRo/WDd1Txuv4Tl3OPEpArbMUN1YKU6Yo5Fxht6w190bqQTmYtE1AzbMKliUWojlLaiv8pvCUOVNgrVGCPa2VZ3Fz+d93XOTmbE5JHY4lLYGinuOg5f62+/LSWLs5S5rvtH9QGA8PTD0sAh5toede9uUpYcs6TUEOlJRm80hsV01eeJsbsk7bddxS+G4OetmWVxt2y8tG6J0wMV6aKfNagj4RSqwrCT6MDzqf5X8bjuabsu7p+uwZKUBHuKYwmXdTSmwQmxZTBBSWmC4T3NgWZ5oy7U9lbPhtzAQ8C4W5+03YU9ySWUW4TLknjLnd7JiVa8SK1M3fMp013ajT3yjN4tox5WRDQGK+LEdpdgkP5hoh7DpVcp08xlw0PejN4VTZPKdmsP5PEGo6AQfMGY3BB0uDAD5BpWqNsMK0wqjtLwOX6INPHn3ZJwkEp4C7Bn2LM2L2ZqFDCyn8aE94H2gVl/MoZx4hUOssVaU31X4j7O0xcTE5EVe3irX2G39IE1DjhOPaBJTS16uoukbwHpOqnFpKXNWZc4KkrZnxE5rpzQYXftxnDpTexNXgXT7Hm9J9ORZR+EEb6cnUljVY2xjyoQ8dngW3taYKPuhgtib7ciusXae5Z9UMRvSGrsiAWNZvdathfdt13LL+sV5RqGvhS2CtrGtq2VTavpqwKy8bYKpWibLMTgA2EoKw4IjNYqfmh7+juKS3Sggi6dJplpX02gRfOaG5N8G9zPEsGdweCBsBhrAS+e55iJYy03bh7Zn2pzI2jEnwJwbHTVStHk7Gno/ZSy2tz+756bPZ0yH5Gc8YxpAmllWYj7nnelCZnjWhbFCsaufVMAF/E4QYMovA133doLQYWzIGEotEyDhtYgqplLy1uvApiv2dB4P1PfegjDE0jB5Cf/AP4IpAS4zXy18+NDNUSTaB3zUdOjEzaMa6sKi4llUlp0D4DK1HQKI966xKNOZd00cpzz6L28aauf6U5cwjBIwzMQxpYmqshAeajnCn8FlDbhSWAAIVWGuxPtab8of24MmeTTwX1xchL/hUgOsxCtgxqcppmRAXAotSMlbM3efGCWQOWi9rG4sUhQQkDikVn9GzZ7GIhoew2lCCjZO1jWLtebUpod/w3h5oWvB8yrQ0X89FD8ZFSONnxjD3jIsG+GkyGO3FxLnYG7e61cDXxtLaBUqvWf9Yr7iDK59MrJl1RA/4I3fueR6ylsKuvGyAKXMn3ypN36mSFospp2iS75K+Vq0vIAyAkOUrRZiekSqgiDPVc+PnRIyeqcfSy1423I/J9jZmYmnGuvBWl8tUxsacqdczuJMoBLT1OT89QS7K3fgxTZvSJsFganM75tPWPQcv3jOW+ZSAW0qkZ7sPU0vwovfUbADv8x3hArzL9ywufmx8a2p9vHeLwnEqP3/7zFO6n1hRKCOvetXl+kKEFlwQsphZ/vbbOivfj1YTIJl06Boz4zfl2jjNEVNFl56RQmwgFhtBnlFiq8IF12hGgKFxgVSU3gpjONTLylgJj/SJMs4UyHO9PZ0yCeaIRggBn7NKol2KXoLoY21Fk5SaNE/1fPjAP44VrlvjZdC4ecEnfMQalmaovrM/jD9xPskGS7ZaWmTAU2oz2T/mJIWXdcT3XGxwZWz2s3F6Vw5d8IO/WevaNmUqC8ncYilLUUq90KJ01iwovPCiF2JrMxjNydq893vPp7VXvjm3/j3rlTVKHz3KJb4Aj1fikLUEduVlA8ylFltQp0cblpk7qbV+qpUlSkntuGqTUgw0DBSwmn4aaUyYzAECgoBzqvC/E3JqftiMtViSTYvZ5DR3jJ95SVq1993//sOJey5F8lnPGjR54H2YDmGYOBzMXbCx8aYjatxzNaAzYJ4UABCm5ye9ZwQV9rIB4FZMUOqHpD5EekbBbXCUNN0tCsdSPz9hlwDaKeZ6Hu6nMLKU3bee5oheMUFA8MQPn8qdYXTGQgGpMTO1yKAfirk4hLhMvKMqcu63xjJmelYIY/McCk6yuOyv6ppYCmM4pLjIrHLSJUBYTcwzfc0oXawQfjtspLp1Wg/ITPODztLOIRVgA+jQnkaj4kngktXzasVhuM5awGuyGI3Zb3MmvBJ3lOSAZIP5P7wtSm9tv+BvBxvrFqut/QNXfhLzR1ASovacd2uoaB5L0rVZ5YxDqrp7ub/QJbyKzzIGSq/9ahxVobkI1oQe1AzGtWntD3vYfKp6a72StSqOiRKTtjY1MH4tzzsv2JWXDbCkxwZG5DonfQIJYTETpygdkzdCq52hky7tuU63tZ9G7d+DeRCg3iHtMmmPmDhB2xZLQlw2qdiEVGJd4nLZMvelwWCxQNiQxkf5SiXaWqk0NVbMPe+Le45rIwGdGGuKf8EFfHlONrPnYIb1BJZsABtQAHCCjxObRNi4LtH7MZH7vSWwcKnyF6XOeKcUkvN0P7lPuq81EueTehnWiuJSx58Gh07gBFaUvZQEiIsugoyQ8gPHbWuGtFZwf07SvfRlNF87OQP3r2WqYzjkvuHacABBe4B1gJJrvPYxujTe7OWAMaWeTZp1xuUF4jZOjafsl1izThVwvCUOI/s7tWBidUnMkXVMJdnUrqJscbmiu8Q3+S61RSL04AsOquKOZqIIxsXtHaloDb9J6Z46LLQWTeDgZywUS5/jv+7xd4p9pm7TRbEmTFkDExPo/9e8Zvh/zoorhGGu7lEFc//czx2C8SlMyQqs+2wtzzsv2JWXDbAkIArDEztQK0za/E4RrAk2bU1jS3XXWjbe6adn+qWJU2IoNbFIYKZOhp7rPUnLzulYTY4EVR6jfCyZO8IW+IrRjwlf4zAn4zXuBENW5m+OyeaIKydgU/UCOkEUvbqZ/S8OByO73/1u3NcjAcCpc1L7BaUODybt2mRV1ADDNSm6c8ofBup0mgKDYwrJebifxk7uYWRwbE3bcVsb38Ep3DB1pyZLFOnaRwiOUyaAgKtdwRPvhS6UXW8FyakyVMAUDikk3KfJBPQd2mGBSakCMS7Ws1c9NdmBtXFmCv3FtVKtsN5p73DDnCrgeAtkf7Nq5gAUi1IC3Ck1aDIHAWBdHSLg0nVc2SxW1fKGftEzWgjAJQsJ3MIFpRTNpUVIOmu7rtf5O2vc0oU9XDPEYs3zLj/WN/TpudYff5DW3fKHrXCMO7ct0x9+Uw9f7/AOg3XSIfbYvVDBGM3fu2rqeYW1ZQnOA3bl5RwD4jBeP5WAERtTHs0fE6jBo72y8b2CZBSDWsUXpBKs5zoZM5Gmm7BnJtr+2KZpc3NPgzp/T1kDYoGwAWv590DwguFjPPW7QBvQicFxFdU4i4D/jSfZDXUzZyzJtIm/nAJj7Cku6G/jFKBaO9WuST2cwr93YlYEOyXX3HxmvK1CckohPgXWGy0x80eg1bWq7qAEMRNi6JDSY77p/5SsrbbnDxymq3FcVYQ4ZXuL5WopU53CYY3ZiOAErvM3nFrDBHK39Ol+e8Fapv9YmuT5rmYZAjQGf+bdWpq2CsAtkP3tnX7ShysNDuEXr6G4xeVtbtn/eAdIi5TKGxw4CMRa6Zmym+riyaZ0T2KhQCyfU2vc0kWem2d4Pl7oGlZen1MK8lzzNGYWN0qMa45xJR3jzm3L9Cf2xN/2WWJPfuqnhoOwz3oHoWMUjPNItz417MrLOQfEtWZbzKcGQ9aeJWNl49sqvk6/LcMlAFgX+NhtQgzWBhQz0I6nKh9bMjbG5o6ppaPznDUgmyM1LlL+PffYsBhMYlB6p9uc/hLQyQqDGa0VbBmL9YgbI5uTspOTsrGm1xIGg+ER6pSMFK6bgynlz7piqE6s/PJtDZCqkGwV4luE4ZxAi1/c+9CutVA3x//pjxTlLwUNez1/4BgNUr41guuN65RMdQqHyexIzFQL1oUw9j6n/FoAMQoa+jU331tXkJYf1ZLof0q8dZY5eDXT6YFnf8ZnDGMUiM3KhJ8kkN4+4bLzP6HKytYWkOzxxViiq+IOx3CZAxw+CA9RkuGyWlXH1rilizw3iqXnR3kO/vPcZMa512d4IiVqqyvpGHduW6Yf30s5jJTE8JyP+IjhGXgGvod+WgX8GAXjvDqDnxJ25eUKB8S1wZAUjDVl41uGi5BqBU3pbjTyqUqwUT6Oydjozd3Ge+ITl1kD6uZIKf7EPWAuhFjiCTCSFnobaKtgazcqN4ZxOoGl5ksK8aURZtYPg4IzwnhszVqFwXx7yh9ayLNrb5HUABFs7Ls8Z+1cW2GYBp/ckGiF8ttr3FhjYDxTZpj4D8+OQLN2lXaXKPdtz58l2TCnZKpTOLQn48qpMU55j/GKlyC4uZf8jpskgcvWizD2PTdILUrnvdYabet2bD2qAnzqeKY1Sqt3UzLQHkU0jUYTj5Q1muJ9Y9/5aRV3OOA6RkvWFu3nQON9XBc5vIytcUsXrs8zEotFaU42YRQlayyoN9agdGbf6n6dK9Ronr4fe15bph9fT02dhBO45n/d0BojcTD+R39b9sIYbZxHZ/BTwq68XIXCRDUYcm3Z+MpwEXYtAoYpYgQICyOcG9exGRvt3J0ApqwBmAK3GdcISC0Cgo8AtYGcMtIaAVPnguh15e1toK2CrbdRzZ1JNtYWsQ6YSOp0uKd2jDZGwnhpwzrv0xivugO/+qsv+5trnYsEMMOv2IAwmDVzbTtFC14U7EwpSgpzFI6xHli+//IvH74XCBg3YRVodf7+5h6SHksAOh1WBWmr8n8qpjqFQ5C0fYpK4p3qe7hiQQ4B5gjgkmKT72MNdF+6IAPCh4JD2TF31gxw6nimNRacVmmyv1mNnPDhQ3ZjbQsxxmPG+GJPqYVL/As+EitkXQlptOUAUy13dY2nqoSjPfuG4mhNZH5yxVRFiRJjrnAbhSaK0hb369JCjcZae4NlHmogkQfmnDip1ANLzAs8vPa1g7KCd+OZxoiHrt0Lc7SxJd3+SsGuvFwl2FpOOgwXs0z57RQBI1RtzPigx+DUGRtLTrJJHbVxv+EbhpNdW4vAZrMhbfxUbfX+sRM6M3RbfGqrYGs3attDx2/4pkyklL3NnngU321pWBcGkJ43xuyEXlOHk61SAzrXCPG2U7SCgOmgnZgMc/EcmQlgTIHxvBr8vbZbsIqhVWhuVf5PxVQJEXTJOsKCBoe1X9c//+eDwJt6D4UuBfuA56QjNshY4cMzrTNhhNatH7BGL3jB4C4wpioArY+Ttf2aeDhrtUSgrqHBqeKb+ERqQPVikY7lfcZUkxvg2f42X8od3La4n6sS7jOHDgqJ79IgtSpK3ouH2utwk7TurXEjcb/jS3genFUenUKNOjQbZzsPFk1rjXYoWBT91O+K0l87hb/FWwxKWTqbr9kLS2njlG0PTgm78nIVoce458y7/maxICgRqw0QxcXJAcHbmE7G6azcwnkFe46dZG3inL49Twn1mp44V4vA5rn3vW8sHDCEMLv2xLBVsNWN6l4Vf/2dqp/GnDidZI4k8r/F2drTc4IS1RYhIHrVgNuAzqVCPPOhgFkHMQwpWpiUXadrtIPBP/vZQ/XUMRfSEqXjvKsIH8tUq8CAW7RJcMCFtY2bkOJiv03Rp79ZxMYa1vWC7q3Bc597uV5TUv3hhzJlTGJ/0AFLJeUIzQPjMg7u4ak1WEuDVyoIPDhrn9EmN8Rq2FvjNVXCa4pxqyhRNKw3pVLMXtK6t8SNGJPSFX6zbFpDa2AdY0mNldO6p9pwatOYh7UxLvsQLeLv9q3rU3PH+r/92984s/VRj7rxHOf2wlrauFrp0FOwKy/nAFszBJaad5NumIj5Nh217WN0nhkbFXrWABsPMyYcCEcBica6tBbB2OmKqdTmHhOM1SWzZg2yUV2vzLhqxCkLH/NtLBUpqJVOvRVnawVBrFbe06YOw5c5twGdS4V47RQNb0n1TtBo0nedcPnZMV5KjlT0LXAl0rjBVqbaCj7CBf5+8AcHvLB8wGtb/4OJ/hRjhR905V3V8hn8UF7tF2OiuKQpaGK/0Lofpdop8mNK4FoaPC++EKAkjLkQWxxV6B3w5uirrRI+pijZ2xQOiqv9VWFN3EhoirKJD5tjMszgLEpIXFPWzTjtOff4Pv2xKMEJ3rdfKVRokUXGXI3ljd5ouN+YHVTgcc1e6NFGjZ9cY927WrArLyeGrRkCa06qqYKpP0zS/RKZjhBtmjWF5tqgX7A1Sr21BrCWMMPbAARvPdnMnebGml++8pXDRhdxH/dUKxgxrmNThGXMOHljhJgHpglHKY8ev7l5tCe0Kgha/FqnVhC0ViuKQ+7B0Fw7ltE0xvSjRLsX03R6N87aUwugF2COPheTkfiNLXAlT/BrYSygkpCiHMRFs7WC8hb8tPQhVkMAsOKATuBJOc616TpvPdUCeexj++Naq4ycZ3rsS1867GUWJ/SVcgBTMVbnSV91z8DnU5864Du9plIVPcrBUiuGuBOKmQrSSf32mxKSNHD73N8OCXg7fPg7cYsOMPgkxca+RRNRXH3nIPM7vzNvTZ46RLe00cbm5Lo5697VhF15OSFsNZWvPanW1N623f3aQnM2SdoO1CJaikxtTYOr1gAnRzEuXEU9N8RUSm8PJ5g3HBincbe9c04pGFMuW0AmZkSoUZqsS63zMpX5ZBy+q0HVGKKTVl2jntUKwzKnxF+sie6vSnQCjSmR8aF7boRh8IfJUg6TursVzvsEfwz0BF9K26e78ZKiaMdAxU8rNGJl8zuVoSO4fB9lFj1YL/trbFxrlZHzSo+luDhMwCnLHgsE3rUkxmorfRkrvC5pr8GFlB5VlCv3s36wDC2JoWppyv7G/xwAjBPYV77HB61dyjGkR1vNLvQdhcUYKLFwRqFyr9IMcYfdZiZjbOoQ3SZ+tE1U4YOC9OIXT1v3ribsysuJYK0CUrVixIrYlp4kTlVoTqCs6Hb/Y5gpy84FYiNTDo7pjxOGijnUwlRLFK2x05WNZnyCHauQmRKMxxT6SoaNU67aPObieYlT8P6xzCc45RLDiCpzwpAwLbEUdY1OFYg6ZrFKrEsCAFNkDH7T8VwMgPelJP4WuMgFrnqCz/+xOtWy92M0dWzhuKrYJh0/QsO70RPFBM2koJ1rvCPZhE7g1hcux5TAtXziPNJj0RRatE8I49yLLsxFJtCSGKu1VarRuqDYqe7vdZ9ICuAWxPfcb2wChpfsuZ4y6re5xgVjbcWz5bDjHSlUOJZdSEnlwlRaw/iX0tnPLThE41+eqSgfJaU2UTWm9Noyr6vdw2gMduXlRLDGlOkkXLVi/xOOgjGXVEo8BZNBjLR5TNB1iZuxQVIs7xREa2xOCsyPGEFOs3OK1lg9m5SPT9XStoBYKxhPUegrAZlSXxX9y/Pa4lxwZR0TlBmrRhhC5pHPziMQdUyJFtfhWalbk9oTmR88uhb9KVC2VJD04Fjl+jyhFXzonBIPXxQ3uIjVo0dTp6An83aaFfdiDar1MA1a87+9nG7yKarmO+9O/SE/obu2pkqPT6SxKQGd9OzAqdNjEyDOetDSsP/NfW2M1RR9STNWCRpfQ/M1EHpJew38G54SizeW9LBEGTVO70zBQutjXOg/Bxrvm8suVDLi2P3/Vh23OtowXiUhcnjFB1J40tzn4ievJuzKy4lgqamcENdQsGrFhCAGqpw64bgk4v1YJoMYadzcQyHaGjeTTsLG5LMtJ8wweozSBmHJwRiccLKJxxStsXo2TqY2O5NsulZn3KAKxnoCSdl/ChDmZv5q7axlxmPKhblp7hih5l2sK9IYCYsafCtgz3gIy8oU2hM9/3msdD3htEaJhksCBN7gFM7SDDTKjM8U21sbg9DCRS5wVQWffSY12bgIOeuUeCZMHa6NNTTlupoZsjWDyrxZtqRGZ+/VrDLP9RwlANLFulfBFy0Zx1gfMXRqn7FqoPnUA0k8k0NF0rOr8nXK9FjvsmfTH6mFdKdeE2M1pZRJxYcfjVvDE1rBfer2GjnwWQf7KWtlP3k+ly0c4P1cP2jJeL75mwdeKDGgzS7Ep3rtIlr4i4Zn+H/pvIxZPSLr7wCNDilZtQ/ZXPzk1YRdeTkRLDGVI2ZMpNWKERQTHSGPsDDMJdaJY5hMlC1MuzaIDCBmWUIa8mGAa0+YVXEwNxsCMzYXTJRQT/PKtlaL8dd6NoR8Na1jEBQDzEqhNfj2Yy4sJLVEeWqb5HSduB44894v+7LL+GoZwRgu2wDZnpmWsuF/18oewqSqgmgO1jtMYexEz9ScOjhLTvpjSrR3irNxMjWmCJPaQZsrLsrSsYrFRS1wFcFnnQk6a4J2KPIp4Q8IHHhAu+gnpe1PlUFFcUCr3u/dVbG1vtaCQAPopFfBN3EVadBalSk0k/L96AHdpc9PWpGMWSWCpzGhvXSfZJ7eO+U23hJj1aOv1C1yAKRATAnuU8ZlZY9/4zcOiofDFVr3TvNmAfLjOy4gvcKM4Tu+Y9iHvexC6d697MIKP9fhGWjEHve+qXlRpL7lWwbFKa1YvBNdqI0T+r4IPYzGYFdeTgRLTOWIgk+11Yr9bTMiXhaKFFZaclLdmi46V1COxcXpDhOQDrzmhNkzXXoHBiXo1aazSZzwx2q1eL6YEN9TdlLPxokkFYnNPWXuU+NG0Tv3plM05uBk0cYVGJuquK5XdK3HCJYoa725AowAI8GMnIqZxOuat66Ino+a4uZ0hunW6plT6zC2rmHefNzwZS1cl5N/2gQY65Q1aG280EUscGVcH//xQ5AiJZ3bIr1uAHygAUoDZQueKbqnSi2FU/ehRXuLgppGkLF8JrhVvRIBr20FX21AQK+PGGWLdRe9oO1YlWTp2QcsMVNWiWMqsrbggCKryPONoz4bHlJdu42xWkJ3lb7gy2HLfjGnGojeU0hOFZeVcbLUWUP0RHnxbvcnsB8vQE/pcn1MdmHW4Ss6PMO6iCOyv8mR3ry8QyCu35Qc401GonUy57FEhIsEu/JyIlhiKmcCtLl62j5ioZHrs0E422DneVIdU7YQrI3hxIYxmEeUj6VMbswk62+bOEKSVccGGjPB+yxVJjHfRMBj+k7EPrfhbXLKgg3IgqPxZa1t0gajpUopRZJfm6K41R0wNldMyjt9j0HXwOLKFOD3S77kpsqP8SXzACNiqUmF31Tl7K3DlBJNQXG/+cEd3Len/SXWoLXxQhfNVw7gFA1RXKwP3FBQkiqeqqtwBMeE05bU0lYIp0Aa3FKwWUjSVqJHH6rZ+mkr+IJeHzH32lspJ58WFqfI0msFJpzZQzLx4OdzP3fY2xXQl/0jxoJQ9d7sLzi0T9oYqym66ynD1spBhBXB3OxZSmFcHz2F5BRxWRknJdhzWIiropUu9O27j80uHDswvdVbDe0m4MchhWJS70+gMJziodbKvZ4Bn/gkfmM8LD5Leu1dTXiTqz2A6wnmTOWYI1PhmLbve1UemRa3xpkcq2xhRqw/aQnfKh9LmNycSRbTw8gITal/YyZ4SkitZ4PZ2ZRwA69xb8BTXB4Zm88I/MTGpBZLlJk0WJRBFMVoiztgbK6eA39ONZRRjC1m+mpNS0pnK4RSZtw4jMG6JJjUvWPrMKVEc4X4m8vOM5zw4LGe9uesQaeqkHu1IfTB5YKRR3AANBf3mflSHLaklrZC2H3WMQq8wwwLZ9xCXIvW2zq0wqut4DvWRyxp36nimoD2LVl6UwLTb4pa4tAoDY985JCZ1yowiaFCT4J33RPhSXGpMVZTdNe6wtqCldWKAJ/myCXTsyKkUjmlS30X+wQ/WlrfJePEk4wVT4slLjF28Ot71xpna0nZ6lqdite5xS2G+wVAU+bwrXqIRtvWyzhyL/zEdQVveDM6Ecx9EXoYjcGuvJwYpkzlGMASbZ8FxvVrYa2Jv7d5UsfEGNrA4aW+4DmTLAbuu7mgMr7rWs8GAzafdPmtbe3bsWGMnsNlRBHD1GxMTI6S6DeG4/NXv3oIXNsSuDc1V/ijKBA0vu/1ZxkTQubqHgLI/LkQ4CBdpo3bZ7116K1rhBdcUQAxU+OLgrrEGrRUobsWwN6ATziuGXAg1YfNN5k5Mj7WpJayxgjgJrzQuR8WitCg6wli9EHQUAitq3VxLXpMVecejsfoznNTdLDuDXvGs+xvY0z9kaVukiow0UWrvHmPffK4xx0On/RJw8m98h8KCnfVVIXdKYvCUlfYUiuCsbKAecba+i51nNzZrHeEfRTiVMbFL+DFYZAlqKcMzTUv7fH0ucPhbW4zHJi46xyAqlKUQOH2XviFH9ebj8zKNVlOVwN25eUcwIL3BN15ZmFsNfG3ypYNrygbxtmDJb5gm2dOSUuLgx5ECfG7plknjZQAT5yLZ3hmGiliFsYGh4IsuYVi8k+wbrr8UmowOczGmlizKT/5lrkSFve4x9CbCe5apXJMCJmfcWUtKVruSR0IQjCpsnPrCn8vetEgZNSNoDCZj+8oQU788DBnDboIFXJPBXBJQWcpSasHtBHFIgHMaCzXL00tdZJ/2csG/FgftOk3wQB3rodf90ehTffmBHvrq0UwOxH39vAY3aEP87Cu1o9Az+nfZymJwIrhuqWxDRGY5omeqvIGPAutU2rghwXZmOvYvXcqHXrMorDGFYaG4cP/8N2zIkzVd7Ef5+q71HGacwKF4Z4yQ3GJAmNt7Ws8oPfMqealoMfT7eE3n4nXcbB5+MMH/FTFJ4HCvXtT7wnP9Z6LrLiAXXm5wnAeWRhLTfxjlpmqbLnG5lnjC+49d0pJw2QoR1MZCISCaPiaZo0xYBDmiRFiWq51oqXQ+NumS40Oio3nu55QCvPLXNKM0OcYTWtKz1imlLUlCqnvx5r2TSk/AGPEfHOCXgpZk6SGw2d83E6tKaSVOicCWO9+92lr0EWokHtKuOMdh/3ilJrCdLUQnOBySkpiTODmnvcc8JWCfjVmCF2jI8KdIu1zz/Jsn1Me/I8m4Z3gixKAFimkaNEpPW5Dlp6em26M7rL26J3yWq0khFLm6P2ULJYf45g7OEXJJuRTjbhmpBiHOSdNmBK31sU4ZlFY6gqTgYgu48rKfmeVechDLlu/j6nvklTkuKPrgSrxZHip9ZYWnfVvs4Y8R3d3hfR6zTnRGOj1b7Ov3/ZtB9xO8eja2bzyA3QAV+EHvXvHlNiLBLvychXglFkYU6bWauJ33VRmz1br0JTFZ0xJS9rpmILkvRg9Zuh9zKhhTOaR1G5/pw+Nd1OI3C/4Vqdq7yBACBq4dmpMumgYpUwP42MqbU3pSzfzMQrpGL65Jrzfc1zT1gJBMwRFLaYWqGtCAPofHjAtgjIn1AhIz7jPfQZm59SftFNMNVkpFS5y+uQaMF/9sQgrOEYfwTfcEBzcN9W6RAixKlgLPzVDyHpR/KyRZxFqlIbEKcE1Rcnz4r5h/QM+R8/WxhhCo+IkKDU9N90Y3andRMnwzLho8QPPoxilWKHvjZdVYM5CGyU7B4Xso7RTMB9zIVTRLtysdTGudYX5nYJ+7tEqIRVr07QWzmVqcUGnGeNWq2L2lVgSz7BnU+gzcUqekbo29py1a/mH57B++IE7ypc5RgE2Tj2rPMu6BG+Vp7/zOw/43sKjvYtylTi/i1KDaS3syssFcy2tjVtZshn5U5m7pzJ7KuNaKoyrxWesCNxYd2c/PQUpvv8wd/em34v323SuxyC93zvjRkqJe6ZiNRc8x+mCkHe97wgW78bgkn0DJ+aY2i9bNvMxCulYzQqCRhCi57VF7lihQKtAtFY460YxM38n8AQwog1WJqc91i1CTMXX9EKiLPpcsa9aM+MinM6OSd+u4B7B8WhKA050lOwQCjHzPN9/fbZ3Wa+e4k2Rhhu0SmhWlxO68uMz446yg2bRuc8TiA7Qs+vQ65RAnSqaqKcYyw2a8Y5YiFrFlUtzzDJYcRW3mWfHPZtAZXPzXPirlYCnxt7CGlcY8Ns7M3fX2zPBrTmL+8D3okBtre9S91UCg1OzyvtizYITuE32Yxt4necInjcu4/UdXpUA49Sy8lx0Ua3BwelrXzuk0YvpW8qjK++HA3PAK+H3WOv/1YBdeblAjHZL3MqpMnt6p7opYVwtPnNF4HpMa0xBMq6U0zan1HbBNG1o43BtepaEiRHC3o2Z+C6nX+9O8TrMJkoL4ZCsBcJavRcMh8De6sobU0iXQItvc1exU+0KWSlwkiJ3cIRWeu671grnsyo44boWQYyLTkwMnIXBwRElVJCkOAXvqQod69nVqN+ydI8s3Xdpvumk6+RujuYv5bS376Ysk8bmvWm9gF7Rnr+rhSy1noyfdSDWw1pFN8Ho6DRdvs25N48e3Rn3/e8/jJGiaz2rq6cqrj3rXQ88Uzr0Ix4xKDCxiOQA4B0EOF4TBWNtsbclrrCa4u1/yrmxpCt4jUNCG7XE/Zb6Lr19lcDgpEOznliv8CZ7hNWs8o/6HOtIgUlmW/oZWWMBvIG2/UnF6Tu8w/jhsH2fMcBJyi1QzFmQKNosb/BU3UzXAuzKyxVgtKmbgjAQiRLsbTrh1tTUdjMmXQ9jxAC91/8C1taaSaeEcSw+S4vALRXYUi0xRDhITItNbSPWcWPm3mWjh8ElADFVR32Xrs5OKgQFXMAT5h0B5yTlhL3VcnJKS0DFtxgUY7c+MRNHSPYsQj0rXE6nBKax1TTZsboPQIEreBX/xCpDwaMAUZgEOC5xQZ4alu6RtYcAnz32scvXcEzx5lIyDvTrfjQIb4RFlEe0wtXgxM26hS5TgLG6YlyLTv/jfxwUckJMID1LylI8W3s0kzibpYJ6Cg9oRDDxU586uJ3QEUUjBeeiMGyt0DrnCvOOxJaENxiTuDjKTS8OqZa4d+3a+i69fdWmF+M57qPoO2y02Vbtc1LZOrEyqaUVy06gF+tWcXqLhTxa/bAcLvMTFz1+MKasX2TYlZdzZrSsHoRmTs4sFHywT3jC5foGPc0+pzfMh4bu+57fuJpabSgnIhswMSOI1POMAwNt05/XnIwqY3MPhQWzaDMPbDgMWvzES14yFNlqxx2om8/1GIFNnMJfCb4zzqQh+jtxL6mxUE9j8UH7zXpgPE4YNjuG6ztKHdxhNnXTrrWcxH9NqOTUjol93MetYwQ9oWG9PUdMBhow5ygQPYtQzwqXejPm7Md7rFeKYWGCbd2HAEsAxdO4Hvzgy8HQp+jvc56xXVvGNyUEetCzTNpvsozSoZpSkrpCsRy4TnFGuEwWmDUzH3sp/bqsCeXGd+5L1hLesRTPS7L+qqBeetjyt5ToXJuxc9HUwnBbXYxTrrCeK9vBjJu4F4fUE/ZrMz5T8DIWsDy/TS/mghxLL06gL0XUvSw1OVSEd8ZVSIlNsHGrdK7B6eteN/C7BId7Lxp1v/+DE/O/Fus37crL4fwYLYUhwWwh2BDPU54ynG4xglazb6t3eh4iZ+prrRjZjJQiJn6MLhst34P0AkrMQ2Dpyag9zRqXvylk1dwd8H4nPhtiaUqtzWbTModiEgkszMnEXJLF4XNjiNsjLqT4uuMaSkVdPwmiwxzcw4326Edv76AMJ0960hAvUS1DTPE+445Y2geqtRRQtgAhYj2B0/rd7jauDI6ZxHNK5IJCRxg/pXiq7kPAGsIPQYxZql3SqwZsbObxvOcdDl/0Rcd1pd4a20UYwdeVqk9TFR5z/5qvGQ4NaJMryHtTn8fhBV0bJ1y6z49MplRpJdj8WPsoPWgV3XqOZ8aNsGQeawR1PWzZh6E7Snl72BpLxU9QefblMQGgPWVyTKkBrL9R0sBUqf21AfZ4kTowXNdoKm6hKGpz6cU10LdWALamxpmmjHiIH9/LAsu9SxSsHkThdV/KUqAv7whNpgHkVNXuiwq78nIOYHMhBMoCBlCtEjZJagE4LRAG9cSMkNsCUL5D0E9/+vCMVnghNpYOTA7TS+aN/wkp4/G89L5IzMOaMtitud7cKEzmYSNU82bM3klDNr8lgBkZt7G4By5iVsUQw8y5g9IAzVx9XjNxwkzgXpaBOdaAV0zRGnim77bEqWAAzPjM+jmJxWWGIfhcGmRt/LgUt9aLxQoejT8WDzTAymPsPaVo6qRtzTEwJu373W8Y71zdh1a57SkQVdGGb0zeuB/0oHnFbY27re6RHvic0kjAo49WwQHmp6Ktn62FIHtQgzDtBbhGm2jRPiGkQnOg7Q5flQBuupe/fLjP/k1na/ODV/j23dI+SksEdT1s2Q9x0yZmyn1iXXLYahWMqoSddxPOMQtZlDTJCdXSnWKArEd1vZcE2Ced2T63D+Dfs/CNBNh6DgVBBholI/flub779m8frDO9CsBoNdYRCiNZEWswOAVOX382bgAXsQrCj7/NV8q88a4Jrr4IsCsv5wA5uSSDoGWkhF0qUtYgMpsOoVY3jE1DQcC4MbdHPerGNTmAZ3gfywxCJLgpAbFYEFoxFdo0NhJBu0SLHzPXm5cUZpo6dw+TsU2SAFjfE3Jgqb+7Cl9zYSmw2eGA0uFH4K1TSeJ6jJvCUH3dfnuOuXqmjZiT2FhX57WA0b/iFQMjS6l9uKlF5KSVws1YJseYuzBlvPO38S+xHsydtI1L9dPK+IJzJ2zCtTYIBFW57fX3qYp23HLolDCfMkGvjUtZEmhp/nDaKjhRsIyNcsNtw+p2Ch9/XUNxA2kBQND72+fog7JkDL2DQlUCxBOhe/PJPq4u0QS0pxP1EpgT1DlswU9qzcB1ivaZI3qniD/3ueOBz0tixk4VH9Z7v/i6Jz/5smsne5H1UPxdekgtcRW26cypSwU/qVPFmk1ZZynzv6rUtQt8lHn8SdZerwIw3u4ebif0L/auHk7XxuH9RePaj1XcHkgiQ8IJkhXmZ65q90WEXXk5B4hvFfPiKmohAgKh1SAy8RlpVhbFhfBICXPMjIIj+MrnEQ45laa/TzqXBvzvGWmXbqN41hItfspcjxk4OQiW8xNXlY1sQ2Mi6mQs9XdX4QsPfNlwhGFQDsyD+dd1NjXFAGPiErERbUiMHXPhXnnBCy4LuwSopit1rca7FjA2AjDZAphUxud5ibHBmChIY8pLD7cpyOVZxlsDbJeknq41icMl5kkhihUpGU3J0opyWxUIf7eKdix+3jFWm2RrcPqS+A331LomrYJl7PYABj31rioA5gRGXcPEiGQNjYHlk6Ihk8nJe87cH0uX9a9FFQPJkst6LIUpQW2elH9r5n3WI++1r7zHO1msppTxubihUzT6HANrRmmwVg5V5mMO8A+Xa1wiY+nMFDnjxtcowfiNOVCE8dO2Czx+yH3sPm6tuOx7fYRYWMf251IryM91XPt4XcotpP2L/ZLDLfA3/jJXtfuiwa68nAOE0dpMhGTiMQDiIGARbIJII7RjaUjvHcIPMyNEokF7nnsx5WzGCJVo03G1BNyDIJ3qbDravfEtOfVMmeuNh7mY4gJs8gTW2igULe9cc7Jqha9NhXGaD4uLucdPTLlJozbvgoOkJ8JL9YMTJnFv9KrxLgUMIg3mzCvtBiKwzD+Cfw56uE36aS2mV9MllwRY5xTcdiLuMUHzcSqF1zQYNCbKANw6zYahVgXCWtdKq6Frli9rljiKVslaGnjbCpkl8RsPfOCNix+CKFi+907ji3m/9661ArZdwyqcQmveT8n/1E+dF9L2Mr5gn+IF1eUM4grxnFPV2fHOuBSsQ61CbS+nKJ25TinjS/bNeQV6R4n0XBbCxAvONTJtYSqd2TMoA/Bkv6S9CJy1XeBjhXRfgnrjsj+PPkI/N+Lat17kkGKM5hO8GDNFBp9aW737osCuvJwDIEI+Vpo4xpqy9onJ4MtGNBhsDSJDxIgZk7AREB9BgNAQej5PYz3+XQIKuM69sXjUpnGJP/GbwF+zUWwCxO49OcVU5mZ+5oIhYEY2BIZBIPgtzVZ36KWmTpvevbV+gfmYq42YXjB8zDnJ5L7EceRd1Q/uRJ7aL36n8JPMlCWnb4zL/4IyMTSlvzEjOKXAJFDR+sRtSMBTGsZO8j1XSKqGeg781oqiawKsa2ZGK4RjisZov+mbBkXQabUX6FjXryoQmKW1No4UWatpslXJqvP3fPduqXC6xKpkjFFw4JQAcU2y0moab/sufz/rWcP/hAshBP9TAra3hn+lU8FYwcYlQj9F8IzbOiSY03r435zxE4raqWJ2vNN6cDknPsIeIZj9TmuNrONasC8UzHPIMTc4iwvsVIHUvcyaxKBVl4g1wcvavZi5oSuBtawVPvOTzMUUc0vTRTjJ/sxaW3u//Z+9HGWhtaKeqo/QX0y49rmrvv/7D4cf+ZGBZyabjXJsTnh66uJMVe2+iLArL+cEzIYi9GUVIfaY42nsrTk+wNcpNZZ5FvNniUH0Np9NiEjdjwAJR5tNRgtitXGZIDGf/G+jxaVho9hYayL/MXM+X77dlLH2jETZp6KouRh7LaRmjP4fE0T1HXMnXdYbz6/KDFcAt1B7TxuUp02AolqYBpymazZGGYE1d/o2p5hbKU+e43OMyWfBeRhV4nCMy/WKzVEmekpE6wqpdVk8lwKU+JO5AGv0UbM/vA+j9E4CiNLrFBZrFSbbtg6o1Tyr9STBveaHRgWVsj6hM0yw1tWoSpa1EnwcXMIj4SFwuBe7MmdZmoutqAoOYYxG0W07vvZdcCcwk+vWsyioObV7X7V0Vhobc2dlLT3fgWGp6b8qiMBeSowLOqS4PP7xN60TdQx4533vezi89KXDu2IVSZkFvMNvP7XU/hJAPxQXfAQ+0UPN1BlTWEPLS2M92syaWrbBe1Jz51/9q4Fm616scSopBmifeV+yx5IAEat4qt+mIJ9npg+Ze5KFmb1MSahW1Km9vHbur5lw7RuDAOBaRNQz/ViPHDanqnZfVNiVl3MEqYUC957//IG4ECXLCCY35t8kGDC81PXIRvOdzWYj+p10TK4RJ2MnBQKIb53FJpVpbWpjmGvzPmWGtLn5bm0mG8Xm5MJJNVt/t4rL1Om71m1YakqO7zcKVWKDqmDunY7Tmdo7MYuUZyfgja13+q5jsrnFGBEe6VfjWSnu5jOKSoo+5Tf8e/6nfdowBu4sgq+dX88V4jcB7zkYivnNpUlG4fKb0ksxgRvjSLCztfEu73Aioxj1WgckNih1fIxXk8yqYHKDiGdKrElOk5Uxm0cyLbK+hAhGSkEnBKsysdSyFFoITXETVCYfBcc7xCZh4MbSMvb6LlklarQAgqae2uGCstBTxLfUDZkTUlUB887EuPicxeWUiktACQZ8hKKJr4SG0U3GzopYS+0vjR1hcfEc64CWayl8698qrEsONGMQy1ELyU5EK56RNWrjVPAUSjneZH5x18c1nEKD1iRKWAL1zTHJBUmWSAwUXmlsSXdPbF5LG1vm/rqZTDx7z14WrK+chvnhR+6bq9p9kWFXXs4ZbAgMtMekehDGRUATYBh/mzJp89gMKcpkU1B0KCmUGGZP2T+ykmzG1p2yxQxZOxGnoRuGR1j7m8JUfcwpy907ffsMI/T8NbEPGRcLE8FC8KYdQQRjW2cEztM9mSJCyPbMya5r5w0oOd7BupRgUEzHOFOLJk3gUlDMelEMfO+ZPsM04dC9dX6f8zl9V4jy+9YVk4yrbCzotipcxoUhYdbG5KSIefnfGPw2V1YJp8RYCNI6wDsx7qQ+Y3pq2VAA0XKYPgUE3inj8G6NqtCmAHgW+q3rS0gI9DYn466tCuYsS+2cp5i88cjwEUxJ+WqhvsuYVI0137gD6qkd7di/xgp3p2jMOTb+f/pPL7tq9RwCCZA+zxYMnqvKsPXiYrAmCRhO5WrZVLXU/tLYkaQT2wcVp4kBqUrk1tiYZGclsyauNvROeUiXafwxFr8ap8Ia6f8oP36nrhT6MB7vsP5pXSIjMsp3qhmja7wt1lLfU/Qpx/blq199ea2FFtS5bJ37rRdk4uED9iEZ4R1wv6Rq90WGXXm5ArCleqe6Cjaj1D8bPum4GB3ixghyKq8BncnEwYSSJrwWembI+PGdHHxP2BFmTi6El42QPiw5WdkYNkx7+rZZnIqdAGq/pcCYKdnfLASej7ljEKl6yVIFbPJaZ2SJOTkR9u28zTVBqfAa06+5ENphjsZqrShTngsvrjM2a4ShVGbdzm+q+Nac0tsqXMmEABQ1Ajnl5eMGQDPmTHGL0mlchDnF1PdRgjB8z05gss/b7rbupfC0VU+59VpTtr/N1zOdyDFT67KmANdSJr/UKuJv16Q/VpSXjNd84ZCyZx2Obcw5Nn57ScAxQZr4iihkV6LuRuLuHJrSXgSNm4f1bkvtT0HdS3DRVpNNDAhaTedl6yPdeOxAQ/l2OKl1ioJf73AIsQ/buk7p2hycBhKn4r7EowD3EPaJXzNmz3Vt6vig27ZVAaUmsTZxv8EDV6RnmUcsWXAo3izp22NxK0vigm6zopKye9cq2hcVduXlggIi08+DHxpBEzLZjIQoQHTZlJVY5xq5zcGYGTIZOwQcwafAHkGXk3tOOxiJTZEgv/b07TrjcnqPctIqML3YB8yF1QUDxXBSd8F8vZOg9sNtkjojwU01J/s7wjzp3b15t5k/5kbApYw3/Me9khLvcU/4PNdXZp2AvXZ+YwrunNAiaFjavCfBtom38UxjiBvAWvgxJ/SRrCBjd411ihBIsT/3Yf5tU8fa3fYzPmN4ToS2z1/1qsul0BMTFCAMWEW449BScLmEga5l8kusIiyHaJXSUIPdA8m0IYjR7Rj01rB1DcFNb/zWDS7jir3DHQa6utJl29FlgoNj2a24WBowXvdSdZ/EKpJ4C4ocAR4lcix2A74oP9zDlBg8sFraqgBvG5l6jzVgdYlFpO5vc0mcSvaJ58fFbF3sL+9gRWENvec9h3FUmuJGFZ+X+Bnz4W7Hu+0hClCsvnH5p5DlVNzKkh50H/uxw3fKHSSxIo0i2wPBGkX7IsOuvFxgQFD8wm0BMRvsB39w2HCuyYbM6Z5Ax/hsjDWN3KbMkG29jJxSc4r3vw1pY2J4GAX3CcbbU07CADHrCPQKPSZpo6XmDUgqanobpew/fDjVO6Xd5z43NicnFghDSedenxFgmqnVedfMn2SLYcZigDw//u+c3vztfu+BD8/NST4KT6xkaxvW9QShZ1hjcU7pwB1fe9YBGHeqpYKMBw4wrLRSIGw812/WEEIcXnzXKl8gCphxYNqezzzOBYPRYu6sUZ4l0wGOAk628M1NYgzWiBke7XrnGEPdwuTnmHWvsnN1O8R1q3bQVDHHXkxX6xoyT64FAru6y5LSbZ9bl2QatgpZcHAeQgfeWEnRTtYkndrRuvWBG2tpvu06VRwk2yZ7qU0hp1B4tmelcKLn9Q5N4T2JJ4Ej+6tV7GomXHWJ+Mz6GWelmezvVJ01T4cjv5NdZ09ZA+uVQ1qacKKH3lqIHfK5cXzWZw3jMOZq9bUXaiFL46xzT9xZFLAlJRLe/M0HBc++A6zDFCrWtGPqx1xU2JWXCwzV7B3XTPL32xbxVblIjAYFYsvJrTVD9uplpFgWsPHSENEGSnZTyk/3AskwAiddriObtsJY7AMGkYyhPD/COtYUwkacis88O0yUcCVMCNMoI5iK8XqO7Jw0lsu8a+aP37EuhYnAPyaEMXkOZdLnhLV18b6YyqMo1cq5a4LjehlQnu29iYmCa2uDNszfvFKrA3P2feKj0vzN2qEngso8pIBX6573RhGqylfW3fu923WydQS9whMaNM4od9x6H/ZhQzyAMRDIQNPJ1C5J5+qY910r0DwB11Eu5toE9Jj8FLOu9E5Aw3FS6+HJ3JUXkPG2ZG2MPUUK4b66htAmxVdMVA4GtTBh4qeC56qQUQyd1s+ryJs5WCvB3N7j7xTaMxetC7hE8ZonPnE8Y8fn1pAVi2XNGppHdT2bD8Wlxqf1Dk1VsfNZguR7lrYxKxtLDFryf7UCJk6FsLfG8Ju4tcS8+JxCk33aumDaGkY1DRuYCz7XU7RZY+CIMguHmbu1r73t4IeyBne9w87PFTckZcU9oan0RbseYVdeLiC0pzjpvnzhYy3ibbC0NkfoKUPtt02+to5CGyvQ1stINVWbLC6VCGc/qRFiE1EIeoFknoEBuM5cCa25LA3zoZRJ3W77HuXkQiDb6J6V9HF4iWAkVFK9NBVsCW33EqQsATVGgpnYuzzHMwnmVO8kgGRaeXc6aVPoBDYad9xHGWstjrYmOC7MqWZYsagRKAR7ToSEA8XJO+EEkzfWMGJrZZ38HasZWrOOOWkSQEn7xtjToRsd1pozVQEz/2c/e/Dtx8oTJSmKlGc4ZSZdNtlIxkwwiYGCO+tD2Pjs275tiJux5pREDJ5y0Qq4CkssWj0rSeidYhGlKwoY+rjjHZfHr1A+XvnKYQ4f+ZGXx+k3AUupZWWomSxxT1YlN+CZ3sPKBq/nWeTNs42zWkngyXu9L/F2cDaWsZPP7T/rrM4SfpTPHcRYMlhcagPPqkSaC5r2vnSaz8EglubW0uZ+9KqwIgXM2iY2JpmNbexTDjyxokbJ97l5Jj4FfRrzWLmJngKbkgljUBWazB0u7dna2874KP/hc0vcqG/3dkNczqmbkV4k2JWXCwZTWQhqMfTM0mIeCNdkudR6FktKyvdgrF4GAZ2+HKn0awMlyC1av7HEKuK9mFobSOY5GE0v4LMX+xA3GrzAQ+17BFLNMsGmibBP/YakNwIMEcMCEeAYDTdTPb15FyZg3oSEewjYWu8mlgR4pjjBtb/hx7y5dZJOaQxrguN6GVbwhlnFVWcM8B13R8ZqTimaFYXED6Zc42CigCYoN83nYj53r7VhycP0rW8UTFlRFGtrHNeUe1NhODj2Du9Eq3DjJ4XxktGVqr2EXdwQsahhxoSa98C/+c0FJ67ZX5SXtjdOgpPHeuOMCY4oi/CJrmO1SxwJZcj4s6fivogQrQIaUIaMyTMFQq8J5lwKrUUrVhJ0xzKA9tCXa+CA0txWlk3GTmK8rK+1BHC0pFWFdaBAf+d3Xk5GSFZiFNi65rG0oaE2nb/WfupZZeA9NbesU7W4JOMrNMiiIruy13C0p8Amg8eapqhdS6tR1lmFvBOPt5dcj0aiuKQjOTeiA1aC0cExsTLXOuzKywWCsU1QT1fcH4H48jUI/PIvH/62GZYEvy6BXr0Mm90J2kbCiDHVBMNmo9uUmBgmhUFjJGPZHvyxS4PHqkUoFUi9i5KSSr9R2mqpegweE2B9IQgSKxMB41qKEIuOAGmWrnZMGNmjHz28I9abWpAswasPeMBl036YNYWI0lXdH0uFTC/DiqAzb/PJSd3zvS9zgxPMilJTi+zFwoJx51SY7tosHpSj1l0AKBbWy/dVAHlPAnl9F5dBcGO9jdFaoIUEK1MIk9Hl3akpY0wUy1yTuRp7zXAytrV1Vab2Fzybl7WRAZf4MnhIVeCaho/W7QuKfY3lATm1U0yMiRXGPJO9g4bc75mxVPjM6ToVV6silBYPcRGfh4DquWzgyXPTzR1O4w6zZ9Ccz9uMncREGVtaczz0oZd7f62h/yjPseC1EOsJty/8Tlml2tgnY+XqtEfSYDH7A1+wL6I44xuCqHuVuMcCyCmangs/bRxVLD3cPFkz46YAoq/EPaWAXEpPtOv8uo1u1OsBduXlgsDWVDl/I2xEn0qYpzClY4gYr/8JLqcoWTwJQk0BK79tnqTeJhMlMTCE5j3ucdPI/Pb0tcUiZBzpAWMcTiuYDkbRlqr3PxcWZcuYMmZjcU+sObrmYlKESh0TM7/5sj7UVNqWSRA+tbXBsUGVbYZVXDKxomCu5m9+KmnWkvTSTr33mc8cGLnxpaO4zwlXQjaCmkLILZM6L8bvHXD0BV8wnBDbOSXIMkHRia8JxD3nBy0nzibMliD03trJONVdKTFoL0JkTkmcsmjN7S9ZGhREwqQGwMuISuwAQWQeYndYBygu9gRh7WQeS1wsKfAS127FSVywmUsyQswzh4K4COxB+Db+sX5ZpxBQc3FuxoFuIuCTHMCt2mbs9MYGp/Xg1YOsERq6+90HAQ+HcO3Zvq8Zb9YixSKNrVp3x/hmLXDo0IcfWAuQWLBArGFtsb4KU5aP1BriCjIne7PuX8qNw1uea/3QDv6TuKda9LOXpn7rBTVe5nj/tQq78nJB4Bjz35o8/yWm9F7wJKZlc3u/jU9pwMAwt/iKMd346t3vZOr0PBWZvwXqCYq5mElfam6KsvVK1adtvdTFQNI4a3okJvmN3zikgdfxrWESp4zkrxlWNVuBsmK+8J42921JeuZwY1Hll/IBX+jIOOMqS8wFRYaQJmCiBKXOCwYPp705BS8pTujaml6euAH0kHLp3pnCa6Ena5H02axNUv7jWjlGSZzbX9aegm3soAbAx2LgvQQeYYiuPcvYjLtWjTVHzxOXlP5Pxp/TvPEmQ+/hD7+MC7/b2LZaM2eK9tJfZypT65g4t2SzGX8sUj6nzCdjp43VWSs86xoZTyw48GYtzI9CGwUcX/NsdNJmEk3xzfA7LkyKeixpyfDKc0K7Po97pz3scQmnplYPzAUvcq/xRnFX4ZaCVhXt7CW4bLMvx3B5myN5/7UMu/JyQeAY898xJcpbU3oveJLQ92xMxEmLe4U1wPMSQ2FT+e1+9xF2rkkX31On5uV5fsQqqA5KkRkrVW8D2+TKYycmB+RUjxmm0m5PSbxaTKJmWPk7Vi1ratzGS2m0NjUWpa3roC6FEyxIXyZrw7KRIN1q4QjzTFO6MeFT8UJJ5fqIMmFMEWqJHRHMDFgsvJtgSBZJAhxjQTQvp2/viDVkq5I4t7/yfEo5+o3VIQHF8GqsxmMt0HoCOJONljpIlD9zY7WJBRIuYuVKGr1KtpSX1hXcK1ZYO6S3tJfMLS4Q67c1C2mqL1SbPZfsM78palxbrqmxOmv3xdgaJc3aPPEU+xx9Gi8Xn95hY+tqnxsbJSP0oxlrFC+HFWOMAoberQ/aSgq1v6t7px720h8MrmRP9dpd4CvWudZCgo9jis2dgvdf67ArLxcEHp5SGgAAiQBJREFUjjX/bSlRPlcO3+c2PmYcE6zNwIWUGiGYOYacnj4Ak3ZyJQSMwSY6heIy1gsG4xFIRwnrlarPBiZECBzC2rOSyp00RIzKydF3rZJ4tZhEm2EVv7mxmk8Cbs2bEBlbb4oF5prCYzFHA/cm46steLikESRzPauF8VFOuGDgz/cJUIUf83DaBP9/e3cCLV1a1oe+HOKAeiVeEcxNoiIkZqkMYmR1E0WkFWSQKDI0KIPMUS5TGKVFQNLMYbBNR8O4bEZDVGhoJIBGpVUEjYlRFhCuRJfgNS7UaJSrnlu/U/77e3v33rv2ruEM33metepUnao9vNN+nv/7jL/7uyumT1hh+NZrKt3G3Ih5G89oS7YBieueL2vIek325IQuB7hkxxxnZMeJrhJtlrDUmN4cz4yUOljOSZRX1prPTGaer7ba9BAgG1p7/m8jt7aJQjJfidaxSTHeNFxJvNhGz+lrCzbji9UWdpz7XIzNkb5ZBykzYK3wsYoGse8c7QSSAS7FIWnMnK8fCd/G17JWk7QyZkvPmGeKliTmne5mzzrUFqBdO1KvCbXr1ZyuG4NNecw/2YD3nw9U4OWE0C529nMzJ65Lh99NTOZhSp2Yb/qm1bnOSVkAFD8MOza5H2hwduEsNhYlot9THmAMinAVDktAtQULw+y02299IPE4mEQbYWWMCc34okRlD5R0U6b3+TBpX9/6smYI2ySxa/0t1hWC7DM10iQ41/XsOrWpW4zUOBIo/EiirifYo8lL7h/zoQ1DWqVdPV/m0u46WV+j6Qpw0TZtiSkl0XKtn5DPngfvhD8tQbSOydcRZ2bnzqG+tZd1m8itbaKQuvOZpIwBRq0GxLGeIfdxTDez7KbPxdgcGVOJOd0vuVo8CwBMskS3Y+BZFukErAAOnhFrjVnNc2ITY222yfMSbJC0Dc7jfxIeM+Q35druD4wCS8YjoHfuet2Ux/yT8yRr7hwq8HJCaFc7+zmq9Dh3ZgfvAeymw/cgxAkvOSwwdu3xACcNvmug1AMBXJK0bhNnsVb4YkB99ZHWRRN0H2DviqGlHhONQFKh2zFSMRNS+jUEEo+aSbTrAkPmaxEThLHHfJOhdB3QMyd968t1+VUkT42xGWOYQ1E7idihBYspZKiooGteeunqc8x9yVdjPqwz82stmn9raRuQOOX5srtGAVUER4AVDUTqPCVSLeA+fkKZI8U2jSVBxiwBDLcU5/ZEHQGOU9ZQd+25hmzGxtda6IuAmxKFNDSffXlaPC8xCzNF0uh1M8tu+lwMzVHy5mS9t8VVfZeaRLR/2u85NvbWjXnwnT64rmsBFm9/+2JxhzucAzDm3/jShNkIACTd+klDflPOT0ADLY/3MS3olKrim/CYTz0PsuaeOPBy2WWXLZ73vOctkenHlov95ouXvvSly93KcrsyQG984xsXl1xyyfKh/n+WO5ibLp7znOcsH4zlk3Ge01Hu7DEshcG8271ktxHVaXZerWbCw4+ZJe9EnOW8UqHYKzleWm3R0IM61LaMQaI83EPiqTbh11g0wRAxcfzADywWz3zmaqeknV7an5wPcpcMtXVOPzal7j30rV0Xvjf21NnddYGBAwUET9TabY0cJoGhHTJtQXarhDWG3CYRWxe1Y2zNh9dYKv1Qn7kvtaBcHyBQGb0vxHyTeZj6fAVUMQsBLAR1oqaS+I8A11ZgL+BeW0SXeCGgRui98UxG42RYNif6JfzaszfVRyXr2zOi8jyn4DjTppp7zBZTopDG5nMoT0vfumvbtg31zZFnP06zMWG1xVXDg9zbugdAgAj/ewaSGylRh0lOyecIT0ktK2MI7Igm6+vHmN+Ua9C4AC7WtDGayuOGqqKfJSByIsHL61//+sVjH/vYxeWXX75Uz9168aIXvWiJeO+wVLN+YIl6OwkSlvSepbfoxRdfvGQgly7ucpe7LF7zmtcsH5R/vnxI37+0Ly8NzOc5HcXOvs3YijFHVUrlnSrDtBJtwixMATP3kGEIzsEI7NAcl91rkrhhOslG2VfjZYhZd3eBmJIIIZoGTovZAcZnY5McF8tldajBeOUrV23CwLXZtQmcRHx024qm9mObuRm6x7roGsDlMY9ZjXeqaUegBehRm8tXY77a6zhHaHV7X+ngu33bdVKsTQD7Osa/7fMVUEUAAlUxG8VvJZFD8fvyfTdrchKUmYeYiGKO8w4s27+591wflTwj/Cza0hBtxJNnr89Prgv6En48NJ/b5mnZhLpzBKBxuKVxSVLKmN+8knuGKVt7HUt7RnsCfMXxGuFxcTR2rYReo3Wm+XV+U+4TH5lNNF1ja+AoNk2njfYOXl74whcuHvKQhyxV20vd9pKAmCuvvHK5uF6+ZMZLbtyhF7/4xYs73vGOi8c//vGH/z9zuUV+x1Jn+MNLF3HnngXaJ+pud1p2ynZ/whCBDg8FYeBBYtP2INqNY4gEWXJcYBg5x4PvewwZ88wOMLszNPVB7dsFJrMoJu3hxWAwfoSBsF3HVDWH9F3ivW6hw0QidNuaSJ1urZpdVv3dhKm150qgBoSYN8LWuLQCLcDCGLbra85995EUaw5g32aMus9XBALQ172na4hgA9qNKe0Koek8ey7rsvX76PpWJV8JDVRbKykO7omUSzbadT4qaau5pDGNc3RqXBHOXsm/Yg10hXEf6AMI9JH/1Nh8TsnTsi8eCIAYN+AgTtz4AT6jfQkUaM9Jptr48AWYOSdV4PU/prHwsjHT/By/xCGwsUk+r23A+vlMewUvn1w+pe9bwuAn4wJ/S5+6nJGLLrpocbVYvB7yPU1NSzQ1P2lGe+gvl0+vV+hPko+8qJe6O+dutdcknqOZABKoyFNoT9G+qFi75yD1llj3YgdH8qVMfVD7dvWpXYQxJKIpAhRTpSKm6iVM5jLXltlhKkNtxSCkK/dd0o2P9eMokxS25xJcydYaDUEr0JgBusBi7n33lRRrCmDfZoy6NEUgALh8SoBCYwj80UQSfoBhn99HX50gc5CIJFoB92IO7WajHdJa9YXmuney8SYCKOn6aYvkMAFIWk1QH+hzTY7FruWZP2lJzrRbRm/k+dfH5GTB9mmIE96ePCwBGc5LNFQofm15RuJgzdS3zjQ/1S9xTNOcjNRTNZfrzMD/9w42TaeV9gpe/nD5pPz1cvtxQ09GQ/7/bTPYQ/xi+o73fR8xLz396U/fTYPPAPXtnOOwhgl6MAi4f/EvzhUkYyLCLNiOPeyxq/edg5GHOCLOeVD72oZZYVqYve85KSKfMQL/ey3dpA4Z2aYP8pg5BHizi/a9z20CqammknVq323MMe25HhP3YNaIej2OpX7rCqK59z2KfDdDY7Urk9W6MgGtn4319NSnnhNGKY8w5PcRMwwwbcefyBhrJgUws46HstG24LLbVmteGzyTtJ82EcxPfC1SUZzApjF9+MPHo2SsC9qbJFsDdto1edxJztLu1Bej6TI2AIhxwJPwGPPU5mEJyAAwAAnzFbOf5zcmO9eNgzXwMmXjsc7MicY0gwp19mkujXV4KYDq8xQz8E+ep0UXz0S0Ea1Oq6mhefkHVk1RLw3tnMNgE21ixym3CODggcEwCPDkG+FMSX2ehHDU1G2Cqk1MDH1tw2ySoh7D1wY7qRTscw33xRi2eZDH2toKma7A6etHl7Jz9m58tQ8DEvlkd9/e3zgDiN3U4GP3aM9N5d84iPrO3BBoxkjoZyuI5s7R1N3npsx0TCOSKs/bmKzGBDlAzjwKFCgl0TrSTsnm22Zu1X5j1GZ7TtmAbjbaCC5CSx/Tv762JheLl2cD6Ei2ZdfxfBDUHLMDrAL6zFUyJ2dt6QMgRhGu3Z6fk5LkLHW99D9+QzYxKXBpPKxrz1CbZn/M7Jf50C/rNw7Wc/rXNXNmvnyOSW+oVIHEhca+5XHRjKYMhTWw9J64pu3rzMAfPQ+LLh47ePnC5Qr5tCUH/TjI2JD/b0TX30O+n3P8Zy5XglfRbnIpcMrFDD1EGETMDl6Yo6FO7Ryhhn4f2p3NNTH0tS1F7nxOCvzklsF8LQtMHPORHM1OzIM817ltrK3t8uoKHO1LRWqCs3u/7Jyp5gmYgAU+NATlJZesHIid5zpyWcRpOin3U5RtSH2fc2nJULIdGyv3i4NidoftWGxiBtpXVNw6f5a73319W9elyR/S3ljfHMKNI4CQWmFT1fNt29vMrX1lA9pstBFc3gFbz5OMsZL59ZkYnOM6rmt90MIwmfjOb/ERY+Z0njabf8+2frhHd20l+RttTWotHVWSszGNpBB6zvrWLp5EWKeSfJzsnfPgB/e3cczs13WwXteWsaiv172u36TXZtuNZtC8WVv4VJyhU4Yi/lPaFZ/CFPKcagY+S7RX8PIZy5G/1RLWv/Od7zyMGEJ/s1wh/v8+pXt76IILLjj8/dHyKf8tcdj1fdH2NLRzJjAJUw8ZL33Mn1YlJpsIQ+Qh8gC9610rRi28uG93NtfE0Nc2D7AHPAXSPOCOS3ikhzcCR1uFQXv4Mek5zm1jbU1BwThXtjsl45ZcOQl/zf0IKAIecHFM6uQY1yQAe8YzzkU+iB5xTf1O4r8IP0KParxPfe94c6TP5sLn7FZTR4iqnHq8OwabmoF2HRU3xZ+F4ybmbW32lYGYkia/T9Pk3JQDSK4ba8mYT1HP97U9mVuNvflzDSbVNhstAaYvET40mfy2mICMe5+JwbXj55KsrsZeH1INnEnEGk2bARNrEOlTcqRkbRHy7v2oR636d1QRLWNaNu22kYqmxZwYZ+3Rx2h7zWtrqu6SvvWZ/brAbKpT7Fj+qT6TXgtgohmktcY3mIXMQcB4shPT+lkDKVKKV6R4ZHjQHw6Ygc8S7d1sxKRz//vff2lb/drD3C5Cpf9sOSOJPrrf/e639FX4vw59V9Cjlk/QbW9728ULXvCCperzzktU+7qlOvNXFz/6oz+676aeGerbOSc5HRWyXU52Zx4QjI6g8FB7QGM6SXIoOLQPGGxiYmjbhqEkyidJ8KJ6d45dGKaN8WJkHmiAykOPgbvWVOe2dW1NlD6mhOEkozDGpQ3AhvsTfq2mQB+SGCvOzijFLu3CU/cE00MET+rHpCCgPsk/0ydMtM/4JK095qb97hvgw5mxj8ltYwbaZVTckEYEJXeQvCbaQxBTp2srYa/vU9Pk92ma4kwLVJjX1qSzzpeGMKPxYnZpMz+0jvDMsNrsGUqRUsBFMVHrB7B0fMxLAWJMDNaA89uSDo4BjoAX6yJ+IK2JyvE0kWp5eSY82ynXoE/tLl4o8r3vPS2F/a6o1VQZX/zGPAAs8Tsyx3HOzQbCJir1tzyHt7/9en+cdUB7agTbuvxTXZNeWwG71WJaA55HFexp+/K7+YuPkvFI+grrOkVWUWp+/Y8eM/BZor2Dl3vd617LB+T/Xe6If+DQ6fYWy239VcsnKk65H12uKBFIoQuXBki5XZ66hMtPecpTDpPUiTQ6CzledkFTVZ/dB5qAfNnLVscn/XyEbvJauI5pw0A8iB5CD+OQD8KmJoa0jVAQzuxBx+w9rNpDjaqNAViEAsbjQdc2Y4DxAzeuNdW5ra+t+s5ZMAUqn/e8c4UdXR8joX3yP0GA0TgWo3vzm8+l82/DNVEy26bKsDEEfFwbSHTtlC9w/YSF9pHztZPt3s7PWGQOtZ05YKxMw1EmRxyiMd8b40RDoV/YgPB4/xtr4Dp+Q1PS5DvGGm6LeBKGWUvmK7mN1vnSRJgBLnbKQIh1GgDhBTgCGUC4ZwgQcUw0hyL4CKa+7LjmwjrwjHYT0SURpF16nsNcw3iliKExM07GwrPjGXJcfCi89B3QOirg0mqqjJGx8Tn1hPQX79F+/dSu+Omkej3hbTwo5Ke0ewhoT41gc1ybRqGbfypmwZj08F7HJqKsq8UEzGhUUrna2vdKqQpjkN/xCrxNQIK1G43eF33R+Vt08cQ47DIRDZmJfjZxcA3d4x73OHwVraeuGpNq3W5+ismkfaA9bISzhyJ2dc65bcVox6dSsAcYqCAs19lcNzEx+E07tMm58UHA0AksjA6j0S7XzVjEPozZYdSY3BzntratBJydIGZ0xRWre2JIIjS0i7OkNkalG3Wue7pf1Prmoo1QSoZPv2HOcTzUdmPDP8H3hLTv9N0ub2ico01wDpV01ylTe9apl3dpBtokodaQ701MOq4FHAADSdePocfHIMx8LAqJBoKg8XyIXIn2BjjUZs+Pe3tmukCzO37tbt19tQn4aX1ckLbHyd28Wnu0RalgTKD1aZvs7JkV9DMaIWMI/LiW+5lvz2DrDug32kHXBnhUt9ZXa9Gzoo0AkWfJeOq/ddIJ8NwrRcum/eYj5tSYs4wpsaC9yW7sO8clYit1sLpVnPcRwQbAAP8twIlvET4J8KaaeEx6iVCM6bYbSm3tmF8ARdtSzNa9EjEWDVmeC6A3BUu/4itWDslnNUz6vIg2OsvUqjExRjsVuxKmH4LI7oXwpf68291WTLNbr2PI98FnD5MHMztTAjsqar97MKfaXDcxMbQCDZOya8FEgBcPcVK2J/NpKiYj36Uq8FznNtdyfSr3VpVMABgTzIPQyD1DbS0oQlb7jCvg0dqt/Z7oCcypBTDG2bzpIyaIoUfFP6R56c5dFygN+a30MfRtzUCbJtQa8r1JDhNkDKIR8TvhS+tifPWlL08JMm7axfJsPIBCQrHV3sQsw9zYVgUOQOIHlgKFfdXYCRbAhYDyPWBrPgNSjEMcTkX1GAtmPmPfNS+4r7ZZC2pOeY+PVSo7a/+3fusKzCQcO0DP8xL/sJyXRG4xLwXcJjDzKP0m9Ln1y2rNqdoEjBHwBLrjgEPrI3zIc5VnY0q7x9ak5xKP85157WpIrR1g1zqgwWzbCTwl90y0LDEXps4RntPWOcKX5ZOKA39bhNT9zR3SDt+7X0q3tIES3//95zSOZ5UKvJxS6tqMaRkwKA9AEH0iDOwSlj7Qh7swjNvDBcx4IFrB1fV9oJKN8IiQ7YYaThWKm+zgW4GG2WNoGIbPBBbGEf8c/Y3wTxZOx27i3OYa0ovbscZPImnII3CMEyaaOlCorQWFyWA6F198zh+D2tdvGDamrO2EpnlBwJFrpx+Oc834nwifTO6Obf1W1jlLDs3f2Nxuk/12qA8YNsBKgGlfd3fsmDhyDkUhpRaXY4CBOD222hvtNXdZ6+Yn4Mb35lL5hKFEY9oW3xnnAzPGNAkczX0KlboHoWZe9Nf/bcK6+Dm4fjRnBCcNURy5rQu+M/yAMl6+B2aSTsAcuXZAvvc4drt2/MH4u2ziN7Hpc27O9Ml676ZXQPphfsyndicBXzYKicIBVvvOb2lsTTJXeQbb+m7d+lCOTWbcdvPQRn0BJ20BW9/jk8zH3Wrv1kX84Mx55ga1azt+PtqEF7h/AJZEoF95xoELKvByCqm78/MQYEgeGg81xidyKELUA4IIKSps57HjesCilozgan0fHM+ejkFF2zIWarjrVNatQLNbxZgjnPQp4ZJAWhgi5pxMsCmCN8e5TfsBF0KB4GkTQ3nH1IxHyikQNGF0qQWlja4D2F100UrQiCpKIciog1MQ0riYP32MWpgACjAi1MwrAIpBigrpjuUcv5V1DD0RN3NqOyWyap3vwFyfI4zb+NIa9pkItNsaaMNPuxFTds7IvdvfWu0NkARoMmMwu/BncO84BVv/Y4nGWgfdpJtPjp2MQ+7ps/t5tqyfaDiZQvTH/Fh77s13K/4gCW8GZAhL4KsdL2vVs9lGnLmfder7mH6txxRW3dTfZdPn3HnCuAEXgp+QNgZtqn9jZ10GtLh2fMeSq8U7berY87wup4/K0q7d1neL2Y8GDgDUXiDJ+LXguI368lxrTzRC5r+v2jvSB/dynHlA1mA2NG3bzQnQG36A3CvBC2edCrycQuraaaNOTRpsDwWmkKRWmKIHgxCktvYwx+Qh/LYbkdNNyOWhTbHCoVDDXdWdGRJoylrRKKX6Kwad3U52LlHjYiKYTEoJTHVuS/tpXFzLOZhG68eAYRlDjBVTw7iBKpQoH9dpgV1fIUjnACf6hzEbI99n14/875UkVebJvAyBgCl+K+sYOlOZeQO6Ikyn1HYSWbWL7Ld9CcDkPaGGj3mkzyQmEotDZZ/mST8Ieu99FIdcxyhayZfAHHTDsccSjaFknTZW2v71X786P2nqA6YTQWMsOSDT8LgWkOXZYqKyjt3Le+sPYi1m7ZlfY5nxMv5MY+7JbKVfcS5PIUm8QRt8Nk6uPdffZdPnvD0PYLTp0J4kH/S86aP2prSCOfOM62eeb+AmRV/Hnuchfxb9p8WNDx9ttHtkQ6JdV165OjYJMrXP961DuDYAOTaK5gcgBCzGHN1TRNN4xXSXkixexiZt1B7mJedYV/qOZx+lf9JJpgIvp5C6kRl5ADz4yTzrAcOAPUxB8R7UmCEwP9fBxDC7dncsH0hXwHgQNxWKQzvvOZFRtA0YI2Zmp43puldUsMnEixEFAAAFfpvi3Na233EYEeESf5PUi8G8InACcAKeEubax7y6hSDt8AGyn/u51XyYT20II0sKdHOZPC3aA8iMgYB1vkXrGHry2cTM0NZ2QpKn9dV2ElmVEg7tNeM8HJPFVJ+jtg/uCYSMmcTGNE9MRa9+9bREfK5p7s1x99ihRGNdsKa/BIx16hoxL2h3IsE8q0KUjQugqO3aoQ3MiABv8vZkvK3FhIrTFsQHJ+Pl+XHNgKU4r8eM1IZaa4NnJVrKqbRpfak+PyFzlPwz+m7cW387zxpQmo2TZ3moNEMfLxmKYIspPGZz5wIh1r/5tznJxoJzvmfRxslvAiJS2T4Rgxx125ISY+azmMLxkvgRZm159vPsxfQd0Bn+ANgal6ICL6eSupEZrf01PiAWvocgzqfZdSZNdiJyYqddtzt2vAcvDML7UDG6KTvvuWpn52AkdndxqstOl2oVw9Z3ORf01VhEffuUp6y3Ebftz04n4xmVtZ0Xxmf8WoET5hjhM8S82qyccnBgXDE9BVii5HfRRy8aFwzMffVdOzbNqjnG0AkWAjdF70KJnMjnvtpOzAAZg24iv9Zmv0kBzakmsSHNEwIWpyTiI6SmlCAQlm59CJO2ZoAZ8+RarsOh2+cIY+p+floRTtpmXIyz37xnXICibt4eayK1eXyv/93q4DGz6gNB7JzkHImmVTutNd8DR7RDc/xd5j7nY+cBIcnvok/GxTnWTgtK122chngJQNIXwWa8vaxp96elAhjNGV6ZyC78JpFcAAPggszJpukEXFdZkHe8Y6Vpi6N4xsHc+IyygUB4hD587/ee86M661TDcAqpLzIj9leLPPVRYj7ykGEGHlav+It4JUcIoThWGwZDbM0emI0HNmBjTo2cdWpnUfVdZ+Ixp1T35RwXXw1CRZ/5uExlLG37M54ELROS/+Msh7kleV8rcKaSa8gAKwoswBKTArhSoyZMNfWJotKO6nyK4/GQVmsoJDmmR9QmactvKCbKLhkz7SQAzA/A2IbAtskOZSTdpIDmNqHccxyap5RLSCkG7bDWgBLXoyUh5NoCfe6nv35PZmhj5X/t0Kc2G24y3A7l7aFxGcvbY5yA9RT0y27edc2LvsX3yvjNzRMytxbW2HmtnxBAHv8fgQKAh/UIyI1Fwa0rsolPmffwSdfHy6KdptG0Rs1jzFj+D9gOJWuy+zzsYatjNg1GsJHiA6eQrXkMb0niS899eDWeDuDQ8NKU3exm8+51PlOBl1NIfczYQ+jhsouw2KMq9lAQKh7qFP3C0JhBXIeaOcmvkvOhWxvmrW9dLJ75zHNMz8OWUvKxcU+tkaMdaoEMqZ3tbtTZ9LD2pXgf24Fvk6ek237jAVAYmwhtIMMYcbTsCpypJNeI8XQt93BPwKQN926dA33vZdzcS5+HqvwGsCQ/TQBW17G2LyQ5CcCozBPl0lfPqa+MWCKrRL8AJ3wA3BeYxJBprlyvm7Z+LtNfZxJbp82bor1ZVy7B82btWxeECfBK8MWkycyRa+V+TASAsDVNmEZTEw2iMW21Fdvm7Wlr+tCIMREnuoXDvut+8zevihmaBwBh6jOzSS2ssfOiPQUi8ZN73WvVHma+dRrZKSYszytTa0Ck99RtS9FZ9zC25tA46T+e2Y1kCjDz/dxNS5esE6ScSUqcWF+JUNM+aywZeY3BWK6ns0gFXk4pDTFjoXlsrzzpCS+MFsNIzgQPRJgfRpUEWBgwNabv2towQBGBS6i1NXf8n104YfT4x0+rkYOG1M6YUJxj3Qsj73MEHAMpm+Yp6QotbTBedtMJ1yRwpCNPkbS5FV0x25/+6RU4IVASJqkPYUqxgTvW3Pkdg2frbtXpXQETwd2NlLFTc812DPu0ELmnddKNyklq9nxuqZ3bb/qm1Xfa4Dp2lW0l31RW3kcl3KlOpOsAbjYGjmHaS7V0/dBPc8JsBiBkjNwvyczMr/u4Tu73Mz+zWLzwhavPwEtfuHerrdg0b09L0bRoU9IoAFf8f+SHYfa0vuUcmRMxtGktrLHzEJ4kWzDQnVpB6xyBp5iwXOt+91tpAoHIZO0FPJNBPGAhZk1z3heWPwTMplJXGxpHfhoYWrJoeGO2TtZuvGhOTq2zQgVeTjGNMWMq5wgzD2yiDlKDA0OMoMR0MC/H2P3YIWN0Hm5e9wAFIR41alsXxefkMZmimo+Zp6t2ToItwMhvmEkcRvscAXddAr6rzdLXJJYztoSOVO4RdBE4xmzqztU8JVV7oqIwLGOTTLxJ2oWpMVkBAWzxaMi+HsFtPgjvVAMHMAlgqvmuQ3Yf8LUbTzSLee2r7eReY2YXwJkQAvT0MdqCCIIx0+SmNNeJdMraMR7GgokBmX/jb32am+zSPSfGw3X7/D18TxAmQ3RXIPYJxW3qTXWBnGhC80ADY64BW6DFc21zEyA2NWJo07atOw9gSHHJvjk0D694xbXzpkw1YVmTIii7INL4xC/LtWI2tGHTj5amgsYhGvPLsVaYAvUR4Kf1afvvebSxnOufdL5TgZdTTkPMuAU2BKyH0+5GMTAM10OdhzZC1EML0MTRN3V2CIck0Wrrr6QSauzvVKnrVPNJ3tVVH8dhNPbf1jwxJ9R2V9osu7Sk9m41B13fhx//8dVubcrONWONmTonmUUxWSptwBGjSrFJzseAx1gUQyu4XYOPhHlKHpk4zjI/ZAzjEHrnO6/u4doRCHaAQ/OH1pldUmaCoE6Ok13uXnfpRDpV8NO2EKrJF8SkChjGf4wgJITs5vuA2Sbaik3rTfUBOWuAmTgRidaqvrhmCh16Fqfm5Nm0bVMiwsY0skzcBHw0I0MOuX1rrQ9ExmQVs5xXHKQ3AY3tHHRTTbR1kVqgKAWAudB/z43NZpuUz7XwV20/y3WM+qjAyxkCNgnvlanT7rF9aAlrD09S0zsWo/bgYOB5oDDqkIfL72245TrV/BAj14aEeo/Zm/dt8037CfgXvWhlZxZ10DKNru+DPk3ZuUawZ9fYMqkUnMNcmeC+5Vum+ey0gjs+Tcn3E4CZ1OVAK+EsUy/h2wVcraljaP7WmV02NStsQ5s6kU7V4BCWNI+0FeY9GsHkXjGPKkTHQbkLzDbVVmzipNwFctFoRqPnOQOckXZ0qx9PBXubOlAPnTcU5ZVaTcYK7wLQreMhh9yxtda3Nr1SOFEb/M5hmEM2Pjg3qqirYbGBMN7u01c0lGbU7/hu68CcjaU16Xv+SWe5jlEfFXg5Q9R1mostPTlNEOHnIfY/RsLUFC2D3U/MCSjAx0PVCqMx1fwQI08+E8x/H/bmOaSNhNUjHrHahXdNJdre5/vQ3blSBWOgYdKuEeaZvBKt9svYYl4p6jdXcCdPTFuyoK23BLAwIWYXOgS41s3fmPaib36tIYLAdzRKcVYEEKMVbLU/c3eXmzqRztHgxO8phUqNa5xtgRjrgZAVytoHzLpaB+siCf84OhPofTTXRNoFctFoxnSXkGvkcwtuww+mgr0pbRuKeuue1zeHLfDynX7F9N3nkLsOFI6ByGxGPDvWZWos0VhymJ2yLvv8rmi8POfJS9Vqb1MjDV91DO1Lqw3SX2OnDfEnKzpHBV7OEA3tihNpQouShHaEDPJAE3xx+CWEMB4MEIOzE7r//ecJnD71sTZoG9qFvXkX9ZWG1NyADSbHTDBkphA1JTFe16TEgTb1bJjZkt+BT4LjMLhnPWt6uvWW6bf5fmKSMk9JECczrM/ya7QOuFPT928ybvG5Sj4cmqcf+7HVcSJf2t+MK+A2t5TErrQ9Yxoc10iBRWCFQM1zk2cmjthj5Q+MLwd4jr2uSbgxl3AondvvvjXeBQF92bdTI8gabsHtrjcKU3I5pQ/GlnnUmsiGIMAr5mnm27YwZ9chd4oJq++Z1vdsRjzTATRADI1IirBuorUzvq5nrLsFOFGb/bkFVI7RN1ryaEWLrk0FXs4QDe088vAlwqj1bbGr8WCniCBGh+k6HnC55JLNioT1qY9jG97G3rzr+kp97cRUAYwhMwWgEMdIjq5dDQdfEynk075osPgctcxzSlmFvmrgySCqrdEWcFbUfqCGSrz14dmHT5H2pkgooWMn6l1/ZOPV3+Q+sd6MFcFlPOaWktjWwXWKBocQSyFQO3JjCazoh+sSqj6vqztjh61uFiFnnDctoTG0xhVcbddDm33b8xsQgGxE9LnN67Mr096U6C+kD461cTKetIPaiKek3k9SBXQ1sl2H3Kmbla4/IL814G5Mi7oO1A9p7aKZc25Xw4VSumQOACtaUYGXM0ZD2gSRJpgIYZMEdpgGBhI7MwZuN4Ax2CHTuGxa3XRIM7KJI2BLu6qvNKa5GXI6DvPnL0TQuU9+b5mh39XQyW5+G+bZJ7j56LgHMJCIGAwyVXPbWk0BMLv2KTJ+0rpbQ3wI4n+hzxGifiOQ4jMFaMWHZK4WaFMn0qkanBQNJOiBTK9oNXznc7QHY2OybfHKKWscOM568OzyC0l1bMdmLLTXONndpw7S3I3CnKrsbeTQ8563WmsxWUfrZb2af+/RGOqjSL9uYc6uQ+4c0J3jPcvaYO63cfYe0tpFG0rD1k3y2E0z4LWttvgsUYGXM0itU2oq7lKNck4VRpuaR22OjjaRlF2C4+NnMfcBW6cZ2TTZ3FTh0PVFmZpuPO0bE3K0VK6N4bY7rC4zTGr3XTDPruDWZiaYpBoHZvSV2QhgMJcJE40ae9c+RX070TaiDOP2Sjh6W22Z6WUTLdA2a2edBidp+VPY0LFJHWDdmU/gc6zuzC6ioobWuL56Vq1J8y/ZGyd8z3cqmVsPzgG89Mncp1L1WMHVOTRWlR340G6CnOksJUy0S9tptJJIzzg5Jyaj1A8LbaMhajcmwFu3JldLU0H9kNYu2bqTryWFKIe0gvuKpDwfqcDLGaU2JDYCGiAROouRYQi+i6NfmAVir+/Lfrtt2GCrGdnkIZ4iHIZ8UdKHqZqbISHnfEybP0tfTg9mBwJFJmQUx7xtmWdXcLuuZIOYop0u5glY6XcKBkaNbY53HQXUtxONpqJNu56SBChmScLM+yZaoG1zAA1pcABAid2sXX46BHFbnM8YPvKR5+rOzCkUOLQ2+oBX3xp3f+cAwjSlQoqZILTXRiOh+fyPPPepXi0UnPN0XymOTWhdVXabBrzFOowpKHWvYt5kPgroxnP4Z0lcJ+kmx1X9nmoO7JuDLt9zn2QdBpq7NBXUj21otBM489LvjH+ZhbajAi9nkIYEtLTyCQO1AyaUMZR442MYvmsZ95g5Zm7Y4CZOo3N2Uet8UdRUYuqYotYfEnIy4TLP9Pk+YNDyOmDmdqfuRbAk0sp92krM8UOYqhFpBTezkft56S/haq7Nobl1P+Ykn7V9W1PBlJ1o/C/MWagtMoex+z8RPceVTXRMg2MMPTu0lNZJzH2Ai4ypmxQKHFobfRuDLgBynqKkKfdhTmN+4VvkeXva01ZAZp9miXVV2bUToEqxSM+W333Gc4yJdWj+tS0maqbNO95xVXKCnxhw5hzPmd/a+kfrtKdAewoyhu+5DzABIPk/gQpztTvr/K5oQmXS9WwbG889HlhFFjenAi9njKbWAyHM7FISCYR8PxV0bBI2OKY233YXNcUX5VWvWgmPqWr9PiHnmOc+97o7sOSrwDz5GLDhY8Ta657az1k34dPJwKvf/JHmakSMNyaJedrxJazXdbTFDjeFOwnVOaaCKZFcfTvRrv0/obuZH+aBmA7suI8zm+iQBgdAIUxp8PqE0NxCgWNro29j0IJCn93P+e5v3aemmXE0nsC6Z2RfmanXVWVP1Jt5xkusResmCdiSIFP7rUWm1oT8WxNAvDEGvtzjQQ9a/Wa8huof9c2Ba6vkDMjf4Q7nnn9rkrb5qqsWi5//+VX5DwBwk0CBMb8rmlhgsgVTANkmQQRFKyrwcsZoaj0QO8lUuI0JIoxonVDfNGxwyETi2mzo1OEJq3VP/jfdXRTh/+53r/qAEYqW8j7VF0Xbhxh8X/v6BEJfnhO7asIJI7ZrxIAxULv2VCemUk/GVow7WVyTB2ITJtf1FUjuHmQHq/qwUhLGpi3GOZZ6fkok19BO1HvyaKR2i2ukWB7Ba52d5Gyi5o4Jo0tzCwWOrQ3zIFmj8ZYWX5FF37eg0O/WRlswk7AnfONH5Nnl+7LPzNRDVdkT9WZOPaueXe1IIcREcCXjrfHzf0xK1mf6pU9xjI52tM+sS7sB1HSdheOn5PnyLLW8jLbFfFr/2pfK7ZuYdcaiKLcNIii6NhV4OWM0Znf3gAMWdpRUqbK8euA91AltnSLUA5Aw59YEEuE0FjbYNRdg3k9/+moHGXODdnI+dT0JvrKLSk4TgiAJ9TA9uz2McMwXpXU+3jbZWXcHZiy1x66acOpqnBzfMs04SxNUGOEm0TfaShgmPXwy+RqjOJ9i2gASTdGUsPK5kVxDO1F+FuYweV6sA9eiLaPFOK3hoXMLBfatDYDemBCw5okwZ6IyXt/zPdf2tzIfNDRxfvY5FeJbrZY52ndm6q6ZMNli+eIA5cBpnLKty5QrSc01bU/Oo+QgEimVvC6u63mmHZETybOR57WtSC9JoLExDq2zcIqOclbu4z2up400O+67jVmt3dC4r+KX20aYFV2XCrycMRryik8NHCp9jORHf3Sl6cAo52YwxZjstNryAoSxHScQgXGMhQ3GXODBp/GRQjsF/jClADAvbQSy7OblL0lRQYwqqmjMk+AgIIbycOgDgU7d7lrbprZvd2AYOD8G5oA+G7fxMeYKr6VIY1vQcJNKzOYCOCEotDuZfBNBhkGbhze+cXW/dWBk0zDfIf8R1Nbd2ibD7r5obqLDuYUCu2sDkOSYar0mSzLBbqz5owGOmRPvzrMukjG3rUacIp8tKNgkMnAq9ZkJ0xaAwdxqhzEAGrxSnZ5W1PMazYh+e96Mp/WWSEdrTRkG6xWASaRUklumqChe1HUW1qY8e9rR8h5k3eMNrtUCj6lFV4+i7lbRtanAyxmjPiYTm3sYHecygr51ZO3zpMdgMB4Pn12jBxHZ8dg9+h1zSQ0YGpE46mEW/sesMJU+p1GM4+d+bnVMq+ZNHaDsSn1vF48RomRB1R7HaaNzvDCJNp9KF5jQCuwiUV53B0bVnTTnXcJcESbfNWltmoOlnefb3Ga1yw0oSuE5Am5qfpltmPCQr8VJZtabJDqcA/K7a8NzJirM+FmP8f0w58aXMCbIW4fxSy9dHes7xIxEe+Aa5jvrRftp12i19uVj0Wcm9MwDD0lEmGijaAKBaJsLfQAcUibEc2oTAZQxqTkuPCIFSK3jXN/z43fH07SGx7TOwqlcrX2tOWpoY7KrRJe7qrtV1LPmrvtV0flMYTIEcep5eMcAPMR2SIRX3jELodEyd+YcxwIedkEYJ/syNTcGiQFT38ZE5N09w0gwV6pzjJnWhBMbgILZdc0OVOo0N4R6KzAxquzSMGm7sJiM3BMDdB+EmWl3TE7akT5kR+r/ABOCXDvshLVTG7xjbJvapgMkMM6+fBWuT+szFHkQ3wBMDqBro3XaHSLzXn5v5xkjNn6Yt3f/GwcMeUp+malM2O+7ZMJ9/ToKinkM8DN+wIJ3/6fW1SbzTEB264DFNEgD41k09rQVWd950YgS2u2cWC/MHMK4Xd9z4GU9mQffmRevbATG2r8tRSOUZwdQAditJSYk/fQOjHg+PdueVWPAaVY01Ld+6yoSMD4wwAlzIjBjTDwn3lue4hlOpfQkYgw/a/MHGT+gJnPT9/y7buYff8JP8B/v/p87fi2g7aOjrNl2vlFpXs4gtb4InPloSTBMO7ehtPEXX3ztejV8UDz8GELquQg3BF4IW9EiyS8SfwuMAhPBQG5723OmCwApYGMKRZ0OQGFwmHPOD2PTdsAG44nt3/f3vOeqXWNZWLdNdjY3jNK15diJarwFE8xvohIwz5e97Jxqu42sGNshDkU/AKZXXLF+RxjB4LP5Nrfdit/7YMK7LPEwh7bJgrtJmQKfCXSRbgB4zEUxbcSB2XPjuy5ANBZCof/Nv1mZAM1PTDDJLxLH6FSH36ePRfvsmDvmZ1rcrJm28CAwaH0bczyoNQN5ZgEf6+52t1ssXve61XVTIDaaWC/PBG2vcXEvx/EpCu9xXfzBMSLrACHACSDsPv+Zf+0xFwBYa/Z2jznjdxxV1s8KFXg5oxQmo+bNC1+4+mxX092Ft2pNzq5Uv5K8YQZs934LkLFDpKkISKD1SHl3QALDSCVb52IGXsBPn1Ag0LXJeYBV2uadkPTgR9viHIwHw0nYqDa2tm19USGWk+86YLLrsNJ16euRXV0r9LQRINTfhA5308BfeeV6B9o+IOZ/EVxjJg5jp3QBpp/aQ+bXLnnTfBhTaFclHjahueaxrl+MsZ5bpsCzwFRLoDtHX63haA0JZ+AlhSG7ANE1H/3o1dgQ+OaImaYFwklKSLjv28ciz461QFth3tq2pGCo9eRYz6o+tWYggM684w+OAdqMRxvFlE2R6+E/qZYeZ+HwHmvX70zbD3zg+MbE9zZn2uGc+NnF7G1eaJanjt+u6m4VXZcKvJxhysNuJ5JwxXU7ag8cYWZHR4OBWbYPeEAKBgnsZJeF2WBkroXhtzbnIZ8J7zQ0zFZ2aO6TiBk7TPfE3DCURCikTk4q/yYVt9+p18OojsPfYp1GpxV6xpk/D0YnjNN4YcTeXQcjfOlLV9eY4rPS7e+6HWFMisbRsQmxBqbkxNCmgKldMuExzYd+0xRefvli8ahHrfq0a6Y/x0dhTDs0p1BgnKtTeJCQJIAT7uza1i8Aw6m7DyCaB3MFnJiPhAh3sxfHif0ofCyGBLf70yZqi/WpLea39VExprSmQLtNTEwvXWDiXJom102RSes3Gp7WJy/h5mjo+ceraF3wDXOSMUzb8KFEyE2lXdTdKrouFXg54zRXrYlZ2MlgHMBB60jrAceg/CZCiFkoTqh28WHEtChd08NQDpWHPWzFzGl3WoaBUWMutADJnMlvAONzjxSX1BbXoS4eKi0/N6pkGxoDTn0qd+2xk07iuqjWjR+VNpX6JlEMYztC32H65q0FRjRkjiF4+KFYB7QCu2TCQ5qPNhoOaNZGYHRbM1J37vVvitMtAJ/q0Ntqh4w97YuIIokKgfM46lrnQEv8k4YAYnwrEmWXCKSsmRSVTHmGo/KxiOB+05tWz2d8P/QLEKNNoeloNSn4COAOIOuv9dvyqNb0REPCqTdABWA3njZO1qb1YkxpXKZkszXu2mjt9z1Xxi4RcnPHYZem6KICL2ee5qo1PXQYJLDQfcATQYQwXMwd48FIMFO/E7rd0vY5d0glzqbfMj/tIzDtgLQRQMIYaGFS44UGCCP0mxouD31ovzA5Lt+KdeAGgwMOE6qJEdN8ED5AC/L7EPObEsUwtCNkwqBFw/S78wQwttlOjdUumXCf5qONhov5we80edbsPe6xMgfObUff3MdU2c2C24J5DqlMB7vI3ZE2AKhxfHd/Qp2QpKUw98Z8aA23m5BoNz0bcXpN+8yr5+Ioshe3oDClAkLWsPWlX32alORuYXpOf/t4lOhCmjjzAgi5HlBtjvAhjr9z8wYFwALmXe2V+6T8yCbg77g0vucrlealaJZaE9Oz06RZAWxCATve453vASd8MTGM3Cv5H1pa5zPh/vxsWmZIeABIYfjZbWFW7o9RaLt030PmheP0rVhH2iJyhJqaycA77VUbLZFkfbQvbeK7uQn1ujtCQvNZzxo2nSTbKc3arplxN9xYH63JaPnixGodMU+afxoLZoEkcJsyZ+vqe3kNgXlaPFlct60OrbIy7RqAAjQZV1olc037ADwCQ3y0ROKMAaFsQtT/oW0wTonIsXGwbjzX2rNvH4sWFMbMkqy7+mksaVbkZQI6Wk2KdmuvF0A6xKNiVgVYWh8sGqyYiqzrxz/+2hqXdVpWmysgjz9OX3JHn2kg+5zWi46WCrwUzVJr+h8zfdvbzlUoJkzsdFLsz3fZPXvoCRnqXbtH56zT8AwxGIw3pQIIHcydCQrDbndbmNm63dY2USVHQRgz5kloE6R2fAkV1ta0CZP1P+1SAMxcB9rujhDgnJOUcJ9mzESjRVgQIMlTY2xo2+zkCZWpoHOT+l76S+MCuCRqbmhs12m9rGGaxJ/4idUcO9594rtlXPUJOHrBC8bNHe2z4jqEt7GipaOZAAaMjb5456tmTe+DtEX6hBe/eDVGgIpxSqSUvlqjQK92WKPGIhmBaXLjb9W3dtdFMiHXsXlh7sQP3CNre4qW1T1pgvxOO5Swc3MQ30AbpIoOOn4q8FI0W61pF3inO60YFQFCsKaoGuYJrMTJ0HeYMQbA3EFAYF6pZtzV8LQMxnXtGAkSyeMI6K7QcT2gaGy3dVSZL3flO+M6b3nLOQ2LcUjOCuSzY5J9lcBm9zc+5mNbB9rjDO/smjGtDwCYBiFhtSkqmftbSwTzOtCZ+THvzA0E39T6XtH20bj4zdqw5rrlHtaBu2h8aNU8IwStfsV/yHokIK1xztEqRlvffdQVxilMyizkur4zTsZGezJW+4g00haO1G94w7mkcQGY7m1Tk/xLSVwHbNGc0WQAM1Ocv1uzaiKypgDIqVrWdv0BW9qGj+FD+nXS626dJSrwUjSb4kiLOWKESfUPSBAsYcRx5iU0MArCBvOk4sUsUhk3Qr5lMASzd0JDzoZEuDi/64sxtts6qsyXu/SdMaaYpzGJk6VX+uz7AJiMaQAMTcS2DrTHHd7ZzUNEAMYHiDCOSYQwsZZS6K8FnbRHAR4pjieTrfmxRmg6aCbiANo3985himy1fQSfMQdcABBrHQgCOJxHmLp2m3G6T+PjGvFn8j2QnwSOfrPLB0QkcewzGfUJY9fzP+d22gHXaMkaYlIaW9ObAPDUH2MGirYu0VHmK+HQ5sncpbJ0ohSBN//PicCZk814rpa1a6JK24DCig46OVTgpWgj8oALV1V7SKl5jApDT12SNgoJA0vRQQwdQ8AkCIZQy2CcC7AkDJtAJnBEubg2U1Efw5oDOObWaxqjdbs65RV8N6dGTtKkJ+lePqdCbhwg0z5CXOG+OGMOXX+qcDru8M6YCAhkOUz4hyDCLjmFjLHfgcSYDoyz+WC6AE4AySQ2C8B1rHnJWiHAnJd6UlMEH3BiTRJ8/FQCMmP+AN5lnG7Ba6vtc13zSGPpXvFPseaTm8j9tbOrKRlqk+dEH/WprdruPjF1pb7QrgC4tgB2AJMx0IfUFst6ZX6Js3UKJPqsTa7/uMetxr1vPQ6t1znawU20rBUddPKpwEvRVoTJYRDJI2GHRaDYTfnO//wU7LYwqDgOdgFGW4maD0s3DBtjBo6cJ6KgzcGwCeDYlWlk3a6OqeGxj121l1CaWiOHcE5OnG724aT/TymEOEEDLmMap7nC6bgZeIQUAd6G/hKGiaIxV7R9Mas5loNochhZf+9+90rjYO0ZL2MFANNs0J6Y6/huWWfemeDGBJ97AkcBIdqUNgR8dE0SrbYvETWeBW1E5ty5KdRJc+K7oWel2ybPl2fG70wewIF2Odb/2uz31752sfiO77j2nG/qvO5eIpyMQ4CfcY5/VkBdnHCjTUyBVWN84YXDDvVj69W7+wseMG/u31cnbVMta0UHnWwqy13RRhShbTfHiREDsuPEdIEWzMLOFNPEnBMePQQwwmAwujhodpNsYSaYb/KQTKkdM7XG01Cto3WCemxXpx92/H7X/rk1cvSJAAvTjVBIpWmvbgK+ofpATCYiiAiaOfV6wsBpyfaRFG4dSQvPL8Kaoc3Q12RURdYW7YW511eh8sbaeAAHfvMCioFAc4H4ZiVnEaGaekJCll1Lf4cEn/vQDGqD60SrCKCaL/NEE2EOU0zROa22Tx/MASHu/5gGU+xUe8ynPg89K11hnIgexwMqQNzVV6/WqH5q381utjIvtnPeBeDGLY7zQAGAx8HY9bp1ppIXBTGZaXcAtVd8z4xxzMn6iDc4lvP/WCTguvpSxtMzRis7VCet6gudn1Sal6KNqCu0k68BUyEsUoMFA45T45hGIwwmIAJTbwnzSwVk2gz38XkbX4xdmEaGBElCfFPplkAleKfWyHnAA1aMmMaA0CEQo4FJOYSAGRFWfQn4snP1TmDRgPEXInC146REVg2Rvolqoz0wt+kz4RiBH/MZTR+BaLwvuOBcP6LJSFkKAho44Evlf/Pmf1oU8yRCxvgS0pzS+8yL1hpw4NxoXAjirFmf/W49tSaJrraPM6hyG0CBdltHAS2OA/zHnpU+k6fnzHqgvRSxZn0yJfk+qfMT0ZM57z7LSQiYxIjWnTZzHnatVgOSvCjIseYp4c7We0zGKFoX1wO8v/mbV/48ffO+zkdFpFF8hGjJsvHoq5O2iZb1KBNXFm1GBV6KNqI+oZ18DRg2R0sCAYPG8DCWMYARBiPCAtNLgbowmJiewpwJGQJoW1+MbU0jQ4IkjJQwImCnlENoCTCTFZS2xLgZg+yG4/MB1Nzxjv3Jy7JzJYja2jAEvLEENGOW2ySy6ihIe4xhNHnJFJscQoRUvrderJ84MYeMe36PaZPAc13j5/hkCTYeNH7GZwhwGK8UP0yqfXPRzm+KCvqNeSomiT5HaMDescAXrQ2NkXZ1TR9zhLFxAQyAF3Mek0pbW6id8/ZZbhMCOse4+c4xNDA3uclqvFp/LmNHY5VoHNGExrc1eXp2b3Ob1ZjFKdm67XvO1vmouD4g5Zr/9J9e27TcVydtrgP6SUtcWdRPBV6KNqIhoY2RSPLkPb4rGNs6gBEGg3FFaGHmqZcEBGBGmLpICuHQGM+uqj5vKrSHBIndp7bbeU4th9DNSiphmmsm14TrJarrPvdZLL7ru65rykmF3uc8Z6U9IOhpAVzPHLiva7QOnXMjq46KtCegDRAOxRyRonyA7GWXrY77wR+89po07vrJtJDaN23afGNrfgjlzF07Hn2CLzWH/B5zZsAMitYBcOiaSa3hu999ZeIABhyXUGHHaZf5mvKspE2EeTQPAQYi81zf+kjdr5baPuZZBrbahIAI2HXtOC97ll07GhB1x7SFmYzvCU0h/yFjHqDpXsbNc+w3ienGNhrrfFQyRkx7cxxwp2hZT3LiyqJrU4GXoo1o3e4PA/zO71wsLr74uiHR6yKYMJW3vnVlWycAgBiMJJlH3Q9wOQmq3KFdXcoh8AOaUg6hm9/GrhlYkXCPGSTZRwlLjJdQ6AIX10j0V4rLJemZ430moJxj/FxT+/aZdG6M1qnmAa4k4UtoeMYRWIhJAhgAPvzeXZPxA9FX45akZo6N6ag7P93x6Ao+axuYdpz5ti59jt9MhD9Q4fiYJLpzjICW5DCaA8bTJvPNvEiYI+AAmJtaoyn3itaTliVaGgAi/jmp8EyoZ90EJHjGlfBIW1IixHgYA+cl+sizrL9jAGBdJGBqnA1luR0C4+u0rCc9cWXRtanAS9FGNEUVK6KBFmYOYTAvfOFicZe7rHanrk/oYljJennFFavwzJOiyu3b1QFZ2ofWlUPo7vb00U4Ww+cYGhNPKDlx2p1lcm3Y/To/x2HAdryEfJxGCdeEEe876dwQrVPNa6tdvXnX3nbsWiIYrY9EYPWtSYAvx5kLYCNaDs7mKerXgsNu/Z+u4DMvqhRzJkbxw0HuybQZ7ZadvdwvfTt6bbWWCflN1rGxA4BoIRJtYz5l711XoylznnHjF2VdMAtZe8YkQE0/rGngN9FULUhwf8+tDQftjPsy7Tg2VckT2v7DPzyuwVi3MaJd0rehRJRjYHxMy7qPxJVF+6MCL0Ub075ygWAwsopy5sMomE/e+MaV9qBlhCdJldu3q9NGjHrMzo66uz0aAWPgGIy6NfH07SzbXBsEjHsT0NG20FDEV8aO33VStkF7Hc934Khoimpe/yQm1IdEtiQ5Xz6nyi8fjQiUoTVJs2HHn3w7xpiAT20kAI+g9aId4DDd50gdoUVw0nDEOb01zcT/Jc+BdfHsZ+92R99G+7V+H7lu1hzQNsXPw7jxQZEFOwUS4+xsrfjOdZPLpQ8kuJbNijHSX+e1Vcmn9nfdxggPMPY+p1BnaBswvuvElUX7pQIvRVvRPnOBJM/Ha16zYqSbMMKjpL5d3TpwZyfa3e3FyZTgSIRMVPV9QqOba4PmJdoI7wBAvnMeQW1nzSfJLttx0t7TJuxbkzVFNe93poeE9yaPS1siIeHJiIkspoSpa5KmgKB6xjNWQCah5zRcTJVXXrmKzOobi/TBcQS+eQVi2gy77i/5Gh+Qn/mZ9eUI5u7op2gJAJv73W81r1M2FzYLzJTCovWFNsjY65s1QxOjX9bYGEjYhQZj3cYIAcC7zABtnVgD5owGLkkL04fjMq8W9VOBl6KtaVOH1ynhiNswwpMQ7rhOkPbt9lonUzvn7IIRoeFafieUmIl8x1chDpLeMdmY2aIV8D2hT+UeDYNig3EsPQpN1pT5JOhT5FM7tZsgieYl/i/6Anw5dm6CMecDb+aCT1HqJ8WPYgwUt30wtl3hDWQDMN///StgNLUcwZwd/VQtgbD4Jz1p+nNgDiK4vawz94nGzrjFf2gIJOxKg7Hu2dm11tc6Ml/mShs9S/rIhOX9OMyrRcNU4KXoWGhqOOKmjHBX4Y67AEBjgrTPObF1MiX4Um3Y/wCL9gAlErjRoCT6BRE8NDRJxU7QE8zJhwKw0HoYNwnL4vdCaB+FJmvKfBqLJOhLRI72p0RCMtomDb1zhnbDQ/MXAOL/PqfQMVCcqJ5kQO4mVCTomfC8833pliPo+jBtsqOfU95i6uZCX2laRBMR1MnzYj0BksadcHccU9UQSNhl6Y2xtu9S62uumXjNY6u9NGc2BvyKmMSqKOPJoQIvRUdOU3wewpQwDwyU6rovuqCPEe4q3PEo8j0MOScSbpxJZQ61AyQ0CEw+CQR2woUJcEIydXWMWXaMSRYWzQXmawxpdAgjkSEJGc4Oc99OiVMEW4CNY81h8tsQKD6nrwmXFmrctzbG5s81NgXFP/7jq3dzQfOTsUsCOCHqrm+N6GPqcTFHcIDt1h3aZEe/j8rfAZaePXOfCDdrJ0VX9Zmp7Fu+ZRgk7Lsq+ZQNxZxNR2vKlIbBe5L0ZdMAvMhpc9y+dUXnqMBL0ZFSnEvtqDGURIl0s2cyl4haIJiFy/rMHs8HYowR7irc8ajyPYw5J9rx2QULOTce7gmYJEzXcXFajV8IYWO8+Gs4JzV99N+1vSskSEjZUccfBqDBpCVJS3bkXVErSLTZ2ItsGRJs2uAcjrT6nyRu+pEU+gQSMAa8EDhdQbhu/oRXz9UOtIn/CDNjDrxk7GhUEu3jnvFRajVpXuYEEDX2m/po7KPydxdYpv0h7WWG0pex6+6jbXM2FHM3HX3ZwrUx4C05m4aAbtHxUIGXoiMl1YGBFwwBgGl3/ZgGYcBZUt4O32EYhLUcFKJPCDOCaogR7sJZ8KjzPUyJ2tJ/3xMo2mU3nHZpA00KIQ/cEYyONbbGynVE2nBS/ZEfWR1PU5GokTiqEsq0BkDBrpwS+wQJnxs72iHBRsggfdBXmVk5JHMaNTf6Gudt7e4KwinzJ0JpHYgaAsXuTYi7hjYZK4LOtaxV495NoJbyGe7NwZgGwzW28dHYdbTfLjUm+4hEnLKhQHM3HX2mzFQHRwCzzVNFGZ0sKvBSdGSE+dCqYCzU6IRmu+tnJsFgCOHWF4FZAGNhQiFcU+CtjxHuwlnwOPI9rLPfMxsZqyRY62ZNTUr6m998NT4K3hk3IaVJZieyyXW0n/NoC4C8uycBBSzswilxSNgkd49olgCtPsHWCj9t1nZE6LuW4/oE4ZT545QpEkd/WxDFPElQdcPHh2p5xbxgTqxjafkBGOuzS84xP8plML0ACts6ke/S72PXGpNdtm1qlJrj5m46dumjU3R0VOCl6EgozIdwwLxTc6Td9TMTEGYYSMJgQzQKcr9ghA960DDj3wUjOq58D2POiQQ2tT0flm46+jYlffLDfOu3XvdaiVqiFRDRY8yNEeDjfADSNYGXOflG+oTTFGFj3gGUoQzMXeGX+ViXsXlOJE4LkICtAKRu+Hifj0xrXgAcXfPhD18VkxzSXjgGKBrzGdnVutnE2XzXGpNtSm9sEqWG3G/OpmPfPjpF+6ECL0VHQmE+hBEBapeawoBehFoKwGGQfQ6YzAWEqx37EEPcBSM6iTsxfh1MaYR+woMThROH5hQaNH59/Uu/aAZarYExTwg14QxQ0tKsE3ZjvgXaOEX74frA1C6F35z5c21r8j/+x8XixS9erTGVnmkGAZJ1PjIxL3jnYwSY78vfYw5t42y+z9xN+45Sy+f2mY/viueFVrIvrP4kzFnRPCrwUnSkzIdwiPNidv40CpgKB0iCE/PoCrypoGEXjOgk7sSACzviJz5xBTiYkOLEGqdnIMZYDvWv2y/+Q2HswA+/EvPwspetwMeYsFvnf6Da9SbaK31JinnUmr2m0tD8mTuJ1rRdNWdrI315/vPP5ffgx8IfyzWiJaKB0ZYUy1yXbr+vXAQzHq1WEgPuSxjuwtl8VxqT44hSyzH4SwC6Z8OYuwbtWhcw7ytbeNH+qMBL0ZEzn9ZfANPnf0GAJscCIQrkrIssGqJtGdEud2K7yBOTa8jQ+tjHrsAF59AkcHO9mJQI3jn9Ms5A4y/+4mr8RfrEIXpI2E0xCbkegT03mqev0OBtb7tYPOxh0wVIXz+BMv5S5i/J7Z773FWum9e+dgVcaE6MR+uHZZ063zriI2MtTU23H+0FwMPh2jX3XZdrbG7ci2nl8stXBVDngsJt2rTtMzBlQ9FGqeExACftWWo+yZlkbpWF6KsldRI1TkXDVOCl6Eioy3ySE8PuiMBgyrAztVPE6N/+9pVmwP/rQEMfc9yWEe1iJzakur/b3c7V2FnXru41AALj5nyCnRA2fgiYIYQ5Rb/gBf3X7PbLuKpg7Xpf//XnAOOYkyPNCCHovgR8m6QtJiGCWpI5x07RfgjflgnW+iBo+KM4B7BVoJPgUbl4qrBv+ykqSNI4u2/3s+u23oBkfXO/pPbv+mEZIya7Ph+ZdWvCtYAmUXL7Drlf5xuSvgjTNtbuTdhvAqDmgJFd5UqasqFIlJr/OfcnizTAwpfOOhUUYCyGogVPmsapaJgKvBQdCXWZj50Pxoa5xw8joCYJ1KS+T0I1VX5Vqe4yvHXMcRtGtA0AGlLd69dP/dSKqRKSY8y87xoEpu+BAG2hcUmiOe0zXqr5Ag1DFb3bfhk3YIdWp+tn1Ofk6LOd+/ved90U6skam3YCn8xR67QfstDKbmqnzAxmLRAwriufis8Erpo7T37yPADKf8U57u3/+Kcga9Aa027tSdbe9D11pQCnro/MlDVx1CH3Q74hzIs///MrLYRnSTu8bwKgplQDz9ik+CXgsAvgNnVDIS8SwGot0eCZW+soa9QcV3Xo008FXoqOjFrmY+cu3wWG1jIWFPMRIUfoRLOwjW1/U9X1JjuxIaFlJ4iRJ+X/7W53bafQbnuHrpH6PsZPpFD8Jwh9fcOw+YwMgZe2X3HWHRrj1jcl480fJPdyndbEYg5jEhIWzE9kTPsBzL3iFavzAxi8Az36CmD4zu8A09zwdGuIEAWQuuar1DNyH32hEYoTOTK2zHDmxlzFXDm2Jtp1ps3G7ChC7nPfbkZqwIUW0zrSn0SUWT9zAdS65+3Od17ViwIsPLc0egCh5JIZ+22B25QNBQ0Z53YbBGPRLbBY1aHPDyrwUnSkFOaj0u6//tcr4cb8EcZil03QRQA6nmDl40EQRcDP2dUypew7zf861T1hgfkTkCJSAAzM1+c+Zj6k/vc5TrqEr754+Y7AMm5tCPWuInMwfLWUjDdzQxsx1ppY+I10nVeHtB+pUQQgpJaMtvvNZ8DO/eLA3VeAcZsolRQfTMVy49mGjzvXPZnSpvg4dbUS+ksDxkm3b2x3JUTb+7YZqQFEGitjCCjSkumPtWO9JevyFAC17nn7pV9aLJ75zNV6Ne/mE1i1LmnU2npO2wK3Fjz2bUq80+rpr/XYpcrbcn5QgZeiIyfMB3hI7pJWwGPAhBamSABgumGQGLFd+n3ve04TsG5Xm4y+R+VzMCQwaUlS8yYABMMHBDD1LjMfErqAHnIdAj6h5oS+cQWKMG87zyk0NbIKBUxl/tqIMffkT0GjxgTVCvsh7UcKHKZiL0r+Gu0ALKwFoCx+PnPD08fAmd2479zbWiTo2vBx99VPwKtrEunu+LtaCesWgPA/M943fdO1HdB3JUT7tCHuzUxE42IMPWPxv/E/bZZ7t/486wDUWJ4VZKxoFF3PmJrv5Bwyx6nnhJIXx/c+b9P3IZ+ykxYtWLR7KvBSdCzUJzQxMkwY87MbZ06KH4bv7fQ5WQIxyRjLr6IVSsnrkF07/5Kj9DnoE5g0P+9850rrEq2ItmD2tEx2pTQwrRAZErqpZURLQJimcjShj4EDNDQbU/szNbLK/y2Y6maY1R5jTssiUduUrMc0HqlaDAQlERzhq1/6ZAdv3OL3NFfgjIEz5LrMC8aeYCZ8rTN918enPGWllRnz9bB+Wq2EdxEv+qQPBD+nXeHjATC7KlDYpw3h+O43js7mJYBZX82nebBO8kxNAVBjGizPm+cVMHI/5LO17t7xHQLgAVyfU0xUkUtarrkbiCkmrMrbcn5TgZeiY6E+oUn4YWoYIGZEOGC6rSkpSeoIBfZ1IbmiZFrTRZijF/8M2o2jSvPfFZiEhcgHfdP2MHT310/RNNpMxd8KEddgUnvPe1af/QbIOcc7wU9AuZZXxtTYJNR5l46QgGIXTLUZZu2i/SYE98YdX5shIBYBZ2yANwIdoHOc8Un/AJiuNmcqrQNn2tr6aqTvfFzS9ylFHqOVcIy1mhBdc2Hs9IsW8Bu/cWXS2EXyszFtCKAS/50kNZR4L/eKWUw7Wn+eTTRYKV7o9zg867v+AUc0WsbFxsMcO9861j4mLmM7pgHtarzM4TqTMS2nKtA2L5W35fykAi9Fx0ZdoUkAElaYmjDahFP3mZIwR5WTqeb97rjkdQhzDNPEyMLMQ/G3oN52/jY1V/pMCQSm72lc7Eq1KU6o1Pj+d//4WegHEBYhQluj7ZweCVbXpiEg5I0RQURj4dquQRj5DnhBc00R6xwhhzQYca5O2vs+EDh0rvNSPRxQY7oh4KMt8HJ/cwUA/NiPzcv3Mgec3elOm5U5oAWUuwU48R2gYw0GOBDmxsS1gTxg29oeCq+e41i+zp9HH60397COotUMcLEe+wpbztVguZ770CpFU9pW0jY23t0boLFmtdk4+H9MA9qn8bJOrBnP/9im5OKLV+H3lbfl/KQCL0XHSq3QxOCoke3GWvt4nykJk3JuBDzTQoBLmKPfqe8x6tjco8lJzgvnCBW2K5zrwLsubFTIJn8HRCADLQRx/FNSTJGQdl6ESLvTv+CC1dgQAPppF53kcsw20XrEH0afNjGvrIui2SZx39i5xiRJxPQTOPNyTQKYVsG1gUxA0DjT7mwCYMbA2VDfx7QbMbskxJtZBCBIjpqQvgGe5pg2EAC78MJpgnrMsXxTfx7Ph3Ncm1lsyliOzSEwqH9dEAUYyaui9EKAqH51owuHNKBDGi/P9Ic/vLrOOkfoytty/lKBl6Jjp5bBEOgY1jpTEsL8CIF3v3sl3GhkWuZI6BHmmCLAQtDTdsQEReDYvTFJzHXgnRKmTWjEhKJf2ueeztEvn/XLjlWV4aEoKmOj7fqmL7QUPusHYUkYRXVPiDzgAcN5R7bZhW6TuG/oXGYUPiVvecti8da3nisVYazMSwQiYGbMtH9TP6VNBNmQdiOmTOMfk541qI3Ck823c4DVgG7+QB/84Grsu/NA0zY3pf+6MgjeAwQdy58HCHY9a44jsrGfSkNzCCxbc1deeV1gY5xkMXZP/UqEUzt3fVFX6zIFGy8a1S5QRBVNdDaowEvRqTMltUQTQZgTIoRDTEphaJg7vxJMFCPEJAGBmG5SBNJnjFciNOrmscrFU8O073Wv1Y7X/bSBJsGx2ghouIfooLvcZRWNMrTTT/G/9NeYSJlPaBFGMREQSAQBIQKUtcJu20ynAT7m4t73nlbdeYr2wzX4JQCZiTTyzgTWAoaYOoznUSYY69NutKZMa8d4Gkvv5hr4st44xQIu5tvvjjfvtIuATubBmGR9Zj25hzHXX35b5q4L2KaUQQAAjRutUNZJ688zl8Y0WNZcF9hoU6LSPAPWb+pG5VnuAxtjGi/PguvqY1IPhCqa6OxQgZeiU2dK6jIqxycTb1eNjEFyhuU3koiHocR4hIyMoJKhYfxDAn6MsbY29/TFTpzQanOIaDtm7thWUzKleq4+U50nOibJ1uJv0PUh2LZQ3xjw2SZHh+vKrKtddtDmgmYMoGu1F0gfzUkSr83JjbKNxmlqVBwNm2sLTzbP+kBQA2HRAgqNJ2wBZ9fNPPCDYRJkImzNminAqe/Wv3Puc59rt31KGQR9N2f3uMcqceC29XqGNFhdYJMMuxmjjEmb1DBh8t2oq7HnwBjpG+BuDdEeVRXos0cFXopOnSmpy6juf//V7n0or4MdL/+TW95ysXjxi6+bGA8RGHasBIZ8JO49JOCnAgxOhRirdmsHgIGRY+LJHnvJJddW3U9JGkcz4fra2HdM60NAIGyTot51jJlr0QIAGUDEtnlyutorxCk3gpbATnI1FAEYUDnkkNwFKtHsbKpxmhMVR/N1hzusosusp1SldjzHXsCF1sB8t/Ngjqw993C9OJ4DOd71xfv3f/8qj07XaXldGYSsQa+73nW/hQbz3CZUm5bQ/a0b4Mo8mhfvEk+a0z6n4XXPARDkOfUsA0lVBfrs0V7Byx8tufQjH/nI5SJ+83Jhfuri7ne/+5IRvni5Qx7IRb6kb1wawX9OzvCGHrZ8Wi9XUKXoTNIUXwuMr62bZPcZZ1bCQV0kDK+bGK81AyTjLSFN5T8k4NcxVsLTTpojMGELqCDXTQ4WbQa6uj4HU5LG0Z4ws4yBp/gQTNUS9ZlhCCBVnhXK1Gbqfv0BGGPq2NT/pK9diU7xAtCMb/x5gBj30a+h3ChdDZHz7M4DGLoaJ6G0UwpkDpkygRWajNaU6Ts+IDR9+sXHxVqhmaFF6YuQ8TuWSCOh3YBK5tA51mtKMQw5LY+VQdhnWoCp89vNC2R+9Vf24Qc+cLo/T/sc0Do+/vGrz1UF+uzRXsHLfe973+UC/f3FO97xjuVD+P8tF+kDl46JD1285jWvGT3vIQ95yOIZz3jGNf9fL9uvojNL66JFImAIW9iXdgPRsNidJaEbcIKByv2Sc2OTR5hsW6Cwj/GPMVYmDcKe8InDKSbtXPfDqGmAhgTllKgeu+dXv3p9Sn9jNEVLNJRhVdgyJ1rCwn0JUcI0an9931Qg9rUrAg5AorVKfSDjaLzMEQAoJJsmo10DfRluOXLrG0EJyBirNp39Yx+7AhvJUdLVyHS1OE94wmoO1pkyo+lrfaec86xnrdpFA+OeMfV5cbS2LrXVmgVEjDXAnfpe2upzn9PyNvO8D8rGwbgaR31s8wIZI+2RLbtPAzY1us1aqSrQZ5P2Bl5+a8lNrrrqqsV73/vepaBYSoolvfSlL13c6U53Wjz/+c9f2oKXxuABAlZu5GkuKpoZLZJoIztQO27MDfMDGnxPaPB7IQCyg8dkgR3CoY1marP1tqnMhxgrp2EmA+fL2cKsYAdO6LpGHHQvu2xcU7FO00RoMS1MSX8eM8wUoNMSgfPTP71qt4ytNFGIwE1CQP0nRDYRiEPaK9dmesEejC3g4nf9Ms7a9SM/shrrZN01Jt1MygCCY5xDe9OGyifE2Xz6vc9EiIb8fGhb1pkyafraZH1MYtYeQKVdqQSeitwAl3VpLJNUzpgDK6ncbeyd2+e0PLVG1TalCKaStgF33mme9KetPB6Tln50K5nvKrqt6PynvYGXq6++erlIr38NcEEXXXTRofnol5fGz2/HBQboiiuuWC7+Hz8EMHddbjMvWToGDGlf/nLJEbxCf4JTFZ05ig8FkGFn3hZ6JKzsZAkcoblMR5xy7b5pSiytOPbGBNDN1ttNZd7HWB1D0BCo7s/GzynXDpoABGbc97u/e7H4oR9aRRltqmmamnNlau2irhnGff1GEKaOUijVn5lkaLI2EYjr0va73/3ut4ps0i/zJ0kdjQvtRCJyaGiWrObw+NYkk9ID5ivp6YEVwtJ8mRNj5veuiZC5z5xbS2MOzsxOr3zl6nrWH43JkGB1PsDE3Ge+3LfVYjmX1okjtjUTwOTeAW/Wgbb2OS1vOs9zaIrjczRgnp8xJ13jCNz5DrAbM9mtqyJddDZpb+DlY0vO9kWdSmSfvtxqfMHyKfXbEN3nPvdZ7ra+5FAz8xvLbesTn/jExQc+8IHFm8Sw9tCll166ePrTn77TthedPhqq5JxKu/xgklWUJsGOnvMjR8elW9bi9a9fORA6p03xnmy93v0OdKTeTZexAjAve9mqDQQqIeTehFTCVN0fI2cVJcAw+CGGPKZpmrIrjbCJwybB3wrjseRyEZSAHuCQjLEhIE1fUkV4Lk0xC/idgNMP40rb5DwaF+PpM+FH4BOQwGe39ICxT5g1QJNIoZgz/NaacWh8JBY0Ly0I7vo/OZe2ByvzWVsoi7/t2/qz5joW0AO8knHZvc094W2MpQIwrolI0k/r0W+pIp4MtV0tyjZJBKfQlFD71gm7z0nX2HuGtCs5l5jS1jlRV6K5op2Alyc96UmL5zznOWtNRpsSn5jQVy+50Rcvucjtb3/7xYc//OHlzmq5terQk5/85KXtemm8bjQv/wBHLTpT1Gfzj6CyU42wjZIOQwQ87K4JOWp+TJ6ABy4c6zw7SNfFmIEZO33L7YUvXDHolrFi0NllOg9z9qLZIQSjmSHEXOcxj1kJROfMiYCZsivtc14FpNp8H10tQbuzNnaOoxGwyweOqPlTfM+4afc20StTzQKEuxBkQtBceXdP40oo65d5ptUIkGpr6xgX52m7cTAnQIFjRPnEAdcx+gQsAEJDDs40dkKSjQNWE7OTdgr97kZgBVhbL+7fVq5uq4zb67m+0GkZZP1uPRsTv3tHfgeSuqBxirlRG+dqMKaG2q9z0s36StQadn4UVd6Lzk+aDV4e97jHLR4gMcUI3Xi5XWLy+QNcr6G/Wj6pIpDm+LPc2upf0oc+9KFe8PKZS47kVXS2qc/mH9MBwJA8Ie1SaZ0YCSuM8+UvX5UKcKwdcHaZhLjrOB8zXir8DkNXW0Yb1b2dO6Dj3CSmIyyzu3dd7SJ0PQquuynz7tuVDgkbgsP973nP6+b76IIdLzt4/YgPRvxMgAZjsrQCX5Ncb1OaYhaQXh6wNE5eNEKEecCoNnm3y/cZOExtHWNtjuM8ndT40YL5P+AWqAGAtMP14/PUOtfqOwBIM5O8LGgoMo3ANqYAUQCVSujJmJyoJH2iiSHU/U5rB3jF90W/UpizsZJPHk8ZaZ/97Pkh41MTMrrnkBN2nHT1hU+Y70QKHVWV96Lzk2aDlxssV57XOrpg+WR/Ysk53rc08t+KE8CS3vWudy0fhr+5BpBMoV+nZ1wSDUxR0RD12fxjOsDw27pIQ06MmPh3fde5cGu7a0zUjjiM1rGEB1zeZbRR3aeqrfu6PqCSgozI94QgoeQ3O9FdMe8IG5offQCQEu0xlO+jC3YAFtWsoxnQB2OXWjEcLrWXknQXQmbMLKDt2gJMRIOCkok32ZN9pq0AVJgFsZg4isYxlvOoNWF8RQpF25G5BR4CjqKBa7UyrqHv1k3XPBmQo43GF3gw9uaCeRJoAE7b5IiAlWulbESAtXYAYAG65i6/pfimdg1FeXXHc5skhXNC7YechpMh2jMIIHpWj7LKe9H5SXvzefkny6fhjne842HYsxwtQqW/b+nhdu+lB14ijX5vyWmYhF796lcvvu7rvu7QNCSMWkTS/7nk6HxeHrPUrX/DN3zD4mYKZBQVDdCQzd+OVX6SFMZrBU6fE2MqHSfPSJvsqy2wiJn3MVpCQLIw96TBwbAJQwKKQLIDR4QR4RqhtCvm7VwaCIKKsI7g1SdCj3B573tX5oP4k3STxfEhcV7yqiDjQajSViARQdoff499UbIsm8sAQGNlHgIYkgAwkWP6rT/mloM2E0ubz4XJjrNtQqj1KwDX/McRGLCxblrnWt8HzHUdu1N8EKnVBAD5ntYHyLLe4riqYKH7BRA6JsBam7Qt99YP7Y72x28JJV9HczQnffM4JwQbOF7nNJzs1uuuVVR0rHleRA0BLABKktS9xBbgbwmg4Yz757Z6S/qM5ZP6H5c64he96EVLwfFnh74rznnqU5+6z2YWnSfUZ/O3WyZUgBCCwOcxJ0YAguperpY47IbaInsECyHZx2gx8aV1dfGIR5yraRRtDeFPEGlLm1PGtQlI4Ebb50RUtL4qHIVpfdxTnwl7baZFAkp8ZwxkzX34w1cApN1Z2+nHT8j9tdE4CBr0Lvmesft3/24FiDbx1ZlD+gR80aYYb21INW6Ao41+yryYd9/J7yOJWbQ17TUBN3MiGib3SFQPcOHa0egkq7DfXNdna8gxceyO6cm1ACjj47yYRwhzv6VY4dvetrp2yiAYS+NuvKMxTNXtaHw2CXveJknh3BDsdU7D+ma9dZ+rTfpVVLRX8CKyaCwh3Zcun5YDHOFvCVjpZtctKppDY8X/1uWKiN+HXa3jCCFgg8YijqJtkb2h/CiSu1n2zrO8k0oeaCCwMHlamGiCsnvnb+G6QnVpbaaAgtZXxX0IDQLZTp4Q1HcCUjvid6PNgIB9xFI5eq2ddesnhBKpE+DiesZC32i19u1oGSdo107FZv30uQUTQIvPcRQFQJiRUkahe01aFYAhY6Jfxg54M4cR2ubcGogzLaWxuaG9ihN3IrFyrRQNbBOJx3lVqLy1pQ+uH/8dfjTuQ1MErGibdQiAtabOuWHP2yavmxuCPeY0TAM2VsZjF+HcRWeHqrZR0XlHfT4UiYYYcgpt/QL4hdvF2x0DFgSNXTShlCJ7BF0YbTQfNBs///MrB1PAB0ABIgAXwhGDJnwdLwyXQEtGXoLL7wnjngIKur4MNDc0LASi9gAg2pGIJ6BE/12fJsX5IlcCcuyG2xDjRBbletkxE4audxSOlq3wXFqPDwUifxVzYrz0kTN1W1kY4EL61CeUuwIZRdOWHCquaZz01fiac0DC/WnTjKtrGwNj0YJb13eO/4GoaE1cI3s1bQQAHR8wQ3gDRRdeuOpXtDmus2nY87bJ6zYJwR5zwm7LeOw6nLvobFGBl6IzQeucQrt+ARj9ne+8AiOEGQZs5xztRhitKI62om+S0QE/yWdBc+BcO3FmIWYdPjGYNqHpvoQagZbz14GCvja7dvppJ0/Io4Ta0gr4HdDxTnjwwQDM3IuAAVjs+uUvSU4Rwhe4aqsoRxuwb0fLVngCklzftJ92C4iID0XXLwn5rU8odwWyY8yxvpuvAAfXNHdApnEG9My/8dBPc24OaXiimQGKEpWWEPUQrY55d3/XdZ1oX8yD/gBHtDs0MKmGvk122V0kr9sk0+1Y5enKmlu0CyrwUnTmacgvgECy2xfwRrjQrBDsYdoo2UQJN+CDwOQM67s2lT6TkBBY59vh29kDRgBHW+MGOCAUgZ0xUNDX5mhNtIMwImQIz4TiEph2+slUoK2uT4gxCQFVABZhHlOGdtIQ6Huy0raOz0fhaNkVeACEcQRSErodis+K/tIuDQnl9pr8hFLx2fEBbTFLASOAhXvFWVdyQ4LffRzrHEUGCW3nAXp+jxYI0daYe3OkzdF4WTspwGi+/M7EwnfqTnfaLrvsrpLX7TLTbWXNLdoFFXgpOvM05hcAfNgFAzDf8z2r1P8RiPJmEDyEOx8Swo/gAhIIIb85tk1PH5+MmKEwf4LYOfGbiA8MZ88hUNDX5jYxW0ASU1H8RpzTak0IIgBFG+Qs8T8wBWgRoMwmrpfraLcssN1MCUfhaNkVeCkXIAxZe4EsYwdkAF6ieQjtdTWkXFMotryb0Z4kw7J5MHfAUipbx+fJXBsHY22MkjMmJiJAJ87D2pDaV0BKnINTnDAmuvi/6Fub7G5bbdautB27zHRbWXOLtqUCL0Vnntb5BRAywADgEuZNuxLNR2ofJYyXgEtSroTiBmzYffNrIfT4UCSUOpqMmKwIUKabIVDQ1+YkZnNv5yZEOn4vrdaE2YnjKaGZCtv6luRpQALfi/vcZ9X2sSrKR+Vo2Qo8JhWgMVXEU3EEiAE25aAZqg7dagy86yfwxrSC2sywAAwwYczMG9NLwFvG2hwCHdaJ44yHaCZmx5h9okFDxld7Y97K/DnfHKk6bn3s0o+otB1F5xsVeCk687SJX0Cr+SAcWydXAt5vgAWB5PcIUNoM5oUrrjin2SCo2tpBwA6BSNV/u9v1gwLfAT+0Bj4DMjQqrkPrIJNpfDdEHwFI+saxlcDlq0N4EtoRjkkmhmhqRLv4bUoV5eNwtCSQlWkAJLUVGRMAZyhzcF9m2T7TinECkFwXQDVWKpW3oe3G9yY3WWndaHxSQqHVaDD78M8RQWa+jSNtV0KtXUO7ktHXMeahm49nE+oDbZX8reh8oQIvRWeeNvELaDUfrbkGeHAuE4QdO0ADPPiez4v7AAb//t+vgE127zFRREvjuq4xBAqAD9oZvip299rDTEFAua57fed3niusyAzinCRZ47NBuA1pS7p+LCfV0dLYEO59An5OZtmh/n391187xDdasSSmM7/MQUDBgx+80pp0fUEUlEwkGZDjN4DFuQmfN3/WEOCivARgYx6Tj2fu+E4BbUVFp5kKvBQVbSCcu9qaFoQQRIQPgQrY+J8ZQx2g1Ltpz22L16Xytd9l6u0TNK1Qjq8KMwQgo93MFa3ZhAbBvdtduDb84A/OC6EdMz2MmWa2Jdce067sKrPslBBfRRmBVMDD+AAltFUA6c/8zGre2nbFsZopEYgBSLRduxI270Vjk3DrthxA8vHMyaOzDrTJLtxmHN7lXBUVHRUVeCkq2sAvoE9bw3dExA5tDUGUAnRd8NN3ruMIxWQifcpTVtEmU4Ry11fF+foxBii6AGpqCO1QIch97fJdO34tNFjxa7ntbReLhz1sNzV52v6MhfgS+iqBp1yBMRTW3Ob+6QIiY5+q121CuwBGQBXQTSh6/GmAYNdOPp6p/i/rQBvwpSo6rR5/rNLIFJ1WKvBSVLRhFERXW0Nw07bwafHqVm6eounh4zJmhukTyl1fFb/zs7DTHwIUuwqh3abo3zpy7ac/fdUPPjsJ8WZ6efObV9qmpz2t//rbZpYdOocZTj+Z9xLinnnoAiJAAtABuoxFzELIuIr8ciwtjuP8DlC0mZyTj2dqHp0x0GaOUlvJNV1rV3NVVHTUVOClqOiYojg2OXeKUCboOIjSoIwBii6AAlr4XzhH1Wlt22fRv1xjyAz1pjetEv8BCW31Z0CMZkLhQ8cwr3Wvv21m2T7SRuCC0AemxgBRtFHeJaYTzg2k6EfmDkARUs1pF8BJbpm28nT3ulPa2Lc+XNccJ+oM+NKHo8iSXFS0DyrwUlR0jDkr5p67TigzPyTTrhIE6wBFABRNDW0GcEPQvvrVK43HmOln26J/Y+YmGiR1gICpbmXvmF1oEBzTd/1dZJbt0lRAZPw5ZEcbxafprW9dhazrp3FJ2n9RTdogX40cOj632pwpQKsFgMakLfcQ8r32uI7j41NzFFmSi4r2QQVeiopOMHU1E0lBPySU49QKkEwFFKKQImx9N9X007fLT4ZbGgo7e/4efRqDdeYmBSMBsWS47RLNgd+G6hftyiw2FxCJNuJX0mqjzJv5UEbAeGkTExgNC+fj5M8BelI7KqHvaAxodQEgUOI6nIFT0RqZD1oX7eRL0xZ7PIosyUVFu6YCL0VFJ5SGNBPq+wwJ5UQStRWNx4TUNqafriYimYFdSwiwa0cTIeIpNOWe6ghpK2Gb/DktcXL121D9oikRZPokimmqya4FRExWAECy4gJsybFDa9X1SZLgUD9oX/xPy4IU8UxWYA69crswJ7kWE5N55EfVB7SGACDgYi0AUcbT96mcrc3d8g5HlSW5qGiXVOClqOgEEsEkxwcNiTBajqIimKKZaLO3EsqEuzwikqZdddVKa9HdXfcJqW1MP4Q9AABouJ7jgBb31VYOtQT2T/zEKhtutDdT7smxVHVvocLqDrU+L0ALk4t7tOUa5vgV0TYp7zA3Qspvxh5okKE4NYnkzXnAA1bz1OdzksgzwEySOloY86Yf7m3cvAMZQIyxM1fGyP26bRoDgO4DuCC/Z33oHzrOLMlFRbuiAi9FRcdEY86qwoNVnPY/h0+CmtAhiGk4hGQ/4QkroaNoomMJfCYIL+YjRSUJ/TEhtU1UDgCgLQCG3b7r8+EgzAl1IIYmwjGt9mbqPSWI016+N/xwXJuQ5txqjPj0fMd3rDf9dP2KtomQcu6VV65Agsgw8wJ40Lz4/u53H/aLAWBEoPHhiaYqIcvGK7WxAE9kjoA+cy1Tb1/+mCEACNDon3Dy5BrSJjWTTlKW5KKiTanAS1HRMdCYsyrBzcGTICNUAgYS5kowOZ/Q4VNC09IKYmYHYMb3Mu0SgkNCatOonBYAMGPRvjDlMIEQvjQRtCIEtl1/q72Zek+Cnk9I8rwwPw3VL5pK25jJ2nPl4GlBAzDgXJmMnauQZ3t9c0mDZBxoyPSFyUk/jZkxcmxqY1kT5tp9+jRfUwEg4NKa7E5iluSiok2owEtR0RHT2M6fkCL8mQ4IuYTkEm6AgN16coZkt98VxM5zTfWN7NoBHNE7fUJqk6icLgCgdQEoArK03f1inuhqb+bcE4BYV79oDm1jJptyLm3U/e636kM0HMa/TV5ojMyhvgIqxrMNvU6hzpiQaJrct/XJ2RR0VoHGovOFCrwUFR0hrdv5c9hkJiJ0mCNaoZYQYf4QTCjAwJAwZY646KKVwH3Qg1Zgoc8hdZOonK4QB7YIWO31P6EMAPids2lXkM6951j9orm0TmMBTABJQrBRO2ZTtR38XqLhoImRq8bY6CctCPDiHoCJqCnX57AL2CCfHeN+1oPP8vbwsYlPzhQAyCnYegOcuqUOKhy66LRTgZeioiOkdbt3zrkiWYTRAjjACo1GzAo+AwvOJ5DGhKlIFQKR38SYsJpb16kV4jRBAAghGU0C4esYAEAbHNPV3hxVoceuX5E2D2kstJO5h3nu5S9fFWNsnXjnaDuMN9OZBHo0L85PvhrjRHtkfMxxgFtATJLhmTvHA22qTXd9csYAINCrP+pXVWHGovORCrwUFR0hrdu981Gw0+YfQZvBN8LuHAjwW0wyMuD6fxdZZAl41xTVwsnX8a49FDocIQ5EAR6EM+EKgPgcXw73Z7rid9PnDLpvE0afX5H7MXER+q3GgqCnJaHV0hdaC59VeAYOUiRzjonN/+bvq77q2vMTh1pOyMbM/AIt2pAq0wEyfnNsnG4ls7v88sXiUY9a9aUPACZay/+7LtlQVHRSqMBLUdER0rrdu9BZjq/ACyHErwVIsEPn2MlcdLe7rapEo22zyI45Dg+BiJgsOAUTuKmCTMOjnUCX/wEimqLv/M5hYbkvE8aQXxHNCnDlFY0FbZHvgRUmH+AlieYAyURV8b2ZY+4aA6r8l0QrvfOdK8BivmN2o3HJ2LXgyhwBPDRz2qBoo/Y86UnnAKB7vfa1q/ZsWrKhqOg0UIGXoqIjpDFfBRoQfg0ASkxChA6h5tVmqyVQ15kO1oW/bhoyHJ8VAh9QScr5aFz0UZRMssYCBEdJUyKKjBUTHd8TL5oKoMUr/ihAGQChT4DDv/pXi8VTnzrd3LUOqAIocrI4xlwkKoyPjLb5Xh+Yl4Ao8+86+kMT1zdPHJutjU1LNhQVnRYq8FJUdIQ05qxK6NC6XHjhSjgRWnKoEFyAAZ8JGg3CFeiI0NrEd2STkOGu/8iDH7xqn/+1n8ZA6vkUFaQ1IqCPOmtr61eEaDX4kKQKtDGX6faRj1z1zTjzcWEqonEBXLQ/Y2IunE/7oRDkxRdPM7FNcaoVGQY4yYdj7GnXzC/g6nquD5BoA82WdgBUTF/u252nfVTTLio6iVTgpajoiGkIcHDwBBAiCFN1GHgBapKGnuB1XITWJr4jc0OGh/xHCHAAxv1SjydOqceVtTUCnKZCAr+UK0iiP+Psd4ArOVA45zIbObZbGNHYO9e4yhYM7Ph/nYltSlTVbW6zWFxxxTnnZ+MMwMS3CVBxfLRXzIqcudPG7jzto5p2UdFJpAIvRUXHQH2AAyARHULA+AzUELKpEQQQIMLN97QEEVpzfUfm7NDX+Y8QkgAVQQpgHXfWVmNJQyFxXsoVtIn+gAQajwjwaEg45ybdfyilCPRRWn9jwAHXWE8xsa3TjAEoimKaR74sQAugIncO0OR/fYnmB6BtaxNtk0OnqOg0U4GXoqJjoi7gAFIieJgFgBTfBWAQZAQu0wxfEsKUJmYT+ryJO3T3ft3rpvuPnISsrUkMxx9HmHHAE7MRQGXcaC8c12pI9IXpTvtpvIAHwAVgQByn+SPpa0DbFCfYMc2Y+fUbJ1vALzWcRBkBK/oAdACHMhnHJBfaNodOUdFppQIvRUUnhFrBQ9tht21nTqjF14HwIdx8D7iM+S4M1U6a4jhMiDOvEHhJODdkXmr9R/YR8jyXtNn4AIDJlQMMBIz4nsB3nHYaIxoamXGBQiAseVaAHL4mtEwIcGgLXk51gh3SjPn+1rdeLF71qtX/7qutwBHQZCyTsI4TtLav06QcVQ6doqLjpAIvRUUniCJ4fuRHVgKTtiWaAMDFLprQisknO+45IdDu0QIl5goCnrAm7Alvgh6Ied7zVg6jcrX0aWhitmj9R/ZJY4As5DdaFo7P/HH0yXcBI0AZjQZ/mNe85tpjBMilujNQ4nj3czytSGuy2ZUTLDMRDZF7uk/bVvcDXt/xjlU7AZgpmpQqA1B0vlOBl6KiE0YEz2Mec243D6zYcRPIhCztAS0Mv41WCzA3BNpL1IxjhWgDSnEUveCCFRiRV+Q3fmPlP6LKc2uyOGoH0HWArGsSo7kAumio2mgj4+f/N7xhBdLaMTI+zksdqSQJ9L/x6PZ/F2PgPMCIOcpct20FlLSfn42svNozVZNSZQCKzmcq8FJUdAKJiUEiOtWUmQ2YZvhcZEfuXY6QruPlnBBopiGFHf2mSrMaOAQ28GJXz1xC+/DlX77SxgANMVsdtQPonJw02kybQbPSpuVPmx0P/ACAbXVo4wCcyArsN+PMfKP/+gfIOX/XTrBTnGzN9eMfv/p8EkxzRUXHTQVeiopOILVmHYACmAFY+GbQHBCyfeaCqSHQTEFAjmsnMy5wdKMbrXb9yehKcwEA+P1DH1olceMwfJQOoHMBmWOBrQ9/ePV/CiIGlNFaAISEfztG+vwrv7LSfLgn0ALEKJTZzcq7SyfYqU625r8SyxUVrajAS1HRCaWu4yVwQfDe6lbD5oKpIdCEu1o+AAG/kJQgEKUDGAEG/mey8D//EaYjAj7tOCoH0KmA7F3vWoUdazfgAWQxeQFqrkG7ISU/bUtyq7QaDvdgNgPgUiWbKaeNqgIkAKJdO8GWk21R0Twq8FJUdIJpruPl1BBo15OGnj9HMvfS6KS2EsFPeNNCIFoLGWgf9rBzRQKPymwxBZDRULz5zdfWzsR3RFuFnQMcnF8d73tmIP33GXhJgjpaGVoO3x9lVFU52RYVTacCL0VFJ5zmOF5O8Z8AQmhegBNalQhvEU20KsALx1A5TfJb/DpoYI4j6dw6QAaUMLtknORykQlXX2hPnAdwXH31CrQ5FpgBZFK/KNcHUvi7+JzSAkxGtFL7jqoqJ9uiomlU4KWo6DyiKf4TySviNxoJAAXI8RtBTctBaPOFoYlxHSDn275tuM7RPrUwfYAMoGLS0lbtYCaS3p/ZR4i5NifMHEhL0jltfO97zyWZA0j0RX9dj9bFOIg4evvbV/3P/VwLqJsCXo5yfIqKziIVeCkqOs9onf9EzEGE8K/+6sqPhSBnGgJSmFP8LpW+NPXAj9/U/4kAnhKyvGtABgwAHkw62pUkfYAGPxUaE/4oQAtNDNCgr47VFxoWYASw8Fs0TQAMiomM6eg971kBGcAu5ir3VdsIqBvr59SQ7qKios2pwEtR0XlIY/4THFiTB4UWhpBtE7kl+oiDrGswoQAENB8cYBFwsC5kedekzR/5yEqDFN8VuW7cD5BhCgM4aJT8D6RoN62Jz9GgpMaRazAz+S1gjQZGVBVfGCAG+OG8C8TIfQMgjZUCmBPSXVRUtDkVeCkqOk9pyH+ia4ZpE7nRToja4cQrz0wENM0MofumN61AQFtJeWqNn00pgICGSLI+4IMmKEUraVC0lz9LkroBKUAD8OE7/UotIeTdMTGZpdxCzgXiXN857snMRINDWzVUCmBOSHeZkIqKtqOywhYVnTGKGYZvB4HKlAIA0GzQOvANEY7dFbCOo63w8rmlbo2fXVELCGiEmHmYtjjhioiiWaE5Arz0IYnmtIc5CBABPGhAAl5iytHPmIq8JwQ82ic5bWh2vLumNgBDzu8rBTA1pHuX41NUdFapwEtR0Rn2i7nlLVemECYX7+r+qLPTly02gr77OQQgDAn2TakFBMAG8ECLEkCQfDR+S2jzzW++yuUSsJLChsAM4JW2Ajauh7y7BgAU0Oaza+qr3wAlZqWhUgBTQrp3PT5FRWeVymxUVHRGqc8vhrD/wR/sD0sOOOh+3medoxYQaButSBxpEd8Ux3jXXv4wNCiAGJAD4ATAACWOQ67pWr5LdJXjXQPAAVQcCxj57HxgRwZe9aD6wN3UHDtHUQeqqOh8p9K8FBWdYYpfDF8O7178YTiXEtYtEchJk98VzskFAxDtss5RCwg40DJ1xS8F0YgkoRxNiagjvjy0LI71W5xvFTZUADGOuEioOHCizbROgBAzkXOY0NxLf93PWMXZV1TTkC9R39jta3yKis4qFXgpKioa9IchvAl67/w1VDdmkvG5/c2x+6hz1AIC5LMoKc67AAqNCdAiHJy566EPXQEPWpRoVlJkEQiKlgXRymi/6/FxAWryco2cT5ODHMO52Tkcb+P8O2Xs9jU+RUVnlT7lYEnH3Yhd0p8sPQk/f8l9/njJMf6PPt1tUVHRWurLVUJrQPiiod/2EQbchh9zegVaVMCmyQAwACpVl5PD5hnPWGlgfKaxoSVRXNFn/is4Hh8aYAIw4XwMwDAH3f72q0reaiQlrJoZSR9FJAFCwJJzX/zi/miusbGrMOmiot3I7/J5KSoqml1np/sbUAFMABXtsWOZZqdmoe0m3QMIaFluc5vVi4Num8MmIENEEg0N8AFwIMcAPExB7v+1X7sCLtoBkNzpTivA8tM/vbpPoqqc4zjX5U8jm+9//s/94KVqFBUV7Z8KvBQVFc2us9P+RtPw3OdeN6PszW62SmrXl2kWzclCOxUQtDlsnJNK0YBMkvDZ0DEj0eQwNzEFATw0NHLcyKKrTfxpmI9ibpJ5OOYl4EWV7bvetR9wVY2ioqL9UoGXoqKijWkooyzTyxVXrDQyfGTaTLObZumdAgja2k5KHwAcqWHk5V40LI5LmDWfFJqZVKZmWgJSfvM3V99HUwME0eTQwOgXx92+ZHVFRUX7p3IdKyoq2oi6GWUTjUQrwvSSQof+T4QSUKKqM2Dgc85JFlrX6nOGnUMxM93kJivAQeOiHUCHaKPkYaFVSZ2ntjI17Y2XdvktkUn+F50EzIjO8lvlbCkqOh4qzUtRUdFGNJRRFmjhFyIrrXf/S9/fZul1vM/5vi8L7TYaDQDm0Y9eaVOAFSn++eMw93DEda+EWQMxjtMPmhrHc/jVftoaIMXLOUxIQBYzU+VsKSo6PirNS1FR0UY0lFE22grfR7PR/tb3eR9ZaIEfDrmuByQBNG2YdcxF/F6YkvivACXJKQNIaY/ryP3ClHTDG65elbOlqOh4qcBLUVHR1gnkWpIwjkbD90kg1/7W93kfWWi7eVdoWICZv/t3VxoYmhe/qePEzKQQZTenDPACSDEZAUHaFbBTOVuKio6PymxUVFS0EXWrU8d0RGshuke9JFlt/R+Kj0vqEvVlof2arxnOQjs1vHqTMGsUZ19gh+YF2GlzygTsVM6WoqLjpQIvRUVFG1Eb2RNhn8gh+VCAFu98W/I9ECCpHOLb0p7jtzGNRl/yt7Hw6k3yrswFO0VFRcdDlWG3qKhoKxrKKCsip5vnZShLLxOS3CtDAGEoJBtwAniGwqs3pbkanqKioqPNsFvgpaioaG/CfkqGXZlqf+EXVn4onHi7GhXHPfvZ1zVPxdRE68PU9MQnFsAoKjrNVOUBioqKjpSGEsity9Ir6ueqq66rUWkT1okA6gvJ3nV4dVFR0emhUoQWFRWdqCR33YR1Qpr7QrL3EV5dVFR0OqjAS1FR0YlKctfVqAAlfSHZ+wivLioqOh1U4KWoqOhEJbnralSAkuRf4ePSF17NN2YovLqoqOj8owIvRUVFJyrJXVejIuS6TTbHjCRzr3f/V8K4oqKzRwVeioqKjjXJ3RSNSvKv3PKWq3pJEuB5F2W06zDpoqKik0+VpK6oqOjEJbnrS1g3J9lcUVHR+U0FXoqKio6Nuhlt1Q1iKqJR6UvBPxZ6XVRUdHaowEtRUdGxUmlUioqK5lKBl6KiomOn0qgUFRXN4hlzDi4qKioqKioqOm4q8FJUVFRUVFR0qmhv4OVZz3rW4sILL1xc73rXW1z/+tefdM7BwcHiB37gBxZf/MVfvPjsz/7sxUUXXbT44Ac/uK8mFhUVFRUVFZ1C2ht4+eQnP7m4xz3usXjEIx4x+ZznPve5i5e85CWLyy+/fPHLv/zLi8/5nM9Z3OEOd1j8hTSbRUVFRUVFRUVL+pSltqOTHmq39MpXvnLx6Ec/evGJT3xi9DjN+Ht/7+8tHve4xy3+5b/8l4ff/fEf//Hihje84eE17n3ve++8pHZRUVFRUVHRyaA58vvE+Lx85CMfWXzsYx87NBWFdOLWt7714uqrrx487y//8i8PO9y+ioqKioqKis5fOjHgBXBBNC0t+T+/9dGll156CHLy+gdK1BYVFRUVFRWdtzQLvDzpSU9afMqnfMro67elyTxCevKTn3yoYsrrf8g1XlRUVFRUVHTe0qwkdfxRHvCAB4wec+Mb33ijhtzoRjc6fP/4xz9+GG0U8v8tbnGLwfM+8zM/8/BVVFRUVFRUdDZoFni5wQ1ucPjaB33Zl33ZIYB55zvfeQ1Y4b8i6mhOxFL8j8v3paioqKio6PRQ5PaUOKK9lQf46Ec/uvijP/qjw/e//uu/Xvz6r//64fc3uclNFp/7uZ97+PkrvuIrDn1Wvv3bv/3Q5CQq6Yd+6IcWN73pTQ/BzCWXXHIYgfTPVWibSH+q3OySyvelqKioqKjo9BE5zof1WMCLZHOvetWrrvn/lre85eH7u9/97sU3fuM3Hn7+wAc+cOinEnrCE56w+LM/+7PFQx/60MPQ6n/2z/7Z4qqrrlp8ljKzEwnY4ffyeZ/3eYeA6LSiT+BLPyrc+/ip5uNkUc3HyaKaj5NFf3KK5QeNC+BCjh97npeizRZf5ao5OVTzcbKo5uNkUc3HyaI/OSPy48SEShcVFRUVFRUVTaECL0VFRUVFRUWnigq8nEAS+v20pz2tQsBPCNV8nCyq+ThZVPNxsugzz4j8KJ+XoqKioqKiolNFpXkpKioqKioqOlVU4KWoqKioqKjoVFGBl6KioqKioqJTRQVeioqKioqKik4VFXgpKioqKioqOlVU4OWE0LOe9azFhRdeuLje9a63uP71rz/pHMmRlWFQhfuzP/uzFxdddNHigx/84J5bejZIXa773ve+hxkqzceDHvSgxf/6X/9r9BxlL5SkaF8Pf/jDj6jF5xdddtlliy/90i89LA1y61vfevErv/Iro8e/8Y1vPKyV5viv/uqvXrz1rW89opaeDZozH6985Suv8xzMKfFSNEz/6T/9p8Vd73rXw/T5xvUnf/In1w7Xz/7szy6+5mu+5jB0Wm1B83M+UIGXE0Kf/OQnF/e4xz1mVdB+7nOfu3jJS16yuPzyyw+rb3/O53zO4g53uMPiL/7iL/bY0rNBgMtv/uZvLt7xjncs3vKWtxwyDTW31tFDHvKQxe///u9f8zJHRfPo9a9//eKxj33sYa6K97///Yub3/zmh+v6D/7gD3qPf8973rO4+OKLDwHmr/3arx0WcvX6r//1v9bQH8N8IKC/fQ5+53d+p+ZiB6T2n/G/bAkmp9BHPvKRxZ3vfOfF7W53u8PiyIofP/jBD168/e1vP/3zobZR0cmhV7ziFQef//mfv/a4v/mbvzm40Y1udPC85z3vmu8+8YlPHCzR9cFrX/vafTbxvKf/9t/+m3pfB+9973uv+e5tb3vbwXKnc/B7v/d7g+fd9ra3PXjUox51FE08r+nrvu7rDr73e7/3mv//+q//+mC50zy49NJLe4+/5z3vebBk0Nf6bqkdOHjYwx6213aeFZo7H1N5WNF2tFjyqP/wH/7D6DFPeMITDr7yK7/yWt/d6173OliCz1M//KV5OaUEUX/sYx87NBWFFOOi0r366quPsWWnn4wfU9HXfu3XXvOdcf7UT/3UQw3XGF1xxRWLL/zCL1x81Vd91eLJT37y4s///M/33dzzTgP5vve971rr2rj7f2hd+749HtEM1HNwPPOBmFi/5Eu+5LC68d3udrdDLWbR0dPV5/Gz8enH3YCizQhwQTe84Q2v9b3/81vR5mP7RV/0Rdf67tM//dMXX/AFXzA6tve5z30OGTZ79G/8xm8snvjEJy4+8IEPLN70pjfVVEykP/zDP1wsd/a96/q3f/u3B+ernoOTMx//+B//48XLX/7yxc1udrPDysbPf/7zD/35AJi///f//p5aWjTn2VB5+n//7/996Ct5Wqk0L3ukJz3pSddxXOu+hhhA0embDz4xdjUcRvnMvPrVr14s1bqLD3/4wzvsRVHRyaYLLrhgcb/73W9xi1vcYrE0pR6C9xvc4AaLf/tv/+1xN63oPKLSvOyRHve4xy0e8IAHjB5z4xvfeKNrL/1dDt8//vGPH0YbhfyPaRRtPh/GtuuM+Fd/9VeHEUgZ9ynEhIc+9KEPLb78y7+8pmQCMbl92qd92uE6bsn/Q2Pv+znHF+13Prr0d/7O31nc8pa3PHwOio6WbjTwbHCoPs1aF1TgZY9kt+G1D/qyL/uyw4X5zne+8xqwQhXIJ2NOxNJZoqnzYee4dH4+tPXf6la3OvzuXe9612LpJH0NIJlCvPtRCy6LxukzPuMzDsfcuhYxhIy7/7/v+75vcL78LpIiJErM90VHPx9dYnb6L//lvyzudKc71XQcMV2wfAa6aQPOm2fjuD2Gi1b0O7/zOwe/9mu/dvD0pz/94HM/93MPP3v96Z/+6TVDtLQlHyxVsNf8/+xnP/tg6Vh68FM/9VMHSx+Lg6Vj3MES1BwsbZk1rFvSHe94x4PlbvFgCQYPfuEXfuHgpje96cHFF198ze+/+7u/ezgffkfLXeXBM57xjINf/dVfPVg6Ux/OyVKLc/AN3/ANNRcz6XWve91h1NwrX/nKw8ivpTnucJ0v7feHv3/3d3/3wdIEeM3xv/iLv3iw9Ek6WPpWHPzWb/3WwdOe9rSD5W7/YCkwa+yPYT7wsLe//e0HS3PpwXIDcHDve9/74LM+67MOlj4vNR9bEnkQ2bBYiu8XvvCFh5/JD2QezEfov//3/35wvetd7+Dxj3/84bNx2WWXHSw1aQdXXXXVqZ+LAi8nhO5///sfLsbu693vfvc1x/hfGGIbLn3JJZccLB2wDpnL7W9/+4Olg+hxNP+8o//5P//nIVgBJJcq1oMHPvCB1wKSAEo7Px/96EcPgcrSqfdwLm5yk5scMoylw+JxdeFU00tf+tKDf/gP/+HBcud/GKr7S7/0S9cKSfe8tPSGN7zh4B/9o390eLzQ0CuvvPKom3xe05z5WGrArjkWb1pqXA7e//73H0ezzzvCb/rkxP3/dvy9m4/uOUvt/OF82FC1MuQ006f4s+x8UVFRUVFRUdGpoIo2KioqKioqKjpVVOClqKioqKio6FRRgZeioqKioqKiU0UFXoqKioqKiopOFRV4KSoqKioqKjpVVOClqKioqKio6FRRgZeioqKioqKiU0UFXoqKioqKiopOFRV4KSoqKioqKjpVVOClqKioqKio6FRRgZeioqKioqKiU0X/P7d482SL/LqQAAAAAElFTkSuQmCC", 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", 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" ] @@ -510,7 +545,7 @@ "pts_heart = heart.sample(1500)\n", "\n", "fig, ax = plt.subplots()\n", - "plot_scatter(ax, pts_heart, 'Heart Domain')" + "plot_scatter(ax, pts_heart, \"Heart Domain\")" ] }, { @@ -525,7 +560,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "pina", "language": "python", "name": "python3" }, @@ -539,7 +574,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.9.21" } }, "nbformat": 4, diff --git a/tutorials/tutorial6/tutorial.py b/tutorials/tutorial6/tutorial.py index 27905609a..b35518434 100644 --- a/tutorials/tutorial6/tutorial.py +++ b/tutorials/tutorial6/tutorial.py @@ -1,62 +1,73 @@ #!/usr/bin/env python # coding: utf-8 -# # Tutorial: Building custom geometries with PINA `Location` class -# +# # Tutorial: Building custom geometries with PINA `DomainInterface` class +# # [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial6/tutorial.ipynb) -# +# # In this tutorial we will show how to use geometries in PINA. Specifically, the tutorial will include how to create geometries and how to visualize them. The topics covered are: -# +# # * Creating CartesianDomains and EllipsoidDomains # * Getting the Union and Difference of Geometries # * Sampling points in the domain (and visualize them) -# +# # We import the relevant modules first. -# In[1]: +# In[ ]: ## routine needed to run the notebook on Google Colab try: - import google.colab - IN_COLAB = True + import google.colab + + IN_COLAB = True except: - IN_COLAB = False + IN_COLAB = False if IN_COLAB: - get_ipython().system('pip install "pina-mathlab"') + get_ipython().system('pip install "pina-mathlab"') import matplotlib.pyplot as plt -plt.style.use('tableau-colorblind10') -from pina.geometry import EllipsoidDomain, Difference, CartesianDomain, Union, SimplexDomain + +from pina.domain import ( + EllipsoidDomain, + Difference, + CartesianDomain, + Union, + SimplexDomain, + DomainInterface, +) from pina.label_tensor import LabelTensor + def plot_scatter(ax, pts, title): ax.title.set_text(title) - ax.scatter(pts.extract('x'), pts.extract('y'), color='blue', alpha=0.5) + ax.scatter(pts.extract("x"), pts.extract("y"), color="blue", alpha=0.5) # ## Built-in Geometries # We will create one cartesian and two ellipsoids. For the sake of simplicity, we show here the 2-dimensional case, but the extension to 3D (and higher) cases is trivial. The geometries allow also the generation of samples belonging to the boundary. So, we will create one ellipsoid with the border and one without. -# In[2]: +# In[ ]: -cartesian = CartesianDomain({'x': [0, 2], 'y': [0, 2]}) -ellipsoid_no_border = EllipsoidDomain({'x': [1, 3], 'y': [1, 3]}) -ellipsoid_border = EllipsoidDomain({'x': [2, 4], 'y': [2, 4]}, sample_surface=True) +cartesian = CartesianDomain({"x": [0, 2], "y": [0, 2]}) +ellipsoid_no_border = EllipsoidDomain({"x": [1, 3], "y": [1, 3]}) +ellipsoid_border = EllipsoidDomain( + {"x": [2, 4], "y": [2, 4]}, sample_surface=True +) -# The `{'x': [0, 2], 'y': [0, 2]}` are the bounds of the `CartesianDomain` being created. -# +# The `{'x': [0, 2], 'y': [0, 2]}` are the bounds of the `CartesianDomain` being created. +# # To visualize these shapes, we need to sample points on them. We will use the `sample` method of the `CartesianDomain` and `EllipsoidDomain` classes. This method takes a `n` argument which is the number of points to sample. It also takes different modes to sample, such as `'random'`. -# In[3]: +# In[ ]: -cartesian_samples = cartesian.sample(n=1000, mode='random') -ellipsoid_no_border_samples = ellipsoid_no_border.sample(n=1000, mode='random') -ellipsoid_border_samples = ellipsoid_border.sample(n=1000, mode='random') +cartesian_samples = cartesian.sample(n=1000, mode="random") +ellipsoid_no_border_samples = ellipsoid_no_border.sample(n=1000, mode="random") +ellipsoid_border_samples = ellipsoid_border.sample(n=1000, mode="random") # We can see the samples of each geometry to see what we are working with. @@ -70,15 +81,19 @@ def plot_scatter(ax, pts, title): # Notice how these are all `LabelTensor` objects. You can read more about these in the [documentation](https://mathlab.github.io/PINA/_rst/label_tensor.html). At a very high level, they are tensors where each element in a tensor has a label that we can access by doing `.labels`. We can also access the values of the tensor by doing `.extract(['x'])`. -# +# # We are now ready to visualize the samples using matplotlib. -# In[5]: +# In[ ]: fig, axs = plt.subplots(1, 3, figsize=(16, 4)) -pts_list = [cartesian_samples, ellipsoid_no_border_samples, ellipsoid_border_samples] -title_list = ['Cartesian Domain', 'Ellipsoid Domain', 'Ellipsoid Border Domain'] +pts_list = [ + cartesian_samples, + ellipsoid_no_border_samples, + ellipsoid_border_samples, +] +title_list = ["Cartesian Domain", "Ellipsoid Domain", "Ellipsoid Border Domain"] for ax, pts, title in zip(axs, pts_list, title_list): plot_scatter(ax, pts, title) @@ -86,40 +101,41 @@ def plot_scatter(ax, pts, title): # We have now created, sampled, and visualized our first geometries! We can see that the `EllipsoidDomain` with the border has a border around it. We can also see that the `EllipsoidDomain` without the border is just the ellipse. We can also see that the `CartesianDomain` is just a square. # ### Simplex Domain -# +# # Among the built-in shapes, we quickly show here the usage of `SimplexDomain`, which can be used for polygonal domains! -# In[6]: +# In[ ]: import torch + spatial_domain = SimplexDomain( - [ - LabelTensor(torch.tensor([[0, 0]]), labels=["x", "y"]), - LabelTensor(torch.tensor([[1, 1]]), labels=["x", "y"]), - LabelTensor(torch.tensor([[0, 2]]), labels=["x", "y"]), - ] - ) + [ + LabelTensor(torch.tensor([[0, 0]]), labels=["x", "y"]), + LabelTensor(torch.tensor([[1, 1]]), labels=["x", "y"]), + LabelTensor(torch.tensor([[0, 2]]), labels=["x", "y"]), + ] +) spatial_domain2 = SimplexDomain( - [ - LabelTensor(torch.tensor([[ 0., -2.]]), labels=["x", "y"]), - LabelTensor(torch.tensor([[-.5, -.5]]), labels=["x", "y"]), - LabelTensor(torch.tensor([[-2., 0.]]), labels=["x", "y"]), - ] - ) + [ + LabelTensor(torch.tensor([[0.0, -2.0]]), labels=["x", "y"]), + LabelTensor(torch.tensor([[-0.5, -0.5]]), labels=["x", "y"]), + LabelTensor(torch.tensor([[-2.0, 0.0]]), labels=["x", "y"]), + ] +) pts = spatial_domain2.sample(100) fig, axs = plt.subplots(1, 2, figsize=(16, 6)) for domain, ax in zip([spatial_domain, spatial_domain2], axs): pts = domain.sample(1000) - plot_scatter(ax, pts, 'Simplex Domain') + plot_scatter(ax, pts, "Simplex Domain") # ## Boolean Operations # To create complex shapes we can use the boolean operations, for example to merge two default geometries. We need to simply use the `Union` class: it takes a list of geometries and returns the union of them. -# +# # Let's create three unions. Firstly, it will be a union of `cartesian` and `ellipsoid_no_border`. Next, it will be a union of `ellipse_no_border` and `ellipse_border`. Lastly, it will be a union of all three geometries. # In[7]: @@ -132,39 +148,43 @@ def plot_scatter(ax, pts, title): # We can of course sample points over the new geometries, by using the `sample` method as before. We highlight that the available sample strategy here is only *random*. -# In[8]: +# In[ ]: -c_e_nb_u_points = cart_ellipse_nb_union.sample(n=2000, mode='random') -c_e_b_u_points = cart_ellipse_b_union.sample(n=2000, mode='random') -three_domain_union_points = three_domain_union.sample(n=3000, mode='random') +c_e_nb_u_points = cart_ellipse_nb_union.sample(n=2000, mode="random") +c_e_b_u_points = cart_ellipse_b_union.sample(n=2000, mode="random") +three_domain_union_points = three_domain_union.sample(n=3000, mode="random") # We can plot the samples of each of the unions to see what we are working with. -# In[9]: +# In[ ]: fig, axs = plt.subplots(1, 3, figsize=(16, 4)) pts_list = [c_e_nb_u_points, c_e_b_u_points, three_domain_union_points] -title_list = ['Cartesian with Ellipsoid No Border Union', 'Cartesian with Ellipsoid Border Union', 'Three Domain Union'] +title_list = [ + "Cartesian with Ellipsoid No Border Union", + "Cartesian with Ellipsoid Border Union", + "Three Domain Union", +] for ax, pts, title in zip(axs, pts_list, title_list): plot_scatter(ax, pts, title) # Now, we will find the differences of the geometries. We will find the difference of `cartesian` and `ellipsoid_no_border`. -# In[10]: +# In[ ]: cart_ellipse_nb_difference = Difference([cartesian, ellipsoid_no_border]) -c_e_nb_d_points = cart_ellipse_nb_difference.sample(n=2000, mode='random') +c_e_nb_d_points = cart_ellipse_nb_difference.sample(n=2000, mode="random") fig, ax = plt.subplots(1, 1, figsize=(8, 6)) -plot_scatter(ax, c_e_nb_d_points, 'Difference') +plot_scatter(ax, c_e_nb_d_points, "Difference") -# ## Create Custom Location +# ## Create Custom DomainInterface # We will take a look on how to create our own geometry. The one we will try to make is a heart defined by the function $$(x^2+y^2-1)^3-x^2y^3 \le 0$$ @@ -174,30 +194,27 @@ def plot_scatter(ax, pts, title): import torch -from pina import Location from pina import LabelTensor -import random -# Next, we will create the `Heart(Location)` class and initialize it. +# Next, we will create the `Heart(DomainInterface)` class and initialize it. -# In[12]: +# In[ ]: -class Heart(Location): +class Heart(DomainInterface): """Implementation of the Heart Domain.""" def __init__(self, sample_border=False): super().__init__() - -# Because the `Location` class we are inheriting from requires both a `sample` method and `is_inside` method, we will create them and just add in "pass" for the moment. +# Because the `DomainInterface` class we are inheriting from requires both a `sample` method and `is_inside` method, we will create them and just add in "pass" for the moment. We also observe that the methods `sample_modes` and `variables` of the `DomainInterface` class are initialized as `abstractmethod`, so we need to redefine them both in the subclass `Heart` . -# In[13]: +# In[ ]: -class Heart(Location): +class Heart(DomainInterface): """Implementation of the Heart Domain.""" def __init__(self, sample_border=False): @@ -209,14 +226,21 @@ def is_inside(self): def sample(self): pass + @property + def sample_modes(self): + pass -# Now we have the skeleton for our `Heart` class. The `sample` method is where most of the work is done so let's fill it out. + @property + def variables(self): + pass -# In[14]: +# Now we have the skeleton for our `Heart` class. Also the `sample` method is where most of the work is done so let's fill it out. +# In[ ]: -class Heart(Location): + +class Heart(DomainInterface): """Implementation of the Heart Domain.""" def __init__(self, sample_border=False): @@ -225,16 +249,24 @@ def __init__(self, sample_border=False): def is_inside(self): pass - def sample(self, n, mode='random', variables='all'): + def sample(self, n): sampled_points = [] while len(sampled_points) < n: - x = torch.rand(1)*3.-1.5 - y = torch.rand(1)*3.-1.5 - if ((x**2 + y**2 - 1)**3 - (x**2)*(y**3)) <= 0: + x = torch.rand(1) * 3.0 - 1.5 + y = torch.rand(1) * 3.0 - 1.5 + if ((x**2 + y**2 - 1) ** 3 - (x**2) * (y**3)) <= 0: sampled_points.append([x.item(), y.item()]) - return LabelTensor(torch.tensor(sampled_points), labels=['x','y']) + return LabelTensor(torch.tensor(sampled_points), labels=["x", "y"]) + + @property + def sample_modes(self): + pass + + @property + def variables(self): + pass # To create the Heart geometry we simply run: @@ -247,15 +279,15 @@ def sample(self, n, mode='random', variables='all'): # To sample from the Heart geometry we simply run: -# In[16]: +# In[ ]: pts_heart = heart.sample(1500) fig, ax = plt.subplots() -plot_scatter(ax, pts_heart, 'Heart Domain') +plot_scatter(ax, pts_heart, "Heart Domain") # ## What's next? -# -# We have made a very simple tutorial on how to build custom geometries and use domain operation to compose base geometries. Now you can play around with different geometries and build your own! +# +# We have made a very simple tutorial on how to build custom geometries and use domain operation to compose base geometries. Now you can play around with different geometries and build your own! diff --git a/tutorials/tutorial7/data/pinn_solution_0.5_0.5 b/tutorials/tutorial7/data/pinn_solution_0.5_0.5 index 82cc8dc2c..d40bbb916 100644 Binary files a/tutorials/tutorial7/data/pinn_solution_0.5_0.5 and b/tutorials/tutorial7/data/pinn_solution_0.5_0.5 differ diff --git a/tutorials/tutorial7/data/pts_0.5_0.5 b/tutorials/tutorial7/data/pts_0.5_0.5 index 740719b69..4279d7ef7 100644 Binary files a/tutorials/tutorial7/data/pts_0.5_0.5 and b/tutorials/tutorial7/data/pts_0.5_0.5 differ diff --git a/tutorials/tutorial7/tutorial.ipynb b/tutorials/tutorial7/tutorial.ipynb index b6d026aa1..ad74cfe06 100644 --- a/tutorials/tutorial7/tutorial.ipynb +++ b/tutorials/tutorial7/tutorial.ipynb @@ -50,35 +50,60 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "00d1027d-13f2-4619-9ff7-a740568f13ff", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Seed set to 883\n" + ] + }, + { + "data": { + "text/plain": [ + "883" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "## routine needed to run the notebook on Google Colab\n", "try:\n", - " import google.colab\n", - " IN_COLAB = True\n", + " import google.colab\n", + "\n", + " IN_COLAB = True\n", "except:\n", - " IN_COLAB = False\n", + " IN_COLAB = False\n", "if IN_COLAB:\n", - " !pip install \"pina-mathlab\"\n", - " # get the data\n", - " !mkdir \"data\"\n", - " !wget \"https://github.com/mathLab/PINA/raw/refs/heads/master/tutorials/tutorial7/data/pinn_solution_0.5_0.5\" -O \"data/pinn_solution_0.5_0.5\"\n", - " !wget \"https://github.com/mathLab/PINA/raw/refs/heads/master/tutorials/tutorial7/data/pts_0.5_0.5\" -O \"data/pts_0.5_0.5\"\n", - " \n", + " !pip install \"pina-mathlab\"\n", + " # get the data\n", + " !mkdir \"data\"\n", + " !wget \"https://github.com/mathLab/PINA/raw/refs/heads/master/tutorials/tutorial7/data/pinn_solution_0.5_0.5\" -O \"data/pinn_solution_0.5_0.5\"\n", + " !wget \"https://github.com/mathLab/PINA/raw/refs/heads/master/tutorials/tutorial7/data/pts_0.5_0.5\" -O \"data/pts_0.5_0.5\"\n", + "\n", "import matplotlib.pyplot as plt\n", - "plt.style.use('tableau-colorblind10')\n", "import torch\n", - "from pytorch_lightning.callbacks import Callback\n", + "import warnings\n", + "\n", + "from pina import Condition, Trainer\n", "from pina.problem import SpatialProblem, InverseProblem\n", - "from pina.operators import laplacian\n", + "from pina.operator import laplacian\n", "from pina.model import FeedForward\n", "from pina.equation import Equation, FixedValue\n", - "from pina import Condition, Trainer\n", - "from pina.solvers import PINN\n", - "from pina.geometry import CartesianDomain" + "from pina.solver import PINN\n", + "from pina.domain import CartesianDomain\n", + "from pina.optim import TorchOptimizer\n", + "from lightning.pytorch import seed_everything\n", + "from lightning.pytorch.callbacks import Callback\n", + "\n", + "warnings.filterwarnings(\"ignore\")\n", + "seed_everything(883)" ] }, { @@ -86,18 +111,20 @@ "id": "5138afdf-bff6-46bf-b423-a22673190687", "metadata": {}, "source": [ - "Then, we import the pre-saved data, for ($\\mu_1$, $\\mu_2$)=($0.5$, $0.5$). These two values are the optimal parameters that we want to find through the neural network training. In particular, we import the `input_points`(the spatial coordinates), and the `output_points` (the corresponding $u$ values evaluated at the `input_points`)." + "Then, we import the pre-saved data, for ($\\mu_1$, $\\mu_2$)=($0.5$, $0.5$). These two values are the optimal parameters that we want to find through the neural network training. In particular, we import the `input` points (the spatial coordinates), and the `target` points (the corresponding $u$ values evaluated at the `input`)." ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 21, "id": "2c55d972-09a9-41de-9400-ba051c28cdcb", "metadata": {}, "outputs": [], "source": [ - "data_output = torch.load('data/pinn_solution_0.5_0.5').detach()\n", - "data_input = torch.load('data/pts_0.5_0.5')" + "data_output = torch.load(\n", + " \"data/pinn_solution_0.5_0.5\", weights_only=False\n", + ").detach()\n", + "data_input = torch.load(\"data/pts_0.5_0.5\", weights_only=False)" ] }, { @@ -110,29 +137,27 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 22, "id": "55cef553-7495-401d-9d17-1acff8ec5953", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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sv6L/R0IvIZjT/y32R6A3Tv/F6zfi/ovs+jWWvitdv7GeU5DOGX90pe+y8YcCeuOuEwCBO0W0JKpxvsj40/iZdp+RffCipSRa2sqbsdkZINDk0nb4YsLO68f9b2dWWIKgXl4ZR4VAy0MvvfSSbL8TTjiBZbolWTIF5R52GF/b/KMiRTfsdDbRXTr6HtLxH3mCJ981sqAzkvClk818JsNP8thM7xOQ9F7HEzglVmQdFXbAGJMnsnNFP886Kmw3LxKidDAReh4CPZ1kdkttQTz4eFb6KGRjgYT4AiTD77D3Ed+NMv6JyAyk4nMZ/4jnShn/WOAfLDEVPclFfX/P4Z9ChGS16RiS8eXZgU7kH/ZSOwCJ6KfSQMdMggcR323i4PkcUjRblOGY2oxI4DH2PuT/B4Qc/on4PETDPFg7SNLNHP6xyDtIxL9n/H2ey2X8g/57kST+KTd8vptl/L2eKxl/GvwzjgrjmI4xG+MY+URyVBhHwQOvj2SqgD/4DHtKzYCeZH2BR9l7t/8+pHKSYUXj8xAIcf5dnuvYU38GgfDbiMQ4/3a3nL/Ldy+bTUmm3Ojwyvm3ua+EkI6xm2/GUcnwb/dcxd77Ip9KjgrbS/Cg3cvb3xV4DpGc9qeZhC4/b/8O3/1I5PAPxebDHXybvW9y3yjj7w7NQCDG+88W95UkppVsbb5/IMb4e9DskfefLT1XM/7k/GQcFdbG6Ria3Fez957wZ5KjwtpY8KDFc/t28W91Xy/j7w2/g5DIv9X9Nxn/bt99rP1TKTe68tq/Q2x/cj49ee3fIfafYOQTyVHJtH+32H98ef0nkdwMr9h/fHn9Jxafh5DYf3xs3Mjyj4TfRjz2C/1fcCOY1/8D3it/8fpNRj8ruH6jPt7+ydALfHY5Y0ttQTL4BLcFHmIzzJItvgBCeAZ/T+NIDn9E32ey5yKKyEWxNtCOAHtayAt9Yss68ZwcB7nbd4lLHPnhUiQ19PeypCJIWR+5TZmzL0lZeY6CtNCRZ6Nsknx5SWDb5CoNVOL2dMx8eSNt3wEV40/rz3lsUp1QbIV/eiv8BfFcQi/8ObfebJTNs32r/IkjO0Kv/Gm/ONK98u9AWmnrlb8g+KWn1974J3vhnxJzXBTa0uJnZGsp4J+1tRZwIVsacQhs/V+OZKoDyl74p0X+mePm8k+IbZVIFvKPi/zjbBs5/8zyTDxZyD9zTJq1yOeSSHYgrYsj1Qt/choERaRX/inBj3iyoxf+vP/z5ZZ8/q3bxZ/PtuTx32b7dyKtjGy1/TOzN7n86Tfj+xbyp1mTX+o/vI/J+afEWQqadcznkvqF/o+0bYddv79m/En3Ov50bmP8LMxyuzMgs5yzPfvvqvhdk8LtrFCwKP1MABp1JhUU2t14MJt6VI5NCSgsgHoQFOp6FpyW/QlUgIo+K4GSRcJTIFymY6qg1E3k77R75DwN8WMqtXuJNoq2z10LFqDSZvabnHM8QhLKjE23VwF/lWYs46/UjC7gr9IMhlJdDwVTqWT5M0mtsgQqpkSQ81eLSgC1dkIBf7Vub/ZOw9pRzl+j4/tpGEc5f414TK1unwL+as14xl+tGSPjr1BYoNYMgVpdDyXjn7GpoFL1g1JZAq3EP/vdtOK5dLpC/jp2fkCvm1zAX6/jCg4D+45y/nrxuxl74a/XjmNLFLo8/kqFBTrNEGjV9VDltb9G3RcqZQn0mlFQ5PE3iucy6Qr7j1lsfzPbRs6ffwaY9YXtbxb7pIXZ5PxNurGMvzGv/xB/vWYwdJp6qPP469R9oVaWwKjN8M/2H4lHL/wt28nfrN+7gL9BbH99r+0/GBqp/bP9R6PuJ7b/6K22v76X/s/7BvWRrfcfXS/9Ryf1ycL+o9GO22b/V/Vy/Sp/xfXb2/ijEvnT0nI+/8wYw5VM+eMPtQVdwJNzeHD+0BLvnQ/bE6+S2k5H5/87is7KDoDSehugO5CuOhY5r7Q/LkkJVY4nAc1YMb9HHVSOF1gkPw0kGufLYk4DDctDoGVruEooVFXQOp8DlNXsmErdXtDaRemsdiw0tvt5TgTooNQfA42VT9Gq9EdBY7maO0QKIzSmi6A2/onZNGbxvcLAZLpa6+1Q6/ZlNoPtTqj1lDWYovTLYXA8DZWGeAFG57NQaceJ+RnqYSx5FQqlDQqFHsaS16BUk8JLw3I9mEooBkYJpaoKppIXoVBx/uSMGO0Ps+OpteNgpPV0kb/GMB0G67XMpjUcDYPlWjagkqRSb74YOuPpnIf5EuiNf2b8FQo7zLa7oNXvx2z8/RTeHspyWB3PQi3yd5Q8D412POOoUveFo/QNNitB/EtK34RabH+NZiRKSikGRgmVqholJS+zv8SfbgZ2B5+O12nHw+l4mMWqkBTUaDgONhsPEDcZjoHdeh3jr1CYYLNcAovpz8xmt1zK3lP8glJhR4n9bhj1PJarzHE3TPop7AanUpajquRZaDVcOVdT+jwMIn9yRmrL3oBKaYNSoUd92RvQie2v14xEvchfo65Gn7KXoFFVQwEtTLp9UOPk/I268ah1/pPdVOl8duOxqLTxJUa78WhU2q5hN2Slwohy68UoMfP2rxDfE3+V0o4axx2wGHj71znvhM1wMDueRlWO/qVPQy+2f/+yZ2HSjYMCGujU9RhU/irUIv+B5a9BrxnEbEbNCBaXQvy16ioMKH8BWpG/Vb83i2dhbawbh3rng8ypoWvIaZyOGvu128W/2nEXrAbe/mplOepLn5H49yl9jrU/cdSq+6Ku7HWp/WvL3oRW7D86zUjU5rR/TdnLUIv8yRmtFNvfoBuPCsfDLFZFAT0sxmNRas/2H0dO/7FbLoFV7D9Wy6UwGbP9x267G3qx/9js9D7b/x3OZ6ER+8+2+r+95A2oxP6j1oyEncWA/fL1q7c/KF2/asN06MXrV6U/EmrLVdL4ozZdyOTYzGa+EEp6L44/auutUIpOvsL6d0B3kDR+KuyP5Uixiyjid5Au/y/wv5IuF1FEEUUU8f8Pv6d0ee7Kapi3Q7ocDAjYe2TbLnl/K8as7CBQOmkKqmUZIPXTZDkLhPgiCPGlUKjroNQdLEW5swDc2HcsTTVlMVWJU8XclkIq+gVbB6bpUlVOkiSSBCYin1C0LtT6A6BU982eS/AgEfmcTThq9IdCqSrPkSK2IEHBuQo9dPojoFCaJVsysRbx2Fy2DKIzHCHjH4/9jERiCVSqOuj0h8j4x2KzkUxuYDMTOjaNneUfiX7O1s5pupovrYgc0xGEw58w9YVBfyCbPpc4ptwIRT5jU8hGwzSoc/gnki0IRWeyp0KL4Ugoc/hH42tZUCQ9cVuNcv6h2EJE4kugVdfBop8q4x+IzkYs0QCDdiTM+j1l/D2RL1j8BKlQTNqsGk0QougJf4aUEIDdsD/0mmz7J1IeuMJfsv1LjVOhVWcSugGRZCt6wrPZ8kK56VCoc/gH4+vhjsyHRuVEhekQKHP4e6JL4I2tgFFdi3LjATL+3ZEfEIg3wqYbilLDHjL+raFvEU12ocQwDg7dsGwbC1E0B2cikQ6hyrgXzJpsiYpYyouW4LcULYQa0/4wqEuz7ZhoR2toLlRKHerMB0GjNGWDUmMb0RH5GXqVg9mUiuzQ0hVZDld0FSyaGtSa9pHxbwvPhy++GU7dYFQax+f0kRRaQrMRTnahXD8WTv3QnKDaKJqC3yAhhFBt3BMW7a/n3x7+HiqFDrXmg2X8fbEGdEd+gk7lQI35YFn7u6NL4YmugFFTi0rj/jL+rsgPCCY2waodihLDBFn7d4VnIprshEO/O6y6EbL27w5/ztRLTsN+MGjqZf2nJ0z1gVJwGg+BVpXtP7FkC7yRb9msjtN4GFQ5/ScSX4tg9AeoVU7Y8/p/OPaz2P/rYc7r/5HYLMQTG6DTjIJB/+uuX4q3i0U+Yeofrf4AqHKu322NP1RJWoh9y8Yflf4w2fjD0vHH5/Es0vpDC3K+7Cwoxqz8dhRnVnYAhNgcCJ6/iEFiaUAzASpa4lFokAq9jqSfou6pk6WhNJwAtShrjvvuQooVxeNBaWrLNdCYqZiYgJjnPKSY6od3Tp39MagNRyItRBByHQshuUq0aWGk5Qzt7izwNdB9OMuLQlAobDCXfcrWp5OJ1fC5plO1Mc5D1Q+2sk/ZlHAs+i387jMl/hQLYit5i/EPh16B33udxFFvPBk2mgZWKODz3oZQ6GnJZrFeD4vlUsbf5T5LVM1w/iWOp2A0Hg1BCKO9+2iWL4VsNE1eUTYDet0eLHCxtfNQpMTAOyVN15f/m8UHxOKr0dx9NNIif3Jw+pR/zqbkA5Fv0ezK8qf4gPoyzr8n+CraPNdLHB3Gk1AjympbPXfAFXhGstFySIXtYsa/wfUXeCPZ9u9f8ihKTEciJUSwuuN4hCn/jch/WMXrsOjHI57swpL2YxBPdbN9aLljt6r3YdDUIRhfi0XtpyDFApPTMKjrsXvVu9CorHCFv8PSzovYDZZsdv0eGF/5PLthbvG/jVU9d0j9p9Y8HaNKb2f8V7kewCb/K5JtqPOvGGQ/h/Gf13EFOsJzJP4TKu5BrfkQJIUIZrWeA2+c6tEo2Dn2q/4XSvVjEEm6MLP5z4ikeF4NrdKKKX1egllTC09sA2a2nIcky52RhkXTB4fUvgityoK20I+Y036VxJ+ciwNrHmcOy3rv+1jQ/Q+J4wDrEdiz/EbGf2H3I1jjfVOyjS25CCOdf2b8v+u4Bq2h7yX+e1XcgXrLFOaoEA9PfL3E/6DqJ1BmGL1N/t7YBsxqPRtJsf3NmjocVPMK498Rmot5HZdL/Ev147F39VPs2I3+GVjuukviWGc+GruV3cr4r+m5H43+lyXbEMflGGA/l/Ff1nUJusOzJP6jyh5Apfkw1n+WtJ/M8sRw/lqMrngZNv041n+WdxyNeKpL6j+jKz+EXlOHcHwN1nQeD0Hs/zp1Pwyv/JBt44/MQmP3WVL/N+kmYUA5yZo1cAdfQUdO/7cZT0a12P9d3lvhZ4UGuc1pvQF267avXxp/PK6jWb6mzPhjL30bGu0e2xx/hMQaxHqO54nkKA2/qi90pR+xJfF0bA7Sngtk46eCLYlrdrqZlTkra7Z7ZmW/ka275MxKMWZlB0Dw35u90AiJn/gFmBaQ9NNAB8kmRGbwInupTtFRYZ+y/ydJXpyOQEgsFB2VzH5pqTJrIvqx6KhkbAnE/Pezf8VCL4oJnPg+lHchJlZtDQceJk11lkdqC2Ki9DfEjp3ln4jPRzw2i/H3i7LiDEeSC1OyqFSqQ3RUsraA/z7mjMTjP+XIezkXjyjPDEU+Eh0VbqP8FR7fvaL09zmkhG5pH1JWeANPMluP/59ikimRY3IL/CG60QGdXjn/cGw+glHOv917u4yjJ/w2YskGplbhjkrW1uEjeXQEwdhC0VHJ8m/2cnl4T/gT0VHJ8m/2PsD+1Rp4GfFUj7RPUgigxc9/40bvk0jl8I8km9EefJe9X+++X7pRErzRn+EKk/RUwBo3/20ztpbgBwglNrEZE+6oZG1r3Y8xZ6QnulR0VLL8l7t4zFNz8CvRUeE2IZ3Eyh4uL93gfQvRlFvaJyEEsdbzGrOtdD+PFBXGE88VSLRio59XzV3ielTGvyu6BG1hku4LWOh6RMZxo/9T+BObEU52i45K1ra051/MGemOLhMdlSz/xeJxtgS/Eh2VLP9l7qd+kf9qz7Oy9g8mWtAY+JC9X9HzTxl/V3QROsI/MP6reni7ZWxNwY8QTDSy9ueOSta2zvMoc0a8scWio5Llv959H2+b0Geio5Lhn0Cjh8fjtAdeQpw5Wtn+0+rnfbTN9yiEHP6x5Ba4gjz1QBvrm1n+JMsmB4b4d+b1f1/4LcQp2VuqQ3RUsja3/95fvH6j0Y9ERyVjS7C0Ar80/iQoBUFO/0mnmpAMc+l42n9fwfiJ2HfYGZGCcrtfuyqKy0A7AiyLY94EFUtwlMyrbJrdPp3ubTpPYE8eJB8s2EVMmMRtufJAAWnBl2PLPS4fMLjNlycdVEjn4X/zs9bSZ0nRwcljKfi2MhkpcGerV/5+ad98/oIo7xaEQAF//hktC3gL+GfOw7OAyvmTBJbyYxRk0RRtUPbe/rRExewF+3Ae3CbnnxLbn7bhz9hZ/pn9EmybrDyT5mToZkRI9tL+ZKMaq5S3Ix8JZutt0BLYDZnshfsEpX3z+cfF7ePiNln2/IbPbXROOf+sjf7mZ60NEhukKJ17HuIp2r6w/en4NHOTzyOXP983j3/ql/knUv6t8ud/89s/yNqfO2j5XPjvvLX2z/yu+cfjfwv7T1LsP4X7ZfsP30be/zM2fh3k9X9x/Om9///26zfd2/Uryru3Nf7wFA5y/lISOPa3t/Fz5wON+0KvY/+v339Xxa7rpu1AKFhBsZzqpQojy9CoUGih0O0vlycrq1i1VIWqHgrVAJn0UUGqIYUDKoreV9hlkkNa4yWo2fFUsp9OYziC/9UfWij9ZZ9RVtfDcxmzwYEraEiFc1QefxM0usmMv5apnLIclapqaDTDoVb3E5U0OdJNzXgolU5otbtDqXDI+Bv0R7J3FKOSz99k4DaKUcnnb2KfAWbxO+byNxmmsn9Z8/iTksKkm8ym2C36A+QcVdUwaIYzmawuj79ROw4qpQNm3XimGMnl7zDy9qcYFUUef6eJt22JcSq7weXyLzHyNi43HiLjTzfSUiNxAypM02T8VQojnIaJbBmizLCPdC46r15VCat2CEyaPjBr+olcuM2hGwOt0g6nfgw0pPjI4V9jIrUXUGncixYfZDeVPmbOsdZ8QAH/WjNXnNSZDyzgX2Piao4688Ey/mqFARXG8VApNKgx7imej3M0qivg0A2CRVMLq6Y+h78SpfqR0CltKNOPZks4mf3omH3E81eb9iQBsYx/PTv/tvnXmg8q4F9t5Gq4WvPUgvan+B9q/wrD3jL+Btb+g2HU1MGU1/52HbW7HTbdbgXtX27ifdVp2Fc8Xrb/lLHfn/cf+Q1dYHErbD/jtIL+bzPw72Rj10+Wv1Jhglm8fs3S9UZQMYWSXjOCLaNq8vo/Kd1+6frNjgdZ/jrx+t3W+KPSF/JXMQUQieoOKxg/c4se7kwoSpd/O4oxKzsA6XQCQvBhpKPfMEmxiuSHYkBsWgiwpaB0fCGg7ssqolKgLbOlOhH3/R3p5Aax+uotLDU1QUisR8x/G0vQpNLtA631ehZYSkjG5rGlH3ra0RiOhtZ8MZMcEuKRjxENPkUnhs50FnSmk0WOaURDz/GlH4UBRsvfoNUfJPEPBR5APPolk+SarDdDI+Y5oJmNgO9WxOM/Q63uC4vtDpajhEBLQT7vTaweiVYzBjbb7VCqePGyRGIdPL6bWYCeXrcfbLYbWdp0QiT6I7xsytkPk/EY2CyXSfyD4Y/gDdCyhACr+WxYTadK/L3BZ+EPUSyKASXWK2AyZPl3+R5AIPIl1KpSVNhvhkHkT0+flKGVgmzJQal23MYCDRnHZAdaPLcglljHtq9x3MZSszOO8fVo8tzOkopZ9fugj/06KJW8/f3R+Wzph2ZZSkxHodp6kcS/O/QZmn3PsPavtp6OSsuJEv9m/8ts6UelMKCv/RKUGrl0lpYCNnoeQ3f4W2hVJRjkvBo23UjpqX9Nzz/giS2BUV2HESXXwSgGxNJSxArXPQgkNsKuG4GRJddCqyInC/DHN2KZ635Ekp2oMOyJkSWXQSXy74osxEr3k0ikAqizHIqh9rMk/k2Br7DW+ypbQhhkPxH9rUdL/Nf53sJG/ydQK/QY6TwHNSae34eWYpb3PI2W0PfQqx0YW3IZSvQ8oDeeCmGh62F0R5YzB2WP8itYoC2BloJ+6noQvvgmtv0eZVdAp7KJAbubsNj1T4STnag0TsRuJRdDLfLvDC/CMve/WNvUm6dihOOMX8V/g+8NNAY+Ys7UcMd5qDLtLbX/avdTaA/NgU7lxKiSy+HQ84BYOsdK1wNwx5bCpKnDqJJrYNLUSu2/quduBOMN7PcaXnKd1P7B+Aas67kH0VQHSvR7YZDzSqn9vZEFaPT+k82klJuOQJ3tfIm/K/QpWn1Ps1mgKsufUWE5SeLfGXgertAMdh1V2/4Ku+EAqf93+B6EL/wV6//V9hth1GX7f6f3VoRjC5n0utJxu9T/aSnI5bmRXb86zW4osd/OUvv/0vUbj/2IkP8+Nv7oDMfAaL70V40/qdALSEZmsPFHY74MKn2Wfzr4CBD9lgsUrNf+rlWXf8+Yla9W1MO0HTEroYCAqaO27JIxK0VnpYgiiiiiiF0Wv6ez8u/l/bbbWZk2unGXvL8VY1Z2EITYbBZUSzMjCuMZLHGa9EQR+QBCYgkUqjqoWWIwnfREkQy/BiG5kSVVUxtOkp5QWFGw0Isswl6tmwytIVsfSUh1IRp6ia0Ha/XT2JJNBslkA6Is8JSUOydArRku2eLxRYiEP+IJ3Ux/hlrNnxAJkeg3iEa/ZdPAFvM5TImT4R8Mv4tYfDGbUbGaz2TSyQx/X+gVxBMNLCmW1XSKxJ9kme7gC0zhQ0syVuPhsiq8PcGX2KyN1TAN5hzJZDSxEa4gBf4KcJqOh1GbldwGYkvgDn3C+FdYToNOzZ/QCe7wbLgjc6BROVBtOQMa8Qmd+HeEPoI/tgwGdR/UWE5j0lvWjukEmv1vI5RohEU7FLWW4yT+SSGERt8biKa6UaLfA9XiUgnjmHRho+9tJNNBVJsOQplhd8nmj2/BRv9HbJmhn+UwtuSRQXdkJRqDXzPp7BDbdJg1lZKtOTgPzeH50KvsGGE/HjqVReK/zj8TnZE1sGqqMMpxDNRKLW9jmtHwfApPvBllugEYaT9U4h9LhbHY/TGCSTfqTKMxxMpnEQj02SL3J4gJEQyxTEa9KStLdcVasMQzk81MjHEciAp9VpbaHF6Hlb7voVFosbtzGuzarKx2vX8R1gcXw6iyYs/Sw2FQmSX+S71z0BxeD6e2AhNLpkGTw/+nni/RHWtFlb4fxjtJ8pzhH8H8ns8QSLjRzzwKI2xZWXkg4cGCns8Z/+HWSehnHvmr+LeG12KV7zt2/nGOw2DTZmW1GwM/oTG4EAa1DeOdR0Gf0/5r/N+gI7IGNk0VxjiOkrX/Ku/H8MabUKobiGG2w7IzDEIYqz3vIpzsQZVxLPpZssVcI8kerPXOYNLrOvP+Msl2IL4Zm/wU+Cug3nIk7Dn9x0OBx8Ev2AxNvfVEGNVV2b4VnoOeyPfQKB2os/5J1v+7Qx8iGFsCnaYOVZbTmXSe2xLoDr7GrjmjdjhKTCdL/Kmoooddvx0w6vaCJef6Taa6EAi+ACEdhFF/mEzyvK3xJxVfjGSEX79q05+gVGWv3zQbP7/jM8uU/E4cP3c2UNVkYTuiL4T82J5dCMWZlR0AIfwOBP8Nou8nsLT5qpIPoVCakPD/A8nQU6ItxdJOa52vsjXamOd8pGJfiWvASagNp0Jnv4cVEwu4jmFl1vnacBJ6603Qm89nBfi83VPEqHtav0zB7HgGOsM0JBMN8LgOpSphIjMl7KUfQ6MdjVj0e/RQZWV2vDTLklle8TXL0hoMvQGP90qJPy33VJR9BaXSBLfvLngDj0s2SilfVUpR/Aq095yLUPQLib/V+CdUOP/BgkI3dx6FaA7/CtvNKLFewArwbeicgmQqy7+u5BnYjNMQTTRgbcfhEET+tLY/pPIDGLWj4Yv8gLVdlM2Tr9lSjolRVf+GTl2FjsA72NBzAxRQs+lzkgWPrf4QKqUJGz0PYovvGdGWgkM/EbtVvMiOs7Trr+gKf8NiCygYt9Z8AkaU3caCQue2ng5/fB3jQLbhzisxwH4GYikPvmk+CTGmOuFBoRMrHkCN+UCWL+SL5jOYE5ThP7X2WTj1w9AeXoiZrZdL/CnHx1F1r8CkKcc636f4vvM+McYhDaumBsfUPweN0ogfu57FYvebFElBoYyoNe6Go/uQQkiBT1pvx8bAjyyGg4JZR9kPw8FVlyMpxPH65ivQFW1kgaRk27/iXEwoOQ7hpA/Pb7oYoaRXsh1bexOGWCezG/2zDX9DivFXMHnrWf3/gWrDQGwKLsOrm/8uxlukoVMZccHAR2DTlGKR+2t82PoE40hOmlNbiQsHPgCdyoAv21/Dd93vizYB/UwjcVZ/Oo4Cb2z5B1b7f5L47+GcgmNqL0RSSOCZjdehI7pZ4nho5ZnYq+xohJJ+PLHhbwjm8D+l7loMt03aJv/G4FK8ueVm8fdMQ6c04ryBj8OqKcVyz5f4ov1hib9dW4Uz+j8GrdKAuV3P4+eet6X272Mcg+PqSL2mwBdtf0djcK5oS2K47QjsX3klUkIcHzdfCHesQYyPSWFC6cUY7TwZ0ZQXn275k6ha4rb9qu5lTos/3oivW/4k6z8H1r7IcuR0R+ZjfvsF7DsT1EoT9qt9FwZ1JVoD72K162ap/9Ny4cSad9k2Wzz/QKv/X1L/t+r2xIgKLndvdJ0Pb+RLqf+XmE5Ffcm9vHBk51GIsaKQ/Pots90Cp/UCpFI9aOs8WFTt8eu3rOQ5mAyHbXP8ScXmIkqV6TMxFwoLDGVfsGy56fAMpP035oyfdVCUfMDGz51tZuWz5f1hsmRigf5zhAIpHD560y45s1IMsN0BEEJcnseDywQg1cifFNIpJKXqpWRLQ6BqzMk1SKdakIp9KUbB86C0ZOQNHuMSWwCByXsFyRZjcRxAPPqJmMcgJdkitEbMZMWviQNFSnwJiIToxgwEWRXYzLlSbFYmHObSWb9Y2TjDP5nchGj0G8bfG5B/t2hsLuKJVawwXyj6bxl/f/g1tkYeji1ANI+/izk8JJ38hD2Z5fLvDnD+ruAboqPC+dPA2xXg8tAO//PiubiNzuMKvc9szT7OkVfHFRBJNsId4e3f5Hshx5aGJzqf5TyJJtvQFSZ5Mt2eOI+W4Aym2nBHFsEfXyM+x3DbBi+XILcEZyKacrGBPxPMud7Lq4Y3+D5kN5qMjV7rfbyy7CrPW+xMmc8pFmJjgNoPWOrm8lpuE+BLNKM5NJ8lRlvq5vJUuinTFi3hJXDFNsKf6MTGwA+iu8R5rPB+jlgqhJbwSnRGNzJLxjbfxWWia/zfI5j05NgUmN/DOS5yf8lu9IJoE9ICfu6hBH3APBeXKbPPqR+kwljm4fLc77rfk2x03J54G9YHFjP+c7s/yrGlsSm0gjkh3kQ3VvsXyPj/7Ka2DWNzaBXao5tk/OeI51jp+wGBPP7fd3/wi/x/6snlISAmhLHC+62sbTI2T7wVmwI/M/6Let6VtX9zeCm6Y5sQSHaiMfh9RsPEtlnt+5TF6LRHlqEntp5ZMn1kmZseUIAtga8RSfXk2BRYJdo2+T8o6D8NPs5tk5e2kfeflsAnzNbofVbW/8PJzSx3D/X/NlE6n+n/foo3SaxhxSu9kS9k/b8n9Aa7riKx+YglVsiu355M9fPIJ2IepOz16xfHpm2NP/GQ/PolRWQywn/TtJQCITN+bgZiGen9zoVigO1vR3EZaEcgnRvBnwEf3Aorm9LHSUDR2z4Qty+0ZQaUNO2bZ8lG4Ke2YUv2zmMrtszg1ht/PmD2cjyRQyFHnhEz93vkHi2zfa/fLWMr2E+RPWYv7U+fceegN/5J6QbT23692TI3nd44Svx7+d0yN9tse/bGv/B8dKMUj96rrbdz8e1Jft1be/B2KLRRro+tt2PGRkG0+Vwy36238/EbP+fT2zF72yfDM3Nc+efZc+XLw38z/4xtm7/bVtp/K/yJfW+/TeazwnNlnbXerqlsP8iMJxw0wyL1ya321630f2qLrbU/u357sWU+20b/3+b4U7CfYhvH7O1YOwdSaSV7/fb909hVUZxZ2QFQmv4krxiqLIdCty8UCjVUhhPEz8VqzOrhrGghSZeVGqpImpFhKqDSHcrWatXaiaxoIF9eETPYigXNtAZK5W+R2fSiTSedKyONTENvpKUfwGiiKdi0+LmKyRoNBq6UMJvOkvFXKiug11NadzUsbP8sfy2l1Sfpo6ov9ExemOVv0lMKcDvLoKnJ4+8wc442wzRWaC7XViLaSszHF/AvMXM1QbnlVBl/kpWWmLicudoqb3+tqhwOw74sg2qV+TgZf7NmGMzaYWyq3KHbXca/3DiFrfWX6MfDqO4jLstwjn2tXJVRzdLMm2W2/jau+Olv4XFFGWkq3SoGWEhWDQy2HZPpLcxO3PpauOR2uP1YiT+T96pKUGsi6bIKw2yHSvyYvFc3EKX6gbBpqlFjpOrEWf4DLXuzWIta0yjYNVUyifJYB2+rIda92BJIrm2caBvtOFA6T2a5ZKyDx+qMd3IJKlnJplaoMdLGpcsUh5LhT0s6FrUDg8xjoVKoMN6RkQzz/Sg2pcrQjy0V9TUNl3jQfyOsk2BQm9HXNILZcyXKE8Rz0HIP8SdbZklkQsmhv8h/rHOajD/JqofbuHR5rINLbzP7mtRO9DOTfFeFEbZDZO1PsUFl+gGwaqpRZRgta//+5n1Y+1caxjDFU24fGWafzv7WmQ5gS4C5tiE26qNAX0tGgpztP/1ENVO99fic/kPXrwbVJv69+7BrI9t/KEV/KUmuFWqUm+Tjj0kzHCYtSffrxarTWf52Km+htLMM0PnXr918Bh9HDIcVXL8W85m/OP5ojPLrlxVPFCXPioLxswwQixwWUUQGxZiVHQBqwnTkXbb0Q9I7pflCsWIpf4pJhp5jtYGU6j5Qk8xPyXmlhTASwcchJBtYgK3GfIEUfCukuhENPsrkzSzAllVcFYNXk5sQCT7Jloy0hiOkPAeEBAXRhmhZIgW98TRodVxeSohGvkI48h6T/prNf2H5UjL8Q+G32NIPBdhaLX+FWgxeJf6+4NOIxhaxtPd26+UsxT3jKIThDjzCa4toR8JhuVgK3kumutHtf0QKsHWYs/LSWGITugNPsilnm/EI2I1Z/qHYYnHpJ4VS86mw5ATvecJfwxX6kAX4VlnPkYJvmawz+C5b+tEonaizXwidulp6om72v8gCbPXqPuhru5CluGcchTA2eZ9mGWEt2mHobz+X5WZhHJM9WO99hslTSw0TmLOS4R+Ib2FLP7RkVGOeIubp4HBFV2C99132hDrQegwqjNng2+bgXGwKfMkCbIc7ToZTN1Div97/OZpD81iA7W7O02HWVIj8aSloBjoiq2HVVmGPktOhE4NXE0IEC1xvwh1rQrl+IHYvOVEK/gwlPZjX/aYUYEvOSoZ/T6wFC3reZUG4Q617Y5h40ya0hNeypROaIRjnOAT9zNng23X+n7DcOxtqpQ57lh6NSn1fif9izzds6cektmC/shNg0/KaPKl0Cj90fywF2O5fcQIMKh6LEBeimN35LrpiLag29Me+ZdOhVvIU68GEF7O7Z/AAW9Mo5pBkgm9dsVbM7f4Q0VQII2x7YZR9r1/Ff31gAVZ6qY9oMaHkGCn4lviv8H6FTcGfWYDtnqUnw6rhwcNsKcj9nhRgO6H0VOhz2n9Rz2vwiAG245ynQCW2fzjpxlL3yzzA1jAWw+3Tpfb3x5uwyvMq4kII9eYDJaeV/TbRFWzph2ZE+lunozyn3lNHaDZag5+xa2yA7c+w6gZL/NuC77OlHwow728/H3ox+Jau3zb/8yw4ndL219ougVocf1JCGB3+x1ismFE7AhXWC2XXb4//YXb9GnWTYTefKfFPJDbCF3iSJWOkHEkmI3fIf2n8SUa/RjLyAQuw1ZjOhVKTvX4ReY8JFEACBTZ+ZoOHd6aYlfeWDd7umJXjxqzfJWNWis5KEUUUUUQRuyx+T2dlxrKhMG6HsxIOpHDCmLW75P2tGLOyA0Dr7OnwK0y+rGAzK5dBIVZCJmVPMvgEhPhiKNT10JivgEJVwm2CD7HAg0glNkBFyyuWK6BQ8sRLqVQbIv4HWFImejrR09OGgnfyRGI1gv5HWFImveEoGIwkOeRTshQAGwg+x5IymU2nw2DISm6D4Q8RCM1gMyt2y0XQ68ZJ/L3BFxCOkjqoFE7rldCKlVRJGeDyP4qImFSqzHYN1CJ/Stvd4X0Q0cQGVrW40nYFlCL/WLINbb6HEE91wKqbjErr+RL/UHwtmrxPsqRYZabDUGE+XuLvjsxDi/9VXjXXcpKU5ZXQHvwcbcGP2MxKP9vZsOvHSPw3+d9EZ/h7ltRriOMCVqiOcUzHsdr9AlxRSkrWByNLLmCVgQmUpn1pzzO86q9+MMY4z5MSjwUTnVjU8zxCyW7UGMdjlOMUtixAcEU3YmHP64gJQQyyHMCWajL8m0JLscj9IeM/xn4YBliymThX+eawoE61gmYmjkONcYjYxgIW9HyGdYFFMKltOLD8ZJTo+JMlKWO+6XoPW0LrUaKrxCGVJ8Gs5jNb4WQI/+6gWZdW1Br7YlrV8dCKsmx3vAeftL0HT9yNodYRmFpxuDQz0RxuxmftHyOSimAP5wTsVZKthLzavwZfdXzNliAOKNsPuzl4GxPm9fyE77vnQavU4ojqQzDQ3F/i/2XHLCzxroBVY8HxtUeiUs9lwQkhgQ9a/o11gU2oNJThpD5HsW1YP0iG8U7zJ2gJt6OfuQ9OqD0SOhWfmeiJuTGj5WO4Yx6MsA3FkdVUiVpM/BZuwYetnyGcCmOScw/sV7aXxH+Vj/OnyJEDy/fD2Bz+C3oW4IeeHxj/aZXTMMA8QOI/q2smVvqWwqKx4oiqY1Gur5Da/6vOD9EYXI9SXQUOrzoRZg2/SURSIXzV8TY6oy2oMfTDlMoTpfb3xV34putN+OM96G8ehb3LjpH6D6m0fuh+CzEhhGHWfTHaPkXi3xxagqWe99lsw0jHEehvzvafjf6vscH/Beujox2noNwwQur/G3xvoy30A+vbI5x/kSpRU//f4HkWnihPajfEeTG7Rth3S/mx2fsIQokGmLUj0Nd+KVS/4vqNxlejx/8IS+poNR4FK5M8c/6x2FwEg8+zeBST6XToc8afRORjxMPvsvFHa74Aau3YXxw/iygig+LMyg6AQEs5wYfFf6kAhRWqsi+gUJYg5vkbhCgpEaiZVVCo+kFXRgoFNUJMnrxCDCZTQqXbH6aSl1iOFW/XgRCkqHsF9KbzYLLdgmSyGT1dB4o1P3gQmtV2D4zmMxCLLUSXi69xZ4LxykregF6/P4LhD9Dlvihn/VqN2oqvoNUMYUUC3WIxROJIyzx1FXOYU9Lacyl84Q8k/lp1fwyoJLm1Gg2dxyAcXy7xt+j3R//yl1mOlZXtU8TqsZx/heUc1DluQjTRgkVth4lF2Xjw3wDnbai2ngZfdAkWdZwq4z+m4jmUGPZGe/AzLO++WuKvhBp71rwLs3Yg1nmewVoPL3hIa/YapQUH9nmfDcrzO27GliBXXZHNoqnD1LrXmdz0383nwR1bwxwLOmK1cRIOrHmI5ch4b/PpCCdJ9cM5jnSchElll8Af78CbjecgmY5Jtv0q/opRjqPRFl6NN7dckcM+jeP73I2+5vHMUfmwhbcxj1lR4Zz+j6BMX4dZne/g2643JRvlKLls8KPMcXlzy6NY6p3LnAeK0yjVVeHywfez/R9e/3c0hblqhqIVhll3w/kDrkE0FcFtq66DL+FlyyGEg8un4fg+p8IV68bfV92EuECqE247re7POKD8QDQEGnDnGl5UkrNP46ohf8Mo20j86FqAJzY+l42MUKhw18ibUWusxvstnzLHgkAcTWojHhhzG3NKHtvwAn5w/SzxrzKU4x+jb2KOx80r/4FNwSbGkY65m30Erht2CSKpKK5e9nd44j6J/2FVU3B6/Qnojrlw3fJbERfiUs6Js/qehoMr9seGwEbcsVrO/xribx+B+T3z8fSmp2X8bx1xK2oMNfis/UPm2GX4G9Um/H34vcxxeXXzE1jk+UHiX6arwjVD72X8n2i4ES057T/EMhZn97+e5Yh5bMNl8CfcUhtPLj0K06rOgjfeiec2kTw7LtkOqbwY45yHoz2yGjO2XJ4T1pvGMX3uRb1pd+aozOrgRUVZTI5Chel1z8Oh64dV7uew0p35bipolRYcWv8Oc1wWd12P1uDnUv83aeqxX+0M9n5J+0kIxFdK1y+VAhhV8cw2r99EshmNHfLxp9xxD1vmjccWwuXKxGbxb+AseZ2NP4nwR4h4L5WNP6ayz6HSDN7m+Lmzzay8tXT4ds+snLzb6l1yZqUYYLsDIIjVQzlIludBOjaXp+GXHBVuS6cakE6sgpDaDCGxNCfqXUAq9i3SgheJ+AIIQluOLY1o+A32Lhb9EmlWJj4bLR8WbeHI++JAkKlgqkAozGWpgVCGY0bhk0IwzKWP/tDrMv4pwY0wqxqdyHFUuC2e3IBIfCXiyc0Ix5fI+Aei3yKZ8iAQ+5mlqc/l380SvQE9ka9ZscBclVGHWD22gxK+5fHvCPLKuK2BD2T8KSakI0TSS2Czn99ouDWFuOBFV3gei1fJOCoZmz/RCG9sHQKJFvTEVkk3DPrbGv4RsZQPneFlCCW7ZEqKdT5RJhr8AYl0VGZb7f1cnJX4VgqM5DcHJVb5ePXmZZ6vCxQ7JCMmLPTMlNnCKT8aAktZ0rGMo8JbWEBXrBVtkc1wxTqxJdyQwz+N1f4lCCWD2BBcB0/CLd3oCXNdXGa81LuE3ehz+X/v4jLRH3sWiCoT/h+9/8E1j9m+6/4xhyMpcAQscC9k//62K1MhmXMMJINY7luNpJCSHJWMrTXSgcZQMzqi3WgIbpY40jZLvCsRTISw1r8BPXGPjP8s8RyLPEsRy3FUcm0/9swv4D/XxXnPdc0t4P+z+2du685USOYcg8kAVvtXsPbPOCoZW2esFS2RzeiJd6A5r/3XBhYjnAxgS2g1fImso8t4u/lvvCEwDwkh6+jy34T347W+b6S6RZn+s8bH96OYptw+wmYTA5z3RpZELmNLISZ40REm6XtCclQytmBiE3yxNYgktyAQXya7finmK5HybvP6DUYKxx9fcOvjT0RMjxCPvFMw/iQin25z/NwZQQnhtve1q6K4DLQjIAalyT+j6Wyl2MSJgu0V6GUf8YkjE2Qrs4ifkYonf5+sTfcLttxqqWkpmK7X82Hr/Gm/3vbh59NIQaryffhnSnZc+T6SrRf+WVuGT/bmkNmeZaTNUzqq2PZM91EgRaZjcXshf1LpKMUgz8Lj0V9NgQQ5Y1P3cky+Pdk00o00g8z2GnEb2X5Kvj09wVOQai4oCDUTiFp4PlWvx8tsTyqefDluZnuNUj4c0Pk1Cv6ZRuST3TfNPsvY8kH7KRW986fttVvjr6SZscJhiXj3xp+uGIlH3vcmvpnvnc8/LeNf+LvRfnwGrBf+Cg37PXsD9R/67Qq+l7h9b/0n0w8ywdG978f7c3bftLR9b32ZPuOqKJ7wTc5R18u1xrmQgmhb1+9/Pv6I+6Fw/JG23+r4ufMhlVaw1/bsv6ti13XTdiBojZWDBlQloB4ChY6kvyqozZfk2BRQ6g6EQj0MSnUN1IbpMp9RazoHCqWZSZfVmvGS80IwWvjygt5wNFSq2hwJoAJmy+WiBJki9o3i51yebDafx2wUo5Ldh5Z6SmA2cTkuxajk8teqh7EigcS/zPpXGX+z/mDoNMOhVdfAbsxIbvmAWmo5h2WWNesmwKwdl7ldMFuN7W/sb5npCFGpk+VfZ+NtVGs5FUqRv0KUJ/excslkP/s50uDLOKocqDHzKeehjgvY34wc1KodhArjPmypZLjzHNHG+VcZ94ZdOwgmTSX6WTJyXM5xmP0kJiutMuyGcv1ISQpKGFdyNvs7yHogLJpKiQv9QnuUcunlbo4joVHqJeks3WjGO/lvTDEqXNhL+yhZWvrRdq4COaCCy7O5TYEKXT2GWMYz/gdVHC/ZGEfLeFTp6+HUlmJ3x96Sg0LYv+ww6FUGDLIMQX/TIJE9t1EcBmGCcyJKtKWiEJfLf4+o5mqOg8oPhE6lY5/Tf2qlGlMrecwBxajQjTvzn0Vjwb6lXKlFMSqMh3i8PoYajHWMYtsfV3uYjOM4+yjUG2tQqnNin9IJMtvhVQfBoNJjqGUQBpkHSMs1/Byc454lE1CmLZH40290TA1PBX9wxQEF/A+t5G1MMSoy/moL9i7l7XdE9XRZ+9cY+mCUbTe2/SGVvN0yPEZYx6LaUAeHtgxj7Vxem2njfUoPZ+1Pkuw+xiEZkTSzHSj+xsNt+8GmqcjpC8Bepdw22l7Yf8Y6+PlHO0/N6XNKphobZOVy7JHOv8j6v007EFVUXVuhwmDH+bL+X27cl1WN1qurUW7ibSpdo9YzoP6F69diPBrqvPGnxMrHH1Mv449JHH8oRiV3/KElHo3xhG2On0UUkYtizMoOghCbj3T8e14byHAyczoI1LxC9Eu25KNQ9YHKeCKbfeA2mgp9F0KigQXYqg1HSYFq6XQE0dDrLG5Fo50MrVihlJ0r5UY4/DqLbdEZDoGWDSwcFNMSYtOqAoyGY6HRcHksIRZfhVDkYyYdtJhOhVrFgwgJ4eiPCMdmQ6V0wkZ1O3L4ByL/RiS+BBp1HzhY/Z8sf3doBmKJjTBoR8BuPFriLwhRdAXfQCLVCat+T9gM2dooiZQHHYG3kUoH4TQeDKtuN8kWSbSgPUhLPgIqzEfCpOFBnAR/bC06Q1+wAFuq46NTZ2vTuCI/oytCwZ921FuPY05Hhn9raBZ6oqtg0lSzKrz09Ms4plPY5P8cvsRmOHVD0NecDXRMCjGs9X3M4laqjOPQxzQxyzHlw2rvZyy2pZ95L1QasvWLfPEOrPTNZFP1w60HwqnjgY6EzugmrPbNZU/yY+xTYdHwQEdCY3AlNgSXwKSyYnfnVJaqPsN/pe8nNIc3wKEtx4SSA6GS+Av4yT0HndE21Br6YpxjssSflnq+754FX8KDIZbhGGHLSniDySC+656NaCqK3exj0V8MNCVQTMjcbr70sWfJJFQZsvWLtoSa2dIPzUjsX7Y3HFpeP4qw2rcOy3yrYFGbcVDFvszpyPD/yb0UDcFGlOtKcUD5XlArVRL/Od3z2NJQP1MdJpfsLuP/Tef38MS9GG4bgt3s2fo/gUQQs7q/ZwHC4x27SYG+Gf7fi/wn5/FvCjexpR/iv2/pvrDn8F8XWIPV/uUwqy3Yp/QA5nRk+C/3/YwtoQY4dWXYs+SAnPYnWfMctjRXbeiH3ezZQF9a6lno/gr+hAf9zSMxyJK9RsNJP5Z5v2DS8UGWSagxDs328XgHVvu+ZPyHWg+CI6f/9MQasCnAA7SH2A6HUc3l4YSu8CJ0hOexqs8DbNNl/b8j/A080RUwampQZ5nOHgK4LYWO4AcIM+n+cJSZDv9V12+KSn6wsSkIs+EQGMRA/cz4E84Zf9Q5408qsZot/TDpsvEkKHPGn62NnztbzMpLS8Zsd8zKmWOX7ZIxK0VnpYgiiiiiiF0Wv6ez8sLisdvtrJw9bskueX8rxqzsADB5sv9uCNGvmSxZbb0ZSi1P5kQBszHfjRDiC5l0WWe7G0o1f9pIJZsR8V6HVHI9VJpRMNrvhVIlSj7jKxD03QAh1Q6Nbj9YbLdLhb0i0W/h890JIR2A0TAdNus1bK2Z4A+9CV/gcRaAZzOfA6v5HPa0RP92+x+FL/Qmkw6W2q6Cxcgzl5Iyp81zJ/yRmVArS1DtuBVmPZ+iT6a82Oy5CcHYQujV9ah33gWD+LREyp4NPTchnNgAs3YUBpXcAa042xGIrcK6ntsRS3XAadgLg5w3ssJqhO7wXKx1P4REOoAa0+EY5LhEmu3Y7P+QJVyjp74BtlPYK8N/leclbPR/wp4sR5WchzrzQdIsyM/dT6ApNJcpICaWX8ayiDKOKT++63wY7ZEVsGlqsF/FFXDo6qRZkK86HkVPbDMq9IMxteoylr2U0B7ZiH+3/YsFSg4wj8WhVedBKz5tU/KzL9tfZXVsxtj3wUGVp0jLBAt6ZuGbro/ZrMG+ZYdin1Iua6Z/f9HxMX5wfSdKf4/FeAdvY1LmvNX0NpZ4l8KqtuK0+lMw2MKr7QaTIbyw6XWsDTQwOfA5/U9DjYHLmruiPXhq4xtoCrdjgLkeFw44GQ4tlzU3BJrxVMMMdMe8GOcYivMHHgeDiscG/NyzBs9u+gShZBQHVozHmX0PZbEihM/bfsKbW2YhlRZwXJ+9cWzt3hL/Vxpn4bO2n5i8+Jz+U3BABZ+tiaUSeHz9Z5jbvRoOrRl/HXIkxji49N2XCOOB1R9imXczao0luGb4dPQ18T7eHnHj3tUfoDHYiaHWGlw7/FiU6LiseZ2/FQ+s+QhdUR8mlAzC34YeCaOa85/vWoenGj5HMBnB1MqxOKf/VGm2Zlv839jyDf7dvgA6pQZn9DsU+5WPkdr/2Y0fYr57JewaC84fMB0jbXy2KZAI4emNb2FNYCOq9GW4YMApqDXy2ZruqAvPNr6GlnAb+pvrcW6/P8Eutv+W0Ga83vQqk46PsI7EyXWnQS/ONq32LcNHrW8iKtDM0GQcXn281H9+dn+DOV0fsRT8e5Uehsklh0n9/0fX21jm/QoahQ77lP+JJfTj/T+OH7qeQmPwRxjUDuxTfjGqjaPE38aHBV33oyuyDBZtLSaVXwublsuCw4lWLHXdiUB8I+y64RhTehP04mwNXb8NPbchKl6/A503scKgbIyJzEK79x6kBD8cpmNQabtKGn8C4vhDqRNo7KEK7hn+keBjiIXfYuOPwXIFdIYjpPFT8N+DdOwbQFkClfUmKLTZZIpFFEEozqzsACR8N0MI87LofF1WA23ZTCjUtYj0nAohPl+Mnqe1WicMZXNYAFmga38IqVbJRhkdLaWfIy30wN21d07UvRI6/ZGwOp9EPLEanV1TZRH3VsvfYLNejVBkJjp7eOr6DMocj8FiOh7uwLPo9v5d/JRP9fYp/wBG3QS0uG9AT/B1iT8t8wytmgWtug/Wdp6GQCzLnzLEjqqezdajF7VOZQUBMzaTdijGVn2IhODGvJapSDH+/JjlxmkYWf4Qq2T8Q+uJohqC8x9oPx+DHZegPfQd5nVkYmQ4di+/E3WWw7HW+yYWuzLyRv4dptQ8jTLDGPzY+QDW+jKqK1rrV+PYvq/DoqnCR81Xoi28VJInG1R2nNb/NTYV/sLGc+FPkOqH6s2QLLU/Tu/3OMIpHx5ffwHLsJrZb7htLxzX52p0RDbjiQ1XSYoT4nFA+Qk4uPJkrPItwnONGQk4x2l1F2N35z74pvMLzGjJVV0BVw25GQPNg/Hy5lcxq2uOpGChINJ7Rt+JMl0p7lz9ENb41zMlCkU5WDVm/HO3O1mA5yWLb0d3zC3Z+ppq8MCYa+FLBHHez3cgmoox1QxFOuxdNhbXDjsTm4JtuGjRgxAo6zI7H3Ba/VSc0W8afnStxg3LeOHHDG4YfgqmVo3HO03f49H1XBGVwZO7X4TR9r54YM0H+LBlgSjvpaBWFd6YfCWqDE5cuvBZLPZsYuejmBa71oR39r4KWqUaJ/3wIDqjXuZYqBRKDDBX4qVJl8ITD+HEHx5AJJnlf1DlaNw++hQ0BNpxzk+PyPif2e9gnDNg6jb5v9c8B0828GKGGTwy9lKMtPfD4xveweftP+bwV+OZ3a9Hhb4Ef1/5KFb6Nsja/8nxf2cBxFcuuwWunPavM9bi7lE3IpAM4MaV17L2T4u28Y49cP6AC9EabsI/1t6Y03+AQyunM4dljX8hXt6clV4TTupzGcY69sVPPR/im04qRprF6X3vR61xBGZ3PIxV3k/E9uClHE7r9xKs2krMbLkEnZHFYj9WQaey45i+M1g8zDfN0xFJtov9X8VivfareZNdvz+1TJFdv2XGaRhe/k9E4muwvoNiZbLXb4X1r6i0X4lwZCa68safUsdjMJuORyT4HML+W2Xjj7XkfWh0eyDluwXpyFuy8VNV+iUbP3e2mZVnF4/f7pmV88Yt2iVnVooBtjsAQnRmTpQ7/Y1BiP+EdDoOIU6VcTNqghTSQjeE5BoIyc0QUk0yG1VaTgtuJBKLWVXkXFlhLMYljNHod+IgkVUFRFj1VIo7+TpvskyJcPQbSXKYBc+ZEo7OZv/yhb+S8acnnWB0AauAHIjJ+SeEboQTaxFNbEE02SyzheKr2EDni5HsNig7pivCK9y6IvMkaWaGS2eI2zrC30sBrRn+9BmhNZSVxxJoUG4Lc1ntluDcnOMJLBFWR4Q4JNAa5gN1VhbsZlWLvfE2+BJ8oM7YumINLB6lJbyOVeXN3W+9/yf2viG4THajIesaVj2YpMtLxKDPDEcF+4ywzLtYxp+2W+2jHDXAYs9SmUolkU5gXWA9kkISq/xrJQkv/fUm/GgKt6I92o3OmEtm2xRqhj8ZxNrAZoRTUUneS38X9KwUpb/reHkI6XzAjy5um+9aw5yGXP7ze9aI8t7VMv603YKedez9910kAc+eKy4ksdTTiISQxEL3RuZY8B5CFZkDaAh2oCXcg7aImzkqzJYWsD7QBm8ihFW+Jjbrk8v/+27O42f3+gL+33Wv+kX+P7pWFfD/2b2WvZ/XsyKPfwIrfBsZ/+W+dQXtvyXUhs5YN7ry2n9zuInJtjeFNrJ4mnSObZmP0hQAawP8XLmKpuW+RdzmXywF5Gb4r/Vz24bA/Lz+o8LGILdtCmTl1awsYjqOtshy1v87Igtz+nEK0VQPPLEGhBLNCCdbcvp/Cr74WsQFD/y9XL894vUb6GX88YljS6SX8Scijj/xKI0x8vEnQeVJ6F+xrwvGz3SCy8p3Ngg5iqDf8hKw66LorOwA0GxJ5mlBAqufowFYdHz+9g4olNngvixo5sUIpcKRvweUCr69Skm23C6rhFLMSMltebJa8Tyk/uGR+BmkoGTbA2pVIX+1yk7CVVGdIwcVOyN7wfciXYTCBE3Bd1NAI9YTogDYfP6k7MnacvdSQCvup1MSn2x3pQFYp+I2Pfsr569TWlniOLWCT73nQq+ysoJz+aAbgEZpgKHApoBBzT8zqix50lmu7CFQXZx8/kY1DxQk9UmuI0PHMEk2Kowo529WmdjSgE7MiCqzqU2wqAt/F7oB65U6WNWmPB6ARcO3J1tujhKaSbBp+PZWjRG586wkPbaK57FrTGzbDMgBsYnHtGlNBfzpWGqFCgYxI20uaD+rxtArf1qqon3z+VvVfHvims/f/iv403657U/8M+exaQrbn9qX+Ot7kRNb1CaYxPpGuaDj61U6mMTaQbnIbE+/eX7/oaBeglGd37eo3/FjGVW2gv5vEPsd1TMq6P8qi9j/C9uZrpvM9Zh//aoVxm1ev+pexh81G/8gjify8UcpHotqjuWPPzQOcqOjcPxUFPIrYtdG0VnZAVBbb5LkuwSFbn8odfuztVqt9Q7Zhag2ngmlegCUqjLozFzyl4HBegtbz1Vrd4dOn8lES1DCbLuTvTMaj4aWyZrFc1HqfOuN7L3NfB7UKl6AMOOg2C1cFkwxKkpFdhDVaobCbuIVUasdf2eOSQYW/QHsRfzrHbfL+Jebz4RBMwBaVakkOc6gn/MGqJR62HTjUG48PMsRSgwuuZm9rzJNg12XTYGuUugxxMllkQPtp8Kgzqo3yIkZ7OAVoSlGJRPzwr6rdgAGWLn0kmJUMjEvhFrjJNSaJjH++1b8VcZ/lP1YOLR1MKkd2LP0NBn//Sv+Ao1Shz7GYRghVhTmra/AtCouDx1l3xt9jLyAHIGUPYdUUUVrYN+yabCLBfwIJrUVB5VzjhSjkolZYG1uqMVepfux96fWnyzFLBBG20ZhtH0U439Wv1NkN9JDKg5EtaESdq0VJ/bJVDvmOLPvsSyeZJi1H/Ytyyo0qADdBQO4BPqA8rEYZqWKuhw6lQbn9OexA8f32RcV+uyNim7wp/alSsbA2QOmwKTO8u9vrsAR1Twui2JU1DkzGpNKhmBS6RDG/8qhR8v4n9BnMupNZXDqLDi7f6YiM8dlg0n6q8Foez2mVGb7CEmIrxwmyqsrxmCErU7G/8JB036RP8WoZGJeCH1NlZhWxRVef+k/nTkmGezuGIbdncMZ//MGnCTjf3jV/qgxVrD2P7aGt1sGlGGX4pEGmgdiD8dEmRNzah2Xt4937Im+pqxChrY/uppLl/cuPRx2jbz/7F/GZdX7lJ/Gqk1nUKqrZ4oybrtIUigR6k0T2Iv4Tyi/Stb/h9hOhE1bD726BEPsvE9nMKLkSnb9WnXjUJZ3/Q4suYW9t5uOgjFHfahUGFBlv569t/Yy/tjE8cdguRKKnPFHpR4CvVGU7Fvk4ye0+0Gh49fGzoY/elI4t9uN0047jS0x2e12nHPOOQgGaZZt64hGo7j44otRUlICs9mM4447Dp2dlH09C+qL+a+33uKJBn8tijErOwhpWtaJ/8SrLos5VjJIUcbaxHIoVX2g1GbljYRkbAFSyY1QaYZJtTLY8dIC4rFZEFIkXd4Das2gHFsckehXTDqo1+0jVUgmCEIAIZpyTZN08GBxtkU8V6oLoegsJh0066dAmTP4xRKNCMYWsKckq5hjJYMwSZ7jK6BT94FFl5XHEnzRnxFObIRZOwyWHCeE+PdEvmcpu8l5MWmz8ljKrNkZns2qFpcaJsEgVoglJIQgi12hy7rSuA+04uwJIZLsQXt4HqtaXGPaR6rjQ/DHW9jSD+WfqDXtKdVhIbiiDeiKroNVU4ka4zgZ/5bwCrhjzSjT90eVYaiMf0NwMav6S85LmT4rIaV6MWsDC5n0dIB5jMxBoaDbVb7FbOp/uHWcNHvC2irhxSrfcnaDGm0fK9WRIXRGO9nSDz1l72YfLdXBIWwONWNTaDOT/o6wDpXxX+1rQEukk8WrDLZk66lQQOkizxq4Yz4Ms/VHnRgUyts4ifk9q9hSyzjHYJTrs32EPqPYDxoWJpUOk81y9MQCbOlHp1Rjr7Lh0OfMmrSEXWzphxyEyWVDZcsx6/1tWOtvYTEsuzsHyPjTPltC3RhoqcIIWx8Z//mu9XDF/Cwupq+5XMb/h+41CKWi2N05SOagbIu/O+bHT+61LMB2z9IRMv5tkW629EP893COkPFvDDajIdjEYlhG2bgTlgFl2yXpdb2pFgPN/WT8V/pWwJvwYqB5EKoN1Tn9J4mVvsUswHaIZSQc2hJZ/6HYFSZdtoxjsy0ZUAXtTcFFLIncIMtElpMlA2+8FW3h5Wy2pd48Sdb/3bH16ImugVlTjUpDVh7OftMI9fFG2LSD4dDzoFwCC8iPfMeuX3JeTNqsg0XLw37KRC0EYNbvA61Y4ZzZhADC4vhjyBt/hFQX4lT/R6GHVjdFqoPGzpfcjHT8Z151OW/83JliVh5fNBEG82/XtUSCSVwyfsF/jeu0adPQ3t6Op59+GolEAmeddRb22GMPvPEGz1LcGy688EJ89tlneOmll9h3vOSSS6BUKvHDDxRCwEF97sUXX8Shh/LcVgRyhvT6wpnv/4mz8t133+H+++/HokWLWAN88MEHOOaYTO2IQsyePRsHHFCYDIj2razMDrZ/VOkylWPnyZAUvdoyEfO/1sZ/mtRvsPFp2kxJd7mNB+xujeNv4U9p7ZVb4Uhr4f+5bev8KbcFr42i+I9tuQN4LihDae7MRi5Hcjr+UxvdqAi5Dkf2XPRsxJ8seuPBErX1YqPU9RnFy6+1EUc6339q+638kwK1x9ZtamXvT4Vbs3GO6f/YlomRoWWg/4w/9YMd2/47uv+QTfEb+s+2+v9vvX5/z/Hnv4mis8KxZs0aDB8+HD///DN2352rsb744gscdthhaGlpQXV11inNgHiUlZUxZ+b44/ns7dq1azFs2DDMmzcPkybxQpz0m/7S/f9/Kl0OhUIYM2YMzj77bBx7bCbb6S9j3bp1sh+ivDz7VPVHBKvn47kU6fiPgMICte12qAx82lpINiHmOR9CcjUUygro7I9ApduT2ZLxpQh4LoCQamFyZovjGag1fIkhGvkKHu8VLOBWo90dTuezUIlJlPzBl+Hx3c4Sxxn101DqfBRKpYkNIC7fvfAEnmZr2nbT6Sh33M6eUqh6ckvPNfCEP2JqH6qQXG69kPNIebHBdQn80R+hUljQt+QOlIrZLSOJJqzquhShxBpoVeUYVvog7AY+xe2LrcDSrisQTbbCpOmH3cofYYUFCR2hOVjS/XdWp8epG4M9Kh6AXpQ1N/jewzLXI0imo6gx7Y+JFbdCo6R4gzQW9TyDle63GP+htqMxsfyvbJBNCXHM6nwAG/zfsMFzQsmZGFvCl7EiKT8+abkbTeGlbKr84MpLMczGnV5PvAPvNN2DzmgjLGonptdeib5m/gRJtV3e2PIgPIlulOlq8Kf6q1AhzqCs8C7G61ueRigVRD/TYJzT/zLYNPwpcXbXt5jR8jZLXDbWPg5n9zuPLfEQ/3daPsDn7ZQULo0Dy/bFn/uezG46VH34iYY38H33QnZjO7nP4Zhey7PD+hMh3L36ZSz1rodRZcClg4/HAeV8qa8t0oNblr+MhmAbSrRW3DTyVIx18DZe7WvGTcveQHvUw5ZW7hnzJ/Qz8z7yXeca3LbifXgTJK+uw31jT0GZnl9T72z+GQ+u/hLRVAIHVg7FXWOPZUskxP/hNd/gpYZ57P2Jfcfj+tHT2A08nkripsWf4NOWlWzJ5NLh++G8wXux43njEVy+4D3M62qEWaPDbWMPwxF9eBK3pqAHl86bgTW+TpTrzXhwwnRMLOczQMvdbbhs3ntoDfvQ31KCxycfj0FW3ke+bVuP6xd+DE88gnEltXh00vEoN/BZhjc3LcJ9y2cikkpiSvUQ3LfH0TCptZz/6m/xYg7/G8YcKvH/+7KP8XnrcmgUKlw05ACcPYhLf33xMK5b8jZ+6tkIs1qP60ceiUOr+Sxha9iN65a+hg2BdpTqLLht9MkY7+RJ6Nb6m3DrilfREfWgzliO20f9GX3N/KFqQc8KPLzuDfiTVFm5H24YdjacOj5L+FXHd3hl8/us/+zhHINLBp3BkugR5w9a38NXnV+w/rNv6f44pe401n9oNu/t5mdYrSJSix1SeTwOquDXKNUjerf5fmwKLYNeacLh1RdglH1f8bu14bPW29Ad2wiTugSHVl2PWhNPwuiKrsZ37TcilGyHlQocVt0Lu47PDtHM5/Lum5AQvLDrdsO48n9K129H4HVs9tzDanw5DVMxqPQBJmve1vhDQfsB79WIRaiOkQYmy5UwsqzafPxMeS8D4vPY+Km03galgWdF3tlAria9tmf/jPOTC51Ox17bA3IuaLYj46gQDj74YDZLsmDBAkyfnsm4ngVNRNAMDG2XwdChQ1FXVydzVgi0VHTuueeif//+uOCCC9iszX/imP5XF8BoSunOO+/s9UtuC+Sc0ExK5kWN9UdG0ncT0kyenAbSfiS9V0BIrGe2qOc8CEmumiAlUNRzDrs4ydHw95wOIdUmOjWNCLj/zJ5KkskWuN3nIS14mC0RXwKPmyqWAtHYAri914my5jTC0S/g9lFcDOAPz4A78BjpGYgVvKEX4QlyKWen7xF4WFHCFKuY2u69G/4Ij9RvdN8If5TzT6X92Oj6G8Jxzn9V18UIid8lnnJhZdf5rNhZSohiUcdfEE22izkbmrCo8wLGP5xow0+dVyIu+JjNE1uJRV08rqY7shSLuu9FkhUzTKMtNAfLXI8xW4P/Cyx3vwoBVBE4hTW+97HGy4sULux5Fev9X0tqh3muZ7A5yNVAM9sfRXOYirKlERNC+LztPrhim5nt7S13oyu6hb0PJj14q+kORJIBlmH0xcY74U30MJsr1o4XG+9mT67uWDeeb3yYOSoEyl76SuNT7P2GwHq81vQKYqwYHRXfW4x3W3iRtu9d8/Bx27+RTCfZU+7Mrln4soOrKN5p/gLfdf/MnqZJbfLKlg+x0M1VOI+un4FlXiqKB4RSEdy35jVsDvF2vWn5S9gU7GDv3fEAk+b6E2HmaPxt8YtM+ktoDrlwxeIXGf/2iAdXL3mD5TghrPS14OblvKDl4p4tuHPFJ4ikqJhhGrM61uKfq7lS46PmZXh2/VwkhBSSlJek8We8vpErnZ5Y+x0+bl7JZjNiQhIPrPwGs9t5v7hl8WdY0L2Z8Q8kYrjqpw+xwU8Ve4GL572D9eJ7VzSE8398mzk3xP+c799Ee5gPuluCbpz3/VtsVqQ15MUl82aw7QjL3K24+mderG+hqwl/X/I5winqI2l83bYO96/gRSI/alqGZ/L4v7aRq7j+tX4OPmtZzo5P/P+5Zia+6+T871r5ERa6qXoyEEhGcdPSd7ExwNfcr13yKjYF+fueWBBXLX6ZOTeUW+aaJc+hS2z/lnA3rl32PGv/rqgbd69+AYFkiNnW+bfggXWvsvdr/A14dtObUv9Z6F6G1zbzIp0/9vyAzzs+lfrPrO5v8E0XVwF+1fk+FnmoqKWARDqOT9vfYMuNhE/bnkRjiKq3A1EhhPdbHkJXlJSGwKett8IVa+TXaNKNj1tuZrmHkkIU37RejnCSf7dAohnftv2N50NJtGFx5+VIiNcvPZQs676OjzHRn7HJfTMEcfxxR2Zis+e+Xxx/woGHEWOFDmlmJYpQ4C7EmHoIEHw3A/EF0vgp+K5EWhxzdjak0srtfhH69OnDZmoyr3vuuQfbi46OjoKJAbVaDafTyWxb20er1TInJxcVFRWyfW6//Xa88847mDlzJotpueiii/DYY3zc/7X4Q3oBu+22G6qqqjBlyhTZuldviMVizMvMff3eEGitVVZJT0A6sZw9TaSTa2USZKRDEJIbkEo2IZ325ETWp9gMC+VYSSRWiMUDc6odJ3iF21h8Ud7PRrJmfkOJxBYWSAej7DMgFCOOuSt+aoREWyBayD8Up4E9jlBinbwydDqMcKIBkWSzOJjlyCKTbYiTdDm+Viyelq326o6RMwH0ROm7yVUNriiX93ZFV+RJlxXoivAbeluE9stVgajQEeFy1JbwSlkVW7oJdERI+ptAV4xuotnih5Q7pTvWDHe8C+FUUCZP9ia6EUr60RzZIiteRw5GY2gDe78x1CALuKRjbghwZ3RDcCPLJZJlr2CfZW5SuUoPetpfG9jE3q/0bZRVGKbt1geamAR4U7BdVpmYnIzNoU4m+yWnJVfe2xH1whunqsXt7GYt9Z60gOWeZrH6c7Nc1YM0Frm5M7fE3SwLlKXtlnpa2PtFPc15RRiVWOJukRwIcmJyj7nC3c5mM9b5uiQbfR5OxtHg70ZzkLhGJP60Dc2wuGMhrPZ25PFPY0kPP9fSnpYC/j+7mrbOX+S42L2lgP9St7ifZ0sB/1W+Vtb+JLPOyKsz7d8Y6mIzXv6kvP07ox7W/huDLUimUzliegFr/NxhWB/YJOs/tN+aQAPvW8GGAun7RrH/bArSNSXv/5vD/IbeFF5d0P9bIxtYsjhXjJywLP9EOoKe2BYEE3St+mX9P5TsQDTlgS++puD69YjXbyC2pGD8CcR++sXxJ0HxfHnjTzLObVymnD9+cudrZwMt1G3vi9Dc3MyWYDKv66/nQc694brrrus1wDX3RUs3/03cfPPN2GuvvTB27Fhce+21uOaaa1iIyP9bZ4UclH/9619477332Iu8x/333x+LF8tzVOSCPMpcD5P2+b1BNX/ksjz6jIJetSzgVi7LU0ChqhYz1eatISsMUChtULPj5UIpFg+DaMuVDqqgVnN1B9XukdsUUIuJlbTqugLpICV9I+g0hfypUCEphDQFskIFK0SoVZXlORZ0AzZAo7TCmBNwl+FvFINoTZoqGUc6hknDA4TNzCaXbpo1fFrdpqmSnY9uABQwS7BreWHBXFg15UwhwWXF8qlGm6YMFrVddmNg7afQwaAysUJ/uSAeTjEIslRbmnfTUKJUx59GSrUlMlkt7Veq4/tV6EvzpLMCykVbpaFEdgMmlOucbLmCAj7zZbUUUEqZXnODQAmkpLFoDKg2yJ9y6NhVBr4EUWO0yzjSMWqNjqwt56ZNM7TV4n59TA4Wj5IB3cBp+95shGojyWNVcGiNeb2H28oM5gL+BpUGVq0BNaZC/rQP42jK569g598qf3G/Pkan7HzEv1rkX2NwFMS3VBnsrP3tee1P7ypZ+1sL21+pZe1frncW8C/XcY70m+f3nwod72+lOnnfovOWiP2uRFch7z9IwanlyzJ2sTBiLuyaMpb4zdCLrN+iKYdBXVJw/ZLMX6ui6zcbsJ9hmQmC17HxRD7+6NjYsu3xR6WqL0ydkBnnehk/oSqMjygiCwqTyH1tawnoyiuvZPEo23rR0gytYnR18VnQDJLJJFMIbS1mlD6Px+PwevkMYwakBtpWnOnEiRNZHAxNNvy/dFaGDBmC888/H+PHj8fkyZPxwgsvsL///Oc/t7oPeZS5HiZ5nL831CQrzskLoDSeAYWWS2f19kfpEhctJGW+BUpVDctJYLLfl/MTaGC2P8oyw2q0I6VKymwvhQl2B8/eajQcDpNUrZkGgXI47SQvBhzmc2DQZmXNOs1QlFh5RdMq+zWSc0IgabLTxKue9nPeBXVO3oUK8xmw6jj/oWUP5ZSTV2CA8wZWsZUKpg0vvVXiT47NqLL7WCl5m24oBufIIil3w9gyvlRVazoQdeZDJJteVYKxpbyi9Aj7CSjTj5BsDl1/jHZyWfDEsnMl54RQZ5qIITYeWT6l8q+yvCljHUejj3EM4z+9z5Us22sGh1SeC5u2jKkspteeL92IyLE5qe4yqJUa1Br7sqyiUjsq9fhTX17ZeZxjd0xwZtdhbRobTq47lb0/tPIgVi04g1pjDY6u5lWHT6s/iqlJMhjnGIEDy/lx/jroRCkPCuHo6n0wxj6Q8b95xGks22sGlww6GhV6B8tVcu3w6ZKTQzfWv4+kqtFqDLFW49wB2UB1o1qLW0fz4LeDq4ZjWnVW9VGqM+OakZzj6f0nYowzmzV0kKUcfxnMJdx/G36A5NQQ9q0chOn1PK7j9nGHyfKmnD5gD0wsq2f8H5o4HToV509MbxgzlTkQdq0Bd4w/LMtfqcIDE4+GVqnCcHslLh6WlY5TPMp9u3Mp/yE1w6R4GEKZ3oIbx3AJ7+kDJmG3XP7Wcpwv8r9s2EGSc0XYu3wQju7D1Xc3jjxaxv/k+knY3dmP8b9t9ElS+xPTy4cegUqDnamMrhx6vMSf4nhuHHEKa/8B5lqcXJfbx3X42xAuXZ5YMhZ7l3LJN8GhteLMfvw6PKh8CgaYs6qbGkMNDq/i8ujDqk6CU5udoh9mGYsJTi7vPbLmIhhycrtMcB6OviYufT+k+gamHpJ+t/KLYNVUsFwrk8qvk65fJTTYq/JW5uBYdUMx0M77O/9uRowpvYu9LzEeilIjj5Uh0ENLP8fNvzj+GK3XZp0T2k93IPTGE9l7lZXGz2yMosLwZzZ+7owQKLHbdr7+U1AALMWRbOtFSzl77rknczooDiWDb7/9FoIgMOeiN9C9WqPR4JtveEhBJua0qamJHW9rWLp0KRwOx38UZ/O7SZd/azTw1Vdfjblz57JgnT+0dJmmVBNrmfROmSMzZrZUD4TkejbbohSfQjJIpdpYvIpKPVBWhZSQTGxESuiCRjNUSuDGjpdOI5FYzWoDaTWjZRJkiraPxikzagp6Ld2wszdqqgEUia9g0kGDZoQsWj8p+BGOr2HSZaNWzj9OVZ7jG9iMioHNwmRB6bopXsWs6S+rgkwIxrcgmupmabxzJcjE3xunaeoQHLqhUOdIGEm10BNdx6alS/VDZUoEmtbujq6DWqlDqW6gjH8sFUJXbBOTblIOilyEkj50R5uYk+LQyr19b9yFnngHC7C1igG0GXRF25ncuNrQRyZBJv4tkWaWpbTe2Bc6seYO509LRlvY336mepa6XWpHIYGNwSZolRr0M9XK1B6hZISlwrdqTKg35XMMojHUgUq9A1WGrMNDoJgVygZLAbalOnl/3xJywRUNYKClAjZtbh9JY72/E8FkFMNs1cyZyZ1xWOVtYzMUI+zVzInItnESK71t0Cs1GGavlM1GBBJRrPV2wqEzYqAYJJsBLe1s8HWj2mSTZkEyaA/7sCXoYQG2mQDaDBoDPeiOBjHYVs6cm1z+a33EP44R9spC/p529nekI59/Aqu97cx5GmqrlLU/8V8faIdDY0J/i3zd3hMPYlOwC1V6O6qN8lkTillpDbtQZypnsy25aA13wR33oa+pGhYxcV2G/5ZwK+s/VG2anJkMqN9sDvGlS+pbuf0nIcTRHG5kuX1qDPUy/tFUCB3RzSxpYblePsaEk164Y5th0VA/yKYJIIQSnQgkWmDV1suqOBOCic2IJbth0Q4uuH4pi3VKCMCkHQnVrxx/KFYuGV8BKPRQ540/aSEAJNdw6bJaPv7sTGqge3/eD/rtUANFg0lct8ec/6p0mWZFaIUjI12mgNuMdLm1tRUHHXQQXnnlFUyYMEGSLn/++edMukycLr2Ux1f++OOP7O8nn3zCjknBtiRVpriVq666ir1uu+22naeQIXlgtDz0hwdlelWV9p6ZlpKZKcuyGRtzTQob0soKKHKeLCSbik8Z08yK7FQKBV9GShuhkGY9pL1E1VCq4OdVQMtsSugKZIUqhRFqVXmvmS0pK61aVQaNmGk2F7Q9qYRUvZR0p6RuNCmcO5hl+OtUpSw7Ls3EyDkqWTE1CvQrXGZSMxs9Kebzp5wrBlWJ7AlT4kFZadVOKeNnLvQqM4wqpyzhVm5SrmRawWZW8vnTMhJlB6WZGDl/bmO1cPKkovT0bVXb2c0yX5aqU2ph1dh6zexKWV1tahvLnJoPs9oIuyYJk1hkMRe0hJFKKaQChrn8nVoLOyflTMkFzRSUaq1cFpzHkW78ZVordCpVwbIJZaot0Vpg1xbmTTCqdNyWl5mWQN+3VJuERVO4n1NrgpBSwJiXBZf4kzLHoIz3zl9n2Qp/NePRW/sTf6faCluOU5Tlr4ddbWWOZD7MagObXaNt8mHRWJAUFDJnJMOffmuNQg9Nb/1H42BOS37/of5vVjvZPvn8KecK9WNjL31EqzSy64ZnepZDo7JCJ5RB09v1q3RCUPV+/WpoGZhdv79+/KFlcYWqnI1ZBbJmyrSrLBMzfxfxv8Lrr7/O8qSQQ0LCFgqGffRRWh3gIAeGZk7CYR68T6CVj8y2tKxzyCGH4Mknn5TsNPPyxBNP4G9/owDuNAYOHIiHHnoI55133n/E7b/qrFDmu4YGHjxGaGxsZM4HRReTtImWcMhTIy+N8PDDD6Nfv34YMWIEy4r33HPPsWmor77KrSvxx0M62Yy4+3SA1fpRQGW5Emozl+Wl4ssQ7vmzGEyrgcH+ADRGvsQQi34Fv5vkw1GW3dHqfBFa3WRmCwRfhNdHmR0FqJRVKCt9CxrNYFEeeAe8wX+x7bTq4agpe5M5E+l0Ak09F8Mf+ZzZzLp9UV/6PJRKA5KCD+u7zkQozoNZS00no6/zbjZoRBLNWN55FqJJzr+f/W+oE6eBSQmwqON8JmGkpZ6RZXei2sxlhe2h2VjYdS2TRasVJkysfBilBj7Fvd47AwtdDzH+BlUZDqx5DDZtP8b/J9eTWOHh2Qud2gE4tPYhGNVOpNJJfNF2FxoCvA5QnXE8jqy9nc2kRFNBvN10C9oiPBBsjP0QTKu6lPF3xzvx4qbb4I7z6POpladhv/Lj2Pum8EY8s/FehFIBNvif0udCjHdyyepS72I8u/FJVotHrzTg4oF/xRDrMGb7qmMWXtnyJnMWHRo7rh92Bat2TPxf2vwhPmzl0559jTW4beTFLKMp5du4e/Ur+N7Fa/6MdwzGbSPPYVllA4kIrlryPFb5eVDnkdUTcPWwY9lNh+SxF/38IlrCbraocNHgqTh7AJ/iX+lpxYULXoM3HmbOzh1jj8ERtbza8Tdt6/C3n95j6hazWocn9zwJE8u4LPjVDQtx++KvWHxHhcGCV/Y/FQOt5ASmcc+SWXh2DQ/KHmovxysHnowyg4mpaC777mP8u4kHbu5T1RfPHnAs9GoNfLEozpw5A0u6uUrp5MGjcffkQ5jT0hTw4k//fgdbAtRHgKt33xcXj+HT+Mu623HGF+/BE4tAo1Ti/n2nYfrA4cw2c0sDLvn2E0RTSZg1Wjw3ZTr2rOazAi+vXoxb533LnL5KoxmvTTsRgxwljP/dP8/GMyt57ZhhjjK8euiJcv5bcvgfyPn741Gc+e3bWOLi6ruTBo7B3ROnMf4U7Pvn2W+gKcj5Xzl6f1w4fLIkrz7vhzeYhFqjUOKe3Y/CUXV8GY2UVFcvnMHa36TW4bEJp2CPUi79nbFlHh5c8ylr/zKdFY/vcTb6mcsZ/6c3foq3m3hdnP7mKty/2/nMeaT+88C6FzGvh9cR2s0+FDcM+wvrP6FkGPevfRgbQzwoe7+yfXB2v9NZ/+mJdeHJhrvhinNlzxFVJ2FKJZ/Bbg1vwBtbbkUkFWCzlEfXXIZR9v2ZrTHwA75ou4Op68ihOazmTknWvMH7Dha7HpSu3/1rnoBVvH43ev6BJj9X+Zg1Q7Bb5Qsso/W2xh9B8MHjOo3VPSMYjKfCav8Hu35p/Ex5zpDGT6X5CijNPK3CzgYhrWSv7dn/vwm6N28rAVzfvn3FHDtZ0GwJOSP06g2UCC43GdxvxX/1my9cuJBF/9KLcMUVV7D3t9xyi5Tsjda2MqBAHQoGGjVqFPbbbz8sW7YMX3/9NfPy/shI+K4HWPVkQhqpwAMQRKcg7D4f6TSXAJLCJ0Ky5lQny/Tod5NDEOV7pcPwu89liZESifXw+kjqKxZ5E7rQ4+Fpq6kwYcZRIcST66Rqyj3Bl+GP/FuyBWNz0R3gHm6r90Gm8MnAFXoLPWFehXZ9z00sV0qGf6P3IfijfMBcyiSMXGGVRgIru29gU8OUaXZh1zXMUSEk02EmV6YEU754IxaKAx2Biqf92Mk5NofmSY4KwRPfjPnd3HNf7vkIDQEqSsjRHF6ChT182zldr6A9kpUzLvN+iVU+PuB/0PwkvPFsYNhXHa+jKcQVOi81PsRUP6wd0ym80fQkfAkPm4LPOCqEmBDFUxsfZdu0Rtrx8pY3pGBHX8KPJxueY+8XeVZJjgqhKdyO5zZxefXHrXMxV3RUCEs8G/B2E5cuP7vxS6zxZ+OpPmn7CTM7eBvfsfIDtEd4gBqd8Yn1X2GFh18XVyx8G35RwksKk5uWfIDuaADBRExyVAihZByXzH+HJVhr8Ltw2+IvpUBUVzSIK+bz33pW20bJUSHQ8swdi7iE9JW1i/GF6KgQfujYgqdW8m0fXPw9lruyUsS31i/HR5t4ccPrvv8CLUHex+mM/1j4HRZ3cafggq8/gi/O+3hCEHDlnM/RGQ4iEI/h4m8/Zo4K459I4PyvP2T8N3h68Pcfv5GCZbsjIVw++1P2/tuWTZKjQljvdeH2Bd9m+W/pnf8DS+dgeQ93tAhvNyzDR41cTXbDT5+jNZTl/8Dy2Vji4tfDpfNnZPmnBVy78GN0Raj9o7hKdFQIpHK6/Oe3mMPRGOzCA2s+kdqfJOe3LOfydiqsmHFUCKTsemI9l2V/3v4d5vdw1Q1huXcd3m/h0uV3Wz5AY4jL8Qlzur/HvB7+3d5qegbueLdk+7T9bWwW1Wszmu9ljj6Brs2PWh5mWZnjqZDkqBDiQgT/br2FLcP6441Y7Hog5/p1Y34nH7N7InMkR4X/bg1Y33P3L44/Qf99SCSy3y0SfgPRCJdsp/w3yMZPIfgg0uL4ubOB0lZu72tXxX91ZoWUPNsKiaE1rlyQnIle/9+QTq7Pk97RZw1Ia4YjLfBBO4sUhOQWQElLErmR0FTt2MerLidJrpjbbikkk3zwibNzKWWS53iCV5aNstwENHXMB1BCLMH3C8skyCxOH9EEn/UKxQv5sxT6uuGIptrl3wsphJNNUCltTNqcayGnJp7ywh9vkvGn9Xd/nA+03vhmttSTWwnWHeNPi+7YFqZ4IKVDZk93nMtqu3MkyBnpZk+c3/y7Yk0y6S/fvgXVhv7wiHlUsq0soCfWCZ3KLDkq/ExphFNhBJMBtEc6CvZpi/J2aAp3sKWGrGRVwJYw/423kE1BuRAy341/RuASZLl0dnOIO1gbAyTvlfNvDHWzQNn2SMbR5aDtmkNutvyQuVFm+PsTUXjiYWzy98h7Dz0N+13s/Qafi80mSJWQ02ms83IeDb4epnAh2XDmCzT4+H7rPC6ZvJf4N3h5267zym3sWN4ejCgpR1uIqofLuTT5vbBqdYilUjL+vngM7mgYm3zuAv4NXjfn7y3kv9bdLZ2zgL+X81/v7S7k7+f81/vlNvab+F0Y4ahAe8Rf0P5NIQ9sWh2TNue3P82AUaxQPv/NQc5xS6hT3n/SVC2b95HmcDv7bhku9P9msS+2hFtlfZxmCdsivE+2R1sK+n9HtBW1hr7wJ7JODDsfBJYo0aQ2SI5K5hvEhCCrOu5n11zu9csdmIxzkjv+kC2Th2lb40+C4vlkY4waSdEGNrblj58bocgpP1JEEX8oNdD/Vyg0Iwqly2qqIaKDQlWX18waKNX9oKLIeFbROOMpK6FQlrCXRj04bx8VNGo+da7VDC+UDmr5tLSB2XIv+jT0Wr6fif3NzW+ShEHDa+GYtcML4kNM2sEsnsTApIe5uR80MGooGK+aSZVz+WuVDuhUDrbcI99HBbuY2dapGyBzOshxKdXxrL2l+gE5jgofLkt1XF1ToR8gk2fSdmU6vuRRZehfIEOu0FNwqwYl2nLZfrTuX6arZPJkqmicUQPxWBMLLGorW+7JlavSsfsYuMqEgiXlVX+VGGDmQcf9zTUFTgd9RhhkqZHJk+mGOlDMdjrUWlUgg6WgWK1KzRQ4ufuR6qfOVMIkvCT1zek9cGqNcOqMGGQrle1D8t6htnJp2SdX3ku2kU7OY5ijXMY/LX5GGF5SLotTIf5DHTyQdkRJRYF0eaijlMWG1Flssv0o7qWv1YFaiw1GtZx/id7IXrTck7sPHXuYk5+L+OTzH1XCA9OHOXvh7xT5OysK+A+z82MOtxfyH2wvZ+3fx0QS9zz+ZidTFhW2vwkOnYkt98jbX4mBlkpp2Se//wy28D5C9Z3y+w99Rqg31cn6JM0A9jHyPklOSX7/p6Bw6v+OPFkz9X+nrgoWTaVYkTzb/w0q+k62Xq9fmxh0b9YOKRh/LNoRvzj+aDSk4MrlmIRaw5dcwca2vPFTQ+fZeZeBtue1q2LX/eY7EBrb3VCoM5JVFVQkT9byuAKj8xkolKK6QGGAwfE4q7isUJpgczwnVSKlwFyb8wWWmlqjGQCHnZZRePAdOTZO5+PsvUm/P5yWK6RBRqcdgzIbSYgBp/l02I1cokqwGg5BqeV89r7GdiUsYpp/Qrn5bDiNXBY5pPQuVkk5w3+A8yZYdNwB2q38UehUfFBXKvQYU/4AC45VK42YUPEgkzUStEobi1kh/lZtHSaW38BK1DPO6kpMruBR37WmiRjrPEviT4qfSeU8enyU/QgMs/IU9IQB5r0wzsllnfuWnY56E29Twu7OozHMylOKT6+5EGV6PnDTwHx49TmoNXLn6Kx+V8Kq4UHPGoUWf66/DBaNnaXHv2DApVLwLKl9Lhp4OZsZqTJU4rz+Z0gBjpQr5aKB57L34xzDcXKfadKNY5ClDmf346UkjqiejCkVWVnq5NKROKEPlxCfN2AqxjqysuYT+uyNAyu49PemkdPR15RpYwWuGnY4htv493l4j5NRqjdLeVT+Mf549m+S8z4+6UQWK0GgwFCKWaEbYz9LCe7Z43AWI8J+e6MND07i0t/9qvvjr6P2lm57o0uqcNN4vsx62uDdcGz/rCx4at1AnDeCR/xfOW5v7FmZVZmcPXw8jujHnd379j4UA+1cpUQ3/VsnHYTRZTwo/umDj0G5gQd96lVqPHbAESgzmmDSaPH0wUezvwS7Xo9npxwDlVKJ/jYn7tvnEIl/rdmGh/fnVYD3q+2Hy3ebnOVfWombJx6Y5T+gd/5XjNkXe1ZkVWJnDd0dh9fzm+XdEw7DAGupxP/mcVMw2sn5Pz6J5PSZ9lfjoQnTWftTeQL6bXLb/5EJp7D2rzeV4saRx0qVnElFdPtoLtOdUDIUZ/SdKvWfodY+uGgQ/20OrdoHB5RnJaITnaMxvYb/NsfVHoPh1myhzUMqDsZEJ+9rJ9edhwo9d2rIaTm25gzUGXlfO6HuBpjVDimP0LG1V7F/U9D5YbW3sb8EncqKw2rvYKUtLNp67FF+o3T9Uo6kSRU8PUKJYR/0ZdXWOX+rbiQGOa/7xfHHbL0GWi2PA2LHNJ0LvViSRGW7C8gZP5WWm6HQZOX1OxPIldu+ZaBdF8WqyzsIrHiX4AIUZijyoucpDiUtuJhDQrJhuS0OQXBBSWqhHJkfgWpvCIIXKiVF0MufPKjiMqW8VrH95E+FyRQF86ahVslllvRTJ4mHQgc1W4aS86d0+mqluSD6n9a646keaJR2qHIqBXNbArGUGzqVE8o8/pTSm7JkUi6V/CJqcSGMpBCBQeUs4E/pwImrQS1XBjDJZMoLlUILvUqueqDp9GDSC73KCG2eeoeeQgMJH3NISPYp55hEIOlnMyq5MlHOMY5gMgS7hmYH5H59OBlFTIjDrrEU8KdaP8TVppUrLOgzksHSEzst4+Tzp3TuVJvGoM7nmEJPLMTkuzqVvI3jQgruaAglepNMpsvaMZlgWWLpZktOQC4o5iWSTKBUbyrg741F2OyFU5/fj9NwRcNsxoSWceT80yy2xKLRwqjJ5y/AFQnBrjNAr85r4xRJ1cMoNRh75x+LsuDZ3viHEwlm+8/5k0Mt7yO0PZUDoEDfXCm0xD8WZAnuMjljcitA029TojMX8k8lWJbh3hL4Uf+JpuJwaAv7T4D6D9Kwagr7jz/ph0ahgVGdf40KCCR9LFBcl6dMojgUujZI1qzO6/+pdAKRpBcGNSX2U//q65cqpqeECAus/U/GH0Ecf5S9jD9bGz93JunyLQsOht4sv4b/E0SDCdw+8ev/SaHe/zWKMys7CoIHQmIV0skNBXE6aaELqcRKHquSB0q7n4ivQiovNoSQSDQgTja6iHOPlxYQTaxBJL4SQlq+pk5xJJHEaoQTqyAIUfm50iEE42sQonT4rMJpzrkEijVZg2CioYB/LOWCN7YOoWRhwj1K2+2ObUA4KV8bJ3jjTXBFG1gK73z+3dFGdEY3slo+Mo5CAh2RRnREG1n9nlxQqvzWyBa05aXDJ4SSQTSHm9AWaS3g7437sDnUjM5oIcfOmAubgq3oicszMBKawl1oCLTBGw8U3Bg2Btuxzt/GcpXk37zW+Tuw1k+py7MxMYRwKs4K+lGOEHJA5BwjWOXpZPET+fy7IiGsdHdiS7CQY3PAi1XuLnTkxYYQKM5jlaub3aDl/NNY66I6T13wx/PaOJXC6q5u9iJnIRcUBLumsxtru7rZDTwXnkgEqzu6sMHVU8g/GMLqjm405WW5JLT4fFjT0Y0OPw8CzcXGHg87X0+4F/5dLqzu7EYg9p/xX93djTUuVyH/aASruruw3t0L/0gQq1xd2OKX92NCc9DH2z9c2P6bAi6s9nSiJxoq6D/r/J1Y7etgtYjy+w/le1nnb2d5YXIRScVYf6R8PPn9n5zqxlAzWiJtBfwpoLw5vBndMa4WyoU33on26CYEEvIxhu0Xb0LPVq5ff3w9K6uRpPwov3L8SadDiCdWIk7jZN74s63xc2dCcRnot+MPn2fl/wOExAokev4EpPmFqzQcD7XtPvbEkYjORISpfvjAo7NcA52FK3tCwRcRYPJkujhVsDkegcHIlxR6vLciEHyavVcoDKgofR163Z4s6VKz61wEo1zOrVKWom/5+9BpBiAlBLG+80REEryejk49AEMq3oda5UAs2YZl7SchJjpFNt1EjKx8geVJ8MdWYVH7mUim+Q2jyjwdI0rvYvypevJPnVcxJRBhmOMSDHacmyNPpuWqNFt+2bPiVvS18Myd87sew0ovV0DQ2vghNfejyrgbe8r7pOU2bArOF/Nb2HFS/UNw6GoRS4Xx2ubr0RHl9VBKtDX4c7/7YVRbWfK2pzbeCJ8YMNvfNAJn97uJrcs3hRvx2Pq7EBW4amaic1+cVv8Xxn+JZykeb3iSKWkIx9ceiyOr+ZLCF+2z8Xzj2/w3g5JVv92njC8bPLPxA3zQylUblI/kjpHnY5R9IIspuGn5y/jBxZUwDo0Zj+9+EfoYyxBKxnDBgmex1s8Dbmk54LlJF8CuNaIj4sPpc59DhxiwuUdJX/xr0ulslmWlpx1nfPcqAkl+4z22fgzuGX8k4/9N6wZcNPd9JsslXDl6P1w8Yi9J/fL3n6jCM1++eGivI3B0fx4jcMf8b/HcSp6J0qBW4+VDjsfEqj5ICQLO//IjfL1ZbGODETOOORn97U4E43Gc8v7bWNnNA2772x149/hT4DAY0BYI4IS330J7kPfxSbW1ePGYY6FTq7GisxOnz5iBQJwHbB43YgTumzqV89+4ERd/9AlTAjH+e++FiybxpY5XlyzFbd98y/jT8teDhx2Ko4bxpZm7Zs/GC4tEmatajReOOxYTamsZ/ws++RjfbNok8X/7pJPQ3+Fg/E99L4e/w4EZEn8/jns/h39NH7x85LFspmRFdwdO/extif/xg0fi/v0OZfy/bm7AhbM+lNr/6nH74uLRfDn1lfULcdvCr7L89zwKR/fl8Rv3LP8KLzVk+rgGz+51KvYorWf954qFb2JOF5fgU5zLi3uei3pzKZttuWTR01gf4MqYOmMZntr9Iti0JnRHPbh62T/RHeMO3yjbQNwx6kKWc4VUQveseZAp3Aj7lO6F8/qfyfiv9C3CS5v/ydICZDLhTqngqRMWuT/BVx28QCddv0fWXIkRYrXyBV2PYZX3ben6nVrzACqNu7HxZ1HX5egKz5JysUyqfgVmTd9tjj/JZCu6XEexJJjMpp2MstI32CzLtsbPnQ25xQh/6/67Knbdb74DkaSqoens05MQeRfp+A/sCSHiuVIeHR/4B4RkE4SUGwHaL6dYoc9zJSt+GIsvkxwVAn3mctNxAH/kU8lRYXsJHnR6eTxIV+B59lQjnSu5GR1+Huuy2fNPxFJZea8v9hM6Au+y92tctzLpcQbtwQ/gjs5j/Bd33ywWNeNY43kcoUQrYimfmEclo1wQML/rTqYwcEXXSY4Ka590DN938sqsG/zfSY5KZslnTieXYv/U8xE6o1x1QHDH2/Gjix/nq843EUhkn/A2hVZjoYdLVt9ueoFJjzNY4P4O6wL09JbGs5tekBwVwrst76M71o1AIogXGt+RqSSeangNCSGBDYFmyVHJZJ59eD2XUM/uXC45Kqwdk2E8vv4T9v7NzT9gvT87Q0Z5U17cyI/z2JpvWDbWDBb2bMYHTfxmTFWESXqcwftbluHHrkbG/6r5n8hmYR5cPoflBaGZgFt/pirUHKQguWbe5yzL7ApXh+So8H6QwjXff8nef7ZxveSoELzRCO74gXN8YekirHZlZ5+2+Lx4YiGXxz704w/oCmX5L2hpwYxVXPp7y9dfs1mLDN5btQo/NlGhzjSu/veXslmMB+f+gGavj83E3P7trGyxv3Qa133xFWLJJHN+Mo4K55/E9V/yPv/5hvWSo5Lhf9cczv/FfP5eL54U+T+wII9/azPeWcNvqjfOnSnj/+76lfihdQvjf+X3n8na//7F37HZLE8sjNsXzpTzn/8Zz/LraZMcFcY/lcSNi3gfmdm+UnJUCFQZ+4HVXO77TtNcNnOSAWUmfmUz7+Ovbv4MPbHsLCoVv/yqg5/jpcbXEE1l+//3rh+wyr+G8X+z6UnJUWHt1/42y8sSTvoxsyNnjIGAz9seYcU/XdG1kqOSuX7ndt7L3reHvpQcFQIVM13T849fHH98/vuQSmVndmLxeQiF3trm+Lkzgh7rhO14pYvS5SK2B2k2WyGfVk6nOqCgUunpwqlvyrOSVkYK9iEpsyD4e1kSEpASuIQxkWwvkC4nUryybCKVb0sjLh6LZlbk0mUV4uLgEU11FHCJJTupGL2UYyUX0VQXVGyJRiiYAo6nAggl5cWwiEc4yaeZA0lXnnRZgF/cPpDsYYGHuaJJvziTQjMrufJMmgnxJbic1RsnqW7e0k+iB8l0EqGUfAqe4Il7YVAlCvYhKTOlvXeJT69ZFmlpmagr5i2QnnZEuRPVFfXJZLX0lz4jtEV8MqUHxcB0RgNSyvlchQihMxJg8SiZHB/5togmIVPFEEgKTMs67XlLQqwisLhMQctF+dLftiD/jTuCQZmNbnYd4kxEa8Avk/dSDEmnePNvDwQKuLQHg2xJxhfthX8oiEhS1yt/WtbpCOTzBzqD/FwdATlHVq3Z/8v82/L5U/uLx6TZlgL+oeDW2z9MEl9dwW9GUnJKPtcR6aX9xd+6M+KX9R/i1B7N9C0fn02QpMtpdMd4/+mKueX9X6FAj2jriZPUW87FHfcwJyXcS/+nZSEhrZep8gjJdJzJl0MFS7rZ6zfKxgy5dDnCxpZtjz8pNkblLl2pkBLatzl+FlFELoozKzsAShblnglAI89XxXIEsEAyzegcmxJQWKDSDGaVkimoNmtTQaXqB6WyhNX7UbDih8oceTKfejbqSAGQOzApYdLxYm1mpvbJXQsWYNHxTKI2A/3NeuU0W2LTczWBUz8p51zkLqhg041hgax2maxZCTVl2tUMgEVTAz0Fx0qFDFUwa2qhVzlQqhvC9s3KIlWoNPDMmDXGkXnSZQXqjDyfQr1pVJ50OY06E1d39DePlPGn7fqZ+JLBYMsImTyTHJm+pkFsiryfKSvr5PJMAysQV6Evg01jlWz0t1JfxoIaB1r6QKNQS4oNstG0O2G0vV9BZeXdndw23tk/m+ND5D/OyTOaTijtJ3smIsdl9xKuTplc3k+S1bLeo1BijLOGLVGMcpKsOcODUrHrMNhWhjqLgwXHZvajbfpaHEz6O6q0kgWRZos0KjCxksurd6+skcUE0P571dRJSzu5syD0bmIN32/P2j4y/rTdhBquQJlcVydx5PwVGFtVxZaIRlVkZcF0LotWi8ElJai321FqpHTtWY70mdNoxMgKKrSnYlWTMzZaAmL8a6oL+ddx/hNrCvlPkPjXyfmnBUyo5sek71/Av6KKtf/okko5f40Og+ylqO+l/evN1P4mjLRXseKHue0/oZT/1mOd9XlVlxWYWMKVMGMd/fOk12nsZu8v9rtBBf1npI3vN8I6TCZdpvcDzf3ZEmmfHFk/8dErjajS18KhrYJJZc+5fpVwaKphVNm2ef06dOMKxp9SNrZse/zR6ShrtOwXgE4sVri18XNnRGYZaHteuyp23W++A6G23QaFjiSGOlbfQu14EkpRimd0PguVli5wDZSqehhLXoVCaWOqIEfpm1Cr6UangVozEo7S11j6abW6GuWlr0DFyqRrodftgzInz/Jq1I1HtfOfLFaFHBqb8ViU27h00GE8CtW2a6BUWFjdnQrrRSg182qvfWwXoMp8KpSsno0dA5x/h8PAnZyhpbegzHggy6tCVVRHlz8Ck5YPkhMq/wmHfjSrymrS1GLPqidZPRFSBR1Q/SisWnIG1HDoBuOAKpIuK2HSlGNq9X0wqyk3hwY1xvHYv+pGMSfKMBxSdTWLVaEBcaj1IOxdfjazDbfui/3L/8zq9FCtk8mlx2O8YxqzHVA+HZOcU5n8kur/HF19DgZb+AB6Yt1ZGGUbx6or2zQOnNP/r6jU8xLzlw26GAPNA6BWqFGuL8dVQ/4Gk5oUQxrcPPxS1BjoZqRCP1Mf3DDsEjbjUaaz47aRf2F/yWkZ6xiMq4bydhxhq8cNw09isSp0Q5paOQ5/GcCrFk+tGs1S5ZOclWrNnNF/PxxXx2Ngzh20D07suweTH9s0Btww6jBMLudOzt/HTsNBVYNZxWFS7jw26XhJSvuvfY7D2NIapjShG+RL+5/MlCwko311yokYaCthEt8Rzgq8dNAJ7OZZZbLghanHotpsYcfcu6Ye/9yfcxxXWY0HDpzGYj3oZnzMoGG4ZhLvB0cOGoqrJu3NHAqjRoMLx0/AaaO4vPrCPSbgtNFjWPwIyYxvPeBA7FPP89zcdtBBOKh/f+ZglJlMeOLIIzHAyZUgTx1zFMZWU0FBJersNrx4/LGw6vXMkXn5hOMwsIRq3SgxvLwcLxw3nfO3WPDs9GNQbbFAq1Jhcn0dHpjG+8HYqmrcf8ihKDGQ6kWFo4cOxdV78/IJRw4eiiv3zPK/IIf/ReMm4LSRIn+dHrfveyD2reP8b997Cg6uH8COV24046kpR0tS7KcPnI5xZZn2t+PlKSfAptOztnv5gFMw0Mrbf7ijEi8ecBLjX2m04unJJ6PKYGXtv2dZf9y3O09/P9rRB7ePOZbFqlBdo8NqxuDSoVyuf3DFbvjLgENhUlGdKy3+1PcAHFPLb+gn9pmKw6r2ZvFTFrURFw48HuOc3Fk/o+9pGOsYw/qqXWPHZYMuRLWBS6/P7ncl6k2DmdKnVFeJ8wdcD4PaxFRBJ9ffhRJdH3b9Uh6jE+tvl67fg6vvg0m8fquN47FvFcXWAQ79GIwuvZPFqlC8W43pCAxxXP6L44/VcilMxj+z+Dulwg677W7o9fv/4vi5s+F/UXV5Z0FRulxEEUUUUcQui99TunzlD0dAtx3S5VgwgQf3+nSXvL8VY1Z2EITEOgjxH6GgJw79YbKcKcn4QiTjS6BS1UGt5yoJAvmJ8dhslkqfZlZ0YhFDbkshFPmcxa/odZOgE5PMsXMJEfgin7IS7Rb9AdBp+FIDIZHywBP+N1tqcRgPYTMlGUSSregJz2ZPROWmQ1lOlQz88Q1wRRawSsnVpqmynCmuyDL0xFbBrK5BtWlfGf+W8E8shX6JbjCqxeUcxjGdQkPgBwSTLtQYRqHCkC37TpLkNf65TP0zwDweTh2fBSGEkgGs8M1n8siRtgmsAm0GPTEXVvqXsORu4xwToM+pNNwcbsVq/xqWhXaCc7wsZ8pa/yasD2xGhb4EE5yjZfwXuteiKdyJgeZajHHwmY7MNPucrpXojvqwm6M/hlj5kgeBJMlfta9EKBHDXuWDWEbZDCjw8qu2NSwG4uDqoVJCMQLVn/m2bQObXZnWZyjMmmyuknWeblbLpkRnxGF9h8hydizqbMWSrnaWDXZK/UAZ/zlNm9HgcWNEWTn2FJc8GH9BwJcNDSyOg5ZraGlF4p9I4vO165h6Zt/+/dDXka0U7glH8OXaDYz/lCEDUWbO5rNp9foxe/0m6DVqHDJ8MMy6bM6O9Z0uzN/UBKfJiENGDIJGleW/pKkNy1o60Mdhw4FD+8v4z92wBZu63RhWVYYJ/eX8Z65uQKc/iN371mBEtZz/v1etQzAax76D+qG+RM7/q7UbkBLSmDI0j7/Pj1kNm9iszrShcv7rXC4WFEwzNtMGD5bxX9TeiqUdHehjs2FKvwHy9m/ZLJUWyBRhzPCnOkskaZ5Y0QcjSypl/efz5tUIJuLYr2oA6s3ZfCSUrv/r9tWs/x1UOQyleotk64h48INrDXRKDQ6sGAWjOptPZUuojdUSomXMvUrHQZ3TfxqCG7ApuAllujLsZh8r478huATd0VZUGfqJS63Z63dT4Ht2/VYbR6NcPzjnu0XRHJyJRDqEKuNeMGv6yMafnvAXLD7FmTf+JJPNCEe/ZrPKJsORUOaMP9saP3cmpKBkr+3Zf1dFcWZlByAVnY2kh8pd03pzGgrtRGicr7ALLhZ6FRHfDVLgmcZwEoz2+9mA4ffehnDoaclmtl4Ps+VSdqPu7DkLYab64QNLufMpmI1HM0dlY9cxiCZWiavQWvQrfxsm3e6IJ7uwsuMoJETVj1ppw4jKj6DX1CEYX4tF7acglabAXkq4Vo/dq95lSzqd4e/xc8dlYixJGiX63TGp6mnmsDT43sOi7ntFHmn0sxyJPcpvZvzndz2OlUw1wG17lJ6PMc4/Mf4ft9wqqn44/8NqrscQ6/5ICFG83HgNOqOk6FCw6ek/9b0btcZh8Cc8eHT9tfAnecCqUWXGpYPuRYmuAi3hJjy4/nbEhRhbyy/XVeKaIbfBqDZhmXcFHlr3GPuc/htqGYxrh17BHJYv2r/H05veEgN30ziofE9cPPA0xv/pho/wbstsyXZOv8Nxcv3BLGj2+mWvYG73Ginu4NZRp+DgyjGIpOI488dnWS4VsmiUajwz8Szs5qxjBe6On/UcKzSYyWo644Bz0cfkwFpvF074+mWWiI0uuL5mBz6YehZb0pnVuhHnfvsecxDIRje316acxByW11YvxU0/zJSCMk8cPBL37ctltXfOnY3nli2SbNdM2hsXjZ/IjnP+xx8x1Uxm0viRww7HEUOGIJJI4KTX3mZ5SMhGyyyvnnw8xtVWoysQxLHPv4HuIA/KtBn0ePfsU1DnsGNdZzdOeeFtROKcf73TjhnnncKWdL5b34iL3viI808De/StwfNnHMdu+G/9tAy3ffIt40tDzbHjRuDOY6awf//j33Pw0g+LJdvfpuyF8/abAEFI45I3P8astZukuJUHTjgMh40aws5/6vNvs9wsrP3VKrx0xvEYV8f5H0f8Azn8zzkFfRx2lhvmpFfeZt+ftb/DjvfO5PxnNzbiLx98KLU/xce8cjzn//qKZbhp9tdSG58wbCTuO4g/cNw5fxaeW7Ew2/577IuLduPt/5dZ7+Hrlgap/R/d52gc2W8Y+/1PmvUS1ng7efsrVXhlvz9hXGkf1m9O/u5fcMV4/7FqDHhzn/NRa3KiIdCOCxc+wRLJEcdaQymenXApLBoDFrlX4c7VT0v9f4R1IG4feSlzWGZ3fYvXml6R+vjeJfvgjL5nM/6ft72EH1wfS7aplX/CfuXHsuv385Zb0Bj6Ubp+D6m+CYOsB7BEjrNaz4E3TrXGFGyM2K/6XyjVj2Hjz/KOoxHPGX9GV37Ixp94fDXau49iBVtZwjh1f1SVf85qjG1r/NzZZlYum3v0ds+sPLr3R7vkzMqu66btQKQC90gXGiEdXwAhNodd9BH/HeJWPHAuEXkbQrIBqVSH6KhkbVSZNC2EEY3/JDoq7GjsRXlXCN7wR6Kjwm2U/6TTx2XBHYGXkEi5pH0oYVO7/xlma/SShDEqcYwkm9Ee5NLl1T0PSY4KoSe6EF3huYz/UtfDOTyorPwnCCQ2I5R0iY5K1rbQ9SzLetkaWZUjT+Zc5nTy77rK953oqHAbZced1fkK+9cPrs8RTGYL90VTYczp5tWC/93xAcsomwlOpARXP/bMYe/fbJohDdSEtYH1WO6jhHkCXtzMKyJnbN90zUNLpBOumI85Krm2Fxs/ZzeD5d4tzFHJ2Oi/x9bzqr9ftq1gjkrmm5Gs9fF1vGrxqxt/Yllo+TemTKQxPL+eBnzgsZVzWYXhzJNBU9CLdzbxKrR3L5wl3SgJCzqbMbt1E/uMboi8h3DrO+tXYqPXzRQ15Kjk2kieSzfjha2tkrw3w+WuObytPl2zjjkqGRvlP3nwOy4TfeXnpegJhbP8YzE8P4+f44k5C9iMRoZjs8eHdxfzfnj/l99Jjgrh582t+H7DZuZ03PfFd/xcovH9xavYTEqXP8gclVzbI1//yJyRxU2tzFHhNv6679+c/+cr1zFHRWr/lICHv+H8X/1pKXqCcv7Pifwfn7sA0WRO+3t9mLGM8793jshftP3U0oI5jZt5+8+dLWvjGWtWYqNHbP8VC+Xtv/B75ows7Gphjkpu+9+5kFfq/rR5FXNUctv/oZX8HG80zoc7nu0/wWQML27k3+2lxq9ZkrgMx7ZIDz5t+4n3280fyPr/Kn8Dqw5O/f+dFi4Pztjm9nyP9mg7/Ak3c1RybTM7XmcPA+2RlaKjkv0Gc7t4Ppbm4Feio5K9flf2PMH+1R54iWXBzh1/WsXxx+t/COmc8SeZ3Ixg6M1tjp9FFJGL4jLQDkCayXvzJqhYZsckwC7QvO0p62y+apnvhDRLsV8oF85kqk2xv7nyQJI18xs8LQvJI+7T4mc8H0LuSelpKpN9kv/Nk/FSOn+kkErLM4QSSJ6s7sXPJYcnmY4iJpakl+/Dn3ZjqZD0JMf3SSMqBCXnRH68tPRZKEkpyOX8I6ItnKQblJx/OBlhU+mUIyUfIdq+l0A1uunEUpRiP9LLPvx3DCSieVWX0wiI21MK+MwsE+OfTjOHheCL8xTwEn+Fgh2L4E/QbJEcgXiMKVsoR0c+epPTMi6U1yeZKMjqmjkeO1c0ViCv9ovbB+lvrnQ2zW/4fL9oHn9xe/GY+fOzgajIP1HIn7bvLeEXHZ8cIto3H8FYfOv8xe0DvfAPijZfL/yl7xbbRvsne+FP+ym33v4kX85Hph/01n8y2/P+IO8/mQzJlLZfpkJTKKQ+SXL7gv6fou9L/T+3sjIHXTcaReEAxB59hNg2r98EGyvk409cNo6g1/FHYCkccqXLCmmc2/r4ufNBYFXlf/scgbALzy/sut98B0Kpp4KA2fqrVE1ZqZsEhUILte4AmTxZoayGSj0cKqq8zJRAWZtGM56t2eq1u0OppFiNrGSY1ngJFKPCP8/+dDYjtzmNUwukgw72GVBu5JllObizUGrk2SqrTYfI+KsURpQa9oBKoUGVcbJMnmxUV8CuGwyrpgY2Tb0ka6ZtyvUjoFPaUG0YAb3SkrOfAoOsXHEywLI72ye3guxw0TbCNkGWS4I4jrTxbKcUo5Jlz/mPsvEYmYkle8iqJ1M15eFWivtQY7xjhFQBlyScpVoH+plqUWMoRR9juUy6PMzaF1aNCaNs9bBqSFabsSlwQDkvrLZX2SB2s8zlP6WKr/UfXD1EJj2lm8uUal58jmJUsvz5jeigah7Hc0T90GzrKxSsGjEVDaQlmv379JeksypR6UMVkPvaHBhgd8ps4yqq4NAbMK66mil2ciW30wbxc+3Xvy/7tzLHV6D4DfY9hgxksRYSfzFuhXDIsEF5/IEDBnPF2LSRQ+T8tRpM7NcHWrUK+w7OyrJVSgUqbRYMrSxDndOOfmUO9lmG/5g+VbAb9ditrpot4eTynzqCn3/fQYX8Dx0h8h9ayP/goZz/oUML+R84kPM/fMhgOX+NBpP69OHtX99P3v5mC4aVlrHK0f/H3lfAyVUl63/tOt3j7pmJu7sTgwgJgQQSLLjL4i67wOIuQRISYpCEGHF3m/hk3N1n2nX+v1On+3b3TODte/D27R+m2NnudF35uvrcc+vWqa+qk76N/SNjEaJQYUBEPILlfvghwrQk/vuPjk7j+P3Gz9QEzuqZENOt3fhheStMxkX1Dhz/ra0YEc6rFY8M7x/Q/VkpVqCXPp2WQXvpeweM8VBZKOJVCdR5OULBOoH7qMsJ6s7UPyhaza5jXcD12ymINw2NVo/wfO7Dn6DlbKYwmmv8HRI35a0wUXvmL79fAGrVpN+cP/+M4moV/e6/v6p05Kz8AdLa6oDL8D7ctl2AOAzSoGeErsutbgPMza/CZT8JsTQZKv0rkEh5zQW2FNTS9DyczmzIZH2g078Ksaf5l92RjbqmFyjBVqUYg9Dg54h2zMRoPYLq5n/C5W5BsGYWIoIeIMohk3rTZlQ0syUXF6KCbkFk0A0ejK0obVlKSz8SkQrJwQ8gXD1GaEaY3fgZqkx7qSFh97DHEazoIURYztS+jzrreaqj0j/icXplwpaCjta8j0ZbIXVPHhb5MJQS3nywzlaE/VWfUxG4JM0AjIpcBKmnCWKx6Tz2VS+D1W1CD/0YjAifK+A/23iYln4Y3uHhUzA4jHedZf/eU7MNR+v3Qy5WYFrMteip7ys0I1xbtgEZjWepdsq8xLlI1SYLEZZvC9ciy1CAGGUEFqXORbSS04LZUtCnuetQZKpEl6BE3Jd+LTkrTAqMVfgoexOqrc0YHJaO+9KnCU0ET9YX4tPsXfQkPCW2FxaljRYcm1/KLuHrnMN0o1zQaTCuS+4n4P8u5yR+LDhH5dcf7DkS42L5jZSVcn/v7CHsKs2lOinPDhiH3uExwhP+a0f3UpJtkj4YLw+bgEQdTyhlSxEvHdiDnIY69I6MxkujxpGzwiSnrg6v7d9HBdtGJSXhqVGjoJRy/MdKSvHegSMUWZjerQvuGTZYcCi2XMrG10dPcfwD+2Juv54C/qXHzmDt2YtQyWS4f8xQjEnnid0Olwsf7zmKPVn5CNOo8cTkUegZxxNKWWTjza37kVFSQYmwz04bi4RQjp8tBb2+eS/yaurRMy4Kz149FsFqjj+3ug5vbN2PqmYDRqQl4fFJoyixl8nxwlJ8uPsIRVSu6dUFd40aDLHHe/nlUjYWH/HgHxSIf8nJM/jp/EWopDI8MHIoxqb58H9w5Ah25eUjTK3G02NGo3d0tGD/1w/uw+nKCrL/S6PHIVHvZ/8ju5DTWEddpl8aNt5n/6ZavHpyNypNLRgVm4Kn+48V7H+8poiWftj4uSaxJ+7pOkKw/7byC/gu/xDhn58yBLMTBwj415QcxJaKU5SgfVvqRAwL544MW4pcUbIZJ+ovQC8Pwq3Js5AexOcYVoJ/delKSrKNVERifuJNiFDwLvBsKWhT+WLU2EoRp0rDNbGLoJbyhN56WyEOVn9GBR4T1AMxPPIu4fqtsZzCxYbP4HAZkBg0BV2DbxOu3zrTZpQ3s/wZN2KCbkaU3/zTYvwKRtMqoi8H6x6HWjXhv5w//2w5K/cenP27c1Y+H7XuL5mz0uGsdEiHdEiHdMhfVv6dzsrdB+b8bmfly9Fr/5L3t46clT9IXNa9lBQmEodAormVCr8xYb6gw7IOTnsGxNJEKDS3EHWP6xwwmb6H05kHmawn1Or5whOK221Ek/FbOF1VUClGIEjNm+8xYWyfOsMS2kavngqtkle3ZWJ2FKDKwHreuBGlnQ2N3Lf80GQ9h0rTL5CIFEjQzYNK6qMMV5oOocp8BHJJMNL18yCX6AT8BYZtqLVeQpAsFl31c6ggHH1nllzXuBFN9lKEKzuhu36agJ/RklmjNFZCP0nTG111vHAXE9bj50j9Ntqmh34IOvlRJqutFThat4+Wg1hDwji1jw6aZ8zH8foTkInlmBA5FmEKH2X4dMNFZDRlQifV4OrYcdBK1QL+XdWncLmlGDGqMMyMHQm5J0LCnkjXlx1DkakWnYNiMT1uoBAhYU0JVxYeQ43VQNVnJ8bwSBMT1uNnef5JylGZFNcVQyJ4FIdJQUs9VuefpSfj2Sm90S2EP8UyyaipwKbCy1TQbUHXfojT+iabvcUF2FdSSE/mt/XuT4XHvPh/zrqMM5WVSNTrsbBPX6Le8nHgwsqz56k7cbfICFzfp6fwhM5yPJafPEsMmSHJCQHLOIwts+LYWdrmqu5pAZThwpoGrD1xkc47Y2B3dInxUU/PFVVi25lsKGRS3DCiN2JCfPgPXirEoctFCNEoceOYftCpffi3nLyM80VViA/T44bRfWh/L/6fDp1HYXUjusRF4NphPYUIiclqx8oDZ1HTbMTg9ARM7OvDX9diwqqDZ4m6PKF3GgalB+Jfd5wlV3vwx/rhL6nEL+eyKUJzw9DeiA324d+fU4gDuUUIUSuxcGg/Woby4t9w8TLOlnPq9YIBfQLsv+L8eUq47R4Rget79vLZ327H0gsZVJqfUZqnduoc0MWZNaFk1OXJiZ1pyU8YP4Z6rCk4Q8uc1yb1RtdgH2X7fGMptlVcoPEzN2kwYlQ+yvaxuss40XAZepkGs+NHIUjmG/8H644j11CISGU4JkePpYKI3uv3UO1uuubi1EkYFjZWGP+/df1anPXIavoRDrcJidqxiFbz6I93/qk0/OiZf66F1m/+sdpOw2j5GWKREjrNLZBKeYSWsFj3wmXbR8vgUr/5888mrb+zc3LrX7iCbUdk5Q8Ql3k1nM3PeHw/N0SSJMjCN0Ik1sDS8iZsxk8FnVQ+DJqwFbRG29hwB6xWVpOA5X04oVYvQHDIP+FutaGsZgZsxPphg9OJcP2LCAm6B05XA7KrJsFJWfdsYnQhOfxLclrM9nycqZxFPXqYsLXlPjE/IkjRE/WWozhVdaeQa8FqrAyPWw+lNBqFLT/jVO1rnvyTVqpUe1X8D5CK1Thd9zkuNi6DCFLqAxKt6o9JcR/S0beWv4QC42Fa92bJuD3012BczGPUDO37osdQYy2k8zHd+Kg7MCRsNkzOFnyY8zgMzmZBtyDpCcpNqbJW4O2s52l/wi8S47EuLyNRnYJLzZl4O/s9AT8rm/96r5cRKg/Fruoj+DTvBypTz4YzK5v/Tt+noJIo8U3BZqwq2U06dgPrE5yGt/rcQ8d55txyHKi5RDpWfn1m3GA83WM27C4nbj78FbF+2M2H6R7rNgW3dBqBBpsZM3Z9RawfShJsdePjoXPJaclvrsOMbd/B7mZ5QyyvQoSfJt1MJfMPVxRj4Y41wso8K9u+ddZtlIOy+vIFPLVvO6QMI1qRpAvG5rkLoZHJ8c9DB/HFqZOQisVUO2RYQjy+n30dHef+9ZuwMzef+vSwZNB5fXrh9SkTYXc6ccN3q5FVXcvxu914auJo3D5sABpNFlz7yTJizbAsU7fbjQ9vnI6J3dNQUNOAGz78gXr6MGH7Lr9/HnrER+FYTgnu/nKdQCXWKhX46W8LEB0chHVHL+KVVTsJB7N/QngwVj1xI9QKOT7aeAjf7vTgb3WTY/HF/XPoOI9/swl7z3vwu9yYM7wXXpg/EXaHEze/vxrZ5T78j80ajVvGD0Cj0YK5/1yGBoafkjTdePf26eS0FFQ3YN4HP8Du9OFf9uA89EiIwrG8Etz5rR9+hQLrHl5AOTQ/nb6I5zfuJIxsjLB8mrV33wiNQo539h7CV0d9+IckJmDJjXPI/vdt2oQd+XmC/ef36oW/T7yKkqJnr1uBy/U+/M8NG4M7+w5Cg9WMqZu+Q52VJ5qzY34+9lpMSeyM/JY6XLvrG+pJ5MW/Zvyt6BkSg+N1+bj3+FIhMZlVSf5x1P2IUumxpeI43slaI4z/WFU4vhr0KFRSBVYUr8eGiu1UpZmN1R66zniu+8N07m8KP8D5ptPkoLhaXRgeNh7zkxb95vVrdTVhc/ECWF2sLxfLHXNhTMyb5LSw+ed0JaM+e5N6xegXs5rmH4v1ICrr5nnms1aqchsftZuqdTvNq+Fofjpg/lSEb6L5888WWVm0/3rIf0dkxW504Jsxa/6SkZW/rpv2B4rTyGl9PLnVjVZXIdy2fVTYzSZ0T+Y6p/0wXM5MauxltW71EjBpC7N5OdxuAyy2Y7A5Lngy7rmuwfAxvTaZN8FJdQxcgq6mhZ+/yrjKM1EwnYvWjSsNy0hX1LzUk7LKPndRVn+FkdOCLzd+R69c54bRUYJKM1s3d+FS4wqhlxD7/yrLaTTYcmFwVKPAeMjDAeKT66XmzcQaKDVfQLU1nzRe3dE6TnM+33QELc4mP50I+2t/Jt2Ruj00Ubo9/zHq9IEaTuHeXrWTXr06thZ/uO4ofba2jHcUZhM/u9lXWGuQ0XiJ/v1jKaf+svfs259tYgWyKqj54P6aS9z6nqTGDeUniGFxuqEIl1sq6Vhe3Td5nEq5tSyT6mGwBnTeZMjFHnryyryzdKPx6tjN4fscTp39+tJJSurkOtag0YZ1ebzr72cZvDMwOxe7WRY2N1KkhSWLfp3B92c3PIb/SGkpLtfWUvO+HbnMxlzHZNW5C5SHcrKkHJlVNXQsr+7Lw5zmuu1iDkVWCIdH9/WBk/T60/EL5Kgwp4j9MerxyiNnSbdsfwYfPR6dwWLDplOc3v3tLn5sdjx2zuLaRhzMLKR/f7/bD38rcCKnFDnltahoaMGec/n0GXNU6Hc8cgFGiw2n88txuSwQ/zc7+Tl2nM2hyArHwXXf7fbgP3aBHBUBP+s6fJjjX3qoDX6rDRszOP6vDvJjs3OxfYrqG3Egl+P/9ngg/mPFpeQEMvtvz88LsP/KC9z+JyrKcKkuEL/3N95SlEWRFWZ/79j68iLXrS444xk/bmH8LMvj3215wRE6l1dndFixqZx/txXFuwPGf5mlFsfqWWFCNzZXclo9c0bY97/Yko1icxka7HU413SK28TTlfxI/R5iCv3W9Vts2AWLizUOZWfi1++lBj7HVBhX0/zjnWPYNuWG5XzeMn7lN9e54G41wGD+8VfnT5fN19m5QzqESccy0B8i/hnwXuE39ytylFnLdpHz14/lmTwC9+Gf8QkiQIFWTwt45hy1/pqO9gtswOf+1WPyY/mqRLQ9oyug4aC/kCtxBfxs4uR6lx85kx/Nu33b/bj1XAGTrb94J1l/BoVP5ybKx5UCh0zn33Cwre7Kx/N2yW3THdbvZnUlnfeGyrfxt7/PSfJvviecz82/bdtuwN7tne4rB0T9b5CBx/M7l98P0B7jlb+b92Yt4Bf5nAzva8D5XLzuyq/hd/0Gfn9Gj+94fuf6Ffxt9yPn8Lfw/8r35p/9F/b/lfHza/Z3eo7Tdj9vrZ5f0wn4Pc62P37vePu18c+2d/+K7krXKJ2z1Xc9/tr122YP32e/Mf+g1fEb8+aV5sIr4/v/Xdiw/z39fdx/qnWQ/550RFb+AJGoFwZ0DIU4EmLFGIhEUqpYyz/nOrG0BySyHpBIkiGnrqNeGqAISuU0iMXBUCmGQiZh2fxsWYYPbL32Fv6qmkohVH9dWNDN9Bqpne1ZJhEL4dYo7VzSJQRd78HIPmcdbWWI0fLmdmm66wM6Lisl4YhWD4dYJEWa7moBH1tWClWkI1TRGXpZLGJVrDu0D3+qdhSUkiAkaHohWBYTQHHsH8JzbnrqhkIhVtHSkXdJZ2gYpzcODhstnIfrWmkdncm4SM5c4lox0TKHhnI689To0QG6EJkO/YK7QyKWYHLMEMH6TNdJG0d/8aow9A1OEc7E/n9sZE+iLPcPTUa8mtFSfQTTG5L5uSbF8jL5jJbq1d3UaSC9zk7txc/joaayif/6TryR3k1d+tJEzj5n+7LqtKyiKZObe/YN6PgbqdZgTGIyLT3M7cE6Svt0LDeC/SUF6zEoIc5Do+Y3r8md06BXKjEoMR6JIfoAjDcO5DhYjgpbAmGUYe+SyLwhXDdzAOuwzZev2B+72c0exPOJ5g7j1G32OduXVXed2q8L33+UB7+I05MjdBqM6JYMqUSMmUM9+EWgfJSu8RHoEh+BhHA9BnTy4Bdx/YQ+aZTr0r9TPOLD9Ryj1/6jOMaJfdJoCcof//UjuI7lqLTDP5jjv36wH35mf4kE03pz/DcN9rM/w6/VYFQat/+cPoH27xYVQX+MGTQ4Lt5nf0bvTuP2HxIbT0t5/vZf2IOfgy33sCXAAF0X1ugUmJ3Mric+RrzjZ24K3++6RD7GhPEjkmJKDP9Os+JG+I0fMcLkOgwO60pLP+MihwdcG8nqBPoLV0Shk5ZR5vkVwP7rEzyIKkL/1vWbqBkHmVjjWTLmui76OfTKclTazj8xnvlHp2VzZKvf/COHRjXzV+dPiYJf7382Yfkqv/fvryodOSt/gNDTiOVHWvqBOARS7f0QUcdkpnPCZlwMlyMDYkkiFEEPQexJHnO7zTAaPqTeQCzBVht0P0QinrzqdNWioeUDON3VUCuGQ8+SzrzJb44CWvphBZeC1VcjWOOrYdBiPYMKWvpxI1p7A4JVvuTbGvNeVBo3UYJbsv4WBMm7CPiLDBtRaT4MhSQY3UJuh1rKqZusQmVm4ypKsNXKYtEn9FbIJZze6HBbcKr+BzTaihGuTMeA0HmQiHm/FZOzEYdrV8HorEeipg8GhFwt4K+1VWB/zQbY3Cb00g9H72BfT6RCUy4O1OykCM2I8HHoHORLbD3TeBZH6o9RguDk6ElIVCcI+FllWrb0EyTVYm7CFIQreE8hl9tFlWopwVYZhpuSJkEr4/RSi9OOJYV7UGSqoQTbhSks+ZAHG1lOyle5+2jJZ1BYCjkr3uTDQkM9Lf2wBNsp8d0wLd4PY105vs85RZGY+Wl9MSzKl3y7qyQP6/MvEYX1jh4D0S00UsD/Y9ZF7C0pQIhSjQcGDEGsJ/mWPaV/k3FaSLB9YPAQKhHPxGx34LOjx5Ff34DuUZG4a8hAIfmzzmjC54dOCAm2zFnxJn8W1TXim4OnaClkcs90TO3FxwGTc8WVWHHkLC0BzR3SC4PTfMmr+y7lY/PpLEqQvXlMfyF5lZKAj1/CocxCBGtUuHPSEESHBAmRkGV7T+NCYRU5IHdMHiwk31psDizefhyF1Q3oGh+J2yYOhNyTfFvfYsJXO06gttlIeS43jOwjJN8W1TRiyZ5TtGR0Vd90TO4XiH/lIY7/umGB+PdezsfmMxz/LSP7C8nDDP+6M5ewP7cQIWoV7hk9BDF6D363G98dPy0k2N43YrDP/g4HPj1+HPkN9dQ1+u6BgwT715pN+OT0MaI3s55NC3v2E+xf0NJASz+sUNy0pC64Jrmbb/zUl2F5Hhs/bsxL7Y+hkb7xs786C1vKzxGFfmHKcHTWRQv4t1aewPH6LEqwXZA8AZFKz/hvdWFzxS7kGgsRpQjH7Php1HWc5hGXFdurNlCCbbw6CROjpkPmSb79reu3xV6CS43LYHebkKQdj+SgiQLGFutZWvph0dcY7Q0IUfnqpZgsO2A0ryWCgV57NxTy7gJ+l+VHWvqhBFvt/RB75s8/W87KLXvnQa719aT674rdaMfScav+kjkrHc5Kh3RIh3RIh/xl5d/prCzcO/93OyvLxq38S97fOnJW/gBhiaAu81K4rezJgBU1ehgiKX8iam21wWr4GC77aSoKpwz6G8QSTrl1u5vR3PI2HM4cyGW9oAt6HGIxf+pxOMtR1/w2nK5KqJUjERp0H0QiXi3WbL+MiuZP4Go1IFR9DcI1rKgaf2prtBxFecv3tJIcGzQPYZ4qtUzKjVtRatgEiUiJtOBbEaL0FK5rdSO7eQ0qTIehlISiV+gdCJLzJ1KX246MhmWotlyiqrWDwhdBJeWUSavLiMO136PeVoIoZRqGRyyATMyfOpvtddhdsxIt9nqkanthZMQsiD34yy3F2FH1M5XS7xcyFENCxwr4M1sysbN6Jz1tjY0ci77BPAzO5HDdSeyvOQ65WI6ZcVchPShFWE/fUH4QJxoyESzTYkHyFMSp+FMzY+YsLdiDi83FiFOF4c60SQiR826vLQ4LPsvegwJjDbrpYnFPl3FQSfhEUmluxoeZ+1FlacGwyBTc0XkYhdiZXG6swacXD1OS7DVJ3TA31dfJmbF+vss8TbkLN3XpgwmJvk7OG3OzsC4rE0qpFHf3G4R+0bzwG9t26Zkz2FdQiDC1Cg8NG4bkEP5kzJg9nx8+gYyyCiSFBOORMcOpszHht1ipGBsrqtY9NhIPjBsGlZw/GVc2GfDpdlY80IghaQm4bexAYq0wYQmui3ccJ+rvpH6dMWtIDwH/iawSrNx7hjDNGdUbo3vxKq9Mtp/IwpYjmVDKpbh5yiD0TPXgd7diza4zOHyuECE6Fe6cNQwJUR78Die+23gcF3IrER8VjLvnDEeIjuM3mKz4au0RFFY0oEtSJO6cPQxKBcdfXW/Al+uOoLbBgIHdE7Hgah/+3NJafLvpOI+sDOqC6aP88F8uwardHP/s0b0xuo8P/7ZT2dh8nOO/ZeJA9Erx4V9x6AwlBYdq1bh38jAkRgQL9v9y7wnqHJ0YFoyHJg6nbcj+Vis+2ncUeXX16B4diQfHDKOCeUwqWgxUaK7KYMDwxETcOciHn7GEPjl9FAa7Hdd06oK5XXsK+I9UFuG7y2z8uHFjl36YEO8bP1tKL+LnkvNEXb6j83D0CeXUX+oBVHwUh2uzaWzfmTYBCZ5O4A63A6tKduBySyFiVRFYmDSNCsfRPOI04eeK9ai0VCBRnYQZsbOgkCj+y+u33paHs/XfU2QlNWg8OuumCfgbLMdQ0ryM5p943fWI8Jt/DOaf0WL6iSIrbD5TKvoHzJ8u6x7P/PkIzZV/Rvm9VWhdHRVs/zzyfxFZcTJnxPi+518SQKSHPGI7RJIwmBofgsPC2C6tnpyVVARFMLqyFDW102F3nPMkk4mhVIxHRPgyqp9SWDUWThdreMYz7kO0dyEy5CXYnKW4WDkFbuo5xJPQkkJeQ2TQQjRbz+Bs1XwPDv6z9or6BqGqkSg3/oKMGkYP5CvsYkgxOn41guSdcKHhG1xo4A3H2Fq0XByEq5NWQSkJwe6K15FnYIyCVtpLL0/AdcnfsKL8WFH0CKqsuTQxMV2KZiBmJ74Gm8uCj3MfogqZ3n4+w8NnYGrMbai31eKtrCc9TQm5bm787RgZcRXyjHn4x+V/CHZlOQePd34cPfU9caj2JD7M/daDka3bS/DPPs8iQR2DH4q34/sixqzieSmsE+1XA59BsFyLVy6sws6qs3QspkvUhGPJ0IfJ8bj58GJcai6nGxtbuR8RmY5PBi+k5Z2rd35BNVa8ibW3pQ/FM72vQpmxCZO3fE1NCb3Jl68NmowFnfvjdE05rtuygkzvTYb8fvJcjI5LwYacy3h45y+e/A0R5UNsuX4h0kPD8PHRY3Rjo9EjElHew7Zbb6Fqqn/bsBWbLmVRcijTJYeFYMMdC4jmPH/xKlwsr+b4RSKMSk/GFwtnUY2Sme8uRa2HNcPkltH98cT0MSivb8Z1by2D1e7D/9zc8bh+ZB+cK6jAonfWCF2b2P8+eXA2hnVPwrbjWXh+8S++vA+JGD+8uACpsWH4ZsMxfLneg18sQpBGidV/v4Wckpc+34rtRy9z/GIREqJDsPzvC+nGfcerK3G5sJqcBXbMYX2S8d7j18JksWP+M0tR12QU8N84ZQAevnEMKuqaMe/576nnENuPyVMLJ+C68X1wPr8Ci94KxP/xI7MxrEcStp7KwrPfbfXZXyLGiqdvQqeYMHy54xg+3caZZQyjTqXE+qduJqfkydVbseW8x/5ibv+1Dy6g/ed9twoXK6ppjDD8ozsl48v5s6jGypQlS1Fj5KwfJosGDMCzY8egtKUZU9YsoaaKXt1royZiYc++OF1bjrlbOXvGO36WTrwBo2NTsLn0Ih4/uU7Ir2LjZ/34u5Cmi8A3eXvwZR5n/bBxHSRVYvWoR8hxeSfre+yrOS2M/zh1BD7u/xRt90bW6yg2FdGSKztmT31vPJz+6G9evwZHJdYW3UI9wwRd5GPoHnwtmqxncLJyQcD80z9qMcLUI2Awr0dVw/1++W9SJETtgELWGQ7DR3C2mT+VETto/vyzRVZu3HPj746srBi/4i8ZWfnrZuv8geIyr/L/F9DaALf9IBV98zkqXOd25sLluASnsxB2R4Zf1rsbVtsuuNyNMNuOw+mq8NO1otn0A71rNO+Em9qs+7Lla42cVlhj4u3efR1MRag2clpwqWFDAMeGrSlXGDnlN79lQwDTx+ZuQqX5OBWN8joqXOdGk70YddZcNNorUGnNFiYs9lpgOgGLqwXFpkw0O+oCGg+ebuDU4wvNp6izq7/uaP0ez+tRoe8P+4+9P1zHu87ureE3E34uznI4WsdppVsrfTo28TY7TMhozCKWhddR8epYfkqOoQIlpgZcaCrzNcRDKw7W5KDZbsapuhJUWlqEmwmTNYW8Q/DOslxqVufPElmVxymkP+dneqzPz8huimvzeGffn7L4q5dhwlgem/N499rVFxhN3TNCWlvRYLHgUHExFR3zOipeXX5dAy5X1aC4oQnny6oCGvqxwmZNZitOF5ajyu9GT+c/zmnS+y7kU2djf/ysTgrZ8USWpw8gZ/Iw/CwSwWTTYW+nb34uVt9k50mO/+f9fvjdrWgyWHD8YjGcTpfgqHh1RRUNyC6qQVl1Ey7lVwkOBzvm4bOFaDZacDanDNUNhgD8P+/j59ifkQ8rw++n23CA67Yeb49/y1GOf+PR9vbfmZFDn609Foif1aI5ms3t73VUvLr82gZcruT2P1deJYwRdsx9eYVoslhxsqyM2hz4jx/vb7yzKI9yXQJ0l8/z71FwifD7j591+fy3WVd8NvDqbXVjaxn/Tj+XcXozHyNuNDnMOF6XR+Pf66gQRrhRaq5GvrEMNbYaFJoKhF5cbJsLzedgdBp/8/otNh6iZqX+uuxm3pG8yri53fzjLY/QYlrdbv4xmnnXZ+cV5k+X/SD+jNKRYPs/l7/uN/8DxZsUGyhyj3mlV9z+yvuwS11GmfK/dg5xO51I+Ezc7pj+Oi8eLvSk5alEK7nC+VgTQ84g8DZT9ImUNWj0JNK22w9SSDxJem2PR/uKWXE5/2AeY8ZwncyzjU/jpxNLA5oHsm/g1XkrcvoLbU/Mj/ZDnCXRKiRX+F0InwTyK+gUnsRbuVjSbh/vsdoeU+S3vUIiCWhe1+r5zKtrh5FtzyIAV8IvlUIhbb8PO7pMIqYGgu334Z/J2Ks/hZfpZB4cnuRWQSfy7ce28SaIEv5W3/be/f1FJmNNOxlz6Ar4ZdIr7kP2l0jo+7Xfx4fj1/DLfxO/tB1+4Xtf4XzMTmT/K+BX/Ib92fZXGj/s9/R/DcD/W+PHu5+Y9TlvY3/P9t6k8AD8YslvjH9Zu2vNK1KR5DevX/4aeP1KfmX+4cwsmd/85Y+lVZjTrjQXimj+/PMJi2Ex6vL/+A9/3WWgDmflDxBJ0MOed2zSEEMk7QKxcjzlmCiDHvLTiSBVTIBY2o1KTatVnPIH8Ataq7kTYrEWasVQ6rzsmf5IF6Z7nF5DNdMhl8QJFEC2TYz+QdLFBt0IsYitp7OuxmyylSFOxynPnVizMU58pFeFJAQJWk4d7BV6J7166Yh6eRpi1ay5mgT9wzgtmi0bMV2iZhhCFZ2gk0Wim268R8cn1AGhsyGXqJGs6Y4EdZcAZ2d8FKteCfQPHo5QeYRApWTfcFLUbL5N5HhaM2efe+nJV0Xxjq4z4yZ5KMH8vyBZEMZGcqbBgqQp/HcgqrEIKZoYDA7tTv++NZVjZDd9dq7h4V2Rpo2hUuVXx3HKq9chuCllOFUGHRSeiH6h8dz6Ht2D3TmVcnpyd8Rp9B4KLD/fgz05dfTmrv2glsppucZLT17Ug1NOWY4KY7Owz9m+oUoVruvKabUPD+eMLXajY7ou4eEYl5JCN/r7Rg4RdAzPuLQUdI0Mp1Lx0/vwUubeG+rNw/tT1dUBqXHomxRDN2uv7v5J/BxT+ndBTKhOoCCzG9pdk/g55o7uDZVCRp976ck3jud5BbdM8eAXi+g1JEiFa0ZwFhTLUSH7S8R0vLT4cIzow/HfPov/RmzZhOEf2TcF6YnhiA7XYcrwboKOybwp/aFRydGvSxx6pXH87Jh0jmv5OSYN6UL7+uO/fTo/x9yxbfBLJZg/keO/dSJrpeCHX6vCjCEc/z2TPfiZjUUipMeEY3S3VPr3PeMC7T+2awq6RIcjVq/DjF6B9r9lSH9oFXIMjo9D/1hG/fXpvL/x9LSuiAvy4CfatggPDhgqUJjbjp/bu/Hxw3JUvGOOxo9CjWuTeD4Xy1HxH/9p2iiMiGDUZTHmJfKyAGzZlOEZHNoDKZpYalUxNNTzvT25KFdFTYZSovrN6zc1aAK00mi/uUSEfmF8jonXzacmqd75h5VHSNRzXUjQfX5zFnOIwqDT8CaHLEflSvNnh3SIv3TkrPxB4rYdg9t2ABCHQqKeB5FY6+sNZN0Gl+MMxJIEyJnO87TBCq+ZzGt8vYFUs4RENbfbQks/rDeQWjECGpUvUc3pakStcSVcrUYEq66CVsE7+zKxOspQZVxH0Yso7XSoZb4Ew2ZbNipMrPS2EolB10Ip9fVNqTafRqX5GFGX0/SzqJaCF3+h8SBqrJnQyWLQRX81JCLuQFGF2+ZdaLCVIlLZCV11vkRZh9uGUw070OJoRKq2J9KD+E2DiclpoGq1VrcFvfQDkazxJRHW2epwqO4Q4R8aOhQxKp4EyaTIVIYjdaehEMswPmoEQuS+/iHnmnJxuiELOpkW02KGQS319XZhlWozW0oQowzF9LhBFD3xhsw3lp5BkbEOXfUx1EHZi9/qcmB1QQblrbDeP6OjfRgbbWasyjtHuS0T49PRL5w5j1xKDc34KfcCBbpndeqOTnrfuntmXQ225OVQgu0N3XoiUsPHCJNjpaU4WFSEEJUK83r3hlYuF/DvyM7D+YoqxOt1uK5vT3IiCL/bjZ/PZKKgrgHdYyIxrVcXH36HEz8eO095K4M7JWBkV1/CYpPJQtViWW7L2F6d0DvZZ+OK+mZsPJpJ5506qCuSo3kXcCY5pbW09KOQSzFrZE+EB/vwn75cimMXixCsVWHW2N7kdHjx7zuVh0v5lYiN0GPGmJ6QSn34txzMRHFFAzonR2LSUD/8dgd+3nuB8lYGdEvAsN4pPvxGCy09sdyWMf06oWcnP/x1zdh0JJOWZaYOCcSfXVZLSz8sIjRreA9E6H34T+aV4khWMeG/bmgvaJQ+/Lsu5dGSW1yIDnMGBtp//XmP/aMjcXUPf/s7qKIty1sZmpCAMSk+/I1WC1ZmnqfclqtS0tAvyoe/1NiEn/Iu0PifmdIjYPxkNVVha3kmRVSuS+qHSFWQz/71BThWl4tguRqzEgaT0+3Ff6T+PLJbihCtDMOk6GHC+GdLqUfqD6HSUkkJtoNDh/xL16/V1Yzs5k2cuqwZiUiVj7pvcZSjwried13WTIdG7vveNvslGCy8dIJOMx9Sia/vkUuYP0MgZT3SPPPnny1nZe7umyHT/M+jRg6THT9O+P4vmbPS4ax0SId0SId0yF9W/p3Oypxdt/xuZ2XtxKV/yfvb/yp1+cCBA3j77bdx+vRpVFZWYv369Zg1a9Zv7rNv3z489thjuHTpEhISEvD888/j1ltvxX+yMHqys+UfcFt3UQa7VPcCxPJBpHO7G2FqehYO+0lIpMnQ6t+ERMaf0p3OUjQ0PQGnIxsyeR+EBv8TEgkvFGaxX0BV47NwuCqhUYxBTMirEHuiHU2W/ShpfJOKwoVrZiI++FGqlsuk3LAWRU1f05NNom4BEnQL6GmJ6MlN36Co5WeKrHQPvQdxWr7E4nLbcLLuE5SaDhIDaHDEI4hS8SUSq6sFe6o+QoXlIoJlsRgf/QhCFbxLbJO9Gr9UfII6WwliVGmYGvMAtDJOWS0zF+Dn8m8oUS9d2wcz426DQsKLsV1sPo+1ZWuoD8mQ0GGYETdbCEXvrTmETRXb6alvSvR4TI5my2m8YeCakp3YUX0UCrEcNyVNxcgIHlGyuRz4LG8zDtdeIgbEg51noncwf6JjCbNvXNqIsw0liNeE4rmeM5Gi5RGlMlMTXjqzGXkttegREoNX+l2DCCV/orvYUIWXTm5HpbkFo2JS8OKASdRYkMm+sgK8cXI/DHYbRU8e6z9SCPevybyAz8+cpATUW3v3x629+3nwt+Kz48fx44WLRG9lywJTO/NOvDanE2/tPIDdOfkI06jx7KSxGJjIozUsYfbVjbuRUVyBxFA9Xp41EakRPFpQ3tCMV9bsRn51PXrER+LFuRMRruNj5HJJNd5cvQdVjUYM65aEJ+eOhdoTLThyoRAf/3iQqL9ThnbD3bOGC0sxm/ZdwLLNJ6m8/fWT++P6yR787lZ8v+4YNu+5SNTfRTcMx7hhvBibze7Ep9/tw+GTeQjRq/HQovHo3Z3TalsMFrz3+U5cyCxHXEwwHr9vEpISeLSgsroZ7366HYUl9eiSFo3H778KYSHc/tn51fjgi52oqTNgUL9kPHTnBKg90ZrjGYX4/Pv9MJrtuGp0NyyaP0LAv3nPBSzbwO0/d2p/zJ3qw79k43FsOnARCrmMaNITBnvs73DigzUHsP9cPkJ1ajx+w1j0S+f2bzZZ8fcfd+NMQQVV3X3hholIifLZ/+X1u5HH7B8XiZdmT0REELf/pcpqvLp1D6pajBiRmoTnpoyFxhMt219UiLcOHqQ+QjO6dsOjw4f7xk/WBXx+7jiNl1t79setPfr7xs+lw1iTfw4qqQwP9xqFaYndhPH/zqXt2FeVjVCFBk/1nIr+YUkeer4Z72etxYXmQsSpwvFY1+uQpOFzTK21DosLl6PMXIFUbRLuSFmAYE+08reu32LjCRyp/Yr6gHXWT8CQ8NsEWnOpYR0Kmr6lBNpk3U1I0t0kzD91LR+hybQKIpEKkfrHoVNfI8yfjpa/w+WZP2W6FyHxzJ9/Nvm9SbLuv3CC7f9qZGXr1q04fPgwBgwYgNmzZ/+XzkphYSF69uyJe+65B3fccQd2796NRx55BFu2bMHkyXzt9T8xsuJofgFu80pPFjwbTDLII3ZCJI1Hc908OO1HPewdlnQYipDIQ4BIjsrqUdTQ0KuTybojKmI7XO565FWO9GP9iKFTTUd8+GdUY+VC5fSAjPs4/YPksNSa9+JsNVsb9knPiLcQo52BvKYfcL7+Hc+nPNQ7JvZbhKn64ljN28hu9rKWxLTMMytpJbSyGKwreQJl5nMCPVklCcYtnZZSot0Xufeg2VEj6CKVKViU+gF1Vv5n1kOwua2CrnfwMNyU9AjKzCV4PfMlgfHD5JqYmeSwZDSex9vZnwTgv6/T7RgVMRQ/l+3F4oL1Abq3+zyC7vpUvJe1DhvKj3nomSwpUoJlQ59AjCoU9xz/FqcbCol9wfIAguUabBjzKCXgTtv5GSrMTYKuiy4Ka8ffiXqbGRM2fQGT0y7QgtmN4aMRs3C5oQZXb1hKn3PrAw/1HU4Oy+6ifCzawtlXXnl/4lRc26U7vjudgdf37fOzPrBq3g0YGBeHl3/ZjdUZF4RzsSTZX+69BfHBetz+7U84WVAmYAzRqLD1sdsocXTGm0tR2dhCLBWWi9E5JgKrH7uROhPPfOk7mG0O4ZhXDeiMN2+fRjVKFr6ynONnjBlGq50xlByWQxn5+Nu7gfhfuncqpo7sjtWbT+OjJYHN5T5/fT56d43Du1/sxIbt52jJgZJSZRIs//h2xETp8ejzq3H2QimnJ4tFCNar8cOXd1By68J7v0F1jQ9/akoEFr9/M5qazbjxnq9hsTqE/caN6IKXnpiOvKJa3PH49z78IuCWucPIYTl8Oh9PvBWI/8UHpmLK6O5YuS0DH/zA7e/9Db584Qb06RyHN5bvxtoDFwT8MqkYP716C2LD9bjr059wKq9MwMjsv+mF2ygf5pp3l6Kyyc/+0RH48cEb0WC2YNKn31GFYa/9p3bvjPdmT6MmlDN+8Njfg+OBIUPJYdldnI9F29cFjp9x03Bteg98m3UCr2fsChg/q69aiIERCXj9/Cb8WHSalh7Z+Ge5LhvGP4A4dQgey/gCZxs564flerHxv3zY05T4/fi5F1FnaxB0iep4/KPXc795/dZZ87G66B4PG4h/g0FhCzEk4lZUm/fhdPUDAfj7RLyBOO101BsWo7rp5YD5JzlyPdSKQbA3vwCXeUXA/KmI2AWxlDu8f6bIyrU7b/vdkZX1V333l4ys/K+6aVOnTsXrr7+Oa6+99l/a/osvvkBKSgreffdddOvWDQ888ACuu+46vP++l4P/nyluK6P1eal87NUGt/0EdSB12g/50YxdaHXXwum8TNRll6s4QOdwXIDbXQ+LPYO6kvrTmg10DqDZyjsd+zLyW9Fg5p2J68z7PUmyXhGj1sy7BVeYfBO1t2ZKlYXTgkuMB/2Oxxr52VFtOQtXqwOl5jMB9GSzqwF11gI02ivR5KgK0LFOrWZGXTbnwOo2B+gut5yi95ktlwIcFSZnmzgt+EzjBZo0vcKS9840ccrn8XpO4fQKSx483ci75h6s5cfk6FupENy5pgI43E6cqC/wa0LYSmX0cw3VKDE2oNTUGKDLbK5Co92Ms3XlVArdnxa8uyyXn6u8qI31gR3FXLenqBBSP8YJu3HsKS6g97vy8/2sz2uVHCgqon/vzs4POJfN6cKp4nLqIHwsvzQAY53RjJyqOpTWNaGsvlmg97LXy+U1RLs9X1hJBd/8j3ngPD//8UvFdJP3PqKwl/0ZefSeUYfZTVfALxLhyFmO/9DJvED7i8U4dqaQ2+R4rtAwkp3LbnfiXGYpHA4XMs6V+OjJ7lY0NJpQUFSLssomVFQF4s/Nr0FziwWXsithMtsD9jt8guM/da4oEH8rOz/HdiSjEBJJG/wZHP/BDJ/9SScW4eh5bv99Z1n3Zz/7M9w55XA4XTieUxqAsc5gRk5FHUrqm1DW0Mb+Fdz+Z8sqYbQF2n9PDj//4ZLiduNnZz7Hv6ckP4D9ReOnxDN+ynPbjB8xDlRw3Z7KbBr3dC60wuZ24nR9MY3/jMY8gZ7MXhvsBhQYK1Ftq0WNrS5AV2QugYFRl3/j+i01sXIBgd+Ad19nLQYOtJt/aswH6J3Bwuco3zeQwGj1dES37rjC/Mk7Uf/Z5HcxgVr5319V/qNiSkePHsXEib4+E0xYRIV9/mtis9nIa/X/+3cLi5b4nnc8Qv1/ZACxc9qoxCH0116kEIk1kLTTMRYDr6gpJZ1/J1UxZHR+RlcMbkMqZE9ZPKzL2D+8MRmECUjhOaZSom+HXy7WEQNIJuKJqv6ikuiglLRPgGPMAblYCY006Ar78O21Um1g91iIofVsr5XxELo/fq2Uf6aXaQMcGXYDCPL0OGFPi4G0ZlBDQkbD9Fak9Re9TAW9nIe02zpAjI0RrAjUsSMHK7gdQpSqgBolXmYP1ykD7S/y7ReqUlFkxB9/iHc/taodIVGvUlKExVuR1l+C1Uqhv04AfrEIarkcwZpAHTu2zvOZXtsGP4t2BHEcem17/N79gnUqoTcPx++G3rNfsE4tNBX0SpBWBalUDKWyPX5dkAo67RXws++rlEEf1AY/w+H5jO3bDr+O42DbtO2s7D0P+46B+FvJFkwYs6ktfva9pb9hf/b7XNH+CjnpA/B7fk/a90rjR+UdP6p2nZWF8aNQtxs/3nEaolC3G/9sjLPxr7zC+GfXhkYSeK0RFlaYUqL4zetXKdEF1Fjh0Va9MP/gV+Yfxv7hTCCvuIR57krzp6jNsf5U1OXf+fdXlf8oZ6WqqgpRUb4McSbs38wBsVgsV9znjTfeoPCa94/lufy7Rap7XqAfMxEpxkKs4MwYjf7vAReiQn07JNJOkEgioAt6LOA4wfqXIBapoJIPhM7TkZSLGDEhr9O7MM10aOU+9g/LrE8IeYreM5qgSupjFsgkIUgJ5rTkbqH3QOrJeWGik3dCko4vyQ2KeJg6LHslTj0McZqhhH9s9EMB+PsEz0KIIgFaaQhGRnir5XKZGL0IMrECSeou6BM8wg+9GLPiFvFzhQ5BqqZTQM2H2fG86/PU6AkIU/jYG4zZMzOW05JvTJ4KtYfhwCRJE0PMBiYPpM8IeCIdGtYVQ8K6Ev6ne1wTcHnPSxqKZG0EwpVa3NeVd2v2ylO9roJSIsOA8Hhck9Tdz8YivDyQL0OyTsn9I3xN1ljp86cHcVrz7X36IzbIF5oNValxb3/erZnlqHhzFph0Dg/H3F6cuvzMpDECw4QJq4Q6Oi2Z8L8wfVzAjXTBsL5IiQhFeJAG93gox155YsYYyifpkxqLyQN9zf3YTfqp68cL1N9efuwZxox54LpR3DZT+yM6zIefOSE3z+D4F93AGGk+/KkJ4bhmAu/6++CicQLDh+zfP4X+GP5H75kYgH/O9P5IjA9FaIgGt8zzNdlkct/tY6FQyNCzWxwmjOoaYP+H7+L03Akju6JHZz/8cinuuZn/jjdM60+0Zn/8C2dx/CxHxcvwYdIpPpyYSUweu34M1XfxyoieyRjRi9v/2bmB9p8/ui+So7j9750QaP8nrx4DpUyK/vGxxAzyt/8LU7j9p3fpgn4xPvyMGfbkSG7/23sNEBpYMglVqnFvX36OR3qNEhg+TDrrw4WO3k/2mAKZXz2YkZHpGBmVTvgf6eLthMxldvxIJGoiESzXYXYczxnxysKkudTK4reu33TdeEQpfdeGVKTAsIi76H2KfiGUngaoTOSSEHQK5vuxHBWxyPeAo5B1RbCGzx8y3QsB8yebO9lfh3TI/wkbiF04/1XOSufOnXHbbbfhmWeeET775ZdfcPXVV8NsNkPleQJpG1lhf15hjg1zWP7da3qtziJa+mHUZbGCTXC+yc/JKtbaz0EiTYRUPkKgB3L8x+Ag6nIPKPycEJaQxsKkrOQ+W9dVyNIFnbvVjibzbqIu65QjoJD6bp5Ot5FyV9j+EWp2E/Q9oViddag2H4FErEC0ejSkYp89W+xltPSjkOgRr2E1HXz4a615qLbmQCeLRoKaJyx6pcR0EfW2MspXiVN3CXjyzjacJepjsqYLopS+9WcnW6ZpPgOry4Juuh4IlfvomWanBRlN5wl/v5DeQmSFSYO9BacbLhN1eXBYz4CnxjJzHS39sK6zw8K7BRTDym6pRGZzOWJVIRgclhqA/1RdMQoM9eiqj0LvUB8FmSrCVuSjxmLEgIh4pOnDBZ3d5cKu0jyino6MTQq4wbCk292FLEegFeOTUuhJWrCjyYT9hUV0g5rQKVXoI8OEVUQ9WVJG1OWx6bxGiVfY8sKl8mrEheoxNDUhAP/pgjIUVjdSB+ReSb4bBS2dZBZR1+K+qbFIjfHZmC1vHDybD6PFjsE9khAd6nuSNpltOHimAK3uVgzvlyJEH5jUN5pw7GwhOQgjB3YS+viQ/Ssbce5SGfQ6FYYNSBXqozDJLahGdm41YqL16N87MQA/26ekrB5pKZHo5ueEMPwskba+wUjOS3KCH36HC4dP5RPWgX2SEOXnoLDPDp0uoN9vRP8U6PzxN5lo6YfhH9UvNQB/aU0TMnLKiLo8sneg/bPKapBZyu0/uHMb+xeWoaC2kTo4907ws39rKw7mFRF1mTkvnSLCAsbP7oJ8Gj8jEpMQGxQUOH6K+bLg+MTUwPFjMWJ/ZQE5yBPi0inR1islxnqcbigm6vLoqM4B4z/XUI7sljLK4eofkhaAP6slF+WWKiRp4pGmTfmXrl+2PFxoOErU5QTNAATJIn2/jduIGvM+wDP/yD1RFyZOVw3Naaw3UJByEsR+84/bM3+KWNRZwWtU/bvk35mzcvX2O353zsqWyV//JXNW/qOcldGjR6N///744IMPhM++++47SrJlP85/OnW5tdXJk2jbxpQ9Oi9j51/V8Z/G9T/Q8TCtt6W7v7DaKFTQ6QoY3QzHr+Bn+/k7MP7C2tB72TxtMbK18P+ujk2U8KzLtz8XSwbkxbTaCisvToWxfkXnrS/RXue+YqVSal3f2vrf1nnD/P4VUwX8bjd9fkWMLrdQ6OxKOi/j5V/VEUZ3639b580V8V82EfC73PT5lTC6nG6IJb+uk0ivjP/XdDRGXK2/rmOJrX8g/n+7/f/o8eNm15Povz3+/6fXL9NeaU74rfmH1ZaibJxfmSN/bf78szgrU7fd+budla1TFv8lnZX/qK7Lw4YNo0iKv+zcuZM+/0+WVncTHI0PotV+BBAFQap/FRLVDNK5nMUwNNwFl/MSROIoBIV8DJliOOls9rOorb8LTlcppNI0RIZ9DbmMRydaLDtR1vAY9QpSywciMfxLyDxFlKoMP6Co8Q24Wy0IVU1Cevg7kIg1NIHkNn6AouYllJUSH3Q9uoU9Q08pLGn2ZPXfUWLcRpVte4TehW4hN/voyZUvocJ8GnKxBiMiH0cnHc8darJXYkPZa6i15UMrDcO02KeQqOHh5zJzLlaXvIMmRw3CFXGYn/gUIpV8Ge5i82msKP4SJpcBKZrOuC3lEeg9tOY9NfuwqmQNNTPsH9IPd6UuglLC8g1a8UPJRmwo301r95OiRmJR6lxyQFjS7LtZq7GnOoMq296SPBnzksYL9OTnzq3Eyfp8aKRKPNV9JibHcoylpgY8cmINslqqEKkMwlsDZmNwOH+CPF9fgQePrEOZuRmdgsLw6Yg5SNdzWjOLnDxx+Bc02iwYEBGHz8fOQqSah7GXXzqLfxzZTz2CJqek490JU4nWzPC/c/gwvj59ijIP5vfqjRfHjqWndNa997ktu7D5YhYtOTw0eijuHD5IoCc/vmoLjuWXQKtU4MUZ43G1pzptWX0THv1uM7IrahGh0+DNBVMxKI3b+FJRFZ5avAUV9S1Ijg7BO3dNp8aCTA6eycerX29Hs9GK3umxePOBa4Qibut3nMOn3++nwmujB6Xj+QemEC2Y4V+87CDWrD9FN8wZU/rgwTvHk0PAkmbfeX8bdu/NpCWfW28eifnX8yWKlhYLXntlPc5kFEGtUeCRR6dg/AReKKyyvBGvPL8W+bnVCAvX4pkXZ6FPf06rzb5cgdeeX4vqqmYkJIXhpb9fh6QUbv+jh3Pw9pub0dJsQY+e8Xjx1dkIC+cRiE0bM/DF53tgszkwYmQXPP3MNVB58H/93QH8uPYk4Z9+dV88cM8Ejt/hxD8/2Y5dBy4Tk+e2+cNx42yOn/UjeuHDzTh1sQQatQJP3D4Bk0ZwWnBZbROe/HQTFcSLCNbgtTunYWBXzxgvrsITS7agoqEFKZEheHfRdHSK5vbfezkfz63dQb9tv8RYfHDj1YjQcfuvzDiHt3YfpPFzVec0vDV9Mi0R0vg5egiLz/B+Pjf26I0XR4+j8WNzOfH04W3YWJhJbJ9H+o7EPb04/ia7BY8cX4ujNYXQyhR4pd80XJPAl7jKzQ14+uxy5BoqEa4Iwiu952FAKC8UmWsoxjvZX6PG1oA4VRSe6nonNQZlcqEpAz/Q9Wuk63dR6kPC9XuucTMOVn8FR6sNaUHDMTn2ScjFKsJ/ufFj5DexrsutSNbNQa+wJ2n+cbfaUN7wJJrMP1NRzCjdY4jQ3SfMn9bG++G2H6b5U6F/HdKAZfAO6ZD/5ZwVo9GIs2fP0p+Xmszel5SU0L/Zcs/NN/MbJhNGWS4oKMCTTz6JrKwsfPbZZ1izZg0effRR/CeLs/l5tNqPeTq9tsDZ9BjcDt4kzdBwB1zOLHrPmEAtDbdR7RXmaFTX3Qinq5wfw1mI6roF9FRid5ahuO4uuNxNpDPbz6C0npf0b7GeREHDCx5aM2t6txNFjW+RjjUNK2xejFY40AonSg0rUNzCGyBmNnyDYuNWehpiHVPP13+MChPP4j9c/Q4qzYyRw5g0Ruyreg2NNs702FD2Cuo8703OBqwvfYmaFbIKl98XvUZ1GJjU2yqxrOg1ioo02GvxbeH75KgwKTblYXnRp/Q+x5CLpUXLYKNmhq3IaDyD1aU/kW5v7XGsLdsBZ6uLN2qrOoBfKjmLaXnRDuyuzvCwfRxYXLAZx+p4k7o3M3/GqYYCchCMTiteOr8G+QbWsRp46MRqYv8wqbMacf+xlWiym2F1OnDbgVWosPCE7CJjAxYdWE03uTJjM+7dtx5NNp4ndbauAo8c5M3aTlaW4fkDu2B2MhsDO4ry8MZRzrhal5mJz0+egMPtpqfcZefOYqln7H968Dg2XrxMT9Osrsrbew5hby5nc7yyYRdOFJTS8QxWG576cRtyq7ldH/l2E/KqPDY2mPHg1xuo9gfrmvzAx6yTLbdxSU0THvxkPUUVKuta8NTHm9BiZJ25QdVjX/qSd6U+d7kM7yzeBYvNwZk0p/Lw2XLO2Ni+5xJ++PE4LROxCMT6LWewbjNnai374Qh27cmk4zPH5auv9+Hocc5w+eC9rTh7hrOMTEYb/vH6RhQV1pLupWd/QmF+Db1vqDfhhadWk3PDHI1nHluJ2hpu//LSBjz3t1V0fOa8vPLCWhhauP2zLpfjzdd507sLF0rxwfvbYbVy/EcO5+CrLzmrZPuui1ix+piA/+eNGVi/gTe7XLr6KHbuv0zHZ3Vhvlh6AEdPcfz//HoXTmdy+xvNNrzy6VYUlHKbP/HJRuSX8/d1zWY8+tHP5Nww+9/3xXpUNXrGeF0T7v+C27+8sQUPr9iMZjO3//mySvpNmZwqLcdL2/ZQM0OGf1dOPt7ew5v2rcvKxGen2fhx0fj5/sJZLD1/hnQfnzuCnwsyafywjt9vnt6HPaUc/4sZW3C8lrPUGIvtbyd+Rm4Lt/lTZ5ahwMjHP2PC/S1jKTn3Npcdr2V+ijpbI+kqLbV4LfMzfv3aavFN4QfkqHiv3+8LP+e/k/ki1V1yUNf3VuQbjuJg9dekKzVuQm7Tt3B75p/CltUoaGElHYCalg/RZGa0bBdaW62oav4HWiy7SWdrfhZuKu/A509b0yPC/Plnkw420H+os3Lq1Cn069eP/piwYm/s/Ysvvkj/ZoXivI4LE0ZbZjVVWDSlT58+RGH++uuv/+UaK/9X4rafDOiCTARCB8u7sMHlvBxAQUarCS5HLpxORulkE4U3s55NsGUe6jKj6ToCujWb7Zw6aLCdafOzuWGwnaB3TbYz1HrdJ2I02/jNstbCXv1ZOFLUWXm31yoLr6PiFfa+1noZTrcdtbZCPwpjKxytFtTbStBgr4bFZQigNzY5amF2tqDMXEShZR9CNwpNnHqZZ8wPSPhjx8wx8Ikpu6UgYK2dbZfdwh2lC03MGfHhZ9tdauHU07MNRQEMC+bQXG4ug93lRE5Lta8zLlphdtmRb6hFiamJnki9+7Ftys3NaLCZcKmhmhwOwfqtrcio5U5lRlVFQHie7X+y0qOrrAgI67PtzlRWCDcp/wVXtt2Zskp6f7qoPKALLzvmxfJqisbkVNYJ9Fj2OavdwYrAldc1kdMi0GOZk9JgQKPRjOziGlqaEPC7W3E+j5/rYk5lIH53K85d5vgvXi4PWFZh4fhLWRz/hYtlAr2X7C8R41Im3+/8eV5HRfhNW1uRlVVJTg1zVLw69rnF4kBJYR0qy5vIGfGnJzMnpbnJhLzcKjidbl+3Y1crMi+xekRA5qXyQFYP+27nS+k9w9MO/2WO/3xme/wXPbqzWey6C7T/5YIqisbklvnsT/htDhRWNlDEizkjAfZv5PZnXZnb2v9sCbf/2fLK9uOnlNvx9BXGDxtTTE5WlwWMf5ZQftozJlmX8IDxwzooN1RSNDLPyDpD+65fi8uOQlMNqm11MDhNfpRnN2ptDWhxGFFqKf7V67fSktmOVVhu4eUFGqzn2lGXGzxzjNnK5ij/jAMpzDY+p7lYrl+b+dPlOIc/o3Q4K/+hzsrYsWM97doD/5YsYcsUoFdWsbbtPmfOnKGk2fz8/P/46rVMRBIWFm7TTZWaDcqvQMsTQSyJ81SqbbOPSA2xWA+5tC2jiXVy5cmfCiqU5E9dlkAh5RVlVdK4NrRCEZSe5FutLC5gImEVJjUe5lCQjDVdCxwKWlk0FX7jtMS2tOBIBElDAqjETGQiBZQSDTUqDPheECFEzhNUwxVhAZMuO0aEgm8fqQwLcDrYfhFKzg6KVYW3oS67Ea3kYelYdWhAR1om0apgCpeHyBmt0x8LEKPSI1KpbdeRViWRQSdTIV7rSwokjCIR4jR8fThepw/AyHIEEnR8+7g2OnYu1rSOSWJIcAD1lOUexOk9xwzRB+joOwXriCHEipD5qwh/SBDC9dqAmihMGBOIUZpj/RJOvfijw/gSSkykLhC/WITYKI4/OkpPibXCuURAVCQ/VkxscMD5WIXY6CiPLia4XX5IVLSOOi/rg9vQmkVAZJQOoeHadvkmjOasDVIhKrqN/cUiRHoxRusDHAtWVyU2JvjX8XswxkX/Ov7YSH07/IxVxJaLWMJtW/uzhGS2pNPW/iqP/eOC29s/Jpjbn/3m7cZPMP9u8Tpd+/HjyUtIDGozflrdiPckdidoQtqPH7UeMpEEwbJAWj/hVwYjRMaacQban1WG1kjVCPNcq759REISPEuyD5xjJNDL+Dyilsa2KUsggtozx8hlSe2oy3JP0TfWM63tXMjmyA7pkP9Y6vL/ryLVvw6IfBOsWH0LRHJO/dUGs4qsXsqhCGrdS5BI46jGQFjI234/gQzhoR9DJJJDJe+BSN3DfpOdBvGhvDBemHoKwtU8H4aJXBKBlBBG/QOSdAsRrOC5Gky08nSk6jmtsFfYfdDIfKyhGPVwpOhYJVxgZNQTUEh8jITuwXMQo+Ksn6tjn4LU0wKeybioe8hZUUuDMCPuXmEiZFVvr0t4BFKxDPHqZEyO5p2UmSjEStyUdC+9HxgyAENDfZRPvVyPm5J4R9drYsahS5CPkZCojsF18TyqdnvqNGIzeGVwaDdMjua01Gd6zCInwyvXJw6jdXmG/58D5lDjN2594KmeUxCrDqYaFX8fOFVwcphj8+7QGZBLJOgRGoWHevO8IiYaqRzvjLya3k9N7YwZaT5abaRag5dG8CaTt7Eooh8tldGT7xvMMT46djhVpPXKmLQUzO7DKaCshL7Or27HTUP7YnBKPOFnOSoKqQ//E7NY6wUd9BolnrtpovCUzmqyvH7bVLrBdk6KxKKZPhurlTK8dCengI8d0hlXjfThDwvR4OFbOf7rpg9A966+MZKaFI4Fc3lH4DtuHY3oaB+zbMigVEyZxKnLjz4+FUF+tVFmzR6Ivn2TCP8zL82EXO6J9omAex+8CpHReuh0Kjzy5DTBSWBVb596cSY5OGnp0Vhwy0jheCwf5cln+VgdNborxk/wUWdDQ7W47wGeXzVn1kB07+7Dn5IcgQUeevQdC0aRMyPg75+KqeN5XsdTd1wl1JNhMndyP/Tvzlk/r981DXKZz/6PzRtL9G5mf1Z639/+f1/A7d8tNhL3jvOzv1yGf1zHx/Hkrum4xo/WHKHV4LmrOE33tj790T/ab/yEheP+gfw4T/QfjQStz/5j41JxXRq3/6v9pwWM/4WdBmFIBLf/K71vgFzsw/9I12vIkQ+SaXBvp/nC9ctqsjzS+Raq7Myu3ynR1wZcvwuS76H36UEj0UXna6qqkYZgbBTXpepvRKiit6ALkqehs4e6HKV/MuAhLEg5DiEaXrJAoX8jYP6Uqm+FWP6fnaf4PxXmyv2eGiut/9df4P9QOhoZ/kHS6m5BqyOLuoaK/WjGTNyueric2RBL4om+7C9OJ6uUWQSZLC2gCykTm6MADlc1lLKukEp8heLYT2Z2ZFFvII28JyRidUC2fbP9ElEHdYoelEzrFdYDqMGWBYlIgRBF54BsfbvLiHpbHhWIC1H4HAYmZmcT6mzF0MmiECz30TOZNNvrUG+vRIQiDkEynzPBpMZaSdTHGFUiNFJtAP5SSxksLguS1UlQSBQBT4z5xhKKnKRpkwIYDHaXAzmGMsglMqRpYwOeDI0OK3IMlUTdTNUG2pEt7eQZahCrCka8JrDgXoW5BcWGBnTShQV0sWVS0NxA1OWuIREBheIokbC+lqinPSMiofb0DOI2duNiTTWF/ntFRQXUT2G5Khcrq4m63C06MmA5gOWqZFfWIlijQlpkWCB+oxn5VfXkpMSHBUYdWM5KaW0TJdj6dxFmUlzViPomI9UU8acgM/x5xbXUtbhLShQVYhPwu9zIyWf43eiaFh1QP4Ut62TnVhH1N61TVEA0wmi0oiC/Bjq9CsnJgZG1pkYTigvryElhERp/qaluRkVZIyXYehNovVJWWo/6eiNSUiPJufHHz87Fqtymp0eRMxOAP7eKoi9dOgfiZ7kqOflVkMtlSE+JDMRvtiG3uJaKx6XEB9q/0WBGfnk9YsJ0iItoY/9GA0rqmijBtq39i+oaqet1enR4QKE4WiarqYPRZkOP6ChyZgT8bjcu1FRThKVXZOD4sbLxU19F46d7aFTg+HFYkdVUTQXi0nSB9m+0G1FgrEGMMpiikP7CclZYvkqcOgqhfl3M6bexVqLZ0YRYVUK767fOVgCb24woZRpkfhRkNv802TIp+hKs6B4w/7hbrdTzjNWGUsp6BMw/bP50Oy5z6rKM92z6M7KBxm+5B1KNb77774rTZMOeq7/4X8Pa0NCABx98EJs2bYJYLMacOXPw4YcfQqv99S7YX331FVasWIGMjAwYDAY0NjYiODj4dx/3P5oN9P+1sEq1kvArVl5kVWnF4ki6ENuKWBxMS0JiUfuqkRJJKO9341fMjY5H/WMiqICcWNR24IuhkERQom5g/gqL0MigkoZDLJK3oxWymisqaRgU4vYXgFyshkYaSpVr2wpb9tFKQ6G4QkVMjVRHAWP2ZNYWv06qh1ysgEwcWCGURTpYiJpTJttiZMs6OoqUtA1hs2JuofKgK1amZcW0wuRB5Mi0Fb1MSSyhIFn7iqRhSjXlTbCqtm3xR6g1UEtlQtRGwC8SEWuI3XTa0lJZ9CZSo6FISVtaKqu5Eq7VXLEyqkYhJ13byqhMdGoFNc8LUrafAEPYEoarlaqqtsUfptdQdVbWoycAv1iEsGAN3IzG22aZht34w4M1FClpu2zCHJ7QYDWC/JwKr6jVcoSGqKHTtcfPIjIsuqO5wgSu16vR6nRDpZK1w8+KyikVUl/Uxh9/iOaKtGYW9WDf+0r4Wc2VcJ0aQVeorMvsFx6kblcZmIlOpaDoCGNxtZVQtYqWpTRtquDS+NGo+fjxc6b8xw9z1tuOHxb1i1RpPeO/jf0lcoQrtNC3qb5M+CUKujZY5dq2wpZ9WPNCtadJ4b96/aqloZC4WVXmtjRcMRTSK88/IsghZXMdFO1pzSI1RJKIP23lWq/83rwT9/9yzspNN91EuaQsb9ThcFDds7vuuouckV8TVgNtypQp9OdfI+33HretdDgrf4C0Okthb1gIuFiysAiSoMch1XJantN+Fi31C9HaypJpZdAGvwuFmi+RmC3bUVvPmoJZIRIFITJsCVRKvvxQZ1iC8saXKGgolUSjU+QKKGXp9FRT2PgWylt4Br5G1hU9o5dALgmHu9WBszVPosrM+wiFK4djQNRHkIiVsLsM2FvxCOptvMdOp6CZGBz5NE0aLfZKbCz7G1ocLJlPhCHhizAg7CbarsKSgzUlLxIDiJXfvzruUfTU8zBwZvNJrCh5H85WOxRiFW5OfgqdtDy0vq9mJ1aXcgpjsCwED6c/hRhVHOFfUbIWmyt5rxDWPO2Zro9QRU1WC+LNrKU4XMeT6/oHd8GLPe6AQiKHwWHB3858g0stPCF7euxgPNFtNjktZeZG3HNsCUrNDRTUfrDrRCxK41VlzzdU4M7DK9Bot0AmEuONgTMwI5GHz3eW5uKhQz8Tu0Irk2PxmOswNJrTar+/lIGXj+6hJ9xotRbLp12PtJAwwv/m4QNYnMGTA7uGR2DprDnkvDhcLjy68Rdsz+a9XkYkJ+KLOTOpqmmLxYq7lq3HubIq0l3XvydemTGRbpqsx89dX65FaX0zb4w4bQTumDBYoCczlk+TyUr1Ol6+eRKmDea02gMZ+Xjusy3UNZhVl33n4ZkY0I2H2tduPYMPv+H4I0K1eP+luUiO5/i/+GYfVq9lSeFAp5QIvP336+nm73S68PqrP+PggWzSDRiYgtf+fh1VlTUarHj20R+QdZEndU6d0Q8PP30N4a8qb8TT9yxFZVkjrTXcdv8EzFvEq8pmXyzDC/d9j5YmMzk7j712LcZf3Zd0R/dexhtPrILd5iTK80sfL0CfQZxWu3HVMXzx5hZyOsIidXjjq1uRmBrJ6ckf7sBPy47QdinpUXjj05sREqYl/P94cR0O7eXsu/6DU/HKW9dDoZRR5Ofpp1bjsiepdtq0Pnj0samEv7KqCY+98CMqqprI/nfcPEpY/srMr8Jjb61Fs4Hb//l7pmDySG7//Wfz8cxXv3D7K+V474EZGNiF23/VobN4c/0+sn+kXouv7pmN1Chu/7d3HMS3RzhLqUtUOL65eTY5o2z8PLLlF2zL5cmsI5MS8eVMNn5kaLZZcevWn3CmhifqzuvaG/8YNYmcllJjE27etwIlRo7/8d5jcW93Po9cairDgye/R5PDTEs9L/e+FtPiuP2P1V/AG5eXErtOLVHipR53oHcwjwrvrdmFVSXLhev30c5PIkYVS/j31nyL4/VrabtIRQrmJb1Oy0Fs/jlW/RzKTZzlE6UaihHR79L843I3o6DmZpjtnF0WqpmP+NA3af5xO0tgbViAVuqVJoIs6AnItff/N2fhDvm9cvnyZWzbtg0nT57EwIED6bOPP/4Y06ZNwzvvvIPYWN8Sq7+wOmhM2uaf/t7jtpWOnJU/QBzNzwAeCjKlrhregdvOKYeGhrvR2uotaOeAselRuF3VcLsNqK2/mxwV2qvVhNr626kwktWRi/JGxpjiiWxOVy1K6ljZe6DRsk9wVJiYHLkoqOel+ItbVqHKzDuzMqmzHkN+8zf0/lzDF2iwcaovk3zDBhQZt9P7vdXvwOCoFvAfr/saVRa+7frSv8PqoSC74cSW8vdgdDTA6jJjRcl75KgwsbutWF78NrEIKi3lWFX6vZBs1+JoxrdFX9B71pjQ66gwYe3plxWvpvebKg7iSB1nDzA525SDH0v5xLc4fzsut3DWB9/2BHZWcabTq+d+RoWF07zZGT/K2oXzjXzbB4/9iGY7t7Gj1Y2nTm1EjcVAlUIfPLSeHBVuRwfuObCOKKN5jfV46chuIdmx1mLCI3s5dXlvUaHgqDDJra/D6wc89OqMc9jhcVSYHC0uxVfHuFPw4e4jxPDxyk8ZF7H5Ar+pvvzjTlQ0cgovO+OHvxzGuWJ+U3riq81oMfMKzYxh8tLS7VSV1mix4dnPNtONkonZaseTH22kbYrK6vH+1z78rHLrqx9uoffHThYIjgqTwuI6fPLlHnq/Yf1pHDrIHRX6rTKKsGoF78u15Mu9yMnkN3omWzeewZ7tnAXy/qsbUFPpGeOtwHef7MZlD0Pn9cdWwuihIDNn4t3n16G+1gCT0Yo3/sYdFSYWsx2vPbICLqcLJQU1+OyNzUIibWO9Af985kd6f+JQruCoMCkuqMUX73Ja9safTuHwvizf+DlViNXL+bbffrMf2dncpkx++eUcdu++RO9Z/ZXqGo6fnXHx9wcFFtSz72+Eweiz/2ufb0VdI7f/019t8dnfZsffPttE2xRU1+ONdXt99jeY8MxyTl3en1soOCpM8mrr8cZWTn1ffvYctnscFSZHSkrx5Un+W7178hDO13JHl8mqrPPYkMev0WdP/IJykw//O+f34Uwdn4+eyFiJFofH/q0uvHR+HWqtBpicVrxxeQk5KmR/lw2vZX7juX4rsLKEP2h4r99vCvn1m288KTgqTGptxdhV9RX/Ls0/otzExxKTassJZDUtpfeVTW/DbPcxfBpMK6nmChNb89Nope7z/Bs4DP+Ey+PU/Nnkj2IDtbTph+dfxf1/KqwHH1u+8ToUTFivPrZsc/z48f/z43Y4K3+AtDpz2lDv2Gd5RF12u8vbsHdcVCiO1Vdphf8Ac8Pdyrq4NsDmYPUT/FOJXLA6+U3Q7Mhr87O5YHLwG4zRwWjBgWFlo4PX8mi25bfJ4peixc6pvw1ETw7E32gvhtPtQIuzNiDD3w0XGh2s43IdnK0O3/clWqSJqMvVNt9NwfPNUGXhk3+5pTKAucN0JWY+sZaYqwLC262ez5iwTrHuNtTNIhOvJZFnYPRkfxuz7WuJulxpaQnYj21XYmqkXBWbyxWAnzk1jTYz8psbAq3PcjyaGvi5GuoDMDJdVh2vKZJfVx9Qpp22r6+n15zqugB6KQvxF9R6jllZL9BjBfzVDUSdZTkR/gwRtl1ZbTOq6w2wO/zwtwIGsw1NBgtKyvlxBRu7W1Fcxj8rLqlrR/1lXZBJV8x0fnkErfwzJv4UZCasqmxpEdcV5dVQroi/MHoyy3GprWoO2I9tV1laj9rKZtL7ztVKTk1zkxll7Lh+5mBVbEsK+LlKCmsC8bvcKMzjTmBxUW07/CWeei+FhbVtWERilJTUCw5bW/sXl9WT/Zmd29q/vLqJcoWuaH+jBUU1jYHjh9m4hts/v6YhcPy4W5HtqamTW3+l8cP3y26sbTd+8hq5LqclUEfnaamj8V9lbW43/svM9URTZtRmAT9aYXRa0Owwocra/vqttPDP6mwl7ajLNTY+j7TYC9rMP630GRMrzVH+c4yUHsro+M62OvZZYJfvP4v8Uc5KQkJCQE881iPvj+jNFxnpa53ARCqVIjQ0lHT/18ftcFb+ABHJerSnIUu7QCRSQCxJbGNmGSTSFEgljG3A1pC9E5cYYnEYdSdVUoKZ/z4SqGQ89KyRd2lHXdbK+dJLkLwLFWPySSt0cp6sRgm1AZOME8HyNHofrkhv5+SEKVKJ2RMsiw7Yj7F+QuWxCJFFUM6JFz9jFWgkOlrnjlEymrRvQmaU4zhVgrDs4z95Ml2Khicdp2rj2jkdKVpOYUwPigtwcpyUgMuTfbvqY9vlt6QHRUEukSJBExywH8sbSdaGIl6jp5wBn/VFCFOoqbttenBYwA2F0UK7hkYIyz5tqac9I3lCb9fICIrM+GzcSp8x6RYTmFDLtuscxSmiXeMi2tFgO8eEEQslLpxRTP3wS8RIjAxGTLgOKoVMoNWybVj34BCdivroBOAXi9ApieNITYkMuGmzG3/nNI4/tVNkO6ejUyeuS+scHeAksBL5KZ790rrGQNwmPyQlLZJyQ6LjQwL2Y6yfuKRwRMUFQ6liuVP8c5FYBH2oBvoQDS33sH8LGCVipHTm50pJiw7ELxEh3cNg6pQW1Q5/aroHY1pgQjDbLjWV2yQ9Naqd/VOTIsj+bWnNMqkYCdEhiA37FfsHqWi5p639O8fw37pzdHi78dMjlk/k3SIiKNcpYPxEcIzdw6LajZ+uYR5dcFQ76nLn4Ega/3EqVmLAD79IgkRNOKKUYVCK5YKGXa+sszn7i1W1v37j1fz6ZT3A2nZdjlbyxqTBis5t5h/+GRPGcAyc05xQeuY08RXmT7HUx1jrkPZSWlpKSbbev1/LFWHy9NNsuV/0m3+sCOt/unQ4K3+AyPT/gEjq7SQsgUT3IsRyTuELCv0aIrHHqxSpoA35FGKWHCvWIDLsG4g8nUhZoi3LWWGlqRWyVCSE/pOt3pKO1SNIDPuQ3oeoRiNR/6DgJAQpeiE19Fl6nxR0PeK0PlpzlHo8UvS30fveYXcjUjVA0HXRz0OillM+x0X/DSHyRGHyGRn5ACKVnF45O+F5Wo/2dlidGfcU/VshUWFB0hNC8p1aEkQ5K6xXSJQyBguT7hB6ioQpwnF7Cqcu9wnugTlxrBMyx5+qTcLCJE5hnBYzAhOjeK4Gk2FhvTAnnpfUv7PTJPQL8XVrnpswEuOjOE37pd4zkaLlNwM2oT/ZYxp6BHMn55OhcxGh9La3l+K9wddSx2VWHv/z0bPplUmwQomvxs6hJ9vU4FC8OWqy0Mk2PkiPD8Zx6vLopGQ8NHiYMJX3iorG86M49XR+v964tiefgJlcld4JiwZzmz88YTjRkb1y89B+mNqTT+QvX38VUiI5S4PdGJ+eNRY9PE3x3r17OsL1GqGOyj8WTUOYTgO1Uo63HpxOr0x0WiXefmQm4U+MDcVT902C1NNTJzpCjxcfnkbvBw9IwS03DRdusl07R+P+uzwdgWf0x6TJPJ+HyYiR6Zh7A6fO3nLXWPQZkCzorr1hCMZM5BTiR16cgcQUj/3FItz7xFR07sHt/+L7NyLEw/JhuSNPv3U95Zeo1Aq88MGNUHkSaxmL6KUPF1DEIz45HI++PEtg8kTFBuOpN+bS+4HD07DgLt7RnEnn7nG4+zFOy7762gG4apqPOjt8dGdcdyOnwN52+2iiU3tl9pyBGDuW/1ZPPDgZiZ5GiQw/azHQNZ3b/83HZlBSLuGXS/Hqg9cgNJjb/+17pwvJy4z6/N79M8j+yZEhePmGq4SeQLEhOrxx01R6PyotGfePZWUNOI6ecVF4egrPr7qxT29c291Hy76qUxruGMjHz+MDR2JYrI/6e3vPAbgmlV+j/xg8DZ103P7MaXmh/1XoHcop0O8OuJHK7NP4Ecvwj37XI0yhhUqiwPPdF0El4ddvkExNOSvM6Y9SRuPm5NsDrt9FKXfT+1TtAIwMv1GYf2JUnTEhipdH6KSbgyStr5NznGYsOgcv5GNQ/zdoPW1GmIRrFyFYzenoCv1bEEnThPlTrnsZEs/8+WeT1lbR7/5jwphA/n8Kxa8zjB5//HHKG/mtv9TUVERHR6OmhkerveJ0OonJw3T/U/mjjttBXf6DhJp3uesAkRYiPyox1znhdrMQdQh1HA3U2eFy1UHCMuH9aH5M3G4LldxnGfRtu5Cy7spML2MMpDZPVXYXW792U4v2wHOxUt0NRF2WS7Tt8JtdjcT88aciEo5WF0zORmIDScWB2f9sqcjkbIFWxgqbBeZrs94/JqcRellwO/aOxWWFzWWDXqZrh9/g4JU12VNeW/yMhsmeGLXSthjdqLeZiPnTlr3DnkLrbEYqENeWvcM64NZbTQhXaSjq4i+sJH+TzYoIlaZdeJ7Rli0OB8LV6nb4myy8siljg7TFX28yExuoLXuHRQvqjSZoFAqo/boBE36XGw0GM9X2UHhqfniFlZZvaDEjTKcOoOkyYSXtWcl9dnNty4wxm22wWB2UWNsWPyuHz1gsrKBbW/xNDSbIFVJo2rBmWJG1xnoTNFoFRUz8heWhMJ0uWAV5m+/mcDjRVG8iB4ZFXQLwWx0wNFsQcoUCcmaTDVaLnfZrh7/ZQlivhL+x0Qy5XAJtO/ytaGwyEXNJ5XEAA+zfbCL6N3NYrmT/UJ2a2Eb+wkrysyq3jGXUbvzY7LDYHQjXXmn8sMrKnE3UFn+dhY8fnbzN+GE6q4kSxduPfxca7CboZSooJG3s73aiyWFEiCyoXaPD37p+bS4zldzXSEKuOP+w6IviCvOP011HDEZJG9Yhm39YOxJGNGg7f/6ZqMvDNjz4u6nLR2d+/L+ClTkt3bt3p8rzAwZwJ3nHjh3E8ikrK/svE2FZgu24cePaUZd/73G90hFZ+aOEj+LQqQABAABJREFU9ftxXEKrMzegrDep3NVwOi5SrkpbcThLYHNcgtMVuE7MxOrMh8WRSRd42wvbZM+GwZ5JtVYCztXqQLM9G022bLjcPLHUK85WMxptuWi051KHZX9hbJ8aax7qbcXt8BscDai0FKLB3n59kfUBKrMUockemCfBhK1zl5hL0eJoaYPRjWJTOQpMZTC7LIH2oBLhFcgzVFD/En9hpfKzWyqR01JFE7C/sNL5l5srqZ5KW/w1ViMyG6tRbOR9UPyFMSkyG2pQZQq0IxOWE5BZW4N6i7kN/lZk1dbiUk0NDG0S25jzc7mqBllVNbB6ki8F/HYHssprqSkhuwEG4DdZkFVai/zK+nb465pNyC6qQVkNTyL2l4rqZuQV1qCmnvdx8ReWp5KXX4Om5jb4WQ5Ffg3yc6qpl09b56EguxL52ZXkLPgLS4LNv1yJgqxKckD8paXRjPxLZSjOqWqHv6GmBfkXS1HpyX/xl+qSehRcLENdZfvvVppXjYJLpWiubzPG3W4UZlUi/1I5TAZre/xZFci/XHFF/Oy7FeRU0zJWAP4WC/JyqqgeTFv89Q1GsiNjC7WVitpmqs9S29De/kXVDcgpqyFHMxB/K3Iqa5FVUUP1ddqNn+paXK5uP35YEnhmXS0u19UGLDcyYQ03WZuInKb2+JmjfrmpEqWm9uO/ytqAPEM5am1N//L1y+afamsRzQk2tylA52p1oNGWQ3/ONvOPu9UEiz0TZvtlT4dlv2O6G+FyXILbmdMOf4f8e6Rbt27kQNx55504ceIEDh8+jAceeADz5s0THIry8nJ07dqV9F5heSes519eHs8zunDhAv2bRU7+1eP+K9JBXf4DxO24AEf9AqCVT6pi1XWQstCmSASbdQdaGliYlE+c6qCnoAnizJ5m43eob3rOk0oqQWToR9B6aM3lDa+izriY3otEKqRGfA+tcigVXbpUcz/qLTzrXiYOQ9+YFVDLUuBwm3C44g402y97SuYnY1QsozUHw+Soxrayu2B28mTEKFV/TIj9ABKxHNXWHKwreQJ2z8TTXT8ZE6P/RvizW07gp9I34fJMLuMiF2BUJF+2OVS7HWvLv+MYIcZNSfdhQAivPLqyZCV2VHPWj1wsx2OdH0OXoC6Uk/J21pc42ciZJHpZEF7v+RhiVVEwO6147MxnyDVyZkCCOhIf9n8QepkG1ZYm3HH8S1RbOethQGgKPhx4G1XnvNRUgdsPL4XRySf+WQl98Xq/mYR/d3kO7j+yjprDMXm811jc130Evf/+cgZeOr6TW18kwnujrsHMVB6Gf+3wXnxzjrM2VFIpllwzB0NiEyin4N6NG7E7nycOhqnVWH3DDUgJDaGn5Zu//xGXqnjIMyUsBCtvvQEhahUqmwxY8NlqVDXzMTIoNR5fLboWcqkUmSXVuOvjn2C0cudsxpDueOWmSYT/wNl8PP3ZJjg8N9f7Zo/AbdfwpZm1287gvW/2CMsXLzwwFZNG8aWNTxfvoe7JTFg9kn++Ohd9eiVQrsbLL6zF0cM8uTE4RI0PPl6I+IQwilY8cfcS5GVxxzk+KQzvf7MIumA1aiub8NiCryhhlknvQSl4/atbKS8l92IZnr75K5g9jsNVswfi0TfnEv5juy7i7/cugdOTjHrLE9Mw7/6r6P2mpQfx2Ytrafiz3JO/vb8A42bxJ6+vXvsZ67/hLBmFSobXltyNXkM6Ef7X7vsex/dwJkxwmBZvr7wX8SkRhP/J275GnoeezJaT3lt2N+FnxeceveM71FbzG2/vAUn4x4c3Ef6cnEr87fGVMJn4+Jk8pReeeOJqwn/4RB5eeGsD9SpicueCUVjooTX/uPMs3v1+D40fZv+X756CycO5/d9dux/L92QIy3ef3HctBqTH0/h5ePkm7L3Mx0+oRoVld9+A5Ag+fhb88CMuVfPxkxoaglUL+fipMLRgzrpVqDTy8TM0LgFLp8+mSOGF+ircuHMlNTFkcl2nXnh72DTCv68qG4+dXA2Hp9fPQ10n4M7OnFb+c9khfJy7TsjZerr7jZgQNeA3r18WZV1T8gZyDPxmpZHocWvqmwhTxNH8s7f8HjTaeP5DkCwJE+K/hkISDLuzAtnVs+Fw8d9GqxiGtMjvKcrisp+HqX6+MH/KVHOhDH6nXcTmzyD/6XVWfvjhB3IkJkyYIBRv++ijjwQ9q5GSnZ1NtVW88sUXX+CVV14R/j16NB9f3333ndAu57867r8iHZGVP0CczS9Qg0KvuC0/odV+mJ4QDI2sY7TvKcJseAsuZwlcrgbUNz0f0KywtuExYhCZ7ecFR4UJ+6y04Ul6X2vaKjgqTBzuJuQ38EzwguYfKKriFZOjFDlNnLp8tv5LWJy+J9tqyxnktWyi93urPoTD7YtwZDZvR6k5g/BvKP9AcFRo25rlaLRXweQ0YF35Eh9GuLGq5EtaFioyFQkTHcfowHeF3Kk5Wp8hOCreJZ8lRZwKubbsAPKNXgo46/BahxXFnIr9ee5O1Nl8T9gZDYXYWMZvxq+e2wyz0xeF+bn0LI7VFhD+J05sCojCvHthH0VTGq0WvHxiV0CzwicP/wKby4kLNVWCo+KtPPvUXk7z/iUnR3BUvCH7v3vqCyw9nkFPxV4paWjCl4f5pP7R9sOoNfievk8VlGHdSU6d/fvq3TDbfFGAjcczcTy7hPC/8s12wVFh8tm6wyivbUazwYIPvuXdhr1P6298vp0YLKzKrNdRIfx2F976kFNnD+y7LDgq3iWTzz/hNl6/8hgKcnzRs4qyBqz8jncEXvrRLqIce+XCqULsWMdt9MmL62D13OiZ7Fx3CmePMDZcK957fCVRlr2y9O1fUFVaj5ZGE754aZ0w/Bnj54MnVhKVOfdCqeCoMGGfffDUKnp/cOt5wVEh/E1mfPUPPo7XLztMkRMBf2kDVi3mv82Sz/eivs5n/wsZxdi+kVPfWRdni8U3frZvu4CMjCLC/48PtwqOCpPFyw+iorqJ7P/esr3C+GH2f/3rHWR/5nx6HRWyv8OJV3/gtY+2X8gVHBUmzRYr3trCv+uSkxm4XOMbP8WNTfjiKB8/7xw/jBqTD//x8lKsucxrJj13fDtMfuP/p/wLOFzFI6TPnVlPlGWvfJS1G2WmRmL9fJq7XvicLbu+k7WaGEK/df1mNh8WHBUmZpcBOyr5HJPTtBJNNl+3ZKOjDJmNfL+K5repGregsx1DvXENvbc0PxcwfzosP8Jl5x3h/2zyR+Ws/G8JY+iwQm2sEi1bavr2228DqswmJyfTuGI9/Lzy8ssvX7EHoH9fv//quP+KdERW/gBppSWcwLBsq6sKItjR2to+vMrqrLSKLe33gQ0udwsczrZLQm44XPwmYqMLXhzQrdnq5Dd4i7OaIhz+nZDZZ0xMzqoAejLbzuzkE6PBUROQ4c/E6GBUWyesnjbxbZeFZGLWBj4wXMuozIy+3NBmSYht1+jgIeh6WyM9xbXt9sqEhaLpacoTBmb71dn4k3yVtSmAKcTW0Gut/Cm5qg09mW/fArubtR4IDEUzqbYYYJE6AlgZ3LYutNhtqPS7KXCMQLXnsyqDkZJ4A7o1GziO6jY6hr+qhe/HIiv+9Fj2dFHt0VU1BdJj6VhNRsqHaDG1x1/XZIRNqWi3D6PSGk021NYZ2uc6eJZSamsNFAUI6HbsiTbUVbdwFo63y7AbqKvhuprKJqIJ++Nn29MxKwPpyXSsqmY47C4Y2ixBMamvbobNbG+3D3NKTC2WdktCLH+G7eM9bgB+lxs1FY1Xxs++twdjDaNQt8Vf69HVtrTDwuzE7G8wtrc/WxZiSzRXsj+jL1c3Bo4ftllNM/+sutkQOH5Yt+Ymzzi+0vjxOLgssuJPT2aJsNVGz9gytbTDUmluoWiit8ZKwHezGuCEvN01wxwVRl/+reu3xVnfbo5pdvBIkNlZEzA38fmH6+w0R/kvHUqEOa2Voi2B84/bo+uQDvFKR2TlDxCxnGW5e5PTmOcrgUjOGgEqIJX18dOJKYFMIusMqTQJEnGEn04CqSSFqMsqeW+IqPmh14uWQKvgoWe9goVp/ScZMUI8VW/DVQPbUZfDlTysG01MIP8aJi5EqfrR+wRNfz96MuPpSBCt6kbU5RhlmqBjGoVYjUhlIsIVUQiS6gUdozeGy6OJupysSYZMxGjBIkHXRcuZC910aQGTJNuml57r+ganBTgkbJLsHcwZQANZY0K/b8a26xfKexgNjUgRaJ1kfZEYfULiKUTeKyRGoHUyJylIpkBnfQQSg0IQrtQI+7FtkoNCqMR+r4goKCQSvyaNIloCIhxxvIqnYH2RCMMTOZNqcFJ8QC4Bu/8NSuIMoMGdEgLxu90Y6GEHDemcGIhfLELv5BiiznZL9tFq2TZalRypcWGIiwpGqJ516fZgFIsQHx2MYJ0andOjqYy+N4zOtunbi+Pv2Ssh4MbMtunvYfn0HpAckMvBvmfv/pxB02cwawzph9/lRq+BfL++w9NoGYcfj1ONu/ZNpETc9N4JAq2ZORLqICWS0mMQkxSOkIggAT/bJjY5HPowDdJ6JUCmkPrwS8ToNYSzRXoMSA7sTCwWod/wdGFpKgA/68/kxTgwuT3+fvy79e+fLOBg27BOzt27x5H9u6QF2l+jliM5IRxxkVewf1QwQoLU6J4YCbmU2R+CbkAa/637J8e1Gz9D0/j4GZLYfvwMTuD7DYtPDBg/jLo/OJbrRsQkCWOcj38R+oXHUSJ6Dz9aPxv/WqkCnXQRiFWGUVKtl9bMnP84VTh1af6t6zdR3a1dZ/cUDWflMbZh2/knQtWf3gXRHBXwDaBV8OVMiWJEu/lTIuNz059N/qg6K39F6XBW/gCR6l+BSDGBd1cWR0Aa8hnEHiqzLvRrSOXMUZBBIkmGPuwHiMWsdoYS0eGrIJOyiZb1aOmJmIgfqPy0XBqDlMjvIJPEUj+NIOVIJIZ9wI+n7Isu4W9Srgpb743SzEBKyOOki9NMQbeQByAVaSERsXohtyNZxymfPUNvQWf9tZCIlJCLdRgc8TfEavhkMS7qQaRqh0EiklEPoKvjXkSogk+gNyQ9i3h1V2L6hMhjcFPyy1BKtJCJ5bin07OIUsYRxTFOlYy7Oj1Nk16oPBQPpz9Mr1KRFN113XFn6p10vM5BKXgw7WbKVZGLZRgdMRg3Jc7kOCL74faUadBIlNTrZH7ieEyP5dTTW1PHYnbCEKJfsg6zT3SbjqHh/Cb1fO+rMS6qC+WvhCuD8MGg65EaxGtQfD7yOvQLiyemT6I2BN+Nng+dXEnN4JZNuh5p+jCiKLNOy0uumks3jxhtEL6ZNhux2iDqxzIiPgnvTeDU036xsfjnlCkIU6uor8vMbl3xxEiep3N1jy54dNwIaBVyak531/BBmD+AUzDvHDcINwztTaX39Wolnps5DiM685vlM9ePx9heqXSDYzTldxZNR0o0pzK//cAM9OoUS/U94iP1+Oix2QhSK4mV8v4L1yE5LpQosqwx37vPzqabZ2R4EN54eQ4iI4Koi/HAvsl49nFOve7eIw5PPjMdwcFqyteYOKkHFt3FQ7pjJ/fErfeNp9L3jNFzw60jcfUcXnXyhjvH4OobhhD9OEivwn3PTceAEdz+9788C0PGd4dMLkVIhA7Pf7IQCZ04Xf+FL29Ht/5JxPSJTQrH60vvhlavglwpw9+X34uE9CjSdeoRh9e+v5siHhExwXjlmzsQERtMx+w3ojP+9h6jywLd+iXh8bdugD5MS87Q+Bn9cOvj/LcZO7U3bn3oKqgZI0ktx/WLRuPq6zkVft6tI3HN7IEcv06FB56YioFD+TX60EOTMGx4OmRyCXVxfunl2UhM5FTmvz9zLXp0ZfaXIDYmGO+8PJf6BzH7f/TkHCTHcvuzTtcfPHEt2T8qJAgf3jsT0SFBtN/gLol47RZOr+6TGIO/z51MuSps/FzTtysemczHzzXdu+CxMZ7xI5Ph7mGDML8/Hz/39R+Mm3r2ofwpRrN/dfR4jE7kjtirgydhYnwaFKz3lEqLz8dcS+OayQeD55HjzuqrJGhC8cXQhXT9sGagb/W9G4maKCrDn6aNwxu97/wvr182F8yMe4RyVVg39l7BYzE++mbSJWonoVfofZCJNZCKVOgWfAvSdHNIF627H+HaBdTPTCIORkLIa9CpeG6DSv86pEpWRkFBZR5UIV9CIvNSmf9c8p++DPSfLB3U5Q7pkA7pkA75y8q/k7rc/6fHIPkd1GWXyYaM6977S97fOnJW/iBxO7Lhth8BxKGQKFkmvq+egcN+Eg77GUgkSZArOcuDCfMTLba9sDtyoZD1EpoYcp0LTZatcDiroFUOgVruK9bFKMk15q1UayVMNRpqma/Ylc3VhArTbto/VjMeSikvFsXE6KhCiekwFXdLCRoPmV89gzprIUrMZ6GW6JGuGx1QM6XElIUySw5C5FHoGjQ4AH+W4RyqrBWIVyUjPah7AD35ZEMGGu2N6KrrjGSNP0Y7DtdlwOyyYkBID8SofC3tWeLfgZoLtP/IiJ4IU/guyApLIw5WZ1OtiKtielJNFa/kNNfgWG0hQhRqTInrHlAz5XRtGc7WVSBBG4yr4tMD8O8vK0JeUz16hEViWGyin43d2JafS7kqLNzurVLLhOUr/JKVTeyNMZ1SkBTiqynQaLZgZ2Yu5SJM7JZGHZEF/A0t2J9ZQNGVSX06U/M7r+SV1+FEVgktI0wckA6ZxIf/fE45LuVXIS5Cj1EDOgXgP3GmCMWl9UhPjUS/Xn74XW7q81NXZ0Cv3ono3NlXfIlReg/sukTsmYHD0xGXwKM43oTVQzsuEj14+IQeCI3wdQOvLmvAiT2ZFBUZNa0P1H61SoqyKnD2YDZFPEZN7x9QMyXzRD6yMwoRnRSOoVP6BOA/vfsSSnIq0al3IvqM7BKA/8jmM5S/0mtYOtL6+L6bzWLHgU0ZxD4aNL47YlN8pbxZ4u6hrec5/sm9EBrhGz/VFY04vj8bCqUUoyb1ogiSV1g7AdZLiNVmGT2he0DNmouXynA5uxIx0cEYMSwtAP/xc0UoKmtA55RI9O+REDB+9mTkobbJiH7pceiWFDh+tp/NJvbXqG4pSAwPHD87svj4uaprGnV09kp5Swt2F+ZTVHBaehdo5b7xw8rxH64spmXMacldAsb/mfpSnGssQ4I6BONjugSOn4ZsFJtqqEK0f9HF37p+HW4bLjUfhs1tRpp2AMIUMb7fxtWMUiNjSLkRrxlLndwFnbMMLZbdVGsqRH01JGJfgqXLkQ2n7RBV8ZaqGBMrsB5Mh3RIR2TlDxCXdR/sjXd4ksRaIZYPgTx0GV1wFtP3MLJGh57EM4VqHoI8tLy6ppfRYvxS0IXqnkWw7kGqY1BQdyeaLYxBwCeW5LCPEaqZAZfbgtOV82F0MHqyCGKRDP2ilkKv7A+LsxZ7y26C1cVZP2y5Z1z8cmhk8Wiw5WFzyb1wtrKEwVboZPGYnrgYCkkQCo0nsKH0Jc9adCvi1L0xJ/FNclhO1G/DpoovaW2a5ZD0D5mAWXH3E/6fy5Zjb+0WQXdNzDxcFT2TJroPcj7FmaZzwrr3fWl3YmjYYHJUnjr/HgpNZaRjIejXej2EbrpU1NtacPeJD1Bnb6G9gqRqfDH4YcSqwqi2yu1Hv4LFZaeMl0R1GJaNuAdBMhUOVOXi3qOrKZeB/TcoPAnfjlxAE/YPORl4/uR2Ial3bmpvvDWU0zpfP7YXiy+eEnRPDRqN+/oMoePcvWUDdhWyXktcPpp8Na7p3JUKwd2wbDWxNpiOLRN9P/869I+PRY3BiLlfrECtgTMb9Col1tw9HwmhwVRb5eaPVlMhMIY/KTwYKx6dD51KicMXC/HIpxs4/lZgQHocPnt0Djks63adwz+/20V42aV6zZieeO5O7vB++u1erN5wStDdtXA0Flw3hHJSXnzxJxw9kifkTTz3/EyMG9cdVqsDjy36Fvk5VaSTyqR467Ob0aNPAhpqW/DQ3E+J9cN20+pU+HDN/YhJCEXh5Qo8dt1H5CgwjCy/5MMNj9I2J3dfwsu3fEE5IgxHr2Fp+Mfqh8hh2bLkAD55YgXlljD9pBuH45EPFhLmr15Yg3Wf7hR0t704Gzc8MpUcjVdv/gLHt58XbqxPfbkIY64dCKvZjr/NfBf5F8tIJ5VL8OaPD6P7oFSq5/LQjPdQX8PHj1avJowxiWEoyK7C4zd/SYXkCH9SGD5acS/hP3E0Dy89sZrsRnk6/ZLw5kc3kcOycfMZvP/xDsHGUyf1whOPTaV/f/z9PqzcfFrQ3XPjSNw8i9v/8c824sC5AsH+/7hzGiYN6kK//82frKZ6O0zHfuNv7rkOfVP4+JnzTeD4+WnRfCSEBFNtlbk/rqTxx8ZPcnAwfr7hJugUSuwty8cde9Z6xj8wJCoByyfdQON/VeEpvHLWd43OTmK0/hl8/ORswurSA4Lurk5TsSB5/G9ev3a3Dd8WPIUqayHvMCaS4uaU1yiXhbENt5XeAgvNPyLIxUGYkrAEWlkc1VbJqZ4NdytLuG6FQpqCLtEbIRXr4bDuhaXhdmH+lMiHQh32w7/NYfl3Rlb6sciK+ndEVsw2nPmLRlY6clb+AHEY/iFcaEzc9uNw2/aR02FsedWzFU9Ks1lWweXMg9NV5XFUfLqGljfhdpthsp30OCrwHLMV5U28s3K1aYvHUeE6Vtwtv/F9+ld+80rYXA3CPg63ETmerqdn65fA1WoTMBocFchp5p2ED1QvFhwVJuXm8ygynqRJa1slpx56mT8ZjbtRaytDs6OBHBV/3ZZKRn20IdeQRxOdV8f+W1HMaYoHa0+To+LVMVrl8mJOPV1beggNds5wYEc0uaxYVcypp4vz9sLq4hM1kzJzA34u5dTZf17YRVi9OE7WFeNgVR5N3q9n8K7N3qTeHwvOI7+lniImzFHx17196iAsTgdOVZSTo+KzPvD6IU4v3ZyZLdBL2ecOtxvvHThM/15+7CzqjWZhH1Yw7pvDHOOXO47TE7UXf2l9M9Yf59Tl9386IDgqTE7nluPIxSK66X34A//+3meKzfsvoqiiAXX1RnJU/HVfLz8Iq82BixfLyFHhOv73xefcDvt3XCRHxatjxd2WfO7purzsKFWaZSCZzmy04advD5Duh493wG5zCBgrS+qxfTXvmPr1q+sFR4XJhaN5OLXnEjkdi1/8iZ/Lk9S7Y8URlOZWob6yiRwVf93S19fDarYh83g+OSre78b+vMfZv+EUOSpeHavfsvTNjRz/kgNoZPRkL36DFT99xendK77cQxElAX9pA7av57/N4o93ElYv/vNninHyaB7Z/7OvuG28uq07LqCENWJsMJKj4q/7auVhsv+5vHJyVPzt/94aPn62nckmR8WrY8UBP9rKx8+yE+3Hz9dH+Tk+PnGUKPTe8VPS3Iw1lzh1+R+neIdnr+54dSn2lRXQZ29d4BRkr3Zd8VkUGOqIZcccFX/d1/nbYHXZf/P6vdh80OOocC1jDO6pXk7/ym5aDavLW3iOzT8mZDZyXVXzh3B7HpSY2JzFqDfybuu2Fja3+eYfl/0YnDYfLf/PJG56MPp9f39V6VgG+gOk1c2oh4EBqlaqLOsE6AJts31rM9FC2wubMC1w0vEChVGamTjpuP7UZbewPXNOAs7jcViY2FwtbejJIppMSEfbtKHxuk1UAMrRGlhFlonVZWLxo/bfC63krJhc7emq3kq1TOd9kvPuY3Ly7U1OC38S9dbeaGU6bj9GwQxgEYlEMAo6axv0gMFpI2YFq5vSVloYnfkKiWrsfMxZYfTltmLwfMZuIP70Uvba4qlCStVI/anXrYDRo2Nl1wNYLCLAYPHsZ7EJN1HhfBZGY3cHdCb2itFsg/gK8VB2fHazNF6BbssozfRqsAqRDNrHzbsdMzEZrQFTITue2XMsY7OZaqF4hdnAZPDs12JuV3WUUZAZM8fmKXQXoGu2XHHKZVhY5MaLJ2Af77nYvn742SvDxrexBjB+CL9nP1a2P6ABIuH3fDdje/uzyr5sKYrRqdvZkm3vYT9dyf7e3zVgH08dFzZW2o0f7ziw/cb4sVoDqMsivzHZ4rC1H/8OGzGGbK7AKr58eyukkvYDiF1f7IHgt65fdu23vX5ZuQIm3vnkSvOP093UhrosEqpv87nySvNnh3SITzoiK3+ASJSseZevfy9EakgUwyASySFXjA+gJ4vFsZBKe0AmTYGMmnf5dAr5AIjFodAqBlLGvD/lOVjN2RwsR4XThX0/XaSGsyFYjop/LRU2/bDPmCQHjfP7nHsECVpeybVLEGuk5qU+iiETqZCg7kPU5c5BAwLoyXpZOKJVKQhXRCNSEUufeXXJ6nRoJEFI16ZBI9UIOja5DQ7lFOr+IT2ISunf1XVEOKc3joro1Y66PCqCd5SeGN3DDz0Pu4+O5J1Zp8X3COierJbIMSQ8mZZoxsZ2Emid7DVGrUO3kCgk60LQSR8aoOsfGYsQhQoDomMRrFT6KM8iEaZ14k0HR6cm07/9m/RO7cp1LEfFv2suuxGxz5hM6pPuw++5H43tkcp1A1kegQe/SES9gQZ1SSAmybC+KQH02KiwIKQnRiAuJgSJ8aE+Wq1YhB5dYqEPUqFHjzgE6ZR+dFwRxozmtho0PI328a8OOvoqbtvhE7sHdC1mjgDLW2Eyciqnp3qPx77bEI9u9MwBATRppUaB3iM6E5Nn0ISeAnWZvUbEhSClRzxiUyORkB4doOs2KBW6UC0t6QSFaAIozyOn8zEycHwP6hPk35WZ5cgQ/km92lGX2We0zaSe7fGP5TYZM6G7z/5iEVQqOTVtZEyqIYNTA+zPGFadUiMRHx2CpNhA+/fsHEP2790plvo4+VOeWR4S4ejKadL+42dyHz5+WI5Ku/HTlY+fqWmdffg9t/bxKTzH5Jrkrr7xz8aPVIZh0YmQiyUYHZXuR88XI1qlQ1d9NOJUYUhUR7IZybOfGD10SdDL1L95/aZrWZkDH62fSQ8dn0fitWPazT8JWs40YzkqaPMN9CreSFWqajt/aiD1a3r4Z5IONtD/XDpyVv4AaW11wGl4Hy4bW38PhyzoGaHrstttgLH5ZTjtpyCRJkOrfxUSKU9WY0tBdY3PweHMhkLWF2HBr0Ii4cmOFns2yhpfgd1VCZ1yNOKCn4HY0+G40XIcBU0fUJQlSnM1kvR3E+WZSZlxO3Ial9CNvpP+BiTrrvVgbMWlpjXIbd4CiViJfqG3IUHLacEslHu0dinyDUehlgZjVORdiFbxydHqMmNr5beUZMsS6abFLkKonCdrsqWgn0qXoMpahkR1J8yOvxkaKU/ILDOXY3nxKjTYG9FL3wM3JM6hst1MLjTlYHnxZnpaGxUxANfFTxIape2pPouVxXsJ77UJI3B1LKdXs3//UHQEG0szoJTIcFf6OIyM5AmZrPjVR5n7sKcyG2EKDZ7odRV6hcQKT5ivn95FSbZJQaF4acBEqrHChC0FvXh0F3Ia69A7PAYvDxuPECVvHpdTX4fXDu5FpdGIUQlJeGrEKCilfA39eHEp3jtwhJ6EGd30nmGDhRvCLxey8c2hU3SjuWlIX1w3oKeAf9mBM1h//CJUchnumTQUo7vzOjEOlwtfbDyK/efyKcH20Tmj0D2Z29hktuGDH/bhfHYF1VF57OZxVOODCVsK+uCrXSgsrUPXtBg8dMd46HUcf1FhLT79bBdqa1owcGAK7rxrHBSeJoLnThdhyWd7KJLC6Mrzbh0l3JD3bz2Pn745QFGIGTcNw2QPdZnh//nbA9jx43EoVHLc+NAkDB7HE6rZUsyyf27GsR3nERwehEUvXIvOng7HLCLy1Qs/UZJtbEoE7vn79YhJ5gnVbCnos6dWojirHJ37JeOeN+aRs8KkOKsCXz6/BrUVTeg/thtuf+FaOi+T80dysPTNTRS9GTNrIG54aBJRngn/5jP46cu9VABuxi0jMfmGoQL+9cuOYMf601S+/6Z7xmPwaD5+WIXdpV/tw9GDOQgO1eCuByaiczc+flgJ/k+/3INLmazhWggevHciUZiZsKWg977dg8KyOnTrFI1Hbh1HzgqT/PI6vLN6P2oaDRjaPQkPzhlFZfeZnMwrxUdbj1Akblq/Lrhj/GDB/r9cysbiI3z8LBjUF3P7+cbPt2cz8GPmRaIvPzRkGMYlpwrj/70zh7CrNJcSbJ8dOI7GMxOjw4Y3L2xHRn0pkrQheLb3VCRo+PhnS0EfZP+MQlM1ugbF46EuM6m1xX91/RYaL2BPzXKaG3rpR2FkxHXC9Vts2InMpmW0BN4l+Hp00s0Q8NcYvkG9aTXRl2P0j0CvGi/MnzbDu3Ba2fwZBqXuOUjkPuf4z5Sz0nPNE787Z+Xi9W//JXNWOpyVDumQDumQDvnLSoez8v+HdOSs/EHisu6Fy7YPInEopJpbIRLr6XPmC9osa+GwZ0AiTYSK6URK4Ymi2fQ97I48KGQ9odPMFyIkLrcRtYYl1E9DqxyGEPU04Vw2Zy3KDMvhcpsQob4KISoefWBisBehqGU95ackBU2HXuELH9dYLiHfsJuKOXULvhZamY9OWWA8gULjSagkevQPnQmlJEjAf7ZpP0rNOQiVR2FI2FQqCEcYW504ULsH1Yy6rE7C8LAxwhOWxWXFjqrdaHQ0o7uuixBGZtJgb8Hm8gNEXR4e3ge9g31LJCWmGmyqOEFPllNjBiItyEeLPN9Yim0VF6AQS3F98mDEqHyUz32VuThQlU/LODenDYZervI9URdewpnaCiQG6bGwywAoJVIhovHD5XPIb2xA9/BI3NC1lxAhMdrtWHr2DPVjGRqfgKnpPjvWGk1YfuosUZcndU3DkCQfZbWwtgE/nbpI+Gf1644uMT5a9vmiSmzLYFE0Ka4f0Rsxob7J5tD5Ahy5UIRgrQrzJvaDTuMdI63YfuAyLuVWIDYqGLMn96WCZN6IwMYtZ1FS1oC01EhMm9xbeEJntOSffzpJ/XD69k/CqHHdfPavM2DjquOUQDtiQnf0GcQjPEzKCmqwbfVxWkKZOHsgUjwRBiaXTxdi/8+nqbDa1beMQmS8j/J8Ysd5nNp1EbowLWbePQFBwRoB/+6VR5B1Mg/RyZGYcfcEyD2UbafDiS2L96A0pwKpvZMw5dYxQoSE5Zps+GInRV/6jO6GUbMG+fBXN2Pj4j20zYjp/dFnZFcf/rxqbFtxmOO/fihSusf58J8pxv4t5zj+G4ciMjbEh/9QDk4eySXq8sx5Q6lwnBf/rp0XkZlZgZjYYMyaNYCK6Xnt//P2c9z+KZG4ZmIvwf4mix1rdp9BbaMRA7slYPxA3/ipazFh1cGzRF2e0DsNg9J946egrgE/nebj59q+3dEl2jd+zlRWYFN2NlGXb+rdB3F+N6s9pfnYV1aIUKUKt3UfAL3CN342lFzA2YZyJGiCsaDTIKrsTPjdLqwvO4YSUy3Sg2JwTdyggOt3e9UeNNqb6PodEua7fg2ORhyv/wU2twXddUORovUtsTXbi5HXvJHmn066aQhR+K5toy0DDeZNNP9FahdAIfX9Ng7rHjht+yAWh0CuuQ0iWgb/84k34fr37P9XlY7Iyh8gTvNqOJqf9vh+bogkSVCEb4JIrIGx5Q1YjJ8IOpl8GPRhrCmbCJX1d8Bk3ebJTXFCp16AqNB/wt1qQ3bVtbA4WMM2Nnk4ERf8PKJ0d8HuasCJipmwu1iPDt5/o1fEx4jQXAWDvRB7ym6Eu5Un1bFckzHxSxCi6I5y8ylsK2OVbvlkKherMTtpCTSySFxo2obtle9DDAktHwXLY7Aw5VPIxSpsr1yOA7XrPDo3UjQ9cVvqS7RmvbjwQ5xrOk0TnKvVhRFh43BT0iJqfPbypTepvTzD4IILNybOxdUxk9DsMOKB02+hyW6gPAGWo/Jc90XktLB6D4uOfyh0iGX5J58Puh9ddfE4XpePe44tFXIjWI2Vn0bfjyiVHj8WnsFzpzdDKhJTkmCiJgTrJ94JjVSOtzL24fNLx0jHEhTZWv7yifPICvfs2IAdRXmQiMWUjDu/W2+8MXoSsS6uW72K6KLMeWG6Z0eNxh0DBqLBbMGMxctQbzJT8gljkXw8Zzo5LQU1DZj76Q+wuzz4RSKsuGceesRFUWPCez5fJ+RGaJQK/PTUAkQFB+Hngxfw+pKdlIvBLsf4iGAsf/EmqJVyfP7DQSz/+QRVSWW1N/r3iMcHL7COxsCLr/+MQ0dzOX6XG9On9sHjD02mpNyH716CgrxqshfLQ2FLG3PnD0Vzown3zf0UjQ1G+g1ZjsQL784np6U0vwYPznifevowmCwv5L2fHkR6r3icOZiF5+Z9wm/Grax7uBKf7n4WEbEh2LbsID54aCkkUgnhj0mJwCd7X4BKq8S3L67B6nc3QyKT0NIMczze2PQk4Xpt/kc4svE0xFIxXA4Xpi0ah4c/uZ1YR4+OexUFF0ogEouJsXTnP+bjuoenornegPtGvoLG2hY+flxuvPD9fRhxTX9iGT04+S047E6OXyLCexv/hvQ+iThzJBfP3fa14IyyKrefbnqUquVu+/k03n9to2D/mPhQfPrD3VCpFfh68T6sXHEUEqmYEoz79k3EP99hDxXA829twMETnvHDlp0m9cYT906iZoa3/30VcktqyV5M9/ANo7FgykA0Gi2Y+89laDCyJFY+ft69fTo5LQW1DZjzZeD4WXnHPPSMjcLhkhLcsm6tkNmhVSjwy4KFiAkKwqqc83jq0DZh/CcFBWPLzFugkcnxzsXd+Cr7iDD+h0QkYcmoBXSc584tw4HaS5TLwpJxZ8YNxpPd59D1++LFtwKu35sSr8M1sZNgcrbg09xHYXQ20fhxw4X5iU+hu34omu1F2FJya8D8MyVhMcKUXdFiPYzsmoXC/CMRB6Fn9Faq1m03rYK1+UlhjhRLkqGJ2ELz558tstJ91ZO/O7KSOe+ff8nISkeC7R8gTuPn3nc8p95VCJeN5V24YDF+EaBz2A/D6cyE01UGk3WrJ1WOMw5azCxaYoDRehwWB6MlugVdVcun9Fpj2gq7i1EfXUIyW3EL79Bc2MJqLTB6r0v4y2/m9MCLjWs8JET+ud1tQm4L78R7vI53tGUTD3NIGu3lFGlhbKBDtRv8dK0oMF1AlbUIDfY6nG06xY/ocS4O1++FxWVGliEXReYSmjjZRMdkY8Uv9Hqw9gwa7c3UwJA5Kmzq+qmU02o3lh8nR4V97v1bV3qEdMsKjpClvJ8bHVZsKuNdc7/M4tRPNuGyJ9IiYwP2V7LCWm4szjwh6BjWI1XFyGysRpmxBduL8rj1PUmNKy+fJ4bFifJyXKqtoWN5dZ+d5MfZmplDkRU28XuTIRcfPUmvP568QDca5lSwP5b38cNRjnHZvgxuK4/OaLFh0wlOQV+yhR+b3XjZPiXVjTh8oZD+vXITpyezGx4VUbtYirziGlTVtODgkVyB/spk09ZzlGNx4WwJ8nKq6FjehNlV33MbHdhxkSIr7MbLdMz+P3o6K29deQxOu4ucCi+WTd/z7rfrGYWXqM5cx3JRdv/Iqcur3+e/LXMq2L7ledU4ufMCbffTh1s9Rb9cFO04uy8TBedLUF1Sh8MbTtF3Yjomv3yzl/JQLhzKRt65Yo7f07F59Tuc3n5g/Sk0VPOmigwL4fd0lN66/DCcdqcPv6sVm77jlOH17Dsy/B4dYwLt/pl3Rl7l+f7e71xeUo8Th3Lp32s89Gx2Lob1zJli5OdXo6q2BQeO5wXYf+OO85RjdCanHNnFnvHj0Xl/4x1ncyiywscB1323m4+f1aevMH6O8/HzbcZpbivPH8uXWneZd5/+7NyxgPFf2NKIvaUFdJ18m+PTsfF3rLYIWc1VqLI2Yn/tJT7+PUntG8pPEPvuckv763dDBf8dLzYfhsHZSPMEmxOY83Gwlndvzm3+ud38k930o2f++toz17F92HdsQZ1pHelsxk8D5ki3qwBOm6+zfId0CJOOZaA/RNrTG/lFyS7OK3CUW51oFV1pH75fYEa9bx96aadjdSh+Tccr4TJhjkdbeiCfbHyvbffj9R7a42fH8joo7ffzOS+B+7j9Xn38ZO6A8O39mUBe8X7GXv27PLMnW+8k6/yV/bj12wcO/W8U7XFeWef9rC1Gf2fHvwmd8N28+1EdDwTi99O1Ox9zTvxqkATqfE5IO/x+DsoV8Tvb2N9zA/eeM/AL+I7lcgTan6inXp3zCudjn7HaH7/y3a60D+FnDoPHQQncx+X32ga/Z3vvNn5fwKdztsHPIk7CflfGyI9/Jftzh+eK+N0+B+VKxyOdHz2/7RgJRO/7rO3YEv2Gjj5j47+Vj+d2OjZGrlw7gT7nc8WvXb886uY7Kqv15D/HtNnPM7cENjjk38D32RXmwl+ZX/5/l9/L6Gn9C7OBOiIrf4BI1Cy86esYCnEkJIoxEImkVLGWf+7pJirtAamsB2SSZCjlQz0/AddrlNOIssy6kcqJMcSWh/jgDA/izcIi1JMgpTLVPl1cEG/ylhg03fMZpzazyTlZN4t0XfU8K5+Fbll4llW+7RTEmi8CfUNmBHRcZs0Mk7UDqUHhgJAJfjoxYpQpiFGlIEIRhTQto0xyGjL7r2/wQKilGnQN6oxIRQRRH70Ux4lRnMI4PLw31BJlgO7qWN7IbUoMXxdnGvbHJsSrY3muwpxEzkphnzNKsUwkxZRYTktd0GlgQMflCKUWo6LTIBWLcX0n1sHa1422R0gUuodGIkkXjCEx8RRq53oRpqSk01r/4Ph4JOr1tL13aljQpy+9TuqSRiF4f91NAzhzYVb/7txKRG3mtSjmDOTr+XOGc6zsc0ZpZZVLp/TnbJS54/v6Ov6KRdTMcFjPZFr6uXp8T46PdTMWi5CeHIn0ZEZdDkafnvH0GdOxZZXRIzojKEiJXn0TERsXQp2QvctO06/lth0xsTstgTBasHdJ7RpPsz+Wo8JORtRaVsukFZjs0U1bOJLuUGxpiO3Lmv6NmcmPOf0OTotnx2NLKaHRegyc2IOWhSbdPFrAx/br1CeJSusz6nKvkV0EHOy4I2YNJMpyr5FdEZMaGYjxTk5zZcs9akbL9qMvX7OIn3/i3KGB+N3A5Bs5BXbavCGB+GUSjLmG232G5zvyjstihIZrMZBRvCViTJna24OfHzctLYr+4qKD0ad7oP3HDE2nJof9u8RT00lOEef2v87zG0/skwatUhGgu36EZ/z0vcL46c/Hz/xevel6oPHDxr9Egmu68Fydm7vzDsXeMR6p0mBsfAqN/znJfQPGfzd9FLoFRxF1uW9winAVsv8fE9kTOpmayutHtbl+r/Jcv2y5h3Ve99cNDuNNGjvpOD2ZX718/knTsTkJiNDe5HFx2NwkofknTM11cs0tAfMna2YoVfLz/dmko+vy/1w6clb+AKFoguVHWvqhBFvt/RBLeGIii3pYjF/B4ciARJIIddDD1HWZCatW22D4kPcGkvdESND91EmZicNVi6rmj4UE2wjtzULyrdlRiOLmr4m6HKmZgiiNL/m2wXqeln5YlCVFNwcRKl9iYrHxMPJadkAqlqNnyA0IU6QJ+C8276ClH7VEhyHh86GT8X4rLOpxuHajkGA7NmouVBK+lmxzWbGtagP1BkpQJ+OqqGsgE3N6bLOjBT+Xb6YlH5agx5wVb/JeubkGP5XtopDzqIi+GBXB62QwudhcTEs/7AlvZtwQ9A/1dV/dV52FLWXniLq8MHU4OuuiBfxri85hf1Uu9Qa6t+tIxKj1wlPn15dPeBJsg/FAr+HQy3nyodlhxycZx3lvoPBI3NN3sJB8WGsy4dMTx4nePCw+gZwVb75DYX0jFh89BaPNhind0jGtu6+nzbmSSvxw7Cw9Yc8d1AtDOvmSJ/ddzMeWU1nUG2jh2P7oHBch4N946BIt/bAE29uvGYLo0CDhSXz15lO4mFOJuCg9bpkzFEGe5FuL1Y7lq46huKQe6Z0iMX/uECH5k+Wk/LDkECXY9umfhBmzBwrJn2VFdfhxySGiLrMaK6Mn9wpIQt30/WGKcEydPxR9hvnsf2zHBexde4J6A82+e4KQvMrw7/jhMC39MOrx/MevRoQn+ZZFL9Z+tE1IsL3xqRnQepJvrSYrVry1EaVZFejUNwnXP34N5B56dWN1M1b8c4OQYDv9rglC8m1ZXhV+/GgbLRmNnjkQo68dFJAEvOnb/ZQLMnXBSPQZ4UtsPbY7E3s3ZtA5Zt8+GildY3z4N57BicO50AWrMf/20YiM1gsRkR/XnMDly+WIiQnGgoUjoNX67P/9T8dRXFZPXa9vmj0Ychm3f32zCd9uOk69gQZ2TSBnxWv/oppGLNlzipYCr+qbjsn9fOPnbGklfjhxlqJ/1w/shaEpvvGzOz8fP2ddpgTb2/sPQLcI3/hZk3sBe8sKEKpQ4YE+wxCr1Qnj/7vcYzjbUEaU5fu6jqKu44TfZcfSgj2e3kCxWJAyljqXM2myt2B9+RY0OXiC7VV+12+drRyHan+mAnE99CPQK5jXWaHrxnIR2c0/EnU5XT8L0WpfYm6TeRfqzOt5x/mgO6CWdxPwOyxr4LTuoflTEfSgMH/+2XJWOv/w9O/OWcm56c2/ZM5Kh7PSIR3SIR3SIX9Z6XBW/v+QjpyVP0DYU4TLvBQuejIIgzToEYilyR6dDWbDR3B4isJpgp6EWMI7kbrczahveZsiK0pZT4TqHofY0wnZ5qxARfN7sLuqoFMMR7SOFX7jFW0N9mwUNn4BZysrCjcVsdrZQri8xnwCuc2rKNckVTcbsZrRAs78ll2UVCsVK9E7ZD4iVT0E/KcbNvLIijQYIyIWIETOn2ycbgf21KxFsSmbqtZeFT0PWil/6jQ7TdhUsRaVFFlJwvTY2ZCL+YVYZ2vAmtJNVFSqp74rZsT6Cr8VGsuxsmQH9f4ZHdEPk6KGCPhP1edhTclhTv1NGIKRET7K7dbyC9hUepYiK7emjUDvEP7Uybb9Pvck0ZdDFRo81GM0koP4kz0rt//phaM4XVOOJF0IHu87igpnMWm2WfH+ycPIbahHz4goPDJwOFQy/mRf0WLAB0eOoMpgwPDERNw5iFUV5vizqmvxxaHjMFjtmNqjM+b06SHgP5ZXguWHzxCm64f0xthuvHAXk22nsrHpRCYVB7t14kD0SuZP9iwKs2bXGRw+V4gQnQp3zhqGhChOq2XMku9/OoaL2RWIiw7BHfNHIETP8bOy+t8tPYji4jqkp0fjloUjoVRy/DXVzfj+q32oqzGg76BkzL1pOC1rkP2zK7Hyy32UZDp6Si9Mmu2rQHv2cA42fMuLwk27aTiGTPRVDt637gR2rT5GxdnmPjgJXQfw78aiGBs+34kT288hOFyHBc9di7hOnBbPmD0r31iPzKM5iO0UjVteuR7Bnk7IxiYTlr60BsWXy5DeLwULX5oLpWcirymtw/cv/4i68gb0Hd8Tcx+fLuBnLKGV/9xIZf7HzB7iWWry4D+YhQ1f7eH4bxmFIZN6+/BvzMDudSehUMpx3d3j0bVfkoB/44pjOMG6RodqsfDe8YhN5NcoY1atWHYYly6WIS4uBLfeMQbBnsiQwWjFtysOo6i0Hp1TI3HbjSOg9ESGqusN+HLdEdQ2GDCweyIWXO0bPznltVi84ziV4J/UrzNmDfUbPwUlWHbUM34G9ca4Lr7xsykrC+szMymyctegQegb4xk/ra1YcimD6MtsbD/afwSS9Z7x43Li86xDyKgvo6Jwj/QYS9cI4XdYsDh/JwqN1eiii8MdnSZCKZH/l9dvlaUIe2t+4kXhgkdgQMh4AX+l+RSymlbTnNJZfy3itXyJl0m9aSPqTet4ZEV3N7QKvnzFtrWbvqOkWrE4HIqgR4X5889JXf49OSv4y8q/JbLy6aef4u2330ZVVRX69OmDjz/+GIMH83XitrJkyRLcdtttAZ8pFApYre37nfynRFYcho/gNPJmgpRLItJDGbEDIkkYWhofhM3CsuVbPTkrqQiJYM3FpCitmQ6bgzVsY8lk/4+9twBzq9reh9/4JCMZd5eOtJ26uyttqVBvKUUqWHGKF3crVijQFupQ2lJ3dx13d0tmkkx8vmfvnZzkzAzce39w78cfZvEMSbOOrOyss886a7/vWkIoXEYgxHcTrbGSWjEGRku1TSdAgPtShHs9j2ZTGS6UTYGF9hxioLcEnxcR5jEXdfrbOF62lAd/GxL0KQIVA2igcqLyFceaskCEO8PXw0sWhfM1m3G2ZhOncxG5YWnMVzRw2VpM6Mln6fozWaf2lQXj4bh3IRSI8E7WGhRrCygvgKxfd/bohgfjHqc1Gh6/+QrH+iEyKWg0FkXORJW+DsuuvQ2jxciBX1fGzsLk4MFIURVh2eUvnbqOAB/1vAf9fDthf9ltPHN9J7e+Ttbjtw1djhh3f6xNO4OP0hjrg6zLe0jlODjuAfi4uOLRM3uxuyCDHo3oojy8sX/yErr/9J83I6WmkrIryBLPsLAofDdpOq2xMv77DajWaLh+LEt79cLq4cNQqlLjjq820aaEdvDiSxNGYl7vbrhZVI6FXxDWlWNW+WrpdAzqFIEDVzPx7PcHbPgTAcWjbHl6PmKCfLB+90V8tYuxngiWgSzzbHt9Mbw8FFjz0T4cOZNJU+VkGSE82Avfvb+Y3rgfemQjsmysH6Lr0zsab74+i9ZYuW/eF6izsX6IzJjbHw88MhZVZQ1YNvVjGPUmrlfOyhenYPKc/nQJ5fEZnzDHIZMqWvDapmXoNTSBLv+8/cA3DBNCcBNiEdaeeB4R8cH48a1fsPFVxuwgeBBSY2XdtTdpUPL24rU4vpXVPSG60E5B+OLq25QK/MigF5B9LZ8uORH7e4/vjtf3PkPrp9yX/ATqKhqojpxzxiOT8MC7C1FZVINlfVfD0GxiOgAPfrQYd9w3ChlX8vH45Hf59m97GL1GJOHk7ut4+5FNPPs/3fc4IuIC8eNXJ7DpM8ZIIzgf0ol53a5HaDXbN1/djeNH07jxDw3zwVff3kvHf8VTPyIz1zH+/XpG4e0XZ9AaK3Of3YBalYYu5xCZN74XHpk3DGV1asx8axP0hLVk85HnZo3EXUO64UZxORZ8wxoG2qflrxdPx6DYCOzJyMSq/fsd/iMUYs/CBYjz8cHHN87jg2uM7UV8nOCujs64Bz5yBZ64/Av2Fqdy/h/p5oPdY+6HWCDA/Ze/QIa6lF6HBGXS3zce7/e8+3ev3wZjNT7JfhQmK+l+znRTQu5HP5/xqGlOwcHSB5zmnxaMCv4Ywa79UKfdjfy6Rzh8ngBidA7aB7kkDoamj2kFW/v8SWpUufod4x7q/k6ZldhNz0KkYMtw/xex6PTIXfjmPzKz8l8H2G7btg2PPfYYXnrpJVy/fp0GK+PGjUN1NbkRty/kR6ioqOD+ioqK8FcWs45Rf5lYgJZ6WIxnWBlpLlBhOos5B2ZTKkzmAhhMN5yae1mh0x+DxdqAJsMVWmbfoWtBjYado0Z3DJYW0lTMgeYva2IdaYuayM3QjtUnfwIUNbHOyNmNjF7KjkaYAlbkN7HOprcaDvB0zZZGFGiv06Jv9kCFWWhFtaEU5foC1BiqUKjN4yYzsk1q401ozRpkNuagztjA6Ygcr2YU2At1KTA4BSpEDlZeoK+HKm7SiZhRrBmY9kAFo5fuLiFjZedXMTbDoXLWdXZbPtPREW5pQYNBh7NV+bQM+e6CdM5+ostV1yGtvgqF6gbcrK7gghFy4zhRnA+VvhlXSktR0dTEaxy3LSWFvh7LykOzycRjWey4wez49Uamre8POyP5LnuuM3rp7otpDvttjKPD17PpZ7+cYsemNlpboGpqxqXUIlp07MiZDO7GRW6KhaX1yC6oRll5AzIyK7iAg7xeupyHxsZmpN4qRk1VI6/x4IHdbBwvHE9nN3onltHBnYwefeIXUjOHNQmkN2eBAMd+YrTaI1tYMEW+gJ1xdPoXtt+B71hnaGqHxUproVw/lkqLvtkDFbuuOKMMuTcKUJ5biczLuVzAQY55ef8NNNZrkHo2EzWldZyOnPPAekZlvfDrdeh1RoeO2P89O/+JnZdswFon+7cz6u7hnZfa2H/mV0YLPvgT+x7MxhY0Nuhw/UIOHX97oGK3sbioFrk5lSiraEB6Nn/8L1zNR2NTM25ml6KqvokLVOhvfJL9xidT8tBs5PvPzxds/nOb+Y/VyX9232D+81NaW//Zn5VFP9uSyTpU2328Xt+M02WF1P/tgYpdl9dUi4yGSpTq6pCmLuGuQ/J6vjYTapPud6/fjMbLMFlJ40SH7mrdUfpa0HSIN/+QB5/8Jjbv1GrZHOV0BaNey7q+G7Wb+WxIaz0sBtYRukM65H8WrHzwwQe47777aLYkKSkJX375JRQKBb799tvf3IdcpIGBgdxfQICj0upfUQQ2UCzvM0htwytud/v29mFPHBIIBSwV6yz2z0hzxNb72EG5olY6xiyQOumc048tFGhLRGxbunEWsUBi21/Ujk7KAWlbC2EQkQaIrYWwd+yvPAoyLVDHtreD+zgdYT3YPiM6Erxw1reAVrIlYgfFOot9e/IE2lrI9u3tQ44uFoogbe94IjYOUrGIxwAn+8jIZ/S1tf2O/YjODtDl7JfYjml7dRbCVrGza9rYIhFBKm27Dzm8WMyYLm2OZwPeSiStxp/YaNfZljCcldx+MjGveSD5AgRoS+2xvfLOR7cXtm+/i5SrYtvGfomI7tvmeLZzEBudk8FkjOygXPs27dlPtmljv+080vbOJxHbxr9typ6Ml33M2viPSARpKz+g+9h+E0k7/mPXtec/dHvqsyK+/zj5vb0iM+98tu1JBqU9nf2aa2P/v7h+RQJJGwq7fXtRu3MT+0wIMsc4+wIJJtm8Y6/ozd+1rX/8HaTlT/j7p8p/NVgxGo24du0aRo8e7TihUEj/feECe5puTzQaDSIiIhAWFoapU6cizfZU0Z4YDAaaYnP++18LwajY3rF8gDgeQheyjiui7B+HjkysoyESJ0EiDoW7fIZNxy52T7f7IBS6wU3WF27Snly6lEiIchV9DXSdCBdxsJ3ES7eJ9lxGdTHKWRAL5bauqIwe2Ek5l+qSvefZKIUi21KPJ+I8WLfmQX6EVkiOyLqp+skiEe1GmvOJMNJ/BqcjkuDeC0EuEfCW+qKv90AuQCEyyn88XERyJLrHoZNbNCNC23Qzw0hnVWCYf08EyLxtFGRG254bPpbqZoT1p2vmdh2ZOGeHszXvJTGDbbRNQrsWwEvmiqlhbM2bYFToCBMdBIhX+mNEEOkuLMRDyQM5HZmMR4bGINHLDyHuHpgWx/Aw9oDmnuRecJNK0Tc0BD2Dg2zBC9M9MpA1fZzUOR7BSg+OgkxuaMuHsHYHcwYk0yaFIid68sLBjOl095je3D7Ufjc5pvRneBCCUaHjaKPqxob6YlC3KPrvu2cyHVk2Ivedgb2jERvphwB/JUbbuh6TAIWO3/Q+UChk6NojAkldQzkqLpGF95LO2sCwickICPakGQiqEwgwdxmj/k5eOAguCildrrHTe6ctZfvNemgcvXbJ8g3RKX3dMWYOG9sFq1mzTLK0QgKCqM5h6Du+Oz3+vOemMx0NvoB+k3oiOjkc/uG+GDV/CLPfdrOe/sgkKNzl6DokEUkDOrFgwaZb+MJM+jp8Zj8EhPsy+8n3FgDznp7K7F8yjGJeOPulIkx7gFHvCUbF/p3JK8GmjJ7JlqIXLBvJG//IuADa5JD8e8Fi5n/kXMT+/gNiER3jjwA/D4wZnsT9NnSMpvaCQiFFj/gQdI0N4o3/fXey35HQ1UmbBWf/uX8c85+5fZMpZsrZfxYPYP5DMCp22jJ59ZbLMaML+/0f7enwceLlCV6+GBUWTa+VFYlDeP4/IjAOCcoABMo9MS6oB6cjMjtiMK0M/XvXb7LnYHhK/Fn5AzpfAMP9Z1FdJ88ZtvmHzTNCgRiJnrPZvEUwdzbaMplNxEJv+Lqx35RgVJznT6E4AWIXe8mEv5d0dF3+i2JWysvLERISgvPnz2PAAHaxEnnqqadw6tQpXLpkS806CQlicnJykJycTNfl3nvvPZw+fZoGLKGhoW22f/nll/HKKwyL4Sz/6zU9i+EirCR1KfSCWDEXAloLhS0JGPUHYDLdgEgUBheiE0i4wmuNuu0wkd5A0i5wk0/jgGpWqx7Vms2UuuzhMgBKuaPugNHSQJd+zFYN/BUjoXRxdCjVmspR2LSX9QZymwB3qQOoVmfIRX7TcYgFMsQrJ0Eh9uV0xdrbKNBcg1zsgW6eEyATKRzdmhsvoUSXAy+pP/p4j4LI9pRFCkVdqDtDqcvhikj09urP2U8wKUerz9B1784e8eju5QBqNpq0OFhxnvYG6u/TFQkeDhsrmuuxr+waTRSTyTTC1dEbJUtdSZd+yJPkneG94OfC6L1ELlYX4kxlPqUuz4nuATeJzNFbpzgbN2orEOamxOy4ZEiEbAImqfSfstKQp2K9gabEJnD2600mbElJobiV/mFhGBbl6J/ToGvGjhsptDfQqPgYdAtx9C8qq1fjl2vpNFU/uUcCovwc/XOySmvo0g8B2E4b0Bl+SuYjRK5llOBiKusNNG14MlxtHYaJ/acv5SAtuwLBAUpMHtUVYtvTNlnKOHwkBcUl9YiNCcDIEYmc/Qa9Cft3X0ddTRO69YpEHycKcqNKh4M7rkCn0aP/yCQkdHPQY6tK6nBkB+vNNHJaL4TagLJE8lNLcHr3VZoVGTd/EHwCHf1bbp3OwLWjKfDwccfEe4bToMNu/7lfriDzcg6lLo+/ZwTENnovsf/IhlMoySpDTPcojJgz0GF/sxH7vz5KcSvdhndGn3HdHfbXNdGlJwIQHji5JxL6xDjsL67Fka0X6LLMyJl9ERob6LA/vQyn992kvYHGze4Hb3+lw/4r+bh2Ppf2Bpowsw8Urg7/OXs6C5np5QgMVmLCpO688T94PA3FhLocHYBRQ538x2jCLydSKG6lV2IYBiQ7/EelbcZP51Og1RsxvGsMkiOd/KdBjV03mP/ckcz3n4zqGuzLZr2B7urSBf5uDv+5UF5Ml35Ib6x5Cd1o0G23/3B5Jm7XlSHU1Qszo7o7/L/Fiv3l11CkrUEn92CMCez2b12/OnMTrtQfgcGiQ6KyL8IUDnq4xlSOvMZ9NPsS5T4OSmmEYz9jOup1+yjA1tdtNqQiVh6BiNlwAWbDKUpdlirmcfPn3w2zEr1x9R/GrOQveuMfiVn5ywUrrcVkMiExMRFz587Fq6++2m5mhfw5OwXJyPwTf8wO6ZAO6ZAO+QsHKxv+hGBl8T8zWPmvUpd9fX0hEolQVVXF+5z8m2BR/h2RSCTo0aMHcnNz29UTphD5+/9TCD3Z1Pg6LPqjlAEk8XgRIikrVGW1NkCjWg2T8TKl47kr34ZYwp5yTeYSVDc8BYMpCy7SZPh7vQOx7WlDa0xFUf0LFGirdBmKcK+XILI19qrVnUFO/Xswt2gQ6DoZMV4P0ZQrkYLG3chUbaBZmzjlHMQq5zDQaosVN+p/QKZ6P82s9PZdgmh3lq0xW404VvUNcpouwlXsidGBDyBM0Zl7ivq59BsU6jLhIw3EjND74e/CioHVGmrwQ9H3KG8uQ4QiEgsi74ZSwp628zXF+LZgCwXqdVMm4e6ou+AiYhfplfoMrM/fC625GSP8e2Fx1AQu3by39Co2FZyiT313RQzEXeHsaZtkcdZln8HPxTcodXllwnCMC+7M0ZPfvHkUR8tyKHXzhR5j0duPZQtUhmY8f+EwrlaXIsLdC68PGIdYT8YyKGlUY/Wpw5S63NUvAK8PHwt/BRvj1KoqvHz0OAXaDo6MwIsjR8DV9rR6OrcQ7x09Q/uz3NElAQ+PGMgtF/18JRXfnrhK7Z8/qAfmD+rO7Le2YP2hy/jlQirNrCybOABjerInUoPRjE+2nMaZG3mUurxq/gh0j2djTACb7391FLczyhAa5Iknlo1BRCizv7JShQ8+OIjColp06hSEx1aNg7c3eyLNyazAZ+/sp0Dbnv2iseKJCZArmP1Xz2Th2/cO0qJwIyZ3x8KHyNIkG/9DWy9gx+fHKIB16j3DMOUeRgsm9N6tH+zHoR/OUurywmemYMhUVjnYqDdi3TObcfHXa/D0U2LZewvRZRArdEYAs5+s/AZp57IQHBOAR764D+EJ7LtVFlbjo2VfoSitFHG9ovHol/fDO5BRbnOu5+PTh76lQNteY7ph5Ud308aI1H8O38b657ZS6vKI2QOx+MUZDvt/OIvtHx+k9k69fxSm3s9oteTf29YexSFCvXaRYsFj4zFkUneOXv31ewdw8WQmZQA98PQkdOnJsn0EsPzJeweRdrsEwaFeeOTJCQiPYBnJiio13v/sEAqK6xAfG4jHV46Bjxcb/8zCKry74Tiq65vQr0sEHls4gjamJHIuvRAf7T4Djd6ACb0SsGLSQG4p6adrqVh/mvnPggE9sKC/zX9aWvD5xUvYkZoKuVhClyUnxDP/0ZvNeP3ySRwpyoWvXIGX+o9En0CWhVYZm/HS9QO4VluCCFcvrOk1ETEeNvub6/FW+i5KXU7wCMHTSdPhI3P/l9dvdtN1HKrYRKnL3TyHYFTgXO76zVH/itSGTTSzm+g5CwnKWdz8U9H4GWo02yEUutBlbW9bJ/mWFj306tdh1hMGpS9cPF6CWNY+W/T/efmjSzktHctA/zXp168fpSkTujIRMmmEh4fjwQcfxDPPkE7Fvy+k30fnzp0xceJECtb9K1KXjeoXYNERRDtByJNJRwKZ31EIxaFQ1c6GyXjBxuwha/o+8PE/SwFkRZVDYbKUcjqZJAlh/gdhttYhpXwELC1a7pjeikmI8f2U1li5VDbDhsZnjJ9oz+U0YCnXnsa5Svv6L5O+/q8iwn0ibtfvwIUa0v3ZIVPD1iJQ0RWHKj7DjYb9NHVL1pXJMs99MV/BUxqAdXlrkKchDc+sdJXaVeyBpxI+oUC7F1OfQb2xjrIGiC5UEYbnEl9Bo7kJj9x4iVa45WiRPr3wSKd7ka8px4PX37MxHsj5gHkR47AocgLOVmfgiRsbeTa+1PUuTAjugY15F/B26iH6mf1y3Tj4HvT0CceLVw9gS94N7lxkrf/whAcQ6uqJ+Ye24mJlMWVCkPV+bxcFTk6/H1KhCKO2fIuypkZOl+Djh19nLUSdrhmj138LrY31QzACE+M74ePJk2iNlelf/0iDDzb6wIqh/fDw8IE4mZ6PB79njR/t8uac8bijZyJ+PH4d7/10imf/t4/dhe4xIXj7+2PYdeI2x2AhoMqtby1GsJ8Sj760HTdSSzh6rKeHAls+X0pBmYvv/hrV1WraK4hgHAiW4ssv7oa6QYclMz5lrBnbfkNHJ+HZ12fSGisPzljLMWZI1n/u8pFY+NAYXDqaipfvXsez/8mPF2LkjD7Y9cURfLWaNcW0f4H39z+Nzv3j8OnD32LfN8cZPVkogFgqxtc330VgpB+eGvMqbp1KZ/RkgnXx88CGrI8piPaexEdRXVxD+/YQXXRyBD6/+jZUNY1YkvAImjUGbr+hM/vjuc2P0BorDw58gbJ27PbPe3YaFr0wA5cO3cJLc/k+/uSXSzHqrv7Y9c1JrFvzC8/+93Y+jM59ovHpa7uxf8cVh/0SEdbtfhSBIV546pEfcesGa6pIaM1KpQIbtq6geJiFy9ejqrqRsn7o+Ef54esPF6GhsRkzn/wWzQbGuiLHHNW3E15bOYnWWJn7zo/M/0n5f4JnGd+PBiwnM/Ox4ge+/7w1czymdE/Ed9eu47UTJ3n+s3XubPQOCcFz545gc8Yt5v8UlC7CsZn3IMxdiUWnfsDlmiKH/8sUODJhJWRCEWafex9VehUNjAi+JcYtEN/3f+h3r19SY+WznCdsjD12BYzwn4XRgXNQojmH4xVP8uwfHPAiYjzGo7JxPUpU9sw4+wYJATvgLuuNZtVzMOl+5M2fbv7HIRQ7lif/LpmVqO+eg/APZFasOj0Klrz+j8ys/NfZQIS2/PXXX2PDhg3IyMjA8uXLodVquVoqixYtwrPPPsttv2bNGhw+fBj5+fmU6rxgwQJKXb733nvxVxWLntRNsVP5yKsBVuMltLQYYTKedaIgE1peNe26bDIXwmQp4ukMphRYrPXQGG7A0tLEO6aqmdED65pJ92FnXHgLqrWsRkSF7qwNdGsXIf3MXmrfWch2JTrWCTa76QKH8CdBibnFiBJdKi0Il6tJ4WiKJChpMqtQoS9GjaEatcYajt5IXot1RdCYm5DTVIBmSzOPFnmtgdErrzdkcYEKOx9wvpbROs/VZtlAt3brBThfk0nfn6hkNE37PqRA1dlqlm07UpbNOxfJtFyuLqYdbM9VsImajnBLC2qatchsqEaRWoXiRjVPl1ZbTWmfNysq0GQ0cvRS8nosL5++P59fzAo7OdlyNCuPvj+dWUBvWpz9AgFOZxRwlFWe/UIBzqUzSv7p66R7r+NcBpMZN7PKYDJZcO12MY8eW6/SIq+oBmVlDaioUNFAhdpvbUFOThXU6mZkpJRCR270TvtdPM1o0tfPs3PZz0deLhxj9NjLx9IYaNXuI0IBLh9n4PaLBxjN1/4FCHD06jGmO7/3moOebCUYLRNSzmbCZDTjxvFUBz3ZYkVDpQr5t4tQlluJivwqrqEh0RFKs7q2ERkXc6BVN/P2I1kbav+xVBYoOtlPzk/tP5zS1v7DzO8uHkltY/+1U8y3LhzP4NtvMCP1WiEd/xvXCh3jaGlBQ70W+XnVKK1QobxSzdGT6fjnVUNNqON5FbTWivP4n7nB/Odips1/nBoZnrzNfONUdjv+k8X852huW/85XVDI/L8ox+H/LTb/ryyB0WrBhepCvv/rtchSVVHqcnlzPa9RaHZTOVQm7e9ev7maW06BCrMmo5Et55fqzreZf0q1jPJun7+cvgHUzSx4N7czf5qN/xoi0CH/LPmvV7CdPXs2ampq8OKLL9KicN27d8fBgwc5OnJxcTHX84NIQ0MDpTqTbb28vNCrVy+KeSG057+qEFBYi7WGRywTCMlyiAQQKIAWXavtvSAQOMB9DhHRCrZiEUuFO+1BGxwSkdJX546pQkhs28uozpkWLIBUyKJvwv5hzcXsdVGskNl0cpEHNOYG3r6kMBzJsJCKtEarAxNERCFyg4utP5CzkOyKTOQCN3Fbnf0z0ijNmfpIAhKlhOmURNeKlkq2J+IldaVPhs51UTylDMRJQLU1eg2P1ucpk0MiFEIhlkBnNvFsIToPSdunGxIouUok8JTzdeT24enClhqJzrlGBmX2yJkdnuSJid/YF0rbUxRh/5AbDHcDI/bbevx4ustpLxnnHKeHqwtl+chdJGjW8+1XusvhJm+79EluwHK5BB6ezB5nO9yV7DPS+8a5kzOxycPL9tt4ufJsIN/N3ZONv9LHnWY4nOuieHjbfjcfd9rLx/m47t6uNEPh4iqDXsv3HwLCdbMdl2c/+b5uLvDwcWtrv215heh49pNsjY87d8429tu+m9LHjW+/pcXx3bxc0VCr4fkeGS8y/i5yCfTN/PH3UMrhaluSajP+LhIo3dr6j9IG2FW6tvUfAqomQv3uN/yHsH94/m9t4fzOy0WBap22rf8LhFCIJNBZWvu/Ah4Sabv+LxfJfvf6VYjcW5UeIOdg8wibT/jzj8ymI+wfxgRy1JUS2+Y0Mh+Sh7i28+ffTzq6Lv/Fuy6TJR+SHSFAWAKqJUtDdjl58iStWmuXDz/8kNuWBCz79u2jmJW/skg8XuDox0SEsuH0j9xs3ZWv8+qbuCjugVgcC7HID94cZY+Jn/IlCAVyuEl7wdvWkdR2RER4McYTaVqolDnYPyKBC+K8H6fv4zznQiF2YIFkIk8keN1N3/f2vRsSWyl/It7SKCR4si6powPv5xg+RKJdeyPGrTe1f1rIvVx3VSKDfCZQzIqHRInJQYwyapdZYXMhFUoR7x6DgT69nawXYkkUozAO9++JRCf2j0wkwdJo9l3nRAyilEq7eEpcsSiKUWcJRoXQKu0S5+GHGeGM1vl8jzEcw4HIsMAY+kfsX9N/DK+6zN2JvRCj9IGfwhUP93aAvulxBg2Hi1iCXsHBmJwQz7uhvDSK0VsndYlH91AHe4M0JXxiNKO3LhzSE4FeDoaSl6scS0cw7NKySQPgKnPcIGKCfDFtIOuou2r+cFqfwy4DkiMxoFsktX/VfaO47rxEZkzqgfAQb3h7u2LRQkcDOSLLl42ETCZBUnIYho91sDdIvZMVT07gqMsJ3cI5HamRcs/jrGvutHuHwy/YESiTwOCulWPo+wXPTIHC6SYckRCMcQsYLXbZuws5+jGRPuO6UfYOsf+hT5dyDBN6jofGIyw+GF4BnlhgoyPb5YH3FkMml6HzwHgMnz3QMf4iIR78hGVih8/qj8R+DmaTTC7B0teYb925bDT8bQ0Uqf2+7pj9CPveC1aNp92mOfvjAzFuDul6Djzw1ESIJY6psM/gTug9mFGnH3psPG/8p83sjbBwH3h7uWLxHL7/rLhnOB3/5LhgjOnv5D9CAR5fxPxnfK94HvuH4Jcemcr8Z9HAngjydPIfhRz3DmH+88igARxmikgnX1/M6sr858X+I3j+PyI0inZdJva/1HM8z/8XxfZBtLsPvGXuuCeaTw9+uNMkigf7veu3q+dgHvtHIpRiXBDrOp/kNRuuTvMPeUDq6sV0IZ6rIBI4giC5JB5+buyYLsqXePOnSDYCYhmj0//thAQbf/TvHyodjQz/JLGaC2E1XqZPCUIZq7FiF1qx1ngbInEYJNLBvMm72XAJRkpdToKL1BGUEUCaWn+KdV2W9YbcBsql52oxokZ3klKXfeQD4CJ2TH4mq4ZiV8hTSpBiMKQiRwZHZ65DifYyRAIZItwGQiJ03HzqjeUo0aZCIfZAjBup6eCwv7y5ACW6fHhL/RHr1oVnf05TFir1FQiVhyHKzUEhJYDYW6o01BvVdPILVTjbaMbFujRKXe7h2Qn+Lo4bJOnEfKaalccf6JsApdQRYNXom+jSD5lQhwd0glzsmLwLm+pxpaaYZlnsNVbsklZXhZS6SkpdHhgUwbP/cnkppS4n+vihe4DDRvL0e6qgANUaLXqFBCPWx1H6mywvncjOp9TlgVHhCFI6bjAENEmwK2T/YYnR3JMxkVq1FufTCyGTijG0azStyWKXkqoG3Mgso1mWQd2jePbnFFTTsu5BAUr06hrOs//27RIUl9QhJsYfiQmOTrXkyfvqhVxKXe7cLQzhUQ4KOFmeuXQyk1KXewyIhV+QI0DUkuq5R1Jp9qLvqM5cZoL6SJUaV4+lUupvv/HduD4+RMryKpF6JpMGCH0n9OAVg8u9WYjsq3kIjPJHj5F8/0k5k4GSzDJEd4tAQt84J/utuHLwFurK69F5UDwiEkN59l/cd4OW5e8xojP8wxy/DenETLAr5PuTvkA8+6sbce1UBi0Q129MF7jY6OFEyovrkHKtAB6erlyNFc7+nEpkZ1YgMMgTPXqxINIut9JKKXU5NsofiZ2c/IdUtL1dgFqVlgYvUSEOG01mC06l5kOjN6J/fDgvwCX+cyIzn2Z5hsZHs2ydTUgn8FMFhZS6PCommutjRYRUZL5UWQpvFzlGkhorTv6TrqpEan0FxXAN8Ofbf7OhgFKXY92D0FkZ9m9dv2R5OLPpKqUux7h1g6fUUQLBaNGiVEuqXlsR6jqIy6zQ722ppks/pAicp3w0REI5b/4kSz9k/hTLSIDetrDh3wGzErn+hT+MWSlc+uo/ErPSEaz8idLSYmYg2naqRhKdwCl78e/o2E9j+U0dKVktbFfHUt0CJ/yHXawtFltvIMF/pLO0WDjE/7+rIzYSLMtv6giwz+mJ0GEHs9/eOI1/LgLmZcW0WovZSs7127r2qtn+no7YSNLu/6nOvtRDnqjb2G+10kxNuzZayHj8ts7OGGlzTLJfOzo6xgR8K/4tnZVj0fDtt41/O9+NnIt8r3Z9xGyhWZDf0rV3rt/T/Z6N/3v7ybl+w39+47ehPmJt+Y91/2f/+T/7v4VWbv6Pr9/f1P32/ENYinTx9zfmyN+aP/8uwUrEN388WCm6958ZrHR0Xf4TpMWqgrHhQViN5wGBOyTKVyGWT6E6s7kIqrp7YTanQSgMgNL7M8hkLMWtN95ERd0DMFtKIBHHIsjnG8gkLMWq0h1FQf2TMFsb6LJQjN8XXBGlksatyKl/hzYz9FeMRme/NyEWkvX6FqTUf46shh9pZiLG40708HucZkksViNOVb2L3MajNMDp7bME3X3m0eM1W5rwS+mbKNTehEzoinFBK9FZyWjNdYYqbCp8D+X6QniIvTA34hHEuLElhkJtPr7K+wx1xloEugRhWcxDCJYzWurV+tv4PHcjmsxaxLtH4/H4++ElZVmevWXnsC5vDwxWEwb5dsVTCfMgF8uo/V/mHMbmwjMU1HdnaF+sSryDrqWT7rEv3tyDfWUpkAhEdFloadxgjp788IWfcb6qEO4SGV7pNQFTIpiNxU0qLDuxC+n11QiQu+GjoXdgQBBbBrlVVYGVh/eitKkRMZ7e+HL8FMR5s6fEY3l5ePrgITQ06+my0Nopk7kiXFuv3sLbh89AbzZhdEIs3po2jqboif0fHzyHDaevUezErP5d8cyU4fQpl3RPXrP5KG1oSNhKyyb1x91jWIpfrWnGc5/tw9W0YrgqZHhq8SiMG5BAdeWVKjz3zm7kFtTA19sNL66aiB5dmP2ZmRVYs+YXVFapER7mjZdfno7ISGb/hTNZeH/NHjSqm5GUHIoX3poFH1/2BL9v6yV8885+WjhuwOgkPPHmLMhd2fhvePtX/LTuBH0/ccEgPPDydBoIEXrvx49spA0NyZIPoS7Peng8R09+Y+Fa3DyRBoWHAg99fDdGzGZLJAREu+auD5B3qwg+wV54ZuOD6DaM/TZZV3Lx6pyPUFVYg7CEYLy043FEJLEMyoVfr+H9e7+kBeBINdsXtq2CTxDLwP36zXF8vXoLLRw38I5eeHLd/RTrQu1/fTd++uIIyP2SdF1+4PW7bPab8fEz23ByzzValG7Bo+MwaxlbBmlS6/DGk9tw41IexaI8+NwdGDGRLbWWlzfgled/Ql5uFXx83fDsC9PQ3datOSOnAi+9vQeV1Y0ID/XGa89MRWQ4G39CQ3/160NQa/Q0s/LmQ5Ph68n8Z8fZW/hg1xlaeHBkcixeXTgOChnzn4+OnsN355j/3NWnK1ZPZP5jMJux+vAR7M3IpEuGhLr8QF/mP6Sf1crje3GuvAjuUhleGzQGU2NYdeZiTQMevLATGaoq+Lu44YN+09DPny3DpqtL8PytzajQN9Dii292W4AoN4YlvFZ/G1/kbbBdvzFY1clx/V6qO4SDFRtoj6BEj76YGfYwZCI5tf9m3ZdIb9hMMyuk63Jvv1V0/rG2GFBY9wzqdHtoUcwQ5aMI8ljGzZ+6+hWwGM/R+VOufB0SBX+J+W8jf7Rmfgv+sfI/waz83cWofg5WI2mYRmD+jTCpVsFqYuwLVd09MJsZ68BqrYGqbjGtvWJtaUZZ7XyYLWVURxobltcuoE8lBnMpcmuXw2xVUZ3GeBP5taykf4P+GjLrXrE1M2xBte4YcupZx9LCpn3IaPgeVpjQAjNyG3cgR83optfqNiKn8QidRCwtRlyq/QpFGtby4GDFpyjSMrS/warF3rJ3UaNnTJWNhe+gUl9M3xMm0HcFb9HaK0arER9nv0epy0Sq9VX4JOd9mhWp0dfhvax1dKIjktNUiE9yvqPvU1X5+CRnJ/S0aytpnpaCr/P3Ut3+8uvYUHASphYLzaDsLLmIHUWMTfBF9in8WprC2A5WMz5IP4pTlWyMn796ABeri+h13Ggy4PGLu5GtJoBn4P7jPyOrgb0nTKClx3bS4IYEGot//QnlmiYujX73vp/p8cvUjVixey9UzazTN2EHPb6fNXu8VlyGl/cdp80MyQ3lWGYe3jtyhur2XEvHNyeuwGSx0ifZLedvYfM5xqL5+uAl7L+SQZ+aCdvn41/O4kwqY4gQ6jKpYEvs1+gMePnLA8gvraW61W/tRn4Re1/XoMXTr++itVcMBhOeeXYbqmtYe4nSsgY8u3o7PX5VhQqvPr0DTY3ER4DMtHK8/RKj7RKWy9pXfoG+2UhvLhePpWP9++y7Hd15Bds+OwqziTTctGLv92ew5zvWUG7ze7/i+M5LNJtB2D7rX/4Jl2xMm08e+g63TpGGi4BWrcPb93yBwnRCyQdenvk+ClJL6Pv6ShVenPYuDW5IoPHsxDdRU8y+W1lOJZ6b/CbNilQV1eDVuz5AUz37bTKv5OLtuz9j9p/PwqePfE9Bu2Sp6sLea/jmeebjR7ddxLaPD8JsJPZbsHf9Sez5mjXr3PLpYdqokWSaiP3fvvUrx3T65NXdtIIt+QFIVdx3Vu9EYS6rDfXycztRkM+artbXafHCM9to7RUy/k++vBPVtczG0vIGPLnmJzr+FbWNeOaTvWjUMP9Jy6vAS1+yMb6RV4bXtx2nzQzJeJ24nYePfmH+s/tmOtaddvjP5ku38MNF5j9rL17C7owMms0jgcs7p8/ghI2htvrcEVyoKGb+bzTg0ZP7kN3AxnXF+R3IVjP7a/Va3H9uO629oreYsOr6d5S6TKREW4vHrn/Hrl9DHd7P/srp+i3A2hzWy61Qm4E9ZV/BaNXT6zej8QqtuUKENC1Mbdhgm38syFLvRJZqB9WVqz9FnY74oIXWVSlVvQVVM2tO2ax6FhZa3oHNn82qR2AxOdh/HdIhRDqClT9BrMYrTih3+gmsJlI3wwCzOYOHgG9p0cJsyobJTCipDU7MHgvMllJYrHXQGUldExOvW7PGyLrmqvU3W/1sVjToWdfYWv3tVtRBAer0jBZc2Xy7FVJfhKpmRucs0bI6KnYhk1CFPpuuTROasnNnZTJJVelLUWuohtai5XVkJoELoS4XaEvo0pDTaCC7iU2s6Y2FvIaEJIOSoma0zBRVMY+6TIC9KWoWKF2vI5Oxw37Sz+RmPbsJXq1ldVScj3m7vpxSODMbahwMCrRQZlCOqo7SllUGPcfMINuQmit1zTqkVVfTmwU3+i0tuF5OumADN0sqeA3lyP5Xi1nAebOogpdqJ9uRz6j9uWU8pgrZ7la+7ZhZpbwuyOSY6QVVNBtDaMp2HQkuCDOosKQO5RUqNDbqefTYqqpGqFQ65GZVwmwmvmY7nsWKjNtsrDJvEvadk/3WFqReZRTYjGsFbai/mdeYLu1CLo+FQ5ZfMi6z3y31XBavCzLZLvtaPs3GFKQUczryebNGj+L0UppxaarXONGCSZBSC3VNI8W4kICJs99sRcYFFphmXMpta/85dmPLuJLf1v6rzO9SrzAciMN+IdJt3y31WhGvQzW1P60MRqOZ0pR5499sQlFhLcqr1GhsajX+1Y1QqXXILqqmS0Oc/1hbkJLLfutbBW3953oe858bxe34Twnb72ppW/+5Vl5O31+uLOX7f0sLbtVUUv/PUle38n8jchtrKG250aTj0ZMr9SqojFoUtnf9atg4lmizeIB7Mm8Uahn1vaY5pc38U2Obf0gneX5aQAyNgVHOLcbLbeZPi8nRSfrvJB29gf7v0hGs/AkiEBFgGn/tViAiyyFSSmvmdzsmzdRCbJVqW+0jUEAkVEImbt0DiVCC2fKKXEJeHTcGMjkoJGx7V3FQG1oh+YyIhySYN5GQScZdwpD7ntJAW5Mxhygl/pQh5Cry4E1ObHtfWqmWsASchTCBFCJX+Lk4wITMDgF8ZYylEeDizU2QREQQIsi2fZDcqxV1GQi0gW/DXL3oerxdSOYlWMGAoQQ46KwjEqJQ0sJv3jLSWM3ZFiDY1YNWqm29D6kMqpS5IFTJXwsmN41gD7aEEuLpwaOekmOEebH0eLAXX0cOH2wDT4b5evJqaBDsQbAPOw8p/tYanxDo406Lw3l6yHlsFPKWNNHz8XZrg1NxcZHA3d0FgcF82ic5tn8gszEgxIsXGBGMRpCNQRMQ6s3TEeyAfygb/6BIP7qtXawWCwJswNbAVjoipFEh6XhMGh7yMAgCpvMO8mqDASE0ZzcvNwRGOMDAzEYB3YfaGOHHs5GMQaANPEzsaWN/GPtuQeE+rey30u9L7Q/1oudwloAgT9rIkRSB47kJsT/AAz5erm06MrvIJHB3c0GQb1v/Ib8nEfKb8/xHKECor7Jd36K+amMHhXsq+f5vtXJ+Soq/tfF/Nw+b/yva+r9CSSvVOj8YUPtFErhL5PCTtXP9StlnnlL/VqUHhPCSsqUjV0nr+UcAV9sc4yIObzXfWbh5TtjO/Cm0zXd/S+loufx/ko5g5U8QKaEnO9VNESkWQyhlTf08vT4DbK3QyeXrrnwFInEoREIvWl7f8RNIEOj9KQQCKRTSzgj2eNhxPIEronzeo+/9FWMR6DrJcW6RH+K9V9P3nTznwtelK6dTSkmHYUb57ON3HxecEAl37YdOSkbrHB/0IK2rYpdeXncgXJFM7Z8b8TDEtsaLRKYE3w0vqR9cxW5YELmEC2RIYLM06gGIhWJEuYZhRigrpU1ELnLBg7GL6fshfskY4c8ox0S8ZB5YHsu69s6OGIgung5aLamoeXc0w848kjgKIQoHa2hIQBymhbNy6a/3ngilreYKkUVxvdHfn7F+Ph52B2QiBs0ilr7QdxSdyD1d5Hhz+FjuKZfUZPlw9ERIRSIk+fvjwf4Oej2pvfLuBIbPGJsUR+nLdvFzd8XqcczGBYN7oFuEgzURG+CL+0aysuErpwxEsI/DRwZ1jsId/VntoGeWjKZ1Vexy15jutPkdsf/FVZMgtTX+I/LQPSNosOLhIafl9e1BDmmu9+yzk+kNNqZTIOYvHeIYf4UUT7zEMACDxnbG8EkO6ru3nzseWM066k5dOgyJtjLzRCLjgzD7QUZdXvz8NATayswT6TO6K8bMY9irR9bew2PdTF0+Bt2GsqaKz2x6iNKj7T/A8vcW0cDDw9sNj35xv8N+qRhPb3iQBjgx3SMx39atmdrv6oIn1i+n7wdP640RdzHKMRGvQE8sf2cBO+/9I5HYO9phf2IwZj/KfPzuJyci0Ba4EOk9PBFjbF2XH35hKtw8HP4zZW5/JPdh1N/VL06FVOoY/xUPjkFAgBIe7nI8sWIc5z+kJsvzj02k498pwh/3THX4D6m98uL9zH9Gd4vDhF4O//H1cMWT05n/LBzQA93DHP4TF+CLB4YyGx8fPAihSof/DI+OwozODPvz5uCxNMi2y91JPTEgiPnP+/2m8fx/dfexNFghNY2eTrqTy3ISHNhLXWZDIhQjkly/Ifzrd4Xt+u2s7I9kT4dvuUm8MCnkHvqedFj2c2F0aiKe0hh0tZVOCPF8EjKnirRKl+HwcWXUdRfPtyAQOr6bRHE3RFI+LbxDOqSDDfQnSYu1EVZTBs2kCCUOCiYRq6UOJnMWRKJQiOkThkNM5jJazVYqIbVXHB1uiehN+TBaqqGQJEAscjwtk59MY8yivYE8pEkQOdVPIYyeBkMmRd17uyTx2EJmqwG1+myIhDL4ymJ5aH29RYtqfT4UYiV8ZXwbNWY1XfrxkvjCW8a3kSz9VOurKcDWU8p/oi9vrqJdW8MVIXC3FX6z25+vLYfOrEeseygtROWcMclUl9HUc6JHKI+lYLCYkKaqoF2XE5SBPLZQk1GPDFU17Tobp+Q/mdfpdXQNP9TNA2HurWxsakRhowqxnt7wd+UXIyuob0C1VoN4X1942gpw2e3PqqqFxmBAUlAAFE4UZPLEm15KUu9WdA4NoGBazn6TGRnFVZBJxIgP9edlUwhWJae4hhZ8i3aiuRJpUOtQWFJLg5TgAL791dWNtJpteLgPfFoVUystqkN9XRMiYwJoITNn+wuyKil1OTYpBC62nkHUflJJ9jYp729FXHI4r34KwXrk3CyCVC5BTNcwHtuGYFVIKXxS8C0ikf9UTMrnF6WXwD/cD0FR/nz7S2pRnluJsIQQDkDL2Z9dTnEukV3CaXDjbH9+SgmlLsd1j6QZGZ79t9iyTlz3iLb2p5ZQ6nV0Ugjf/iY98rMqaGG8iBi+jSqVFoUFtTRICWqVtaqqaURZhYoCbAkA2lmKKxpQq9YgJtQXSlvhN7v92WW10OoNSAwLgFzG95+08mr62iWklf+YzUipqqLUZRJQOy8nEaxKRl01vFzk6OTlCCqJ1Bm0yFXXINhVSTOUPPv1KlrNlgBsfWUeba5fle36dWt1/VbqC2GwNiNYHg2pUwkEMv/U0/nHCh+XRN78Y23R055npOuyQpLEm3/I/Gmh86cXRDaSwd+RDRT21UsQtio6+Z+ItVmPkgde6WADdcgfEIECApFfu5UXBUJXiIR+EApbV6YlaWBPWEVk4nHUWrCLWORD06qkqi3veAIBpGJfCK1yCLmsjU0HUoWS7GdttX5Msh8SKMQ+EAmlbWiFZMJxFXtBLmprh0woh7vYEwpxWx1Z9lFKlJCL+FVTiXhI3Kj9Lk7BiN1+b6kHXIRSSIWOiZoIedLzlbkTGF6bNDUpfOXn4gaZUNyG1kxqrvjJXXkZFru42nSkqmdrUbq4wN/sSlkUrcVbIaf2K5yKcdnt93VT0DopslbUWXIDIU/L1naoouTG46t0hVQsbrPsQ5YQvD1daYXT1qKQS5nOva39ZNmHBCmuTjds7rt5sWrB9gaGzvZ7+brTIIX06OHZLxTAO8CDLqe0XqYRS0XwDlTSTElrWjAJGEiWwznDwtnv7gKvQC8ayLSx38sN3sHecFW2rWir9FPS7lH2BobO9hM75G4ySOxZG579nnT827XfXwmpTNzWfrkEXn7ucHfKsHD2K2Tw9nHjBXyc/W4u8PF2hVs740+W8Kj/2BoYOtvvq1TQIIX0eOLZLxDQbJ3lN/zH39UVMuI/rZZ9SKVmUujQ0ynDYhc3sQy+Lm7wcqpZxNkvltPrzU3s0u71i9+4ft3FXpBaXXhZV8f840sBtq3nHwGkkJC5DrK2tGaBAsLfmD//VtLBBvo/S0ew8ieI1VwCY/1CtNBePwKI3Z+AxG0F1ZmMN1FftwAt1nq61KP0+hByBUtxa5oPobxuOUXHk2AlxPc7KFxYar2qaSOKG16m+BSJKBDx/j/QwnD0qazhPRSqGbvGTRKP3kFfQybyhbXFjAuVz6FUy1D2AfJ+GBz0HsRCFxgsTdhf+hSq9QwMl6CchKEBT9BJQ2WsxJai56AyMTDfMP/FGOjLqkuW6HKxPv8N6CxNtKbCrLCV6OnF0sA3Vdfxdd7nMLWY4CKUY2XsI4j3YJTJgxUn8W3BdjpZe0s98ULSw7SwFLGfsH92lDCWRrRrMN7qtgxeUnda7+GF21txoooBf/v6xOKdHovoWnqjqRkPXPgRtxsYy2RGRE+83G0yDVpKNCosOrWZ0jTJFP541+FYnsSqu96qrcDdR3agwdBMl3reHTQR02JY+vxIQS4eOvwr9BYz3KRSfD1hGgaEsKzSphs3seb4CYohCHBzw8ZZM2hhOGL/O0fP4NsLDBwYH+CLb+dPh6+bK0wWC57cvB9HUlnPooFx4fh08VRa5bZRp8fKz3chpbCS6u4c2AUvzBlNb65lNWo89O5OlFarqf3LZw7G3ZNZ+j8jtwJPvPYz1E3NdKlh9crxGDuULR+du5CDNa/voUBQhUKK11+Zge626rR7dl7B5x8eokGHr5873vpkPsIj/aj933xwED99z3pGRXUKxBtfLYGXrxsFtb71yCacO8DAjT2HdMKL6+6hXYo1ah1emLsWmddYr5px8wfh4ffm0Zt+ZWENnp32HioKauhaw90vzMCcx9lSZdbVfDxPGEB1GprlePyr+zBy7iCOnvzGwk9hbDZB4SHHyzseQ7dh7Lvt+eoovnhiE7M/2Atv7n2Kdmum9r+4Ez+tZU0tozqH4o2fV8HLX8nsX/4tzu1jDJqewxLw4ncP0C7RmsZmvLB0PQUYU/vv6ouHX53O7C9rwDPLNqCitIHav2TlKMxZOpRul5lZjmef3k4ZQGT8n3xqEkaPYUsdZy/n4uX39tKu2a5yKd5cfSd6dGXjv+PoTbz/I/MfPy83rH1yBqKCmf98uOcMNpxg/tMp2BdfLp8OH3fmP4/v3I/D6cx/BsWE47O5Nv/R67Fk1y7KTCMyu0sXvDZmDA1aCD1/wYHtKGpSUf95svdQrOzGlsoI0Py+s5vRYGym5fff7DMFU8LZUvGZ6nS8lPIjZde5imR4q/ti9PRmhR0PV57A94Vb2fUr8cTqpFUIkbPr92DFRpytZQ0XA10isCTqJbhJPOn8c6byBRRr2LUdJO+L4cHv0PnHbFUju/puaI03qM7XdQ4ivd+g84/VXAx9/QJu/pS4Pwmp20r8PYX8Qn8EJCvAP1U6MCt/gpjUz6CFdk8m0gJz07uw2i7Khvr7aB0B25ZQNzwCi6UKFmsTymuX0UCFiLVFi7LapbQwUrMpF8UNL3FAWpOlhqMu1zaf5gIVIlpTLjLr3qLvc9TbUaplEwWRquYryGxgXYyv1K5HjZ5RqIlkqvchp5E1F9tf8THUJkbVJHKqegPKdGzbTYXvo9mioe8JQ2B78Vo0mhpoozN7oELEYNXji7xP6DalugqsL9jGge1UxkaOuny5Pp0LVIgUaivxRc4u+n5n8QWctAUqRK7W5WFTAWt29knGcaSpGGuCyE9F17GvlDENVl/ZhzItG2NyxvdSTuJGLdt2+YlfoDayMTZZrXj87D5U6zRoMhrw4OG9NFCh42g0YdnB3ZQFlFtXh1eOHefAjrVaLR7bx6inp3IKuECFSG51Hd44xGwkVOWjtkCFyMXcEqw/SVgQwGe/nkdasWOMd51PpTVXiLzx3WFKd7Xb//nOs0jJZUyP59/dgyYbBZYwfF7/9ABqGzTQag1Y8/puGqgQISyVF17ZRZdBigtrsPb9gxzYtL5Og7deZtTly2eyuECFSFFeNb56Zx99v3fTWZw/6GBh3DyXgx1fssB341t7kH2T0dmJHPrxHE7+zL7bRw9/j6piRmEnX+D7NT8h4wpjCr067xNoGhgFlgQT792/DnUVKmgbdXhjAQtUqP0aPdbM/ohSjoszy/DZ4xsd9lep8fbSL5n9h1O4QIXan1nOdYPe+90pnN9/y2H/mSzs+OwIs//DQ8hOKXHYv/0yTu5lQc2Ha3ajukLN2f/d2mMce+qVl3ZB4zT+77y9D3V1Gmh1Brz07h4aqBDR6U147u1fKAuooLwO7/3g8J96tRYvfsX850x6AReoEMmrrMM7PzP/2Xz5Fo7YAhUiF/JL8PVZNsYfnDuHlEoW6BLZlpqKPRmEaQg8c+YgSjXMfnLGd66exvVq5j8PXdjh8P8WK56+sgfVzU20UvSLtkCF2m8xYvWtTfSBoay5At8VbnFcv6ZGrM1ZT99nNV3jAhUi1foS7CtntOYs1U4Ua1hnaCKVzVeR1sBozWWq96E1OnyrVrsVdTp2HEOr+dPU9A4sNvZjh3SIXTqClT9BrObsVtQ78hnpbkta3Je1ajxIakAUwmwuI/2BnfeAtYV0ca2H3pTXKt9nQbOZTWIaI3l1WuultGZG62w0FrRKvbag0cSehOsN+Tx6shBiqIzs5lOrJzVKnG0E6ozFlLqsMtXyEP4ES1JnqKRYFXugws7UAp1FR6nLZK2bNxawoqyZTbRF2qpW1GUr8rXsabFAW81b+iFnLdSwGhE5jQ4Kpp26nN/EakmQmirOOiJ5jbWUulmubeQxLMh25AmUYFUMFgvPfrXBQLsu59c38Ee/pQV59SQzBuTW1vNS8ESXXc3syKuu45U5p59VsZt4bnktj6lCKpfmV7Jj5pXWcd177VJYUU+py1W1TXz7rS0oq1RRrIrRaOHjmDR6qNU6lBTZAgf7GFtbUFLIbCzOq+FTf8nNNYf9NsU5VTzGTIvtM/rbZJTz6MmEulyczfYrzCjj6YiUZJVT6nJNSR3ve5P6LYS2TD4nGBLuXNYWaFRaqGubUErscRoOcuySLOYjxVnlbe1PYzc6Yo8zq4fan23zrawKHj2ZLBEV5zHfKsytpkGesxQX1NJAkIwzz36LlRaKI1gVo4k//k0aA9SNOhRVtPIfawv9PYnkV7XyH9KtuYL9Nrk17fhPDfsts+rq+P4vFCLX5pNZqto2/p+rqqOFFCuaG3nsO4KlKtY2UKyK0RaosLFqQZO5GWqTDuW2a5UbY1hRrmfjWKMv5TEHia7SVpNJ1Wr+odeUkdHDdbRuimO8BBBDb2JzmtXM17HPHEHb30r+CBOo5Z+9DNQRrPwJIpSQZYVWa8/ieAgEMohErSl7EojE0ZCICVqfrCHbJy4hREKCJ/GBnAJ0nX8aQk9mFU3dpfFtqMsEZEvEUxpHi8E5C/mMiI9LXKtJxgxvGUv5BrjEtKEu+8kiIRZK4C0N4OkI68dXFgQfqS9kQhnHBiKv7mJ3uIs96HKPM92Z0BsjFAx0GeUWzJs8iS7OnVEY49yDYLaV6rYL6VlChAJqnY5JtuukZGDfJK+ANtTNTp5+lAUR7qbk3RwI7iXSwwuhHkq6zu8YfQF85HL4uMgR5+PD24ccO8GPgRbj/X3bUJc7BzFAZnyQH83MOE/W8cEM7EsBtU7HJE/gnUJsx4zw59GaiRBQJmEBBQfwac2EzhwW5IXAQCWlKtsPSZlnSgWl2kZEkbV/p/EXCRAVG8At+/CpywLE2gCx0YnB/Jt2CxCVyPoNxXQJ5dlBMiBRSWy/2OTwNtTlyKRQ2oOH9ANy1hHWT0hsIKUgE5yLndZM7FX6edC/8ITgVvYLEdUljFv2aU29ju3OKspGdw5pE3RE2SrixlBArbP9VkTFM3ZcbEJQG/ujYv0pCygoyJO3H2FdhYR4I9CfP/60e7JSAU8PBe0DxPMfoQCxoey3jgtq5T9CAZLCmP8kBLT1H/IZkSQ/P77/WK1I8GO6zj5t/T/ByxdSkRhhrqTEQCv/d/OmJQHkIinP/0njUE+pK0LlwW2u33AFG8dAeQTvwYbMDSFyxsDylrWdf7xkrKeZK52jnANhM+S2Oa39+ZPp/nbSEaz8n6UjWPkTRKp8EwKxvYmfCBKPFyGUJtN/efqsh1BoY6cI5PD0/gIiEQHbuiLE9xsIBW4c0JZgVkgDLxdJNCK93yKrt1RH6hFE+3xE3/sqBiPGk+Bh2GTiIeuCeJ9n6PsY5QxEujtozSGuwxHvxWidfXyXIljhaJTYxXMmYtxZZ9MJwY/AVxbGTT6jAx5AkJwh8hdFPkHBtUQkAinmhT8Kd4knXEQutLy+zMYEIFTmFbGPUgxJsDwAy2MWQGzrG0LqNjwUZ6NQeydgYcQ4bjKM9wjD8thp9P2dYf0wMdhBax7mn4T5UQwf83DCSPT1ddBqF0b3w/hghj15o88kxHiwmwGZtF/oMRbJ3uwm++XI6fCXM9AnYRF9OuwOCrZ1lUjx5fipXCdbTxcXrJtwJ32yjfL2wptjSSdndnmEKD3w/kRGgR0SG4kHhxJaum0cgwPwzFjWGXp2/2RM7cUwO0RGJcVgydBe9P3KyQPRp5Ojfs784T0wticb49VLxiAyyNvRoXf+cCRFsRvp609NhY8ns580QHx51SQKtpXLpVjz4p30lfqBhwtee4WVxg8N98Fjz06mGAsiAYGeePolNsa9B8Vh/nLSaJN9gU6dQ2nXYSIT5w/E6OmObrsDxnbBjPuYjyx6ZgqSBzkot9PuH4mhU9l3e+STuxEez4JKctNf9vZcdOoZRf/94taH4RXAaKkEO/LshhX03wQ0++LWRyF3Z/5D2D4Es0LtjwvCqs+WckyegAhfPLWelWbvPaoL5j99h8P+HpF44HWGr5q4aAhGz3JQhgeMS8aM5ayk/qJHxyK5v6PR5rS7B2OoraT+oy9OQXiULzf+y5+cgE6dWSD2yprptMM1tV8mxvMvTKX/JqDn15+eSl/tQOc3n51G7Y8I9MJz94zh+v6QuitrHmD+MygxEsvGOfync1gAnpjG/GdOn2RM6+7wn9EJMbhnEBvjVYMGoX+Yg/p7d48emNSJ+c/bg8cj1tOH8/+X+49Csh/7PdYOmEVB6Xb//6DfnRRsqxDL8Hq3hVDYwLMeEgXe7r6YZjaD5AG4P3oR1/eHXL8rY5fS93HuPTDS/y7u+g1VxGJiMKMuxynvRLS7g/Ic5joMSZ7z6fsQ5eNwlznoyP5u98BbwSjzMuXbEIjtjVpFkHq8DJFt/uyQDrFLB3X5TxLavMtaCwjcIGjF3iE4FFJqn7CBSMdRvs4Is6UWYoKEb4Wst1r1tOS+hOr4Tx5mqxYWqw5SkW+bxl8GC1m/boHMie7MztWCZksDRAIpZE51Vez2a80qSEUKHhWRCMGhaMwqKETutCU83w4zmsyNNKNCaqzw7TBCY9bBU+rRhtlDaMt6i5ECa1vbrzbq6FMlecprbT+hYZKMibuEbyN5Wq3Va+AmkUEhbm2jFbXNWsoGIrRPZyEdlEnVWl+5gkcTJUJ6t6j0evi5EjYX337ScZmU3Pd1VbSxX6XTU1u9XPnsEfJZfZOOZkzc5XyGBckW1Ddq4SqX8ais1H6LFQ0qLa3tQQIWZzGZLJRa6+XlSp/6nYX0/iEl97182haQ02kNtOQ+0bW2v0mlpfYoW1Fxif2qmibKBnJtxZoh7JuG6ka4ust5VGJ7FqahSg0PHzdIWzFjSAdlVbWaBjCkZw/P/mYjmho08ArwbGt/kx56nQFe/h5t7W+w2e/Tjv11GsoGIna2sb9OC1c3Ga8bM7XfYkV9vRZKpZxXc4Xab7JQarm3p6LN+OuNJjRq9fBRtvUfrd5IS+77uP9n/lOn01E2kLuslf+0tNB2Eu4SKRSSdvzfoKFsIHvNFc5+qxkNRg28pe5tmhmSlhoasxaeEpKd5NtvsDTTatZuYs925x/GAmw7/5ittTTjLBZ6tJl/Wqw1EAjc28yffyvq8mev/HHq8sqX/pHU5Y7Myp8lpN+PKQ0t5mxeFVaqslTBZEylTQ1bCym7bzClwWRha8LOQnAqOlM6TNa6Nhd2kzELjcYMmK2sN4ldLC0mqAzZaDBkwWzV88/V0ow6Qy7qDHkUuc87l6UJlfo81BqK2tjfZGpAWXMh6ox8LAqRWmMNSnTFUJlI6wC+EOwKKd3daOLbSPqPkDoruZoyCvTj2Wg1I6epAtlNFbR/ibMQEGBWYyX9I0BAZ2kw6JCuqkKOuqaN/dXNGqTVV6G4qa2NJY1qpNdWo1LLt5FIXkMDMmpq6A2Cb38LsqprkF5ZjSaDM+4IMJotyKyopn96E3+MdQYTMstqkF1WQwMQZ1Frm5FZXI28sto29tc1aJBdWI2yKjtQ2yEVVSrk5FejxtajxlmKi+uQm1cFlQ3gytlvJTiVKuRlVkJrA486Bw95GRXIz6ygwY6zNGsNyM8oQ35GOQ1AnKWxXou81FIUZVW0sb++Uo281BKUE7ZQK6ksqkV+ailqy9v5bXIq6TFJnZY29meUUZ3W1v+Ib385tdGgN/Lt1xnpd87Pqmxrv7oZeTmVKMxv6z8EUEvGsawdG8ur1cgpqkZ1HQOhO0thZQOyS2rQ0NjKf0g5/4oaZJRVo0nfyn8sFmRUVdO/1v6jNZmQXluDjNoa3nIREcJ2S6+rQraqro39NfomZKgqUaxlGBdnqWiuR05TOaoN6rbfrbkSRdoSqE2txr+F4FQKUaHPh96qazP/NBhy6BzUev4hJIJmYzqajaSPlLnd+ZPg//5mz888IV/tj/79N6W+vh7z58+ngZCnpyeWLl0KjaatbzvLunXrMHz4cLoPCVxVqrbzVGRkJNU5/731FiOG/LvSQV3+E8RqSoGhbj7Qwm4YIvlMSJTv0B9E33wYqvr7KBOIiJvHM3BzZ9VpG5q+Q7XqedtCpAhB3h/Dw5XRmovrX0WVhiHwhQI5Ovl9D3eXfrTY243qh1GjY4waqdAHfYM3wVUSCZNVixNly2hROCLukgiMCv2GZlg0pmr8UrwSWjMDFQbJu2NS6Lu05kpFcw62FD0Lg23i6aocg0nBj1L709VXsanofVhsk8v4wLkYGcBsPFF9FFuKN3Hr2kui7kc/H5bq3VCwE79WHKPvZUIpVic+iCRlHAX4vZL6LS7UsSZynhI3fNjjIYQq/KE1G7DiyjpkNTImQ4TCF+v6LYNS6orKZjUWnv0Glc1s4uzjE4kv+y+k6/Kp9RVYeOoHNJnYxD8jshve6jOZ2n+0JBfLT/4Cky24ebLHUKxMZjZuTL2Bl84cY6MvEOCDkRMxtRNLw79+8iS+vc4YCXKxGN9On46+oaG0/sWDO/bieDbrleLjKsePi2cjyscLWoMRd6/bgfRyNsZRvl74YdlseLrKUdnQhMUfbkOlivlI79hQfLH8TpplySiswvL3dkDTzG6udwzqjBeXjKX2n72ah+c+2AOTmd2cHpgzGIuns6WOXXuv46MvjnLLF6sfn4QxIxh+6Yu1R/DTdtJzBbQI2pvvzEFy93CaJVjz+FZcPMX66Xh6u+L9b+9BaIQvdBoDnl70FXLT2PiHRvnh/S3LaKG0mvIGPD79Y9SUs4koeUAsXt24jGYpcm4V4ZkZH9MibUTGzBmAVR8tpPZfPHgLr939BW0uSOTu56dhzmNsqXLP18fx+dObqfuT5aMnv1iKEbZlnHUvbMfPtu9Glo9e2/Ywug7sRO1/9Z51uHSYMcE8fd3x7i+rEBoTQIvcPT33c+SmMsBtaLQ/3t/5ELO/QoXHFqxDTSW7KZMKta+tu5tmSrIzy/H0yk2UYUVk7KRuePz5KdT+8xdy8dKrv8BsC26WLhmKBXOZ//x08AY+WH+cG/8XHpyAsUOY/3yw7SQ2H2H+4yIV45NHp6NnJ+Y/j363FyfTmP94u8mx4cHZiPT3otm6RRt3IK3C5j8+XtiyZDa8FHKUNzVh1vatqLA13uwfGorvpk6nWZaU2krMO7iN8/+ZsV3w7uAJ1P7jFVl4+NJOzv9XJY3AsgS2tPpTyXl8mPULh1l5vsscjA1kS8U/FG3HgcojXBuNp+IfQaJHJ1r0bUvR28hsYiwlV5ES98a8Dl9ZCJ1/jpat4M0/Y0NJWQUljOZyZFVNh8nCfMtNNgCx/htpnSiLKQX6urlO8+csyJTvtsnYdMh/X0igUlFRgSNHjsBkMmHJkiW4//77sXnz5t/cR6fTYfz48fTv2Wef/c3t1qxZg/vuI/dCJu7ubesu/Z50ZFb+BDGqnwdaHE+vluadsBrP0ScEdQOhHDueIjSNb8FsLobFUo9q1Qu8ZoWV9Y/TVupaYwoXqBAhnxXUP03fV2oPcYEKEZNVhcy6t+n7bNUW+kTDnctUivQGRhm+UvsNdGZHhqai+RYy1fvp+0MVn9GUrl1S1EdQqL1J7d9W8hkXqBA5WLkF9YYqaMwabC3+wWEjrNhYuB4mqwl5miIuUKHjYzXhizwW1JyuvskFKkRIM7UvchmFcVvRWeQ0OjJMpc31+D6fUSE/zTiGGr0jwr9aV4hdxexm8OK1/dCaHE/RPxXewvnqAmo/oSo7Z2HevXEaJU0qNOib8fLZ47xmhU+dPEgZRKRKqD1QsVcOffbwYfb903O4QMWesn/rMKOebjx7HZkVjuxBcb0K606ygGHtvnOoaXTYfy2vFL9cZOPw5qajlPpql73n0nA5o5ja/9rnB7lAhchXW8/Sp3l1YzM++eoY72n9nY8PUAZRdlYFF6jQ8TeY8f47v9L3Z46kcYEKHX+1Dl+9f5C+/2XDWeRnOMa/vLgO275ivrbh3f2oq3I8YadczMXh7aTTOLD2qS3Qax3+c2TrBdw8TaqYtuD9Fd9SyrJdvn/tF1QW1dDOy18+s4Vzf8Lq+fCh7ymDiFTJtQcqzH4TPnyUUfDP7L3OBSrU/gYt1r38E7P/29PIT3fQ28uLamkXaWr/J0dRV+PIPqVcLcDhnxmF+JO396PZFigSObzvFm5cYf7z1nv7uECFyPrvTqOiQkXr3nz07Qne+L/5xSE6/iT4tAcq9srFr37P/OfwrRwuUCGi1unx7m7mPxsuXUdGJd9/vjrLfscPLpyj1ZTtcqm0FDvSmf88d+EwtGaH/TtzU3GugmVIn7lK6PgO+z9MP4ESbQPURi0+znJQkAno/a30HZQhVKAt4gIVIuSa/jp/A32fqj7PBSpESP2lAxVsjslUbW0z/6Q1fM9+C/W7MFkcmVmN4SLqNNvZ76t+rtX8uYPOn39L+QsDbDMyMnDw4EF888036NevHwYPHoxPP/0UW7duRbmtaWZ78uijj+KZZ55B//6ONhjtCQlOAgMDuT9X17YFJH9POoKVP0Fa6BIOPy3bYiH0PyNaWsiTaOtlIbKMUdN2HxhgtTbCaG69JGTlLnQ9PS6fuqw3M0fS0ayJs86KZlsmpclURbe1CwHSas2MMtloqmlDXW4yEzqkmaux4iyN5gY0mlQ8SjMRQmVutuhQb+SnAcl2pOw+kVqDug11udrA0tPV+kbe0xRZbrGnp8ub1TQrYxeyhl6lZzef1vRMIpW6JhitFlpjovX1XdWsQY1Oy2NlECFU5kaDAZVNrZatyD62VCjRtaYul6nZTbyqUcPTkZtFlZrtV1HfxKMnE/urbbqqej49mY5FvQYmswWNrZZp6BjWa1Bvw2U4C6EyazQG1FTz0/bEjtoadi6i41N/W7gaI7VVah4Lh+5XyY5VXdbAoyeTTEidLUtBsi6tbamtUNElmSaVrs0EW1ehprVT2thvMNGy/a2XhAitmdRmsR+3NXW5upT5T22lqh37mY3VFSq+/UIham3BFxmTNvZXN1I8SlNT++NfR3A9rX4zQmXWaA2oauD7D9msRmX7rVWt/MfagvKG3/Af0gnZFuCWNTby6MkEA2P3yQptW/8hn5FsitrU1v+r9U2oMza1uWZIoKIxkaXihrbXr4mNf6OpjscOJPOGysgCLDbX8DsyszmJLI+SINJ56U0EE53LyFxJ5i/+/GNtZ1n8byGka/If/fsvyYULF+jST+/eDpD96NGkcKUQly5d+sPHJ8s+Pj4+6NGjB959912Yza2WAv+FdAQrf4IIpaTqrB2cRpxJBKG0BwOSSbo56YQUQCaWxEMijqAl+B06ESTiKEpddpV2hQAEQGd3TBHcZCw97iUj7ADnSUYIbzmLaP3lvVpRB1vgJ2fsmhDKBHKeSCwIUjA2RKRbd6cJSGCjIyZQ6nKoPIbrrkw0LkIFAlzC4Cfzh4fYg9ORV39ZANzE7oh2DYdEIOYYA0SX5MEo1J2VUbxJkmzTw5OxGnp6R/ECEjJJ9vBirJK+vlG82o1ku94+jLI6wD+Ko27S0RcI0d0nhIIJk30COR25EbhLZIjz9EW4hycF1dpvDmSbSKUnfOQKdAkIgEwkcoy+QECXgIj0CmNVVLnRFwgwMIpVLe0TFcrDEpD7X+8otl+fuDC+/VYresYwXd+kcO4GTO0XCtA1JoguESXEBHC0ZnIuV4UUUWE+CA7yhJengtuPbBMS7Enps53igyCVirjAj9CTyRIQkS7dw/mdfYUC9OjLqKfJfaMppZcbf2sLuvZh499tIOkl5WS/2You/Ri7pvuQBI76S7Yh7xN6RVHqclyPCE5HbCXA3IjEYARH+VNwLKcTCREc7U+7NBMqMmkDYA88iI4sAVH/6RvT1v4hjOaa3D+2rf02G7v1jebbb7Gia2/GLuvRO8ox/gLWyTmhSyhdIorvFMjpqP2uMkRF+CIkwBPeSv74hwZ6UupyYkQALaNvPx/RkSUgeq6otv7TvxP7bfpGtvWfPhFsvwFhfP8h2/UNYYylQUERrfxfgB5+wXSJtItnkMP/aXkBGeLc/REi94G31I17cCDXTKjch4Lao9q5fhPd2fhHuCa2oi4LEOPGmDv+8p68ByJyBfvL2bKSO63MzfsG3JwmlA5qM3+KpA7mYoe0FQLYdf4ztMLO/V+ksrIS/v78vlhisRje3t5U90fk4YcfphmaEydO4IEHHsAbb7yBp5566j86Rkew8ieIVLkGQhmhSMoAoR+kXp9DaKMye/msh0Tay1ZfJRJevpshFBJ0vQtC/bZASil7EsgkXRDq+wMtPy0VByHOfz2kIlLvQAoPl0GI9vmAHs/TpRu6+L4BKWmYKJAh2HUyOnmtorpwt7Ho6r0CEqErxAI5Ej0XI9ZjBtV191mAJOVUiAUyyIQeGOT/KMJc+1Dd2MAViHPvR3sHuYm9MD3sOfjYqMyLIp9EuGsnWl/FRxaIpdHPQS5ypaygRzs9SRsYEopjmCICD8c9TjMGPjIvPJO4gr6KBWJ09UzAg3Gs+2qSMhJPJcyjWBWpUIxRAb2wNJphGMYEdsOyuLFwFctoDYhFUcMwPYxNaPfGDcFdkX1o6X2lRI7VXSdioD+jO77SczxGBneC1NY7aO3AGRyV+asRd6KnXwitLxHh7okNY2ZBKXWhrKBNd8xCrJcPpSh39vXH95Nm0JtHkLs7vp42DcHu7rQL88DwcLw3gVFPu4cG4a0p4+CjkNO+QFO6JuCxkYOpbmK3eDwydhDcZKRztgT3DuuD2f3YRL50bB/MGpxM8QtKhQuenTkCAxNZsPX0/FEY2i2a3uAITfntFXdwVOa3npiKLp2CIRELERLoiQ9Xz4C7qwtlBb3/+l2ICPOhFOXYmAC8s2YWvXn6+Xvg1bdmw8/fnXYB7tkrCs88N4UeLzE5DE+8cifFqhC8yaiJyVjy0GiqGzapGxavGgcFYcQopLjr/uGYOJeN/+yVYzBxwSDI5BK4eyqw4tUZ6DWUBQkr35qDfmO70gCDsHqeX38/wuIY9frFjSuQ2Cea9uUhTQxf2/EI3JQKyih6/adVCOsUSCnKMclhVEee4vyCvfDK5gfpK+nC3GNYAp78nFHfE3tF4fGPFtGghhxj5Iw+uPtZ9t2G3dEDi5+YSHsRUfuXj8TE+QxfMvu+YZg0ux/F77gr5Vjx3B3oNYgF0A89OQH9h3SChPQO8nHDC2/OQngk859XX7oTnZNCKNOHBIjvvDELbm5s/D98YSYiQ7wpRTkuyh/vrybl+wUI8HbHhw9No6+kLk7fxHCsWcr8p1tkEF6bO45iVYj/TOqVgEcmMv+Z1Dkeq0Y4/Of+QX0wtzfzn+W9+2J+124UP0Vo9i8PH4khESzYWjNgDEaHxdIA21/uhi9GTOOozJ/1n43u3qHU/8PdvPDN4PnwkLpAJpLggx73IcLVn5YYiHMLxns9ltquX288Ef8QfKTs+u2iTMTyGEZPDlPEY3rowxSrIhZI0c1zGMYEsvIIEW5j0c17OTf/JHkuQpwHw7cFeqyEr9sCir8jZRrCvF6Fh5y1NJApX4VIRnxQBoHQHzKvLyHkqMx/LxG0/PE/ImFhYZRdZP9788038VtClmhag1tb/2VmOqqb/zfkscceoyDc5ORkLFu2DO+//z5dYvpPgqwO6nKHdEiHdEiH/GPlf0pd/mjNH6cuP/oiSkpKeLbKZDL6157UEEZjHZ9R2lqio6Pxww8/4PHHH0dDg2MZkCzVuLi4YMeOHbjzzjt/9xgnT57EiBEj6P5kOen3JC0tDV26dKFBUny8o37T70kHG+hPEqspC1bjeUDoDZHLRF7NFKPhCkymG7SarcxlHJeeJ3GiznASRlMOzazYmxgynQUNzQdhNFdSFpCrlDVPI2Kx6lGlPQizVQNfxRAoJOwJnYjBokKphgBHrbQonFzsaBffZKpEkeYCxEIpot1HQOpUz6BGX0hBtQqxEgkeQ2gmxS6F2iwU63LgIw1Akkdvnv1pjSmoaC5HuCKCa2JIx6PFikv1Nyh+JdE9DtFu4U42GnG65iZ0Fj16eyciRO7nVGNFixPVqTTVP8y/M3xkDsR4uU6F01VZ9KlwbHBnmoGxS7a6GuerC+EtU2BCaCJ9krTLtepS3KgtR7ibJ8aExfHsP1VSSEuTk8zKgOBw3jLNodxcVGo06Bsagi7+rAIsEUIp3Z+RRdkbw2KiEOHtuDBVumYcTsuhGIhRSbG0gy5nf30jTqfnQyYRY2y3TnB1qjmSW1aLy5nF8HJXYHSvOF7Nl9vZ5UjLq0CIvxJDesbw7L98oxCFpXXoFOXPNdGj9lusOHM2G7V1TUjuGoZOtkwHHX+9CaeOpUOnM6BP/1iEhLEsDpFGlQ5nj6ZR+weOTIS3r2P8q8oacPlkJs3IDBmfTDMwnI9kVeDm+RwovV0xZGJ3rqAbkfSrBci6UYjAcF/0H9uFZ/+1kxkoya1CTOdQJA+M49l//sBtikMhSzmxXcN49p/ed5Oyl/oMS0CwLQvC2X84ldKbB47qDG8/J/srVLh0JpsWdxsyujMUTvVgCvKrcf16ITw9XTFseAKvZkpKRhkysisQFKDE4H6xPPsv3i5EYXk9OkX4o1dSGM9/jt/IRbVKg55xIUgM5/vPwdtZtNbK4PgoRPg6/KdB14xDmTnU/8fEx8LPzeE/pY2NOF6YR7OCE2PjafNNu2Q11OBcZSF8ZApMjEzg+f+NumLcbihDqKsXRgbG8/2nPgvF2mrEugejh1cs7/q92nANDUYV4t07IdLVMceYrAakq8/CYG1GrFtPeMuC/635x2guRWPzUVprylMxGSKh2781f/6t5I/iTlpsxUA9PP7twMrPz4/+/SsZMGAApR1fu3YNvXqxYoTHjx+n1xIB3P6ZcvPmTZpFbb3s9HvSkVn5E8SiPwljw702kFgLhNJ+kHpvohecTrsRjapnbCtuVrgo5kDp+T6dMGpUr0Cl+YrT+Xg8C2+Ph2gdldza+6FqJkwG5pzRPp/Ax/UOWKzNuFIxH01G0sRMAKFAgl6B38PTpQeazbU4UrIIzRYGnJUKPTAm7Hu4SUJRp8/D7uKVMNHGiS1QSkJxZ8SXkInckdd0BTtKXrEBZlsQruiKORGv04DlQu1h7Cr7mq5NE31vrxGYFbac2r+jZAuOVB3kdHeGzMKEoMl0onsv60tca7jNrXs/HLcUA31700Jwj934GHnaMqojhajeTl5JsSy1hkYsubiWvpK93CUKfNtvJUIUPshurMSis9+g2WKkVoa7+mDzkPvhIZHjVEUuHji3nU7w5L++fuH4fuh8OmH/kHUDz186RNfmCVbmrpiueHsgmQwFeO3CCXxz+xqne6rvEKzo0Y8e54G9u3EsP59bZf94wiRMjo+nheDmbNyGjKoaqiPLRBvmz0TP0GDUNGkw6/PNqGlizAYPuQu2L5+LMG9PZJfXYNEn22ghMGq/ryc2r5pLtzmXWoBHP9vN7G8BesWF4PNVM2jA8vOxW3jnu2PUXnKpTh7WGc/dy2jNa787gW27r3K6BxYOxYIZ/Wig8eLLP1HarR038fzqqRgxPBF6vQmr7v8OeTlVVEcKsb3z6QJ0Tg6jbJmH536Butom+t3cPOT4ZPMyBIV6oyCzAo/N/YIWaiM2Bkf44OOdD9JtrpzMwMv3rqcYEWIHCS7e2LSMBiz7Np3F2tU7KLaE6MfO7odH351Lbf56zS78vO4Ep1vyzB2468ExdHJcc9+3uHQklbuxPv3pQgyb0pMWsnti1lrkpZdTHVleeuuHZUjqFYn6mkY8POsz+j3s9n+8fSWCwryRn1OJx5Z+S/cn9oeEe+OTDffBzV2OS5fy8MLq7XTciC65WzjefX8uDVh2H7iJ9784wo3xxNFd8PRD4+m/P/7xJLbsv8bpVswejEVT2Pg//tUenLqdz43/G/dMxLje8fT3X/DFNmSWkwJooL/xt/fNRI/IYFQ3aTD9282o0TD/UcpdsHPJXIR7edLaKjN3bqH+R/wn0tMTu++aDw+ZC06U5eHe4ztt/g/0CwjDD2PmUP/fWnAFa27t467R6eE98GoPRsv+PHsPtpec5nT3xUzE/MiR9Pr9JGctbqhucdfv8hhSlqAvDVS+y38alXrCaBLQZaKFUa8hTJH4u/MPqa2SW3UnrC2kPEILpOIodArcC5FQ+bvz598us/Lhq388s7Lqhf+arRMmTEBVVRW+/PJLjrpMALd26nJZWRlGjRqFjRs3om9f1hme4FnI39WrVyk1+fTp05T5Ex4eTvEuBLhLALok60I+J/9etWoVPdeGDYxl9u9IB2blTxBT0xvchUbEarwEq+EkDToa1a/YtmKgNL1uKyzmXJgtlbZAxaGra3wbVqsOGsNVW6BChAUQJarX6L8qtfttgQrTkeJuuQ2sFH+Oaiv0lnpuH5NVg8wGRi++VrcB5hYDZ2OjqYyjLh+vWs8FKkSKdSnI11ylk9av5cyZ7Myfqw0nUG0og8rYQAMVZ90vZTthsBiQ1ZRHAxW7jvy3sXAn/fep6us0ULHrLFYLvi9gXX+3F5+n1TTt35oUjPux8DT995fZJ2mROHtkXaqt56jLb90+Sm2123G5phinK0nhuxa8dpXRe+2g3u15KchT16FKq6GBirPuvStn6c3gankZDVQcow+8fprRS/elZ9FAxa4jnZw/PMlolpsu3ESdhlTfZTqNwYBvz7JzfHn4En2i5uyvU2PXJUY9/XDnaS5Qob9VThnOpxbSm97HP7Lz2p8pfj2VRp/kCSOFBCrOuq9/OAO9wYTUtFIaqDAd+/viSzYOJ4+k0UDFriPF0b6z0ZN3b76AhnoNNZ7oSOZip61D84+fH6ONB+02VpTU49BORmH95o09XKBCJOVSHq6ezKBBx9evsjoeRE/k8LZLNJNCmEQkUHHWbXjnVxpMpF8poIGK/buRv69fYzTbU3tv0kDFriO06A3vs47GuzddoFVoefZ/y/znx69P0YyM3f7y0gYc3M06o3/1xTEuUCFy+1YxLl/Ko5+ttdGT7d9t/9FUFJXWo6ZBQwMVZ92X28/R8b+ZV0YDFefx/+An9jseuJVFAxW7jhQH/PgQ85+NV2+iTuvwH1JwcP1Fdo5PL1+gFHq7/xSr1diWxsbojasnuECFyKWqEpwsy6fXxDupjDJt1/5cfAP5mlrKyiOBirNufd4B+jCRo8mlgYpdR/7bUsw6W6eqT9sCFaYljMHjVT/8y/mnSv0RrLYHJSJGcxHqNFt/d/78W8pfmLpM5Mcff0RCQgINSCZOnEjpy6Tom11IAJOVlUVrq9iFBDaE4WOvoTJ06FD67z179tB/k+UpAq4dNmwYOnfujNdff50GK87H/XekYxnoT5AWa2MbL2qhlWXNAL1A+WK1tq0WadOgpaUZFno8vlhslWpZxVqWibHvY7Jtb7TyacYttgmDiMHS2IqeLOS211u1bezXWwg10wJTC78KqEPXNs4l5yMlunVmflVRIs0W9pnG3Mw9ydm+Mf2MiNbEHyvaSdhW4bbR2MxnEQkE0Ni2bzIa2lzDTSbSqsBK66a0FjUpntVOKpZM+M1mU5uqtPZzUDv0BgrCtTNSyKvaVoVUQ17J47JNR17oZ2Q/nZ7PYhEAmmama2o2tKlMST4jSwmk829r0egMELYzaZHjk5sl6b7cWuwFz4jOngmg+5BuxzZ6Lrm5sydpx3ezV7jVqJv5DQQFAmht+2kbWXl43vma9JSZ07oKLtu+ud2CX+T4JHOjaVWV1r4Pt68tE8N1a1bbdOS7tRoPnd3+Rn1b+2068tp6/Ml4kaUoUqOmtWhITRl+ZXrufKTEPvnt2uxjq+PS2NzWf8hnv+U/dl9UG/Q86rLA2SfboScTHWn2aWhVBZrqTHqI23Egcn2RBwKdmV+VlojOdv2Sa9/5+iWvelt5g9+bfyxWQn12ZgoJYLXNab89f/4N5Y8GHC34rwrJhPxeAThSibb1tf7yyy/Tv9+Snj174uJFVpPpj0hHZuVPEJELacjl6F8KgQIi2QAIBFJIZSN59GShKBgSSRKlKUso4t2hc5H2glDoDTdZL4qYd6Y8eylYgzAf+RAbzdjx0wW6jqevoW4jWlEHrQh1G07fRbuzVybshhThyjAyiR4ElW+nPgohEbggwrUbpS4nuPfkaM2Ewugp8UGQPBL+LgGUCeRMXY52jYWb2A2d3KPhJnblUZ77+TAKdR/vREqTdO7qOtSvO30dFtCZR10mkyfBrRAZY2taaD8euWCGBTI2ysSwJF73WNIbqJ9fJF2iGR4Sw1E3yWuQwh1JXv6IVHohxtObp+sZEAQvFzl6BgVTxoUz5XlCHMNTDI2JpP92bpI8IZHROkcnxdIAg7O/heFWqP3dHHgM+/1oWGdGGR7bm+AIbPYLBFDIJOgTH0aZJAO6O2i1hAIb4OOOuHA/hAR5ISLU20FrFgrQOT4YSnc5OncOhYe7ixMdV4ChNuZO3wExEIkYA8Auw0ayqrcEo+LctZgEAgNtusHjujrZz262/UYwjNLQyd0d9gsFlIlDKtwSJk+fEUmUOk11IsL08aTdmgnOJCw2gEddTugVSavNJvWOoowjjg4tFGDwJOYjvYcnUGqxcz2VIbaGhANHJ7W1fxTzm8Gjk3jjT+zvP4QB+4aPSOLZL5dL0J3QpyUi9OsdzRt/f0KtjvJHaIAXIoL5498lNghKNzm6RQdD6erCo5yP7sF+/yHxkXRbZ/8Zn8z8h2BUWvsP+Yz6WGwnh/22e9aoKMY4nByR6PB/4j9iCQYEhlO23ZCAOCd6vhCBcg/EewRQ6nK4wp/MSLb9hOjsEQGlRIE4t1i4ivjXbx9vVnsj1q0X7fTufP0meQz6l/OPUuFosGr/Bh7y0b87f3ZIhzhLB2blT5CWFhPMTR/CYjgCgdAXEvdnua7L5OmhSf0yjMYrEIsj4a58FWIxA6uRpaDqhudhNGfBRdINfp5rIBIxsGOzMRvFDWtgtFTAw2UIwjyfgdDWYLC++TJd+iFZlkC3SYhS3k8pz0SKmw4jU7WJLkHFed6FaI+pNhtbkNKwgy79SIQu6OVzN8LdWH0Wkso9U70J2U2X4CpWYmTAUq7rst6iw97yDSjUZsJXGoQpIUvgI2NgQbIURMrtlzeXIdI1CrPDF9BghUiJrhwbCrejzqBCsmci5offCamIAQJvqXLwfcF+aM3NGO7fE3PCR3ON0o5W3sIPBadooDIzbCCmhPbh7P8h/wJ2lVyHXCTBsk4jMCSA2UiKX32cdgrHyrPhI3PF08mj0NXWdZk8Yb569RgF2UZ4eOHlPmMQ7s4AjWQp6KVzx5BdX4tkv0C8NGgkDVaIZNfV4tVTJ1HR1IQhERF4evAQuIjZGvqlohJ8eOo87esyuXM8HhjYl7shHEjJwvozV+lT/Pz+3TGjdxeH/advYNelVMilEiwb2x9DklgNE5PFgi/3XMCpW3kUYLtqxhAkRTJArFZnwEc/nsLt7DKEBnjisUUjEOLP7CdLQR+uO4qC4lokxAXhkaUjobQ1GCworMHnXxxDdU0jeveKwv33DofM1iDx1vVCuvSjbTJgxNjOmLNoMHdDPnUwBTu+P0PtnzKnP8ZP78XZTyrcHt55lZa/n/fgKPQdxgIgshSz6cMDuHgkDZ6+blj67BR0Sg7jMizr1uxC+pV8BEf6Ydkr0xEUwUCXZCno8xd2oji7AnHdwrHslRk0WCFCegx99couWia/59B43PPsHZDZAMm3L+Ziw/sHaZZl2JQemL18JAXrUfsP3MbO9aeZ/fMHYNyM3pz9uzZfxKE9Nyh9ecF9w9B3MPMfUqH2+29P4/y5HHh6KfDAslGITwjixn/tNyeQklmGkCBPPHLfKAQHsvEnS0HvbziO/NJaJEYH4rGFI2iwSCSvvBbv7ThFC8T1T4zAw3cOobR1IpfzSvDJIeY/E7vH477hfbnxJ8uM31wgS7AtWNC7O2Z1d/jPtzevY0dGKqUvP9x3AEZERnP+/8HNMzhakgMfF1es7jUCyb7MfpJ9fDv1EK7XlSDC1RvPJo9HmCubY8hS0MdZv6BQW4l49zA8FD8VSgkb/1JdGTYXb0W9sR5dPDrjrvCZtOw+kUJNCk5U/0CzLF2UQzHYb+a/Nf/UNn2Deu02Sl8OUK6Ch3zkv5w//3aYlff+BMzKE/89zMpfWTqClQ7pkA7pkA75x8r/NFh597U/Hqw8+fw/8v7WgVn5k8SiPwGL4SQEQm+IXe+GQKikn5NYUN/8E0zGaxCJIqBwu5tS95jOBLV2I4ymXEpd9nCdyz2hWKwaVDVtoGX23V0GwFvBikoRMZhrUNL4I8wtWvgrxsBbzlDZRJqMhchvJKBGKyLc74CnzLH8UNWchtym47QwXJLnVLhLHHTKvKbLKNBchVysRC/vKXARuXP231CdRrEuG97SAAzwGU8LwlEbW8w4XXMclfpyhMojMMh3GJchabbocajyOKU+JnnEo58Pe0InUm9sxN6yM5S6PMg3GcmeDhuLtDXYW3aZnndCcC/EurMnRCK3G0pwsDwFMqEYd0X2RZDcQfk8WZGD01V58JLKsSi2L5RSueOJuiCVoy4viu9FK9van0h/yLiFPEJd9vHH7PhkLkOiMRqx4dYNmn0ZEBrGS8MTtsam6zfpNmM7xaJ/uIOyml9bj53XGfX6zm5JiA90UAZvFVXgwM0sSl2eMyAZQV6OyeZMWgHOpRXC080F84b3gIfC7iMtOHAhAymEuuynxKxR3en+9ozAriO3UFRWj7hIf9wxsiv3hK7TGfHTr9dRV69Bj65hGDbIUcugvl6DX3Zdo9TlwUPi6ZKHXUqKanFgD+sLNWZiMqJjHT6ScbsEJw+lUurv5Fl94B/kGP/Lp7Nw9Ww2PLwUmDpvIC28Zrf/+J4byLxVgsAwb9wxrz+tbEvtN1mwb9sllObXIDohCONm9uYyJDqtAXt+uEAZPqSy7uCxDup+fW0T9my9RDE2g0YloZutyi61v7AWB3dfp5mVMZO7IzrOYX96WhlOHk+n1Os7pvZEQAC7RolcvJyHS1cLoFTKMWNKL7i7O8b/0OkMpOWUIzjAE9PHdacF4ezj/9PJ2yisqEencD9MHeIYf0JL3nLmJm2p0DcuDKOdlgEJW2zzxZuU+j4mKRZ9o538p64e228z/5neJQkJ/g7/uV5Zjr25mZS6vKBzd4S4O/znRFkuTpbnw1smx93xvaGUOcb/19JbuK0qRajCC3Mi+1LqP7XfasGv5edQrKtGrFsIxgf1465fvaUZx6qPQmVUIcEjAb28WIaTXhumBlyt/5U2Pk3wGIAI1+R/a/7RGa5BpdtDC2J6uy2CVMwq8P7e/NkhHWKXjszKnyBm3TaY1ISeTCYxKwSiCMh890IgdEWT+g1oNWs5nVQ6AF6+BFkvQEXdvdDqD9qwKWZ4KBYgwPsd2rgwo3I6dCbC+iGThxlhns8h0OM+GC0NuFg2DQYL6dHB+m908/8Y/q5j0GgswNHSBbC2MFAdwZqMDP0OXrJElGqvYX/pE9zaMKmxMjPyW7hJ/HG74RAOVnwEIUQUFOcpDcLi6E8hFcpxsOJHnKzZZdNZEe3aGUujX6Rr1l/lf4Jbqmt0grO0WDDYdwQWRNxDG5+9mPo2inUl1AYLLJgfPhOTg8dCbdJg5bV30GBsojgBglF5PukeDPLthiJtNe65+AlMLRYOf/Jl3xVI8AjFpdo8LLu4gcNakBorO4euRIBciR0FN/Dc9V8hFgjp8lG4qxd2jboPrmIp3rlxEl+kXaA6AlAcEBCOTaPn0lF44NhuHC7MoX1WCBh3XkI3vDl4LAXlztixhdJFSfBCdKsHDcN9PXujXteMyd9toqwN8gUI4+WzO++gQUt+TT1mrPsRRovNfoEAW5bOQZfgAFzMKcb9X//MYSPcZDL89NgCBHq6Y9f5VLyy+Qi1g1yOYX6e2PL0PChkUqzdcQYb9l+hVVJJH5neCaFY+wRJuwOr39+D01dymf0WK6aOTsbT94+hzfRWPPkj8goIPVZAcRwr7hmO2Xf2gVqtw333rkdDg5bTvfLKdBq0FBfWYuWSb7jGgwQX8tG6JeiUEIQbl/Lx7ArSJZd9AVKj5Ivty+EXoMTBn6/io5d2QSQWUpxIUJgP1m5fAblChu8+OIjtX5+iOtKHKLlfNN5Yfw+jjj/8Iy4cS6PYFALGnXBXHzz8ynQKKn5s7pcoyKqgNhDdvU9OwIwlQ6Bu0GLFrM8oa4n4IMF4vPD+XBq0EPsfXLiOb//6exCXGIzr1wrwzBNbbf7TAoVChnXf3kur/e47dBvvfnSQYmHI+JNKtes+XQyFXIovfjyDH365zI1/z86h+OiFWXT8n/5sL07ecIz/ncO6YvXiMTCazVj00TZklTn857GpQ7F4RC80aJtx59pNlDVm95+P591B8U55dfWYtvFHGG2NE0ngs2P+HHQJDMC50iIs/HUnh+xwl8pw4K7FCHJzx7bcW3jm0n7O/yPcvLB3whK4SqT4OOMI1uee4fyfdCtfN2AxHbtX0r7D+dpU7vqdGDQAq+Lvotfvm5mvoURXTHEr5PqdFTob4wInQGdW4+u8h6A1q+gxrLBgRthqJHgM/N35R6M/i4Kaedz8IxK6Iy7gMCTi4N+dP/9umZXwd/54ZqX4qX9mZuV/ArD97LPPKIqYVMIjxWUuX3Z0hG1PSLU8Qp8i23ft2hX79zOK7V9VzJov7O8YLNRSAIvhBC3sptV8ydMZjedgNqXBbCmFVk8olyRWZIyDRt0PlPXTpL8MnYnQWq2crqLxc/papT0Ag4VQHy0cmK1AzTo05zfuohMF+dz+l6tmlMOUhu02EiL73GjVIrvxENVdrGXbkImHBCQNxjLkN12hbKDTNXucdC3I06aiQl+IOmMtbqqusiPagouztSco6yejMQeFumI6cZKJjsjuckYvPVNDCsWpaQNDEqiQqWtnCaPV7i69RAMV8rn9j7SxJ7Ip/zwdKfvnZC1+b+lNqvsqi1E/CfuBPJEWaupxqjKH3si+Tr/E6Yit56uKkNFQhVJNIw4V5rDRt4EaN2feohiXy2WlSKuppsey6z6/xo6zPzObZlbIxG8HQ667xCi8266l0ECF3NTIH3m6//Eys3Hj6es2qjb7I3iFPdcYBX39YXY9kOORcxZVN9BMC/n3j4cYdZXcDMmN9EpGCXJKalBZ04hTl3M5+isdv6O3KcbiVmopcvKq6fntgNMfdjA0/smTGTSzYteRm+7WrUy3f/d1eqMnn5M/0vhvj42e/PMP56mr2nWEQXP0V0Zv3fYNo+WSoIIct6yoFldOZ9PtfvruDKcj9t+6mIf8zEpUl6tw/miajT7NbDyw/Qo9burVAuRllDMbbbptX7NznD6cSjMrJPCh9pP5wnaOA7uutbF/93ZmP+lCTc9FdS2U7XPkEOve/OM29v3pPtYWlJY14NKVfPrvLXuv8sb/WmoJcouqUVHXiBPX+eO/61QKZXhdyytDRinff9YfYb/xwdRsmllx9p9vTjMbt95KoYEK1bUw/yEZPLrNrWv0/HZdo9GAn7MY9f2LtPM8/y9oqseJ8jx6nWzIc1wbxP8u1xUgq7EKVfoGnKtN4V2/+ysu0HIB2U2kCGSRbbaw61h5gfTGs9CYG+g8QeYEEnxcqP3pX84/tU1f2+Y6sg+5RhrRoPvpd+fPv6X8xanL/+hgZdu2bbQvwEsvvYTr16+jW7duGDduHKqrWTfO1nL+/HnMnTsXS5cuxY0bNzBt2jT6l5rKagr8NaW97pEWm2dZ29XxGw7+a11LC/uMBECtNA5dO/uRgMPx2ooeaNfxEPy2/agdLHPT9phkUmzve9l17RzPtj1j+zg3VGQTqUPHF/tn5NW5yzO5ydr3M7e3n5URnVt3lqXbW6081gXfThZMtD0e+6z1ftR+282qPZ39M3JO5xwmzSrZdO3ZQm6oZPvW3YDpsSy/bT8NhiztH4+9trLR6Wbb/n4OHS8JS+y3BRLOzQN5+5HaH+2NJQnobNmD1kKDkHaOZ++YzHTOnZX5Nv7mfq3tb/Xd2rOf+n+79rc/xvR8vzX+Tn7gzK929pF2fcvavo+TQ/xL/ycBTzuJcxbwW377+m3vmrfNEe3NP/br/ffmH/sc5fwNHJ/91vzZIR3yPwxWPvjgA1oshlTCS0pKogVkFAoFvv3223a3//jjjzF+/Hg8+eSTSExMxKuvvkp52mvXkqWUv6aIFAt5HUMh9IdINgwCgRhyxRzb50wnlnSBWNIZElEkXKT9bT8B07u6TKSUZXdZP8goY4gsD7GZzd+dnYMs94iFbpQ+aNeFecylr5Hud9g+Y9RmcnOPsqHxEz1ZszeSuiXpWVL5NsaDNF8EeniR/exaIVzF3ohy600bFPb2GsnTBbtEIVgeSbsux7nF08/YMUn35N5wFbsiwaMTAmR+NIVspziOCWAUxoG+3aAQufB0k4NZIzeCUSFCNOSPTLOTg9la+YxwxuognxNKMekKOz6Y0WkXRPfmdVwmzQyHBMZCLBRiVkyyY/QFAiR5BSDJOwARHp7oFxhK0/RML8D4yDgoZS7oGxKKcKWSbm+/ryzsyuixZLmHLOE46+b3ZLpp3QmFmtBS2R8Z/xk9GNZiVn9mK/mcUFpJ5dIJ3RmOZPbQ7o6Ov0IBfD1cMSgpki493DGkM7OPdDMWChAf7kf/SNff7omh9DOiI3/D+8bBw80F3bqEUsYKOZZ92Wmajfo7ZEg8XcJh9GWmmzqV0crH2CjAlFpLapm0tGD8HWy/iTZWDVlaIVRkiUSM4ePZd7tjbn9ex2JS4r734DiIxCKMmd6Ls4/sF5MYjJiEYASF+6CLrdsx0wswaExninXp2juKVp0ly0N2GyfPYeW+B41OomX+mc7mP3cxzNaYSd3oD+1s/7iprHvvpCnslexDdISWPGIUozPfeUcP3vj7eLuib+8oOv6TRnbhjT/BBsVF+iHU3xM9Otn8x/b9RvSMhYerC+2mHeqjZONv85HZg9nYEowK9R+n32ZOP6YjGBVqn91/WlowK5mN8fykZHo9UP8h/i8S4Y5YxsZa2IldN3YfJ80MhwVH0+rQ08J78Pw/wSMQ8R6BCJb7oqsymrsKyf8H+ybTqtGd3OLp9e18jY7wY3NFvMdAyIQKWzkDpuvlPfFfzj/ebvNt4RfRkY7UEngqpv7u/NkhHfI/w6wYjUYamOzcuZNmR+yyePFi2oNg925WldJZSIlekol59NFHuc9IVuaXX37BrVss7ewspGujc+dGsjZIOlL+b6nLLbA076CpSwoQc1tJ66kwnRlazVcwGa9DJA6Hm/ujtOsyEVKttr7pY9YbSNoFXu4raSdlIiZLDcrVazmArb/bQg58qzUVoFC1nvYGCnAdh0A3B/i2Tp9CU68UX+JxJ/zlDmBcoeYcchqPUIBtstdd8HGJcdCaVYeRr7lCAbYDfOfAQ8KAfeQJ7GzNXg5gOzJgJu26TMRg0eNA5R5U6MsRLo/A2MDJkAgZeE9lbMSusn1QmRjAlgQrdvBeqa4aO0uP0ZTzEL/uGOrnaAefqirCTyUX6BPelNB+6OXNbCRysioT+0pv0c7LC6MHopNHIGf/T0W36NKPl1SB5QmDEaRgY0yeTL/JuIybtWUIc/PEQ10H0a6zRHQmI9bevMh6A/kEYFk3Aj5k4MkanRZrr1xEtVaL/iFhWJjcncNrFNQ3YN2lq7Rg14T4OExKdIBXb5ZU0KUfkqq/q1dX9I9ygCdPpudh73UGkFw0tCfig/04+3dfTMPZtAJ4uspx7/h+CPRiAGeS9dh86BoHsF06pR/cbeDbZr0JG36+iMKyetobaMHUPpDawLf1DVps3H4BdXUa9EgOx7SJPTjwZ0lJHbZtuwStxkD74Awf7ujplJFait07r9AMwcSpPdG9F+vsS+TiqSwc23+LUqCnLxiA6E6O8T/8yzVcOZMND08F5t4/HH42ei/JoPz8/VkOYDt32QhaBp+IXmfE1q9OoDivmgYxs+4bBqkNvNpQ24QtX55AfU0TBdhOntuPA9+WFtZix/dn6ZLR0DGdMdSpBkxGSin2bL9M7Z9wZ0907+0A3144l4OjRxhAeOZd/RAd4+8AMR9OwcUr+RRgu3DOAPj7eXDjv+3Xq0jNrkBIgBKLZ/SnXa/p+BtM+PbXSygsr6O9gRZPcIx/XZMW6w5fRo1agz5xYZg9qBs3/oW1DZTeTpYCx3WJw4SuDv+5UV5Bl36I/bO7dcWACIf/HC3Mw67sdOo/93brjUQfh//syL9Ny+6T3lgruwxEsKvNfqsFG/PPI6WhFCEKL9zfaRhtUUHttxiwuegoSnRViHELwezwUbQ2CxG1SY19FXtsANtEDPcbyV2/dYYyuvRjsGqR6DEYScoh/9b809h8BCotwW25wNf9PsilSf9y/vy7YVYi3n4NQpc/gFnR61H09D8Ts/JfDVbKy8sREhJCl3ZIkyS7PPXUUzh16hTtF9BapFIp7RdAloLs8vnnn+OVV16hPQtaC6mcR3St5Z/4Y3ZIh3RIh3TIXzhYeev1Px6sPPPcP/L+9v88dfnZZ5+lmZjWmZX/pZACSBbdBlj0xyEQ+kBMsidi9kTa0mKArukTmIxXIRJHwtX9KQhFPlRnsapR1/guzay4SLrA2+NxCG2dkA3mcpSrP4DRUgkP2UAEejwAgYBVtG0yZqGg4UuYW5oQ4DoBwW7TuZR4te4yctRbbU820xHsSqrTMslrPIqcxoMQC12Q7DUX/vLOnP3XG3YjX3MZCpEnBvotgJeU0QrNVhNOVu9EsTYL3rJAjAmcSwvHEdGZtfi14idU6MsQJo/E5ODpkApZZqjOUI+dpbtRb2xAZ2UiJgeN457MCrRl2F58iBaFG+LXE6MD+nP2X6vPxc6SMzSFPCWkPwb6OiqPHiq/jV/LbsBFKMGimCHo6sl+Z7Iu/0PeZZyszKFF4R5MHIYIN1b4ijB7Pk87h2u1ZYhw98JjXYfSwln2EuYf3jiLHFUduvgE4NEegyC3FX4rb2rEB1fO0a7LA0PD8UD3PpT1QSSzpgafXbxEMyuT4uMxs0tnzv4LBcXYdPkGtWl2z2SM6MQKdxHZfzsLu6+n0+Jg9wzpjW5hjJZNnqI3n72BM+kF8HZTYPm4AQj3Y5kJwuxZf+AybuWXU5bQiikDaeE46gdaPb7adR4FZfWIj/TH/dMGwMVGC66qbcQ3286jpq4JvbqGY96UPnSJhvpBQTU2bb8IjdaAEUMSaHM+u/3XbxTi513XKEvljsk9MKC/oxPviWNpOHIwBTIXMe6aOwCJSSGc/bt3XMal87m0qNrCpcO4Ts6E2bPluzNIv12K4FBvLF42HJ62wm+kzP9GklnJr0FsQhAW3D8cLi7M/uoqNTauO4na6iZ07xOJWfMHcvbn51Zh8waSWTFg2KgkjJvUjbP/xvVC/PwzK8o3+Y7uGDDAQZ09dioDh46RzIoEc2f0RVICe3on2+48cB3nr+XDS6nA0rsGIjTIixv/7/Zewu2cClqUb9n0gfDysI2/To/Pf72Agoo6JIT544HJA2jBPyIVqiZ8dvg8qtQa9IsNw5JhvR3+U1WDL89eQpPeiAmdO2FGN4f/nC8pxvc3mP/M7ZqMUdEO/9mTl4GfctJoUbgHkvuih7/N/pYWbMy5Qun73jJXPNx5KCLdbeNvMWN93kncVhUjTOGN5XFj4CWzjb9Zhx8K96NYV4lYtzDMi5gAF1vhxnpjHfaW/4QGYz0SPDpjbMAk7vqt1hfgQs0WmllJ8BiKrp5jefNPnnoznX+iPGYiyGn+Uet2Q6XdQYvC+bgvh0LWkzd/WvUnbPPnIxDY5s8O6ZD/SbDi6+sLkUjUJiNC/h0Y6GhZ7yzk8/9ke9Ikifz9/ylmzVqYNR/a/iWCxXAGLn6HIRD5oEn1BAzNu1hjL+MFmIyX4OVHmouJUVYzDwYTafhnQbPhHAzmbIT4bqI1VjKrZsBoISBkCxr152Cy1iPc63k0m8pwpXweLLTnkBV1zedgbTFS3Eqd/jZOVazkgLQVurMYEvQpAhUDaKByopJloMh6c7H2PO4MXw8vWRQu1m7BudqNnK5Qew1LotdBIfbEz6Wf4baKBQ/52lQUatPxYNz7EApEWJv7Dop1BRSQl92Ujkp9GVbEPkFrrLyS/hYabKyf1MYMNJqasCDiLlTp6/DUzQ9pDyECYbyhyoTJasbE4CFIVRfi8RvruK4j52sz8F73e9HXJx4Hy29h9c0d3Pr66ZosbBm0EtHu/vgy8ww+yWCNz8i6/NmqPOwbs5xO3M9c2oc9RenU/kvVRbhSXYxfx99L8SyLD+9ASl0lXbK5UFmMbFUtvhszk9ZPmbFrC6q1GqojtNH6Zh2eHzQCpWo1Zm3ZCr3ZTG8SZ4qKKANofvduuFFajnt+/JlZ39KCkzkF+GbedAyOicC+W5l4cvsBG/5BgFOZBdj54HzE+vvg66OX8NnBC8x+oQDns4qw6+lFNHB5ZdMRHLySSYGk17JLcT2nDFufX0BvfA+/9zMyCqvozfZaRgkKyurw4WN3QttsxLLnt6KuQUMBt1dSiqFqbMZDi4ejokqNlU9thsFopvtduVEIk8lMl4nS0svw1DPbbEDgFly8lIe335yNPr2jcPxoGt5Yw5oSkuWMi+dz8eX6pYiI9MPm789go42tQ3Ap1y7l4+vNy2hQ8uFre3H8UCpjAl0vRMrNInzxwwM08Fj90A/IsbF+bl0rRFF+DV79aB6tsUI6Q5PuzwRwe/1KPtQNOjzwyFhUVqjw6LINMBhMdL+rl/NhMllwx529kJZWiqee2uqw/2Iu3n57Nnr3icbRk+l49Z1fOfsvXM7DN58uRmS4Lzb8dBHfbDvH6S7fKsQPHy6hgctr64/g0MUMeszrmSW4mVWKH15dSMd/xdpdSC9m438luxT5lfX4ZMU0WmNl4WdbGevH2oILOcVo0DTjyTuGoVSlxtzvt9GmlsR/zuYXUQbQvN7dcL28HIt+ZgwZMl4nCvLx/Z3TMSQiErvzMvDwiV85/zlWnI99dy5CJy9ffJ5+Fh+lneL8/0xVPg6Oe4AG5WtSf8bB8tvU/6/V5+N6fSG2DH6QYrteTPkCOU2MtZeiykGxrgIvd1lGa6y8m7kGapOKXr+ZTWnQmJowM2we1MYq/Fj4GMxW0v3cikLtdVhaTOjhPRl1+ls4W7Gcm38qdWcxKOgzBCgGQK39BWX1ZG5iV7Cm+SiiAw9BJukEi+YzWGzzZwtEMBrOQOp3iM6ffzv5i/cG+scCbMmSTq9evXDsGKOmEiFPbOTfzstCzkI+d96eyJEjR35z+7+CmHWseygTC9BSD4vxDC36Zg9U7DqLOQdmUypM5gIYTDecUO9W6PTHYLE2oMlwhZbZd+haUGPrUFqjOwZLC2kq5kDslzWxjsZFTeRmaO8cQv4EKGpilMPsRgf9m0wy5Gkmv4nRA2+rDvB0zRa1bRIyc4GKXVdjKEWFvgA1hioU6vI45gDZJrXxJrRmDbKaclBnbOCxCk5UM3rppboUGGyBil0OVzLq5ZHKG6zJnu2MBN53qJJRd/eUsg7LdoYPwbQcrmDU0+2FTEdHuKUFDUYdzlXl06Jve4rSOPuJLrexDukNlShsbMDN2gquORy5cZwozYfK0IwrFaWo0DRxOvL/rensXEfy8mhnZmeWxbYUptubksn1naGjLxDgl9vpVLfreprDfhtt9WBKNv3sp4tsf2qjtYXW4riQVUTL8NsDFbuuoLIemSXVKKlWIS2/kmMLkWOeu1UAtaYZtzJKUV3XxGM17TnGznHuUi5tdujMMvr1MOuQffRYmq1vEWMikRv3kaOMhXfowK02jJeTxxn1+oCtezHVWVqgVulw7XI+LZpmD1TsuuKCWuRmVaC8pB5ZaWUO+60tuHwuB43qZqTeKkZNVSPd3i4HdrPf+PyZbOj1fPv372HnP3aU2C/g2X/Y1r35gO3V2f4TpzPZ2By9zdORwI4ELMR+e6DCjX95PbKKqlFSo0JqIX/8z6QWQK3V41pBGSrVLFC0y87L7PzHstr6z44bTLc7K4Nevc7+83MG858d2Sl8/2mxYl9+FvO//Bt8/zfocNbm//ZAxa4r0NYgs7ECFc21yGoq4q5D8nqlPh1NJi1yNFloMNXzrl9SloBITtMFmKykcahDd1vFuq+XtDP/FDexAFGlZRRmxxVsgVq3l9nVzvxpNbL54m8nHdTlvy4biCzRfP311xSHkpGRgeXLl0Or1VJ2EJFFixbRpRy7PPLIIzh48CDef/99ZGZmUkzK1atX8eCDD+KvKgIbKJb3GaS24RW3u317+7CcgQRCAUvFOov9M9IcsfU+dlCuqJWOMVOkTjrnnrQtENsq0dpfnUUkkNj2b9teViyQcEDatvuJIBa0/c4SG3CPvPIoyPQzdiw7uM/5GxDWD9NJaPDCWd8CDgxrf+WdT0RatAloBqW1kO3b24ccnWxPGiC2FvtnslY6so9MbNOJxW10UptOKhZzAF1q/+/sR+0Xi2zMoXbsl4ghk4jat18kgrQdnf0zwoJpTaG2g0Lt4FaeHdx+4jadku3bk4qw7e1Htiesoza2SMXt7kO+gFgs5M7JO57tXMx+PoWds+N37JdKRb9pv70iLU9H7Be2P/5kvKS/Of5C7jfn7cP5gYh3w+H5j6gd/+H8Tsz3fye/ac+XybXEmEPt+I9QzF2P7V2/EkHba5s0NWV6Sas7JjmH8xzjrCHzB9uPzXfOtrRw81b7c2HbOalD/tnyXw9WZs+ejffeew8vvvgiunfvjps3b9JgJCCAlcEuLi5GRQXJIjAZOHAgbVG9bt06WpOFMIkIE6hLF0e57b+aEIyK7R3LB4jjIXQZSTEmCvdHnHQCSGWjIRKTrsuhcJfPsOnYBe3pdh+EQje4yfrCTdrTdrmzSSVEuYq+BrpOhIs42E7ipdtEey6juhjlLIiFcltXVHKzk6CTkgGVk73n2QjBRCeEi8gTcR6MRTTQdwF9JVVqyRl9ZVGIdiPN+UQY7j+T05Fzxbv3RqBLJLylvujrNYib4IiM9B8PF5EcCe5xiHOLocey62aEMuo0waj4y7xtljD64+zwcVR3Z+hAuAillDJJJlmJUIRZ4YxpsDh6MDf5kn29pK64I5SteROMCh1hm66Thz+GB8bRG82DnQdzOjLVjwyORYKnP0LcPDAtmuFh7AHNPZ17w00iQ9+gUPQMDOaCFyKr+rIO1ZPj4xHi4cFRSMkNcGV/Rt2d1zsZComEfm6nly7uy2xcOqQ3R1smr16uckzryTBDy8axrCGxlxwvLsgXQxOj6b/vm8gou+QmSOwZ0jUKcSG+CPTxwPiBiZyOyJxxPeEql1JKc5dOwRyVmJ7/Lmb/yCEJCPT3oFkHSl+GAItms/NPndITLi5SprPRe2dMZ5Tl2fMG2PYR0lelpwLjJrAy6wuXMlwC7YYsECAqxh/9BsbRf8+z68SMhtxvcBwtge8fqMTICV05HZHpc/tTWnXXHhFI6hrKs3/hvew3HjG6MwKClJwtZKP5d7PfeMpUYr+E+27U/hmMjTJ3Zj/uc2q/UoHxo9mcco9tbOz2x4T7YmBPNv5Lp/bnjf/g7lGIC/NFkLcHJvZN4I3/vJE94eoiRa/oEHSPCKL22/1n5Rg2xpM6xyNYafMfSl8WYPkQ9hsv6Natjf8s6cH8Z1kya5Zp1/m4yDEzjvkPwag4+3+80h8jgmLptXJv7HCe/w/xi0eceyD8Xbwxwp/9tvZrdFrIcCjELohzj0e0axy7fm2d3ycHTaeviUrCJgpg5Q/ofEHmDzbHRCvvgqjV/BOrJJRlwNdjBUdbJn8ioQ88Xe9i5+fNkY758+8ogpY//vdPlY5y+3+SWAwXYTWcBoReECvmQiBk3YfJ8Br1B2Ay3YBIFAYXorM9bZACS4267TCR3kDSLnCTT+Oe/qxWPao1myl12cNlAJRyNukQISX3ydIPoS77K0ZC6cLqNBDRmspR2LSXpmkj3CbAXeoAqtUZcpFv6w0Ur5wEhZh1vyVSrL1FsSpykQe6eU6EVKTg7E9vvIQSXTa8pAHo7T0KIlu2gyzFXKw7Q3sDhSki0NtrAGc/waQcrz5t6w2UgG6ejmCz0aTFocpzFNvSzzsZ8R4OGyua63Gg4irrTRPYA+GujF5KJLuxAocrUuEiEmNqaG/4uTB6L5FLNYUUq+IlU+CuyJ406LDbf6g0CzfryhHm6om7YrrRIIj+ZlYrfspNRZ66Hkk+/pgSlcjZrzebsDntNqp0GgwICcfwcAcFtqG5Gdtup1Bsy+jYGHQPcvQvIpiEXbfSaar+ji4JiPZlQEcimRU1dOnHRSLGjF6d4efBfITIldwSnM8sgqebHDP7d6U3Pbv9x2/mIrWgEiG+Hpg6qAu9idnt33c2nfamiQ/3x9j+8Zz9BNOx+2gKahs06Nk5DP17OOxXNzbj10O3oW02YHC/WCTFO2iilZUqHDycQouhjRrVGeFhDtxAXm4VTp1Ih1QqwYRJ3eDj6xh/gke5ejGPBjGE8kyCDrv9505mIjO1DIHBnhg/tQfEtkwCWYo5uu8W7ecTEx+I4WMdQF+D3kQr6tbVNKFbr0j0GeAA+jaqdXTph1ShHTgkHomdQ3j2HzqYQsd/9KjOCAt32J+bX40TZzJpJmXSuGT4eDvG/3pqMS7dKoSnuxxTxnSjQZ/d/pPXcpGaV4FgPyWmDu3isN9qxd6L6SisrEd8mD/G93aMP8Gk7Lh0GzWNWvSNCcPgeIePN+iaseNGCu0NNCo+Bt1CnPxHrcbOdLZ0OTU+EdHeDv9Jr6vGr/lZlLo8O74rAhQO+y9WF+JMZT71/znRPXj+f6IqHamqEgQrvOh1w/l/ixXHqi7RUgLRbqEY5teTd/2eqTkBtakB8e5J6Kx09P9pNjfiluogjBYdYt37I1jBgjb7/FPURKpeWxHmNpE3/+iNaWjU7aXUZU+3uZCIHH2brNz86Q2RYg43f/7d2ECRr/1xNlDh8/9MNlBHsNIhHdIhHdIh/1jpCFb+35D/56nLfwUh9GRT4+uw6I9SBLvE40WIpCz9bLU2QKNaDZPxMqUzuyvfhljCnhJN5hJUNzwFgykLLtJk+Hu9A7GIZRJ0xhQU178Ak6UCHi5DEOr1CkS2xl51utPIbXiP9hEKcLsDUZ4PQ2jLdhQ2/oJs1fc0axOjnEv/GOjQilv1G5Gt3gexUIYePksR5T6C7kOQ/aer1iFPc4EygIYHLEeIgmVCdOYm7C3/CsXaDHhLgzAlZBn8XEKZHYYabCv5GpX6UoQpojE77F54SBjltlBbhI2FP1DqchdlZyyImAsXEbtIrzekYWPhbugszRjq1xtzwydzqej95ZewpegEBfdNDx2C6aGDqf0ki/Nd/knsLb1Gqcv3xY3CqEC2jGCwmPBu2mGcqCD0alc803U8evmwTsIEMPvS9QO4WltCG7y92msiYjxYRqlEo8bqCweRo65FV59AvN5/HK3+SSS1tgovnD2CCm0ThoZE4qVBo2hjOCKnCgvw9rnTtI/QlPhErOo/iEv377idinUXr9Kn1sW9emBRr+42+1vw1ZnL+OlGKs2sPDh8AMYnsU7OBpMZ7+47jRMZeZQB9Mwdw9ErkmULCGDz9Z3HcINQl32VeOGu0YgKYE/b5bVqvPbjMeSX1yExwh/PzR8NXyXzkczCKryz8Tiq65vQr0sEHl8wAgpbtubizQJ89sNp2kdo7OBE3Dt7ELeUse9oCn78+RIFjs6c3BMzJrGnbfLvH7ZdwP7DKbSo2pIFgzF8cDxHT/5i3Qmcu5ADL08FHlw+Gl27MB9pbGzGRx8fRmpqCYKDvfDYqvEIt2U7SBbkgw8OorCoFp06BeGxVePgbct2ZOdU4pNPD6Ompgm9ekXioZVjILdlOy5dzcdX356imZXRI5KwZOFgnv2bf7pEwa3U/skO+zfsuoRfT6TARSqhy2Ij+9vG32jGx1tP48yNPHh5yPHY/BHo3skx/m9uPoabeeUI9VPSMY4KZONfVq/GK9uPIa+qDp1D/fHirNG0+jCRtPIqvPrrcVQ2ajAoJgKrJw6Hq8zmP/mFeOfkGUp9n5KUgEeHDOT8Z3t6Cr64QYryWXF3ck/cndyD85/PUi5ge+5tmllc1X0wJkYkcPT8t1OO4FhFFqXur04eh96+4cx+kw7vpf+CW6pChCp88FTSnYi0ZSur9XX4Im8zZQHFuEVgecwceElZWYISXQF2lnwHlakO8e7JmBG6GDLb9VuouYIz1d/AYNEiQTkSA/0Wc9i24sZdyFV/S+ebKOU8RHnM4+af+sZPoNZugUAgh6/yCbgrJnPzp7nxDVht86fY4wUIbfPn30462ED/Z+nIrPwJYlS/AItus42hQyYdCWR+RyEUh0JVO5tSlhmzh4D2fODjfxYQSFFUORQmSymnk0mSEOZ/EGZrHdLKh8HaouWO6aWYjCjftdAYM3GlfLoNjc8Q95HKFYj2ehgV2tO4UGlf/2XS2/81hLtPQlrDdlyq+YSnmxT2OQLkyThW8QlSVPto6pmV4hdjcfQ3UEoD8V3+SyjQptLzsVL8Hni00+cUcPd6+uNoMNbSwIKsYAfLw/FE/BtoMjfhqduraYVbgvsnuj7evbEi9gEUasvw+M23bIwfcj5gVtgEGrBcqE3H6tusKaNdVifNw5jAXthSeA4fZjJmExGy37p+D6CbVwTW3PoVOwqv2c4loGnuvaNW0oqdi07+gEs1RZQJQdb6SYXPoxNWUuDiqF++QZlWzekSvPzx66TFqNPrMGLbN9DaWBsEKzApOh6fjrqDdmKesmWTg7FBMDN9+9OA5XhuPu7/iV+V+b3J4zGtcyI2XLyONw8xeqkdJvnjkrvQMzwEa3Ydw47LKdy5JCIh9j62GCHeStz/2U5czSulN1+CcSBYl73PL6EA3OkvbkBlfSOniwvxw4/PzUNDUzNmPPUtrbBKbtIEozG6Tye8tmIScotqsORpm/1kDRzAkpn9ce9dg3DuSh6eeZ1Rr+3y/KMTMW54Z+z45SrWrjvO0619dx66dg7Fh58cxt59N+mSAzkXWSbZ8M29CAxU4okntuDmrWLODk9PBTZtfIDiSRbf/TWqq9W01w6xn1SU/fKLu6FS6bBoyTo0Nxu5/YYNTcALz02lNWLuf3CDw34BsGjuQBqwnCf2v9bK/lUTMXZ4Z2zbdw0fb2D0drt8uWYOkhNC8PaGY/j5xG1mPxl/sQjb3lxMl32WfbSTUsbtY0yW6XavWULtn/LWBlQ0OMa/U5Aftj02D/XaZoz/+DvojA7/Gd+lE96fNRGZ1TWY+v2PPP9ZObAfDViOFeZh6T5GD7fLh6Mn4M74JKxPv4JXrx7n+c+O8fPR2z8UL9/Yj20FfP/fP2YFQl098dDVr3G9IZ+ej2DEPKWu2D74CQrAffD6GtQYGOuHXKORriF4r9vT0Jgb8Vr6KhhsrB9y3ffw6ofFkQ+jRp+PHwtW8uaffr7zMdBvEaq0p3C56iGe/T383kCo+2TUN32NGtVLTlcvEOa/CwpZX5jUL8Cq28KbP6V+RyAQs4D375RZiVrzxzMrBS/+MzMr/5Ouy393sehJ3RQ7lY+8GmA1XkJLixEm41knCrIFLdZqmM3pMJkLYbIU8XQGUwos1npoDddhbWniHVPdfIS+q28m3Yedw/MW1OiO0neVujM20K1dhPQzIkUaYodDyHalWlZBmGRUnOnJlhYjynQptCBcvpZQH+30ZCs0ZhWq9EWUulxnrObojeS1tLkQWnMTcjX5tPuygxZpxU0V6x57S5XBBSrsmITOzKijF+vSeewFAvC7WMuom6erGU2W+2YCIS7UMurv8cosHgXTYDXj6v/H3leAt3Ul4f5ismSUme0wM0OTNklDbdqmnDIzbpkZt8yMKaSUYhpOw0yOHTOzbDFL75tzpCtdy+1C8/bt63r6uVI0F34dnXvu3Jn5Zzp4/ZMtbTUCBZle2112lJlbUWvpQp2tW6Q7bGqFye3E3tZmWD0egV5Kr6trK9n7zXW1PUYf+LWK69ZXVrObVgSjhH3GMJbxbcL70A14UwX9/sDakkrRudw+P3ZVN8Lr82N7eX1UE8UgOqwOHG3qYNTlxg6zSEeU5m6bE4cqmlmtlWha8MZ9Vez9jgO17CYffkShl407ObatuyuFhNYw/q27+X6bt1WIxp+227GbfzfyqAj05ECQeVr2H6xn9U/27K0V4TCZ7KisakNjYxeam7sjDRYDQZSXt8JsdqLkSBPzmkTvtzV0/l17asT4g8BvW8s5/l294N/F8W/aVSnGT7Vi9tWw9xtY9+So8ff6sLeMj/+OUvH4d1ocKG/sQH1HNxo6xeN/pLGN0c73NzSzfJTo33RdKT//bzV1MfNndTnXra2phjyKsUSGx9pajn91Q2T8eY8gKTY0ct2a5tJe5z/VL9pliswtP9Vl8tD12YJmVzta3fxBg+8XQJW9HhafDTWOcrgCTtF1f8jMqeN1dnoVf4NKKy890OrYGLP+0GckNifv8B75BjI4XNx4DLh+7WX95F2q+6RPwtJnrBwDoX4WYlowfUbhEAUg4YmqYl0ipEzfU2Ssgq1clthzD9bgkETBXqO7ohLVlm+v7HFMutkrQ32IiP3Dm49BWIDUMq6jpNqe+NUyPUukVUpjnwI0cj20of5A0UJPZ0qZGnHyWJ1Ozt37erlORF2mBdmg4NsbSBfl56ObDTVWIyH2TzT1lxbgeEFHjdXEEq/UQCGVQiuLpWEmKLWIV/H+KNFChpJOrkBijycfOnaCin+WoFaLamQQpqTQ9gkadQwtNT6kS9RqmPcmGn9CqMcPeUt6sGoRr1Wz0Ea4IqoIv1aN+FB/GhF+qQQalRLxcbH440MJr/H6HvjJW6Dn28frNTG0YGqMyM4ZrxF62zD8gSAMoR4/8YZY/Aa9mtGQwxVpRTqDBnp9L+NP31ejYMcT4ZcA+hBG2rcn/vD2hl7w68P4DT3w0/wJHZOSamPwx4XGP1QROFpo7A2h306EXyqBVqnk8yAaP+0T+ixREzt/aG4wnVotJgVLIvMuSdXL/AnN4d+b/3KJDJpQRVqRTqGFXq7tdf6rpSroZJHE6bBoZfz6VcsMohortKbw9YNo1r2sP6E1htg/nAkUFj+k0sTfXT8RWrf+chKU/Pm//1HpM1aOgSgM9wj0YxKpaib7o1itPv4R0YWo1l4EubwYcpkRSXpORw6LMf4+VopapxyLRC2n+oaOiJzEh9i7VN0CGFS8Ey6JTKJGUeIt7H1xwtnQyCOVfsmI6Z/I69mMTr4IilDOC0mishD943nMeEbaFQLDhyRfNw75ceMY/oWZl4rwT0xeAKMqi+WmzE3ndMawnJy1DEqpEv3iijEhaXwUeimW5Z3N3k81jkV/fYSZQtufl8ebXJ6WMw1p6siCF6/Q4ew8TmG8tHg2dLJIPYaiuFQszubUy9uGzhUYDiTTUosxLa0fw3//mHmiZfC84nEoNCTDqNHhuuGcshqWu8ceB7VcgTFpWVhUNFB0Q3lgCu86u6j/QIxKj7A3iJnxtymcOnrB2NHIMEQW+iStBpdP5LH3a2dOEnIWSPoZU4SOzLctnCHGPyCf/RH+O087TnQjPWvaSOSnJSHZoMOlCzjlNSw3LZ3BSvkP75eJORMizfHoJn3LMj6Ox08eiKH9I/iJGXPlORz/0kVjkBZq4MfGP16Lc07h57jgnKnQaiP4C/JSsGAOzxm6+orZAkOGZMK4QowfV8jwX38dlWKPYFyyZAxjGCUl6XDeMk59D8uVV8xipfCHDMnCcVHNFQn/ddecwN7PmjFIKJMfxn/ZRZzWvHRxL/hP5fgvWTpZYPiQFOakYOEsjv/Gs2ey+jRhmTw8n/0R/tvPFI//mceNRH56ElL0OlwxRzz+ty7m4z8qNxPzo5oTEv67F/DxXzhoAEZl9pg/Mzj1+qIRo5Gpj+BP0mhx5Wh+Hd04YqqQM0UyICEFZ/TjDJ07hovn//S0YkxPL2b4bx54ktA5mY1RzmTk6YxIUBpwek6kASrJBfmnQCVTokDXH6MTJ4mu39OyL+DnNcxEhiby2xCzcGrqJex9Qfy5MetPccLF7D3lqEglEYaPUjEQCTpOeZYb7hatn5LQ+vmXlL6icP+29OWsHCMJ+GqY65J5TVS8xkpYWMVazwHI5DlQKHnCaFic7u3wMOryYKiVke7DlJBmca1n1GWdaiw0ikiPEyqv3+FYx6jLSZrJUMsji583YGO5K+R9SddOE55sSBy+TjTat0MmVSFXR0mhkSfAbk8jGhwHoZHFoyBUYyUszc5qNDorGHW5UDdMhL/SVopWVyOyNHnI00XopZQQe9B8iFGX++uLkamJ3GC4e/ogHH4XRiQMRIoq4kmiTsxbOg6zJ+SJKYNhCHlPSDrcVmxrPwqVTIFpqQOFPiYktbZO7O6sZV6TGen9ReEkqlh7sKuZUZcnpfKbUFh2tNaj0tyJQUmpGJmSKXpy3VBfzajLY9OyUJwYocBSeGlNdSWjLk/JyRXdYChpknJXaP/jigpFT9ntNjs2VdSwG9RxAwqhUUQW6NqObuyubmBdl2cMLBAVIyttaENJfSuykuMxvl+OCP+e8gbUtHShf7YRQwsiNwoWOjlYjfYuO0b0z0RBZgQ/hTd+21XJQkXjhuUiLSWCn5JuKXeF9p88tpB5K8LSabKx0A8ZCJMnFIu8JhTWodAPdS2eOL5IFI6pqGhFWVkLMjLiMWpUngj/gQP1qKvvRFFRKgZFGSGsou3OKtY1euiQLOTlRWj2FF7asr0CDocHY0blCR2SBfw7Ktn8mdQTf7edJRcT/qljioQ+SiT1rV0s9EM5KVNGise/rL4NJbWtyEyJx/gB4vHfXdWA6tYu1kF7WJ54/Om3brPaMDo3E0VG8fyhOUKhosn5uciMMnApaXtNdRUL58zKK0CCOoK/zWnDhsZqlmB7fE6x0MeKzR+bCTs7apmXZWZGP9H8P2ppQqmlARmaJIxNKhLhLzFXoMHZyvJV+uvzRdfvEct+WLxdKIgbgHR1hB5O5fWrrNvgCTiQqxsFvSJVtP5Q7gp5X9K000Xrj8/fBjv1/5GoEac+QeiDRhIMrZ9EXZaqyECMLaz3V8hZKbzv0T+ds1L1wJ3/kzkrfcbKMZRg0MeTaHv6lEM6SS+VXf9Ix38a/+/qqGS1tFcdd9NKeqleGQj6eXG4XjD+I11v1WxJ/EG/wObpiZFi4f+qjhZKknDjNPG5KBmQF9PqKb4A4egdvy8Q6LWa7R/pWOv6YPBf1oXd/NFhKwF/IMA+7xWjPyAUCutNF2a8/LM6hjEQ/Jd14VyR6LBJH/5/E/9/cv784fz3Qx7lfTkW1y9pe1sT/mj9IZYiC/7+zhr5e+vn/03pM1b+/5A+6vIxkGCgG56uaxDwbAEkeijiH4Jcw8M4fl8tzKZL4fcdhlSaBn3iS1CqePjB5dmHts7L4PPXQyEvRlryW1AquPu42/Erak23sF5BFBYqNL4qFFFqtHyKiq4nWY8go/Z4DEp5AnIp5XsEcaTrRVR2f8jyQvINp2JY8t/YU4o/4MGWtsdRbV3FKkuOTLoYQ5N45VqX34IfGx9GvWMvVFItjku/HgMM3G3d5WnB53WPodVVDb08CUuyb0Z+HHef1zkq8X7NczB52pGqysSFBTchPURr3te9F+/VvA2bz4YiXTGuLLoGCUoe4lnVshEf1HzFCk+NSxqBa/qdD41MzfC/W/0DVjSsY/gXZEzGFcWnsAXYE/Dh8ZIv8WvzPubyvrjoBJyTz13FZo8Dt+75HDs6KhEnV+OuYYtwYhZ3kdfZunDtti9wxNyKVHUcnhm/BBOM/Alyf2cTrv3tazTYzSgyJOOVaaegX7yR6VbXVeCWTT+hy+3EmNQsvDrrJKEI10eH9uHRLRvg9Hkxt7Afnpl9InPRE/6nN2/GW7t3MW8tdc29d+ZM9pTu8flw14+r8f3hUhZyuG7aRFw6iYeIuh0u3PjFD9hWVYc4tQr3LZyFhcN4GKq+sxs3fPQ9SpvakWrQ4YkzT2RFxkgO1bXg1vd+QJPJgoLURDxz0SIUpfMn+A0HKvHA+6vQbXdhRFEmnrxsAYzxHP+Kdfvx/Ocb4fJ4MXNUMe6/ZB6jNRP+1z7fjE9+2gW63y+ZNRw3Lgvh9/rw+OursOq3UsaWuXjpJJx7Eg9RWKxO3PvsSuw+UAedVoWbLzseJ0zjoYKmlm7c9eS3qKhuR0pSHO69cT5GDeW02iPlzbj36ZVoabMgNysJD9+2GAU53IPy285KPPbSzzBbnRg2IBMP3bqY7U/y9ar9eOnDDQz/9HH9cM810fh/wyc/7mY3fIb/vOME/I+89yt+3l7KiupRh+rzThwn0JNvf+sH7CitQ5xGhTvOmoV54/j4N3R248Z3v0dZUzuMBh0eP/dEjCvm43+woQU3ffoDGrstKDAm4vmzF7HGlCRrj1biju9XocvpwujsTLxwygKk6jn+jw/sx2O/bWQ9guYUFePpOfOgU3L8T+3ahDcO8qKI5wwagfsmzmL4iZ58544fWa8rmv/XD52GywdPEub/Lbu+wPaOKjb/7x6+EPOz+TXa5OzEvQfeR4WtCclKA+4eejZGJXIPaKWtBs+Xv452dycy1em4sf8VyNZy71aJeRc+r38FDr8VedoBWJZ/MwwK7gE91P0dtrS9Cl/QjcK4qZidcQcUUp4vVN71HGrM77F4Rbb+dAxKvoOtP4GgG22mW2F1fs1aiiTF34wk/dXC+unvvg4g1qRED6nhAUg1i/CXlD7q8r8tfTkrx0A85rsQ8GzjMylogbf7RgS8nKliNl0Mv483TAsE2mE2XcBqrwSCTrR0nA2fv5HpqLFhS8e57KnE7WtAdccV8Ae6mc7u2YvqDl7Sv9u1C2Wm++EPOkJMoDWoND3NdPW2lSjvfgcBeBGED9WWz1BlIUogsN/0Lqqsv4TYPm7s7nwFDXbeaXZty3NocBBbh5gEdvzS9Bg63Zwp8Vnto2hzcdaKzdeF5XUPwemzMkPj9crHGHWZpMPdgjcqH2dekU53B16tfIkZKiTV9iq8Vf0Ge3/EUoE3qz4N0SKD2GXaj49qqNkjsLp1Jz6rXw1f0M88KN81/YZvGzmb4L2qNVjVvE9gO7xS/hO2tHOG0EMHv8Ouzip2HVt9Lty590tUWHnn7qu3fY6jFupeDXS47Lh8y2fo9jhZhdoL132GJoeF6WqsJly0/jN2k2uwmXHF2m9YjRaSfe1NuHEDb8hGTQ7v3rgaDh+NMbCqugKPbeGU5K9KSvDqzh3wBgLMW/Ph/n14fx9nQb3823Z8d/gIe5p2+3x4at1vWFfB2Rz3r1yNHdX1HL/Ljb+t+BnlbXxcr/tgJcpbQmNsdeDq975lxo3L48NVr32Nli6rEEa6+vWvmVehqdOCW1//nt2ESQ5VN+Oed3izuX1HG/H4h2sYrZke4DfsrcSLX3DG2I+bSvD+yh3w+gKsuuyXv+7DF6s4/ndXbMMvm46w41Ndklc+3oTNezj+p17/FXsOcfw2hxsPPf8jquo45jsf/xZVtfx9Z5cdtz3yNTNuqMLuLQ+tQFsHx9/Q3IW/PfQVO35Lmxl3P/ktLDY+/iXlzeyYbB6XNuCpt1YL+DftqsArH22M4P+O8Psj+H/hTf7eXrkdP20L4ff62Hf+bT/H/8gnq7HraGj8nW7c/e7PqGzimG94ZyUqQuPfaXXg2re+ZeNKFWove+9rNJs5/rqOblzxPh//RrMF16z4Ht1OPv77G5tx63d8/Hc2NuKedWvg8HoFJtkTv/HxX1F+GC/v384aEPqCAbxfshfvlXAWzkuHf8M3NYfZ/HFRXZX967C2kTOEHjywEjs7q4X5f8eeFagIzfm7D7yHKlsLe2/yWHHn/ndg8TrY9ft46fPocJuYrsXVhidKX2DXb5enHR/WPs0MFbauOMqxvI6XPWh2HMTG1r/Dx7q+B1Ft24wtba8zXZPtW1Sb36T+8mz9qbd+glrLx/zcludgdVJHaT+CcKHT/AjsTs5iDJjvATzbhfUzYL4ZwdD6+ZeTP1tqP4j/WekzVo6BBDw7oyjI7BMEvFS3wQ2/74ioszKCdvi95fD5qPZEVxSzxw+fn+o5dMLpoXLbXlG3ZruHdx82u/f1+NkC6HbT+QGTa38MddDk4rTgNid1zY1uIChDm5N3ciWacnSGPxkRrc4yRl1uc9dEURiD8ARcaHfXo9PdCoffJjB7yF3c5e2A3WdBnYPqmkTGg3SVNr6wHrVWiRL+yPg4YuW6Ekt1DHX5iIUbTfu7aDGO4KftDpq5EbXHxOuoRB/zcHcjPH4fysxtkc7KCMLh86DC0s5oy2S0CLTOYBCNdgs6XXYc7mxjBocw+sEgdrc18XO1NMWwknY2c4NzT3OTyK1P2+1t5vvtamgUMZ1ou70NvCfWrrpGMf5gEAcbW5k35mhLh0CPpc+pdgcVIaMnfrPDFaHHBoJo7rKiy+5gORYUmhDwB4LYX8XPdbAyFv/eo1TrBzhY3iTKNSF3PH3Gxv+IGD9td7AsrGsQdUGmY5ZWtDBvRmVtu6CjJ2+ny4ua+k40tZphsbpE9OSWdgu6LQ4crQ7hD0bwHwqd69DRZjH+QBD7jjT+Q/xkpInGXybFgQqu21vRKOqQTPgP14bGv7n38a83dcPsjIw//X5N3VaYHA6UtLQxYzV6/oR/6729zJ8dTXz8d7c1sh4+wvyBBHtaOcadbQ2i+U/b7eng++3prIuZ/4do/gd8qLI1izqjO/0e1Nhb0erqgM1nF12/HR4TLF4bGp3VMddvrZ0bDy2uwzGswmYnX2O63XuFXmbhb8DXK56bJ77TyuH07OLH8Maun0FvpBN5n/QJSZ+xcgxEIiO3cI9uvDJKSFP2QsuTQCrLgoxVqu2xj0QLmTQeyphiSEQJ5gluGjm9RlMHZdDIuVtaK8/s0dFYAm0o+VavzBIZMrTIxCm4Ll6RIVqA2PaKNMYQ0vZCa45XGJlLmFgC0aKUqqCRxSFZFUmGDONIVnH3eKoquQd1WYq00PZp6iQRrZP2S1PxaqFZ2mSRIUNPgBlq7pbO1iaKaJ0kGZoE5i7vSeuk95naeFaptmdHWo1MwSif2XHiWDDdXKjxITuXPl6Ekc6bY+BJhFkGsY7OlRVKvs1NSBBhpNyDrPjQMRPiY/BnJhhYuKInrZneZiToWV+h6JouDL9SDoNGjczkWPwZyTyJMyOlB36pBFlGHp7LMMaznkDCuSRAeij5NistXnQ+qrCaEUpszUxLiMkPSTcaWLiIKMM98RNjJzlRF4OfEl71OjUyUsW0VTp2mOVD5+yJPzONb5+RYvh9/Kli/OR5yQjrUnhjxGihRoW/O/6Jehj1vYy/Qg6DWi38rtHjnxnPxz+7xxyh3z03no9/dly8UC8ljD8873LjesyfYADZutC804p1JJnaBCgkMsaoi344ICHGHVWq7Xn9qqRKxMm1SFLyUGjkO0uQoOTXqF6R3oO6LIMhtI7Q2iTWSUJNV6nbdF4MdVnB1k36OHb9hCySbP2Xkj420L8tfcbKMRAl0ZMlkQVWpj0fUuVE9mRnSHiZloGQRgKd4X7I5FmQSRORkvhU1E+gQGrSi5BIlNAqhyDdEKlEK5XokJ/8LHtv1M5Fqm5h5NwyI/ol3cneF8afjSRVpOGYXlmM/iHq4KjkywXjhCRLOwnFhgXs/az0G1hdlbCMSDgZ2doRDP+SnJshj2oZPzf9EsQrjaxuyuk5lwoLIRk25+ReDblUjlxtHhZmRKjXVGb/onxOb5yQPApTUyKltBOVBlxQsJS9PzlrBgZHNTXM16XjzLzj2fvLiucKxgnJxJSBmJ/Jqcv3DFsMgyLCmjgrfwLGJRcw/M+OXwKVjD/tEdI7h89hxgrVqHhk/Ins6ZWNvlSGZycvZpVthySn4bqREeqmTq7EM9Pms/cnFvXH4n4RWnOqVof7pvK2BReOGoVRUU0N+6ek4KrxIerpzMnMKAnLjKICnDKcd31+YPHxzMgIy7njR2JCPnUdluDJM0+ESh7Bf9uiGchMNLBaH1R6P/yUTlVvHzn3RGYgUFO9cLdmEq1agQfO552tZ43ph7kTIviT43W46Sye+3PG3FEY2i+Cvyg7BRcs5vgvP2uqyIiYNLIAC2byrr+3Xn6CUM+E5NQTR2HUUM6auffGBVAqIk/b1150HDM8iKVz65VzIvjlUtxzw3xWGbZfQSouWMq7HZNo1ArcdS2n2c6c0B8nTInCn6jDDRfw8T9j3uhY/CfxcbjqlCmMzROWycMLsGgKx3/X2ceL6qacOXMkxvbn4085KtHjf+vJM5CRaGC1bu4/STz+Tyw9EUq5DIPTU3H11Mj465QKPLGIj/+84n5YPCCC36jT4Z7pfPwvGjoGo1MjN+kBiSm4eiQfh1tGzGQVacMyM7MIpxbya/3eEeL5f3bBeIxL5qy3e4acw6rVhuWafichTZ3IaiFdUniucP1STZariy9m12+mpgCz006NMmI0OCPnGj6mcdPRT89p/CRaeRKmpnJdnmEZElSRpqpxyn4ojL+M/07xt0Mhy43sp54FQ7jrsuFhQBIx8CSa8yBRRn7/v5T0GSv/tvSxgY6RBAMWBLxHmCdFGkUzJgn4O+HzlUEmy4ZMHrlgSXw+qpRZA4WCaq9EupCSuLxV8PrboFEMhDyq4BL9ZDZvGfwBG/TKwZBFUwCDfnS7S9gTToJqMEumDYs/4EanuwwyiQpJKqpDErFV3X4bOtxVrFBcsor31QmL3WdGu6uOGSmJygg9k4RyVjrclLyaifhQAl5YWlwtMHu7kaXJRlyoKFwYf62jkVW5LdDlQh1VP4WeGMut9cxz0l+fK2IwUA+gMmsjVFIF+ukzRGwhq9eFo5YWRl0u0keolCQmtx3llnZmpOToxBib7BZG+6R+QeG+QGGpMpvQ5rBhYJJRKMAVxn+ks51Rl4caU6GNqn9BHpNDba0sdDAsLU3okMzw+3w41NzKegMNSksVhQMoV6W0pZ0VCAsnaQr4bQ5UtHYyIyU7Sex1oJwVypegBNtwAm1Yalu70GG2ozgrRVREjiVC1nfA7nJjYF6aqPAZ4S+tbmXhlUEFaaL6KZSrUlbVyqi//fJTRd4Im92Nipo2VqAtnCQbli6zAzX1HcxIIS9MtLR2WNDY3M0SbMMJtGGpazKxPJei3BQRBZnwU9sAol4PKEhjxsw/hd/rYz2TCH//HDF+ylU52tCOxDgNCjNix7+ypZMZKdnJ4vFv7raiztSNwpREURdtkurOLkZX75+aIqKwE/7Sjg5GUx6amgatQoz/YEcruw6GG9NF9VMoyfagqRlqmQKDE9PE88frQpmlhXkSe87/bo8N1fYWpKsTkaERf7dOt4nlq2Rq0pEYSoAPS7u7iVGXM9S50Mr1Ivyd7kpGXTaq+7Hk2ojOD7PnMC2IMKiGiNafQNAFt+cAoy6rFNRhOyqcFLACFDKXJkIiF6+ffyk20F2PQvYn2EB+YgM90scG6pM/IxItJDJjqHJtD5VUB6k0ldVg6SlUyZZCQlJJbNVIuYxCINQbRVxtkp6YVLIU+CUaSCWqnkeEWm5ki4Y4fkweGgU0cgqnKGNohbTgaOWJUEtjLwB6sopTJIRCQmKhSrbxigRoZLEVMfVsgQtC3aMKLuFPUBhYnRSlVFwhlDwdySpy9RNlsidGGVJUevakKO0lhJOijkN81BNmtGfEqNYhURmri1eqmZGiV/QcRyBZrWUhK61cGYPfqNWxm0zYayPgJ51Ox1z9PWmpZLgY9Tr2pN6TlkqVaqnQWLjSqQi/Sska5IUr3kaLXqtizQuJxdJTEvUaBCRBaHtUYSX8yQlaqN0KKBVi9zvhSk6MY2Ge6PwPhl8uQ1JyCH/PEI5agcTkuF4r62o1SiQmxQkVb0X449RISo5DXKjCbrQkxGtZwc5wA8No/EmJOqg1CiiV/wJ+mYx5YsjT0xM/jX9ywu+PP3mgEnr5bgYaf1/v45+k0yBI49+jCjHhT9FpWdhOJYvFn6rTcVpzL/Of5irNuZj5I1PA+DvzXytXsesmXCla9N1Y2CceWlnsfnFyut6DMVWsCT95VBQBNVtLxCKFWkbrD/UUEl8bEijZA5lEooqlNUs0gNT4161cGxIhUfZP7P+/Kn3GyjGQgK8eHtMyBFmvHwnk+lugiLuK6byefTB3EsuHkmkV0Cc8C7WWV361O39BW+cVLDteItEjPfk9aNSc1txhfQ+NXdT4i2o4pKMo9ROoFf3YU01V15NosPCGfzrFAAxPfxdKWQoCQS/2tN2GZjvPsjdqJmFc2vOQSdXw+K1Y03gjOtyHmK7YsBgTU29ni4bF04xv6m+FxUvJfBJMNF6Mscm84myT8yiW194Pp98CKeRYlHU9hiZwt/th8058XPscfEEPM2guKLgNRXHctb6+7Vd8Vs8p1AmKRFzf7zZkaLIY/k/qVuD7ZuqnBORqs3HHwBtYRU2qBfFU6XvY0smT8kYmDMTdgy9lVTWtXidu2/cmSix1TLcgczxuHngaM1oaHSZcs/MdNDhMzKl9Zf85uLCIu9YPdjXi8q0fsWRahUSKh0efjEU53H2+uvEort/2FXtijZOr8NrUpZiYysNQH5Tuxv07VzOjI10bh4+OPxPFCSkM/6M7NuCNgzypeVCSER/OW8qMF6/fj+tX/YCfKnmvmmk5eXhjwUmsKq7F5cKFX32Nfc080fKMYUPx8AknsJtOfbcZF36yAnVdZob/xplTcMUUHn452NiCSz/6mjGAKNTw6MlzsGj4IKGn0C2f/Mg8NnEqJV48b7FAa17+2z48/vV6hj81Pg5vXHEKCtOSGf6/f7MJH6zhCdv9s1Lw6tWnsIq4hP/Ot37Emj084XnioFw8e9VJrCqr1eHCtX//GodCibonTxuKO8+jwl4SNLabce1TX6KhjeO/8rSpuGAhx19S1YIbnv4KZpuLJbXec+lczJvM8W/cU4m7X/6BeTyouuxTN5yEMYM4/i9W78MzH69j+I2JcXjp1lNZYTvC/8IXG/HhLxx/v+wUvHTTqcyY+CP8FocLV7/6NQ7WcGbMkklDcc+ZxzP81OPnstdXoL6T479u/hRcMjs0/g0tuPyDyPg/vGQOFo3k+NccrcQNX0XG/9WlizEhn+P/cN8+PLiO40+Li8MHp56K4mSO/7GtG/DGfp5cOijZiA8WnsbnT8CP6zZ+h5/qeDLrtIx8vHncKXz+eFy4ZPMn2GfiycRL80fhodEL2Pz5o/lfZq3FvQdfh8VnZ6GeG/ufjVlpPHy6t2svXq18Fd6gFxqZBtcVX4eBBh6i2t75I35oeotdvwZ5Es4vuB+p6hyGf0fHKzjYtZxtl6QswrzsZ5nxQuvPwbZb0Obg13aSejJGpr3M1h9/wMzYj24PZzfptecgJfFJtv4EffXwd50P+OnalkAadxOkcVf+i6twn/zVpS9n5RiI13w7gqx7MkkQPutTCHg4ZdJiugzBoDm8JazdN8DvJze1FW2dlzNDhe0VtKO18yJWGMnlLUdj171CIq3P3466juvYe5NzvWCokNi9FajopJL+QI1lOZrtawRdu3M7KrrfYe/3db6OTjdvCkhSYfkO1VbeXGxtyzOwelsF/Nva30KLk2+7ov4xuEIUxgB8WNn4HKxeE1x+Bz6uJQqjh+mIJfRBzVOMRdDsbMTy+g+ERFqL14x3al5j7/d2HxQMFZIGRxM+rP2Mvf+heSO2dhJricuB7jKsaOCG19tVP6HUUi/ofmjagdUtfIwfOfQ1mp2c5k1nfOXoKhzs4kbNDTs+Zws9G/1gAHfu+QbtLiusXjeu38oNFTaOPg+u2vwlY3FUdHfgvh2/ComQ7U47bvhtJR+r+irBUCE52tWBB7fxbrgfHtyHn0OGCsnmhjq8tptv++zmzTjYwm+UJJ8dPITvjnDq9d0//MpqdYTxP7t+M/Y1cqPgus++h8Xp5vj9Adz+9S+sKqrN5cYtn/zAbpQMv8eD6z9ayVg0Va2deOwrfqMk6bTaccdHnDq76XC1YKiQVDZ34qkVnHr9+br9WLs30jCPmvi99wvH/8rXm1ESutGTfLPpEH7ezvE/+u4qNHdE8L/y5W84GGLa3P7iSljtHD9he/CNn9HRbYPN6cZdL3/PDBUSh8uD2174jm1T3dSJpz9aK+A3me249/Wf+JgeqBYMFZKqpk48s3z9P8T/8vdbcLguPMeBr7cewk+7eUmB+7/4FU1dEfzP/7gZ+2v5+N/wqXj87/zqF7TT+LvduOEr8fhf/eVKPn86O/HA2gj+DrsdN/3E8a+rqxIMFTZ/TB14aPM69v6D0j34OWSosO/aUotXD/Fmo38/vA4Hu/iYknxRsxcr6w/+w/n/SMm7sPqozAFYSYBnj34Mk9vMQrBhQ4XE5XfhxYoX2fXb5qrH901EQeb4rb5ufFn/HHtfb98qGCokXZ4abAt1c6+3fII2B2+4yn431zbUmN/i25mfgNsTubatjo9hc/AO2X7LnUCohANL/bc9g2Bo/eyTPglLn7FyDCTgO9qDekefUSdX6hzb2KPxoB8BXy2rrxKEO3oPBILUxdUEt5e6sEb7+/xw+fgi7GC66J/ND3uoJoHVQ7RgsVvZ6uW1JLrclT0y9eUwezgtuNNNtGAxfpO7llGXLd52EXsnAD+6Pc3o9nTAF1roIrRIOxw+C1rdfKGP+mZocfKFttHZLCS1hnV1Dr5Q1TtaRO5tOmudg98gq20tIqYEucjrHLyWRKWVx/ijpdrezqjLzU6zaD/ars5uQrPDzOq1ROO3eF3ocjtQaTGJR59yJMyd7H15d6cII+lKTbwOR7mpU1SmnX3Wxfcr6+gU0UspRFRh4jUujraLdew7dZgYdZbqeESzRygXpt5kZrkS1J1ZwB8Eu6l2OZyoaesS4w8EUdXGz1XVYhLjp27HoZoiVc2x+KubOf7Khk4RPZm8JNXNJkEXTf0lqWk2Mepya2cs/oZWM/vc4xXjJ6OGisDVNsfip+OFMfbEX9Hwj/FXNHfE4Kex4LpY/FWtvz/+dZ1mNJl7GX8XH/+qrh74g0FUhn5rmiMx86ezneMwd4pDn0H6jH83qhUkmj8SKSotHX88/wM+tLsJi3j+N7k60OnuFAwVfqogHH4Hq43U4Y4YRVwXQLubP4x1e2p6UJf9MLn5GmP3VsasP/QZiccbXcKBfQN4fSHDnr2K15+gT9wl+y8jfQm2/7b0GSvHQKQKCn30iD3LB7DYrJRlwEfrFJDJCyCXEVuC8jzCC5cUUmky606qVvTv8dPIoFFw17NOSRVuoxcmGfRKHnoxKAewYkwRCcKg7B9yyfbvscj4kKjilSyN6uKYRSZZVQi5VIEERbpoP2L9JKoykag0MqpyGD+xCnRyA/vLUBNNOrrVvRRZmhwh7BNtPJCOkmxJCnRZMYsufUZSrM8SGTlUNKswjjM/BhgyYvJbivVpUMrkjNYcvR/F/fN0yYzuSR2ZI6MvQZJKy/76JaSI9iFa6MDEVCHs05N6OiyFJ0YPSjGyBElhjIP8M5LBqUbRTYqewAeGdWnGGOppf2MylHI5chLjRftRKCIvOYEl21KehYCf8jh0GvZH4Z7ofYhi264IGfYAAQAASURBVD+DJ732z0yJof4OyuHfjfoLkWdDwI8g+mVzjANye+D3ByK6vNQYGi8xcSg3JMsopgUT6ycnPYHRhimxN3xI1n1Yr2FU54KsWPzF2Rw/nTMGf94/gT9bnNBM29FYkAzMMsbg75/Bxz+7t/FPSUBWgoHlLInGX6thf/2Se+Cn+ZMSOldy7PwZagzNn8RU0fwPhj4jGZyQLsYfDGBAfNofz3+pHOnqZNFcplBQlsaIFFUKVFKVcJ3SK+WY0V+qOjvm+k1X8/BokqooputyioqvMfpe1p84tl4BSiU17YzG6INSwdlwkA+OLeMQquT9V5M/UxBO8ifzXf5/lz5j5RiIMv4xSORFoX/JoDDcC6mS50XEJ70FKSWOkUg0MCS+AqnMCKlUh7TktyEJdSKlRFvKWaHS1CpFIXKSnmRlqdnx5dnITX6evU/STENePFEF+WKiVw1DUdId7H2+YSmy4yKU4XTtLBQl8G6pI5MvQ5pmjKAbGH8G8uI4LXhW+i1IVOYKi8+01GuQpuGLxWm5dyJOnih0WD05+2/s3yqZBufl3wpVKPlOK9Pjgvy/sV4haeoMLMu7ROgpQnVXLirgMegRCUNwatZCYTEsjMvDsjxOYZyXMRWzUiPdmicmD8cp2bzs/8WFczEyVCac4cqZhuNSOU3yrqGnIF/Hx5gW9JsHLcTgeF6r5oUJZ7DEQxJiUTw99lSWiEvl8V+echp0cp4YSWyf16aczp7MCw1JeHzyiVCEntKp/sVzU3n57xnZBbhh1GRhKR+eko57JvIcnnOGjsApA0MLMIA5hUW4dBTPD7hxyhRMzMmJdLgdPQoLBvBF/pEFJ6AoJUm4ed09ZyaGZXLW1YtnLoIxjidGEovomdPmIyVOx5I+nz93kdDJOV6rZjkrhD8/NRH3n3GC0K+GDJvHzuHU38mD83H5iUSr5zgG56bhllN41+LTZgzHwkmRjrozRxbhvBP4nLni5CkYOzCC/6zjR+GEcRz/nReegPwMjp8Mk5vPmYnBoaaKj1+/mOWTkBAL56ErF7B/U3n8x69bxF5JDHFqlrNC+PPSE3HXRRH8ZNg8eDnHP2loPi5dHME/KD8dN5458x/iv3rBZIzrF6lfdM7MUZgzmuO///QTUJAawX/7yTMxJIfjf+HsRSwpOjz+T58eGn+lEi+dFjX+GjVePZ2Pf0FiIh474QRh/mQZDHjmRI5/Rm4Brh87KTJ/UtNxz5TQ/Ok/EqcUDo3Mn9xiXDqEXw/XD56JCSkRWv/5xeMxP3vwP5z/9wy+mJUHYOMvVeD2Qeezf1M5gWuKr2GvJMTWu67fdSwHLEWVhZOzrhY6sScoU3FaDq+gna2bgFFJ1Mmdf4MU9UBMTL2W6/RnIiOOd1AnMWpnIz/+IvY+yXAb1KpIl22D7lLoQi1JZFT6IWr9lOrvgUTB2wX8JaXPq/JvSR91+RgJa94V6AAkcZD0YO9QHgqV2pcSLU8izqwPBj3w+zsgIyZRFM2PJBBwspL7cllqTBdS6rhMeoUsJabxl8dvZk8/KpmYfUQ/tctvYtRlpSwuBr/D3wWlVCuiIjIcQT8rtU9sILlUnP1PoSKbzwK9ggqbifO1qaS33WdjbKGe7B2n3wW33414hSEGv9VrZ96XeEVPjEF0eWzMYxIn74kxAJPbBp1cDU0P9g4l7na67UhQaljHZvFYcV2KWieiiZJQSf5uj4sxiXqGF4i2TNVwjRpdDP5uF6+Mm6TpOQ+C6HQ4GJtGrxKzR2j7DpsdcSpVDHuEvACddgejv6qiapYw/D4/THYHkuO0Ipo0w+/xsSq3KQZtDH67ywOnx4tkvTYGP5WTJzxE4+2J32RxMAxx2h74A6SzQ6dRiajQYfyUdxIfp2EGS7RQaXw6ZrJBK6IZc/xeWOwuZtzE4HeG8Bv+RfxWB/P46DWx+Dttduho/FX/wvj7Q+Ov62X8vV50u1yMHdb7/PHCqInFT20e2PxR9zJ/3HbGBtIr1P/0/Kc8lC6PlbGBerLvfAEfLD4LDHK6tnv8NgE3nH4b4uR0/Yq/G9GWfQEnNLKkGPxefzfzail7WX/8gQ7GYJT2YB3+0fr5V6IuF9/+KGSqP0FddrtQ8fj/JnW5z7NyrIT6/XgPI+grD3VLjlIF2uD3HmZNDXsKld33eA/D5xfneZC4fJVwekvgo4u4x4Vt95TB5imBn+oTRJ8r6IXFUwqrpxT+gEt8rqADXe5ydHvKEWAdTqPO5adS8+XoovL6PfDbfCa0uSpZrkpP6fK0ocVVDbOX5wZES6urCY3OWlh95h4YA6h31KPWUQeHn/d/CYs34EO1vQE19ga4/Tx5NyxOvxuV9kZU2ZrgD4hj3GavA0etTaiyt8bg73DbUGZpRr2D5w1ES4OjC6XmFrQ6eYJltFRaO3GkuxWdbkcP/EGUdrfhsKkVFq875uZV0tnG/sjYiRa714vDHe0o6WhnYaBo6XI6cbi9DeWdHTH42+12lLS2oa67OxZ/txlHWtvRauF9mKKlqsOE0pY2VidEhD8QRFlLO440tbH6Lj2Nh9KmNpQ1tTFjJ1ocbi9rqEhN/aLDLSTddieONLaz/I+Y8bfYUdrYjoaOWPyNnWaUNbajtTsWf01rF9uPDIye+I82t6O0sY3VR+kNf2lv+D1eHGlpR1lzL/idTpS0tKOivfNfG3+zGSXt7WixxeKv6DahpLMdnc5e5k9XaP54epk/3a1s3vWcPw6/h9VSoT8ywHvO/3JbI6un0hN/l8eCans9Wlw8NyZaOj1t7Brt9sZeG53uBrS6qlidpZ7rT7eb1ooyeAPi7x0IemDzHIGdrU3i9YdIBF7vIbbe8Q7L0couBH0lLH/lL/b8LJa+nJV/W/qoy8dAAt6D8HaeCwS54SDVnAZ5/BPsicPj+hVWE1Vx5AuPRn8btHruNrXY3oWp+67QDJQhJekFxIVozY2mB9Fhe5O9l0g0KDR+gDj1RFY/paTtKpicnPWjkCZjeMZyaBUF8AXs2NZ8ISwezuTRKfIxKeMjKGUJsHtb8WvjpXD4OCMiVTMax2U+z+oktLvK8H39jfAE7EzX3zAPM9OJ1ixBhXUbvm14BP7Q4jLNeD4mGc9k77d1/ISVTSGMkGJpznUYkTid/fur+o+wrp0zIJRSJa4s+huK9YOYofJC+UvY282ZAQa5HncNuh3pmnQ4fS7cfeg5VNk56ydLk4ZHh90EgyIOba4u3LD3BbS7+Q1jREIxHh1+OYvLl1oacM2ut2D38cVxQeYY3DXkNIZ/Q2spbtm9HN5Qr5NrBhyPS4p52OPjyp14eP9PbPTJff7EWKI1c/fzI3tW452jO/hvJlPgnRlnYHxqLstJuXz911hdXyHUYvli3jkojE9iT8tnfr8chzr4GNNnK046G4lqDZqsFpz25XI02/gcmZiVg/dOOoU9JR9sbcWyz7+A1cONs1OHDMETc+cw/GvLK3HtV98zJgrJjTOm4MoQrfnjHfvw8E/rIviXzMOiULfmJ37cgPc37xHCF2+cvwRjC7IZ/hs+WIl1JTwpMilOgw+uPAP5xkTmbbnolS9wpIEnLlM46YNrz0CCTsOKz53/3Gdo6eb4xxZn49Url7C8jpK6Vlz2wpewuTj+xRMG44FzOf4NBytx65sR/NcsmoKL53H8n23Yhyc+D+GXSvDwefNwYqjb8dMrNuCjdSH8SjlevmoJxhRz/De9vRLrD0Xwv3v9GQwr4b+wB/4PQ/gpIfncVz9DS6jx4LjCbLxxEcd/uKkVF7z7JWxujn/JyMF4ZElo/I/2GP+ZkfH/cO8+xvoJj/8zJ87D4kE8DPXQb+vx9n7OWtLI5Xhv0amYkMnxX76W5k9lZP7MP5vPH68b56z9GIe6eFJ5oT4Jnx9/HhJVWrQ4zVj2G7H0uFFNFWpfm7iMeRlp/l+/+w3Y/Xz+z88YizsGL2X4d5oO4snSNxkTiOSc3EU4LWcee7+hncoLvM/XLEhxfv4VGJfESyf82vwWdpi+4WuMRIUz8x5Arm4o87JuaL4dDXbefFEtS8Tc7NdhUOYyb+/+lmWwUVE49r0LMCpjORSyRFb8srl9Mfx+nryrUk5GuvETltcX9B6C37RMWD8l6lMhjX88xmPzV5C+Oiv/vvR5Vo6B+KhraJDf6EkCzi8R9GzmlWa7bmTJZGFxWp+A31cHv98EU/fdUaayH52mmxiDyOE5IBgqJPRZvelv7H27/SfBUCHxBrpRZeLU5Wrzh8yrEhaHtx6V3fw4B0yvwemLeGjanHtRZeF03E2tz8IbiHg4jlp+RqNjN8P/Y+OzgqHCtm1/H92eFjh8Vnzf9FYEIwL4quFlFhaqc1QLhgrH6MUndRzHDtNOwVAhoWZqn9Rx6vLK5nXMoxKWZmc7VjRwevV71T+i0xPxfhzorsAvzZzW+WTJN3D6Ik+oPzTtxk4TsbGCuGf/V8JCTfJS2Wo0OroY6+eR/T8Lo09Pu3fvXskYRFQlNGyokBC9+Y4dP/Bj15YKhkrYZf/QTv57vHNoF/OohKXW0oWX91I3buCZbZvRZo88hW5vrMcXJbzmzb2/rmZel7CsOHwYW+rqGP7bVv4i8gL8fcNmVpeFWCeP/LxejP+7VYzBcrixVTBUSOize77mlNJfDpQLhgoJhYmeXMmpyx9t3MO8HGGp7+jGW6v5OLz0w2a0R3lvdlc04Jtt/Kb0yPI1zOsSlu+2l2B7Gcd/7wdi/C+t3IzGDjO6bU48+UUU/kAQ93+0ijGIyPgJGyps/L0+PPAxx79qX7lgqITxP/01x/9hL/jfDOF/YdVmRjkOy66qBny1i+O/f+Ua5nUJy9f7SrC16nfGf31o/J1OVkclevxv/2UVozIfbGsVDBWG3+fDbWv5PP6hpkwwVIT5s4NT398t28m8KmGptXXh1ZIt7P2LR9ag3RWFv7MGX9fxMXqm9CvmdQzLj827sCs0/18o/0A0/z+uWxlqYmjF5/UfiFh5H9W+ya7VZmeFYKiQUHmC75s4PbnWtlYwVNh381uwq53Tmhst7zOvSlicvjrUmXnJgi7LE6xkg7CfZyusdk6B9pvvFa2fQdcKBD38e/dJn4Slz7NyDCTIQjhit3LQ3wIJPAgGY13HAX8rglJn7D4gqrMFXl/PcEsAXj9/2vKwV6moW7Pbx59WXP5W5uGI7pLsCu1n97aI6Mm0ncPHF3abt02U4c+297XDH/TC1SPMxLb3dUIh9YgokSREZXb57ej2iENCtJ3Z2xVqU9/Fkmuju712hrbvdHfxp6mQG5i26Qx5UlpdXcwrExbKgelwc/d0m6tbxDAiaXeZWZEti1ccZuLbW1jOS899iMpM9OUWR4/QGutCzW8ULXYre4oWddu1cyOKvCbROnpptvNjkWclmnpK7I1w6KDZKqbH8s9srMiZuUeYhuG32uBSqWL2ISot0WdbzD1d8xDCRK3mHvgDQaHGCIVionX02ho6FnV0jqb3SqVStIV05G3piYWORSEZiyMWf7vZBpcnFj/l31BYp2dIiDYLn6utOxZ/8+/gD0bj747FH9aRtyUGv+WPx9/p62X8/X5Y3W7BeyaMPx3P/gfzx8bxk9eEmDvhecnmT2guNjnNIqYQzf9WF9e1usT0fDbGbjN8QR9soRor0WLy0LFUMdcvUZmdfgcs3h5hZ1B7D36NOnxtPdYYP+w+vsa4fS0iHVvRQjrqKC+mJ8vgD4e+A7HrJ0Lr1l9O/mwoJ4j/WenzrBwDkSrJdRpOQCPXpQwS5Sjm4pQphkfppKxSrUzRH3J5XoglFNbJIJcVMPqyRjkcEtb8MOwGlSFOxRt7GVRjesxYKRJCVW+T1eN6UAcDSFLzpoFpWtovuoaJH6makex9lnZMFD2ZTAkZUtWDWTJturqfoCONSqpFiioPScp0lngX1pEbmT7Tyg3I0Raw5odhxg/piuO4e7y/vl9MZ+jBBq4bGt+/B3UziCHxvE/IyETCERHablhCIXs/NqlYoGey0ZdIMSQhl7nIB8dnCrRO2oYq1VLvlFxdElJUOoEOyrrf6pKQpNJhaFI6VFJZVJNGCQsBsXOlZYti6nTMKRm8l9LETHKFR/DTDWRiBmfQTMrOFeEn6un4LM7YmJyXK1CXOX4Ja4hIIYqhGdT9OjSOEgn0KiX6GZORm5TASraL8CclIEmnxZCsVNZQL+xFJ1ruuAJ+rtH5WWL8Egkm9gt9t+JsEX7abEwR329cvxzx+AcCGB3STRiQK9CTGX6pBMPzM1giK7GNwrRgOlecWomijGRkGxNYcm94P9omx5iAxDgtBufE4qcQEMmowlj8E/pz/ON64CfbZGwI4/jCWPwUFmO/W2GuMI5h/COy/3j88xISkKIVjz99lqTVYlhqGiujHz1/KAT0u/Mnk8+fCal5bF5Ez58JoXk3PqUgZv6PTc77/fkfnwuFVIEiXa7QXZl1YZepkavNgFGVCr080nmZXo2qNMTJ9cjQFEMWdf3SNU4hIJJU9YgeDzZSpGv4GpOgnhCz/sSrechMo5rao3u7D+rQmiZRTupl/eRr019N+qjL/770GSvHQOTxD0Ciok6kKtbfQk705BAVT5/0FuRKMhQUkMryoE/+CFIp1W5QIz1lORSsaRf1aBmKNOPHrPy0Up6BgtR3oZBlsn4aevVU5CZzV6tBPQr9U55kuSqUVZ+qOwn5ibcwXYbuRPRPvA5ySRxkEi0K4y9Grp7TgockXoB+hlMgk6ihlBowNuUWZGj5YjE17QbkxU1h+StaWRLmZD6IxFAzwyU59yBLM4gxfRKUmVia+zDUsjgopEpcWHAvUlXZjKKcoSnABQX3sCe+RGUyLi+6BQnKZMglcgwwDMWyfE5dLo4rwqWFF7NcFYVEgcnJE7E0h3d4nZoyhsXUaUFVS1U4JWsO5qbTIgeclXs8FmZOYfRLvVyLa/qdirFJPL/hlkEnY1rqYJa/kqzS49ER5yBfx+tT/H3M2RiekAOFRIZsXRJeGX8+61BLuSJvTz0XRfoUVmBrUHw63pxyNrv5ZGgNeHP66cjUUh8iGSanFeDpiZy6PNqYhaenLmC5BnSMkwsH42+jeQ7M4qKBuHXcNOgVSmjlClw5cgLOGcwX3SvHjMc5w0aw/IUElRoPzJiF6bmcivrA7NmYXVTIbnDEHHl58SIUJXMq7SunLsLIrAxW3yM3MR5vn3kKDGo1YxS9vewURnmmAnODMlLx5jlLGP70eD1eWXYSMuL1rIv0xKJcPH4az1MYkZeBh8+Yy3I9VHIZFo4aiBtO5GN84qgBuHb+FGZQECPp4lnjcPokTsG/+IRxWDp1OMt/IZr0Hacdh8kD+Ry54/RZmDm0kBkY1Kfo6UsWoSCd43/2skUYXsDxZxvj8fI1p0Cv5ayaV649BQVpSYyiTHVQXrpqCTNe0hL1eOGKk5CeqGf9iMgYonwWEjKCHjonhF8hw4KxA3Hdwgj+634H/6XHjcMZE0P4NWrcddJxmNKf47934SzMGsjxE035+TMWodAYGv/TFmFkdtT4nxUZ//dPOxXFSUmMojw4NRXvnMLHPyNOj7cXLEGmno//lOw8PHs8py5TV+Wnp82PzJ+iwfjbGJ7ntSh3MG4eNgNxChWbP1cMmoSzi0cz3SX9puH0/HGMfk/9f+4cNh+TUzmV/6aBJ2OqMTT/lQY8PGwZ8kLz//ZBl2GAnh4eZMhQG3HvkGtYPyC6fq8tvg1pajLmZcjW5uHqolvZ9WtQpOCM3PugV1D9HwUKdCOxOOsmdjyjZigmp93LclVovSjUz8WoFH5tG3ULkJ9wI2SSOEglWuTEX4ZM/VlMF6+/FnG681j+nVSSgKSER6FRc8q51PAAoKISBUq2fkoTXooqBdEnfcKlj7rcJ33SJ33SJ/+z8p+kLve/+c9Tl48+879JXe7LWTlGEvCWIeDZAok0CVL1fFHNFK9nF3yevZDJcqFQc5YBCdmJTvc6eL3lUCqGCU0Muc4Pi/MnlquiU02ARhkpkkSUwE7HD/AHbEjQzIBGESkW5fF3o9W+irlq07SzoZKHCtKxvJVmNDs2sTor2XHHQyGNdGGlktlNjt1QyxJQqD8O0qiaKU2Ow2hxliJemYHCuEki/BW2vehw1yNdXYiCuAhGyi85YN4Bs8eEwrhBLDQkYAx4sMu0Ay6/E0PjhyNVzStxkli9Nuww7WH7j0sahQRlpAtrm8uEHaZDrFbE1JRR0MojF32VrRl7TBVIUOowM3UE5FE1Uw501eKQuR5ZmiRMTx0kwr+5vQJV1nYMis/AuJQCkZt9VWMpWp1WjEvJxZBEXi2XhCilP9QdYeyNGZlFyNfzp3CSLpcTP9WVsfyIuXn9WJfcsDRYzVhbV8mejucXDkCcMlIPo6yzA1sa6pCk1mB+cX9RzY7dTU3Y19KM3Ph4HF9YJMK/sboWlZ0mVgV3Ym6OKMyxqrQCrVYbxuVmYUhGZIxdXh9+OlzG2C/TiwtYRdywdDucWHW4nCW8zh5cLBREY/Ogy4L1pVXMOzF3WH+hIBpJeUsHtlXUIVGnxdzh/UT499Y04WBdC7KT43Hc4ELx+B+tZaXtqYpsuAljGP/qQxUsd2RsQRYGZ4nx/3ywjLGPpg0Q46fE419Lytn4Hz9IjJ/6L60vr2JenXmD+rPmg2E52t6BLTV1LIxz4sB+//T4b6ivQUVXJ4akpGJSVq4I/8+1R9HisGF8WjaGpaT/c/PH7cCqpiMsp+X4zIFCQUOSZmcXNrWVQi1VYHbGMKGgIUm1rRl7u48iQRGH6caRovl/1FqBSls1jCojxiSOEOEvte5Hq6sR2Zp8FOt5JWwSYv2UWzfD5utAtmYY0jQ8HEviC7hQb1sNb8CODO1k6JWR383r70Kng/pQ+ZGknQulLLL++Hz1cLpWs1pTWs0iSKWR7xakliGerYA0CVDPi6k59ZeRvpyVf1v6PCvHQPyu9fB1XRpKEgtCopwARdIH7IJz2T+E3XyHkBSr0pwBXcLTbMEwdd8Pi+11QZdguBMJhmtZHYPajothdREDgi8sOckvI0G7GP6AEwdblsLhJXoyRZWVGJL2EQzqMXD72rG1aSncfp44q5AaMDHzc2gVOawuwrrGi+ALUsJpEHGKXMzO+gBKmR719m1Y1Xh7KBYdRLpmJOZn/50ZLPu7vsfalheEpNgh8XNxQsZNDP8vze9ga+e3gm522jJMM57GDI13qp/BYctuIe69LO9ajEqczArBPVn6MOqddTw7RiLHzQOoW3M/dHm6cdfBR9Hl5Um1cXIdHh56J9LURlTbGnHL/ufY/jRhM9VG/H30zYiTa7G94wjuPPAuW+DpvxEJhXhm1OVswV5Rtx1PlEQwLsoag7uHnsLwP3XoZ3xQtVXQXT/oeOZup+NctfVzrG0+GsIfxLPjT8GCnCFw+rxY+usHKOmiZGaiZcvw0exzMMaYjTaHDYu+fx9toWTceJUa3y04H7n6BBzpbMNp337CCoER/oL4RHxz8rlsm/W11bjk+69D+MHyGz486TR2w/x4/37cs3YNw0uX6tIhQ/D4CdzgfWztBry9c4+gu2XGFFwxcTzH//l3WFtOvaK4PLtkPhYMGcAKqZ39zmes3gjpKMzy3nmnYXRuJmPLLH3lE7RbOTPDoFHj8yvPQk5SAqtNcu5rn7H9CSMZCMuvPotts6m0Gle//y3HT3kiBVl469JTGf7Ptu7HQ1+vFTAuGTsEDy49gY//yg14f1ME/w0nTsElx41nhtJ1H36HdUeqhLyVp86cjxNHcPznvP4ZSpvbmY7O8e7Fp2FUXiZLfF36WgQ/hXs+v5zjL21tx1nvReFPSsCXF5/FQjobKqtx+Zch/JQfkpOF98489R+O/8Ob1+Gt/buFpNi/TZyGq0ZPYMe5dPVXjPUTHv8XZy7CosJBfzx/nFactu4t1miT4Vdq8MVxlyBHl4hySzMu3vY6XH5KbAdytMl4f/JV0Cs02NF5BPceekuY/8Pji/DEiCvZ/F/duh7v1nwszPEZxim4tOB8hv/bxg+xvv0HQbcg4ywcn3YSW3++aXgAlTZisvFvsDDrdgw0zGSGypqGi9HtoX5kEkglChyX+QpSNCPg8bXhQMtJ8Pg5I04ujcfw9G+gVuTC4y1BW/tiBIOU8BuEXFaItNQfWUg86N6AYNcVwvoJxXhIkt79jxks/1HPyk3HwLPy7P+mZ6UvZ+UYiN/6WORCo/97tiNAFyAVb7M8GNqKJ6W5nZ/B76uAz98SMlQium7L4wgEHHB4doYMFXY09tfcxY/T4VgZMlS4Lggv6rqfYf+qtXwIj79T2IfqHtSYedflkq434Q+6BIw2bwOqrZyeuL39ZcFQIWlx7mMGDOHf2MoxhpNiD5t/gclTD4u3kxkq0bq1rR/BE3Cj2l7GDJWwjv77tulD9u+dXduZoRLWES3664Yv2b9/blkLszdCT3b4nPi+iXdo/rTuZ3hCCzVJs6sDq1o4LfjViu+FhZpkf3cVtneWMqPpudIfRRhXNu5Gjb2dMYLIUInWET3U6fNgd2c9M1Qi+IHHD/DfY2VtCbvRhH8ZbyCAZ/bzrr/vHtmNDpddeHiyetx4PdQ194U9W+Hy+wT8tZZufF7Gu+Y+unmDcKNkv0dTA9bXVbPPHtnIabnhZ4ovDh9mTfHIY0KGSrTu2Y1b4PR6sbu+kRkqkdkDPL6aH+fHQ2XMUAnriJb73NrN7N8fbt2HTptD2Ic6C7/zG/8dX127nXk0whipmWKY+vvUDxsFQ4VkV3UjNpXVMKPjqe83ijB+veswa6pI7B4yVKJ1L/y8hRkTe2sbmaHCdfzvyR9C+A+UMUMlrCP8z6/i+D/aJsZPzJy3N3P8r2zqgb/LjC/3cvyPrwvhD+l21DdiY1XNH4+/3cYMFZIwE+fp7b+x8d/V2iDQk8NYHtqx7h/Onw8rd6DTbYvg97rx9lFO4X2rYi3cfm5okTQ6TPi2gXdvfqMybGhx7QFzJXaYyDsTwMd1X4jm+Ib2zWhytcDsNTFDJVr3Y/Nydv02Og+HDJXIN1jf+gb7V51tVchQ4ToqLnnA9Cr7V7P1PXj8HVHrjxWNFr6fxfIsglHrj89fA5v9U34UyxOi9RPeHYCbz5u/mvy3J9iaTCacc845zBBKSEjAxRdfDFsvBQ+jt7/22msxYMAAaDQa5Obm4rrrrmPGVLTU1dVhwYIF0Gq1SE1Nxa233gpfqGP5f4Wx8q9+cZKZM2cyqz/674oryOr+75VggG6wPWYRo/z6qGhA7PZBCwK9UILZshekEvux1VQDQf4Z10X/bAH4AnxikHEiOg+ZMqHPvH5LjwZkEkHn8dNrDxoplfOHH75gLHXT7bfBHVWXRXw+KtEdS5cMf+b0OURN0sLdXkl6VrPlOv4ZUTBFDRAlEth9YZ0zhoZJBeIolOMJiKuAkli9LuaC7yl0fJffC6vH1es+JBaPq0fX6CDMoe3p5iL6btRJOHQes5vwRLOguDHDjunm3iLR+dwexmyhGh09hbanAnQx+Cms6PXFVKVlxwsVbCNqc3RDPNqHujWT2Gi/6K7XwdBntJ+Dl7AX8EvomFxHrz39s7QfhUKoRkosFrdQgK0nfjIownjEx/MI+/4efusf4Dc7e8HvjsLfE6Pb/YfjH/7teuInz0nPqrSI/q3/YP7wORmNPyjMH6LgR89/WhdtoTlp87l6mf9Usp/mf+w4O3wOFoLtKXQMMlbc/kjNk7CEP/P6rTHrD60tJGSc9DxiuMJ2INDdg7pMnqrQOseKwfX4BUIF4v5yEjwGf/8Xhe7Xhw8fxq+//orvv/8eGzduxGWXUVHT3qWpqYn9Pf300zh06BDee+89/Pzzz+xeHxa/388MFY/Hgy1btuD9999n2917773/PcbKv/rFw3LppZeiublZ+HvyySfx3yxS9UJR92RItJCqqNmaEgrVcSJ6slSaCbl8MBTyAijkxSKdSjkGUmkStMqxkEkTRJRng2YBe5egmRnqkBz56VJ089lrqu54US0VWkjSdLNDzfj4Kxfu9s3UchZCoZ4y8SM0RblEg0zN6BATYHwUdVkKvdwIo7oQScoMpKiyRbpszQDW0LBA1x9aWZyIMjkinjOPKEeFGAfRN/WxSZzeOC5xFKu7EhbCOC6Js2mmpESojLQv3RgmJHM65XGpI0XdkzUyJUYmFkEhlWNyygDh5kCUzjR1PPob0hlNuSAuRaA1E710RGI2EpRajEqmV02EsgoJ5mVzevXMzCJGbY2+4czP47q5uf1iqKdzc3mzvBML+0fh52vO7FzOeFhYPEDUvZe6+U7KzmFMkpkFBSJ6bIZej0FGI/ISE1CURIyMiG5UZgYSNWqMys5kfWyiKbfzBvGcg+n98tm/o5sMzxvCsR0/uFjUNZrGmPJWSOYMi+QshEvhzBzIqeMnDu+BX6lgVGEKMU0bGIVfKkF6gh4DMozITU5AgTFRoDUzunBuBhK0ahbSIcZRNOU5fP5p/fMZYyga/9zhIfyDYvHTZ2yb0PcXxj8IHNef458/KHb8J+T+8fjnxyeiKCFJNP6j0zJYteIxqVmM8RU9/vPzB/zD+XN85gARdZ/mzwmZnPE2Oz3S4JCFbYJBTEsNzcne5n9CP9brZ2TCMBE9OVmZxDqfp6jSkarKFOnytf2gk+mRqRkMtVQvKlnQ3zCNvc/QTQ59HsGfE3cCe03WzulhkARY3goJ5aigxxWgVtP2VAZ3fsz6CSVfL/5y8l9srBw5coQZGm+99RYmTJiAqVOn4sUXX8Ty5cuZQdKbDB06FCtWrMCiRYtQVFSEWbNm4ZFHHsHKlSsFz8mqVatQUlKCjz76CCNHjsSJJ56Ihx56CC+//DIzYP6f56zQFx88eDB27tyJsWN551kaiPnz56OhoQGZmZm/61mhL/Tcc5yq+/9Dzkow6IXf+ncE3KsBaTLk+juErsvkQXGYH2BJtjJ5PnTxD0Am55RJCgWZuu6Cx1cGlWIkkhIehEzGk+1c3jI0d90Pj78ZevV0pMffAWmowaDZtY2FfnwBC4y6xcgyXMkozyTNtp9QbX6bLRS5hnOQree0YPqZy82foNr6LTNGBideigwdp3ySK3d3x1uotW+GRpaA8carYVQPFJ6oNrS+jibnYUZdnpl2FRKUPNmUQkE/Nb2BNnc9MjXFODHjElZnheFw1uObxg/Q7e3EAP1wLMw8m5XdJymzHME3TSuYl2Vc0kScmLFQaHS4tWMnvmv6hXmB5qbPwnGpUwX83zSux68t26CSKXF23jyMS+IJgdQn5Z2qX7C54zASFXG4ot9CDDTkCE+dzx35Afu761icnzrSZmn5GFMo6LGDP6LC0oYhCVm4fdiJzFghKbe04ZH9q1ihrimphbh12GyWGEuyrbUWz+zfwArILc4bgiuHTBZuaCurj7DQD90ozx80Gmf0GyHgf/vgbnxRdhAauQLXj5mM43L5zZKKj/19+xb8Wl3BanfcMXkGhqelC0/4j2zYgF1NjchPSMC9M49DbgJPKKVQ0AOr16G8oxPD09Nw9/Ezkajhc6S8vQOPrNqAFosVUwrzcOusaSwxlmR7dT2eX7uFeVkWDhuAy6aOF+qd/HSwDG9v2sVCOOdMHIlTxw4V8H+weS++3nUIaoUCV82eiOkDCwT8L63ainUllUiK0+KWBdMwNJvjJ88GVcilJNvclATcvngmckIJsRQKevTbdaho7cTQ7DSmo9L4JBWtHXh85QZWsG1yvzzcdGIE/46qejy/agvziMwfMQCXzYjg/5Hw/7aLjf85E0bitDER/O9v34sv9x2CRqHANdMnYka/CP7nN23FmvJK1pDwtuOmYVjGPzH+dhvu27QGR00drHvyfVNnMWOF5GhXBx7YvoYVBZyWmY87xs2AWv6P58+PDYfx1tHNDP+5ReNxWv4oAf+nNZvxXQMlwStwafFsTEkdIMz/92t+wpaOQ0hU6HFp0SIMMOQKodSP6z5HmbUC6epUnJd3JlLVPOmVQkFfNbyHZlc9crVFWJJ1PnRyPdN1uGuwruU1WH0dyNONxvTUi6GQ8oTeNsduHDS9wryveXFzMSjxAmH96bB/j0bz6+z6zdCfhzT9GQJ+m/0N2O3LGX3ZYLgZGvVsYf0M2p4HXGtZgq3EcNt/tOvyfzJnZcD1fz5npez5O1FfXy/CqlKp2N+fkXfeeQc333wzurp4AU8SMjjUajW++OILLFmy5J86Dhk7d9xxB9rbebiWPCjfffcd9u3bJ2xTXV2NwsJC7NmzB6NG8Tn+/8xY+Xe/OBkr5I0hWOnp6cxiu+eee1isqzdxu93sL3pS5OTk/E8mIPVJn/RJn/TJf6+xMvC6P2+slL5wZ8zn9913H+6///4/hfHRRx9lIZqysjLR55Rj8sADD+DKK3k9nT+Sjo4OjBkzBueeey7zsJBQNKW2tha//MJbTpA4HA7odDr8+OOPzNPy/5S63NLSwr6k6GRyOZKSkpju9+Tss89GXl4e87wcOHAAt912Gxu8r776qtftH3vsMTaQ/6/F71rHkmol0kTIdBdAIuWUWzK6PM4V8BJ1WZ4LtY4y8dXCE4XN/gF8vgooFEOg054tPKEQLdlkewc+fyt0qskwaHkYiMTjpy6778MftCFJOw8GdcRlavdUo9H2JUuOzdSfDL2SP32RmFwHUG/7hVGXCw1LoVVE6LgN9i1otJPXIh6DEpZCJTMI+Mssv6LVeQQGRQaGJZ7MKtsyjEEf9ph+Yt1Z09QFGJk4R8Dv9juxpfNHWLxdKIwbimGhMBCJ2dvNmqhR3Hxkwlj01w8WdM3OFmxo38hCQFNSJiNXG6FFHrVW47eOXYy6PDd9OoyqCOVzR2cJdpqOIF6hw0lZ06FXaAX8v7TswWFzHTI1STglezJUIQ8JleNfUbeDJdz2N2Tg5OwxgofH7nXj4+rtaHNZWfXQOZkRjNSj5aOKnbD53JiTORATUiPU8SpLJz6r3MeejE8pGI5BiZFrYE97I1bWHGFPxuf2H4WsuMjCuLa+Cuvrqxh1+cIhYxhLKIz/66Ml2NvajFxDPJYNHQW1XC54BD45tJ8lfA42puL0IcOEJ3TKaflg71602WyYmJODef0jYah2mx0f7tnHtpnTv1hEea7qNOGLfYcY/iXDB2NgaoR6uq+hGT8cLmPnP2vscGTGR/BvKK/GxsoaFoZaNn4UY+KE8X978Aj2N7QgJzEe54wbwQqqhfEv330AVR1dGJRuxGmjhkbwuz34aOc+xvCZkJ8jCuMQ/o927WPbzBlYjAl5Yvyf7z/EmUfDxPj3NjXh+zKO/5wRI5AZdWNaV1OFDTXVzDN1wYjRiFdHjX951PgPiRr/gB8fl+5DpdmEwUmpOKP/8Ah+rxvvl+1m1PdJaXk4MXegeP5U7oDd52FhngnGyPypsbXjq3rel2tR9igWsgzLYXMt1rTuY4URT86ehDR1oqDbbTqEPd0lMMh1WJB5HGPJhfFvM21Bla0SKSojZqXOZgXhwtfvjs5f0O5uRIa6AGOSZgvz3+N34EDX17D7OpClHYViAw8Zk7h8Hag0fwZf0IZM3WwYNdxzTuL0VqLd9hlbf4xxp0Kr5KEqdkzPbjgd33Lqsu48yOW8qi/D6V6PoHsjWz+hPU9YP/9ycoyoy/W9eFZ+T26//XY88QQlMf9xJORYGGSUm0IRlT9rOB0Tz8o/+8XJuPizVhrJ2rVrMXv2bFRUVLCY2H+jZ8Xv+Aw+Rk+mRSwAiSwPipTvIJHqYLc8BpftZUEnV06CIZmy4CXoNF0Mp4tqElAOig867blISnwKgaAbNa2L4WKsH1o8fEiLvwfJhivg9ZtwsHk+vCzrnhZGP/oZX2WxYbunCtuaTkMwGI4DSjE+81MYVEPQ5tiO35qvEnJF5FIdZud8Dq08DeXmldjS9lgoFyYIvSILC3PfhUKqxZa2N7HH9CmkkLF8kmztSJyU8xQ794r6R1Fm3cbi3ZSMOypxHuZnXsOaGb5ScSeaXbU8vwR+zM84D9ONi1kDtUeO3AmrlycF0zEvK7weIxPGocnZjPsOPwhfgMc6aeG8e/AdKNDl40B3KR4seVHATzVWnh1xF5JVifi5eRv+fnQ5yz+h6ZyhScHLY26BRqbCaxU/4aOadaxKLSW4jk4sxN9HX8qOc+veT7C+tYTtR7kmp+SMw11DT2bNDM/e9DbKzC3s5kO6W4fMwQXFk2FyO7D41zcYa4PGgJIYX5y0FHOyBqLS3IHFP78LD8NPeRUSfDnnPAxLysDm5hos+/XzUI0LGmMVflp0ITJ0BiwvO4DbNv3CMFKeQp4hAT+cfB50CiWe2LoRr+7dwarUUu2QSVk5+GjxUjYKV/34HVZVVkAmlbJk0LOGDscjs05gSaFLly/HkfZ2jj8QwB3Tp+OSsWNhcjix8N0P0Wl3sOSTQCCAl5csYkZLZYcJp7z7MevRQyegfT8770xWcn5rdR0u+vgrIX81TqnCd5efi3SDHl/sPYS7v/+VYSQjJzcxAV9dejZ0SiWeWfMb3ti8k+MPBphh8e6yUxn+a79YidWllQL+M0YPw4MLj2eNF8949zNGNw7jv+346bho0hiGf/GbYvwvnrqIGS1Ub2bJe2L8ny87E0PT07C5tg4XrFghZEbEqVT44bxlLAfls8MHcceaVQL+vPgEfHfmuQz/E9t7jH9mDj5axMf/inXfYFVtuTB/zuo/Ao9NmcsaX576ywc40t3GckhId+foWbh00AQ2f05a87p4/kw4HSdkDUS1rR1n/fYqM4L41SvB+1MuxeD4LOw2lePmvW8KNVK0MhXenXATUtUJWN26BS9XfCzM/3S1EU+PvA0amRorGr7ATy0/sCq1dK4B+oG4qf+tbP5/UvskSiw7hOt3XNIJODn7SvgDHnxZey063JXC9TvFeAVGJZ8Ot78La+rPgNtvYjgo3DMh7Wlkxc1ihsqh5kUIhNYfym0Zkr4COtUwuF2b0Nl5Vmg9C7K2I6lpqyGTZSLo+AJBy13CGglZLiTJX7P18y/nWbn2GHhWXvzXqMsUjunsFPdr6ykUkqGckn83DGS1WjF37lwWAaH8VNonLMcqDPQvJ9jSlyFj5I/+CASFcNraIh1ow1+cGEKk+2eFEn1IyFjpTciipB8t+u8/LT4bp+7x7soBBP3VCNCTQtAPl0BP5jqfZzP8vhL4/Q1wun4KE0jZFnbHRzzHxU00S+rIGxB0HdaX2KvJ8QO8rI6BX9A1hTqbNlg/Z4YKJdnyvwDqLR8zXbn5ozBZmP0Rc6jO+j3THezi3VfD+1i89Wi0b2WFofaZPmc6WrBoiwYHFYGrhNnbhjIrUX9pD7647u36GW6/A9X2EjS5qpkmrFvf9jV73d21jTU1JE04mXZVC8exvm0DM1TCOlpcV7fyjrQrm/lrWEex+PXtnBa8vI7TiulmSDf7Rmc787TQvz+t5dRTumHQt9/dVYkKazOand1Y11rCRz+U1PhV/U7GsNjVWYsj5mZ2rLDuzaO80+xPDSWsDgYZPuFkyDfLOL3004p98AT8go7wf3CU01vfKtnFzkWfk97ideOrKk6dfWUf/x50LrpZVpu7sK6+iiWLvrmf01Pphk34tzTWoaSjDY1WC36prOD4Q0mlnx46wHIsdjY24nBbGztWWPfqDt59+MfSo8wzwTCGdG9s38leP9t3EB5/CH8gyPJWPtrNF5h3t+3hsyfA/+g83xzgT2NvbObHpnPROWtMXczTQsd/Z+vuCP4gsK2mHqUt7Wg0W/BraaUI/2d7DjK69M66RpS0iPG/HjrHTyWx+N/c+vv4Pwzj3829Ffy34fi/LuElAF7dtV2Ev7q7C+trqnsf/6Y61lm7wWbBL7Xlovnz6dH9jPWzo60eh7taOf6Q7pVDnCb/U8Ph2PlzlFOvV9TtYoYKnyN8ni+v4dg+q9sYNX8CjO32Swsf23Bn8vD8b3K1YU/XYTb/VrX+HNLRtU1F4I6gwVmPbm87SizbRdfvThN5Ox1odB5Au7tcdP3u7vyErzE22qZDWEdIjna/x17brJ+GDBX6nK8lLVa+tthsb0StdX4Eg1Y4HLxkQdAuXiPhrwHc/Lr9q8n/C+qy0WjEwIED//BPqVRi0qRJ6O7uxu7du0XOAnogCN+Hf88QmzNnDjsGGSXRhgoJHffgwYMie4BIN3SvJi/MPyvyf+eL098/kugvTjGsf/aL95SwNZaREQlZ/PdJdAY8oi68YGw3UZKgD0HJ73HM6UKO1ZHhw1976mgJ4p+JmUAhXWh7vn/0TJcIOjJKekr4s56UyLCut32YjhkZv388WkCjuy7zfUK6mC7UVMeB6/wBvthG4w8v9tEMirDQZ3Rz6s1xGF7we8cfuYlES/imE8024RgjN9ue+7GbS0hHSZDR4y+J2s/X2/nYzZEzWmLwB4iu/jv4o27wPff5Xfz+f/zd2GsUFBH+Xs8X/F38tH10B+Se+/0h/uA/j1E8/uL9JKIx6QVj8I/H/3fnT5QRJdrnd+ZqtLETowuK94uZ/78zJtHbB3rV/f71S+Gb8Foj+l74g/UnvMagF53wWS/rXfhYvax3va+pfwE5RmGg/xsyaNAgzJs3j7FxX3vtNXi9XlxzzTU488wzBUJMY2Mji3R88MEHGD9+vGCoUA4KeWbo3/RHQnaCTCZjejJKli1bxpi9lAZy99134+qrr/6XkoKl/4kvvmPHDmzevLnXL05WHelJKisrGaWJDJyamhpmpZ133nmYPn06hg/n7Jr/RpFpl4k6hkKaCqlqBiQSOatYyz/nOpl8CGSKIZDL8qFk9LwwDVACjXo+pNIE6FQToZARY4jCMtztmxh3Hn/VzoNMohfp0vT8/JlxJ4c+kwru1kz9aUxXYOCsIO5clrHKk9lxvDncwIRTRR2XNbJkZOkmQiqRYVD8PAEfuXVTVMVIURcjUZmBXO1QgcZI/w3QT4ZGFoeCuMFIUqaFaJEc48RkTmGkHBW1TMN04ZDO9BROfaQcFXJzc01IZ+SUyTmhhoYchZRV56TGhySLs/g2vCIudX82sCaHtM2CzHHC6NNx++kz2V+2NgmjE/MFFPT/WWlDWJPDMcl5yNFyWnA4bHBWAe8sS+EeajQXrTuniMfsTykcxs8T+g5kKJ1exNlA5/QfydYZNvoSCRRSGRYV8Hj+eYNHiToup2p1mJldwEIPpw+kMY7oqKz74JRUFqoYn5nNzhXWzy3qx3Itxmdns9Lw0RjPHcmp3xTuoRCICP9ojpFyPNj4MmpzqGLrCM6mOXP0MI5fEsIvk7GKuGz/sSNFHYuNcTpMK85n+E8dOUSEn3JT6I+aAlIbAAG/BCyUQ7ku43KzmT4a49ljOcY5A3rBP4brTiH8kh74h3P8Z40YHoN/4QCeR7Js+MiY8Z+Rx/GfPqCX8U9ORZ4+ARPSosdfgnl5/Vmu0fi0HOTGJYgwLuvPGxLOyRwUM3/OLuTzZ1E2pyDTHGHzB8DJOeE5PlE0/2n+zE7nuE9Mny7SJSoMGJUwmIV+pqZME+lyNLnsjzqk5+sGi67fIYaJ0MjjkKkdDoOCmqhGrt9hiSeF1hjKeYkLhYy5rjCeN0s16ii8J15/UuM4G0irozWKXwGsq7JECY2GH1OiO7fH+mkEVBx3n/xn5eOPP2b3ZDJIiLlL9OU33uCF/UjIgKHUDjJOSCiMs337duY5KS4uZk6F8B/l1ZCQwUKhIXolJwYl39J9/cEHwwVT/wvK7VPIhwwU4lxLaeE69VS88MILiIvjPSHIICkoKMC6desYC4i+HH0RKi5jt9tZ7gnFycgK+2fDO/9vqMtBBJxfsNAPpImQx10NiYwbZPTU4bK9AZ93D+u6rNFfx0pMk1C1Wov1Ofh85VAohsGgvxoSCbc0ff52tFueFxJsE+MoMZfblk5vFZotr7OCS0na+UjWUZ0XLt2ufai3fMK8LNn605GkiXixmu0bUGf9ETKpCv3iqdR7/0iPH8sPaHRsgUqagOFJ50On4L1YeCjoC7Q4S2BQZmBc8jKoZPz38wRc2Nz+Wag3UBEmpZwGuZQnr1q93VjbtgIWrwlFcUOZsRJO3mt1NePX1u/h9DsxOnE8xiRGkm8rbJUs9ENPhMelzsAgQyQxcafpADa272AU6EWZs5Cvyxbwr2rZzqp2GhQ6nJV7AlJDyYfk0fisbpOQYHt+wWxWopyNo8+DtyvXo9rehgH6TFxQOB1KGXc2drhseP3oxlCCbT4zVsL4q62dLPRDSZRUf2V+TqSnyt6ORnxwdBdz859VPBKT0iLJk6vrK/B11WGWoHnJ4HFC8i3h//zoIRb6oQTba0ZORGYo+ZY8Am/t34W91JvGEI9rxk4Ukm8dXi9e3rlNSLC9fMw4IXm1w27HS9u3Cwm2ZKyEkz+rTV14Y/suFgo5cUA/LBgUScLe19jMklcJ/xkjh2FifiR5de3RSnx3sJThv2DiaAxMMwr4V+w/jI3l1UjQanDl1Ams43MY/7tbd2N/YwtyEuJx5fTxrMQ9w+/x4rVN21muzKD0VFw2ZSyUYfw2O179bYeQYEvGioC/swtvbt3FQkZUP2b+4Aj+vYR/zz7mLTmT8Ecl366prMQ3JUcYBfriMWMw0BjB/0XJIRb6oQTbq8dNQKa+x/i3hcZ/dPT4e/DSgW2o6O7EkORUXDFsAuukTNLutOOlQ5tZ64WJablY1n9MBL+1E28d3QKrz4V5WYMxPzsyfw501WN5zTbm4Ts1ZyzGpXB6O8nm9hKsatnDznFGznQU6cNrTBBr2ray0I9eHoelOfOQokoUvCirWn5Blb2S9QZamLEIWrlOuH7Xt36JNncDMjWFmG5cIly/Dp8JOzs+gt3XiWzdSAxLOElYf6yeWhb6oVByVtwJyI4L1UthVO+9aLW+T5UyYdSfhXj1JEHncq6Cw7mCUZfj4i6DQsFDAOwW5FzByu7T+imJuxIS2X/Ok/6fzFkZdNWfz1k58sr/Zrn9vt5AfdInfdInffI/K/9JY2XwMTBWSv5HjZW+rsvHQCjO63e8j4BrHSSsKNz1kMj5E3Uw6IbL+iL8nt2QyvOh1t8CqSyZ6QIBM8yWp+D1HWVdlw36myGVcsqh19eIDvNT8PmboVVPRZL+KkgkvKKtw3METeaX4A+SZ2UhUnRLBZaAybkVDZYPWXJblv4MpGipgi6XRttPqLeuhEyiRnHCBUhUDxfwl5k/R5N9M9SyJAxLukTopErMgD2mD9HqPAyDIgvjUi6GRs6LYrn8Nmxu/wCd7jqkqYsx2XguFFJ+IZo97VjX9gkrHFcQNxxTUpawsBJJk7MWv7Z8DWfAgVEJkzA+ibdYICm1lGBNK3WNDmK68TgMT4hUrt3auQOb2rcyz8rCzLkojuNPneSF+aFpA3Z1HUa8Qo+zcucjQ8OfmomZ80ntrygxVyNTk4ILCuYjQck9Q1avE29VrkKNvQ399Vm4uOh4qGWc1tni7MZr5WvQ5jJjXHIRziucJlS7JZbQ60c3sWTceVlDsCR3lIB/a1s1PqjYwXIXziwcjeMyIpThlbWH8XXNIahlclw2aCJGJmeF8AfxXukurGusRLJahxuGT0W+gT8ZE7Pk5f1bsbutibGEbh49DclqrVDC/++7NqO8qxNDU1Jxw9gprOAcG2OrBc/u3IwWmw2Ts3Nx+chxjHVDcqSjnXlkqCz8wn4DsHTQUAH/lvo6vLdvL8NE7KLZhZEn+5WlpSwplTwrl40bh5GhPDLa9v29e7G+qhrJWg2umzQJ+Ykh/D4fXtm2HXuamlnV3RunTEZyqGaSxeXC37dsQUWnCUNSU3H95EmsYBvHb8VzW7agxWrF5NxcXDp2bAR/ezte3rGdeYYWDBiApYOHiPC/u5+6dgdxNuEviDAIvysvxVdl3LN1+ajxGJUWwf/e4T1YV1eJZI0WN4yewirUCuN/YCt2tzciT5+Im0dFxp9K5//9wEaUmzswNCkd1w+bJox/s8OMF0rWo8VlxSRjPi7uP1mYP0ctLXizfAMrWDgnYyhOzhkt4N9tqsDndbwo3MnZEzDFGKH+rm/bgzWtO6CSKnFaziwMNPA1hifSrsW+roMwKPQ4JXsxKwBHQsy8Va3foNp2FCmqNCzIOB1xCoNw/W5o+xgd7jrmGZ2eerZw/dq8rdjd+TbsvnZkacdgWOJZwvVr8ZShousN+IJWZOjmITtuiYDf6tqMNus7nLqsPwfxmuMF/E7HN3A6vmTUZZ3+KiiVPDRG28LxIaMvU1FNSdy1kIQKZ/ZJn4Slz7NyDMRHxojt76F/yQBJPJTGXyCRJcPedR28TmoYSMMsg1ReCL2RMvTlaGtfBI93fyiZTAq1ahaMKR8iELChumUmCwFxnQSJcZchNfE+uH31ONQ8DwHWc4gnoeUlPoRU/TKYXXuxu+XsEA7+s45IewvJmqlotP2IPW23C/knUsgxPfsz6JVFOGh6GwdNPC5JsWilVI8FecuhliViTdPDqLCu5nRDSBGvzMFp+W8zKvMnNTegxcVZA6Qr0I3FKbkPMUbQyxXXwuo1Cf2IJiWfhLkZF8HkbsOTZX9jPUvCutOyL8KUlDmotFWwjsxhYZ2Q+92CIfHDsKVjO16ufCuEkfIOZHhk6D3I1mbis7qf8Ekdb8pGcXmqMfHSmLuY4fJ4yUdY28aZLKTL1hrx2thb2I3j8h2voNTSwFzulCMwMWUAnhp1Iew+N5Zueh4dbovQz+ec/Cm4adB8NDq6cPLaV+EKeIXky3uGL8CZBeOwt7MBZ63nzIhwMuTbU8/G1LQifFd7GDdupe7PvK8L0ZS/m3sR+sUb8cKBzXh2P2cbUS5DvFKNXxdfym6KN2z4Ht9WHWHHI12BIQk/nnQBy6c45duPcbC9hWGkEMOMnAK8O+9UVj/lhOXvos1uYzo658UjxuDuKcehwWLG3E/eh8vnE/A/NHM2zh02Enuam3D6F59x/CHdeyefgml5+fiutBQ3/vhjBL9Uiu/OPRf9kpPx4tZtzLAQ8KvV+PmC85lRctMPP+G70iMsWZThT0zEyvOXsf1P++RTHGptjeDPz8dbpyxh+Oe9/z4LYYXHn8I2d86YgQazGfM++kCE/8HjZuPcESMY/qUrlovxn3Qqpufm49vyI7j+1x9E+H9Yugz9klLwwp4teHb35gh+lRq/nnYRN1w2rYwd/0UXcvyr3sdBU3MEf0Yh3p55BgsPLlz9KgshhvFfWDwRtw+fw+bPaRteYj2owr1+7hq6CKfnj8eh7lpcufPVcDtU9vrs6IsxIbk/1rftxhOlnFnD855keHH0rcjTpePrxu/xZQNvKkpzXCfX4snhDzLD5cOal7G7a7Mw/42qDPxt4ONs/r9XfSuanZHrtyhuDM7Iuw+egAMrapbB4esQrtGhiWdgovEaOLyN2NS4JNQUleuGJN+NPMOZsLl342hrOP+Nf4ti44cwaKbD6fga5q6ro/L35EhJXQW5oj+Ctpd5BVth/TRAYvwJEmmkjtJfxrNy5THwrLz6v+lZ6eu6fAzE71ge/S8gaELAs4kVfYsYKlwX8JXD7z0Mn68aHu+eqKz3AFzu1fAHuhh12edvitIFYbZzCnKX41cEWJv1SLY8FWEiabGvDCW4hTuYStBi452V6618MQszlCinpcnGKY+VlrCOM4rcgW40O7azolFhQ4XrAuj21KLDVY4uTxOaXWXCYkavVfYdcPotqHWUwOKNLHQku7t49+SDll2sWVq0blsnpyVv79wiMIXoP3q/rZPfRDa285shPxdnOWw3cVrpqha+DR/FACw+G/Z2lbJ8lbChEtbVOVpRYWtEg6MTJZZ64YZBr1s6SmHxOrDXVINWl1nUePCben6utc1lcNKNJkr3ZS3vHvxd3UF2IwzzNgj/N7UHmG5FFX8NM0yIrfFjHaf+floeqT9A5zS5ndjUVM1orN9WEb06KOgqzJ04bGpFjbkL+9r4jZLhDwaxrq4K3S4ndjY3oNkWuVHS/5eX8A7Pv1ZVss7A0fiXH+a6b0uP8NkT6kBM3+WrI5zeu+LwYTH+QAA/hmoofXbwoBi/04nfamtZ0bfvjnBDRcBvMqGkrQ21Xd3Y39Iixl9djW6nEzsbGtBsjeCPPkdv+KlOCvuNynrBX8rxf3nkUAz+7ys4/k9LD4jxu5zY1Fjzx+NvNWFfZ5MYf1Mlut1O7O6sQ7MzYuiSfF7D58j6llI+f6JoHV/V8bn1S8tehjncP5mMkp+b+H6UkxWZ43z+bGrfy+dkW6RDMc1xq8+Gg+bD7PoNGyphXau7EQ3OGpg8zWhyiq/fCttOOH1WtDr2w+5rE12jZeaV7LXVsRb+IDVBjOjqrSvYa5ed1hrx+tNp5zqng69REYakH07nd/wTQcdGGQh2Ae7f8FeU//auy//N0mesHAMJJ8WKRRkaXnmv2/e+D93eFCxT/vfOIY3RSYTPpDHHjNaF8XBhT1qhXh+yXs5HTQw5gyDcTDEicolSqGIbsx/kkEsUveyjEF57UjDpeCSKUHJfRENPwApBF938kL5BeHuqaNtTqIkhLfxh13u0KKVy9tdTOOND1qsunHirlPLCedH7qNhnEJIrBZ0ksp9KJhM1rwtGbU9hodjz8e3pCb6n0H7hRNqe+IkBRfv2drzoVxH+MMYex5REbc/wR3c0jtqedL3il/wRflnv+GUyIcn2n8cfwij/I508dvzlfzD+0n8w/rLfG39paI702Cc0p4iF1HP+CHOkx7yj+Rs+lrKX+R+ep73Nf7rOGP+nl/mvkCh6vUZJKNQj7eV44TWi9/WHryOSP1h/uC4aSzCyfaiit3jX3teX/+/lv7iR4X+79Bkrx0Bk+utD72jxkEIiHwCpehbLMVHrr4vSSSBXzYZUPoiVmtZqwi5TvjjE6S6FVBoHrWoi1MqxgruUJNlwM3tN0i2CUpYlUABpm4z4a5kuW382pNSxlKiBIXpyjuF8pitKuDBEiCSdFCpZInLiOHVwWNKl7DVMR4xXFiNTO4UtXKOTOWWawkaky9VNQpKqCAZFKgYZZoV0fEEdk3QKlDIt8nRDkK0hFk/E2JmZStUrgdEJk5GkNApUSvqGc9J5ZcSZqbOhkqnY55yeLMfsVM40oBwVWnjD/+kVekxPmcx0Z+byrtNkmNBx87SZGJs4hP37nLw5UTpgQvJgFOoyka5JxNx0ThmmkAzJ6blToZOrMCopH8MTcrmxFNJdVsy/64nZQ5GpTQi54vn5rhgwQ6CgamUKFi4I05PPL+YdpS8bNClEm6UbiARJKi1OLeA5QzeM4LRsOhcdcWCCEbOyqDuvFNeO4N+RboKEf1Z2EQYlGlmp/pOLOZsifEO9aNgYxCmVGJ+RjdHpRD2N6G4cz4+zqP8AZOkNAoWXbojXjuNsrHOHj2AdhwX8MhkuHMXzCihHJbwPw6/R4NQhnMVCuSZhHKQbkJKC4woKGP6rJ00U4T+usAADjSms1P1Jg3g+RhjjhWNGc/xZWRidkSHCf/2kSRH8BjH+a8bzcywbNjIW/wiO//JR41izQwG/WoPTBnJa8w1jJovHPykFs3IL+fgPj+hE46+Lx8n5/PuH58iFA8YzWvLYlDyMSqKO5BHdNYP4HJmXOQwZGvH8uazfTKZbkj2J5UyFdXKJjM1JEspRYd85dNXEK+JwfDpn+p2StZjP8RARP0eThVGJ1HpBirnpp4TmP78OhxhGIVOTi3hlKobG8/OGr9HxySdBJdMiQzMSqeqhQikDdt0mX8ReM3UnQiOnXB++ltA2xQmXM51Rv0xYfzg9WYFU/cVMp4u7KmrNIoMoGRotpzVTjkr0+gl5f0DFsfVJn4SlL2flGEnAvQ0B90bWNVSmPRMSKU/ipOH1un6G37sXUlkOlKQLPdVQ8SW74/NQb6Ch0GpOFhLVAgEnC/1QZ2atagp0mkiirM/fhXbbp6w3UILmBMSpIuWKnV4KAVC12ADS4hZBp4gkSJrdZWiyU28gNXL1S6CWR4r7tTp2o9lBvYESUBx/MhShUteEv9q2CW2uEtYbaED8AsgkcoHWfNi8GiZ3PVLVRRhoiCTKegNu7DatgtVnQr5uGPrp+U2DxO6zYmvnWpbbMjR+LPJ0kb4vHe52bOn4jXlfJiRPQro6QmGstdez0A95VGYapyJRyRN9SQ52H8Xe7iMwyOMwJ30KK8cfxv9bx0GUWmqRrk7CiRkTmfeBhFzpPzXtRi31BtJn4vj0EQJ+yimgirYdLgvGJhdisjGSKNvtceCLmt0st2VW+kAMT4r0OGmwd+Or2v28t0vuUBTqUwTdka5W/FBPvYHkOL1wJFI1fI6QbG2pxcamanYTPavfSHbTC+OnSql725uQo49n/WcUYfyBAFYcPYzK7k4MTknD4qKBEfw+Lz45fACtDhsmZeViZi7vMEzS5XRi+eEDLDfk+MJijEqP6hFlMePLEt5I9KSBg1CYGMkboMTWH0K9dU4fOhSpoRIEJNvq67GphnoDaXDm8OHM6AjjX1VegX3NzciJj8fSYUND3gWO/6vDJZx6nZqKRQMHRPB7vfiUql6GqNczCnrgP3SQ5+YUFgmJvgL+I1S9NYiTBgxCURR+qvz7QwXHf8agYUjVRY1/Ux02NtTw8R84QoT/l7qj2NvRjJy4eJzRr8f4Vx9k/aAGJ6ZhUd5g0fz5rHoPy1uZkJKP6enFovlD1Wpp/sxMG4jhiRF6dbPThB+bdjP8czJGIU8XuUarbI3Y2L6X9Qaamz4RSapI/5wSSxkOdB+GXhGHWanTWan9MP4D5p2otVcgSWXEpOTjRNfvge41rLdXuqYIgw3TBfy+gBul5u9Y3kqGdjRydJESCB5/N+qtXzLqcpp2FhJCifoklFPXafuCuQCSdCdDrYgkOHu9h+FyrGQJthrdWZDJeHkEhtO9HUHPJt4bSHOGsH7+1XJWhlz+KGTKP5Gz4nHh8Ov/mzkrfcZKn/RJn/RJn/zPyn/SWBl62Z83Vg698b9prPRRl4+BED05YHkMQfcaRr2TGe6GhIVxyEPSBWf3XfB5djLqsjb+McgU/CnL56tHd/ff4POWQaEcjoSEJyGTccqhy3MA7bSfvwla1QwYEx6CNOTtMDvXo7H7UdZHKFF3MjLjb2bVcklarF+gwfwGS5TNNJyPTP15PGkvGEBl95uot33FPCv9Eq9Gho6HSPwBN/Z3Pocm+wZGXR6ZcjNSNNxb4/ZbsKX1abQ6D8CgzMbktFuRoOS0QounBWta/o5OTy3S1P0xK/0G6OT8SbbZWYFfml9libaFcaMwJ/1yKGW8GNtR6x780vwh60MyImEaZqefJbipt3Wuw+rWlQzvdOM8TDfO5UmrlJDa/D1+69jIqMuLM5dgbBKvKusJePFx7RfY3bWfMSDOyzsDAw3cW2P12vFa5WcosVQiQ23EVcVnIlvLe1O1OE147ujnqLW3oJ8+Bzf0X4okFV8AyiwNeLb0G7S5zRif3A/X9z8JWjn3dmxpP4oXy37m1OXMEbi83/GCt+bruj14p2ITp84WTMTZBRNC+IN4vWwTVtTsY12Xrxk8gxUE42Psw2P7V2NN41HGALp75ByMNeYyHSVs3rPzZ+xqr0eePgmPjJuHonjuram3dePOrT8z6uyw5Aw8MnGu4K051NGCe7atRrPdiulZ+bhvwmzWGJFkfX01Htu5gfWxObloEG4aM1UIt3xeehCv7t/O8F4wdDQuGMJptfTvl/dsw+elh5hn4saxkzG/iBdjI2bOo1vW49eaSqRotLh36nEYl8G9TZTwe/fG1djV3Miq7j4y4wQUJ3Lqfr3FjDs3rEK5qRPDjGl4ZOYcVj2W4W9vxT2/rWaJwtOz83Hf1Fki/I9v5/hPKh6Em8ZG8H929ABe20/J4UHWvfqCwVH4D2zF5+UHOP6RUzE/f6Aw/o/sXoPVDeWMOn7v2OMxLjVHGP97d/+MXR31yItLxMNjT0SRIUXwot2393tUWNoxJDEDD4xaCKOaj/8RcxMePbgSrS4LJhmLcduQ+cL82dpRhpeP/sT6+8zJGIlLi04Q5s8vzdvwWf0aNt9PzprOOoiH5//XDT9jbdsWNv9Pz1mISSncW+kNePBF/XLs794LvcKAM3LOQT899wQ6fFZ80/gmauylSFalY0nW5UhVc8q8mV2/z6PTXYNUdX8cn3G9cP12ukqxo/1pOHxtyNCOxzjjTayxKUmHYxPKTU+zrsvpuoUoSrwW0tD6Y7ItR7v1FSDoR7L+IiTHXSSsP3brC3A6PgUkGugNt0CtWSisn0HL40Bo/ZQY7hLWzz7pk7D0eVaOgfjN9yLoXB7KcqdFUwFZyi+QyLNh6zgLPs/WEHtHxuh4htRNLIGsrXU6a2gY1lFFxxTjz/AHOlHbMiWK9SNFnGYRMpJfZTVWSlvmizLu0w3XITPhZnQ61qKk7TIRtgEpTyM17mRUmz/EEVO4W3aoBH7G+0hSj8ae9sdQaaGsfc5BoFyXeTlfQqfIxE/116PFuTdEb5RBLYvHaQXLWeLch1UXwuIl1oA/VIq/EGflvwqH34xXyy9jFTLDtMhBhqlYkvM3tDhr8HL5LQLjh7Acl7oUx6efiUPmPXizijo6R+TcvKswLmkafm39BZ/VU7fqiNw24C700/fD21UfY03bRoFBJJfI8cyIB2BUp+Cegy/gkLmCMSEo2k+x/lfH3geFRI6LdjyGNlcXcbSYrjAuA6+MuRndXjvO3PwknH63QGuelTYc9w87B+WWZizb8nKIcRKiBRfPYgbLhtYyXLuDs7bC8uioU7EwewTer9iGxw5wRlQ4TfLjGRdgdHIu7t39E5ZX7hHORWGSX+ZdgWxdAs5d8wm2t9Wym68slOuydtGVLNF09jdvotFuFnQDE1Px/YIL0Oly4LgVb8Lu46wZynVYkD8AL85cjCOdbVjwzQcRxgyA60ZNYgbLmtpKXPzLVyL8fz9uPpb0G4K3D+zGQ1vWifB/cdJZGJuRhbs3/IpPSg4I56IwyeqzLkSOIR7nfPs5tjXVR/BrtFh/zsUscXT2p++whowC/mQjvl+6jOP/9G3YvVH4CwfgxRMWMvwLvxLjv3b0JGawrKmrwEW/9sA/YwFOKR6Ctw/vxEM714rxn3gOxqZl457tv+CT8r2i8V+96FJkxyVg2bqPsb1dPP5r5l/Fxn/+r6+gydEt6AYY0rBi1qUweexYvO45OHwe4ZhzMofiidGnsyaaF2x7UTR/LiycjUuLT8C2zsO479CbIvx/G3guZqeNxQ9Na/BeDW/8F5aHht6CgYYifFz7Pja2rxPN/weGPoYUlRFvVj2AKtthdh1yWrMBtwx4kSWuv195sej6NaoKcXbBy3D5u/Bt7VL4Ak7h+s2Lm41pGQ/B6inD9sZTQ0wh/gsUJlzJDBaLczVqOy4QYcxOeh6JulNht70Jq/k+0fqTlPI1lKrxCJjvB3qsn5KUn9j6+ZfzrFx6DDwrb/5velb6EmyPgQTdq6OofPTqRtC7k3VA9nl+i6IZ+xEMtMPvOwKfrwZ+f61I5/UeRCBggstDMWuriNZsd/HOwlbXph5p4UGYnfwmaHKuDyWwhUUa+oxyUviNJrwPLUAdTk75bbSvjzoeNSJ0o921B/6gF83O3VH0Rj+cfhNM1HXZ0wizt1lonkjbtLsr4PSb0egogzvgENEiy62cellh2x9lqHDtEdb9leLue0P9hLjQwlti5vTMfd38NfLNpDhs4ZTT3V37hOPRqzfoxRFrObwBHw6YjwoNEum1y2tBjb0Rjc4ONLs6maES1hGl2ey147C5Fna/S0Rr/q2d04y3d1bwBonC+YANrZweu7H1qIh9RDcp+oxkbRN/De9DyY+bWirZv1c3lonORU/6O9vrWAfhLa01AgWWXttddpR2t6HW0oU6W7dIR5Raoj1TfovV6xHovfS6up6fa1NjbY/ZA6yq5R3N19ZVCgmhYfxr66o4xpqKGPwb6qvZv1dVV4jORfh3NDUw/Jsb68T4HXaUdrSj1tyNOotZjL+jjdGG97Y2w+rpgb+W4/+tN/whbGvrq2Lwrwt9b2p1EIO/kX+3VQ1HY8Z/e1s9H/+2Xsbf3IY6mwn19i6RrsTcgi6PAwe76mHzuUXH3NBayt7v6CyPmT8b2zgtfEfnYdH8oflPn4VbTYjmv0SKfd1ct697T8z8L7eWsYJwlbaDwnXIac3daHHVotvTFHP9toWu3w7XIXgDdtH122DndYA6nVtC54r8Am32Neyd1bmmh7NeCquLG4huJy+TEPkFZHBTETimjF0/4eWdtP9q0kdd/velz1g5FkJJYSJaIf2Tkt8UAMuO76GSJrKGhbFCWfJayNjxRHtAFtpeznTRnVSJNcNdt4qYYxL1kifhKannRpQhQwtQeHtiBvXEr5QaGANILuGhm2gh74pKFmvV84JyGmjk+hiNRsY/08r0IuoyGU3a0LF0oZ5DEZ0EWjn/TC/XiwwZOoYurFNQYzUx/ji5jrEp1L1QrPVyHQyK2N+FbhQamZL1FxLjAGtwSBKv0IpqZNANMUHJt09Q0jZR340VeOP7Jaq07Ok7LHQDTlBFdD1mD9tPIZVCG6qIGi2JKg3iQ/v2xK+TK5i+J/6EUCGqRLVGVKMkzIwJ60S/jSSyH20jxh9AQmg/Ygb1xJ+gVv8uftqPCsf1il/xx/hpzH4PP+l64g+PUyz+yPgnqXrBr/x9/IlKjfC79sSvlSsRr9T+W/OH5l20n5t0+tBcNCjixPM/GGTzmCROro+Z/3RtUCKtMlSeIFroGlSHrsferl+VLD5Gowxdo0q2ZojXHwVbP6hhHb0Ge123qLK3+EHKD2l4net1/extfeyT/2XpM1aOgUj1dwv0YybKGZCwrssSaOMfFl2ISu2FkMmLIJMZEae/UXQcQ/x9rMmXWjkOcaGOpKEzwJjAK7sm6hZBFypTzTQSDTITqDItkGW4ACpGK+SikCUhJ57TCvsnXgVZKOeFRK8oRo6e0xpHJt8UoiZzSddMQYZ2CsM/KfUmEf5BCachXpkLnTwR45PD3aa5TE+9AnKpCtmaQYxZEBZaSOdkXMHeD0uYihxthFmjkCoxN4MfZ0bqiUhU8nwGkji5Acen8XGgHBV1iOFAkqnJwrQUfo5leWcIOS/s+yQMZX+E//Ki00UL+cKMGcjWpiFRqce5IVpzWC4vIuqmEsPi83B82oioMZbixgHU0RqYkzEcwxIi7A0V5Z8M4B2lzy2YhHRNZJFNVGpxcTHvHnvtoBmMFh2WfgYjTs3jeUF3jzxBYJiwcUgvYn+E/4Gxc0XL+Pn9x6LQkAyjRofrhk8R4b977Cyo5QqMSc3CooKBohv6AxN42fNFhQMxOpU3wCMhZtLt42YI1OdwA0WSJLUWV47kLJAbx00RckZIBiSl4IyBw9j7e6fMEhg+JMQ8oj/C/+D02SL8FwwbxRg6Rq0O142dJMY/ZSbHn56JRUU98E+dzfEXDcSonvgncPwXD43Ff9XwEP6RU8X4E1MYs4rknrHHi8Z/ZmYhZmby8b9/9Dzx+Pfj45+ijsNVAyNznOS2YSewfKSRibmMohzBL8UdQ3l+BjHOhsbzfKTw/Lmq34ns/ZLsGUhVR+aPQanD6Tn8e1OOSpjhQ5KjzcSsNP77n5Fztmj+DzUMx9D44Qz/SZmXiub/5OQTYVRnset3Qkq42zGXGWmXsevXqB6O/DjeCT38QDHeeAt7n6abj3hV5Npg+W9JvKxCStwlUIQauJLIpckw6qlqLViOikQSeRiRywdCo+XlDCT6O3usn9MBlXhs/zLSV2fl35a+nJVjJEFfDYKenbxrqOo4oY8Pic97GH7PAUjlOZAruREQFjdVq2XU5cFQKiMUZEpIc7jWwRdogUY5HkpFhN4bCHpgdq5mZfn16qlQyiMLhC9ghcmxlnlOkjTHQSGLLH5uXwfanb+xIk5p2pmQSSNPhzZvPdqd1NE1ARnaqSL8na5ydLhLoVdkIEMzRoS/0XEQXZ46pKiKkM5qq0TwV9p2M+pyjnYwUlSRGzy5p0utuxh1uShuBBKUEXovJd1S7go9IQ82jBK8JyRmbzcOmQ8yA2dE/EhWkyUsLa42HLEcZV1nRycOFxXDqrI1oMJG/YuSMTy+vwj/we5K1DvaUBiXiYGGSD8SViG3s4yV3B+WkI98XYRmSeGlTW2lzNU/Ppm+d2SMKemWclfoyX16Wn/RU3a7y4ZNrRXshnZcen+hjwwJVUSl0A95WY7LKBb64JCUdLXiYGczsuPiMTktX4R/R2s9Ks2dGJSUipEpkXlA59/QUIVWpw1jU7NRnBAxAim8sbquAjavB1Mz80Q3eEpapdwV2p9qjYS9JyRtDjs21FWzBNXj84qEPj4Mv7mLhX7IOzMrj9coCQuFdw62tbAclslZuWL8TQ2o7DZhULIRI0O9egT8ddWMej02PUtIyg3jX1NbwUJdU7Ni8dN3o1WN4Y/y0rQ5bNjQGMKfUxwz/jSWNP7hGjeR8W9hZfUph2Vyqnj8d3XUosraiYHxaRielCWaP5vbKtDutjDjpVCfKpo/FFYk6vK45GKkRRkolHS7vfMQ876MTxos8vJ1eczY113CisCNTRzODGvhu7lacdRWhjh5HIbHU4ftCP4mZzUaHJVIUqaiKG6YCH8DXb/uehjVhTHXb5NjGxy+dqRqhiNeWSBaf9od6xl1OVkzCeqoByTqBG9xUsg6AL16NuQhrwvT+dvgpv5pEjXU6hMgCSXssvP5aoHQ+kk1VqLXn79Szsrwi/58zsqBd/43c1b6jJVjKMGgL1QMSdKrLszY+Wd1/Kfx/xs67qYNt3QX63jCbm8YA4Tjd/CTLpzx/8/qeGw+IDRA+2d1tNCT9FZ9k3T0pNgbRn/Qz0tm/Y4u+unzn9ERRjqfTNq7juq0hFkc/yx+2oeV7uoFI7UHYIXCetXRuXp3hP6ejmMM/su6cJglumKtgD9Av9nv4Q8Ihdr+evj9vf7Wf6T7oznyR3Pr/8b8p5oqvV1rf6Tj1yjp/jPrzx+tn/83pc9Y+f9D+qjLx0CCgW74u68DiPUj0UNqeABSzSKmC/hq4TBdjoCvBBJpGjSJL0Cu4u5vr2cfuk2Xw++vh0xejMSkt1hjLxKbcxVau25iCbdUzTYj+U3IQ0WUOqwforn7YQSCThg085Cb/HcW4qEFpK77aTRZqOFfEGlxZ6Eg6V72lEJPQyXt96DZ/j1j+xQlXIP8hEvY8Tx+M7a33oZ25w4opHEYmXIHcvTcNW31NmJD8+3ocpdDI0vB1PQHkK4dw3RtziP4tel+2HwtSFDmYk7mQ0hU8U6wldZt+Ln5GdYrKFMzGIuz7kacgj8d7zL9hF9b3mWFpwboJ+Lk7BsYrZnw/9zyKTa2E3U5iInJJ2Bx1gVsIfUGvPi07i3sNm1hhsX8jFNxfDofY5vPhlcrX0WJpQRamRbL8pZhYjKvatrqasczZa+j1tGAREU8ru13EYbEc8ptubUWT5a+jTa3CdmaNNw26BLkavlT4taOQ3i6dDksPjuGGPJxz5ALkBwqwvVtw1a8XPE93H4vphmH4M7BZzJaKmF+tXwVPq7mRe2W5IzHTYMWMgPE4/fhgYPf4KemA1BIZLii/yxcWDRNKBJ2864vsb29CnFyNe4ZsQALsnkYgZI4r932OY6YW5Gq1uPpcUswwcjH+ICpCdduWYFGhxmF+mS8PPk01hiRZE1jOf62/Xt0uZ0Yk5KFl6eeKtCaPz66B4/uWQunz4s5OQPwzOSFLERC+J/auxFvluxg78/uPxL3jTueeRko6fT2rT/ju+oSFjKhqrtXDJ0g0Huv2fAtNjfXQq9Q4aGJc3BSIadl11m7ccW6r1FiakOaJg7PTV+ESRk8DLK/oxlXr/8WDXYziuKT8NrMJeiXkCIkxN665UeO35iFV2ecjFQtx//R0b14dDfHPzenP56ZEsH/9P4NeKt0O3t/VvEo3DvmBAH/nTt/wMq6w8x4uH7IdFw+iF+H3R4nbti+gnXMpmJ8D4yaj4U5vLptvd2EG3Z8jlJLCxv/J8acgvEp3MtwuLsBt+1djiZnN/J1KXh69NmCB+W39hI8evgLmL0OFlZ8eMS5SAnR4lc2bsYbld/BHfBiSsow/G3g2dCE5s8ndd/hu6bVbP7MSZuGCwuWsvlD8/+9mvewrXMbm/8nZ52M+Rm8crPdZ8MHNc/hqO0QNDItTsu+GKMTeYioy9OCz+seQ6urGnp5EpZk34z8OD63Wp2lWNX0AKyh63de5gNICl2/TfaN2Nn2ADyBbiSrRmBS+hPQhIpINlk+RWXXE2z9SdGegIEpTwjrT5v5cZisrzP8ibpzkZ74IFt/iJ5s7b4VbtYrTQGd/mZo9VcJ62ew+4bQ+hkHGB6AJERr/svJnw3lBPE/K305K8dAAuZ7AA8xWoJA0IKA+WYEvZz94TBdhoCPN0wjJpDDdBG/OINOmDrOgd/fyHR+XzVMneeypxKvrwHNnZeyGi0kLs9etJh4SWqbawcau+4M0ZqDsDh/QXP3o0zXbv8KjRbq2upFED602D5Es/V9pqvqegXN9pUC26e86xnmyiXZ2/4IOpzUTC0Ib8CKnW13w+LhLIr1Tbeh281ZE05/J9Y13QK338wMjR8b/sYanpGYPQ3s34Tf7G3Ftw0Pwem3hmqulOLHpifZ+zp7CX5sehVeRmsOosy6HatbeKfi3V0bsK7ta9aALQA/tnT+jM0dP4eavH2NXabNjNFAbIdvm5bjUIgp9EHNByi1cLaFw+/AG1VvoNHJx/XpstdQ76CmkEC314InS1+BzWuH2+/BA4dfQYebj3GTsx0PHn6VPbm2ukx48PB7sPrsTHfEWocnjnBK8oHuajxT9hVcfuoaTSyhw3itgnd8/qFpL96r2gBv0A9fMIAv6rbh81qirQNvVKzHj42c3usO+PB86SpsbOXz4oH932NnRzVbh6w+F27f/RUqLHxcr976GY6G3ne4bLhiy3J2c6UKtRdt/JQ1zCOptZlwyabl7PhEZ75q0wpmRJBQw72btvKmcTvb6nH3jl/g8NEcAX5tOIrH93Cm2IqqQ3jl0FbWwI/wf1C2B++V7ma6Fw9swTdVJcyb4fL78Pie9VjbwOfInVt/wdaWOnY8i9eNGzd9j6Nd7Ux32dqvUBZ63+604+I1XzJchP/81Z+jycHx11i6cMHqLxj+BpsZV274OoK/owk3/PZ9BP/2CP5VDeV4LIT/q+qDeLVki4D/w/LdeP8obxL4Uslv+LaWmvtxts+TB9ZiXVM509275wdsb6/h4+9145Yd36A8NObX7fgM5dZWYfyv3vYpMy6pQu01O99Hi9PM57WjE9fuIkp1AC3OLty1/yPWFJOEGmY+eJA3Oz3UXYUXyr+Ei3Udp+aZB/FmFW8SuL59O75q/AW+oJ9XV27ZgJ+a+TX6XdN32Nq5VZj/XzR8gf3d1LEd+KL+LVTYOCPN6Xfgo9qX0OysZ//+rPZRtLlqQ0Z9F5bXPcSaFdL1+33DbbBFXb8/NNzO66F4m7Gl5VZ4Avy7mdyHsKPtHr6daxfKTfcJ60+HYzUqTbzcgNnxBTqtL7H1B/Chy/4eTLZ3+HVpfQ5uJ9HKybPigt36CNwuYgEBQfO9UeunFUHzLQh6+W/zl5O+nJV/W/qMlWMgRFOO7oLMyIreg7xYnO+IiIKMoB1+71H4fHUIUndRIbPej4C/AYFAJ9xe6iLrjZqZfrg8fNF1ePb0+NkCsLs59dfq3kP8nyidFFY3v6F3ueimE83CkcPs4t1+O1yUIyLG3+U6DH/Ag25PRZQuCF/QCbOnGhZvE9wBi4jeaPO1wunvRpurEgGQSzdMpwygycmpvw2OUpawJ4wd64TMKZi19jJR40Ryd9NnJJU26hAbzaKQodrGF7Sjtgg9mR8ziGo7dS32os7RKOjoc1fAjQZnM1pdncwYidBLiXptgsVrQ4W1kd0wBDJ3MIASSw17T7Tm6IZ4tP/+bk7hPdhVG0M9Pdhdx97vNdWK8BPFdn8X1+3pjNB7w8c82N3IvDFllrZIZ18E4fB7WBGyOns3M1rCIQ/ahjwsJrcdh7ta4A1GOCek29NB9XyAPe2NovAI7b+jjd/Ydrc1xlB/aXuSnW0NMfh3h3WtDTH493e2MKOgtKtdjN/nRXl3J/O4dLtdYvx2C6uxQhRsb6AH/tC59rQ3xeAnA4bpOhpi8O/t4PtRUb0Y/KEx2dXRy/ibmtn4H7W0xox/pbUdjQ4TzF5nZP4Eg2h2djPq8lFrk2j+kOFxyMx/a5pHPefPQTM3+sqsVTHzhz4jKbeWi/DLIEOFjdOxq+xHYuZ/vaOS5Ya1uckIi8x/qn3U7q7v9fq1hq7fbk8pe9iJXL9+dLo4ddri3hez/pjdnGbscNMaJV5/nG5u7Ho9O3rcaeXwhdY0eOlVvP6ArYF/PemjLv/70mesHAuRUfJoj5gvy4pXsiJwYlqeBFJZVqhSbY8usRItpNJ4KNjxokUKuYwXSFKyQknR1EHqUsvd6ip5lqitO52LPiPRKnJ7UJf9UIcSc3XyLJEBwbfPYOEiSrjtiV8nT4dWniw6HolcooZKpke8IpKMGvrGrPEhSYIyVYSRdAlKXlE2MUYnQaKSu55TVGki6iZ5XpJUPGSQokwR6UiSlcmsOBYl3PakdaaokpCoNMR0ZFZJldDJtUhTR/rJkNDNJVXFEwXT1Yki6ikdI1PDt8/QJIZi+SH8EmJW8eTJbG2S6Hx0A8vU8mNmaxNEtFqSTE0CC7cQo0g8+kCmNh6p6rgY/BqZglFkqZicCL9EwhrvsXPFxYuov3TeXH1CrzqCRJ+R5MaJMRL+bB0Pa1DPop74s3QGVvitJy2Y4dcZWEgnBr9cgXilWjinGL/hd/HnxHH8WbqEGPzh7907fr5fji4xdvy18b87/hmaeKSo9DH4KXHaoFCzJpki/FS4MZRES3NLNH8gRYaah0dTVUli/JDAqOJzy6g2iua4H36khOZ/sjI15vql64aoy7wsgPi7xSuMf3j96qIS9sPfQBtKolWz9US8/mjkfL1Sslfx+qMIFXaTyfJiqcvhde53188+6ZOI9Bkrx0BkhocBSSTZSaI5DxLlRJYopkl4gW6DYQ1UhnshlWexGgOGhCejfgIF4hNfhESihEo5FEn6G4TjSSU6pCc+x97Ha+YjQRuhNStkqchMvJ+9z9BfCH1UU0Otoj+y43lcuDjx+tBCwyVFMx2Zet7teJTxbiikEfxFhjORoh7L8E9Ne0BoD0/4x6bcAJ0iHWqZAdPTbxYWSaI+z8q4CzLqtKouwsSUsyMYpRqcmMmpj4MMkzE0PkJL1MsTMTeD585MNc5HXhStOU2dg1lpnF69MHMpklWRpm5DDCMxMZkf5/z886GVR5gFs1NnY6CeN/W7rt9FUEgjT3vn5y9lxgrVr7iq6CzBkKGaLDf2P59tW6zPEro1k2hkKtw6iH+fGanDcHzaSEGXpNTj2v789zgzfzKGJURoqUVx6biwkHePvWbA8cwACctUY38szua/1X0jFwl1OEjOKRiP8SmcdfLMuCVQyTh+QnrH8LnsRkosl4fHzBee0unG+syEk1hlVWqqd+2QCK1ZJ1fiqQk8B+DE3IFYnM/zSUgoj4XKy5NcNGgsRhsjN4kBCUZcNYznddw6arpgFJDMzCrEacU89+HRSfOYkRGWCwaOwaR0zvp5fsYiEf57xs9mHaMJ/2OTqJN2BP/fpy5k+IckpeG6ULfjMP6npywI4R8Qg/++EP4LB4zDqJTIHO8fb8RVQ/hxbh42U2TEzcwoFrpePzh6vmj8lxWNwwRjHsP/5JhTRfhvGzqPdd0mltddQ0+KjL9EhodHLGXzh5piXlAwOzJ/5CrcNfR09n6acTiOS42UHkhUGXBlMb8OF2TMwgB9hHWTq83Eqdnz2PtTs04VjBOS4fHDMSWZ/8ZLcy6FVhZhDU1LmYviON5UcUnOzZCHGqeSzE2/BPFKI7t+Z6bfJLp+j8+4k12/CaoBGJTIr0n+22gxLpWvMSnauUjVRfJJVDIjipLuYu+T4i6BRjkmolMMQIqBh6+1htsixgkZNqpZUGv5mEgMD4rWT2iWAcpI48S/lPSFgf5t6WMDHSMJBqwAhXyIuiyP0IxJAv5OBHxHmUdFGvKChMXva4TPXwO5vFjUhZTE4yVXbhtUioGiQnH0k7m8RxhNUKMcBlk0BTDoh81ziD256JTUJj6yUFEPIKvnMKMu65WDRNn6Xr8VZs9RKGUJMCgjnVJJXL4udHuqoFNkQK8QP/HYvG0wextYvyCdPEIvJTG5G2D3mZCizocmqogc4W911bAqtxmaIiilahEzocFZxWLn2doioUMswxjwoN5RzajLWZo8EVvC4XOg3lnPqJtZmsgNi8TitbK8FaMqGanqyIJP0u7uQrOzndVeSVKKn+gbHG3o9FhQoMsQUUgJf6WtmdFM+xuyWSE5YYyDAZSaG1noYHB8logJQgm5JeYm9gQ+wJAuwm/1ulBmpkRHLYoNqT3G0Y5ySzszUsgLEC1NDjNqbV0o0icjVSMu9EXdgIkuPSA+VUThJfxHutsYdXloUhorZCbgDwRw0NTC8A9PThfVH6FclUOdLay2yeCkNFE4xuJx44ipjRVfCyfJhoVCO0e7OpAdZ0BOyIsj4LdbWL5KcXyykEAbwW9idOOBicZY/F1R+BVi/IcY/gDrlxSNn8JSFN5h+BPTRfhp/Eu7Wxl1udhgjBn/CmsbMzaze4x/i7Mb9Q7qLG6EUS0e/zp7OzrdVhTp00VFCAl/lb0JDp8LxXqaPxEKPuGustWxsE6RLk80fyisWWOvYV3Hc7W5ovlDuSpNzlro5Hqkq8Vl6u0+M9pddcxISQx5MSPfuw0WbyNLsO15/Vo9tXD5OxCvLIYyqlAc4bd7Sxl1Wa8cErP+UF8z8txqlNTFPLL+BIMu+DwHAYkacsUQ0frD18/S0PoZ6VD9V2MDjVz2yJ9mA+378K4+NlCf/AmhSq9SIxCqGCtSSXWQSI28/XmMLgGyYCqkktiKkjIZLR7UG6VHRVWJBHKZkRWEI8NDLFIoZbTYEsVR/PPysA7tp4yhFVLNFbUsGYqY6pX8yYrcxvQ01lNUsjjogslQRRWcC4tWHg8JAqwqZk/8ekUCVAGV6KmP6ah/jzwh1MtH7BomwyVekcD6mvSkdVLNlQSFQVSXJSxUTCtJaRCqgUaLXq6BR6WHLqrgVlgSlBRCCopuJmH8ySo9C1soo7w2JPSkTTctuun0DBPQjZPCN8pe8FMIx6iJQ3zUE360ZyFVo2OVU3sKeTTSNDrGwukp1BRRIgmKbuZh/HQ8qhYb9hoI+CUSpGl1nBbcAyOFdajRIO3TkxZMlV5TddpeK7vGKZRMF12zRYRfp4Ne2Rt+DQvS94pfq4POp4BKHos/9XfwU0Va+m4qaSx+Gn8aE4Pyd8ZfrRMZTGEhjwz9pr2Nf6JSB4kkIDJmw/iTlXpoZTR/FDHzh+Yqzf+e84dYQInKeCgksfOHKtXGKwxCpehoUUk10CviWeXanqKWxcEfTOr1+qXK1nT9yqOMkTB+pSwFst9Zf+QsxB3o5faihFSWColEFUtrZutnSq/rZ5/0CUmfsXIMJOirh7/rfMBPSXQSSONugjTuSqbze/bD0XleKJlWAU3C01BoudvX5fwF3SbazgWJRI+E5HehUnG3tdn2Ljq7qTIu1WLIQIZxOZSK/uyppsX8MDqsr7Pt1IpByDd+AoXMiEDQi4qO69Hl+Inp4tXT0N/4BqRSNbx+C/a3XQKLmzMIMuKWYmAy0QqlcHgbsLX5cjh8lKhIDfGuRb+QG7jTdRgbm65nzAByFY9Luxf5IVpzne03rGu+D/6gGwqpDsdnPoYMLXdxH+j6BptaiRkQgE6egpNynkKSKo/hX9/2NnZ2UuNEwKgqwOl5j7CKmsQC+rL+aRyxcAZNoW4kzsq7EwqpCk6/HW9WPYY6B0+qHZ80C6dlX8oW7Q53G14ofxwdHs5sWJx5OualL2bvq2xV+Hv53xm9mRb7iwsuxqRkHtrYZdqHFyveZF2byaC5pf/VGByiNf/YvAFvVX3BkhKTlAm4f8i1yNGmM/xvVX2HLxs4A6VAl4nHhl/JKuJSvY0HD32Eje08OXBsUn88MvxCVqXU6nXi5r3v4HAo0XJR1jj8bdApDD8la16z8x00OEwsqHBl/zm4sIiHjw51N+CaHR+i2+tgoaoHRizBgixeQXRtcylu2fUlYxfFyVV4ccKZAq3246odeHT/zyw/Ik2tx9tTlqHIYPw/7H0FuFzV9f0ad3vuL+5CSCCBBAjBCe4Ul0KxFi8Ut6KlOBSH4hR3DxqIEHd5eXlu467/b+9z587cmZcECu2v//A23/Amc+beWXPmnH33PWevvUD471j2GZ5YJ/qYVl2emnECV2QlFs1F37+Bj9oEs2p65WA8Mv0YXgnyx6PMPlrsFgmrxwzeATdPmc0XfVJ/PvnLF7A56GH8tOVyzhixRbG0rx2nf/USPPEIBwu373QwDh0kaMGftq3Fn75/nVc8CP8jM47GtApBnX1u/QLctOgjgd9kw9O7n4Bh9rIc/rV5+HfL4b9k3uv4uF0kc0+vGIIHdzlW4E9EcPbc57HUI5Jqj2zcEddPPEjqfw/O/uFpuf9py+6MYaIq7kpfK/604GmmIFP/Xzv+SOxfI7YBiQl2w/LnmYJs1hhw68TTMMklViXfbvsaD69/nfGX6R08RhosYvw82/wG05PJGs21uHbMBXDq7Uimk3h4w6NY4BFJqePsY/CnERewynI4GcLD6+/AprBIqt2ldCaOaziD8XviXXi66Xq4453ctnflidij4kh+3hFZg9dbruYSAjR/96+5BGMcs7Y5f5t8L2F5323sf4yaCkyrfhQ2/RDG3+y9De1+Ibho1o3CmMpn+AYpk0mgve9cBCOCHWc27I7asqegVpuQTvvg7zsJqQQRBACD6XhYnLez/yH/mfGcJvtPWC+CyioqXm93NkBd/rdtIGflV7CU/y+AREHmvPrg35CJCxYO1VjJZAQFkBg+Ee/FSKe6kE4H4HXThIyKozIhePvO4MJI8cRa9HmvkpPVUuludLvP5+ckDJYNVMiiibXo8Ij95K7AP+EJC6ovmS/6Ldr9j/Dzjd574I/R9pCwjuCr6GIqM7Ck5wZEku0y/tWe++CJiqDmu84rEKclWkaTxLwuem8v4qkQvui4lh0df7N0GJ+1X8UF4tyxZnzVdZ+cLBtOuvFJu6BXbwzOlwMVst5YMz7vFN9nXt/7WOX/Xm5rCi3FNz1CRfeDjpeZ4ZC1ee7PscgrhBifb34C7niv3PZ2+ytoCgmn/uCGBxGSKMhUNOvxjY/DG/cinIzgvnUiUOF+TMVw99qH+T0t4U48tvEVmX3hjftxz1pBr57nXikHKmTNoU48sl5gfKP1W3wtBSpkP7rX4YVmIeT26IaPsMonWCtk77TNxyedgo11y/I3mEkieh94aO3HWCYxhS5d+BJfaMmIYXLtktfREw1wpdxsoEIWSsbxp3kvc8C0wd+DW5Z8ICdy9saCuHzBG/x8Tud6OVAhI2bRLUs+lgOEj6VAhWxu1yY8uvo7fn738jlY5smOEeCVpsV4e7MYT1S/pC2Uw3/XsjkyC+e8716DLy7GODF8Lpv3DrojAaYI/2muCFSy+M/99l9cmG29vxc3Lvowhz8axKU/vJnDv7Z//C9snI9PpECF8Xc34bE1Yozct+pzrPBm5yjwWvOPeK9V/FY3LH1T0f/3r/kUSz3it/rzouc50Mz2/w3L/oXeqJ+3AK9f/hwHKmSRVBxXL32G+39zqBMPrn9Nxu+OB3D76ufEmPAslwMVspZwB56S1JQ/6/4CCz3iYk62wr8K77WLG493O17F5vDG3Hfrm8M1h8jebH0QXilQJ/u06zm0hAWL7u3WmxBNBeX5+0H7XQgm+rY6fwPxjVjed6vsf2KpPvzYfSU/90S+kAMVsnBiHTa5hRSIJ/g0gpH3c22xb+AOPCie++9AKiF8Cp8z8iLiETEmM/6rFf4zE7wbmbiYG9ubDbCB/n0bCFZ+DUuuK6De0WrLBqYuZ9LtBRnyKS4UJ+qrCEchLM1BDRWBSyTpopw/KlNI8GcAMa7fkv+zpTh/hSzCtQmUWyfiNSAUX6vASFtEoYRwfv5EPj1ZWCDRhFQmjnCS7tbyaZEpBBOUi0KUznj+N+agJprywRvPXZRFS5pL8pP1xjYXUZd7YoIWTJRKJasnw6+RdUapjkcOB20RdUeFg2uPtiqom+L9bbzH7467FZRPel93jHJR3FyvIvdJGYRSYQQSQbRHRF2N/GNaI+KudXO4q4B6mkZTqIOfN4e6FMvzGek1so2BTgULhLYoNoXEBWZDgPpSib8p1MPU2c6oT3EcvY9WADoiPjlQyeL3J6JMnW0K9ilHD+VIBEWtEwpk8rdAqC1bx2WDv7do64ECB7K1vhyFOot/g9yWoydnjdooEKE6KoX4SS26I7wF/LEwmgLF+DcEereM39ctYy3Enz2O6qYU4t+YPWc//U/9FU8n0dVf/0fc6Ip6uT0ffyAZYdXu1kgucMiOkZawGActkc6i8dMcEuO4LdJexGpri4oAkeqm5I9xWiXslNq6Yso2sp5oK5LpOALJHsW8ofnrTXRsdf4GEzQfMwVzXqq1lFhf5H8of4WMbrIK/U9c8j8prjWV72O0SCUlJeyk0jeJ13I3JgM2YGQDwcqvYdoxxTRk3UixN6tpKOhmHdTawdBo6pmqnKMVqqFWl/JDpx1RcIwGet0YeduniDqoF6wGs350waTPSK8BVv6bfyFNwqoXWx4O/agiGqNdP5xZQIW0ZjV0sOnqYdVVSYrMOfxGjZMfLkOjgi5Mx5cahvDzCuPgIupypVEk1FUZBzElOd/oNbJa0yAFDnpftVEkK9ebG4ucfI2xnhMRyw1KyifRmSuNlZxsa1AbZJz01661wa6zoc5cpcBPxw8yi6RdSrZVquaqMdwmWA5DrTVFFz16jWy4vUZxkaKiZUOtgg460l5ddJEdZquEXqNFndmlOI5YJw2WUmakUJ6FSpHrYEaJwYKhtnLFMUTLHWEXydsjHZVF1N+xToFjlLOScWUtI71GNsapTEil941yiERgYh8VUn9HOMs5t6Xe4lQcR3k7g2wu1JqdnLOhwG8g/JTgWlaEn7Z7tojfJeF39IPfIfCPclQV9f8IqW2ko6ao/4dT/6u1qDWVFPV/vbmUKexGjV6B36mzcJ5TQz/jh7YLs9s+heNniFWM4wZzPVOS841eI6srGP+0AlhjEm01xsFF47/SSMm5ejh0hEWtyPty6Wu3On9tOpqr+fVeNLDrBUvPrB9V5H+serGtZ2AfpcRv0Au/RQm1Sp+WhIZ9Wf/+E1Il7+3OBthA/7YNBCu/gmkctwDaLINGA7XtGqh0gtZpLnkUKrXE7lCZYHI9ADUluaotcJY8LiuRUqKts+QpLk2t1w1FuetvshKpVlOPipIH+LnNNBMV9otkJ0MZ99XO6/h5pfUElFkE1ZfMZdoX1fbf8/Mhrj/BZczRAetsp6DCLHJPJpZfB6tO5DqQYxtb+mc4DeRcgOnVd3DiLX8zlRG7VN0Mo7aUk273qrmF/5IZNHbsXXMbl8Z36euxZ9WlspKzTVeFfWpIWRUYbJ2MXctOkPFXmUZgVuVZ/HxKyf6Y6NxTxjjSPhW7lon8nv2qjmU6ZtZ2KzsAE50i9+SEhjNQZRQXA3LaR9edhEaLCI7OH3Y+HDqRtEd7/2cPOZv/TQrOF4/4A4xS8qxVa8ElI8/llZFaUyXOG3YC5yiQVRhLcOGIUySMo3FiIykhC/wjbPU4e6hQZD6kbhfsVzVFxjijfCyObRS5J78fuh92LMmxrI6pn469KkWQedW4I5hNwvhVKlwy+iCMcQhGx98mH48yiWViVOtw2ySicFtZwfnenY+VlZwpsfX+qcfzRXewrRQ37XgwdNIFmAKbO6aIcbFb1VCcP3p3Gf94Vw2unCAUdo8fuiMObxSYyPapHYEzRgjZggvH7YFp5Tmhx1OH74wD68Xv8dedZmOovUwOHq6ZtC8mlIjf4+HpR6HcKMY4sXDu3eUwzi+h8vgPTj9Kxk/Jq5QfQ6XxCf8tU2bL+Cmw+dvUw7aJ/7ghk3FYQw7/3jUjcfpwMUb+OGoW08GzdtKQqdi/Rozx68YfisGWMrn/Lx9zIMY6RXB6+6QTOJk62/83TzyW/03yCjePP5lzVcQYN+OvE0/l/q8zV+CikcfJ46fS6MLlo4XC8STXGBxTd6CMf5i1AacNEvklsypmYkZZjrK9o2sSDqgSit6za47CcNvoHPW6fH/s6BK/zaF156LMUCfP3wOrz0CtWdwAHFp3LeeDkWlVBsyuuZL/vbX5a9UPwsTy6+UEfZO2BjtW3Cr5lN1R5/ijPH+t+vEY5KLcOsBpPRl281EyRqtpP7hsIvfEbLsMWn3uuxksZ0BvFHIZqgL/qbJdLfvP7c0GtoH+fRugLv9KxuJd6V7WtshXExVtSWTSvRyQkOKosi2OdLoXamILFTBjSHsjnfZCo6YMeuWdRyod5JLXWj5OeVebTHl59UKnURY3o586ke7jDH6t2laEn/amtWpLUfY/7WNHU24Y1A5o1Mrs/1QmwdRmk7akSPCMSnpHU36YuU2JnxSXE5koLMQ4KMAfTgZ4Wd2itRfhDyZ9fMdIGihKjFSB0wej2gRDAbOH7kL9CT/Tmmm1RYkxCV8iAIfOBm0Bs4dK8geTYU5+LLzzJtoplUx36WxF+KnMOt39O/WWIvyeeJDv2K0FrB/C744FYdEaYcqjEguMKfTFQ3Dq6Lsp8dNWBFFrKYDJp+mSUUl4XzzCwUEh/mAihkgqgTKDpQh/tjIurXIU4u+LhZhNYyugX9L7KbeEtHXMRfjT3Ea04EL2ESko0znLjJZ+8ROW8n8DP2F1bQm/RgubrhB/mtso0be//nfHg3DozEX9TwrK9JtSvZ1CwUIaP4FkuN8ChJFUlPOkiMFWiJ+SwQmrTVc4R2mryc9juL/xH0p6YdCYFaUARFsKIZqjGjvPnZ86f1PpKCfWGzVl/fqfVIaSpsuK8KdSHvY/WukmJx8/+UGoDFDn1XXalv/cnqjLOx7/y6nLP77426QuD6ys/FqW8SCTXMn5K4XxXybdjXRiBTJJkbeRb6nUZiQTK5BOibyHfEsk1iMep7L3vcWBRWIlYvHlSGeEtkrWSLAwkljBj3Q6qvysTAih+EqE4qskhdO8z0p7EYivRJDyVwrwU60FX2w1QknBpMi3UKId3vhahCWNkXzzxpvhjq1DNOUpwt8X24je6HrE06ECjFQifAOX7E+kYwUX5ig6o00sykYBSL6FU5RrsgldsdYi/P6EF22RJvTGlLkoZL3xHrRGmuFJKDGSdUQ7sTm8mQOdwgsDlfHfFGpBOCWSL/MvXk2hFm6ji1W+RVIxbAi1cY2NVDpVFOCsD9I5O4vw98UDLAHQFukrxhj2YF2gA93RbBJ3zjYFe7j0OwVBhfjXBTqxxt/OWkSF+Nf4O7DW38HBQr5Rqfk1/k6sCXTyBTzfSC9ntb+D65EU4u+JBbiNRAELrS3i4c/qkjSO8q0p2MttFIz9HPz0nenRH/71wXasD3QU4Se2z/pgK5r66X933I8NwVZ0RIv7n2QbNoVa0RsTCbr5RrlPzSEqbR8owt8SbuGxVTh+KHhuizTzI56O9zv+OyKbisZ/NBVAV3QDJ6wX4g8l+9AXW8f1VAotlGiDL74G4WTx3KCctmB8NeIpd9H8DSdWIxxfjlSB/6Gbr1hiBeLkfzLK70ZSI+TrUomVRf4HGS+wBf+5XdnANtC/bQPU5V/BMonlSLlPYhEuMpXxSKgdt/EdRzL6KWIeWgoVjlNnuwx6q2D2REJPI+i7WhqBGtic98Io0Zr7vNcjEBQsGZXKhMqy52E07MJFl9p6z0AoKhgQGnUZGire4K0juttZ23UMIgnB0jBoh2Jk5evQalyIJduxpONYxKSgyGGYinFVT/Iqiy+2Ags6TkMyIy5qNdbDMa7sZsbfEfoSP3ReRvJp3Dam5HyMcp3Bz9d6X8WCXtquyvDy8y6V12OQTSxbf999P5Z7X5HLeO9XeyeqzTvwXd67rdejKSQYHSaNE0c33gOXvo5XW55vvgJdUZF4V6Kvw0mD7oJZa4cv3ovHNl4JX0IEboMt43DyoGu55kpreCMe2XATomkhHDfFNRPH1v+B8S/z/ognmu5lWjTZQTXHYL8qUXH28+5P8cLm5+TtozMG/x5TJVrzc82v4IPOT+Tto8tH/gmj7SM4J+XO1f/AfI9gktCKzM3jLkaNqZJXW/6y9D5sCImk4FpTBe6YeDEXlOuOenDhovvQI13UJjqH4a8TzuZVljX+Vlz84yMIpcSFd//qKfjz6GMZP6n3Xr3kORZHJDtr2H44ZbCgnr7aPBd3rXqHRw/lTFw/4WiZVnv3qvfx/CbBhCHq7v1TTsGOJYMZ/8ULXsSX3SIpskRvwVO7nIlGaxlCyRhO/+5JrPKJMUJKws/OOJML1ZFg30nfPI5OKajYqXQQHpl2EufVrPC24/Rvn0EwKYLLw+p3wM2TDmX8X3SuwUXzXpHx/2n0LJw1QlQefrHpB9y67H2BX6XCXycdgdl1YhvnzhUf4J8b58r4H5p6EqaUDpLxz+nKw7/rmRgk4T/rh8ex2i8STxstZXhy2tmMvyvixVnzHuGEWbIdXUNwz5TTuP9X+1vxp4WPyv1/YPUUXDnmaMb/XS/Rk59mJhDZ6YMPxAmDRMXcd9u/wsPrBZOH+v+SUSdhZoXYBnym6V94t+MzMQ/Vevxl9PkY4xjOgcp96x7AIkmEkPKkrhp9BapMVYimIvj72lvQEhEJ55WGalwy8lpYtTZ44714eMNV8CVEwDTEMhanD76ax39HZB1ebL6SiyySjXfsg9k1FzL+TcHv8GHbdZJWFzC17AxMLhVbUuu8r+DH3rvk+Tu18gY02kTF3NV9t6PZL0RQ1SojJlc+ihLTFPY/63vPhjciGE1adSlGV74Ko24I0ukgWnuOlLTNAJ12KOrL34JGU4JUqh3+nsOQZsIBoNXvAnvpc5zXR/4z4z5F9p8wHgE4bi1asdke7Jdu5ah+w8HKwMrKr2ApUg3N5O7+MtHXkIl/x3cIMe8lnEyWtUTgTqSTm5n1EyS15jyxwoD3EmYQxeJL5ECFz5eJoddN5wECkXfkQIWPSnvQ7RXU5e7AE4gkhPoqWSy5CZ1+keuyyfN3xFK51Q9fbB46A8LRruy9AUlWURXWHnwD7uhcxr+g+1rZ0fF73Q/w3RgpLy/ovTtP7CyN77tvZoZBb3SNHKiQJTMxfN11Oz9fF/hKDlTIaJvoq66H+fl895vojubomZ54O+b2vszPP+0iCmnuDq8ptAI/esTF4LXWxxHLW0Va4JmDdUESkszgueZH5ECF7N32V7guCy21v7hZKCmTEZvi6U1PMoOoKdQsByr8m6UTeGyjcNxz+36UAxX+PRIhPL1JULHfbp+DplBu9akj0otXWz7i5083vc/VcLO21LseH3UIAcq/r3mNV12y9mHHAiz0iDvMm5fnLvRkj67/CO0RN69k/G3VuzmxRdB7X+dtoVW+NjlQ4XGQSuLGZYIm+knHcjlQ4XGQCOOulYIe+9zGuVxFN2st4T48vu4rfn7/qs+4Gq7cx32b8MZmQbO9ccm7CCdzqwBvtizG9z1UhTiDq358U77Qk9276nO0hqgichi3LfsgTywyg+sWv8UMqJXedjlQyeK/fomgLn/cvlwOVArxv7DpW16JyRqxpp7cIFSL/7H+Y/TGciscizwb8U6rENL72+rXFf3/fscCLHCLFcY7Vr2owP9k0/voiPTBnwjhkfU5Cj71/71rX+CxsiHYLAcqZESPf3jDP/n5PPd8OVAhCyZDeGGzGOOfd3/Iq3xZ64l14aNOoZb9cdeLCOSt/m0MrcQCj6DFf9TxIK+6ZG2Z7xNsCi1m/J913KaYvz/0PgF/vAOxlBc/Sjca2fk7r/smnr9085INVMRvE8eKXqG67A6/JwcqZMm0F5s9N/FzT/AxXlXJWiK5Ce7A/fw84r8D6XRu9SYZ/x6xsPjeGd91Cv+J6OtAXNCytzsbWFn5t21gZeXXsDQ5SCULBCly+nGxvFlgmXQXMrxEWnAMiOrsR6poSyiNVFpcRJLJDinGzKk1J6QaBYlUYVsGcelctLKipC5rEE8J5xFlrEos0WQXr6Yk0sXL89FkDzTqWNEx5NTiqQBCRVtCpLYrVkSCiR6+i1OovSYEjkCijxMP80mTAek4b6JHQc+klRC/dJfpTRDVVYnFl3AjmUkinFJuIZD5Ex6uj5FPaeb+yyQQToXRFyvYtkIGnoT4HamN7qIL1ZrJaCuA7walZWw6Lrs90BX18F21jJ+L2Ym7/O6oV8EQIeuJ+jhIITpsofXG/LBoEkXHUKBCNUGyqwf5+Hui4nek7ZZ8/ETn7Yh6c20qlcy2SWcItziuPeJTMJ0If1dUXPxptaUQS2fUz0XaSJm40LqjRJEtxk9UZqIv94efjhHH9oNfqpHCbQr8dJxfLotfiL8nJvVJAT2Z+z+W7f9cEJ+1vpift2gy/fR/MBmBO+4tHj9x8Z3ccY80xnPjpy+eHcduaW6k8o4TY4tWVgrHP41xMn9CSU8mo3lDRSJjUo2kfAsle5HJmPudvyS7EeNyBYoWRCVfEWdfofQ/8ZRYLUn243+SUpso1ZC/daVBms+1Jf9ZvC01YL9t+4+trNxyyy3YddddYTab4XQq9UC2ZHQncO2116K6uhomkwl777031q0TPP3/ZVPpaesgm4BGS5caqPQ78BKnWjchr00NqGxQa0dAo23kEvy5n0ADtWYQVOpS6HUTSPJQ0WbgzwBMhp0Lwms1zIYZ/MxqoPfk7wWnYTMIxoDDRH9VCuqyw7gTPy81Tsv7LHKlGjgME5m67DSMyaM1q6FVW2HTD4VNVwujpkSmRdJ7rLo6GDUulBlGSuKHWVqwBlUmsT1RYx5XpKxcbxFVMxss4xXUZXLW9WbBChhiob/59SlSGGQRbI7h1nEF9Go1Gs3DJQ2VHK2TPosScEk7pcJQDrvWLrfR3wpDBWxaGwZbGqBTaWXGBrWNtgkq5Wj7MMWFjd4zXqp6O94xTHFBJPzjHIKVsYNruEL7lrVrnIKxtKNruEyP5dGjUmOMo5G3KEbZ6+TkTHoPJeAOtlSyinOJ3iofR+8hSi2VeB9tr+VjVXlttAVENqmkUXGRpeOnlgomxpSyQQXU3wwmlwoGDVXGLcQ/pVSwg6aVD5bpyVn8E111vEU01km04Gw/qjiBlbR3SOOo1GBR4G+wlHBS72hHTQF+FaaUDNoy/jKBf3LJ4CL8O0rHTS4ZWoR/kku0TSkZVtT/Yx0NjIHYXur8/tcYMchShWpTGZw6m3wcjZEaYxkcOiuG9DN+xtiFXtgI23AFfnrPGLtg+Qy3ji4a/8NtRBUGhljHFY3/wRZx3CDrDnnjn85IjDbSE9Oj3DAyb46qoVdbUGIYLM3VwvlbzyX27YaxLMmRP39LJF9hM0wp8j82g6hWbO7H/5j4NUDHlbnzf4EktFmxQmYJ5fCzv9SLKs3bow0wgf7HgpV4PI6jjz4a55wjys7/FLvjjjtw33334ZFHHsEPP/wAi8WC/fbbD9GoMonuf83U9hsAA+UR6FkfSO18ACqJimdwPQq1ji7GOqg0jTCWPAuV2sGsIGfpC9Cw6KEOWt04OHkPVw2ttgYVZc9CwzLpehgNu6G85D4pWJmMqpJ7OFdFBSPs5iNQ7hDVJV3mQ1DjuJx1htQqMyrt56LMKvan6x1/QLX1d6wnpFU7MbTkOrhMu3Hb6LJrUGGexQ7KoCnDDhX3wKoXF9JpVXejxDiB66uQg5tR/SD0GhuzgvasuQ92/SCmKLsMI7Bn9T2M36KrwL41t8OqJc0jHWrNkzGzWiizVpvGYN/qyzlXhQKakfa9Mb1c5MCMse+BPcpPgUFtho5o0qVHY0fXgdy2e8WR2LlkP+hUBpg0VhxU83sMs4kA6PC6MzDWPpl1huxaF04edDEqjIJ6euaQizDIQjVjtCgzVOLc4X+GWUvMEz0uGnEZqoxU44RqZzTgT8Mv4QtTqaEEl468AKV6F9dlGecYjXOGns7nG2EbjAuGncy5KqTrsnv5zjihQeTA7F4+GSc1HgSzxgij2oCj6vbBAdUikDy+YW8cVDMdBrUONq0Z5w8/ElNKxIXowlGHY9fysXxxJF2YG8afjEaLoLvfOvEkvnBSfY9acynunnQ6bBIr6P6dTsMgawVTZEfYqnHP5FMZf6XJgXsmn4Qqo4MZNjuXDsWNEwWldIKrHjdOPIJzPYjVc2DtRFwwSlB/D6gZjwtG7cUBhVmjxxnDdsMxjSIH48zhu+GYQTtx/gjpF/1l/IHYtUIEYldPmI09K0cyfqJZ37PTMRhiE1Ts+3Y+FhNdVPNGg3pLCR7d5UTW0yH8/5h2Mr+PCrSNslfh4aknMv4qkwMP7HwCqkx21iOiYOSWSUfI+G/aIYd/dt1E/FHCv1/1BJw3Yh/B6NHoceqQ3XFUAwX3wClDZuLw+qlMP6bPv3T0IZhaJgLQi0cdhhnlYxh/qd6Om8efJPf/jeNOwxh7I/dxjakMt008i5lc9NvfPP5clmCgtqHWWtww/hxp/Lhwxehz+S+Nn/HOUTh/+Kl8vmHWofj9kDM4V4U0fnYtnYaj6wV1eYprFxxSc7RgtKkN2LfyYOxWJtSb96w4HNNK9pXH/6E1Z2CENP73rToXw21TWTHZqnXhiPqrUGoQNVgOqL0JVaaxPEftuhocVHcH63nR/N2j5gHY9RSEauE0jMDu1ffy/DVqq7Bj5UMwaqopvEGJaRrGl98m3RDtiMEld3GuCt2MlZoPQ73zctFmOgyl9ivY/6hUFrhs58NhOVn4Lev5MJhP4vINKpUTFsfN0BuFpIHKfh1g2Ev2nyrn/bL/3O6MVv1+6eM3av9x6vLTTz+NCy+8EF5v8XZIvhGMmpoaXHLJJbj00kv5NaJnVVZW8jmOO+64/2nq8oAN2IAN2ID9/2f/Tery5KNvhraANv9zLJmIYuGrV/8mr2//MzkrTU1N6Ozs5K2frNGPO3XqVMydO3eLwUosFuNH/qD4v7BMYi0n1UJdApXxAEXNlFR8AdLxxVBpG6Ax7CNnuXPdk9gcLjtNFR7FUql0vkwKkej7nL9i0E+DXqpSS0aUwGD4HaQzAZiNs6DXiiV+smTKA1/kfT7eYd4fOlZAFRZLtsIT+Zyz+0vNB0KjzikUB+Nr4YnOhU5dggrL/rwikjVPdBG8sWUwa+tQYd5Tgb8rMhf+eBOchpGoMOUKohHrpyU4B+FkDypMO6DUOCqHMR3DhsAXSKRDqLdMhUOfk7SPpHxYH/ia6ZFDbTNg0eZqxfjiXVgfnMfFrUbZZ3BNiax1RZvRFFzKzKGxjum8kpK1TaE1LIBYqq/EGPsUBf7VgSVctpwqhA635YrOUX7Jj54F8CY8GG4diUZLrqAY5SvM65vPtTImOMdxRdysUbn+ee4f+fidSibBqc+pyHZH+7DQs5zvynct25HFE7PWHGrHEu9a2HVWzCibpKjZscLXhDWBZlQZS7FL6TgF/vnu1dgc7sYway12cA3LjblMGl93L+Pci4nOIRhhz/VxLJXA511LmD0zrWwk6sxleX0cwpxuosSnsXvFOLkgGllHxINve1ZznZJZlRPkgm5kGwKdWODewFVc96oar8C/1NOMFb4W1JpLsFv5aAX+7/vWsuzASFuNomge4f+yeznn7uzgGoKR9tqfhj8Rwlfdyxj/jHLCn3PoXVE3vu9byatbu5dPhFmb6/9NUv87+un/1f6NWBvYhEpjKXYumaDAv8S3Au2RDgwyN8gimNnxs9CzEJ64ByNsIzCoYPws8nzP7J8x9okoN1bJbaFkAMt93/NW6Vj7zrDpckrtvng31gXmca2U0QXjvzfWhJbQIpg0Dgy376EY/92RZeiJroBNV4N6y24K/N2R7xCMb4TDMAplJrHVI9pS6A1/jFiyi7eLbVKRSP5u6Qi84XeRygRhN86EQSooyb9byo1w5D3GbzYdICkwS8clW5GIfUp0SeiNB0GV53/IfyI+l/0njPsX1ZzaXmyADbQdrKx89913mD59Otrb2zlnJWvHHHMMT66XXxaZ44V2/fXX44Ybbih6/b8ZeaZjXyLtoSqstF+eAXQ7Q1PyDE+4ROg5xP1XyYlnGtMxMDju4O8U8t2IaOhRuc1suwIm2/l8oe51n4Yos36EYyl1PQyz+VCk02G09hwqZd3T/rQeteWvwGTYCYlUF9Z2zkZSYv1o1A4Mr3oXBm0jwvFVWNF5lFT7IAOjdhDGVr8FrdqOvvCXWNp9jpRLkoHTsBN2qHqKA5Zm/8tY0XeThCODOuvhGF9Gas0qLO79O9b5npPbxjOt+TTGP6fjz2gNfS3j363qRgyy7YNEOoq3Np+HvhjRk1W8dH1Q/d9RZRrH9SBe3HQu/6U2o9qG4wY9AIe+Bt3RJjy76RKp9koGLn0NTh18D0vcrwssxIvNN/MeP/3XaB6LkwffyA57bu/HeKPtMTmpcYprTxxdfw7jf7P1OXzR857cdlD1cdin6lC+0Dy8/j4s8S2S8w7OHHIOdiqZilgqhptXkfpzC7fRMv+fR12K4bZh8MS9uGrZX+VkXKqKe/O4v6DSWM61OK5a9jfEpMTMamMFbp9wOSxaMxa4V+CmFY/K+CnP5aZx5/MF8932b3H/uldljPtWTcXFI45j/I+sewuvtn4pt505ZDaOb9yL8V+99Gl817tSxn/NuBMwq3IHRFNxnLPgIawLtHMbbc/cu+NZGO8cxIm7Z/xwP/9VSVVZH9/5fN5+otokZ817mI8nh0H5MU9MPZ+3pL7rWYPLFj3LCa303yTXYNw/5QzG/3rLD7hj5Zu5Pq6djKvGHsn471vzHl5s/lpuO2f4/jh58EzGf+WSZ/FNzyoZ//Xjj8feVRO3ip8SX8+edw964xJ+rRmP7Pwn1JhKsTHYztRxKsRG+GtNZXhgx4t5S2eBeyVuoP7P5Pr/lvHnMf4PO77GPza+JGPcq2IXrm5M+J/b9Cre6/xEbjuu/nAcWnsA479//f1Y7F0s46fKyVNLpyKejuHva25g1g9nh6m0uGD4XzDEOoITvx9Ydzn8SUrwVsGsseC8Ybej1FDJtYWebbqMj8+O/9OH3M3jf1NwHt5uvUaev7WmCTi84Q4+9xrvG/i+5055jg6zz8auFX9h/Mt7/4YN/n/KbaNL/ogRztN5/q7oPhd9EWIbCfyjy+9GhWU2Byrrug5HNM//DK18CRbDFCRTXejo2h8pifWjVjtRXfEBdNpGpBKrEOg9HGDWYQZqzWBYy9+BWu1AJvYlMlzeIec/VVzNW7fdraxMOfKXr6wseO23ubLys3JWrrjiCh7kW3usXp2jFf437Morr+QfLvtoaVGK6P03LO0XUupy4llinpiAmTTifkHrkxWUI68gk1zPmfAiUMm1hQN3IJOOIB6fJwUqkPlqHp+gJwcib+XRA8lFJtDnE7Tg3sBTSKZ65WNS6QB6JNXlVu/9SLPCqsAYTW5GT1AEgOs9d8iOjswbm4++yFeMf5X7zjwcQGvwDS4WFUn2SIFKrm2Z+yEk0xF0R5dKgUoO/4IekXOzIfCZFKiINqqOO69HqLgudr+BMDtq0RZLh7DQLSjQ3/S8wMJsMsZ4B5Z4RR993PkUJ71mExebwys4gKGLxrvtgoKZbVvg+QLdsTZmUlCgkt/2XsfLfDHYEFzHgUq2jf57teVF/vcP7nkcqGTbiHH0r1ahuvxh5+fw5RWQI2Xnd9sFxldbPlAwSDqjPfisS9Azn2p6U/4csuW+9VjoWcn4H90gKLvZto87f2BRPGISUaCS3/bkxvf5Yr7cu4kDlXz8D60TFNhPO5fwhV7Gn07h0Q1CqfvVzd9ypdbsr0bKwi80C+rykxs+4xWN7J1NW9jNytFk9695Xw5UyBZ5mvBd7xrGf+9qZR+/27YQm0I9zE6iQCW/7R/rPmL8S73NHKjk479/7bvbxP9ayzdK/KkoXmoW1OXnNn3ERfqy+NsjffigUyh8P7HxTTlQyfb/Aqn/n5Jo6dm2z7rnojXSxYwfClTy215ueZOD2fXB9Ryo5ON/qeUl/vdC91yZnkyvE62e6PRk3/W+zxWas+M/mgrjq563xPjvfhGJgvG/2CPG1tfd/1DM37bIUjQH5/H8nd8r5l22bb3/PfgSzYgku6VAJde2yv0Az19fbKEUqGTbMtjgFjkrnvBbUqCS/QYJdHjv4H8Fgo8jle6Rj0mn/fAHHuK2SOAequkgf1Y61Yx4WPRJxn97kf9ETIy7ARuwf2sbiPJJTj1VJIptyYYMEYmZP9eqqsRSaFdXl2Jlhf69ww4ikaw/MxgM/Pg/Na7iWLBAxQWOKDNeWYWVm+j96f4KHpHyMpXY9/d/DC/B+gvogWmkMsLBpbgt/7wiYOE2fk8+dVAltyX4rxJ/Mk0l71NSgKM0en+63ziXsESZvlx8jLiIxFNBBXWTnGxcaqPgRIk/Lb1G9OpgkQBiTKIlR/lvAQ02RSXvU0golGWR9/7i/idMFKwQfbnQItJr4WS4AH9Gfn9hNVLRJl4LpkJFLKKQ1BZKRoposPQabYUQxbrQiB7bH346v5AIKKYLU+CRPVZJnc0w3Tn7HkXvZzLycf5kRIlfpUJQPme0H/xRxh/fAv7+6n3R+YnS/O/jl86bKcZPFHCFgCCtbEpt/fU/BZpbwh9KhqHuZz2ezkErZ/2Pn4g8jorHjzSOuahbPmMvIxc6jKT7Gf9Sm5gjhTRwGm+pAmVlYTQ/Nf0W7BDzl+Z+oWVf69f/pIX/SfN7lP5HvEaN3iL/k8m2sa/sz39uf6ZKi8cvOf63aj9rZaW8vByjRo3a6kOvV2pP/FQbPHgwByyfffaZYumMWEG77CLob/+rpjLOVqiXQmWGSj8NKpUeGgMJ2WX3vzVQqWug1o2BWjsIau0wRZtWNxkqtQt6/RSoVbRXnaMMmyTRL4txlvR67qezmUSbw7R/EXWQ8lbISiTRQgmx2O4xCaZBJbfl8GtUZriM03gbqJwZQzl6o1FTBbt+JNMcbTpSghUY6W+pYTz0aicqTBOgV9vz6JRqNFhF1VXKURHH5JzaEJsQ+6MclWyNiayzHmYVbBrKUcnHT457mE1QH8c6ZuTRLImeacQgK+VN6DDKtqOMgyikTl0pqk2DUGaoQoWBlJBzbYPMw2HR2DDUSn8tCsrzZJfYz5/gHM+Mj3xV3Z1LRNtOrkmKWhiEf6cSEWjvUiro2dnzcZtL0LJnlBHGbE/R9pcBE5xEvdZi55IxCnpsucGJIdZa3saoN1co8BNrharljnUOgl1rVuDfo0LkPFGOh6YA/6xKQRPdvWJsEfWaXhPvGa/En8lgRrmgzu5dNUGBn5g4RBUm/LuUjVTQkyuNDgy3VaPOVIpGS3mOlq1SY5yjgfV3xjvoe5gVlOE9K8ZvE/9u5eOL8O9WLhSBKUclHz8FMruUiu+2W/mkLfb/ZNdYRf+X6V0YbKlDlbECNcYqRf8Ptw6BTWvFMOuwovEzxSXyuShHpXD8TJIECSlHpZC6TK+RUY5K4fgfbhNtI2x7KMa/TmVCnXkH3mKtNe+ioCebtRUoMQyHhZXTByvmr8tA89YJh4FydpwK/1Mu+RGbaU/pmJz/cZoP4r+Uo1Lof8RrgM40W4Gfvp3OKOUnGg8s8p/Qiz7Z7mygKNz/Xs7K5s2b4Xa78fbbb+POO+/E11+LJd9hw4bBahWJVRTc3HrrrTj8cFFi/vbbb8dtt92GZ555hoOXa665BkuXLsXKlSthNBr/Z9lAmUwC6eA9yEQ/A9Sl0NivkFVD6e4h5r8J6fgCDlD09uuh1gpJeNoKoiq2qcRaaPUTYXHcALVaJNQlEmvgobZUB4yGPeBwXMW0Y7Jw9Dv0+W/nuxyb+TC4bBcw5ZDME3obPf6H2ZmV2U5DqVUkJtPP3Bl4Ej3BV/k8dc4/wmkSCsdUPKrJex96wp9Bry7FsJLLYTeMl1dEVvXdAU9sEczaBowtvQJmnaBF0lbQot474ItvQIlhDHYouxQGjaip441txPyev7NmULV5KnYsOxdaSWCtPbwI83oe5xWVYfa9MankBBn/Wv8XWND3CuOf6DoM45wH5CWTvoml3o+5fP+M8t9hmE0ECbSU/kXXC1gTmAeLxoF9qk9FrUnUtaCl9Hfan8Gm0GqU6atxSO1pnANARltB/2p5Gp3RVjSYh+KIupNh0YqE0vZIK17e/AI8CTfG2MfhiLpjuOw+2Sr/arzW+gbfQU8rnYqDqg+UL6xze+fj7faPGP9+VbOwZ8UMGf+7HZ/j8+65XH796PoDMdklLqS0lfF883v4wb2Ma3ecNvgwDLeJMUJ3///Y8AZW+ppQYyrHOcMO5xofZLQVdP/a19Ec6sRIez3OHX44HDohntgU7MQDa9/iBNspJSNw9rDZsgjfj+4NeGzDh7wisnfVDjhp0J4yftpmeX7THL5QHlm/Kw6u3VnG/9Lmb/Bu2wIY1XqcMXQv7FoukqZ5K2b9J/i6exXXeblg5IEYLalGE/57Vr/HWzt15lJcPOpgTrQlo62gu1e/hY3BLoy21+GiUQfDIYk/bgx24r4173DBtp1Lh+Pc4Qf+JPyfdy3Gi81fMN7D66djdo0IaOnfr7d+iQ87f2D8Jw7aD1NLx8j4n2t+D9/3LYdTZ8UZQ3L9TyssTza9htWBjag2luOMIUejyij6n7aCnm56Ea2Rdgy1DsLJjcfCphO+rS3Shhdo/MQ9GGsfi6Prj5bHz7rASrzT/iqvshBdeZ+qQ2T8S7zf4svut3j87Fp2AHYq2UvGP6/vLSzxfgKd2oDdyo9XjP/ve57GxuBcmLRO7FZ+FipNItk3ngphfu+9nGRr19Vh54oLuUaSmL/dWNZ7OwKJ9ayyPr70cuil+RuKr8N69y2IpTrhMk7HENdl0EjzNxidy1s/5H+clkNRYT9fnr+h8FvwBR5k/Hbr6bBZfifjj4UeRzz8MsuHGG0XQcc3XsJ/ZoL3AtHPBUHB/uf/quryfzNnZafDfnnOyvw3f5s5K/+xYIW2iyjoKLQvvvgCM2fOlJeSn3rqKXlriaBcd911ePTRRzkhd8aMGXjooYcwYoSoh/BTbIC6PGADNmADNmD/i8HKzof+8mBl3lsDwcp2Yf9XwUomNgeZ2Fe8jQPzyVz4jV/PZJCMvIF04keoNQ3QWk7mgnCiLYFo6J9IJTdAqxsDg/l4+Q6FRMFCoSeRSnXCYJgOU94yKrF9/MEnkc4EYTEeCJMxR3mOJdbDG3qRk+uclqNh1OfouMHYj3CH3+HPr7CeCIM2Rwd1h+fAHfkSOo0LNbZToNPk8HcE34YvtgQmXT3qbb/jglKMMZNAs/9VBBNNvDXUYDtCxk+05PW+l3n1hSjNdVZxh0gWSfZhlfdf/J5G60xUmXNbJN74ZqzyUlJmGiPs+6PUmKOzdkRWYq1/DrQqPca7DoZdl6MMbwjMQ1NwAUxaByaXHAKjxibjX+adg9bIGrj0ldipZDZTP7N3pPP6PkJPrA3VxsGYXLKXfIdLtNJvez+EL+HBMOtYTHBOVag4f9XzMb9nonMnBeW5M9qBb3q+5JWJXUqno84s7tDJ1gc34Ie+eVyQbq+KmSg1lMpti73LsMS7DFatFftX7QWL1iLj/6pnHtYFN6LSUI79qvdg6jOPg3QKH3V+zQmftDWxd+UuMn4SVXy3/Uu44z6Md47A9LIdFCrC77Z9hXAqil3LJvKWR9Zaw134sPN7Hj97V+6MwdbcGFnl34Q53T8y9fegmhmoMOZotT/0rcR89ype2TmsdndmEmXxf9q1AKv8zag2leLQmhnQa3L43+v4Fi3hbt7a2r9qqgL/2+1fMdYJjmGYUf7T8X/SJXS5ZlVOxSBLDv/aQBO+6V3A/bdf1e4oN+Ro8Ys8y7DYu5xXRvavmsVMriz+7/rmYkNwI8oN5dircpbc/zR+vur5XKK+N2J62R6K8fN174fM8KHxMzFv/JDGz/d9H3Be1RjHNKk6rbC+WCsnztLKxHjnXqg0Dv5J478lOBet4e9h1DgwxnkUDBq7jH9z8H30RZfBoqvFMPsxivnb4n8ZoUQTbPpRqLMRS0vgJ1HUjsAziKe64TBMQ6klt42cSHWjL/g056PYTQfAmud/SCk+FH6B8VvMx0Cvy82NZPxHxCNvs/+hAnHqPP+zJf+53QUrh9z0y4OVt68ZCFa2B/s/2QYKv4oM05MpXzkNaBqgKn0DKrUFcf/tSIQektpSUOt3gbGEBPRUCHh+j0SUhO5oDzgJg/kEWJ23C+HCnkOQ4Kx7ch5J2O3Xwmr7A1KpPrR27S1l3dM+bwqVpY/DYjqQA5WNXfuzTDsZ7VUPqnwbJv0E+KPfYk33SfLesEZtw7iqD6DXVqMz8ArW9f2FK1mSkzFpGzGp5k1o1Basc9+NTb7HpLYUXMapmFz1BJ9nQdfF6Ax/znvYVL6/wXYUJpZfy0l9n7eeBl98LeOntgmlF2Kk8yREU1683Xwyoiw7T3vvKcysvhWN1j3giTXj9eazkMqIpEZynIc1PIhy40hsDv2IN1uukPHr1WacMPhR2HTlWOr5CB923EPZNhwkOPXVOGXI/dCrTfis8xl82/ua1JZGo2U8Thp0I5/nheY7sNI/j3MLKFdgp5J9cFjdOUimE7h//TVol+il1HZQ9YmYWXEwgskAblt1JQIJKakQaZw55EIOWjoi7bhl1XVIppMy/itGXY1Gy2Cs8K3EnWvulnMVTBoTbh5/PUr0JZjT/TUea3qWczFoOlYYK3DLuKth1BjxfPObeLPtI66yS+yUsfYRuGbsH/k8t69+DPPcS/kCmcqksG/ldJwz7HcspnfZ4rvRFGrj1UvK4zh98GE4vG4v+BJBnL/wdnjjAU5GpbarxpzBF31iGV3w411IZJKiHoRKhbt3uAjDbfVY5FmLK5c+LMrqZ8A1Sh6ecjnn0HzQ8T3uXvOyjJ8qvT40+RKYtAY8sfFdvLT5M26jPBFSm7594h8Y/00rnsJ3fctk/AdW74I/jTiWNXYuXvR3Bf4zhxyKI+tmbRV/a7gTFy++A4l0knuZjr194iUYZm3AUu9q3Ljyfrn/Cf/dE6nSqwtfdH+DRzfm+r/SWIG/jr+K+/+VltfwXsf7cv+Pso3E5aMu4fP8Y+N9WOJdKOOfUbYnTmw8ncfPPeto/GyWx88hNSdiz4qDEEr6cf/aixFMeuW23zVejrGOaeiNteDJDRdK41+wK08ZfBeqTcO2Ov7X+t7FN123SbkkGd7mObTxCejUZizrux+rvU/L87fCOAW715A/UmFx95/QHf5Mnr911qMxtvwGTqpf1nEUQgmijov52+j6C2rtZyKZcmNd1z4S61D4n4bSR+EwH4BEYh26evaT/Q/5tcryt6HXT0Qi9g1CfSfkcmtUNtgqPoZaU71V/7m9BStTD/7lwcoP7/w2g5UB1eVfwTKhrEIyXaTSQGoTwNTlFBIyPZnaMkjHv0M6uRLpVCsSUaJcUqwoLm6x8PN8txKLfY8Ey6yn5bZAUKiXBiPvSHUMUnKbN/Ag//UEn5McBbWl+OLsDjzFbZ3+x6XPEm3EHOoNCcpti0/Qm8kp0WdGkk1wR+Yw/mbfU3ltGXii3yMQX41Ish2d4c+yBFJ+z+YArZYE0Rv5Ed74av78bNtqjzjPpsBniKSE8KBIplVhuVtQoFf53mFHTa/zI5PCco/AuMj9mkQCFW3xdAirfII6+r2kzEyOn87ribdhY2A+s4Hm9r6Z15bBptBSdEabWBhxpZ9UjzNyUuN89yec47IxtAptkSZuybZ93i3O86Pne15toZZsMu2nnYJW+3XvHA5Usm20OvFFt1Co/UiiuWbbiB3yba9QFn6rXagG04WXeqUz2sUrLfTvd9rF8XQxJPzL/WvQHGpj8cQf3Esk+qvA+HHXt5xjsdy3ARtCraKHpYTTV1vE53/ds4hF9fibZdJ86fhXi0hq/6DjO6Zi00U5Re/IZPBOu6CQvt4qKMApqY0YNJ92CuryS82fKvC3Rnowz72S//1qyxdyG2Fd7F3HNU+6Ym5827dUgf/9jrmc40LU4UL8L2/+dJv4P+r8lgMt8asJ/O+1C3r3Ox2fK/qf+mlOj1C9fqtN2f8d0S4s8orCeB92fqTo/1WB1WgJt6Av3ovF3gUK/N/0fsG/6wYeP5sU4+fTLjF+lnm/RSDpyWtT4ese0bbI8yGv1giExMRLY4H73W2O/yVuQUEWbWn4Ey1oCZFqegprvM8p5m93dD688bWIJtvRHf5UMX9bg68imQ7CH52HEN8o5eZvm09QkH3hd6Q6Tjn/0xMQqunBkNL/sJJ8SMz7WPAxhf8hdmM8/K+t+s8BG7B8GwhWfg3L5GfAZy0lTc5+uGbs3Po7JntcagvH9PdZGWTk1wqPywVCWaeTM1rVkNqy584/UnLOhWqu4lzCYfZndIGmIKG/Y/L/5rekt4ZDer9w7JmttCkt+1p/+NNbwJjFn734FB4j/qYUTA6yVBZHHhNFfHbuYpu92CmOy/R/nGgTdSf6byNKav/4xQW+uE35Wfn02Fwbf6YC4tbwq+TjklvCT7VL+lm45YCnn2Oy+PprS/+b+LP9l0r3hz/33fr7PB79W/hu/b2+tbEljx+IACsf49bGgfy9tzb+f+78zVAo17//Ecf1fz7xmT/H/+T7q2L/I79/i/5zO7T/cTaQ2+3GCSecwKs2JEB8xhlnIBgMbvX9F1xwAUaOHMniww0NDfjjH//IKz/51l9NtpdeEnV2fqoNBCu/gqksJyoVQ0lN2UBlrbVcsVa8LtrU2rGCuqwZBC3T89Ryu854AFd91OunQaMhRdscxddiOUX8NRHzxKZos1tFgrLDcrT0mlp6ZOC0HM9t5dYTpJFOr2uYllxqFpTnGrsSv15TAZdpd6hVWtRaj1Dgt+lH8/62WVuPEuNkBf4q817Qa+woN+0Ii7ZOQVEeaj9aUlaeCZ3aomgb5RBCbiMc+0tnUkvLzxmMcohcnfFO8ZcCBWojWuYIu2AzTXIdrFCcpRL9g61E/9ZgBxfRI1VyW5VxCKpMg1Gir8IgCylKC/z031j7NJi0pJo7mkvzC+qpwLhr2b78l7Z7jBoTt2WDlt3KBAWTclR4Iua1zSjbnf/uWSGJtnErqVdrMa1EMG32qdxT0ebUOTDROY63HmZV7KpoG2SpwyBLPaqM5RhjF2rB2f+mlewAq9bMFViJsZKPcXaNEK3ctWwCCy0q2wRjiXJU6CXR+xTMAvtVCQrpgdVKHFQ5dmaFyDU6tFYcLyqyqlmMkUQaNWoN9qsmqnp2VKpZ8I8epFA83jFURkH/n142gXNd6PVqGb8wypHZFn7KUVEV4Kc8HrJ9q2YU9L8GM8omS21b7v/dy5XHNZgb0GCuR7mhgmUYsr81/TfJOYVzjfobPzPKhNjiWPtUGNTK8TO1VNCCJzhFXlf++N/Btc82x/8Y5xEKxXSTphR1Fio9oMVg2yGK8e/Uj2TRQmL2uVhBOTd/K8z7cK6a3bAzDMxYzM3RKpvwEQ7TAUX+p9QqxAot5kL/k4ZFYgPpzScq/A9UOuhMh2zVf26P9ksUl1X/BeVlClRWrFiBTz75BO+++y6++uornHUWVWfv36jiPD3uuusuLF++nCvWf/jhhxzkFBqRaTo6OuTHYYcd9rOwDeSs/ArGXRh5javWQu2CynoOVBpR2I7uOhKhx5GO/8jaQHrrBbnk23QYkeB9SCbXseqyyXouK5mSpVI9CAbuQSrVBYNhV5gtp8rJb/HEBvgCDyGd8cNiOhhWs5j0ZOHYQniCT4v8EssJsBiFfDuZN/wpesNvsDZQle1MmPWjcxo/wX/x1g9pAzU4z4FBWyPdtSWx2fe0SLDV1mOw8w/QScl7yXQY67yPIRhvYm2Roc7TWUmZLEpJtJ4nEEmJBFsKVrL4ffHNWOF5nqnLg6yzMMiWS77tiqzAcs8bjH+0k2iuueTbjYG5WO3/lLWBJpUchXLjkLwk2o+xMTifE2x3KTsOdp1Q/c1uBbVF1sCpq8LuFcdwiXLux3QUc7r+he5YK2pMQ7B7+eFcm4UskPDi067XufT5UOtY7Fq6r5w82R3twKdd7zL1dJJrKnaU6mQwxuB63vqhO+jdyvbEKLvoY7JFnsX4ru97KcFzX77oZfF/2fMtb/1QgudhNbNZ+ZnHQSaFd9s/4+TQSmMZjqw7gEv0cx+nYvhX64ecVEoJtkfU7QOdhN8T9+OVzR+hL+7jBNQDq3eT8beFu/Gv1k95y2W38h2wW/mOiiRa2vqhLZQDqnfFxLzk1e97l+Oz7gWcYHtE3Z4YYq2R8X/UOQ/z+lZynZffNe4jJ9/Sisa/WueIBFtjKU5o3JdL3Gfxv9D8CefKUABzTMPerHycxf/i5o85QXiCYzgHKz8F/xp/E97rIPxp7Fs1HROcOSbhfPdSTlYmCvHBNbM48Mvin8P9v5zrpBxee6Ci/z/s/FhKsC3DITUHy/0fS0XxQefb6Ii2o8HUiH2rDpL7n8bPx11vSAm2YzC9LDd+emPt+Kr7DS7qNs6xK8Y7c3O0Lbyat34I/6SS/THIMuEnjf91/vfQGvoeBo0DE0tOhlVKvqX5u9b7PNyxZbBoazHadSarpmfn70bvP7giNd2EDHGeycrrPDdSPWj1PYh4qgsOwy4crGTnbyyxET2Bh7iopMN8EJzSTQ+3xRciGHxSClROhNGQ+26J6CeIh19nbSCj9SxodKO36T+3t5yVabN/ec7K9+/9Z3JWVq1ahTFjxmD+/PmYMkXUBaLA48ADD0RraysLDf8Ue/XVV3HiiSciFApBqxXzmW7i3njjjZ8doOTbQLAyYAM2YAM2YL9Z+68GKwfe+MuDlfevZVmZfKy/RiX3J598kqvUezxZyRMgmUxyjTMKQLL10LZljz/+OMvg9PQQCQRysELBDokOU5X7P/zhDzjttNNkUc3/r1SX/382ynNA+J9Mv6OicCpaPdE2Sm0xJIMPSisrjdBZL4ZKIyir6bQP0cDfkE6sg0Y3DkbbxVCpxV1nKtmGYOBOLgqnN8yAhVddREXJeHwlvIF7OBnXYj4EVrMQtiMLRb+BJ0BsnRSc1pNgNYllZDJP6C14Qq/xykq5/RxYDJNk/F2BZ1iRWUdLyI4LYdQJldh0Jo5m78Pwx35kltAg14XQa8RdZyLlx3rv/QjGN8BuGINhzvOgkfBHkp1Y5X4I0VQ3ykw7Y7jjFBm/N7YOK91PcjJuvW0fDLYdLOPvCC/Aau/LjGmE43DUSRVsyTb4P8U6/4dcXG6C63hUmEQFUnrvUs8b2BT8HiatCzuXnQKnXtAiSU/o+74X0R6mgmXVmF5+Csxap1zC/8vu59Eb24wq41DsXvE76KTCV754D77ofgH+RB8GWydgetnhvK3EGCOb8Hn3a5yMO8G5K6a4Zsn41wWW45veD/jOeJeyvTHGLrYasrow89xfM3V578qDMMgiVJLpvV90f4LlvsWw6ew4qPoIVEhKzsTsea/jHWwIrmeW0GE1R/B7+LdOhvFGG60atWOQuRGH1R4Cg0Y4rL6YG6+1vsmFy8Y6RmF2NSlpizvj5lAr3mx7j6UAppVOwcxysX1Ftty3Gh90fM65L3tX7obJrtyd/be98/FVD60M6XFIzb4Ybhss4/+gYw5+9CyHQ2fD0fWzUW2qkPG/1voh1gY28tbVsQ0H83uy+F9peQet4Q4Mttbj6LqDYdDoZfyvtr4Nd8zD+A+u2U/GvzmcxR/GtJKdsEce/pX+Vfi481PeQtmzfA/s4MpVrp3n/h7f9X4LvdqA/asOwBDr0G32PzF7Pu56E03BtSgzVGJ29TGwSv0fSYXwcefL6Iq2otY0GPtUUeFA0f++eC8+634R/ngfhljHY0b5YfL4IVHOb3te4pL4o+27Y4Izp8ROysmLPa/zasM410EYYp32k8b/Gt8raA99C6OmBONLzoRNL1btiJm3zvMYPNHFsOgaMLLkPBjy5i8Vgwwl1sOmH4PBzgvk+RtLtqPddzfiqU7YDbuiyn62PH+j8ZXo89/LReHs5kNgt+T8Tyz2DYLBJzjHzmI5CcY8/xOPvIV4+DVpZeUcaPU5/5MJP4t0bA5U6hKorX+ESptTqd6e7NdSXa6vF79v1qg+GYn6/hLr7OxERUVOJZuMVkZKSkq47adYb28vbrrppqKtoxtvvBGzZs2C2WzGxx9/jHPPPZdzYSi/5afawMrKr2CZ4IOiAiMb7cfaoSr/gCdezHMR0lESI6Nu1kClGQxDOdUR0SLYexhSiaVSMpkaWsNMWEuf4Rorvd0zkU5lWT8qmK1nwe64DolkC9q79kSGRcFEElqJ8zbYracgEluAzd3ZZTbxs9aVPw+LcSY8oTexue+CvP1rLUZUfwCjbgTavPeh1fd3Gb9W7cCEmo85cFnVcym6Q+/I+M26QZhc8xYf/0PH7+CLLZeSiNUoM+2GyVUPc/2UL1qOQDTVKyfrDXWchHGllyCUaMeHm49DKhOTk/8ml/8ZwxxHoSeyDB+2np2XRZbBXjX3osYylR31F5035Pb0VRoc3vAEXIbBmN/7T/zQ+5TcZtDYcMLgp7ia53ttt2O1/wu+eFGbS1+HU4Y8xFTmp5suQ0dkHeOgtqHWyTi28Tquf/Hg+gsQSLhljLuUHor9qk+HO96Ne9ZezKJy2bbDan+PaaX7YVNoLR5cf21e72fw+yF/wUjbRCxwf4dnNj2Yl9uhweWjbkG1qQ7vdbyJd9qFYB7lMpi1Flw35ja+cD6+8R/4wf0946e2SmMVrhtzI1+4b1p5KzaFNklJmypMcIzHxSP/hEgqiiuWXgtv3Cszlg6o2ge/azwWPdFeXL70BhZVzGrlnD7od9inaiYHFNcuv1ORIPyX0X/EROcYDlTuW/eEAv/tE/6COnMN/tXyPl5ueUfGT9skf590LQcl9619Ct/0zpfxUxBz58SrOLflmuV3YGNws4x/B+dYXDH6fMZ/2ZLrZNYP2YHV++CkxqPRE+vFFUuvV+A/bdAJ2LtyJtYH1uPmVbcp8F868iKMd4zDD33f47GmRxT4rx1zA2pMtVvt/39uehALPd/K+MsN1bh81G3c/w+uvwqt4Y3S+FFhpG0STh9yJWKpCO5f90f488bPrmWH4IDq0+CNd+HxjUSPz42f/arOw44ls7mOyqvNF0rIxTc4rP42NFqmbHX8L3M/gWVuwTqknBW92obZjS/BqHHhx+4r0RZ8n89FbRZdI/aoIxVvDRZ2HAd/fLnsf0pNu2Ni5T+4xsryjn24xkrW/1TazkCD62r2P02dsxT+p8J1K1zWUxCPLUBvr9L/lJQ+D6NxJuLhNxH25vwP+T9b+QfQ6EYgHXyAK4Dn+09N+YdQqXN1iLaXlZVdDvjlKytzP/h5KyskQEzV4be1BfT6669zIdc1a9Yo2iiAueGGG3DOOeds8zvus88+HNxQ5Xqdbsuq2ddeey3nsPwc4eGBBNtfwTJhQZ0VlgIyHiD2jSjDLwcqoi2TWo9MYgXSqU1IJRblZb2nkYx9jnTag0T8B6RT7XltGURCVJsFiEQ+RIZl1nPZ8kGpzU/7wewIsgqmKvhCgh7oDgll1yxDiYIIb0jQIruD+VnZKSTTbvgiX3PRqFygItrCiQ0IxlchnNwMX2ypQtCsN/Il4ikv3NEfEUl1KVgFzX5BQW4LfYlkJqpgKWz0C2XZpsBHUtKhSHsnp7wx8L5Uhl/8Fd9A0II3BgQtdoX3XUVbNOXD5tACpoGukgKVbJs7vhnd0Q1wxzvQHlkj46C/64PzEUkG0BxeCX+iV4FxoaRwu9I/H4l0LtAim9cnaLU/er7JE6kT+Be6BfX3h76cimyWZbLII6iz3/SI7yF6Mc21XFb6lzENOhuoZNsoP2JzuBnd0W5sDG2UL+b0niW+pQgmg1gTWAt33K3QKfqiW8hdLPAsYbG9fFG/z7u/kXD8IOMXF2cVr6SQzen+rgj/3L6F/O9Pu8TxWYyBZBBLvau46Fs2UMm2tUU6sSnUwqrT64Mi0Mqek+jCwUQIq/3r0Bf39It/oWdxEf5s23d9Svz0PEsP/7bv6yL8893zttr/NH6ygUq2rSvWhtbIJvTFO9ESXp83fjJYHfgRYRo/oZXwFY4ft6AZrwvMLRo/i71CNXq1jyjYyvGTpSdvbfxvkOaPaEshlvaiI/wDz99soJJtCyY2whdbhUiyGf74EoX/6YvMQSLlRSA2H/FUh8L/9Eg+Ihj5qMj/+IIv8N9wpNj/RCR6cjyi9D90fDwi5m26H/+ZieXG1HZlvxIbyG63Kx5b2wKirR0KRrb2oK0Z0ubr7qYAFYptIGL8ZIWGt2SBQAD7778/bDYb56ZsLVAhmzp1KufB0LbQT7WBbaBfw6SKtMrX9FIsSF1coNyqMkCF/gYXuVf6kfsRg5QSb8V5C46R2rJ/89uyCXNq/rx8tdSM/H510XF0Lr3ESqBiUkr8dM7seYs/T9dvm4yjH/zZ1zTbbMuT1EVGrkSr6Qc/sSUk3kQRTZPatKr+JxMt1ffXln1Nq9IWUWCzOLLJlbkWyAm79Feptptro22hos9TUz+q5YJj+UafU/hZue+mga4f/Nn3EwspH78qr634nCroVFq5LR8/fYPs+7MVXZX9peWtgS3h128Jv1rDAoL9nS/7d8v4tcWzKYtfGg/5ase6bfQ/83/6w7/V8aOFpp/vRmMu97dg/EhjPDuO+j9ua+O//+PE/BUF35QYDf3OeeFLtFudv+QXfo7/yb1/y/5H9m2KQ/89QdzfyjbQzxUgpse2jASDSeZm4cKFmDxZbF9//jndQKc5uNjaisp+++3HAROtqPwUHb/FixfD5XL9rDybgZWVX8EoR0UYOUY1oB0BGGbyUq3Wen5emwpqwyyotKO51LTOlKUcCodksJwBldoKvWEadPop8nIpmc12Cf+1mA+DVlOXowBCBYeNlo4Bp/VUqEmxlF/XsKNwWX/PbZSjkjuGtnpKUWIVdOJa54UK/CbdSLhMlIehQaPzXPEdJfwlpj1h0Y2CSVuDasvBeW1Ao/1kaNUWlBp3hMswUd5uIhvl+gP/bbDuC4u2Snak9J6xrtO5bYTzSGjVJrmNHP9o57HcNqHkd/Lr9NeocWK4XZQApxwVgUOcr9QwBIOsRN3UYJcyQZ1US21DrFNRbhgCh74C4xwz89qAnUsPhUFjRqNlLOpMJNIngh2ymRWCAj7ROQMuXblEZBXU2r0qBPWaGEOk+CxaiF6qxW5lpChLFNqD8o5Rw6q1YVqpoDUfVCMS1+izqMdqTfUY79iBL5QHVR8iByHcD46JqDPVc6n+XUunKdr2q9yHK+OOtA3HcOtQebuD7Ig6cZ5dS3dCuaFUpuLSdzyiVmDct2oPGDUGGT/Rew+sFkwtylGh6rXZNpvOhpnlghZMOSoCh6DjNphrsaOLqL9qHFUnzp3FQeKNjeZalBlKsFvZzoq22dV7waQxYlQ/+I+S8O9SujPK9Ur8h9WKz9+rYhbn7OTwa7FvlciZ2L9KiE3K+LU2TC+bsc3+369KzNEsjrH2SagxNcClL8ckp6DXaqQxslvZbKa1EyW+3ky05tz4mVUpBEXHOPaAQ1cpoafPA6aXibYJzoM5ZypLXaaAY5LriG2O//Elv1eMf4d+GGrMlMejwQjX2Yr5W2HeHXb9CBi1Nai0SNRhaY7W20+BVm2F1bAzrPodFfO31nGR8EPmQ4v8T6ld+A8LMxaV/sci+R/KUcn3P7TFo2eqMzhHJd//QDsSKoOgZQ/Yf89Gjx7NqyO///3vMW/ePHz77bc4//zzcdxxx8lMoLa2NhYgpvZsoLLvvvsy8+eJJ57gf1N+Cz1SKRHkv/POO5x0S9Tm9evX4+GHH8Zf//pXrs/yc2wgZ+VXskzsB2TiXwttC9OxHHTw65kM0tGPkE4shkpTD435GKikuyUqtBSPvIp0YgM0urHQmQ6VE9UymQjCoedZmVlvmA6DMTd5Uyk3AqHnkckEYDbuD4MhR92kPWVfiJZV07Cbj4BeJ5I4ySKUmBt+hxNsS6zHQafJaYv4o9/DG/mKtYEqrMdDk4e/N/wxArGlMGrrUGU7ildPsvjbgm+ytohdPxpVlgNl/Kl0FJsCryGa7EGZaSdUmnMUxljKi43+Nzm3pdayB0qNOW2UYKIdG/zv8Z3vYNt+cOhFojJZX2w9NgY+Z+rmSMdsmLVC/ZasNbQYm0PzYdI4MNZ5EPSanDbNusC36IiuhkNXhfHO/TmIyNKal3o/Yz2WKtNQjLHvLuOnpfqF7o8RSLoxyDIew225Pg4lA5jv/pQTbMc4dkKDOUePpZyW+W6hDbSjk/RzcnS/1nAzb/3QHf0uZTPh0OW0ddYEVmGlfykHMUR5poteFv+P3oVoCm1Emb6M67bQRVjgT+Ob3m/REelEg6WB67Zk8VNOxxfdX8ET92KMfRQmOHN9HEgE8Xn315wbMsU1EcNsggJL1h3txZc9VP00gxnlO6PGlFv+3RRqxfd9C3glYlbFdLj0Of2WFb61WOxdCbvWir2rZnDQkcU/z70Y6wKbUGEs5eMoCMrip8+iraHBlgbsWjpFgf+zrq8FfsdI7FCA/4sewh/BZNcOGGbN4aeclm96xNbNLqXTUJ2HvyW8mbd+KEGY+tGpd/6k/l/qm4/m0HqUGMqxS+meivGz0P0lumNtqDENxg7O6Yrxs8D9MVOXSfsnf/yEk34s8X7IuVHDbdNQaxbq1WT+eCdW+ki1O4NR9r3gMtT/pPHfFV6IjjBRl50Y5jiMaxll8VOlaU90Gcy6WjTYDlfM347gGwgnNsKqH4NKy2wZfzodRXfwBSRSXbAbd4HDJAL7rP/xkm9KB2E17QdTnv9JJlsQ5m2dNMymI6DN8z+pxErEI0KbTG8+Duo8/5OOfS/7T5XpONl/bm85K7vuc8Mvzln57pPr/mNYacuHAhQKMNRqNY488kjcd999sFrF77Fp0yYMHjxYFiSeM2cO9tyz/8CyqakJgwYNYvozsYMoUKHxOGzYMM5/oaCIPuOn2kCwMmADNmADNmC/WfuvBit7/wrByqf/uWDlf9kGclZ+BSN6csZ/GxD7TFCX7VdBxds4VPjNi7jvGqTj86HWDoLOcQvUWkGZTCVbEPFegVRyLTS68TA7b4NaI1E+48sQ9P0F6VQHdIY9YHPcKAt7RaKfw+e7GWlaWTEdDof9ct5rJvOFXoSHtIJIddl6OhzWM/huiRLyev33wRt6CSqVCRWOS2A3H8THpDNRtHpugS/yMXTqMtS5roPVKJboKeFuo/saBGILYNQ2YkjpLTDrBP5oohVr+66RqI/jMKL0Rui1Ym/UH1uJlX03IZrsRJlpOkaVXslbRGTd4W+w0v131iGptR6Ika7zeMuHrMn/FlZ7n+G7vuGO4zDMcZyMf4XnaWzwv8N3luNLf48GSck5mY5hfs+D2Bz6hhkQUyv+iCqToKxGU35803U3OiPLYNfVYrfKS+AyNMp3sZ91/h198WZUGkdgVtWFXP2WrCuyDp92PYhAogeDLDtiVtW5LIxItiG4AF90PclFvcbaZ2L3ipNkWuoizyf4ruc1TsbcufRg7FxyEOPnVYTu17DQ/Rl0agP2qjwW45yiKiwxi97reBar/PNh1TpwcM1pGGQRBbPCySBea30cTeE1KNNX4qi636PCKGjZfbEevNzyGDqjrag3D8Gx9WfCrhOrBS3hJryy+Wl4E26Mso/HUXUnwyCtdqzwLcGbbS8hko5gJ9cuOKjmKHmb49veL/FR57ucxLlnxb7Ys3xfGf8HHe/g296vGP8hNYdjcslOMv5XW17CEu8iZtAcW38ChtvEalMoGcTzzc9ifXAtKgwVOKHxVFSbxGpTb6wHzzU/jfZIGxrNg3DioFPhkPBvDm/Ci5ufgSfuxhj7OBxTfxILC24L//d9X+DTrnd4vOxevj92L99Pxv959xuY554DnUqPfauOxkTntJ/Q/wG80/4PbA6tQom+GofU/gHlRlFMjpg977c/wNR3Ehs8oPp8WKXVsq2Nn+bgPHzX8yjiqRBGOPbC1LLT5PFDooRL3c9z/5N6Mj22Nf5T6RhWuO9GR2gO05LHl12OUuMkef6u6rsR3uhCmHSNGFN6PSx6sRIVTbZiQ99ViCTWwqIfj6Glt0CvEfM3HF+GNvfVSKQ6YDXujlrXDSxsyr9p9Av0eoX/sZkPR6n9Mtn/hEMvIsRaZWmYrafDTFvbEv548AHEwy8L1WXbxdBJSvLkP9P+W5GR/KfGfrXsP7c3Yy7UL8lZwW/XBlZWfgVL+64HIi/JFF7KQVGVfQCVtg6xvhOQjhPrIyWVki6BsfwLTiALMD25TW5T60bDVvY+Muk+uLtn5GXdq2EwHgx7yUOIJ1aiq3tfRca93XYRHPbLEIx8go4+kb+RtUrX/bBbjkRf4DF0ea9XDPlBFW/AbNgJm91XoTf4vIyftqnGVH8Og7YeK7pOhC+aw08Vbnes/Zz3o+e37c+CaNk2q34kdqx+A/G0G9+0HIAk4xfnrLLsj4kVd8EfW4uv2o6V2BAC/wjnWVz/oT30Fb7tFHvjWdu54iY02g7Eau+L+LE3S28U32Gf2n+g3DQR33XdhdW+LOtK5IocMeh52HTVeLflIrSHF8v0ZNrrP37I85ww+M+Np8Gf6GaWBLWVGYbg+EEPI5zy4YkNpyGRFqwlahtp3x0H1V7JNTKe3HiBzNggHNPLjsfuFSdirX8+Xtp8kwL/YXUXYYJzT3zb8w7e7xD06qydNfQWNFpG443WRzHP/YnMYKFchYtH3oMSfQUe2XATNgZXcvAjaMF2XDHqXt4KumXlJfDEe+U2yqW4dORfEUz6ceOKSxCTWCeEf0fXVJw6+Hy0hjfjttVXy4wZsgOrDsdBNUdiqXcRHtpwtwLjaYP+gKml0/Fp10d4pUWwPrJ2+cirMMw2As83P4OverL0cEoY1eKGcbeizFCOu9fcjrWB1TJG2ma5efwd/J5rl18Bd7xPbqsz1+Oq0TcgkPTjuhWXcYXbLP7Jrp1x5pDztop/ue9HPLYxR70mO7HxXOxUshu+6nkPb7c/q2g7b9gNGGwZtdX+f2rjdWgKLZdxUP9fOOIhTsB9ZN0f4OPxI9oqjINxxpB7tjp+eqMb8PKmPyjG/06lJ2Fq+anYHPwWn7b/WYFx96prMMy+31bH/5KeW7Ap8C95/NM2z151r/O2z8KO0+COzpfnqF7jwvS6D3n8L2rfB7Fkzv9QLtqE6reRTPdhdfvuSGdC8vx1mg9CY9mDiMVXYnP3fgr/U2K7EKWOyxCNfAKvW+l/HK77YTIfiVjwccT8Nyr8j7n0X9AadkLKdy0yBf5TU/YR+8/tbWVlOq2saH/Bykoyim9/oysrAwm2v4bFPlVQeIEYkJjPCqSkspyj+aWAdA/SydVIJzchndqsaEsnliOTdiOR+JHzUfJphbGYoDBGo18VKFplmM5MFo5+VrBYpkYoKmi1gYig3maPIecUjArqoy/8sQI/3ekEo0R9jMMXVeJPpHsQItXlRDOiyRZFWzC+Eom0B77oEiQzQcU5e8Lis3oic/Mu9AJLh9TWESbqr0aBn14jawvlqKdkdAFoDwtaanPwm7zzkRBeHJ2Rxazg3Bb+UUFPjqTc6ItthC/eBl+iI09gMY2e2HpEUj50RFYhng4rjtsQEBTephDRzZX4iY5Kti44X06oFBhVWBdYwM/prj3f6H1rA4tkOnQ+SyWZiaMptJKpy+uDy2UKr6AFe9ERbUZPrAt98W5FG1FqKZ9mU2g9oumIAv8y34/8fHVgueJCT7bEJzBSUTSRtJrDv8y3WLzHu6gAvxor/Msk6u2PCvyJTALrAmsY/+rASgVGf9KHtkgLemLd6I33KNqIkk204abQBs5Hyce/1Ldom/hX+hcV4V8pHbfCt7AI/xr/km30fwIbQ0sVOIJJL7qizfDEO+BNdCrauqIbEE75tzp+WkILi8bPRh6/lHc1t2j8t4S+2+b47wzPUYz/dCaG3uiPPH/d0e8VczSe6kWQVZebEUsq/Q8pLVPZgnCMjg0o5q8/IvxIOFbsf4JRoUwdixX7n5jkf5JR4b/y/U9SUlbO9OM/MwnlfNlujNYGfunjN2oDwcqvYZRUW7hAp6LlbB3A2fEFTWonP4qNsuTNUKtchUdAzecjaie15SupqqGWKlKquU1Ji9RIn6PhAkv5jjAlnQvQaIrxazVOplELdpHSKAlXpynGT45WozL306aCTtJD0nObEr9BwmFgrPm0VBX0arvcJkQHIV8ASAeFzMh/lfgNajvxQaDth1Zu1NhhkPSNCvHTUr2pqE0Fo6SnYtLYCqizavn99FfZpuL3k9EdeSF+s6JNid+ssfLWRrYiqrLNBrNGLMkXXoD1GiMs2uLkxOz7qa0Qv1Ur8BceR5hImI+MVkTyAwE6R/b91FaIn9oIv6Ef/NSWPW8hfsMW8Ft+Cn5NMX5zHsZC/Nm2Lfe/ltldhWbS2mR9KSV++r2MWx0/NPbya6yI8SPGsRiTyvFD43hb41/MKSV+vdrB85fmY6Hp1E4u/FhogoFngZb9QUGL9P7+/I9GvWX/Q8Ks/Kwf/yP7wH79ZzG+7cH+14UM/5dtIFj5FUxl+4tMP2bT7w4YBLNEZ79RMRE15lM5Z0WtKYfBmqUMCzPZr+V8Eq1+CgzGQ/Na1LA6buZnZvOh0OtyJdzp/U77VfzcZT0TWk1t7rPUpXDZBHWaclTUqpyDNehGyYrM9a7rpPouwuzGPflB+IeU3KDAX2U9BSbdEOg1ZWh0nKfAP6TkSmjURjgNk5gZJGOEGqNLBcYay/5wGXIl3DUqI0aXin4Y7jweZm2OvUHMhlEuoShNe/TZnBcyh34ohtoF9ZJyVLI5L2R15mmsOkv4Z1RepMA/znkknPoGWKgsf+lJCvy7V/wBWrUBNaYxGGWfqbho7FUlKNxj7HughmnNwqhGxp6Vp/HzaaWHwCEJKPJvpXVgepmgNe9VeRwMEsOEfw9jA6aUCLXmg2qIcp5z5FQJlR6E//DaMxQX0hml+zPDiHJTsrTarB1WexIzXQZbRmCyS9CKyegifXS9WJ6f4pqGwVKZfzKqd3JYjaCH71W5P1z6XNVQusDvVyXo6ZSjks0ZIaPKr1lF6WPrfyfnjHAf2ydgnGMC4z++4WQF/lkV+6DKWA27zoGDqvPHOFGgj2f8Qy3DGWc+/mMbTt4m/j0qDijAb8feleIzKEclv/+rjPXYuWTWNvv/oBqi3ubwTyudjXJDLaxaF2aUi/mTtb2rzuB8nq2Nn+H2Wag0jpHbKP9kl3JRmnys6xhYtDmGDG1ZTig5cZvjf1zpZRyYZ63CNJ3Zd4R/VOk1Cvz1thNh0Q/m3JQ6h5I6Osh1Fc9fs34KnHniqPQL1LrE9qbVfCiMeqX/KXOIuW2xnAlNnv9Rq0thkUo3GGwXUY2HXJt2JDOC+Lnt6gL/uQdUBqFSPmADlrWBnJVfyTLJZiA+X9wlSDVWspZOrEQ6sZSpy2r9rgrxpmTsB6SSRF0eLWtl8PkoIS32BZfc1+l3glaXU7+l7aVI9GOmDhoNu0GrzTkIUkIN0ZJrJg2LaS959YQ/K9XNWz+U4GYz7gu1lPBHFk1sQjD2A7RqFxymvRT4Q/GVCMaWwaCtg8OoxO+NzkcksREW/SjYubZKDn9v5GvEUj0cvFj1Q3P9kUmgMzQHyUwI5aZpMOUFKKQXRLkrdIdWbZ4BvXT3SBZJ9qEjPJeLwNVadmONlKz546289UMOvs6yi+Li0xtdh57oGs5hIRXnfPxt4WXwxDejzDAUVXlBCOFvCi1AMNGHWvNYlBoacn3M2xw/8FL/IMskWeGZjOioawPzmKI33DaF78KzFkh4sC6wiIt5jbLvpFg16Y118NaDRWPHKPuOCvxtkU1oDW/gHIph1nEK/BuCq9EVbUOtqRGNeRdxSihd5V8KH1FnLSNQZcqNEdqeoW0h2ioaaRuHkrwLPG2/LJW2dajWSP4qhy/hxQrfMqYuT3DsIOsQkXVHu7A2uAZWrZXbsjo+ZC3hZmwKNXEOyyjbGAV+2i7qjHZw7ZjBklZPFv8Kxu/l4KX6J+InOjnlrhD+MfZJCvz+hBdrAos5wXaMY/JP7v+OSBPaIuvh0ldiiGW8Av/m0HKmvlO+Sq155E8eP02BuYinQ6i3TIZNl9NjoaTblhBRr9Oot+yqWAHc2vgPJjajL/Ijr7JUmXdTzN9AbBV88eUwaetQYhRBvNwn0Xk8f0mB3VYwfwNRqmjbBYthCowF/ofy4zjB37AbdHn+h/TKYrzlk4bBuJe02iK1pbp564eKwWkN+8g6aHzO5CZkJP9JNVby8W9POSsz9rz+F+esfPPF9b/JnJWBYOVXtEwmKRVDUvXbls2Y/6lt4qdJ/RttYpk2K+mubBMJu1vC+N/ET/ki6l8Rf5pwbAE/tfX3WVtr2zpGLtquuKj9FPxUm0Nou6h+dlt/n0VGFVbzVzbyMVIuyM9to0CBLD/g+CkYCYd6K22a/3H8/27/b6lt2+Pnvzf+/3/3P9tLsLLbzOt+cbDy9ZwbfpPBygB1+VcwoidnvBcC8bliqdN+A1QmiRac3Iy45xxkkisBdSX0znugMYgl7lR8CcLUlmrlrSGT6x8s7EUWi36MgOcSZDJuaHVTYC95FBqpiFIo9Cz8vhu5cJzReACcrnuhVlvYgXj9t8IX+Aff2dgsJ6HEeRPfpVDSXZf7MvjDbzDbp9R+CUrtYhsnmfKgpe9cBGPfQK2yodb1VzgtQpAslmzGxp6zEUms5CJyg0rvg80othgCsSVY23MBYqlWmLRDMbL8IZj14g6sN/w51vReiWTaA7thR4wpvw8GrbiDbPG/hHXuO5DKRFFh3htjy2/lJW7Cv9ZzLzb6nuGVlQbb0RhTegXjp6TZH7tvRGvwA8FWcv0BI6QtoljKh7mdf0FXZD4Xw5pcfgUabftJqxlt+LLjCnhi62DSlGFG1Q2oMotl7J7oKnzRfg2CyU44dI2YVXMzC8ORbQp+iy87bkc07UOlcRz2qb0RFq24g1/ueRvfdD+CZCaGIdYZ2LvmCs51Ifzf9jyFhe5/cSLceOdszKw6hy9kJFz3Yce9WOWbw7kQ08tPxNQyUcEzkgrgzVYSJVwMg9qC/arPw1ipuq4n3olXNt+KrmgTbNoSHF53CQZZx3NbW3gdXm25A95EN8r0dTim4c+8vUS22j8fb7Tej3AqgAbzKBzXcBlsOpFb8EPfR/iw4xkuXDbavjOOqv8jb5EQ/o86X8DXvUT9zWBq6T5M4xX4E/hX6yNY5PmG8e9bdQxmVhwq03ufb/471gWXwag24/C632OSS1SH7Yt14Z+b7kJ7dBPsWheOb/wThlqFWjBp67zQ/Dd4Ej28tXJi46WoNIoiaCv9C/CvlgcZf6N5JE5ovBR2iRa8NfyfdT2HuX1v8vPJJfvhgOozZfzvtN2P5b6vGP8eFcdjevmR2+x/b7wDb7XehJ7YBli1pTiw5s9osIgViK7IanzYfiMCiU7eWjyw9nqUGAZtc/ys8b6BBb0PsEZWg2V3zKi6Bjq1mTEvcz+ENR6iLmcw1H44JpXT9q0Y/0t7rmetHxr/I13nYJjzdJmevLT7Yk6mpQq0o0qvQ7VV0IIjic1Y1X0eQolV0GsqMLLsbjhNwv+EYkvQ1HsO4qlWGLRDMaT8HzBJ/ocS8tvdlyCVdsOkn4K6skflIpL+4DPwSP7HbDwAZSX3yf4nFLgNkSD5nwyM5hNhpZILKg0n7Ue8f0YyQqw9HW8LGWznyP4z5f2j5D9tUNtvgNokth8HbMCyNpCz8itYxnctwPRkytYOIOO7FJnEOm6Le85GJimpWKZ7EPecIYKbTBShvpOQYcFCCmo2Iew+he9KUslW+N1nIUOCiFwIaBECHlGSOhb7AT7vFRKtOYNo9EP4fSKfJRR+Fb7A/fSpdBQCoafgJ7l2umj474Gf5NlJTDETRa/vFgSlDP82z5UIxoh1QAJvfrS4/4hoQmDe0HMWItLzRKoHG3pORzLl5Qq1q7pPQ0zCH0luwqru0xl/NNmGld3nI5n2ykHN6t7L+LknuhCr+25AKhPhz+sOf4Z17r8JHMG3scH3OGsRkZ5Jc+BFbPILuuxq92NoCb7PqxkUeC1334sOiSGxoOdWdEcEy4K2kb7vuha+2AZum9P+Z3hjGwXGVB++aL+UgxuqzfJx6yUIJYVwlz/Rgk/aLhPL34lOfNJ2LaJpP7d1U1DTIfq4PbwMc7ru4QsNfV5T8Ft8103OGVjp+wTz+17iO9w0UljifRuLPUJkbm7vi1jlI3pvmtkmX3Y/iQ0BUbL6w4770RwiUUgglg7hnbY70RNt5n+/3PxXdEvPg0kPU6NJbJEu1M8138CCefz7xtvxfPONvKrgjXfjxebbEU4Fpcq5a/GvFkF73RRahbfb/oE402ozzFL6qOOf3Paj50vM6XmDBfwI/9y+D/Fd7wfc9mnXv7DI87WM//2O57DKLxg2r7U+ivXBFfw8mg7jpc33oTMq1FSf3XQHOqPEOgEzmZ5quo2DG8L/VNPN8Cb65G2Yp5r+yvg98R4833ynjL8lvA4vb75vm/iXeL/AN73/kvHPd7+PH/pI4Rz4qudlLPd9KeP/rOsZrA3M32b/v9V6A3pjTWJ+Jd14o+U6RFJ+Hj/vtFyBYEKMH1+8Fe+0XLnN8dMVWYLve+5EUhr/LaGvsbD3IfHdAu9hledppKXxv97/Ktb5hMjfOs8/0Bp8Tx7/q9z3oCssxDFX9l4Pd5TGUgbJdADLey5HMC78z8rucxBKrBW+KNWLFd1ncXBDFWrX95yEuDR/Y8lN2NAt/E882YqW3rOQSgv/E4kvQnuf8D/R2A9w5/mfcPRDuH0inyUWeRWR4AOSFloS0fDTiISeFG2B+5CMvCGxj6KIBW5FgtmLVPrhmjz/6UfadwkyEubtztK/wuM3agPByq9hCaJO5oudpYHEMlEsLrlaQUFGJox0ch3SlOOS8eaNPlJkbuMaK8nEcmnC59SOE3FBz0zEFxb8bGnEeaIDUdrzLaQO8vupkJzkDGTTIhIT5wzHsnVUcueMxJewU4wmVinwU+2FaGIt0x5FMJLDH0u1IZHuQzC2UhJPy6m9+mOCQuqLLi7C74kKHJ7YYlmLJIvfGxPU2b7ojwVMCS3cUUE97Y1QHRUlfndsJVLpOLxxUsbNqcfSRcIXb+IVl1jaX0BL7UQ05eUcl3QB/s6IuBjT30JWBgUwZB2RlUXU5Y7wKilgEJTb3DfToC2ykp+3hFYoGCL0vo7oWl4N6I5tUij70kW6J9YCT7yLVwTy8fsSPUydbY808cU6i1/QgkXA2RJao0h4peM2hQSO5vCaIvybw+Ki0RRaXYS/OSTOuYnblPhbw+sZf0d0s0JZmfB3RVtZloCCkXz83kQPQkmBX4gH/jz8LeHVRfhbw6vz+liJX9nWX//H0RNrUvR/IhNBX2wzfIl2Dkby8QeS9Jtsffz0RJcXjZ+uiBjjvdGlBdRlFfqiy7Yy/sVx3hjNceX498eW8/wN841G/vwNI5xYzyumqYL5G0+1cY2VaLzY/4Ql/yP8iXL+0g0UmfBRyvmblPxPKi6CqZxpkZLaBE1ZiT+TEN97ezNVJvOLH79VGwhWfg3T0NJ1wZ61hqp06rkInJKWp4JKUyPpYhQcozJBpXZAU1QMSS2/ptHWF4TXGmi0YulfqylsU0miY4BO11hEHdTxuQA9H6/EotPUEnEY2n7w60mEkSvtKo9RE5NJ7YAxL+Eui9/A/QGYdNSWT93UwKwTGEkcUUnrVPFrZBZdvcKR0wXArJPatDWKCwCZWVvNxbGIUVSIn4QUSVdFeWEgZoYReo0NNp1SDp3ObdOJJXC7rqoAowYOfbXUVqloo9Zs8qRTX63ASBdgErQTbULYMd8cugrerjBzgqVyD58YR8RGyb8wk+lUBhjVFrj0FUX4nXqRBOzUVxRctNWcOEpG4nyF+LPHleorFdRfwp/9HEr8LcRPxxF+SlgtpAU79WWwaZ2K82XxmzSEv/zfwi/alPhJsFJ8N2UfE36n/Ntsqf91Eq1Yid+uq+Atnf7Gj2Eb48eqrS4aPzZ5HFcX0bLpNdHWz/iX5gYlzhbORRIq3NL8NWhrpC2d4vlLFGV9P/5HJ/mRYh+jgVYrKkJr+vE/auk4tabY/2Tbtuw/B2zAcjYQrPwKpiJ6siov2cl0EqCfyolilKMi5NH5ndDZr4GaAgG1E0bnbXk/gQ4m571cGVarGwdzHq1ZpbLA5vw7PzcaZ8NoEiqxZGp1BRwOURnSbj0ThjxaoU43Cg67WL4td1wBnRTUkFmMs+CwHMPPa123yXUUyEqtp8FiEKwfylHJSb+rUOe8VgpWnFyeO4ufqM/Dy+7myphWwxg05NGaNSoLRpXfzs8rzPuiyiL208mIQjmyhKjfwGD7iZJaszCrfhiGOoRq65iS82TnTFZlno5Gm9jXnlLxF7keC9lwxzGoME2WqMs3QCPLzaswpexCWHRVzLKYXnmZfJFSQ8fVQukCVWYcjh1LT871o9qEmVVX8vOhtt0x3LZXrh+1JditQnzXSSWHo9qUo6WWGQZh5zJBb92tnErJ5y5iQ6xTMM4pKQJXn6+o2zHZdTAazIL6e3j9JdBKwnNk+1WdCYe+HGatDQfVnisHAly1t/4irqxabRqMmRVH5/pYbcQRdWIcjHVMwwRJLZj7WOfC7FqR+zC9bLZClLHK2IA9KwQ9ev/q4xVB0CjbJEwpEXkdR9Sdpaj7smvpARhqGSuoy41/VOA/pOZUDkYI/+F1ZyvwH9vwR8ZPooCzKo6SjzGojTi6/rxt4p9aejDq8kQBK4yN2K1cnGfPypPkoIbHiHUyJrr22mb/z675M9PTs7Zn5R84WKF6KXtWXZwbPyot9qm5cpvjp9E6E4Ot4ncnM2lLsVO5mOsjnMejzCjykbL05NEuQYsfVXIBzHk3ARWmGai3iZyh0WU3QJc3/ome7DIKUctR5TQnc/N3SMlVHMhQHaWGktsU83dQ6b08f436cSiTlJTFd7OgpkT4H7NpNix5/kejqUCJU/gfk+VMaPPKKmi0I2G2CXq0gUryc1AitRn2hE5SXdbYb1b4T5XpZKj0Oer6dmWZX+HxG7UBNtCvZJl0AKAtH6LeaYcp21J9vPWj0tRCLa1mZC2daud8FVF7JedMyZLJDUz30+pGKSiA9JMlkyuZuqzTjYdanSv8xErOiaX816CfKCs882dloojGl7LqskFHFNhcrJpK+xGNr4RGUwKjlGSXtUSqj7d+KEgx5AU8ZLFkO1fDpARbvZRAm7VwognxVA8suhGKQnGEPxhfw1Vu7fox0BTg98VX8l+HYaysECswxuCNrYJabYBTP1KBP54KwhtfyyspDkn7JGvRpAfe+EZYdNXyXWzWgoku3hJy6BsUKrZk3ngLwsk+lBiG8MUpHz9VwSXqablxOF+Mcn2cQjdtA2TSqDSNkBV6+fdMx9EZXce1NSqNQxT4o6kQuqMbuTZLWR7NlSyU9KEnupmDFFohyDdfvAfueAfKDHVyAm3WemNtTJeuNDZycJCPvzO6CbF0BDWmIYrCZ4S/LbKR8deZhyrwk4YOtdHFu8Y0SMG2iaRC6Ig0w6K1yUmych8nfbz149KVocSgHOPeeC/64p2cYJtNoM1aT6yd8VPQ9HPwt0c28OpFjWlYUf+3R9ZzLZQq4+Cf3P/hpBe9sWZeOaNVmHwLJLq5GrLLQLV7Sn/y+PHE17PqeIlhZNH48cRW8/gvMY5RsIVo/NPcoODDoR+lwJ9IBxCMreaCjRTk51s81YdwfB2veBp1yt8mnmznfBWjbqhChZ0sltjA5Q6MulFS4cgc/gSVY8gEoNdNKPI/yQTl/6Sg1Sn9D+XKpeLLuHSCWkfBrLrAf66S/GeOJr29sYF2n37NL2YDffXtTQNsoAH7BaYyAeoyoJ/KkFBbodKU91u1VqVyQKWugCp/ZSZ7GIkiSisrymOoMiQdY85b9ZCPgkZdIS3HKn9eWhYmh0THFNIKqVKtTlMuV5zMNxIwozaqwVJotO1j4LbcxSRrenUJVEQvzXNmWfwGbRm0aWPeXV8Ov1FTxk5Pmb9CGHV8J6ruBz/VnDBrSqDLq8uSNWJa0ApI/gUja1RZNJ1xQZ9XcCtrZo0DaqQVF5Msfioqp08b8lZt8iuqugStuWBpmy6cxIjRqPVF+OmCS4XeshVL882gNnERtWzF23wzaa1wZJwwaoorlVJRNMKfFTDMx0+fFU8bFKseWfwkJkiJloX4Sc/HoXNwsFJIC6bVD4fOLlfszTej2gSnzs6BTKGZtRakMg6Y8gq2Zc3GVWe3hl+/RfwUbPXX/9RGv9nP6X9ielFbf/1PKzJpnYtZRP2Pn1S/44fGalJN/V88fszaEu7/wm0mGv80N/od/yoTz6lspWhFm9oi2vqpOk0rqrSlq+nH/2g15H8yzPQpxE8rKqpM//6HxViZvlx4edGLG7J+8Av/Wd6//9yO7JdWoVVtV0sLP88GgpVfwTLJFmQ8pwGs9aMCrBdBZf0Dt6XjS5Bwnw4ws0cHrfN2aEyCFpyIfoyImyo8RpnybC55AlqDoAVHQ08jRCwjEkJTV8Fe+iIXhqO7moD/JoSDj/D7tNoxcJW9CI2mHJlMAn3ucxCJCgaEwbAHykqf4r3oVNqHzt7fIRYXGjE28wkoc93BTiOe3IzWnuOQSG5i/GWOK1BqF8u34dhibOo9iZkBtFRcW/I3uCxia8Ab/hhNfefzHRNVxx1a/oRMa+4MPItmN1W/TUOnqcLoyn/CpBvG+Js8t6PN/zi/j8TTxlU9zRVxqVjcip5L0BMWWiMu43RMqHiIq2omUn4s7DobvphIqq21HoWxZdcz/nCiFXM7zkaYtYpUGOW6AMNdZ/L73NEV+K7jfMTTXg5+JlfcgAabqK7bHvoS33ddiVQmBq3KgunVd6PCJNRe13pfxYJeEvVLw6Qpx6za++HQD2b8C3sfxAqvYCm59MOwT+09MGlLmAU0p+N6bAqSVgtQY94Je9fcxlVxY6kA3m+9HN1RkQw6yjEbu1deyvj98Q683Xop/AliZqgwtewMTC49QfRjZDXeav0Lq0dTldJ9qi/DKIfYvtgQmIv3225hdotebcYhdTegXqLVLnK/jc+7Huagyaotw9ENt3JhMsI/p/sJzO8jZhhQbhiMYxpv4eCLWDTvtN6GtYFvuY3Ugg+vv5ZXIqKpIF7efC3aIyIpdaKTaMEXMH5vvBMvNl8Fb6KD2/aoOAW7lomqsu2RtXhl87XMoCH8s2svwjjHnty2LvA93my9TcZ/VP21aLSI6sY/ut/BZ52PSPhLcWzjX2X8X3Y/jgVED5fwH9Vwq4z//ba/Yn1AaO00WHbEoXU3SP0fxJstV6IzKhKexzoOwF5VF22z/4ne/mHrZYilfYx/96orMcxOQqLA5uDX+KrzWh4/RJmfVX07qsw7iu/mfQU/9v5NHj8zax+EXRo/y/vuwTqfEFW064djRvXDMGpLefwv7r4cnWGho1Nm3BWTK++Tx/+irrMU45+2fwh/NNGCZV2nIMpaPyoMcl6MeqegBQdjS7Cu51QuIUDzd3DpnSiVyhL4Ix9jc9+58vxtLHsSVqNQAvcGn0KPl6rf0s1GFerKX4Ze8j8+/00IBB/m9+m0Y1Be9pLsf/ye8xCX/I/OsDscJU9yldtM2oeo+xSkEyLRXmM6DgbHrYyf/GfKc4rsP9XWi6G2CvwDNmBZG8hZ+RUs478aYPVk/hcywbuRiYtM/YTnPOI2S20JJL2XIZPq5mXPiJv24aPSYSGEma6cRDKxDiGi82WZBukeBD0XyGJh2UCFLJlcg4DvOn4eDD2FSPR9uS0W+xoBlmsHPL7bEYsLR0cWCD+PYPh1ft7lvhQJvtAL/L2+WxFhhgGwue9sDnRESwKt7ou4qiVVym3qPY8dHRmxhDb2CvyRxHo0u6+X8RPleX2P2AP3RObIgQpZKLEOG/sErbPN/zx6WFRRmCc6F82+x/j5Os998MWIpSCsLfgvdITe5edLem5AhNWfBcrVnvvgkZhCP3RdirhEISWG0sLuaxFJ9jDFeW7XFXyh4X7MhPFd56UccBBbaIF0oSGLpvrwXZfo47bwd3KgQuaNN2Fej6AFr/K+jk1BIc5G1hFeiKXu5/j5/N4n0BMVF3qy1b73sM4vqONfdN2FQKJLxv9D7+PolJhC77fdyBda7mMk8XHHHQgl+xBLhfCeFKiQxdMRvNN6A28jEFvls64H5UROoty+1yZyhjYG58uBChltb3zeKajXi9zvYG1ACOeRNYcW44feV/n5l93PoiOSo5Mu8X6EFT4RlL3fcS98Mn567zNok5g2b7TcgmgqION/r+1uBBNurvT7RuutMv5EOoLXW26W8X/a+VAefg/ebbtDwj9PDlSy+L/oEvNhiedtrJcCLbKW0GLM7xPU3+96nkJXVCohwMKGH2CN//Nt9v+n7dcgns7h/7Lzrwgne7nS7Fed18jjJ5EO44uOK3n8+ONN+LH3rrzx42Y6PVln+Bs5UCELxDdiaZ9Qim72v4TO8Ke57xb9Hht8ovTABs+9ReO/Uxr/6/r+wuUCsvg3ef8Gf1QEBRt6z0Eyb/5u7LsE8VQ3z9/Nveco5u/m3t/z/I0n1qHHe7WMP5XuQSffVBF1+TM5UOHvnVwDD99UAZHQ04jn+Z9E7BuEA4KWHQ/cxVW8s5aKvIRU5E3x3P8Xhf9MB/+GTFwpnLnd2ICQ4b9tA8HKr2HJtQXUO3ptA1OXkW4vyJAninIz56qwOrNsNBB9rLqcTlKNkPxBmUIquV484/oD+T8b7RGLu8UEtymXjhOMDYgrKMhkWiSSohZDTEFvFEYOi6iPCXYiSvzxZDPvdWcK8FNQQ6qtVL67EH+UvxPlsawvwh+S6riEEhuKlr7F+8FKsfkYaZUkFBf1U/yJfHqysECC6K9xRJKdCvz0vlCiFeFEJ6vS5uOnff94ygt/fLMCP100/fFNUnCyqYB6moI3Lr6bJ95U0Jbh95O5YxsVLBC6S/fGRS0PN9Njlfg98WbOsQgkuwtotfR5lMvRzd8vH38sHWTVaHe8VXEuOt4dF8Fob2xzEXW2J7ZJblMydDLo476g/JFNBfg16MueM9pcwMKhui9EbU/An+xRMFyIUu1JdMDP+BOKvoqmgwgT/lgx/r5Yi3zeIvxRUQelL9ZcjD8m8PflUZCz+N3Sd9tS/xP1PZTsKup/WoEJJTuL+p+CGqrh4+ffNX/8pDiAyQYnhePHF5PGeD/jP8hziY7b8vgPcU0VJf5wYgPPX1FHRTl/Y4lmJJJtxfM3I+ZvnH2Ncv7GJV8hfIxy/iYk/5Pi9yjxpyT/k+ZaU0r/k5Z8GpLF+DOSv9jeTJX+5Y/fqg0EK7+GaccUU+90I8R+LmfA53ezDirNIEHbY0XULK1Qzcqk9BBVbPOP0bB2EH+UbkwxdVAnGAQ63diiSa/n9wN6/biCcyblNkq2LcRv0I/mvXG9pkFxHC0l67WDmd6oLsCvVZfyg7Z7CvGbdIKlYdGPLMJvZWzE/hkl1WfJWoZfI7MZ6PvnO/kkrHwuYk2MKnLytLxOuQmCQZE7jlg/Vl0DLLoaaGifPA8/qT8bNC7e7lF+Zw2cUtKiSz+0SDWXkiTJSgzDii569BpZqXF4AXU2iRKD0MIpMwwvwl9qGMIaQnadkvJMCZdOfR3s+iroWFFalafwTLkfDpTqGxR0YTqetkvISMOmEH+lUWCsMA6R6rPkrEI6rtI4tAB/CuVStdbCNjJqI2aPU6ekBVPeSIm+Bg59ZRF+yvGgB233FOE3Chz0PYrxi4TMcsNQBX663JYbRbJ1uXFYEf4y6bttqf8pt4gSshX9Dx3sujpYddWcJ5I/fowal5TgXTx+HFJlZ4dheBF12cVjG7DpRxaNf7teJLvb+x3/os2qL/Y/NM9o/hr6mb9G3SAuW1A4fzXS/NVri/1P1lfo2A8p5y8l2eZ8k3L8aKTj1NqxRf5HLfm0/vynSpfTWRqwAfuPBiu33HILdt11V5jNZjidxYld/dmpp57KyVv5j/333/9//pdSOW4BtFkRNg1UtquhygYQrkdE4hibEVqiJ3OyrQVm16OAlDxLybemkse5NLVGOwQWJy0Ni+RBCmxsLlHB02CcCYvtYtnJ6HQTYXfQlgtgtZwMsylHWTUZ94NN2vstsf8ZRsN0uc1u+T0sJqGsWlVyJ/RyBr4GFc4bYdSL3IeGsseh5ZoqQmG1vvRBTralpNshZY8yrZFMq3ZiSLnAT6rMQ0pvk5WcDdpaDCsT1EeXaXc0sNqrwG8zjMcQibpcazsOVdJ+OlmZeW80OM7g58Ndf0SJcWe5rcF+EqosYmxMLL8OVp248NCFZWzpn+E0iJLu06r+xkmJ/M1URuxUeSvnB2jVZuxadSe07LApidLOOSuE365vwNSKv8hKtlSXZddKyr8Bai3TMLHkdBl/qWE0dir/Ez8f5TgMw2y58dpo3Q3jXYK6vFPZGagx54QqxzmPwlCbyN3Ys+pSuPQNMv4ZFeejwiic9UG113PCJfexyoADaq4Syb1qEw6uu5b/8sjS2DlnhUqzlxjqsG/1RTJ+qudyYO3l/HywdTJ2LTtBxl9lGoFZlUL1dwfXgRjrEErQZMNsu2CnUlGSfvfyk+R8ErIpJYditF2oLh9Q8yeUGepl/HtXno1qk7iQHlF/NePN4j+09s8yfmrL4bfhyPprZfz711yowH+QjH8Kdik7MYffOAJ7Vp7Nzye4ZmO0I0cLHmrbFZNLBHV5l7JTUWfO0eJ3cB2B4baZ2+z/vWtugUkjWD4kILhn9XX8e1DS9szqv/JfHuNEZa6+jfHb9I3YqeIqGT/V/JlWKei9leZdMcp1tozfaRiD8WWXiPFiOwa11pzacaV5FgY7BHV5qOtPivFfbz8ZlZYD+Pnwsltg1uX8z5CSa3he8W9Y/ohUE4kCXSOGlN0vz9+GssdkJXaN2sk5KzT+9bqhqHDRNpaYv1Rjparkfsmn7Am77RIZv143EU7J/xjNJ8FgylHO9cb9YJZy9/S2S6DW55TAtebToTEKSRJNgf9U266R/ed2ZwPbQP971OXrrruOg5TW1lY88cQT8HpF6fVtBStdXV146qmn5NcMBgNcrmIWyv8cdZmy39O9nCirKmC/sEAXtREtryB7nhRMqWqtSl2moPmJtgjSaa/E/FHeeRBtmUpeq9XlRcJfXCY7k4ZGcrK589FWTS/fcakLWA9c5j/dw9pA6n7wJ1M9TF8kh6fAkYkjmeqDTlOMn0p6U5Vbco6F+JPpINLpiHScEj+VA6e7T72mpAh/PN3H+HUF7CPCH0v1MfOBAhElxiRiKTf0VHAvT2lXtCU4p8CoKVHQpAXGKOe7GDXEQCrYXkuHuJ2OK8QfS4nKpqQAXYg/kvLwio8hr65HFn845eFE00L2COVxhJMeDkhotSXfaCuFqLVmYhkViMpRSXvKF6HgoBA/5YwkMlFYNK4i/FwZN5NmGm8h/nDKy/iNeXVVsvhDSS/0GrOCSpzFT3knpi3gJ2q2Rev8WfjjEn5zP/gpGZmwmraAnxhEP6//k4gkPRyQULJuIX6ixlOCdaEo4bbGTyodgYEYNwX44ynKMaHx7/pZ4z+R6oVGbS1i39H8pTaqrdLf/E1xW3nx/JX8j+Zn+p80+58M1P3MX+EjDVD143+25D+3J+ryzJ2u+sXU5Tnzb/lNUpf/YysrN9xwAy666CKMH//zImQKTqqqquTHzwlU/k+NSueTWGFynaRImmfpbqQTK5ChEvuFh6VakE6sRCYlmBT5lqI6K9RGk7gwsEis4rL8mYy/oC2OZGIFPyjYUbaFkEgsR5zbkgUOxo14fDniyTVF+JOpLsQSy5HoBz8l5kYTK5DoB380uZ7bkv3gjyRWI5xYjlQBfnKe4fhKfpD+kKI/KAmZ21YV4U+kvQhSG+WvFOCnWi/B+ApEmC2hNGISBeKrEE3mEkSzFko0IRBfjXjKXYTfH1sHX2wVJ+oq8Sfgi62GL7amCD8l8dLrvvhavgDmG7FNvHRcfGMR/kiyF57YGgQSylwOsmCiHd7YWoQljaN8o9wJaoumPEX4qc6HJ7YW8QL8dPF1R9fBHVvH+jf5RqXm6XV3bH0RfgoQ3LG18FL+RwF+qjVCbf5ENokyZ4FEB+MIJXqK2nzxZm7rD787vp7P2S9+xri2CH+2/73xdT+r/6OpXvjiqxBKFvd/ONEGf3w1Ij9z/ITia3i8kpZP4fgPxVfyo3D8pLcy/ontE0ks53pIhfgTqW5Ead73M3+Tyc2I0RztZ/4mEusRj6/gG5xC/MnEyi36nxS1JYv9D5EIUlQfKlGMn9iSmS35zwEbsP9F6vKcOXNQUVHBQcqsWbNw8803o7RUuUKQb7FYjB/5Eex/2zI0ad2nsIghm/EIgGl5KqSinyHpOVfS2gA0tkugtYpqnInQM4j7iWVCk1MDg/NuaCVac9h3I+Khx+QaBNaSZ6E1TOP6I373mYjHBGuGVmScZa9Dqx3Kdzt9vUciIelqaLRDUV72Ft/hJJNt6Og5BClJuMyg3xVV5S/wSg+xhNp7jkZGwm81H4ty198ZfzDyMdp6fy/jF7RmUQ21L/A02pneKPDXl9wDp0VUt2xz34je4GPy9tGQ8mdhNQr863vPhlcSUaQ98tGVr8KoG4JUOojlnb9DiLWRqGT4EIyvepULXVHxuSUdxyImOVWHYSrGVT3Jd5mkg7Ko8xSkMuLCVWU5AqPL/sr4e8JfYEnXn5gJQTbMdSEGO8W2wSb/i1jZ91cJvxoTy29FraRWu6L3Tmz0/1PePppa9RBKTVMY/9zOS9AZFqwfg6YEu9c8yUv/dLc8p+0seOOCCWPTNWLP2ic5j4ESej9rOxNh6aJWbpqMPWru41UKd3QVPms/j48nG2w7CNMqrmb8LcFvMKfjKha3I5tUejYmlJzCz1d7X8MPPX/LbV9UXoshEq32h+77scL7slwGft/au1Bl3oFXOT5tvwqbQ4I1Q3kWB9c/wEXx4ukw3tn8J/TGRFKkQ1ePwxofhFHjYMG+NzefJws/Vpt2wOy6Ozmvoye6Bu+2XMRF8shG2PfHzCpSy1ax+jCJ+gmtHNoOOxM7lp7Ez5d73sC33ffI+Pes/guG28U2zvfd92O59xUZ/361d6J6G/iJkfN+y/nok/E3YHbDw4w/lOjCh61nyf1fadoRe9Xcw/i31v/toa8wt/Nyuf/HlZyH0S5RMXeD72Us6SWWVYbxT6m4GfW2A7Y5fpZje745AABeRElEQVR0X8DjkkyvLsWUmn/CohvMq41LOk/iwJrMpB2MSdUvyeN/WefRiEvj326YhjGVVJbAgHB8KdZ2Hc9F2shKLEejseQuxu+PfILNxNKT8Fc6/owKqSyBoCdfLc/fypJ7YTeLsgR93usRYPVkMX8ry56H0bAL4/e6z2BVeDK1ugwlZW9AqxuKTDoIX9/RSEn+R60ZCkf5m1zQkggF0d4jkWHCAaDWT4Ox5Fn2P+Q/U+6TZP+pMh4JteO2ohWb7cF+qb6P6jccyP1PJdhSfsqzzz6Lzz77DLfffju+/PJLHHDAAUilCpg2eXbrrbfy8lr2UV+vrND437AMUYczwtGxRV8H4t+JSrPeSzmZLGupwN9EXZa0B3H/9QqxsJj3cmYQJeNLc4EKf0AMYa/Ys49F35UDFW5KexDyiXyKUPAxJBLC0fEZk5sQCIq9Zo//dqRSubu/WHwuAqGX+Hmvh1RUc/iD4ZcRiX3N+DvcFyrw9/pu47osyZQH7d5rFfhb3UT9jbHzzAYqjDETQ4tb4HeH35MDFTLaJtrsEaqt7f6nEEqszHVjshmtPkmR1vN3xFK51QNfbB46A4LCuqbveqRYBVZYZ+h1pj1zTYueKxVJi+s99yCSaGXWz8q+W/Pwp7Gsl2pmxOGNrZQvNPzNMjEs6RH78q3BT+RAJbtkv6xPBAzrvC9wFV25HxOtWO0RqrPL3I/wCknWeiI/osn/Nj+f13MHU3ez1hR4F12R+Yz/m66b5Qsl2aK+fyBAInopH+b1/D3Xx0jju+6/MoOlN7paDlS4jzMxfNN1m3TuL+QLPRmxV77vfkDC+Cr6JGYKGa2ELOrLUq8f5xWSrHVElmC1T9BUv+66W4F/rf9DtIUXipouHbfKgUr2PFTXhPB/132fAv+XnXdI+NfIgUoW/9ddt28T/wrPK7zqk8PfiiVuQRNe3PcPRf93RRZhvf+dbfb/vO7rFP2/3P0gQiSCmfJiSe8deWKFaSzsuX6b46cr9KEcqGRXBNf2ie/W5n8Gwbhg1pDRSuBmn6Blb/bezZRj+bvFfkB3UNDKN7uv4lWXrLlDryIQ+4bxU6mB/PHf5bud528q5ZbqqOTmb7f7Ep6/dPOSDVT4u2Vi6HWLvJpo5B05UMlu+fh94rtFQo8jled/0qlNiLAKPJAI3IVMOud/0vEfkAyL3zhF1Oc8/Jnoa8jEcxT67coGclb+O8HKFVeIu6WtPVavztWS+Ll23HHH4ZBDDuGto8MOOwzvvvsu5s+fz6stW7Irr7yS9++yj5aWbL2Q/6Kl6W6ngFPGgUFcqrGiHGA0aTMpWvYu5KER1dnfz5ZQGul0p+QAOoqog6mUWJ5OFbVl5JWUJL8nP+jTSO+ntkJ6ozhXBnHesy7En0x1IpnuLjqGqJBUtj+RLMafSAn8cf6rxJ+Vqac2JaskI71flPVXUjc1iEvBl1htUWKJJTv5blLUmFDip6AnluotOoYcdZJkB4qW9DOISheKCP9VUk/D0veltkJabXZ7JpSg/szHr+Z6L2Tibl+JhY6jLaVsjZh8o4tuJNlXRBemCyVti4Sk8+bjp9ogjCPZU4QxKH1f0ZavaJxBUDoX1SEpxB+SzkmrLoVY6FyEP1awzSG+Wy/CSXe/+Il+nV29+fn46Tgl/rC0vURU40L84W31PxJI9Nv/PZwfVTx+4kikAlsdP1Ees8rxk60RRGO2cPzTa6KtrXj8S59DZQQKsST4+8YlZeXCbaEuJNM9/c7fdNov+4W8b4bUVvxPWqqRUtyWkV6jtrYi/5ORztm//5TaBmzA/p1g5ZJLLsGqVau2+hgyRKnL8kuMzlVWVob163N3S/3luFCiUf7jv276XfO6kpylBmBdHoOU1Z5NTlMDKhtU2hFQkcYOleeXj9MwpRlEXdYT68KQ53g10ErCXjr9TgXORw2dQQi76bn6bf5ecBoGKQPfZJhRoL6ahNEwLa9No8Bv0O8oqI+6iQr8lIBr0I2EXtsILbOccvj1WqKrlsKkn0Dargr8VumzbIYpRfhtEkvJbpxaQN1Mwy4xIBymaQUXoiQcxp2kSre7KPqfHLndMJFF2ez6cXm0VDW0KissumEwa+uh5wTkrJCbBmZtA0sEOAxE29bn0Wo1KDGKyrZlxh2K8JdLGMuNk4uop+UmUdG00jylAH8K5SbBDqoy7ZR3kRL4S43jeIui1JCjZdN7qFKqUz8Ydl2tSO7Nw2/T1XFSb5lhpCQDkMNfZdpB+qwJRcrWNWYhPldD2yyKC0oGNSbBoKllJpMSf7XErqk1Ty7CX2EkzSc9yg0jFfhJ1sBlGAS7rgYmSo7Nw0/fyfQL8FeZJhVQxzO89SXaJhfhr9xW/6v0cBnGKMaPTm2FXT8UFm2dSI7Nw0/KyEx938r4cRp2LBo/JSYxR539jH9HdvzzGFeO/+zcsBmnF81fi2ESz18T04qV85e0v3TaRmh4/mbbNNBpBjN9majIYv7m5nbWj+gMOxfh17P/IN9U7H900rzXsI9U+h+1fqpAzMcp8av04nfb7oy6Lv0LHhn8Zu1nBSvl5eUYNWrUVh96vTLb/5cYMYn6+vpQXS1k0v9XTWW/DjBQCXQ905RVzvuhkqh4RF1W6SZJ9VUaoSt5mjPhSczLVPKcJHqoY2EvY8kzXH5aramGpfQpqFgmXQ+tYQbMrN5MDmEyKzBTrgoFNAbTEbDar+A2k+kw2Oy0+mVjPSGr9XyYLUL91WG7gKnNtP+sVjlR4vwrTEZB3Sxz3QqzcV92UJT5X1n6OJfWJqstewImVnLWQacdhLryF1hPhFgFg8qfh0FLNSp0MOnGYlDZPxm/XluNwRVPQacRMvU24ww0lAr8VsOOGFxyFwc1FMyVmg9DvVNsEZWZD0aD8xJoiJGkMqPW/gdUWUXZ83rHH1Bt/R1LBxBNemjJdXCZRJA2svQ6lJn3IrfJKs7jKu6DRS/6f2Ll/XAYdmCMFIxMqnoMOo2dWUE7Vz0Kq24IF9iy60dhp6pHGL9JW4WdKx/gv3TOMtNUTKoghWmgxDiBS/ZTrgpdDBqsB2JcqcgBqLfuxzkNFBBRDY5RzlMx1C6onKSeO9R+JOcvEE16x7LLUGUWjnxK+WWotezGFziiye5WdSscelHDhOiw5cZxXN/DpqvFPjV/h15jY/xU5p/eRxTZEsNw7F1D1Gs1LLoK7F1zOyzaCmY40cV89+qrJbXesdij6irO9aCL8VDbvtipTOTwDLXtxTklFFAQ/h1KTsAYp1D23aH0RIxxHMr0Y9LPmV5xIeotIlicUXkhGq3TRV0bTQn2rbkRLkMjt+1bezMqTWMYIwUoB9bdCYPGxqya2XV/g1PfyG1lhmE4sO4OGf++NbfDKuGnYGhm9VXbxD/Etjcml57FAR3hn+A6kenkZONKTsEIx+Fy/+9cfilqLFO32f+7Vt2FUuN4qT5PHXarfkDu/xnVD8GmG8zjx2EYiek1D2xz/DiNO2BsGVGexfipthyM4a6LuK3cMhuDnBdBo7Ly+K93nIUam6C+1zrOQaX1BHn8Dy65Hk5p/DeU3AyHaW+ev0RTHlL2Dxi51hHQWPY4zPrJUn2kRgwuf06ev7VlL0Iv+R+qtVRT/hzj12prUFH2LDSS/zEadkN5idiy0+snw+G6h3NVqBSD0XQEbA6hKK03HQqT7c/sf6gkg9F6HgxmkZ+ks54LrflEoQGkckJvvxFag6C+q+03AIZZsv9UOx+Q/ef2mrPySx6/VfuPUZc3b94Mt9uNt99+G3feeSe+/vprfn3YsGGwWgVtkIIbyjk5/PDDEQwGmUF05JFHMgtow4YNuPzyyxEIBLBs2TJeQflfpi4P2IAN2IAN2P9/9t+kLs/a4QpoNT/tWtafJVMxfL74tt/k9e0/xga69tpr8cwzz8j/njRJLLl+8cUXmDlT3NGvWbOGO51Mo9Fg6dKlfAzVZKmpqcG+++6Lm2666ScHKv+XlqEy1PG5gLoEMO6vqFmQji9EOr6Yt37Uhr3lLHeuexL7Epnkel5Z0UgihqIthWT0A6RTndDqp0Kjz1HAiRIYi7zL+kJ6457QaAfnPivtRixCiY8pGIwHCAVUyZLJVsSin/CqjtF0MNTqXK2JeGI1awmR0rPZdLACfzQ2H9H4j7x0bDbup8Afjs3h0vx0Z2aWRNCy+AOR9zlXxWyYBlMefqqv4ou8y/okNuOeMEgF3RhjygNf5H0+3mHeXy5oRRZLtsIb+ZzvCkvMB3JNiayF42vgj34HraaE2/JrpvijCxGML4ZR2wAX3YHm4fdEvkIksQEW/Rg4easph783/DFiyS7ebrJJRebIiFJKiZLE3igz7wazrjHXjykvukIf81ZFpXkvGLTZgoBAJNGOrsiXXFys2rIf14SRMcbXoTfyA9fWqLHsq8Dvji6GJ7oMZl0dqswzFfi7I98hGN8Ih2EUykw7KfC3hT5HNNmNUtOOcpXULP6W4CdIZEKoNk+HVZdLSqfEUTqOjq+17AmjVhTUy+bdtIe+ZmG9eutevIKRNW9sPboj83kbpN66t6LmSG9kCfpiK2DV1qLGsrsCf1dkLpeidxpGyiKSWfytoc85P4S2ywrxtwY/RjITQiXjb1Dgbw99xsfXWGYp8IcT7egMf8WrKzXWfRT4A9z/33Ntn+qC/vdEF8EXWwqztg7l5lkK/O7I1wglNsKmHw2XaepPHj994feY/eY07QGTTqziZMe/O/IB1TSAy7yfYvzHk63wRz7l+es0H6QY/9H4aoRi3/CKpd18kGL+Rnj+LoJO2wBLwfyNxL5AIrEOet14mArmbzT6PlKpTuj1U6Hnrel8//MOs3+K/Y8HCcn/6Iz7K/xPOtmKVOwzovtAa5wNVR5+8p+cVEtK7cYDimq+DNiA/cdWVv6v7P9iZSVDAYfnD7lNRd3OUJU8xRMuFXoeST9l3ZODyEBtOhpaiZYX892MZPgxaTcuDZ3tz9BbSQU1jYj790jGSH1VOBaT637oTIcgQxf63sOQSq6Q2vSwl70MnX4Ks328PQcgzcmvRDl0wlX+HjTaRiQSK9HbcygXciIcGs1glFe8D7XagUj0M/T2nSLjp/3p8rKXGb8/+Ax6vVfIGK3m41Duou0GFXq8N8DLrAHRVmq/EiX2Cxh/S+8ZCDJrQOCvLX0QDvOhHKhs6D6M66+I/AA9Ble8DIthCif+re2cjaSUjEjL1cOr3oVB28i1JVZ1HYW0hN+gHYwxVW9Cq3bAG5mDNd1nyvhthqkYVflPvuB0Bp7HRneu/yssR2MoVddVqbDRfSva/E/IbYOcl6Le+QfGv6L7XPRFPpfxjy6/GxWW2VzIa37HCVybhbMwVDpMrnoaTuMkxJI9mNt+NGKcPA3o1HZMq3kFZl09/PE1mNt+ElJceyIDs7YR02te5C2prvDXmN/5RykXI4NS4xRMq/4Hn7vJ/yqW9t4iY2ywHoodykltWoXlvX/DBmadiLbRJX/ECOfpjH9u58USa0ng37nyVtRZ90MyHcEXbWfAG18j49+j5hGUGSdyYPBp60lcV4SMtkv2qnuWtz+8sXX4rPUMJFn4LsMBzj51z/CWSEfoW3zdcYmMv9y4I6sMU8Cy3vcaFvbcJmMcbDsYO1Vcw/gX9/4d63zPyW3jS87HKNdpjP/bzkvQEf5Kxj+t8q+ot+7L+L9sOw2+PPy71fwDpcYdGP8XrSco8O9Z9xwsujr4Ymvxdfupcv9btA3Yo/Y57v/u8NdY2HW+jJ/yS3auouquOrT4X8LKvhtljLXWIzC27CbGv859G1r8T8ltQ5yXYJDzrG2OH6Igh5n1Jsb/2MrnYDdOZrbPys6DuS5KdvyPqXqbg+xIfBXWdx0uj3+SvBhR9Q6/JxD5HC29p8rjn24OGstf4vnrDT6Lnrz5azcfhwrX3xi/23s9/Hnz12n/C5zS/HW7T5dYPwK/y/UQTOZD2f94eg9V+B8n+5+dkE51IdAzGxnZ/zhgLX+X/Q/VVon0HQlI+FWawTCVvQWV2oF07EukPWcp/KeGt8R129/KysQ///KVlSW3/yZXVv6nqMv/v1rGf7sy+ykxD4h9JYon+cVedbYtHXmVRbpoYotAhV8VhwXu5LuWVHy+FKhkj8sg6hPKxLHoW5KjyLYlEObPJ+rgk0hzASdxDBVsCksKqQH/3yWFVYEjlWpGOPQiP/f6blTgJ1pzNEp312n0SbToLMZg+CUWQCRGkAhUcm19/tuRTocRjs+XApUc/i6PKDfuDb8lBSqijRg7RKck6w08hSRfaMQxtPLS4xfUzXbffUjn4Y8lm9EbFNTHzZ6/KvAHYj/AF5nD+Dd5lP3fHXoVkeQGvuMVgUqubZP3br6Y+GILpQtNDv8Gt6D+dobelwIV0UbFxYgOTdbs/yfizBIRx9DKyyafoC6v9zyCVB7+cLIFLUGher2y7275Qsn9GF2A7jBRT9NYIdGis22bg28hmGhCJNktBSq5tlXuB/hi3hddnEevFliWsoo00BL8WApUcviX9wll7vW+l7jSb/YYKni3xiuovyvcj0sKw+Kzgok2bPS/xc8X996nwN8T/REd4W8Z/+Je0TfZtqbAOwgkNnFgIQKVXNsy90OMvze6RApUcvgX997N/2oNfiQFKjn8KyT8G3wvFuFf6xWru2s8jyr6nwq8NQeE6u9q910K/O7oAvRI/b/aLdSes21twdd5JYXGjwhUcm0bvX/f5vjpDb8jBSq58b/ZK36brsBTXGk2f/x3+sUc6/Ldoxj/VOCtLyhKD3R5ifqfwx+OfY9g9AvG31swf/08f9fz/BWBSq7N67+N5288Pj+Pniyw+KTzRPvxPyG/6KNY6CmpgGXW/wQQkxTi44F7iZMsYyQx12RY0OvT/tuK/CfdAG6Xlv4VHr9R+58rCvf/pXExo0w/r1FmvLKKpmjzbyGrOw1kIkVVIfkQ6bUMUynFnVD2mExWAp7b8jPuhcPgdzGFMZ8poUJacU4lINGWlCXkFW1Fn5PDLyQCBJ58y1aqFX+V+Emtmdv6wU8Om0xQkJX4c23F+KkyKDEmiI5chIXe32/BqTSXGC+sKpo9X+6vEn+W3krBSb7xpUh6TbxHyWLJvl+cs4BeSuXMiZbeD35Sh95S/9MFWbQXHpPFUYyf1ILz39MffqJQF7JwlOcspMeGmFnUH/54qhjftvAnt4I/2/+Jn9n/W8O/tfFDbaot4t/6+BFjXIlfjG1qC2xx/Kf6m79Sm5irSvzic/qfv9n5tqX5m/Un/fsfXxF+4Vu27n8yXMJBiT/bxv6wX/85YAOWs4GVlV/DjAcq1EtZTVk/DSqVHirDTAUFEOpqqHSjmaas0gxVUAfVuh0BlQsa/RTOmM+nHNIeL5nOsKf0eu6n05sO5r+Uo1JEXTYKYT2TSYiGCRPL1kajqHZqMh2qVH9WWWAwTGf8JuMsBUZiCJACq047GDpmEuTajPrJUKtLYNJPYWG0fPx26fMpR6UQv8Ms8DtMhLWAumkW+EvMBxThd5iIgQWUmg9S4Ccmhd24C7M7XCZl/+s11TDrRsOka4SJxd9y+G2GSdCqiXpKf5X4yyUcpabdJLpqDn9WULHCsncBdTaNSovAWGXZR+moaUvKvAf/q8ayn1L9VmXm/BPahqg0zVDQY02aKlbitejqWbwxRwsm9d4J0KudKDFOhE7tUFBuay1CoLDKPF06X+6iUm8V2ChHpRA/vSbes1cR/hrLbnnH56lvq0yoME+GRqVDtXlXBX6zthJOwwjeRrLpBinwlxrGM/5SI30PJf46CX9lP/hrrWIcU45KIX56jdusexfhrzILNko1/37K/v9/7Z0HmBvF2cf/kla9XrcNLhhjjE1JAtiA6dVAAAdCh4DpECD0JJBQPkIgQALB9ITeQxIgJKGYngCmEzrYuGLfna7oTr3srr7nfXe12pV0Z/t8ts/n+T/PuWi00u9G74xmZ95S753K9tPIETdlfo9jBFdHJvvxOcdb7Cfk/h6cK7CfiHd3/fcq20+j/wC9yOd+VfZPfiuksO/AKvsPebXfKcQFSa3j1+/eicevr2L8So5RcPcxft36+HW5toPNVmfh9+hFB10ctWPld+vzD/moVIUu6/OPxHOkld/h1vhtPLdZ50+bnqphuElEAw1cwmdlEFQsFlBM/hHIvqI5iIV+blQNJSdYOgoq5t8HpHFwhi7XcqxwoqR25Hsvhyp/A7tza7jDV8Jm12ohKYWvke29ihPEOdy7whP+JTvWkQq5t5COX893Oy7vTHgDZ3PIISmbeQaZ5B28Bez1z4LXr4U+0secSv0J6RSdZXsRDF0Aj2cvg783fj0y2RfhoPT94V/Dpec5oLu3zp4rkMu/x6HLDZHfsKMtibaSo7FfcT0hj3MbNEX+Dw69eFm28DXaY1dwzSC/Z1e0hC+FXS8Ql8y+hfZeylYaR8Q/E03BMn8s9Q90xO/gu/jG4Cw0BI4y+NsT96Az9SSHb44K/wwRr/ZFSsnHvuu5CbHMHDjtjRhT90sE3Fsbd7SLuq9BIvc+PM5x2KTucnh0h0zayv+2+yqk8/P4+ZvW/4pTm5NS+XmY330Nckob6jzTMb7uYnYsJXVn3uWjH3rtEYEDsUn4NIO/NfkcFvbS8ZKKMaFjsXHwMIN/UfwhLE08xQ6em9WdiWb9y5L4v47djrbUqxwSPbnhQqNqNN39f9Z5I7pzH8PvHIOtGi5hHwwSHQV92vk7JArz+fnU5tKLJ8bz3+J/nTdwzZoW747YsuFcgz+aeR+fdd/OCczGBGdgUmSWwU/HRF/FHuD+3yx8JDYJzTT4v+l9jLPuEv+U+lMxyq/lx9GOku7EstQbcEv1+F4DVciebPB/1HETOrOfsO/LD5ou5L81/g581Hk9evPfot49Gd9rvIhLE5DosY87b+TfscW7A7ZuOMfg78i8j8+7b0VBSWJ0cAY2j5xk8NMx0Tex+3kxsmn4SIwL/cjgX9D7MBYnnoFk92DzutPR4tvF6P9vYrcimn6Vc+9Mqqf+31K3nyS+6vodenIfsp/RpIZfsg9SyX6+6b4ayfx8hNxbYWL9pStlP73ZuXz0QzuCTf6DsVHoTIO/K/UsH/2wg3bwBDQFjjT4OxN/RnfqCbb/lvD5CHn3NMZvtPdGJDIvQHI0oiXya3j1qum0M9PZcyUy+vhtjlxtGb/dsct4/Lqd30O9afwWChT8cDkniHO7d0M4fCnPG6R87i2k4r/j+cftnQlf4ByDP5/5B7J09FxU4fbPgttfHr9y6h4UMn/h13EGfgbJU+ZXkzejmH1ZyzNF6RfWYtXltemzsteUi1fbZ+Xlz2/YIH1WxGJFSEhISGiDlVisrB8SPiuDpGLuNRRzb2g7I76fsJc7P14sQs08DbXwEWyOMXD4j+dkaFpbAUr6Eajyt7A7J8PhPdK4Q6GwwEL6fhSVdjhcO0LylrdRVUoXn7ofxWISTs/+cJpCnqlSczb1KG+zun0/huTU7nBJhfwHyGWe4cRMXt9P4JA2Mtqy2ZeRy77C28D+wMmw8za2Hp6c+Svy+Q/hcIxFMHCiscND/InUg1yd1eWcgoD/GIOfiir2Ju/hCCWPeycE+KhGE0X7UBE1KrwW8BxgCXnOF+ajh/iLRYT8h8PjKvOncx8gnn6G78zqAz+B08Qfz7zMToUOez0agyfpx1B6eHL670jnPoRLGoPG4Akc+lzi70g+jGzhW/hck9HgP8rgp7DS9sQDHKEU9OxoOYaiKs7t8Qe4cGK9bwZCnvKWNYVBdySf4J2tpsBh8LnKIbeJ3EfoTD3L7z8ieCzcJv7u9GvozrzOd+ajgifA6SjbTzT1DOI5Cr0ejY2Cx8Fudxs7Aq2Jx5AuLEDAtQVGBA43+GlH4Lv4w1xaoM4zDc181KSJopaWxh/h0N9m3z585FFSKr8Qy5J/Zf5RwZl85FFST/ZjtKf+zfwbh46CV6KkYZo60q+jK/MfPgYZEzrOwt+a/Ad6c/+D1zkao4PHcEK1Ev938Sf00N9J2Cj4YxN/CkuJX45ySHCLf18L/3eJh6GoKTT59rGEDNNrtSae5J2VkYGZCLgmlW0k+zGiqX9x/40KHgOPib+//u9IPY1k7iO4nWMwMng8J3Mr8XckHzHsp9F/pMV+OhL3s/0EPDuizlcevxTt05m4n8dI2Lc/txu/G9v/Y9z/kQr7z+Q+QILt34MI2//G5bGReRnp7Cu8MxIKnGyx//7Gbyr1IGR5PpyUesB3tGX8ssO+0g6Xeye4vQeu9PxT4JpjKpy+H8PhLNu/mv8IcuZZfn+H/1jYHWX7V3n+fJ3nT5vvBGP+HHZa3fo+xWG1t7BKEjsrg6Bi+kkU45fpaz8VcIyBreEp2Ox+yPHroaTu1NsUTi3trKcICxvysTOhclFCOgOW4fAeDVfkt1w4LNt5KFQqmc5nwzJcwcvgDJwGVelGomNf3eueznkV+Orugsu7PxSa6Dr3B4p5ncyOcOMzkFxbI5/7D3q7jimnALcFUdc8h31QUqlH0csFFzV+hzQOTU0vwG73o6f3GiSStxptVK2ZwprpdTq6TkY6+7zBH/Adh8Z6imjKYVn0IOQ56kfjrw9fgUjwDChKFxa37wOFa5No/CMb/oyAd3/kCvOwuH0Gl5lnRtgxpuVZeFxbI5n9D5Z0lPnt9iA2bZkDpzQK3cnHsCx2scFIaf8ntDwHh92P1p7rEI3fZrQF3DtifDMt5mxYyNWfX2AfAnKmbPAfg7EN17FT5ZdthyJd+NLgHx25DCNCp6KgdOPT1gP0qA2Nf7OmO1Dv248XKp+1HsQ1Ykr8U0b8DX73VujJvIkvoicY/JI9gG1G/htuaSTaEn/BvK5LORMqbf97pbH4/qinmX9B7EYs7b1bb1M4JfvWLffz63zecTa60i8Z/CMDR2Ji49X8/u+3Holk/itmoLYJdT/HmPBJyCsxzF02k2vbaJ4DKrZp/iOa/fsglV+Auct/bPQ//e5TRz2GkHsKujJv48O2U4zaQZTjY8eNnoZHGoFlib/ii85fG/yUKXjaRn/lPDLzuv+ARb1/Mvhp4bTtCC1c/H/Rc9GRftng3yhwBCY3XcX87y4/GgkT/2b1l2BceBbySjfeXX4IR12V+Ldqmo0m5v8W77UeZuHfduTjCLm3RCzzFj5pP4n5i3r/bzfqWbilEf32/+LY9VgWv9PgD7l3xJQWbfzO7zwDPZkXDf4m/9EY13At28/XbT9ChqN+NPvZKPIrtIROg6x04+u2ffWoN81+xjXexYsWWqgsqLD/cS3/4PIVqex/8F3H0frrFdn+x7W8xPYfTz2KztiFho3Tcc9GzS+ucPzGuk9B1jR+fb7jEKmj4+UcYp0HQzHx+0O/hi9wxgrnn1TngZb5x9/4FJcPUXJvIt/9k7Jvii0AT9NzsDlGQk3/BWr8UtP8ORaOhqd5/hx2OytbXLj6Oytf/n6D3FkRDraDoGKqFAJIzmUqoCwCKPdKUYGS+rOprciJj4rylygqy6DmXtC94DWnNCXzGPu4qPl3ocqf6R73Wls+qVUfLmSf1fMYKEZbTg9PzqYf0ScKRf9RkU3RFxuQ5irIRaONPPGzaa1qcTJxq4VfkRcgl9USayX00MNSWy7/X67sTIUR09nnLPzJ9MPs45LJzUWey8SX+XsSWrruROZZKFx9tczfndBCT3uTj+gTtc4IFbGEFvrbnbDy0/v06PwdcSt/Xl6ABIdeK4jqoZ+ltmTuTf4SySuUYI4maroH1zi6Uo/yGX8i+y7SvNAq87fGbzeqRmt5MMr8y/XKuNHEY/pCpczfltBCf1vj91r4yd+lI6WFLi/Vr9c4VGTkhejm0GsF3+mhz1pbET3ZubwIocJ2Xek5Fv7W5BO8o9KTfQ/JPH3RqEbbol49BDb1nJ4HRjGcUTUfG+C7xF+4/+lx7UflHRjSkt4H9PfS2oi/NamFLi/s+ZOFPy0vQmeaQvcVLO69z8Ify87lRUhWXo6O9EsW/mXJvzB/LPseEpX8PXdrfZx6jne2zPyL49r7L09W83+X0Pi/i99v8Jf6vy351Ar7f3n8zxb+OPlrFL7U7UcbvyXGjtRjbD/J7DvIFKzjt40XzBS6/6yeR6hsP9G4Nn5jyYer7L87ofVfLHG3aaxp9h/X7b8nPtti4wV5AdIrGL9U/DRbMX7T+vgt5N6BUsGf1ueI/uafQvrRqvknn9JCx8lfxWz/FO0jZzT7V1NWRigLeadaSMgscQw0GCqaPeBLUkxVq2o831brmlL4YK02/bGiOdqBHzB54FdeV55Iq9tKjLXbSpNzLX6a8G01X09rq9kfJe6qtqLxWLEfxmI/r1nzOn5+3/1PE3lf/LVer/T+1RzlPq6+rtymVrVR6Gbpd6tm0R6j62v1v1wR9WK9rt++qmE/xu+G/tqsr0k7FGofbaXXKvbJX7uPy/w1+qNkB/0x9ve7VfUJ7bAMsP/7tR9aYNVoW4k+Ro220tis9ZmWH+trvuh7/Na+RmPobxyu6vxTnreq+788J9Tqy9r9u96LPg7bal6/gUrsrAyCbP7jSv/Sw5ObAPcusNkkzlirPa5XE5Umw+aczEUN7U7yFSiFYdpgd8/gs1qHaxq3a9uzmmU7fVpBQqeXUlEHLW0uvVih23ivUmhtER6f5o3vpSJiPHnQ4w7A5oSbQx4Bv/9EC7/d3gKPZw/m9/uOtvDT2Tb5p0iOcXBzeGGZ3+ehFPgReNw7cruZMRSgIxDA76VU+Fb+sN4W9h9Rwa8irEcz1QWs/JTdMuTT+BsCVn7J3oyAh9LSS6j3H2nh9zinwOuawllxA+5pFv6Id38OOaUMuNRuZmwOagXZ6nwzuNCiua1Fb2vyH6Yfk5T7v1mP5hjBBenK/BSW3OjX/HhGhaz243I0o85LaekljAgcZuH3O7dg/xTKahp2b2/hb/Tty74WEc/28HLEWZlxo9Axenj1PnwEYjO1jQ5pfTwqQJE/Vv5RQa0Q40bBI6r6f0RA88MgP5QSP70uFZNs9O7MGWwp46uZn9LSk38K8UW4AneZn/xniL/Osx280mgL48Z6Qb8m377Mb/nd9PcfUYs/oPGPDB5Z1f+UUXZF/U8Zj639P5lLM9S2nxmcUZkep6KBZsbGoF5Q1Lt/lf03lNqM9yrzR3T7jwSOr+h/F0I+rchkKDDLwu+wt8C7gvHrcIyDq2L8ejwHsK8aPW6vmH88/hXPP07vj6v4nfr8I/mOsc4/cMKhpzOwV82fzbDpRQ6Hm0To8sAlfFYGQdyFmb9pWRfJQSxwJp/Fam0ylNQ9KOY/4pBlB4UZ2zWuopqGnLwNql4bSAqcXna+VTqQT85GUdUdbMlpt+S8R8c0FJ6sJuD0HmjkWSk50WpHPyo8vmPgdGvhpSTKSplL/51rc/gCpxnOt1wjJP04Z60lB9tg8FzD+Zb4E8m7kM9/AEkai1DwPE7RT6Jsl72Jm7XaIq6tEA7+1OCXlQ70xCm8tw1e93SE/CeWwxsL3yKWuF1zsPX+EEF90VFyIowlaetbQcR/HHyeMn8i8yJ6U39nB72G4GmG8yE70aae4KMfyV6PptC5cOnOk8TfkfiT4WDbEqYQXo1fUdNoi89GtjAfPtcUtITONJwnC0oHlvfeajjYNgeON/gz5MQZv4u3/KkOUYO+6GDG3EfsmEuhm03BoxE2OU92p19mZ01yUB0VOgl+3fmWw7KTf+WjB6rGOyZyJtwmfsqUmtAdbMeGz4LkCBn8i3vvQLrwLQKuyRgTPo1zg3AfK51Y1HOH4WBLX+gl/lRhIRb13MNHLi3+/TAisL/FiXZp/FG++944eATqTc6rHelX2VmW+MeGTzScb4l/efLvfPRDDqrjI6fDI2n2T7svS3rv1xxspdHYJHIGp7gv8S/ouQupwre8iNkkcqrBn1M6sbDnTsPBdnSw7PyZLizE4t4/81FOs38GWvQ8JaTe7MfsfEs7HOQDU2eq99SZfgXR5DOag3B4luF8u6L+Xx6/hz9XCnnfOHw2JHuZvzV+KzK6/YwMnWGxn7be2YaDbVOgPH5zhQV89EP2E/EdiIj/IIsTeSxJodcK6vzHwm+y/2TmRcRTf2MH87oK+0+kH+eyGTR+64I/g7SS4zeZ+CNkeR4vYgKm8asqHUgl/2g42HrI6XUl5h85/yEKKQp9V+DyHQPJXXaeV7IvQck8rdUG8p8Mu+58S/zFzF+1ox+qRs3zZ9n5eTj5rOy92fmr7bPy0rybNkifFbFYERISEhLaYCUWK+uHhM/KIIjCDJF+SL8zaICNkiTpiZfIs16lu5DCh9rRT/A82OwNWpsaRyHxB6PqskRteuIlVVnObSonhZsOp592XbSMkkrhS+QSszkpk9N7EJzeI4xKqoXcm8glySmTkjIdC6ennLmTwpZz5JRHocuBM+B0/cDgpzDFfI7uzBrhD15gVFIlfrr7oh0b2joOhi6B3aHxU1r9ZPwG053ZhbDZfRqjvAzxxA16UqmdEQicZfDnC18gHqdaJwn4vAfD7zvK4M9k/4t4kpwaVQT9x8PnLWd+TaafRir1JPdROHgW3O4yfzx5DzsVOhwNqAtdxBk6mZEiG+J/RCb/gZbULnQJJJ2f0o53xW/gqtEe55aoD10Iu85fkJehs/cGyEorfJ6dUR8s82fzX6AjfguXHaCjqAiHrGr8qex/0ZXQksLR0VXQxE91kWKpv3JSr6bgGfCZ+KkuUjz7MiR7I0aEzzcqURN/tHc2UrkP4HaOw4jwRQY/pWlv7fk9soV58Lq2xKjwBUbivby8HMt7f89J+YLu6WgJnWHwU1FI2jVSirQz9EM0+g83+KlyNdWoIabm4DGI6FmCSV2pf6Ar9Xct9Dp0OgJurZI6Pbc9cT96s69y1d+NwudxAr4Sf2vvbUjkPoDHORYbhS+E08S/rOcmZArz4Gf+8+DQ+XPM/wfklTaE3Dvx+60MfyL7JqKJe7XQ8eCxCOtZXkmxFPX/35i/KXQm/Cb+/vqfPmvuf2kcWsIXW+yno/dG5ArfwOPaCk1sP94V2k8u/wV6EjezM6vfdzCCK2n/abL/tGb/QbJ/0/hNpe5FLvsSj99g6EJIpvGbTtyCQv59jvLzB63jN5O4EXJhHiQKXebxW55/svEbtarv7ulwB85YqfmHon4KKdoZVSH5joWkJ57kNnKuZ6daL6TAabDriSeJv5h+kMOXbbyzci5sUrkS9bCSWqQtgtW7fgOV2FkZBBWTt2kZbFnkDxKCjcLy7PVQei5EMfsP/bzWATg2gaOR/i8h13UYihw1Q85kdtjdu8Fdfy/nWMl07G3yurfxtqk79Guo8lIkO/bVi4JpTmie8DVw+Y+HnP8Ayc5DS1T8p7/+ITg9uyGXfhrJnrNN59cSwk3PQ3JORCpxE9KJGw1+8pupb3qNJ7We7rORzTxl8Duk8WhspigUCd0dB6FQ+MTgd7n3QH3jQ5yjIRrdnbeQS/z+wGkIh6+ALC9FW3QPvWaJxl8Xvg6BwAnI5t5HW8chFv6Wxkfh9eyOZPopdHafZeEf1fIiXM7NEYv/AbH4DQY/bXOPbnkdDkcj2rrORsLE75TGY2yLxr80ehByJn6fZw9spPMvbNsdsom/LnAamuuuQF5eigVte+lF5TT+EXW/RX3gBKRz72NR9EcW/jFNjyDg2Q09qaextLvc/xQKO2HE8/A4J3KRurbeUsFC8jkIY9LIV/hLcXHnuehJP23wu6XxmDjyeb7+6/aZSOfL9hPy7I4Jzfdzjo8vWvfmI4gSf3PwFGxc92vk5KX4rHWGhX9s3dXsk5PMfYAv2w+38E9segBh767oSj2DBV0/s/BPGfkveJ2bcZHJZb1aoUHNZyiMLUfO4UXJgs7z0JV+xuD3SJtgysh/8/Vfth+KlIk/7NkNE5vvY/7PWvfhKsQl/pbgyRhT96sV8n/TfpiFf0LTQwh5d0Us9TSWdJ1j4Z848rkV9v/SLur/pyz9P2EEhbtLWBg9GNl82X4Cnj0wpunBfu2nIC/Fd+1W+2+MXMc+Xf3Zfyr9FLpjVvsf0fwinM7NuUhpImG1/6Zmsv8GxGPnIFcxfuuaKF2ChF6qnmyyfyrlEWp4gOefeHQvPoIu8bv9p8Abvrzf+UehI+iuH1v43fUPQnLvCiXzD8g951n4nY3Pwu7cDGryVs5ga54/HU3PGzd1w2pnZfzPVn9nZcEfN8idFeFgOwgq6tVDNVE0TAzIUdXWgmmhorcp84HCFygqi1AsfGzyeleh5l5FUe2Bkn8XRbXV1FaEnNYqrBaoGiqXWS97y+f16sl5nlRtpgqmNuQzf+O2XEarUFyOEFCQzzzLj2hJ5Mr8RbUbeQ69LpgWKlqbIs9DofAZFHkhCoWPLPy0M6OqMeTz7/CdmZk/ndJCSDPZ51Gs4E9SyDXtSpA/TQV/Mv2k9pzUE1X8qbTGH9dfu8Soqt1I6/zlhYrWRhWjc/nPUJAXIlfBTzszihpDOvcO5Ar+Xv09KKW5WsHfk9T6v7dG//emtPDSWMra/3Sm36vzd+nXG32sdiORpdDfgmmhorXl5HnI5D9HTl6EdN5qP/HsK5CVHiRz76JQwd+pv0csPaeKn5LYMUfqmSr+Tj28ulP/Pcz83al/8iPRCn5Z7UY8+x9OmlZeqGhtWXk+0jp/qoKfdmaIP5F7D3nFav8deoXh/vhpQVLJ35XS7L+7Rv/36Pz99/9TVf2fzX+GvLwQ2bzVfpJkP0r/9pPOVNt/yX77s/9Uutr+0/r4TdWw/5xu/+WFiv67UcX0wmdQlUVQKuy/oI9fmecfK38u/dgK5x+Z/FEq+OW01v+KHmZt5lez/9Leucb8Wcz91/SYkJBYrAyO9IyQ1sdcevfWOGkjJzbdka2igZ9Pnv61Xw812myGU1zpb2tb6fnuio+72M91pfepzU/Pt/XJ74QN1fzl91pFfpjbavOXMtJar3QPiN9G/LZB5Dc+t4H1P93BV4qcOEuOnNXvV9t+So6rpb/N15Qeq8VvXFfDfuz99T8V8eyHv8/+pwi6tcQ/0P4nB+9avzPbv22Q7Wcl7H8g41ebD2rbPwbIj/7mnxr8xvNr2UKtOXBYSM9gO9AfDKuDkFWSWKwMgshHRRNNDHZAmgi4KXTWAXvgp6Y2G0BVk6VJnGra7tGKxJUmFIf/JNjsAdhd02B3bmssXkiuwPn8t9N7MGycproUAmiDK3huOYSQKj7z43p4sv9kbvMGzjRdQ0c9DXD7KBwVfFZt5ndIW8Dl2Yv5A8HzLPxuz96QpMlwSBvD4y1tuTu11/GfCrs9AJd7BzipcrSJP6i/h887Ew7HxhZ+ilDg53Aq8DI/h2cGT+U28lEx89vtDQhwWDJQF7Lyu6Qt4PNq/PUhK7/Ps7deNXpjBCv4IwGN3+feAZ4K/gb9PcK+Q+Cs4G8M0fEI+CjIbuF3oj5wCreRj4SZn3w76jhUG+wjYeb3OCch5NmT+Sl6ycwf9OwFj3MLuKSNUOfTjpxKX6jNQUqzHuDQa7/Laj8jwxdojP6D4Kqwn5FhzX7pKIUqDht9TFWfgydpjOQzYlxj56irRj0smHxUzPxe5yREdP5R+mtrjDaEPXvC69yCSw00+GZa+Om9iD/gnooA+2NoxzXae5y/Qv4mToVv7X/qk776vz5w+Ar7v0n/bMv9vzf3P5V6CPsOtdgPfdaOFdiP3zcTUoX91K2E/YcC1fbv92n2Tz4qZn5J2oKLlBK/L2jld7n3hoPH70Zwea38Hv8pPP9IrmlwVMw/nuCK5x9Or2Cef+CEU59/yEelfI2do34cHOoM9lEx80PaHDauLj8MtToLleJqpupfCXV3d+PYY4/lI6ZIJIKTTz4ZyWSy32tOP/10bLrppvB6vWhqasIhhxyCr776yvKcJUuW4MADD4TP50NzczMuvvhiyHJfuX5qS/isDJKKuXdQzP9Hqw1ENX44F4Qelpd7EcXC/2BzbAybl5wBtcmBM9xSyLP8LWyU+8BzkOGoRmfacvpRrg1kp9Blz+7Ge9FWbSH1KNfmkDz7QnJpjoIkRV6KPG8dq3D5fgSHtKnRJlPmVto6tnk4/4rd0WK0USVVOvqx2+vg8VH9mTJ/LvscClRbRBoDL9cPKfNn0n+BIs9nBz2Pd6aJP8Pb0+Sg53JP57wtBqPSjRTVRFIT8HpnGI6CJDrT1458VAR8h8LpnFBmzH+OVOYffGdLdYgkE38m+xYyuVd5Eg/5rfyp7HPI5j6ERLlJ/Fb+ePovXNvI7doSARO/qmZ4654q0/rc0+HXKzxzPyrdXL+IQk+D3v0MR1lmlJeih48cVIR9P4LbxJ/Jf8FHP3RXXhc4Ck4TfzL7NhLZ1+Gw16EhcCx/6ZX4ezPPI537CC5pNFehNvN3pf6qh15viTrfwSb+LDqTj+ihszsh7C3bj6zE0JF8jGsbRbz7GI6yJPIJ6UzSln0R9f5D4HWW7Sed/4Iz+BJ/Y+BIzkdSUjz7Nnqzb3Dob1PgaAt/LPMCUlRbRxrN19lN/J2pvxm1deor+KPJR5k/5Nlxlfi7kk/q/DPhMfFT/1MGWeKvX4X+j2eeQzpP/T8G9Vw/qszfk3oSeXk+O2hT3pOVsR+y/wSNDXIw98yAx72S9l/4HOl02f4dJv5c7i3ksq9x6LKvwv7zNH4LH8HhGA1PxfjNZZ7kNPk0fl3eQyzjN596FKraDolKhJjGb3/zD/m0yBmyHxWSdybspvlHLXwJNfMvnn8cviNgM9mPmptrzJ8271HG/DnsfFY2OQeSXhtrIJLVHF5aOHuNse6///5obW3FXXfdhUKhgFmzZmH77bfHo4+aXQWsuvvuuzFp0iSMGTOGFztXXnklPv74YyxcuBAOhwOKouB73/seRowYgRtuuIFf/yc/+QlOPfVU/Pa3v11pNrFYERISEhLaYLVWFytjz179xcriW9cI65dffonJkyfjvffew3bb0c4g8Pzzz+OAAw7Ad999h1GjVi73zSeffIJtttkG8+fP5x2X5557Dj/84Q+xfPlytLRoC+w777wTP//5z9HR0QGXa+WO/MQx0CCIw5N7r4Ia3RVq549QzL9fbiOH2Z6fQY5Oh9x1DO+iGG3yd8h3nYBc+47Id5/GieBKUgufcTHDDLX1XIKimjLa5OxrSHfsh3R0J+TjVHisvJ1WSD+BdHQPpKO7cghhaS1K4YG5xC1ItE9HMroXCnSHY/Bnke75FXrbpiHRcQDk3LtlDjWGVPdZ6G3bHonOw/kuzLyLE+88Bt1t2yHedRJXYzUY85+it+NgdLdtj2TsQgt/PvsquqN7o6t9KpLxay38lJyuu30XdLXvhHTyHgt/Mn4zOtqmoaN9d2Qz/7Tw9/ZcimjrduiMzkA+946Fv7v7DLS1/gCdHYdyAjuDkXYROo/mtq6uE6GY+PP5T9ARPQhtrdsiFrsAqok/k30Fbe17Ynnb9ujp/a2FP5l6DK1t07G8bUckkn+28PfEb8J3rdOwrG03pNJlfops6YpdiqWt22J5+37ImvjJ4be963QsXv59LI8eymHWxmctL0Vrx1Hc1tZ5ol5zRlMu/wmWRX+Ixa0/QLT7fAt/KvsqFrfthYWtU9HZa+3/3tRjWNS2Mxa17oSehJW/O34zFrZOw+K23ZGo4I/GLsXC1u2wpH0GMhX8rV1nYMHyH+C7GvzLOo7mtuUV/BRlQ9FaC1u3RXv3BSvNH089hqVt07GkdUf0VvDH4jdhSes0LG3bDclV6P9o1+lYuvz7aK3gJ/uJdh6F5a3fR8cq2E8u+yo62/dCtG0qEr0rb//pxB8Ra98RPdE9kauw/2TPrxBrm4qejgNQWMnxS7sgqc7jkGibinTXyZbxqxQ+RaZzJtLt05DtudgyfpXs68h27I9sdGcU4jdY+JX0X5CP7oVcdHfIXJm5zC8nbkUuuivyHftByfzbxJ+D0nsl5OgukDtnWubPYSdKc7G6P2tIb7/9Nh/9lBYqpL333ht2ux3vvFMeE/0plUrhvvvuwyabbILRo0cbr7vVVlsZCxXSfvvtxwu4zz+nGmwrJ7GzMghSe68EMlpZdG3954St8TnYpI0hU6XR/Du69zylkq6Ho/EldiDLd+wDKMuMNps0CU4Ka1a7kO3YkyokGq9p9xwId90tvJWaocqmJo97Z+BcuIIXQM6+jFxM8zEoyR2+CZLvUOSSf0Yu/n/6o9pWr6/hr5Dc2yPdcynyHJFT5g82vwqHNBrJzqMh598uM9rrEWr+D/P3RHeDauKncvDhxudQVLvQE90FRRO/y3MQgvW38VFUrGOGhd8XOA/+0EXIZecg3l1Kna8pGLkFHt9hSCX/hETvFRb++san4HJPRW/sl8ikHzLxu9DY8jokaTQ6O45A3sRPx0TNLW+yP0C0fVcu6FZqczono7HpeahqF6Lt001RG3Z4vAehvv4OzhHTHt3Xwh8Kno9w6GJkMnPQyZVly6qvmw2/78foTdyNWAX/iKan4XFPRWfsF0imyvzkoDxqxBtwSqOxvOMIZHNvlfvY3oDRI95i/qVtu3BByVIb+eJs1PwC8y9p28nC7/cejJaGOzjHx5Lofhb++uB5aAhfjGRmDlq7tNIHJbXUzUbIfxhiiT+hs4J/46an4HVPRTT2S/RW8I8d8Trzf9dxBDK5ty3840Zo/b+4bVcUTPxu52SMbn4eitqFxW3TTVE/dgS8B2HkCvhTmTlo77L2f1PdbAT9Wv93VfCPWon+b6vR/xvp/d/avkuV/bQ0vdCv/RQKX6Crgt8fPA/B0MX92n8m+Wek41da+EMNf4fTvT2SPZcil37YYv+RlRi/SUohYBq/lFHW3/gvnn/SHbtb5h+H54fw1M3m+SfXeZCFXwqcA2fwfCjZlyHHNB+bkqTwH+DwzYScuhdK/DcWfmfDE7C7toPSezmKFfOno/EFnj+H3c7KmLNWf2dlye1YunSphdXtdvPP6oiOZB544AF8/fXXlsfJx+Sqq67CmWeS31dt3X777bjkkkt4sbL55pvjX//6F++qkE477TQsXrwYL7xAhT81pdNp+P1+/Pvf/+ajp5WR2FkZDOWoemxpxUt/54DCe1oF1XxpoiMpgNoByF+jKC8GlCWWtqL8OaB2Q6WQwmLC8poqvwclXfqPPkmU1phFyBROyG2vVHj/2yHnXuV/yVnKLVKSlnNBpvIApXDECn4l/w7zy/n/WhnVDijyl1DkRVAr+KlSK4U9y4UPuaqz+TXzOe3987k3qvhzXKaedlxeruKncGjuYq5wa+XP6ZVZtevN/FkU8nOZP1/Br6pRXjDJ8iIoymJLW6HwKYd9UgI8jb8c1klfJKRstpo/w9WbqVTAS1X8Wf6dgLT+GZn5M1nts9GuL/MXkUUu9zbzZ/nzNvWxGuUFE4VeyxX8VOmaviizNfhL75+u0f/JrNa3FLpdyZ/KanaX0p9j5k9ntf5P1uCnytvEn+EQVCt/jvkXoVDBnyt8ymHDxK9W8Kf0/u+fv7r/td+J+Kv7P70a/S/LC/uwn65+7Sdfw35Ktt2f/edr8Bd0+8/z72+1f3kF41eVF6FYMX5VffxySHPF+FX08avw52nlV3Q2Sr1Qya/ynASouh2V+antDd3fr3r+LBbew7DUIDnYjh49mhc/pZ9rr722z7f8xS9+wb5I/f1UOsSuqsgp96OPPsLrr7+OiRMn4ogjjkA2S7l4Bk8ig+1giJxqaRFiDiuzRTQve/KO5zss8/MjejGwStHOi197vaqQyEj5vSylN+18t8TPstNzzBtlNk7wprU16J745clJez611esJ6MrXam21+dkJzlbrDoTu3Hyw2ar57Tq/vQY/OQWW2yr49etq8WvPp+vqeRFi4df7n6IrtDtc0zsSv94vlfyUwZadpCv4S33lqMXvqDe1WfmpMJzWVt8vP30Jmq/V2vrmL9V3sUqCze7XOdAHRzW/o5/+dxjX9d3/jpr8ffe/o09+rf9r8TtWgr9W/zv66X/HAPvfsYL+789+bP3YT3/2r42RyvHbN//Axy/x63NNDQ5bv/NPNT/NdVpbPYoWftVoqz1/1urfYSDOQLv6GWyX1thZ6UsXXnghTjzRumNXqfHjx7MDbDRaPgokUcQOOc1SW38qLZo222wz7LDDDqirq8NTTz2Fo48+mq99993y0SSpvZ0SDmKFr2uW2FkZBNmClxrhfyzXroCbqrbaYA9daakJbvP+BDZpPGyOJjiMkGdNjtBlWg4H57awe8rFwfgYJaxtAUveg2B3lqMHyLPeFfwF/5PCBM0FwGiCcHLII+Cm0ENb2cPeLm0OV6kic/gKC7/k3oN/iN8X/o2F3+WbxRFGdkcTvIFSyKomX+hyTgUuubaDy3OIhd8fvlrj8B4CicMijQ6BP3SZxkGhzxwWqV9lb4AvqIV+B0MXwWbil6RJHJnEbZGrLPwu955wc+ipDeHwby38Pv/JkJwT4HA0IaCHY5YUCl/B/C7X9vB4rfxh7gfA5zsELhM/PT+i8wf8p8JRwR8MUNZaIBK6CHYTv1OahKBfqxZcH/k/C7+Hwnt1/sbItRb+kP9kuJwTIDmaEAlq4cglNYSv4FT+btf28HtnWvgbI9dojL5D4HFZ+RvDGn9d4BRIJn5aoNQFNf6GCn6XNMmoiN0UuUrLz1HqY8+e8Ov8zRFr/4dN/PUV/d+k83tc2yNQ0f9Nkd+skD8cOLWKP6Lz19XgX5n+b6jo/6D/ZI7QIfsJVfR/ROfvz368FPpeYT/BlbB/L6XCN/E7pM2Niup+nhvK/JSJ1rkS49cVKIU1a6IM2dr8sx0cnoMr5h/tCNnhPQg25/ct848z+HOtzT8LMBcgtNdDCpyhtdFcYfOXL5MmwuHVQvftwV9VzJ+7webeDcNSg7SzEgqFLD/9LVYonJiidfr7ISfXHXfcET09Pfjggw+Ma1955RWoqopp06atwq9Y1KJIczn+P73up59+alkIzZkzh7nJoXdlJXxWBkl8rJN/T7tL0HOsGG2UsZbS6jtGw+ba0QgPJKmULVJeAJu0BeyubcrXFOno53VOeU2Th90Uwkjbu1TBlEIHHe6dYTdNEFQJVclRWxESTbimuyRyoKOjH0riJLn3MeqAkCgjLW0d012a5NZyNJQkFz6Hkv8Edmk0JNd0C38h9w6HLjsod4kphJH4C7lXOeW+5NoeknMzC38+O4f5ne6dLV/wFM5MbRx67dnLuHtnRiXKzok0oXo8xO8rM8oLkc/NZX7KBWPmL1DG0cL/IDnGwOXe2cKfo2yj8nz2N3BV8Odyr0Kh0GvXVDgr+DPZFzmtuse9i1HhtsRPbVz12rO3ZZeAHEjp6IdCZ72efYw6RMxIGVFzc3kHwFvBTxl3c4X/wekYA08FPx23UOi1yzUFngp+ei+qek1f/i7nRAs/+adw6Kx7F84ZYvSxmtCOXIoq/N69qvjTev/7K/gpo2tG5/fX4M/q/N4q/neQ59DxyVX89F5ctds1Fa6K/u+Ln/qfj3yKKnze2v1P/L5V7P+S/VT2f476n+1nCtyrYD90LESsbvcuRoXzFdk/jd881c+hG5Qa47egj1/nKoxfOfcOVPlbHr8OvVZPiV/JvaZVfef5x8qvZl/m8Wt3T6+af1Q6uioWYffsYZl/KICA5jRa4Ng5D0yZv0jHUvr8STlWzPzDymdl1Omr77Oy/K41GrpMux4UrVMKXSaH21Lo8rJly7DXXnvhwQcfxNSpU7FgwQI88cQT2HfffXlRRFFD1113Hd58802OLiJ/l1LoMkUTXX/99Whra8Pxxx+PU045RYQur6vQZc0r3mGZDMxtlJ2zr+tqtWkfjTKANm2btlTS3dqmOfz1xSj4Bb/gF/xDjX/YLFZG0mJl4Nl5ZTWPl1rX3GKFjnzOPvtsPPvssxwFdNhhh+GWW25BIKDt6i1atIgjfV599VXsvvvuHI5Miw7ajYnFYhzxs+uuu+Lyyy9nR9uSyMGWHHRfe+01dqw94YQTeFEjSSvviSKOgQZBFJ6sdp+IYvsUFKPboWgOK5SXQO48CEr7JMjRnTj5UUlq/n9aKF/bROSj+0A1hUWSZ32+fTutrfNwFM1hwalHkG3fEtm2ich1n2GEFdIEQqGE2bZJyLZtjnzvFfrkoIUH5mIX8OPZtskoJO+08Ge6jkW6bQLS7VtDzjxTZpSXINOxP9JtmyLdPhUKR3bojPn/IR2drrVF94Ra+MZoK2TnINn2PSRaxyPVcahe1FBTPvUQEm1bING6KdIUsm3iz8Z/h0TrZki0TkCm59cW/nTsPH480bo5cok7LPypzmP49RJtW6HA9Wh0Rnkx4tEZ6G3dBL1t26Fg4pfzHyPeviO3xaO7Q6ngj7dtw22Jjh9Z+HOph9DbNgm9reORrODPxK9Db+sE9NLvVsGfiP0M3a3j0d26GTKJ28t9rMYQ7zwa3a2bINY2hYtOmvl7ovuhu3Ucutu2RYEjU8r8sfYduC1GoaIm/lz2RXS1bY3O1rGIdRwCxcSfST2IzraJ6Gwdh97uUyz8FEre2ToenfR791xm4Y/HzuXHO1snIF3B39N5FL9eV9tkZCv4u6P7MkdX2w84+aDRx/mP0dW+A7d1R3dbp/wD7f+B2M9A7T8bOx/pts2QbpuEfMX4zXUdx/NBtv17kDP/KP9u8hJkOw5Etm0zZNp3gDII84+afhRKdBso7VtAjp1l4VcSN0JpnwKlfTKU3qss/ErPRdrj7VtCTd69UvPnsNMQz2BbX1/PuyiJRIIXRPfee6+xUCGNGzeOP2daqJBot4Qiemg3Jp/Psy/NI488YlmokMaOHcvPoyggyq1y4403rtJChSR2VgZBauxcgL3lS85jdtganoXNuRlkCjOWKbeBdkfB2Rub3uC/8+07AcW47rBGFZlHwNX0OqC0Ik+hy5ANz3+baxpcDQ9Dyb+HfJd21lt6L4fvGLjCV3PRsELvRRY2Z+jXkPwnoUCl4JM0QZc/blfdPXB49kQ2dhYUjqgp83sbX4DdOZHzuRTleSZ+L3zNbzE/5WAw89scI+Bt+i+KynIko7tZ+B2uHeBvfIxzuKSNyqzaezl9x8IbuYYz72Z7SqnDNblDl8MdOAXZ+A3IJ2+18Hvr74PTsxfS3WdCruD3N70Ah3NzxKP7QpW/sfCHWt7mrfR421Quc1/mH4lQ85vMH4/uauGXXDsg0Pg48ye7Smn6tfdy+Y6DT+dP91j9GDyhK+AJnIJ0/HpkkrMt/MH6B3irP9F9BmcZNfOHm+ZAcm6Onug+UCr461rmMj/l1SgWew1+u2MkIs1vcRHJ7ujOFn6na0dEGp/gHBw9XaXK0Np7eXzHIxj5LbLpvyDRY/Uj8YeuhC9wKlLx3yFdwR+qf4CP3Hq7T6/ir2t6ifkpn04lf0PLO8zf1bZ9FX9989trnX+g/T9Q+xmI/ecTN6CQvM3C7667F5JnL+RiP4XKEUFlfnfjczx+KR+Kdfx64Gl+c8DzD+VAUbo1X5nSe9m8R8MRvgpq5u9Qey+x8NuDl8HunwUl8QcUU3dYnZgjf+Kjov7mz2G3szLitNXfWWm7e4NMeip2VgZDhfctVUh58Bc+5bsJClO2eMCTZ748n3dcUOwxedZTReZlnONApRBmFEwDW0Ex/6H2Cvy3+WNT2e+F/1X4oDp00LiOGM3rUglqXnOkoirPlfxK4X/MX5S/quBPQZXn1eTnnA1qF5RCNT+Vj+d/Far5tfcnjmp+RefXnmPlV/rl/0RL1id/Wc1fmMd3nMUq/u84RwxVpa3kl/X3kmvwk68Pt9Xk164r1OCX9eRX5GtQyS/r/a/U4FcK86Awf8zCr/bDXyi9F9uIlZ/CvDWO96tD3wfM//F6zt9//w/UfgZu/+/1OX5VbrPyq7r9V4/fdJ/jd2XmnyJXarbyl8KMtedY+bXnU1v1/FPkvuh7/hyWUtXV/9lAJRYrgyEHZeqrcAhjpzMXYKOwPvMZrI3btLoYFdeQw5k9zDWErLLrxcMAO7eZDZbu6MZol1e12YzrtOeY308xki7Za/BrUQku9uiv5LetgN9elczJDrvUN79d569usxnREXbH2Cp+7fn989tq8FNbX/wU0kyOiIPHrzE6+uF3VH029Fj//PZ++KnIZCV/6TFHDX6NjdpGryL/6D75tevWX37HkLGf0nW1xq/2PrYa9m9bjfHb3/wD/tvKr81/pfe08mvPp+ifGvzGa/Y1fw5DDfFjoKEssVgZBNlC/0d/lB/wHg+4pmmhy5E/mMqx23hblCcLewRS+BrTR+CEFPkDZ8a0k2e+OazZ5ocUuYH/affsD7s5rNDeDGfocv6n5J8FmymsmcIDJb3qszN4kT6p6Ze5dzeqnrrD11ryGki+E7l4IvF7IrdY+F2hy7XJ2h6Bi64z8bsjNzO/wznFGhZp88PD/QBIngMgmcKabfZmeDh0mqpGnwyHiZ/Cq9166Kk7dLGF30HhmT6taq4ncp0lb4rTdyIcOr8vMtvC7w1dwV8cFGXhjVxn4fdF/sj8EkV2VPD7Ijdpz/IcAGcFv1cPK3f3w+8LXWJ8OfLruPcwql77K/jdvlmQXDsxfyByq4XfF7qCo0eI3x/5nYU/ELlF59+SswIbjDY/gjq/izIhG9W+KTy2GYHwVXro7CmWsHIKj/UFNTv0hX5u4afwcI/OH4z8zsLv8Z0Ep84fitxm4adjmRJ/IHK9hT8Umb1O+Afa/wOxn4HavzNYaf+7Q9LHr4vmEdP4dfhOgN21A/O7Ijdb+OlYuDR+BzL/2NwzYDOnVbA3wcGhx/S0EwFzWLM0EXa/lvXUHrgAMC+CKDxZr3re1/wpJLRWfFbIa/jqq6/mOG0KVSJHnOOOOw6XXXZZv4WLKOsdJbF5/PHHOU6baghQKl9zXYEhGbqsJgDacqXQO2lCRVsXioV5fCdRuhsy2pTlHPZskza1VCElqfICQOmAzbm5NQSQ4tjlL7XQQWlLSwgvObRRmHQRKuzOrYwKq1pbDmrhUz5vt0mTLd76RTXOqbQpsROddVsZaWv4G+a38x2SiZH5FzG/uYozSZEXsGOewzmpip+OZ6jPHMRYwU+MRUra5dy6gj8LJa/x251TqvgVnd9Rwa8qXex3QJO0owY/ZeN19MlPoeOTLCGkK+KnIyi6w6zFL+v8jgp+lfm/4Dt5qSb/13znXcmvKMuhygvhkCZU8cvytxzuKtXgV+QvOPTaWYNfZn4FknObmvzk7yDV4KfMwPY++GX5a94VqeZfpvc/5S5Zt/wD6f81YT/92T+1ceivNKWP8VtvCTMuj19t/rEP0vzDc10xCRBHBT/4GEwBasw/3GZzA1XzT9/z57DyWWk8afV9Vjrv3SB9VtZYBltK30vJZKjU9IQJE/DZZ59xSWiqHUCewH3p/PPP57oCTz75JH+4FEZ16KGHctz2kBZvoTbyNmp1W4CTwBkZGy1ttO3aTJ5o1U2cNZT+UZ4M+L8U2mdvgq3o0wZ+1ZZtM2wcPlj58Tr5TpSuqQortPmYsTp7Jd8Waa9ZlZmTWMKAowU2e/XAoaRWxT747fYm7fEa/HxEUJPfpX2h9cFv74Ofys07+uGX6DX74Nee5K/B39wnPzP2wS/1wU+ZUjXGvvhbavLb2X5G1Ox/h72RK93YavA77M1w0OM1+KUB8kv98Et98kdWwI8++e198K/N/if70doGz376s3+7vT/7b649/9gDPDaqM2MPfP4p0vjtY/4B8ys15x+at2rz9zN/DicNUgbbDVFrbLEyY8YM/jGn86UCSXfccUefixVaLd5zzz0cOrXnnuSNDq7guMUWW2Du3LmcxncoqigvRTE2S6/1YwMC58OmZ24sFj6B0n0ywM6ETtjD18GuZ7ekehlqD20X53hBY4/cCbtb+x3V1ENQE1fraalb4Kh/gO84tPDA66Ck/sTP4+KH1OZoQrFYgNzzM6h6rR2ba2c46+/mO3m68ypQeGDhY26ze4/kbWCaNIg/3328we8IXghJz3xL4Y2F7pMMfinyOzj07KiUmE4mT35kmd9Zd7fBr6QehMyFE4l/BJz1D3Jiu6HEP9D+VxO/QzH9Z+3DlybBUXc/bI5G5ld7zkcxp/HDtTMcdXcO+/5Xdf4i18qxQQpeZGRO5vDY7lkGvzNyPSSDfw7yJn5X3Z/g0Pnl1IMoxK8y+N31Dxn8cuJayCZ+d/2DBn+e+SmyB7C7doFrHdn/mrCfYuwUQOcveg+HLXS1wd/f/FPsPtXgR/ha2LzaMXKRErv1nGfYPyJ3wObWjl+KqYdRTPzG4Ef9fQZ/MXE9kL5H598cqLvP4C9SNFxOryPlmg7U3bFa/EJC68RnhRYjFMfdlyixDGXNo7LUJVEq4DFjxnCZ6VqioyLaYjP/rG0V47/Sqyfz/1BM/gHFvDYoldjZAIdnkgoc2kdHI5zpsfRFyZeloPacxYmRivJ8qAn9i4akdkLRQxqpWFjpi4Yvk+dB1quZKumH9BBGvS3/FhQ9H4Oc+L2WRVeXmnkCqp6PodD7Sws/5UpQ85oXfyH2Uwu/TOXidX45RufaerGqYgqF2JnMrxbmQy590fCbdUDWQ0qHEv9A+r9IWT1LXzQMNg8qT+oUaPEwiqWJmpR/C2ryrg2g/3/BkTAlfjlxg8Gfi51l4S/0XGTw5yv487EzDP4CVxgu8+f5S1Xjlyv48/GrTfz6Fz0vNN6EvA7sf03YTzF5kzVCJvMkkH12hfNPkRhN/MXen+v8yfJCRecv9vzU4C+WFlo6f7FHT4lAxRNLCxXmp+dqpRxAldtzpoKL+bdR1POpDJR/uIkS5q3uz4aqtbZYmT9/PmbPno3TTz+9z+eQbwv5s0Qi1q1Y8lehtlqiapPm6pNUjXKty8jDYH7sW+2MVl1e4SFPIYJLOJeBMVGwyNM7TklbUJQXVmwVKvx6/CzO2WL+2Khas1Yxs1iYX+VVrz2f/jaHUJMkU1s1P09YffDTHTSddVfz92r8yoIq/uIQ4x9o/0Opxa+VVNd4KqIalOHf/+U8NmWp/fCrq8FPvhd99b+W1KySf97a7/81YD/ghHOV/N+uYP7JA2prH/PP8j7tHzX5F+j//LaKX3v/vvi/HTD/sBT5+6ir8VPccI+BVnmxMpBy01RPgI6EDj/8cPZbGUz98pe/5B2b0g9l0FvrkiZXD1LnRK7Bo4XlmbvZCTjGaSF9fBZcCiu0a2GG9nrdwcx8jQNwahkByVmvKnRZ2tLUZh70Ra45xG3MaH5NGTZ+PmBzTqkOfZQ275Pf5hinhTdW8Tf0yV96r6HCP9D+523vPvjpSEJLpLVh9T85O1eFLuv8Wsi8ld++xvi3GLL2v7r2A2cNfn6t/uYf14DmH6wiPznZ9s0/acD8w1IidHntLVYoUocKFPX3Q/4pJVHtgD322AM77bQT7r67nGK5lqhcNKXspcqPZlEq375KSVO1ycoKlGtbNgoBlDbV/+eALfgr2Jxbaf+L3K45lbE8sFN4r6ORS7HbKayz5Hxni8ARofNpmrw2gT1EBZ50T3qKYgn/nv9pd+8KR+BcY5Kh95HCWuig3Xcs7N5ydk+be184AqdoLxG8ADZX2efH7psFu+dA/rcz/FuOBijxOyg82bW11lZ3p4VfovBMdmT1w1l3R5nfHoGz7i7mt0vjIXFYc5nfqYcuDyX+gfa/zX9OeZJ3bgV7iKpu09x/DGyeQ8uGQcXm/CcP+/6nEHYzv9PE7yJGcibV+V0UXq3zuyr4XXV3G/zO8HUGP0WxuPTQX4d7N0gcFlzi3xound/hOxYOb7n/7e59IQVOXev9vybsx0a/szmc13cC4DlghfOPjUKvTfw2Ck/W5x9uM+w/DBv5rOj8ttA1Fn5bWPMztLl3AfxnW/htoV/qTEcDHlN2YffewGryCwmtlXT7tKNCC5Vtt90WDz/8MByO/itp0s4IVW587LHHuIASiZxyyW+FfFZWxsF2nYUu01mi2qlF/phC+bQ2WWujsLwK73ltq7aLPeHNYX5aWxZQe7TIn4oqpHTmjGJGv85a+ItqbdDdj5bQyvx6RZ3RXRW9IPgFv+BfH/i7dP7gIPJ3867QUOUfTqHLewWPhUQ7XgOUXMzj5cQjG2To8hpbrNBChYodUQGjBx54wLJQKe2SVJabJlFlRip4dP/99/OHcc45WnKit94qFxAbqlWXhYSEhITWL63VxUrgmNVfrCQf3SC/39ZY6PKcOXPYqZZ+Nt7Ymr65tD6iyB/aOaFKjCXddNNNRmlqc1I4ISEhISEhoQ1TouqykJCQkNAGq7W5s7Kn76jV3ll5Jf34Bvn9tsZ2VoSEhISEhIRM4r2B1dgfKA6rvYVVkihkKCQkJCQkJDSkJXZWhISEhISE1oYosZtN7KwMRGKxIiQkJCQktDbEi43VSJlf3HAXK+IYSEhISEhISGhIS+ysCAkJCQkJrQUV1SKKq3EMVNyAd1bEYkVISEhISGhtiKsmr84xkIoNVWKxIiQkJCQktBYkdlYGLuGzIiQkJCQkJDSkNex2VkorT8oYKCQkJCQk1J9K3xVrY9dCLuZW6yhHRgEbqobdYiWRSPDfo0ePXtcoQkJCQkLr0XcHpcRfE3K5XFzA979t/17t1xoxYgS/3oamYVcbSFVVLF++HMFgsKp0+dpYodMiaenSpRtc3YY1IdGfgyvRn4Mr0Z/Doz/pK5AWKqNGjeIiumtK2WwW+Xx+tV/H5XLB4/FgQ9Ow21khY6us8ry2RQNNTF6DJ9GfgyvRn4Mr0Z/rf3+uqR0Vs2iBsSEuMgZLwsFWSEhISEhIaEhLLFaEhISEhISEhrTEYmUQ5Xa7ccUVV/DfQqsv0Z+DK9GfgyvRn4Mr0Z9CG5SDrZCQkJCQkNDwkthZERISEhISEhrSEosVISEhISEhoSEtsVgREhISEhISGtISixUhISEhISGhIS2xWFkDWrRoEU4++WRssskm8Hq92HTTTdnLfTCyF26ouuaaa7DTTjvB5/MhEomsa5z1TrfddhvGjRvHSammTZuGd999d10jrbd64403cNBBB3HGU8qS/fTTT69rpPVW1157LbbffnvOON7c3IyZM2fi66+/XtdYQkNQYrGyBvTVV19x2v+77roLn3/+OW666SbceeeduPTSS9c12norWugdfvjhOPPMM9c1ynqnJ554AhdccAEvmD/88ENss8022G+//RCNRtc12nqpVCrFfUgLQKHV0+uvv46f/vSnmDt3LubMmYNCoYB9992X+1hIyCwRuryWdMMNN+COO+7AggUL1jXKeq37778f5513Hnp6etY1ynoj2kmhu9dbb72V/08LaarBcs455+AXv/jFusZbr0U7K0899RTvCAitvjo6OniHhRYxu+6667rGERpCEjsra0m9vb2or69f1xhCG+CO1AcffIC9997bUj+L/v/222+vUzYhoVrzJEnMlUKVEouVtaD58+dj9uzZOP3009c1itAGps7OTiiKgpaWFsvj9P+2trZ1xiUkVCna8aNd0+nTp2PLLbdc1zhCQ0xisbIKoi1z2vbt74f8VcxatmwZZsyYwf4Wp5566jpjHy79KSQkNDxFviufffYZHn/88XWNIjQEJa1rgPVJF154IU488cR+nzN+/Hjj38uXL8cee+zBUSx33333WiAc3v0ptOpqbGyEw+FAe3u75XH6/4gRI9YZl5CQWWeffTb++c9/cqTVxhtvvK5xhIagxGJlFdTU1MQ/KyPaUaGFyrbbbov77ruP/QSEBt6fQgOTy+ViG3z55ZcNJ1Dabqf/0xeEkNC6FMV3kKM3OSm/9tprnO5BSKiWxGJlDYgWKrvvvjvGjh2LG2+8kT3cSxJ3swPTkiVL0N3dzX+TD8bHH3/Mj0+YMAGBQGBd4w1pUdjyCSecgO222w5Tp07FzTffzKGhs2bNWtdo66WSyST7oZW0cOFCtkdyCh0zZsw6ZVsfj34effRRPPPMM5xrpeRHFQ6HOUeVkJAhCl0WGlzdd999FA5e80doYDrhhBNq9uerr766rtHWC82ePbs4ZsyYosvlKk6dOrU4d+7cdY203opsrpYtko0KrZr6midpDhUSMkvkWRESEhISEhIa0hKOFEJCQkJCQkJDWmKxIiQkJCQkJDSkJRYrQkJCQkJCQkNaYrEiJCQkJCQkNKQlFitCQkJCQkJCQ1pisSIkJCQkJCQ0pCUWK0JCQkJCQkJDWmKxIiQkJCQkJDSkJRYrQkJCQkJCQkNaYrEiJCQkJCQkNKQlFitCQkJCQkJCQ1pisSIkJCQkJCSEoaz/B6f1kz7jE2VKAAAAAElFTkSuQmCC", "text/plain": [ - "
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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "points = data_input.extract(['x', 'y']).detach().numpy()\n", + "points = data_input.extract([\"x\", \"y\"]).detach().numpy()\n", "truth = data_output.detach().numpy()\n", "\n", "plt.scatter(points[:, 0], points[:, 1], c=truth, s=8)\n", - "plt.axis('equal')\n", + "plt.axis(\"equal\")\n", "plt.colorbar()\n", "plt.show()" ] @@ -155,57 +180,60 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 23, "id": "8ec0d95d-72c2-40a4-a310-21c3d6fe17d2", "metadata": {}, "outputs": [], "source": [ - "### Define ranges of variables\n", - "x_min = -2\n", - "x_max = 2\n", - "y_min = -2\n", - "y_max = 2\n", + "def laplace_equation(input_, output_, params_):\n", + " \"\"\"\n", + " Implementation of the laplace equation.\n", + "\n", + " :param LabelTensor input_: Input data of the problem.\n", + " :param LabelTensor output_: Output data of the problem.\n", + " :param dict params_: Parameters of the problem.\n", + " :return: The residual of the laplace equation.\n", + " :rtype: LabelTensor\n", + " \"\"\"\n", + " force_term = torch.exp(\n", + " -2 * (input_.extract([\"x\"]) - params_[\"mu1\"]) ** 2\n", + " - 2 * (input_.extract([\"y\"]) - params_[\"mu2\"]) ** 2\n", + " )\n", + " delta_u = laplacian(output_, input_, components=[\"u\"], d=[\"x\", \"y\"])\n", + " return delta_u - force_term\n", + "\n", "\n", "class Poisson(SpatialProblem, InverseProblem):\n", - " '''\n", - " Problem definition for the Poisson equation.\n", - " '''\n", - " output_variables = ['u']\n", - " spatial_domain = CartesianDomain({'x': [x_min, x_max], 'y': [y_min, y_max]})\n", - " # define the ranges for the parameters\n", - " unknown_parameter_domain = CartesianDomain({'mu1': [-1, 1], 'mu2': [-1, 1]})\n", + " r\"\"\"\n", + " Implementation of the inverse 2-dimensional Poisson problem in the square\n", + " domain :math:`[0, 1] \\times [0, 1]`,\n", + " with unknown parameter domain :math:`[-1, 1] \\times [-1, 1]`.\n", + " \"\"\"\n", "\n", - " def laplace_equation(input_, output_, params_):\n", - " '''\n", - " Laplace equation with a force term.\n", - " '''\n", - " force_term = torch.exp(\n", - " - 2*(input_.extract(['x']) - params_['mu1'])**2\n", - " - 2*(input_.extract(['y']) - params_['mu2'])**2)\n", - " delta_u = laplacian(output_, input_, components=['u'], d=['x', 'y'])\n", + " output_variables = [\"u\"]\n", + " x_min, x_max = -2, 2\n", + " y_min, y_max = -2, 2\n", + " spatial_domain = CartesianDomain({\"x\": [x_min, x_max], \"y\": [y_min, y_max]})\n", + " unknown_parameter_domain = CartesianDomain({\"mu1\": [-1, 1], \"mu2\": [-1, 1]})\n", "\n", - " return delta_u - force_term\n", + " domains = {\n", + " \"g1\": CartesianDomain({\"x\": [x_min, x_max], \"y\": y_max}),\n", + " \"g2\": CartesianDomain({\"x\": [x_min, x_max], \"y\": y_min}),\n", + " \"g3\": CartesianDomain({\"x\": x_max, \"y\": [y_min, y_max]}),\n", + " \"g4\": CartesianDomain({\"x\": x_min, \"y\": [y_min, y_max]}),\n", + " \"D\": CartesianDomain({\"x\": [x_min, x_max], \"y\": [y_min, y_max]}),\n", + " }\n", "\n", - " # define the conditions for the loss (boundary conditions, equation, data)\n", " conditions = {\n", - " 'gamma1': Condition(location=CartesianDomain({'x': [x_min, x_max],\n", - " 'y': y_max}),\n", - " equation=FixedValue(0.0, components=['u'])),\n", - " 'gamma2': Condition(location=CartesianDomain({'x': [x_min, x_max], 'y': y_min\n", - " }),\n", - " equation=FixedValue(0.0, components=['u'])),\n", - " 'gamma3': Condition(location=CartesianDomain({'x': x_max, 'y': [y_min, y_max]\n", - " }),\n", - " equation=FixedValue(0.0, components=['u'])),\n", - " 'gamma4': Condition(location=CartesianDomain({'x': x_min, 'y': [y_min, y_max]\n", - " }),\n", - " equation=FixedValue(0.0, components=['u'])),\n", - " 'D': Condition(location=CartesianDomain({'x': [x_min, x_max], 'y': [y_min, y_max]\n", - " }),\n", - " equation=Equation(laplace_equation)),\n", - " 'data': Condition(input_points=data_input.extract(['x', 'y']), output_points=data_output)\n", + " \"g1\": Condition(domain=\"g1\", equation=FixedValue(0.0)),\n", + " \"g2\": Condition(domain=\"g2\", equation=FixedValue(0.0)),\n", + " \"g3\": Condition(domain=\"g3\", equation=FixedValue(0.0)),\n", + " \"g4\": Condition(domain=\"g4\", equation=FixedValue(0.0)),\n", + " \"D\": Condition(domain=\"D\", equation=Equation(laplace_equation)),\n", + " \"data\": Condition(input=data_input, target=data_output),\n", " }\n", "\n", + "\n", "problem = Poisson()" ] }, @@ -219,7 +247,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 24, "id": "c4170514-eb73-488e-8942-0129070e4e13", "metadata": {}, "outputs": [], @@ -228,8 +256,8 @@ " layers=[20, 20, 20],\n", " func=torch.nn.Softplus,\n", " output_dimensions=len(problem.output_variables),\n", - " input_dimensions=len(problem.input_variables)\n", - " )" + " input_dimensions=len(problem.input_variables),\n", + ")" ] }, { @@ -242,14 +270,17 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 25, "id": "e3e0ae40-d8c6-4c08-81e8-85adc60a94e6", "metadata": {}, "outputs": [], "source": [ - "problem.discretise_domain(20, 'grid', locations=['D'], variables=['x', 'y'])\n", - "problem.discretise_domain(1000, 'random', locations=['gamma1', 'gamma2',\n", - " 'gamma3', 'gamma4'], variables=['x', 'y'])" + "problem.discretise_domain(20, \"grid\", domains=[\"D\"])\n", + "problem.discretise_domain(\n", + " 1000,\n", + " \"random\",\n", + " domains=[\"g1\", \"g2\", \"g3\", \"g4\"],\n", + ")" ] }, { @@ -263,7 +294,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 26, "id": "e1409953-eb1b-443b-923d-c7ec3af0dfb0", "metadata": {}, "outputs": [], @@ -271,13 +302,18 @@ "# temporary directory for saving logs of training\n", "tmp_dir = \"tmp_poisson_inverse\"\n", "\n", + "\n", "class SaveParameters(Callback):\n", - " '''\n", + " \"\"\"\n", " Callback to save the parameters of the model every 100 epochs.\n", - " '''\n", + " \"\"\"\n", + "\n", " def on_train_epoch_end(self, trainer, __):\n", " if trainer.current_epoch % 100 == 99:\n", - " torch.save(trainer.solver.problem.unknown_parameters, '{}/parameters_epoch{}'.format(tmp_dir, trainer.current_epoch))" + " torch.save(\n", + " trainer.solver.problem.unknown_parameters,\n", + " \"{}/parameters_epoch{}\".format(tmp_dir, trainer.current_epoch),\n", + " )" ] }, { @@ -290,17 +326,58 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "id": "05a0f311-3cca-429b-be2c-1fa899b14e62", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: True (mps), used: False\n", + "TPU available: False, using: 0 TPU cores\n", + "HPU available: False, using: 0 HPUs\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1499: 100%|██████████| 1/1 [00:00<00:00, 68.34it/s, v_num=2, g1_loss=0.000142, g2_loss=3.78e-5, g3_loss=0.000105, g4_loss=3.2e-5, D_loss=0.000561, data_loss=2.71e-5, train_loss=0.000906] " + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "`Trainer.fit` stopped: `max_epochs=1500` reached.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1499: 100%|██████████| 1/1 [00:00<00:00, 54.70it/s, v_num=2, g1_loss=0.000142, g2_loss=3.78e-5, g3_loss=0.000105, g4_loss=3.2e-5, D_loss=0.000561, data_loss=2.71e-5, train_loss=0.000906]\n" + ] + } + ], "source": [ - "### train the problem with PINN\n", - "max_epochs = 5000\n", - "pinn = PINN(problem, model, optimizer_kwargs={'lr':0.005})\n", + "max_epochs = 1500\n", + "pinn = PINN(\n", + " problem, model, optimizer=TorchOptimizer(torch.optim.Adam, lr=0.005)\n", + ")\n", "# define the trainer for the solver\n", - "trainer = Trainer(solver=pinn, accelerator='cpu', max_epochs=max_epochs,\n", - " default_root_dir=tmp_dir, callbacks=[SaveParameters()])\n", + "trainer = Trainer(\n", + " solver=pinn,\n", + " accelerator=\"cpu\",\n", + " max_epochs=max_epochs,\n", + " default_root_dir=tmp_dir,\n", + " enable_model_summary=False,\n", + " callbacks=[SaveParameters()],\n", + " train_size=1.0,\n", + " val_size=0.0,\n", + " test_size=0.0,\n", + ")\n", "trainer.train()" ] }, @@ -314,47 +391,63 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 28, "id": "dd328887-7c18-4b96-ada4-c9eec630c069", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ "epochs_saved = range(99, max_epochs, 100)\n", - "parameters = torch.empty((int(max_epochs/100), 2))\n", + "parameters = torch.empty((int(max_epochs / 100), 2))\n", "for i, epoch in enumerate(epochs_saved):\n", - " params_torch = torch.load('{}/parameters_epoch{}'.format(tmp_dir, epoch))\n", - " for e, var in enumerate(pinn.problem.unknown_variables): \n", + " params_torch = torch.load(\n", + " \"{}/parameters_epoch{}\".format(tmp_dir, epoch), weights_only=False\n", + " )\n", + " for e, var in enumerate(pinn.problem.unknown_variables):\n", " parameters[i, e] = params_torch[var].data\n", "\n", "# Plot parameters\n", "plt.close()\n", - "plt.plot(epochs_saved, parameters[:, 0], label='mu1', marker='o')\n", - "plt.plot(epochs_saved, parameters[:, 1], label='mu2', marker='s')\n", + "plt.plot(epochs_saved, parameters[:, 0], label=\"mu1\", marker=\"o\")\n", + "plt.plot(epochs_saved, parameters[:, 1], label=\"mu2\", marker=\"s\")\n", "plt.ylim(-1, 1)\n", "plt.grid()\n", "plt.legend()\n", - "plt.xlabel('Epoch')\n", - "plt.ylabel('Parameter value')\n", + "plt.xlabel(\"Epoch\")\n", + "plt.ylabel(\"Parameter value\")\n", "plt.show()" ] + }, + { + "cell_type": "markdown", + "id": "f1fa4406", + "metadata": {}, + "source": [ + "## What's next?\n", + "\n", + "We have shown the basic usage PINNs in inverse problem modelling, further extensions include:\n", + "\n", + "1. Train using different Physics Informed strategies\n", + "\n", + "2. Try on more complex problems\n", + "\n", + "3. Many more..." + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "pina", "language": "python", "name": "python3" }, @@ -368,7 +461,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.9.21" } }, "nbformat": 4, diff --git a/tutorials/tutorial7/tutorial.py b/tutorials/tutorial7/tutorial.py index 3c55f1ca7..69d51d729 100644 --- a/tutorials/tutorial7/tutorial.py +++ b/tutorials/tutorial7/tutorial.py @@ -2,7 +2,7 @@ # coding: utf-8 # # Tutorial: Resolution of an inverse problem -# +# # [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial7/tutorial.ipynb) # ### Introduction to the inverse problem @@ -16,63 +16,76 @@ # \end{cases} # \end{equation} # where $\Omega$ is a square domain $[-2, 2] \times [-2, 2]$, and $\partial \Omega=\Gamma_1 \cup \Gamma_2 \cup \Gamma_3 \cup \Gamma_4$ is the union of the boundaries of the domain. -# +# # This kind of problem, namely the "inverse problem", has two main goals: # - find the solution $u$ that satisfies the Poisson equation; # - find the unknown parameters ($\mu_1$, $\mu_2$) that better fit some given data (third equation in the system above). -# +# # In order to achieve both goals we will need to define an `InverseProblem` in PINA. # Let's start with useful imports. -# In[1]: +# In[ ]: ## routine needed to run the notebook on Google Colab try: - import google.colab - IN_COLAB = True + import google.colab + + IN_COLAB = True except: - IN_COLAB = False + IN_COLAB = False if IN_COLAB: - get_ipython().system('pip install "pina-mathlab"') - # get the data - get_ipython().system('mkdir "data"') - get_ipython().system('wget "https://github.com/mathLab/PINA/raw/refs/heads/master/tutorials/tutorial7/data/pinn_solution_0.5_0.5" -O "data/pinn_solution_0.5_0.5"') - get_ipython().system('wget "https://github.com/mathLab/PINA/raw/refs/heads/master/tutorials/tutorial7/data/pts_0.5_0.5" -O "data/pts_0.5_0.5"') - + get_ipython().system('pip install "pina-mathlab"') + # get the data + get_ipython().system('mkdir "data"') + get_ipython().system( + 'wget "https://github.com/mathLab/PINA/raw/refs/heads/master/tutorials/tutorial7/data/pinn_solution_0.5_0.5" -O "data/pinn_solution_0.5_0.5"' + ) + get_ipython().system( + 'wget "https://github.com/mathLab/PINA/raw/refs/heads/master/tutorials/tutorial7/data/pts_0.5_0.5" -O "data/pts_0.5_0.5"' + ) + import matplotlib.pyplot as plt -plt.style.use('tableau-colorblind10') import torch -from pytorch_lightning.callbacks import Callback +import warnings + +from pina import Condition, Trainer from pina.problem import SpatialProblem, InverseProblem -from pina.operators import laplacian +from pina.operator import laplacian from pina.model import FeedForward from pina.equation import Equation, FixedValue -from pina import Condition, Trainer -from pina.solvers import PINN -from pina.geometry import CartesianDomain +from pina.solver import PINN +from pina.domain import CartesianDomain +from pina.optim import TorchOptimizer +from lightning.pytorch import seed_everything +from lightning.pytorch.callbacks import Callback + +warnings.filterwarnings("ignore") +seed_everything(883) -# Then, we import the pre-saved data, for ($\mu_1$, $\mu_2$)=($0.5$, $0.5$). These two values are the optimal parameters that we want to find through the neural network training. In particular, we import the `input_points`(the spatial coordinates), and the `output_points` (the corresponding $u$ values evaluated at the `input_points`). +# Then, we import the pre-saved data, for ($\mu_1$, $\mu_2$)=($0.5$, $0.5$). These two values are the optimal parameters that we want to find through the neural network training. In particular, we import the `input` points (the spatial coordinates), and the `target` points (the corresponding $u$ values evaluated at the `input`). -# In[2]: +# In[21]: -data_output = torch.load('data/pinn_solution_0.5_0.5').detach() -data_input = torch.load('data/pts_0.5_0.5') +data_output = torch.load( + "data/pinn_solution_0.5_0.5", weights_only=False +).detach() +data_input = torch.load("data/pts_0.5_0.5", weights_only=False) # Moreover, let's plot also the data points and the reference solution: this is the expected output of the neural network. -# In[3]: +# In[22]: -points = data_input.extract(['x', 'y']).detach().numpy() +points = data_input.extract(["x", "y"]).detach().numpy() truth = data_output.detach().numpy() plt.scatter(points[:, 0], points[:, 1], c=truth, s=8) -plt.axis('equal') +plt.axis("equal") plt.colorbar() plt.show() @@ -81,133 +94,166 @@ # Then, we initialize the Poisson problem, that is inherited from the `SpatialProblem` and from the `InverseProblem` classes. We here have to define all the variables, and the domain where our unknown parameters ($\mu_1$, $\mu_2$) belong. Notice that the Laplace equation takes as inputs also the unknown variables, that will be treated as parameters that the neural network optimizes during the training process. -# In[4]: +# In[23]: -### Define ranges of variables -x_min = -2 -x_max = 2 -y_min = -2 -y_max = 2 +def laplace_equation(input_, output_, params_): + """ + Implementation of the laplace equation. + + :param LabelTensor input_: Input data of the problem. + :param LabelTensor output_: Output data of the problem. + :param dict params_: Parameters of the problem. + :return: The residual of the laplace equation. + :rtype: LabelTensor + """ + force_term = torch.exp( + -2 * (input_.extract(["x"]) - params_["mu1"]) ** 2 + - 2 * (input_.extract(["y"]) - params_["mu2"]) ** 2 + ) + delta_u = laplacian(output_, input_, components=["u"], d=["x", "y"]) + return delta_u - force_term + class Poisson(SpatialProblem, InverseProblem): - ''' - Problem definition for the Poisson equation. - ''' - output_variables = ['u'] - spatial_domain = CartesianDomain({'x': [x_min, x_max], 'y': [y_min, y_max]}) - # define the ranges for the parameters - unknown_parameter_domain = CartesianDomain({'mu1': [-1, 1], 'mu2': [-1, 1]}) - - def laplace_equation(input_, output_, params_): - ''' - Laplace equation with a force term. - ''' - force_term = torch.exp( - - 2*(input_.extract(['x']) - params_['mu1'])**2 - - 2*(input_.extract(['y']) - params_['mu2'])**2) - delta_u = laplacian(output_, input_, components=['u'], d=['x', 'y']) - - return delta_u - force_term - - # define the conditions for the loss (boundary conditions, equation, data) + r""" + Implementation of the inverse 2-dimensional Poisson problem in the square + domain :math:`[0, 1] \times [0, 1]`, + with unknown parameter domain :math:`[-1, 1] \times [-1, 1]`. + """ + + output_variables = ["u"] + x_min, x_max = -2, 2 + y_min, y_max = -2, 2 + spatial_domain = CartesianDomain({"x": [x_min, x_max], "y": [y_min, y_max]}) + unknown_parameter_domain = CartesianDomain({"mu1": [-1, 1], "mu2": [-1, 1]}) + + domains = { + "g1": CartesianDomain({"x": [x_min, x_max], "y": y_max}), + "g2": CartesianDomain({"x": [x_min, x_max], "y": y_min}), + "g3": CartesianDomain({"x": x_max, "y": [y_min, y_max]}), + "g4": CartesianDomain({"x": x_min, "y": [y_min, y_max]}), + "D": CartesianDomain({"x": [x_min, x_max], "y": [y_min, y_max]}), + } + conditions = { - 'gamma1': Condition(location=CartesianDomain({'x': [x_min, x_max], - 'y': y_max}), - equation=FixedValue(0.0, components=['u'])), - 'gamma2': Condition(location=CartesianDomain({'x': [x_min, x_max], 'y': y_min - }), - equation=FixedValue(0.0, components=['u'])), - 'gamma3': Condition(location=CartesianDomain({'x': x_max, 'y': [y_min, y_max] - }), - equation=FixedValue(0.0, components=['u'])), - 'gamma4': Condition(location=CartesianDomain({'x': x_min, 'y': [y_min, y_max] - }), - equation=FixedValue(0.0, components=['u'])), - 'D': Condition(location=CartesianDomain({'x': [x_min, x_max], 'y': [y_min, y_max] - }), - equation=Equation(laplace_equation)), - 'data': Condition(input_points=data_input.extract(['x', 'y']), output_points=data_output) + "g1": Condition(domain="g1", equation=FixedValue(0.0)), + "g2": Condition(domain="g2", equation=FixedValue(0.0)), + "g3": Condition(domain="g3", equation=FixedValue(0.0)), + "g4": Condition(domain="g4", equation=FixedValue(0.0)), + "D": Condition(domain="D", equation=Equation(laplace_equation)), + "data": Condition(input=data_input, target=data_output), } + problem = Poisson() # Then, we define the neural network model we want to use. Here we used a model which imposes hard constrains on the boundary conditions, as also done in the Wave tutorial! -# In[5]: +# In[24]: model = FeedForward( layers=[20, 20, 20], func=torch.nn.Softplus, output_dimensions=len(problem.output_variables), - input_dimensions=len(problem.input_variables) - ) + input_dimensions=len(problem.input_variables), +) # After that, we discretize the spatial domain. -# In[6]: +# In[25]: -problem.discretise_domain(20, 'grid', locations=['D'], variables=['x', 'y']) -problem.discretise_domain(1000, 'random', locations=['gamma1', 'gamma2', - 'gamma3', 'gamma4'], variables=['x', 'y']) +problem.discretise_domain(20, "grid", domains=["D"]) +problem.discretise_domain( + 1000, + "random", + domains=["g1", "g2", "g3", "g4"], +) # Here, we define a simple callback for the trainer. We use this callback to save the parameters predicted by the neural network during the training. The parameters are saved every 100 epochs as `torch` tensors in a specified directory (`tmp_dir` in our case). # The goal is to read the saved parameters after training and plot their trend across the epochs. -# In[7]: +# In[26]: # temporary directory for saving logs of training tmp_dir = "tmp_poisson_inverse" + class SaveParameters(Callback): - ''' + """ Callback to save the parameters of the model every 100 epochs. - ''' + """ + def on_train_epoch_end(self, trainer, __): if trainer.current_epoch % 100 == 99: - torch.save(trainer.solver.problem.unknown_parameters, '{}/parameters_epoch{}'.format(tmp_dir, trainer.current_epoch)) + torch.save( + trainer.solver.problem.unknown_parameters, + "{}/parameters_epoch{}".format(tmp_dir, trainer.current_epoch), + ) # Then, we define the `PINN` object and train the solver using the `Trainer`. -# In[ ]: +# In[27]: -### train the problem with PINN -max_epochs = 5000 -pinn = PINN(problem, model, optimizer_kwargs={'lr':0.005}) +max_epochs = 1500 +pinn = PINN( + problem, model, optimizer=TorchOptimizer(torch.optim.Adam, lr=0.005) +) # define the trainer for the solver -trainer = Trainer(solver=pinn, accelerator='cpu', max_epochs=max_epochs, - default_root_dir=tmp_dir, callbacks=[SaveParameters()]) +trainer = Trainer( + solver=pinn, + accelerator="cpu", + max_epochs=max_epochs, + default_root_dir=tmp_dir, + enable_model_summary=False, + callbacks=[SaveParameters()], + train_size=1.0, + val_size=0.0, + test_size=0.0, +) trainer.train() # One can now see how the parameters vary during the training by reading the saved solution and plotting them. The plot shows that the parameters stabilize to their true value before reaching the epoch $1000$! -# In[9]: +# In[28]: epochs_saved = range(99, max_epochs, 100) -parameters = torch.empty((int(max_epochs/100), 2)) +parameters = torch.empty((int(max_epochs / 100), 2)) for i, epoch in enumerate(epochs_saved): - params_torch = torch.load('{}/parameters_epoch{}'.format(tmp_dir, epoch)) - for e, var in enumerate(pinn.problem.unknown_variables): + params_torch = torch.load( + "{}/parameters_epoch{}".format(tmp_dir, epoch), weights_only=False + ) + for e, var in enumerate(pinn.problem.unknown_variables): parameters[i, e] = params_torch[var].data # Plot parameters plt.close() -plt.plot(epochs_saved, parameters[:, 0], label='mu1', marker='o') -plt.plot(epochs_saved, parameters[:, 1], label='mu2', marker='s') +plt.plot(epochs_saved, parameters[:, 0], label="mu1", marker="o") +plt.plot(epochs_saved, parameters[:, 1], label="mu2", marker="s") plt.ylim(-1, 1) plt.grid() plt.legend() -plt.xlabel('Epoch') -plt.ylabel('Parameter value') +plt.xlabel("Epoch") +plt.ylabel("Parameter value") plt.show() + +# ## What's next? +# +# We have shown the basic usage PINNs in inverse problem modelling, further extensions include: +# +# 1. Train using different Physics Informed strategies +# +# 2. Try on more complex problems +# +# 3. Many more... diff --git a/tutorials/tutorial8/tutorial.ipynb b/tutorials/tutorial8/tutorial.ipynb index 71913a352..796b0937e 100644 --- a/tutorials/tutorial8/tutorial.ipynb +++ b/tutorials/tutorial8/tutorial.ipynb @@ -5,9 +5,9 @@ "id": "dbbb73cb-a632-4056-bbca-b483b2ad5f9c", "metadata": {}, "source": [ - "# Tutorial: Reduced order model (POD-RBF or POD-NN) for parametric problems\n", + "# Tutorial: Reduced order models (POD-NN and POD-RBF) for parametric problems\n", "\n", - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial8/tutorial.ipynb)" + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial9/tutorial.ipynb)" ] }, { @@ -17,11 +17,7 @@ "source": [ "The tutorial aims to show how to employ the **PINA** library in order to apply a reduced order modeling technique [1]. Such methodologies have several similarities with machine learning approaches, since the main goal consists in predicting the solution of differential equations (typically parametric PDEs) in a real-time fashion.\n", "\n", - "In particular we are going to use the Proper Orthogonal Decomposition with either Radial Basis Function Interpolation(POD-RBF) or Neural Network (POD-NN) [2]. Here we basically perform a dimensional reduction using the POD approach, and approximating the parametric solution manifold (at the reduced space) using an interpolation (RBF) or a regression technique (NN). In this example, we use a simple multilayer perceptron, but the plenty of different architectures can be plugged as well.\n", - "\n", - "#### References\n", - "1. Rozza G., Stabile G., Ballarin F. (2022). Advanced Reduced Order Methods and Applications in Computational Fluid Dynamics, Society for Industrial and Applied Mathematics. \n", - "2. Hesthaven, J. S., & Ubbiali, S. (2018). Non-intrusive reduced order modeling of nonlinear problems using neural networks. Journal of Computational Physics, 363, 55-78." + "In particular we are going to use the Proper Orthogonal Decomposition with either Radial Basis Function Interpolation (POD-RBF) or Neural Network (POD-NN) [2]. Here we basically perform a dimensional reduction using the POD approach, approximating the parametric solution manifold (at the reduced space) using a regression technique (NN) and comparing it to an RBF interpolation. In this example, we use a simple multilayer perceptron, but the plenty of different architectures can be plugged as well." ] }, { @@ -35,44 +31,37 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "00d1027d-13f2-4619-9ff7-a740568f13ff", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "We are using PINA version 0.1.1\n" - ] - } - ], + "outputs": [], "source": [ "## routine needed to run the notebook on Google Colab\n", "try:\n", - " import google.colab\n", - " IN_COLAB = True\n", + " import google.colab\n", + "\n", + " IN_COLAB = True\n", "except:\n", - " IN_COLAB = False\n", + " IN_COLAB = False\n", "if IN_COLAB:\n", - " !pip install \"pina-mathlab\"\n", + " !pip install \"pina-mathlab\"\n", "\n", "%matplotlib inline\n", "\n", + "import matplotlib\n", "import matplotlib.pyplot as plt\n", - "plt.style.use('tableau-colorblind10')\n", "import torch\n", - "import pina\n", - "\n", - "from pina.geometry import CartesianDomain\n", + "import numpy as np\n", + "import warnings\n", "\n", - "from pina.problem import ParametricProblem\n", - "from pina.model.layers import PODBlock, RBFBlock\n", - "from pina import Condition, LabelTensor, Trainer\n", + "from pina import Trainer\n", "from pina.model import FeedForward\n", - "from pina.solvers import SupervisedSolver\n", + "from pina.solver import SupervisedSolver\n", + "from pina.optim import TorchOptimizer\n", + "from pina.problem.zoo import SupervisedProblem\n", + "from pina.model.block import PODBlock, RBFBlock\n", "\n", - "print(f'We are using PINA version {pina.__version__}')" + "warnings.filterwarnings(\"ignore\")" ] }, { @@ -80,7 +69,7 @@ "id": "5138afdf-bff6-46bf-b423-a22673190687", "metadata": {}, "source": [ - "We exploit the [Smithers](www.github.com/mathLab/Smithers) library to collect the parametric snapshots. In particular, we use the `NavierStokesDataset` class that contains a set of parametric solutions of the Navier-Stokes equations in a 2D L-shape domain. The parameter is the inflow velocity.\n", + "We exploit the [Smithers](https://github.com/mathLab/Smithers) library to collect the parametric snapshots. In particular, we use the `NavierStokesDataset` class that contains a set of parametric solutions of the Navier-Stokes equations in a 2D L-shape domain. The parameter is the inflow velocity.\n", "The dataset is composed by 500 snapshots of the velocity (along $x$, $y$, and the magnitude) and pressure fields, and the corresponding parameter values.\n", "\n", "To visually check the snapshots, let's plot also the data points and the reference solution: this is the expected output of our model." @@ -88,31 +77,30 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 83, "id": "2c55d972-09a9-41de-9400-ba051c28cdcb", "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ "from smithers.dataset import NavierStokesDataset\n", + "\n", "dataset = NavierStokesDataset()\n", "\n", "fig, axs = plt.subplots(1, 4, figsize=(14, 3))\n", - "for ax, p, u in zip(axs, dataset.params[:4], dataset.snapshots['mag(v)'][:4]):\n", + "for ax, p, u in zip(axs, dataset.params[:4], dataset.snapshots[\"mag(v)\"][:4]):\n", " ax.tricontourf(dataset.triang, u, levels=16)\n", - " ax.set_title(f'$\\mu$ = {p[0]:.2f}')" + " ax.set_title(f\"$\\mu$ = {p[0]:.2f}\")" ] }, { @@ -120,55 +108,21 @@ "id": "bef4d79d", "metadata": {}, "source": [ - "The *snapshots* - aka the numerical solutions computed for several parameters - and the corresponding parameters are the only data we need to train the model, in order to predict the solution for any new test parameter.\n", - "To properly validate the accuracy, we initially split the 500 snapshots into the training dataset (90% of the original data) and the testing one (the reamining 10%). It must be said that, to plug the snapshots into **PINA**, we have to cast them to `LabelTensor` objects." + "The *snapshots* - aka the numerical solutions computed for several parameters - and the corresponding parameters are the only data we need to train the model, in order to predict the solution for any new test parameter. To properly validate the accuracy, we will split the 500 snapshots into the training dataset (90% of the original data) and the testing one (the reamining 10%) inside the `Trainer`.\n", + "\n", + "It is now time to define the problem!" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 84, "id": "bd081bcd-192f-4370-a013-9b73050b5383", "metadata": {}, "outputs": [], "source": [ - "u = torch.tensor(dataset.snapshots['mag(v)']).float()\n", + "u = torch.tensor(dataset.snapshots[\"mag(v)\"]).float()\n", "p = torch.tensor(dataset.params).float()\n", - "\n", - "p = LabelTensor(p, labels=['mu'])\n", - "u = LabelTensor(u, labels=[f's{i}' for i in range(u.shape[1])])\n", - "\n", - "ratio_train_test = 0.9\n", - "n = u.shape\n", - "n_train = int(u.shape[0] * ratio_train_test)\n", - "n_test = u - n_train\n", - "u_train, u_test = u[:n_train], u[n_train:]\n", - "p_train, p_test = p[:n_train], p[n_train:]" - ] - }, - { - "cell_type": "markdown", - "id": "c46410fa-2718-4fc9-977a-583fe2390028", - "metadata": {}, - "source": [ - "It is now time to define the problem! We inherit from `ParametricProblem` (since the space invariant typically of this methodology), just defining a simple *input-output* condition." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "55cef553-7495-401d-9d17-1acff8ec5953", - "metadata": {}, - "outputs": [], - "source": [ - "class SnapshotProblem(ParametricProblem):\n", - " output_variables = [f's{i}' for i in range(u.shape[1])]\n", - " parameter_domain = CartesianDomain({'mu': [0, 100]})\n", - "\n", - " conditions = {\n", - " 'io': Condition(input_points=p_train, output_points=u_train)\n", - " }\n", - "\n", - "poisson_problem = SnapshotProblem()" + "problem = SupervisedProblem(input_=p, output_=u)" ] }, { @@ -176,129 +130,29 @@ "id": "3b255526", "metadata": {}, "source": [ - "We can then build a `PODRBF` model (using a Radial Basis Function interpolation as approximation) and a `PODNN` approach (using an MLP architecture as approximation)." + "We can then build a `POD-NN` model (using an MLP architecture as approximation) and compare it with a `POD-RBF` model (using a Radial Basis Function interpolation as approximation)." ] }, { "cell_type": "markdown", - "id": "352ac702", + "id": "cb5f3ead", "metadata": {}, "source": [ - "## POD-RBF reduced order model" - ] - }, - { - "cell_type": "markdown", - "id": "6b264569-57b3-458d-bb69-8e94fe89017d", - "metadata": {}, - "source": [ - "Then, we define the model we want to use, with the POD (`PODBlock`) and the RBF (`RBFBlock`) objects." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "0bd2c30c", - "metadata": {}, - "outputs": [], - "source": [ - "class PODRBF(torch.nn.Module):\n", - " \"\"\"\n", - " Proper orthogonal decomposition with Radial Basis Function interpolation model.\n", - " \"\"\"\n", - "\n", - " def __init__(self, pod_rank, rbf_kernel):\n", - " \"\"\"\n", - " \n", - " \"\"\"\n", - " super().__init__()\n", - " \n", - " self.pod = PODBlock(pod_rank)\n", - " self.rbf = RBFBlock(kernel=rbf_kernel)\n", - " \n", - "\n", - " def forward(self, x):\n", - " \"\"\"\n", - " Defines the computation performed at every call.\n", - "\n", - " :param x: The tensor to apply the forward pass.\n", - " :type x: torch.Tensor\n", - " :return: the output computed by the model.\n", - " :rtype: torch.Tensor\n", - " \"\"\"\n", - " coefficents = self.rbf(x)\n", - " return self.pod.expand(coefficents)\n", - "\n", - " def fit(self, p, x):\n", - " \"\"\"\n", - " Call the :meth:`pina.model.layers.PODBlock.fit` method of the\n", - " :attr:`pina.model.layers.PODBlock` attribute to perform the POD,\n", - " and the :meth:`pina.model.layers.RBFBlock.fit` method of the\n", - " :attr:`pina.model.layers.RBFBlock` attribute to fit the interpolation.\n", - " \"\"\"\n", - " self.pod.fit(x)\n", - " self.rbf.fit(p, self.pod.reduce(x))" - ] - }, - { - "cell_type": "markdown", - "id": "4d2551ff", - "metadata": {}, - "source": [ - "We can then fit the model and ask it to predict the required field for unseen values of the parameters. Note that this model does not need a `Trainer` since it does not include any neural network or learnable parameters." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "af0a7f9b", - "metadata": {}, - "outputs": [], - "source": [ - "pod_rbf = PODRBF(pod_rank=20, rbf_kernel='thin_plate_spline')\n", - "pod_rbf.fit(p_train, u_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "41a27834", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Error summary for POD-RBF model:\n", - " Train: 1.287801e-03\n", - " Test: 1.217041e-03\n" - ] - } - ], - "source": [ - "u_test_rbf = pod_rbf(p_test)\n", - "u_train_rbf = pod_rbf(p_train)\n", - "\n", - "relative_error_train = torch.norm(u_train_rbf - u_train)/torch.norm(u_train)\n", - "relative_error_test = torch.norm(u_test_rbf - u_test)/torch.norm(u_test)\n", - "\n", - "print('Error summary for POD-RBF model:')\n", - "print(f' Train: {relative_error_train.item():e}')\n", - "print(f' Test: {relative_error_test.item():e}')" + "## POD-NN reduced order model" ] }, { "cell_type": "markdown", - "id": "a5bac005", + "id": "89125805", "metadata": {}, "source": [ - "## POD-NN reduced order model" + "Let's build the `PODNN` class" ] }, { "cell_type": "code", - "execution_count": 8, - "id": "c4170514-eb73-488e-8942-0129070e4e13", + "execution_count": 85, + "id": "2edc981a", "metadata": {}, "outputs": [], "source": [ @@ -308,19 +162,16 @@ " \"\"\"\n", "\n", " def __init__(self, pod_rank, layers, func):\n", - " \"\"\"\n", - " \n", - " \"\"\"\n", + " \"\"\" \"\"\"\n", " super().__init__()\n", - " \n", + "\n", " self.pod = PODBlock(pod_rank)\n", " self.nn = FeedForward(\n", " input_dimensions=1,\n", " output_dimensions=pod_rank,\n", " layers=layers,\n", - " func=func\n", + " func=func,\n", " )\n", - " \n", "\n", " def forward(self, x):\n", " \"\"\"\n", @@ -344,7 +195,7 @@ }, { "cell_type": "markdown", - "id": "16e1f085-7818-4624-92a1-bf7010dbe528", + "id": "9295214e", "metadata": {}, "source": [ "We highlight that the POD modes are directly computed by means of the singular value decomposition (computed over the input data), and not trained using the backpropagation approach. Only the weights of the MLP are actually trained during the optimization loop." @@ -352,75 +203,61 @@ }, { "cell_type": "code", - "execution_count": 9, - "id": "e998cad5-e3a7-4a3b-a1a5-400b6ff575a1", + "execution_count": 86, + "id": "2166dc87", "metadata": {}, "outputs": [], "source": [ "pod_nn = PODNN(pod_rank=20, layers=[10, 10, 10], func=torch.nn.Tanh)\n", - "pod_nn.fit_pod(u_train)\n", - "\n", "pod_nn_stokes = SupervisedSolver(\n", - " problem=poisson_problem, \n", - " model=pod_nn, \n", - " optimizer=torch.optim.Adam,\n", - " optimizer_kwargs={'lr': 0.0001})" + " problem=problem,\n", + " model=pod_nn,\n", + " optimizer=TorchOptimizer(torch.optim.Adam, lr=0.0001),\n", + " use_lt=False,\n", + ")" ] }, { "cell_type": "markdown", - "id": "aab51202-36a7-40d2-b96d-47af8892cd2c", + "id": "9bc5c5e8", "metadata": {}, "source": [ - "Now that we have set the `Problem` and the `Model`, we have just to train the model and use it for predicting the test snapshots." + "Before starting we need to fit the POD basis on the training dataset, this can be easily done in PINA as well:" ] }, { "cell_type": "code", - "execution_count": 10, - "id": "f1e94f42-cf80-4ca7-bb5e-ad47c1dd2784", + "execution_count": 87, + "id": "1f229d30", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "GPU available: True (cuda), used: False\n", + "GPU available: True (mps), used: False\n", "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", "HPU available: False, using: 0 HPUs\n", - "/u/a/aivagnes/anaconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/setup.py:187: GPU available but not used. You can set it by doing `Trainer(accelerator='gpu')`.\n", "\n", - " | Name | Type | Params\n", - "----------------------------------------\n", - "0 | _loss | MSELoss | 0 \n", - "1 | _neural_net | Network | 460 \n", - "----------------------------------------\n", + " | Name | Type | Params | Mode \n", + "----------------------------------------------------\n", + "0 | _pina_models | ModuleList | 460 | train\n", + "1 | _loss | MSELoss | 0 | train\n", + "----------------------------------------------------\n", "460 Trainable params\n", "0 Non-trainable params\n", "460 Total params\n", "0.002 Total estimated model params size (MB)\n", - "/u/a/aivagnes/anaconda3/lib/python3.8/site-packages/torch/cuda/__init__.py:152: UserWarning: \n", - " Found GPU0 Quadro K600 which is of cuda capability 3.0.\n", - " PyTorch no longer supports this GPU because it is too old.\n", - " The minimum cuda capability supported by this library is 3.7.\n", - " \n", - " warnings.warn(old_gpu_warn % (d, name, major, minor, min_arch // 10, min_arch % 10))\n" + "13 Modules in train mode\n", + "0 Modules in eval mode\n" ] }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "a5ebdb14ddcb457da6d72432a4aa7a61", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Training: | | 0/? [00:00" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -516,52 +491,69 @@ "u_idx_rbf = pod_rbf(p_test[idx])\n", "u_idx_nn = pod_nn_stokes(p_test[idx])\n", "\n", - "import numpy as np\n", - "import matplotlib\n", - "import matplotlib.pyplot as plt\n", "\n", - "fig, axs = plt.subplots(5, 4, figsize=(14, 9))\n", + "fig, axs = plt.subplots(4, 5, figsize=(14, 9))\n", "\n", "relative_error_rbf = np.abs(u_test[idx] - u_idx_rbf.detach())\n", - "relative_error_rbf = np.where(u_test[idx] < 1e-7, 1e-7, relative_error_rbf/u_test[idx])\n", + "relative_error_rbf = np.where(\n", + " u_test[idx] < 1e-7, 1e-7, relative_error_rbf / u_test[idx]\n", + ")\n", "\n", "relative_error_nn = np.abs(u_test[idx] - u_idx_nn.detach())\n", - "relative_error_nn = np.where(u_test[idx] < 1e-7, 1e-7, relative_error_nn/u_test[idx])\n", - " \n", + "relative_error_nn = np.where(\n", + " u_test[idx] < 1e-7, 1e-7, relative_error_nn / u_test[idx]\n", + ")\n", + "\n", "for i, (idx_, rbf_, nn_, rbf_err_, nn_err_) in enumerate(\n", - " zip(idx, u_idx_rbf, u_idx_nn, relative_error_rbf, relative_error_nn)):\n", - " axs[0, i].set_title(f'$\\mu$ = {p_test[idx_].item():.2f}')\n", - " \n", - " cm = axs[0, i].tricontourf(dataset.triang, rbf_.detach()) # POD-RBF prediction\n", - " plt.colorbar(cm, ax=axs[0, i])\n", - " \n", - " cm = axs[1, i].tricontourf(dataset.triang, nn_.detach()) # POD-NN prediction\n", - " plt.colorbar(cm, ax=axs[1, i])\n", - "\n", - " cm = axs[2, i].tricontourf(dataset.triang, u_test[idx_].flatten()) # Truth\n", - " plt.colorbar(cm, ax=axs[2, i])\n", - "\n", - " cm = axs[3, i].tripcolor(dataset.triang, rbf_err_, norm=matplotlib.colors.LogNorm()) # Error for POD-RBF\n", - " plt.colorbar(cm, ax=axs[3, i])\n", - " \n", - " cm = axs[4, i].tripcolor(dataset.triang, nn_err_, norm=matplotlib.colors.LogNorm()) # Error for POD-NN\n", - " plt.colorbar(cm, ax=axs[4, i])\n", - " \n", + " zip(idx, u_idx_rbf, u_idx_nn, relative_error_rbf, relative_error_nn)\n", + "):\n", + "\n", + " axs[0, 0].set_title(f\"Real Snapshots\")\n", + " axs[0, 1].set_title(f\"POD-RBF\")\n", + " axs[0, 2].set_title(f\"POD-NN\")\n", + " axs[0, 3].set_title(f\"Error POD-RBF\")\n", + " axs[0, 4].set_title(f\"Error POD-NN\")\n", + "\n", + " cm = axs[i, 0].tricontourf(\n", + " dataset.triang, rbf_.detach()\n", + " ) # POD-RBF prediction\n", + " plt.colorbar(cm, ax=axs[i, 0])\n", + "\n", + " cm = axs[i, 1].tricontourf(\n", + " dataset.triang, nn_.detach()\n", + " ) # POD-NN prediction\n", + " plt.colorbar(cm, ax=axs[i, 1])\n", + "\n", + " cm = axs[i, 2].tricontourf(dataset.triang, u_test[idx_].flatten()) # Truth\n", + " plt.colorbar(cm, ax=axs[i, 2])\n", + "\n", + " cm = axs[i, 3].tripcolor(\n", + " dataset.triang, rbf_err_, norm=matplotlib.colors.LogNorm()\n", + " ) # Error for POD-RBF\n", + " plt.colorbar(cm, ax=axs[i, 3])\n", + "\n", + " cm = axs[i, 4].tripcolor(\n", + " dataset.triang, nn_err_, norm=matplotlib.colors.LogNorm()\n", + " ) # Error for POD-NN\n", + " plt.colorbar(cm, ax=axs[i, 4])\n", + "\n", "plt.show()" ] }, { - "cell_type": "code", - "execution_count": null, - "id": "d3758c39", + "cell_type": "markdown", + "id": "b062369e", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "#### References\n", + "1. Rozza G., Stabile G., Ballarin F. (2022). Advanced Reduced Order Methods and Applications in Computational Fluid Dynamics, Society for Industrial and Applied Mathematics. \n", + "2. Hesthaven, J. S., & Ubbiali, S. (2018). Non-intrusive reduced order modeling of nonlinear problems using neural networks. Journal of Computational Physics, 363, 55-78." + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "pina", "language": "python", "name": "python3" }, @@ -575,12 +567,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" - }, - "vscode": { - "interpreter": { - "hash": "812fc65ca8c4f5385369e756893b1e5d443bf42489b0b3ab8df91541fbfe2649" - } + "version": "3.9.21" } }, "nbformat": 4, diff --git a/tutorials/tutorial8/tutorial.py b/tutorials/tutorial8/tutorial.py index 980404e4e..4f3f5bfcc 100644 --- a/tutorials/tutorial8/tutorial.py +++ b/tutorials/tutorial8/tutorial.py @@ -1,127 +1,103 @@ #!/usr/bin/env python # coding: utf-8 -# # Tutorial: Reduced order model (POD-RBF or POD-NN) for parametric problems -# -# [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial8/tutorial.ipynb) +# # Tutorial: Reduced order models (POD-NN and POD-RBF) for parametric problems +# +# [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial9/tutorial.ipynb) # The tutorial aims to show how to employ the **PINA** library in order to apply a reduced order modeling technique [1]. Such methodologies have several similarities with machine learning approaches, since the main goal consists in predicting the solution of differential equations (typically parametric PDEs) in a real-time fashion. -# -# In particular we are going to use the Proper Orthogonal Decomposition with either Radial Basis Function Interpolation(POD-RBF) or Neural Network (POD-NN) [2]. Here we basically perform a dimensional reduction using the POD approach, and approximating the parametric solution manifold (at the reduced space) using an interpolation (RBF) or a regression technique (NN). In this example, we use a simple multilayer perceptron, but the plenty of different architectures can be plugged as well. -# -# #### References -# 1. Rozza G., Stabile G., Ballarin F. (2022). Advanced Reduced Order Methods and Applications in Computational Fluid Dynamics, Society for Industrial and Applied Mathematics. -# 2. Hesthaven, J. S., & Ubbiali, S. (2018). Non-intrusive reduced order modeling of nonlinear problems using neural networks. Journal of Computational Physics, 363, 55-78. +# +# In particular we are going to use the Proper Orthogonal Decomposition with either Radial Basis Function Interpolation (POD-RBF) or Neural Network (POD-NN) [2]. Here we basically perform a dimensional reduction using the POD approach, approximating the parametric solution manifold (at the reduced space) using a regression technique (NN) and comparing it to an RBF interpolation. In this example, we use a simple multilayer perceptron, but the plenty of different architectures can be plugged as well. # Let's start with the necessary imports. # It's important to note the minimum PINA version to run this tutorial is the `0.1`. -# In[1]: +# In[ ]: ## routine needed to run the notebook on Google Colab try: - import google.colab - IN_COLAB = True + import google.colab + + IN_COLAB = True except: - IN_COLAB = False + IN_COLAB = False if IN_COLAB: - get_ipython().system('pip install "pina-mathlab"') + get_ipython().system('pip install "pina-mathlab"') -get_ipython().run_line_magic('matplotlib', 'inline') +get_ipython().run_line_magic("matplotlib", "inline") +import matplotlib import matplotlib.pyplot as plt -plt.style.use('tableau-colorblind10') import torch -import pina - -from pina.geometry import CartesianDomain +import numpy as np +import warnings -from pina.problem import ParametricProblem -from pina.model.layers import PODBlock, RBFBlock -from pina import Condition, LabelTensor, Trainer +from pina import Trainer from pina.model import FeedForward -from pina.solvers import SupervisedSolver +from pina.solver import SupervisedSolver +from pina.optim import TorchOptimizer +from pina.problem.zoo import SupervisedProblem +from pina.model.block import PODBlock, RBFBlock -print(f'We are using PINA version {pina.__version__}') +warnings.filterwarnings("ignore") -# We exploit the [Smithers](www.github.com/mathLab/Smithers) library to collect the parametric snapshots. In particular, we use the `NavierStokesDataset` class that contains a set of parametric solutions of the Navier-Stokes equations in a 2D L-shape domain. The parameter is the inflow velocity. +# We exploit the [Smithers](https://github.com/mathLab/Smithers) library to collect the parametric snapshots. In particular, we use the `NavierStokesDataset` class that contains a set of parametric solutions of the Navier-Stokes equations in a 2D L-shape domain. The parameter is the inflow velocity. # The dataset is composed by 500 snapshots of the velocity (along $x$, $y$, and the magnitude) and pressure fields, and the corresponding parameter values. -# +# # To visually check the snapshots, let's plot also the data points and the reference solution: this is the expected output of our model. -# In[2]: +# In[83]: from smithers.dataset import NavierStokesDataset + dataset = NavierStokesDataset() fig, axs = plt.subplots(1, 4, figsize=(14, 3)) -for ax, p, u in zip(axs, dataset.params[:4], dataset.snapshots['mag(v)'][:4]): +for ax, p, u in zip(axs, dataset.params[:4], dataset.snapshots["mag(v)"][:4]): ax.tricontourf(dataset.triang, u, levels=16) - ax.set_title(f'$\mu$ = {p[0]:.2f}') + ax.set_title(f"$\mu$ = {p[0]:.2f}") -# The *snapshots* - aka the numerical solutions computed for several parameters - and the corresponding parameters are the only data we need to train the model, in order to predict the solution for any new test parameter. -# To properly validate the accuracy, we initially split the 500 snapshots into the training dataset (90% of the original data) and the testing one (the reamining 10%). It must be said that, to plug the snapshots into **PINA**, we have to cast them to `LabelTensor` objects. +# The *snapshots* - aka the numerical solutions computed for several parameters - and the corresponding parameters are the only data we need to train the model, in order to predict the solution for any new test parameter. To properly validate the accuracy, we will split the 500 snapshots into the training dataset (90% of the original data) and the testing one (the reamining 10%) inside the `Trainer`. +# +# It is now time to define the problem! -# In[3]: +# In[84]: -u = torch.tensor(dataset.snapshots['mag(v)']).float() +u = torch.tensor(dataset.snapshots["mag(v)"]).float() p = torch.tensor(dataset.params).float() - -p = LabelTensor(p, labels=['mu']) -u = LabelTensor(u, labels=[f's{i}' for i in range(u.shape[1])]) - -ratio_train_test = 0.9 -n = u.shape -n_train = int(u.shape[0] * ratio_train_test) -n_test = u - n_train -u_train, u_test = u[:n_train], u[n_train:] -p_train, p_test = p[:n_train], p[n_train:] - - -# It is now time to define the problem! We inherit from `ParametricProblem` (since the space invariant typically of this methodology), just defining a simple *input-output* condition. - -# In[4]: - - -class SnapshotProblem(ParametricProblem): - output_variables = [f's{i}' for i in range(u.shape[1])] - parameter_domain = CartesianDomain({'mu': [0, 100]}) - - conditions = { - 'io': Condition(input_points=p_train, output_points=u_train) - } - -poisson_problem = SnapshotProblem() +problem = SupervisedProblem(input_=p, output_=u) -# We can then build a `PODRBF` model (using a Radial Basis Function interpolation as approximation) and a `PODNN` approach (using an MLP architecture as approximation). +# We can then build a `POD-NN` model (using an MLP architecture as approximation) and compare it with a `POD-RBF` model (using a Radial Basis Function interpolation as approximation). -# ## POD-RBF reduced order model +# ## POD-NN reduced order model -# Then, we define the model we want to use, with the POD (`PODBlock`) and the RBF (`RBFBlock`) objects. +# Let's build the `PODNN` class -# In[5]: +# In[85]: -class PODRBF(torch.nn.Module): +class PODNN(torch.nn.Module): """ - Proper orthogonal decomposition with Radial Basis Function interpolation model. + Proper orthogonal decomposition with neural network model. """ - def __init__(self, pod_rank, rbf_kernel): - """ - - """ + def __init__(self, pod_rank, layers, func): + """ """ super().__init__() - + self.pod = PODBlock(pod_rank) - self.rbf = RBFBlock(kernel=rbf_kernel) - + self.nn = FeedForward( + input_dimensions=1, + output_dimensions=pod_rank, + layers=layers, + func=func, + ) def forward(self, x): """ @@ -132,67 +108,99 @@ def forward(self, x): :return: the output computed by the model. :rtype: torch.Tensor """ - coefficents = self.rbf(x) + coefficents = self.nn(x) return self.pod.expand(coefficents) - def fit(self, p, x): + def fit_pod(self, x): """ - Call the :meth:`pina.model.layers.PODBlock.fit` method of the - :attr:`pina.model.layers.PODBlock` attribute to perform the POD, - and the :meth:`pina.model.layers.RBFBlock.fit` method of the - :attr:`pina.model.layers.RBFBlock` attribute to fit the interpolation. + Just call the :meth:`pina.model.layers.PODBlock.fit` method of the + :attr:`pina.model.layers.PODBlock` attribute. """ self.pod.fit(x) - self.rbf.fit(p, self.pod.reduce(x)) -# We can then fit the model and ask it to predict the required field for unseen values of the parameters. Note that this model does not need a `Trainer` since it does not include any neural network or learnable parameters. +# We highlight that the POD modes are directly computed by means of the singular value decomposition (computed over the input data), and not trained using the backpropagation approach. Only the weights of the MLP are actually trained during the optimization loop. -# In[6]: +# In[86]: -pod_rbf = PODRBF(pod_rank=20, rbf_kernel='thin_plate_spline') -pod_rbf.fit(p_train, u_train) +pod_nn = PODNN(pod_rank=20, layers=[10, 10, 10], func=torch.nn.Tanh) +pod_nn_stokes = SupervisedSolver( + problem=problem, + model=pod_nn, + optimizer=TorchOptimizer(torch.optim.Adam, lr=0.0001), + use_lt=False, +) -# In[7]: +# Before starting we need to fit the POD basis on the training dataset, this can be easily done in PINA as well: +# In[87]: -u_test_rbf = pod_rbf(p_test) -u_train_rbf = pod_rbf(p_train) -relative_error_train = torch.norm(u_train_rbf - u_train)/torch.norm(u_train) -relative_error_test = torch.norm(u_test_rbf - u_test)/torch.norm(u_test) +trainer = Trainer( + solver=pod_nn_stokes, + max_epochs=1000, + batch_size=None, + accelerator="cpu", + train_size=0.9, + val_size=0.0, + test_size=0.1, +) + +# fit the pod basis +trainer.data_module.setup("fit") # set up the dataset +x_train = trainer.data_module.train_dataset.conditions_dict["data"][ + "target" +] # extract data for training +pod_nn.fit_pod(x=x_train) + +# now train +trainer.train() + -print('Error summary for POD-RBF model:') -print(f' Train: {relative_error_train.item():e}') -print(f' Test: {relative_error_test.item():e}') +# Done! Now that the computational expensive part is over, we can load in future the model to infer new parameters (simply loading the checkpoint file automatically created by `Lightning`) or test its performances. We measure the relative error for the training and test datasets, printing the mean one. +# In[ ]: -# ## POD-NN reduced order model -# In[8]: +# extract train and test data +trainer.data_module.setup("test") # set up the dataset +p_train = trainer.data_module.train_dataset.conditions_dict["data"]["input"] +u_train = trainer.data_module.train_dataset.conditions_dict["data"]["target"] +p_test = trainer.data_module.test_dataset.conditions_dict["data"]["input"] +u_test = trainer.data_module.test_dataset.conditions_dict["data"]["target"] +# compute statistics +u_test_nn = pod_nn_stokes(p_test) +u_train_nn = pod_nn_stokes(p_train) -class PODNN(torch.nn.Module): +relative_error_train = torch.norm(u_train_nn - u_train) / torch.norm(u_train) +relative_error_test = torch.norm(u_test_nn - u_test) / torch.norm(u_test) + +print("Error summary for POD-NN model:") +print(f" Train: {relative_error_train.item():e}") +print(f" Test: {relative_error_test.item():e}") + + +# ## POD-RBF reduced order model + +# Then, we define the model we want to use, with the POD (`PODBlock`) and the RBF (`RBFBlock`) objects. + +# In[89]: + + +class PODRBF(torch.nn.Module): """ - Proper orthogonal decomposition with neural network model. + Proper orthogonal decomposition with Radial Basis Function interpolation model. """ - def __init__(self, pod_rank, layers, func): - """ - - """ + def __init__(self, pod_rank, rbf_kernel): + """ """ super().__init__() - + self.pod = PODBlock(pod_rank) - self.nn = FeedForward( - input_dimensions=1, - output_dimensions=pod_rank, - layers=layers, - func=func - ) - + self.rbf = RBFBlock(kernel=rbf_kernel) def forward(self, x): """ @@ -203,109 +211,105 @@ def forward(self, x): :return: the output computed by the model. :rtype: torch.Tensor """ - coefficents = self.nn(x) + coefficents = self.rbf(x) return self.pod.expand(coefficents) - def fit_pod(self, x): + def fit(self, p, x): """ - Just call the :meth:`pina.model.layers.PODBlock.fit` method of the - :attr:`pina.model.layers.PODBlock` attribute. + Call the :meth:`pina.model.layers.PODBlock.fit` method of the + :attr:`pina.model.layers.PODBlock` attribute to perform the POD, + and the :meth:`pina.model.layers.RBFBlock.fit` method of the + :attr:`pina.model.layers.RBFBlock` attribute to fit the interpolation. """ self.pod.fit(x) + self.rbf.fit(p, self.pod.reduce(x)) -# We highlight that the POD modes are directly computed by means of the singular value decomposition (computed over the input data), and not trained using the backpropagation approach. Only the weights of the MLP are actually trained during the optimization loop. - -# In[9]: - - -pod_nn = PODNN(pod_rank=20, layers=[10, 10, 10], func=torch.nn.Tanh) -pod_nn.fit_pod(u_train) - -pod_nn_stokes = SupervisedSolver( - problem=poisson_problem, - model=pod_nn, - optimizer=torch.optim.Adam, - optimizer_kwargs={'lr': 0.0001}) - - -# Now that we have set the `Problem` and the `Model`, we have just to train the model and use it for predicting the test snapshots. +# We can then fit the model and ask it to predict the required field for unseen values of the parameters. Note that this model does not need a `Trainer` since it does not include any neural network or learnable parameters. -# In[10]: +# In[90]: -trainer = Trainer( - solver=pod_nn_stokes, - max_epochs=1000, - batch_size=100, - log_every_n_steps=5, - accelerator='cpu') -trainer.train() +pod_rbf = PODRBF(pod_rank=20, rbf_kernel="thin_plate_spline") +pod_rbf.fit(p_train, u_train) -# Done! Now that the computational expensive part is over, we can load in future the model to infer new parameters (simply loading the checkpoint file automatically created by `Lightning`) or test its performances. We measure the relative error for the training and test datasets, printing the mean one. +# Compute errors -# In[11]: +# In[91]: -u_test_nn = pod_nn_stokes(p_test) -u_train_nn = pod_nn_stokes(p_train) +u_test_rbf = pod_rbf(p_test) +u_train_rbf = pod_rbf(p_train) -relative_error_train = torch.norm(u_train_nn - u_train)/torch.norm(u_train) -relative_error_test = torch.norm(u_test_nn - u_test)/torch.norm(u_test) +relative_error_train = torch.norm(u_train_rbf - u_train) / torch.norm(u_train) +relative_error_test = torch.norm(u_test_rbf - u_test) / torch.norm(u_test) -print('Error summary for POD-NN model:') -print(f' Train: {relative_error_train.item():e}') -print(f' Test: {relative_error_test.item():e}') +print("Error summary for POD-RBF model:") +print(f" Train: {relative_error_train.item():e}") +print(f" Test: {relative_error_test.item():e}") # ## POD-RBF vs POD-NN # We can of course also plot the solutions predicted by the `PODRBF` and by the `PODNN` model, comparing them to the original ones. We can note here, in the `PODNN` model and for low velocities, some differences, but improvements can be accomplished thanks to longer training. -# In[12]: +# In[92]: idx = torch.randint(0, len(u_test), (4,)) u_idx_rbf = pod_rbf(p_test[idx]) u_idx_nn = pod_nn_stokes(p_test[idx]) -import numpy as np -import matplotlib -import matplotlib.pyplot as plt -fig, axs = plt.subplots(5, 4, figsize=(14, 9)) +fig, axs = plt.subplots(4, 5, figsize=(14, 9)) relative_error_rbf = np.abs(u_test[idx] - u_idx_rbf.detach()) -relative_error_rbf = np.where(u_test[idx] < 1e-7, 1e-7, relative_error_rbf/u_test[idx]) +relative_error_rbf = np.where( + u_test[idx] < 1e-7, 1e-7, relative_error_rbf / u_test[idx] +) relative_error_nn = np.abs(u_test[idx] - u_idx_nn.detach()) -relative_error_nn = np.where(u_test[idx] < 1e-7, 1e-7, relative_error_nn/u_test[idx]) - -for i, (idx_, rbf_, nn_, rbf_err_, nn_err_) in enumerate( - zip(idx, u_idx_rbf, u_idx_nn, relative_error_rbf, relative_error_nn)): - axs[0, i].set_title(f'$\mu$ = {p_test[idx_].item():.2f}') - - cm = axs[0, i].tricontourf(dataset.triang, rbf_.detach()) # POD-RBF prediction - plt.colorbar(cm, ax=axs[0, i]) - - cm = axs[1, i].tricontourf(dataset.triang, nn_.detach()) # POD-NN prediction - plt.colorbar(cm, ax=axs[1, i]) - - cm = axs[2, i].tricontourf(dataset.triang, u_test[idx_].flatten()) # Truth - plt.colorbar(cm, ax=axs[2, i]) - - cm = axs[3, i].tripcolor(dataset.triang, rbf_err_, norm=matplotlib.colors.LogNorm()) # Error for POD-RBF - plt.colorbar(cm, ax=axs[3, i]) - - cm = axs[4, i].tripcolor(dataset.triang, nn_err_, norm=matplotlib.colors.LogNorm()) # Error for POD-NN - plt.colorbar(cm, ax=axs[4, i]) - -plt.show() - - -# In[ ]: +relative_error_nn = np.where( + u_test[idx] < 1e-7, 1e-7, relative_error_nn / u_test[idx] +) +for i, (idx_, rbf_, nn_, rbf_err_, nn_err_) in enumerate( + zip(idx, u_idx_rbf, u_idx_nn, relative_error_rbf, relative_error_nn) +): + + axs[0, 0].set_title(f"Real Snapshots") + axs[0, 1].set_title(f"POD-RBF") + axs[0, 2].set_title(f"POD-NN") + axs[0, 3].set_title(f"Error POD-RBF") + axs[0, 4].set_title(f"Error POD-NN") + + cm = axs[i, 0].tricontourf( + dataset.triang, rbf_.detach() + ) # POD-RBF prediction + plt.colorbar(cm, ax=axs[i, 0]) + + cm = axs[i, 1].tricontourf( + dataset.triang, nn_.detach() + ) # POD-NN prediction + plt.colorbar(cm, ax=axs[i, 1]) + + cm = axs[i, 2].tricontourf(dataset.triang, u_test[idx_].flatten()) # Truth + plt.colorbar(cm, ax=axs[i, 2]) + + cm = axs[i, 3].tripcolor( + dataset.triang, rbf_err_, norm=matplotlib.colors.LogNorm() + ) # Error for POD-RBF + plt.colorbar(cm, ax=axs[i, 3]) + + cm = axs[i, 4].tripcolor( + dataset.triang, nn_err_, norm=matplotlib.colors.LogNorm() + ) # Error for POD-NN + plt.colorbar(cm, ax=axs[i, 4]) +plt.show() +# #### References +# 1. Rozza G., Stabile G., Ballarin F. (2022). Advanced Reduced Order Methods and Applications in Computational Fluid Dynamics, Society for Industrial and Applied Mathematics. +# 2. Hesthaven, J. S., & Ubbiali, S. (2018). Non-intrusive reduced order modeling of nonlinear problems using neural networks. Journal of Computational Physics, 363, 55-78. diff --git a/tutorials/tutorial9/tutorial.ipynb b/tutorials/tutorial9/tutorial.ipynb index 9ef256bda..daf81ec59 100644 --- a/tutorials/tutorial9/tutorial.ipynb +++ b/tutorials/tutorial9/tutorial.ipynb @@ -20,31 +20,35 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "## routine needed to run the notebook on Google Colab\n", "try:\n", - " import google.colab\n", - " IN_COLAB = True\n", + " import google.colab\n", + "\n", + " IN_COLAB = True\n", "except:\n", - " IN_COLAB = False\n", + " IN_COLAB = False\n", "if IN_COLAB:\n", - " !pip install \"pina-mathlab\"\n", + " !pip install \"pina-mathlab\"\n", "\n", "import torch\n", "import matplotlib.pyplot as plt\n", - "plt.style.use('tableau-colorblind10')\n", - "from pina import Condition, Plotter\n", + "import warnings\n", + "\n", + "from pina import Condition, Trainer\n", "from pina.problem import SpatialProblem\n", - "from pina.operators import laplacian\n", + "from pina.operator import laplacian\n", "from pina.model import FeedForward\n", - "from pina.model.layers import PeriodicBoundaryEmbedding # The PBC module\n", - "from pina.solvers import PINN\n", - "from pina.trainer import Trainer\n", - "from pina.geometry import CartesianDomain\n", - "from pina.equation import Equation" + "from pina.model.block import PeriodicBoundaryEmbedding # The PBC module\n", + "from pina.solver import PINN\n", + "from pina.domain import CartesianDomain\n", + "from pina.equation import Equation\n", + "from pina.callback import MetricTracker\n", + "\n", + "warnings.filterwarnings(\"ignore\")" ] }, { @@ -77,36 +81,42 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ - "class Helmholtz(SpatialProblem):\n", - " output_variables = ['u']\n", - " spatial_domain = CartesianDomain({'x': [0, 2]})\n", + "def helmholtz_equation(input_, output_):\n", + " x = input_.extract(\"x\")\n", + " u_xx = laplacian(output_, input_, components=[\"u\"], d=[\"x\"])\n", + " f = (\n", + " -6.0\n", + " * torch.pi**2\n", + " * torch.sin(3 * torch.pi * x)\n", + " * torch.cos(torch.pi * x)\n", + " )\n", + " lambda_ = -10.0 * torch.pi**2\n", + " return u_xx - lambda_ * output_ - f\n", "\n", - " def Helmholtz_equation(input_, output_):\n", - " x = input_.extract('x')\n", - " u_xx = laplacian(output_, input_, components=['u'], d=['x'])\n", - " f = - 6.*torch.pi**2 * torch.sin(3*torch.pi*x)*torch.cos(torch.pi*x)\n", - " lambda_ = - 10. * torch.pi ** 2\n", - " return u_xx - lambda_ * output_ - f\n", + "\n", + "class Helmholtz(SpatialProblem):\n", + " output_variables = [\"u\"]\n", + " spatial_domain = CartesianDomain({\"x\": [0, 2]})\n", "\n", " # here we write the problem conditions\n", " conditions = {\n", - " 'D': Condition(location=spatial_domain,\n", - " equation=Equation(Helmholtz_equation)),\n", + " \"phys_cond\": Condition(\n", + " domain=spatial_domain, equation=Equation(helmholtz_equation)\n", + " ),\n", " }\n", "\n", - " def Helmholtz_sol(self, pts):\n", - " return torch.sin(torch.pi * pts) * torch.cos(3. * torch.pi * pts)\n", - " \n", - " truth_solution = Helmholtz_sol\n", + " def solution(self, pts):\n", + " return torch.sin(torch.pi * pts) * torch.cos(3.0 * torch.pi * pts)\n", + "\n", "\n", "problem = Helmholtz()\n", "\n", "# let's discretise the domain\n", - "problem.discretise_domain(200, 'grid', locations=['D'])" + "problem.discretise_domain(200, \"grid\", domains=[\"phys_cond\"])" ] }, { @@ -115,7 +125,7 @@ "source": [ "As usual, the Helmholtz problem is written in **PINA** code as a class. \n", "The equations are written as `conditions` that should be satisfied in the\n", - "corresponding domains. The `truth_solution`\n", + "corresponding domains. The `solution`\n", "is the exact solution which will be compared with the predicted one. We used\n", "Latin Hypercube Sampling for choosing the collocation points." ] @@ -155,16 +165,19 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "# we encapsulate all modules in a torch.nn.Sequential container\n", - "model = torch.nn.Sequential(PeriodicBoundaryEmbedding(input_dimension=1,\n", - " periods=2),\n", - " FeedForward(input_dimensions=3, # output of PeriodicBoundaryEmbedding = 3 * input_dimension\n", - " output_dimensions=1,\n", - " layers=[10, 10]))" + "model = torch.nn.Sequential(\n", + " PeriodicBoundaryEmbedding(input_dimension=1, periods=2),\n", + " FeedForward(\n", + " input_dimensions=3, # output of PeriodicBoundaryEmbedding = 3 * input_dimension\n", + " output_dimensions=1,\n", + " layers=[10, 10],\n", + " ),\n", + ")" ] }, { @@ -175,20 +188,91 @@ "for all dimensions using a dictionary, e.g. `periods={'x':2, 'y':3, ...}`\n", "would indicate a periodicity of $2$ in $x$, $3$ in $y$, and so on...\n", "\n", - "We will now solve the problem as usually with the `PINN` and `Trainer` class." + "We will now solve the problem as usually with the `PINN` and `Trainer` class, then we will look at the losses using the `MetricTracker` callback from `pina.callback`." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: True (mps), used: False\n", + "TPU available: False, using: 0 TPU cores\n", + "HPU available: False, using: 0 HPUs\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 4999: 100%|██████████| 1/1 [00:00<00:00, 154.88it/s, v_num=1, phys_cond_loss=0.033, train_loss=0.033] " + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "`Trainer.fit` stopped: `max_epochs=5000` reached.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 4999: 100%|██████████| 1/1 [00:00<00:00, 104.00it/s, v_num=1, phys_cond_loss=0.033, train_loss=0.033]\n" + ] + } + ], "source": [ - "pinn = PINN(problem=problem, model=model)\n", - "trainer = Trainer(pinn, max_epochs=5000, accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional)\n", + "pinn = PINN(\n", + " problem=problem,\n", + " model=model,\n", + ")\n", + "trainer = Trainer(\n", + " pinn,\n", + " max_epochs=5000,\n", + " accelerator=\"cpu\",\n", + " enable_model_summary=False,\n", + " callbacks=[MetricTracker()],\n", + " train_size=1.0,\n", + " val_size=0.0,\n", + " test_size=0.0,\n", + ")\n", "trainer.train()" ] }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot loss\n", + "trainer_metrics = trainer.callbacks[0].metrics\n", + "plt.plot(\n", + " range(len(trainer_metrics[\"train_loss\"])), trainer_metrics[\"train_loss\"]\n", + ")\n", + "# plotting\n", + "plt.xlabel(\"epoch\")\n", + "plt.ylabel(\"loss\")\n", + "plt.yscale(\"log\")" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -198,14 +282,24 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", "text/plain": [ - "
" + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" ] }, "metadata": {}, @@ -213,8 +307,12 @@ } ], "source": [ - "pl = Plotter()\n", - "pl.plot(pinn)" + "pts = pinn.problem.spatial_domain.sample(256, \"grid\", variables=\"x\")\n", + "predicted_output = pinn.forward(pts).extract(\"u\").tensor.detach()\n", + "true_output = pinn.problem.solution(pts)\n", + "plt.plot(pts.extract([\"x\"]), predicted_output, label=\"Neural Network solution\")\n", + "plt.plot(pts.extract([\"x\"]), true_output, label=\"True solution\")\n", + "plt.legend()" ] }, { @@ -226,12 +324,12 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -244,21 +342,21 @@ "# plotting solution\n", "with torch.no_grad():\n", " # Notice here we put [-4, 4]!!!\n", - " new_domain = CartesianDomain({'x' : [0, 4]})\n", - " x = new_domain.sample(1000, mode='grid')\n", + " new_domain = CartesianDomain({\"x\": [0, 4]})\n", + " x = new_domain.sample(1000, mode=\"grid\")\n", " fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n", " # Plot 1\n", - " axes[0].plot(x, problem.truth_solution(x), label=r'$u(x)$', color='blue')\n", - " axes[0].set_title(r'True solution $u(x)$')\n", + " axes[0].plot(x, problem.solution(x), label=r\"$u(x)$\", color=\"blue\")\n", + " axes[0].set_title(r\"True solution $u(x)$\")\n", " axes[0].legend(loc=\"upper right\")\n", " # Plot 2\n", - " axes[1].plot(x, pinn(x), label=r'$u_{\\theta}(x)$', color='green')\n", - " axes[1].set_title(r'PINN solution $u_{\\theta}(x)$')\n", + " axes[1].plot(x, pinn(x), label=r\"$u_{\\theta}(x)$\", color=\"green\")\n", + " axes[1].set_title(r\"PINN solution $u_{\\theta}(x)$\")\n", " axes[1].legend(loc=\"upper right\")\n", " # Plot 3\n", - " diff = torch.abs(problem.truth_solution(x) - pinn(x))\n", - " axes[2].plot(x, diff, label=r'$|u(x) - u_{\\theta}(x)|$', color='red')\n", - " axes[2].set_title(r'Absolute difference $|u(x) - u_{\\theta}(x)|$')\n", + " diff = torch.abs(problem.solution(x) - pinn(x))\n", + " axes[2].plot(x, diff, label=r\"$|u(x) - u_{\\theta}(x)|$\", color=\"red\")\n", + " axes[2].set_title(r\"Absolute difference $|u(x) - u_{\\theta}(x)|$\")\n", " axes[2].legend(loc=\"upper right\")\n", " # Adjust layout\n", " plt.tight_layout()\n", @@ -284,11 +382,6 @@ "\n", "4. Many more..." ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] } ], "metadata": { @@ -307,7 +400,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.9.21" } }, "nbformat": 4, diff --git a/tutorials/tutorial9/tutorial.py b/tutorials/tutorial9/tutorial.py index db4b7a333..ae03c1892 100644 --- a/tutorials/tutorial9/tutorial.py +++ b/tutorials/tutorial9/tutorial.py @@ -2,46 +2,50 @@ # coding: utf-8 # # Tutorial: One dimensional Helmholtz equation using Periodic Boundary Conditions -# +# # [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial9/tutorial.ipynb) -# +# # This tutorial presents how to solve with Physics-Informed Neural Networks (PINNs) # a one dimensional Helmholtz equation with periodic boundary conditions (PBC). # We will train with standard PINN's training by augmenting the input with # periodic expansion as presented in [*An expert’s guide to training # physics-informed neural networks*]( # https://arxiv.org/abs/2308.08468). -# +# # First of all, some useful imports. -# In[1]: +# In[ ]: ## routine needed to run the notebook on Google Colab try: - import google.colab - IN_COLAB = True + import google.colab + + IN_COLAB = True except: - IN_COLAB = False + IN_COLAB = False if IN_COLAB: - get_ipython().system('pip install "pina-mathlab"') + get_ipython().system('pip install "pina-mathlab"') import torch import matplotlib.pyplot as plt -plt.style.use('tableau-colorblind10') -from pina import Condition, Plotter +import warnings + +from pina import Condition, Trainer from pina.problem import SpatialProblem -from pina.operators import laplacian +from pina.operator import laplacian from pina.model import FeedForward -from pina.model.layers import PeriodicBoundaryEmbedding # The PBC module -from pina.solvers import PINN -from pina.trainer import Trainer -from pina.geometry import CartesianDomain +from pina.model.block import PeriodicBoundaryEmbedding # The PBC module +from pina.solver import PINN +from pina.domain import CartesianDomain from pina.equation import Equation +from pina.callback import MetricTracker + +warnings.filterwarnings("ignore") # ## The problem definition -# +# # The one-dimensional Helmholtz problem is mathematically written as: # $$ # \begin{cases} @@ -52,51 +56,57 @@ # In this case we are asking the solution to be $C^{\infty}$ periodic with # period $2$, on the infinite domain $x\in(-\infty, \infty)$. Notice that the # classical PINN would need infinite conditions to evaluate the PBC loss function, -# one for each derivative, which is of course infeasible... +# one for each derivative, which is of course infeasible... # A possible solution, diverging from the original PINN formulation, # is to use *coordinates augmentation*. In coordinates augmentation you seek for # a coordinates transformation $v$ such that $x\rightarrow v(x)$ such that # the periodicity condition $ u^{(m)}(x=0) - u^{(m)}(x=2) = 0 \quad m\in[0, 1, \cdots] $ is # satisfied. -# +# # For demonstration purposes, the problem specifics are $\lambda=-10\pi^2$, # and $f(x)=-6\pi^2\sin(3\pi x)\cos(\pi x)$ which give a solution that can be # computed analytically $u(x) = \sin(\pi x)\cos(3\pi x)$. -# In[2]: +# In[15]: -class Helmholtz(SpatialProblem): - output_variables = ['u'] - spatial_domain = CartesianDomain({'x': [0, 2]}) +def helmholtz_equation(input_, output_): + x = input_.extract("x") + u_xx = laplacian(output_, input_, components=["u"], d=["x"]) + f = ( + -6.0 + * torch.pi**2 + * torch.sin(3 * torch.pi * x) + * torch.cos(torch.pi * x) + ) + lambda_ = -10.0 * torch.pi**2 + return u_xx - lambda_ * output_ - f + - def Helmholtz_equation(input_, output_): - x = input_.extract('x') - u_xx = laplacian(output_, input_, components=['u'], d=['x']) - f = - 6.*torch.pi**2 * torch.sin(3*torch.pi*x)*torch.cos(torch.pi*x) - lambda_ = - 10. * torch.pi ** 2 - return u_xx - lambda_ * output_ - f +class Helmholtz(SpatialProblem): + output_variables = ["u"] + spatial_domain = CartesianDomain({"x": [0, 2]}) # here we write the problem conditions conditions = { - 'D': Condition(location=spatial_domain, - equation=Equation(Helmholtz_equation)), + "phys_cond": Condition( + domain=spatial_domain, equation=Equation(helmholtz_equation) + ), } - def Helmholtz_sol(self, pts): - return torch.sin(torch.pi * pts) * torch.cos(3. * torch.pi * pts) - - truth_solution = Helmholtz_sol + def solution(self, pts): + return torch.sin(torch.pi * pts) * torch.cos(3.0 * torch.pi * pts) + problem = Helmholtz() # let's discretise the domain -problem.discretise_domain(200, 'grid', locations=['D']) +problem.discretise_domain(200, "grid", domains=["phys_cond"]) -# As usual, the Helmholtz problem is written in **PINA** code as a class. +# As usual, the Helmholtz problem is written in **PINA** code as a class. # The equations are written as `conditions` that should be satisfied in the -# corresponding domains. The `truth_solution` +# corresponding domains. The `solution` # is the exact solution which will be compared with the predicted one. We used # Latin Hypercube Sampling for choosing the collocation points. @@ -111,7 +121,7 @@ def Helmholtz_sol(self, pts): # arbitrary dimension, see [*A method for representing periodic functions and # enforcing exactly periodic boundary conditions with # deep neural networks*](https://arxiv.org/pdf/2007.07442). -# +# # In our case, we rewrite # $v(x) = \left[1, \cos\left(\frac{2\pi}{L} x\right), # \sin\left(\frac{2\pi}{L} x\right)\right]$, i.e @@ -119,68 +129,101 @@ def Helmholtz_sol(self, pts): # network. The resulting neural network obtained by composing $f$ with $v$ gives # the PINN approximate solution, that is # $u(x) \approx u_{\theta}(x)=NN_{\theta}(v(x))$. -# +# # In **PINA** this translates in using the `PeriodicBoundaryEmbedding` layer for $v$, and any -# `pina.model` for $NN_{\theta}$. Let's see it in action! -# +# `pina.model` for $NN_{\theta}$. Let's see it in action! +# -# In[3]: +# In[16]: # we encapsulate all modules in a torch.nn.Sequential container -model = torch.nn.Sequential(PeriodicBoundaryEmbedding(input_dimension=1, - periods=2), - FeedForward(input_dimensions=3, # output of PeriodicBoundaryEmbedding = 3 * input_dimension - output_dimensions=1, - layers=[10, 10])) +model = torch.nn.Sequential( + PeriodicBoundaryEmbedding(input_dimension=1, periods=2), + FeedForward( + input_dimensions=3, # output of PeriodicBoundaryEmbedding = 3 * input_dimension + output_dimensions=1, + layers=[10, 10], + ), +) # As simple as that! Notice that in higher dimension you can specify different periods # for all dimensions using a dictionary, e.g. `periods={'x':2, 'y':3, ...}` # would indicate a periodicity of $2$ in $x$, $3$ in $y$, and so on... -# -# We will now solve the problem as usually with the `PINN` and `Trainer` class. +# +# We will now solve the problem as usually with the `PINN` and `Trainer` class, then we will look at the losses using the `MetricTracker` callback from `pina.callback`. + +# In[17]: + + +pinn = PINN( + problem=problem, + model=model, +) +trainer = Trainer( + pinn, + max_epochs=5000, + accelerator="cpu", + enable_model_summary=False, + callbacks=[MetricTracker()], + train_size=1.0, + val_size=0.0, + test_size=0.0, +) +trainer.train() -# In[ ]: +# In[18]: -pinn = PINN(problem=problem, model=model) -trainer = Trainer(pinn, max_epochs=5000, accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional) -trainer.train() + +# plot loss +trainer_metrics = trainer.callbacks[0].metrics +plt.plot( + range(len(trainer_metrics["train_loss"])), trainer_metrics["train_loss"] +) +# plotting +plt.xlabel("epoch") +plt.ylabel("loss") +plt.yscale("log") # We are going to plot the solution now! -# In[6]: +# In[19]: -pl = Plotter() -pl.plot(pinn) +pts = pinn.problem.spatial_domain.sample(256, "grid", variables="x") +predicted_output = pinn.forward(pts).extract("u").tensor.detach() +true_output = pinn.problem.solution(pts) +plt.plot(pts.extract(["x"]), predicted_output, label="Neural Network solution") +plt.plot(pts.extract(["x"]), true_output, label="True solution") +plt.legend() # Great, they overlap perfectly! This seems a good result, considering the simple neural network used to some this (complex) problem. We will now test the neural network on the domain $[-4, 4]$ without retraining. In principle the periodicity should be present since the $v$ function ensures the periodicity in $(-\infty, \infty)$. -# In[7]: +# In[20]: # plotting solution with torch.no_grad(): # Notice here we put [-4, 4]!!! - new_domain = CartesianDomain({'x' : [0, 4]}) - x = new_domain.sample(1000, mode='grid') + new_domain = CartesianDomain({"x": [0, 4]}) + x = new_domain.sample(1000, mode="grid") fig, axes = plt.subplots(1, 3, figsize=(15, 5)) # Plot 1 - axes[0].plot(x, problem.truth_solution(x), label=r'$u(x)$', color='blue') - axes[0].set_title(r'True solution $u(x)$') + axes[0].plot(x, problem.solution(x), label=r"$u(x)$", color="blue") + axes[0].set_title(r"True solution $u(x)$") axes[0].legend(loc="upper right") # Plot 2 - axes[1].plot(x, pinn(x), label=r'$u_{\theta}(x)$', color='green') - axes[1].set_title(r'PINN solution $u_{\theta}(x)$') + axes[1].plot(x, pinn(x), label=r"$u_{\theta}(x)$", color="green") + axes[1].set_title(r"PINN solution $u_{\theta}(x)$") axes[1].legend(loc="upper right") # Plot 3 - diff = torch.abs(problem.truth_solution(x) - pinn(x)) - axes[2].plot(x, diff, label=r'$|u(x) - u_{\theta}(x)|$', color='red') - axes[2].set_title(r'Absolute difference $|u(x) - u_{\theta}(x)|$') + diff = torch.abs(problem.solution(x) - pinn(x)) + axes[2].plot(x, diff, label=r"$|u(x) - u_{\theta}(x)|$", color="red") + axes[2].set_title(r"Absolute difference $|u(x) - u_{\theta}(x)|$") axes[2].legend(loc="upper right") # Adjust layout plt.tight_layout() @@ -189,17 +232,15 @@ def Helmholtz_sol(self, pts): # It is pretty clear that the network is periodic, with also the error following a periodic pattern. Obviously a longer training and a more expressive neural network could improve the results! -# +# # ## What's next? -# +# # Congratulations on completing the one dimensional Helmholtz tutorial of **PINA**! There are multiple directions you can go now: -# +# # 1. Train the network for longer or with different layer sizes and assert the finaly accuracy -# +# # 2. Apply the `PeriodicBoundaryEmbedding` layer for a time-dependent problem (see reference in the documentation) -# +# # 3. Exploit extrafeature training ? -# +# # 4. Many more... - -# diff --git a/utils/mathlab_versioning.py b/utils/mathlab_versioning.py index 577a8ec3e..d7e17a7e4 100644 --- a/utils/mathlab_versioning.py +++ b/utils/mathlab_versioning.py @@ -4,8 +4,8 @@ module = 'pina' -meta_file = os.path.join(module, 'meta.py') version_line = r'__version__.*=.*"(.+?)"' +pyproject_file = 'pyproject.toml' class Version: @@ -34,11 +34,11 @@ def __str__(self): def get_version(): - with open(meta_file, 'r') as fp: + with open(pyproject_file, 'r') as fp: content = fp.read() try: - found = re.search(r'__version__.*=.*"(.+?)"', content).group(1) + found = re.search(r'version.*=.*"(.+?)"', content).group(1) except AttributeError: pass @@ -48,13 +48,13 @@ def get_version(): def set_version(version): - with open(meta_file, 'r') as fp: + with open(pyproject_file, 'r') as fp: content = fp.read() - line_string = '__version__ = "{}"'.format(version) - text_after = re.sub('__version__.*=.*"(.+?)"', line_string, content) + line_string = 'version = "{}"'.format(version) + text_after = re.sub('version.*=.*"(.+?)"', line_string, content) - with open(meta_file, 'w') as fp: + with open(pyproject_file, 'w') as fp: fp.write(text_after)