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Bump the requirements-txt group across 2 directories with 3 updates #722

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Bumps the requirements-txt group with 3 updates in the /demo-notebooks/guided-demos directory: pytorch-lightning, torchmetrics and torchvision.
Bumps the requirements-txt group with 3 updates in the /tests/e2e directory: pytorch-lightning, torchmetrics and torchvision.

Updates pytorch-lightning from 1.9.5 to 2.4.0

Release notes

Sourced from pytorch-lightning's releases.

Lightning v2.4

Lightning AI ⚡ is excited to announce the release of Lightning 2.4. This is mainly a compatibility upgrade for PyTorch 2.4 and Python 3.12, with a sprinkle of a few features and bug fixes.

Did you know? The Lightning philosophy extends beyond a boilerplate-free deep learning framework: We've been hard at work bringing you Lightning Studio. Code together, prototype, train, deploy, host AI web apps. All from your browser, with zero setup.

Changes

PyTorch Lightning

  • Made saving non-distributed checkpoints fully atomic (#20011)
  • Added dump_stats flag to AdvancedProfiler (#19703)
  • Added a flag verbose to the seed_everything() function (#20108)
  • Added support for PyTorch 2.4 (#20010)
  • Added support for Python 3.12 (20078)
  • The TQDMProgressBar now provides an option to retain prior training epoch bars (#19578)
  • Added the count of modules in train and eval mode to the printed ModelSummary table (#20159)
  • Triggering KeyboardInterrupt (Ctrl+C) during .fit(), .evaluate(), .test() or .predict() now terminates all processes launched by the Trainer and exits the program (#19976)
  • Changed the implementation of how seeds are chosen for dataloader workers when using seed_everything(..., workers=True) (#20055)
  • NumPy is no longer a required dependency (#20090)
  • Removed support for PyTorch 2.1 (#20009)
  • Removed support for Python 3.8 (#20071)
  • Avoid LightningCLI saving hyperparameters with class_path and init_args since this would be a breaking change (#20068)
  • Fixed an issue that would cause too many printouts of the seed info when using seed_everything() (#20108)
  • Fixed _LoggerConnector's _ResultMetric to move all registered keys to the device of the logged value if needed (#19814)
  • Fixed _optimizer_to_device logic for special 'step' key in optimizer state causing performance regression (#20019)
  • Fixed parameter counts in ModelSummary when model has distributed parameters (DTensor) (#20163)

Lightning Fabric

... (truncated)

Commits

Updates torchmetrics from 0.9.1 to 1.5.0

Release notes

Sourced from torchmetrics's releases.

Shape metrics are quantitative methods used to assess and compare the geometric properties of objects, often in datasets that represent shapes. One such metric is the Procrustes Disparity, which measures the sum of the squared differences between two datasets after applying a Procrustes transformation. This transformation involves scaling, rotating, and translating the datasets to achieve optimal alignment. The Procrustes Disparity is particularly useful when comparing datasets that are similar in structure but not perfectly aligned, allowing for more meaningful comparison by minimizing differences due to orientation or size.

[1.5.0] - 2024-10-18

Added

  • Added segmentation metric HausdorffDistance (#2122)
  • Added audio metric DNSMOS (#2525)
  • Added shape metric ProcrustesDistance (#2723)
  • Added MetricInputTransformer wrapper (#2392)
  • Added input_format argument to segmentation metrics (#2572)
  • Added multi-output support for MAE metric (#2605)
  • Added truncation argument to BERTScore (#2776)

Changed

  • Tracker higher is better integration (#2649)
  • Updated InfoLM class to dynamically set higher_is_better (#2674)

Deprecated

  • Deprecated num_outputs in R2Score (#2705)

Fixed

  • Fixed corner case in IoU metric for single empty prediction tensors (#2780)
  • Fixed PSNR calculation for integer type input images (#2788)

Key Contributors

@​Astraightrain, @​grahamannett, @​lgienapp, @​matsumotosan, @​quancs, @​SkafteNicki

New Contributors since 1.4.0

Full Changelog: Lightning-AI/torchmetrics@v1.4.0...v1.5.0

... (truncated)

Changelog

Sourced from torchmetrics's changelog.

[1.5.0] - 2024-10-18

Added

  • Added segmentation metric HausdorffDistance (#2122)
  • Added audio metric DNSMOS (#2525)
  • Added shape metric ProcrustesDistance (#2723
  • Added MetricInputTransformer wrapper (#2392)
  • Added input_format argument to segmentation metrics (#2572)
  • Added multi-output support for MAE metric (#2605)
  • Added truncation argument to BERTScore (#2776)

Changed

  • Tracker higher is better integration (#2649)
  • Updated InfoLM class to dynamically set higher_is_better (#2674)

Deprecated

  • Deprecated num_outputs in R2Score (#2705)

Fixed

  • Fixed corner case in IoU metric for single empty prediction tensors (#2780)
  • Fixed PSNR calculation for integer type input images (#2788)

[1.4.3] - 2024-10-10

Fixed

  • Fixed for Pearson changes inputs (#2765)
  • Fixed bug in PESQ metric where NoUtterancesError prevented calculating on a batch of data (#2753)
  • Fixed corner case in MatthewsCorrCoef (#2743)

[1.4.2] - 2022-09-12

Added

  • Re-adding Chrf implementation (#2701)

Fixed

  • Fixed wrong aggregation in segmentation.MeanIoU (#2698)
  • Fixed handling zero division error in binary IoU (Jaccard index) calculation (#2726)
  • Corrected the padding related calculation errors in SSIM (#2721)
  • Fixed compatibility of audio domain with new scipy (#2733)
  • Fixed how prefix/postfix works in MultitaskWrapper (#2722)

... (truncated)

Commits
  • a8e8337 releasing 1.5.0
  • 0fd1f96 Fix overflow for PSNR metric when used with uint8 input (#2788)
  • 6377aa5 docs: adding PESQ example to gallery (#2755)
  • a8cbae7 Fix corner case when single empty pred is provided to detection IOU metric ...
  • 801cec8 Improve CI stability for clustering examples (#2785)
  • b1f4db2 build(deps): bump sphinx-gallery from 0.17.1 to 0.18.0 in /requirements (#2784)
  • 8187e51 build(deps): update cloudpickle requirement from <=3.0.0,>1.3 to >1.3,<=3.1.0...
  • 1335c7b New segmentation metric: Hausdorff Distance (#2122)
  • 13f7b94 fix(lint): emergency bump docformatter (#2779)
  • 0990ecf Add possibility to control the truncation in bert score (#2776)
  • Additional commits viewable in compare view

Updates torchvision from 0.12.0 to 0.20.0

Release notes

Sourced from torchvision's releases.

Torchvision 0.20 release

Highlights

Encoding / Decoding images

Torchvision is further extending its encoding/decoding capabilities. For this version, we added a WEBP decoder, and a batch JPEG decoder on CUDA GPUs, which can lead to 10X speed-ups over CPU decoding.

We have also improved the UX of our decoding APIs to be more user-friendly. The main entry point is now torchvision.io.decode_image(), and it can take as input either a path (as str or pathlib.Path), or a tensor containing the raw encoded data.

Read more on the docs!

We also added support for HEIC and AVIF decoding, but these are currently only available when building from source. We are working on making those available directly in the upcoming releases. Stay tuned!

Detailed changes

Bug Fixes

[datasets] Update URL of SBDataset train_noval (#8551) [datasets] EuroSAT: fix SSL certificate issues (#8563) [io] Check average_rate availability in video reader (#8548)

New Features

[io] Add batch JPEG GPU decoding (decode_jpeg()) (#8496) [io] Add WEBP image decoder: decode_image(), decode_webp() (#8527, #8612, #8610) [io] Add HEIC and AVIF decoders, only available when building from source (#8597, #8596, #8647, #8613, #8621)

Improvements

[io] Add support for decoding 16bits png (#8524) [io] Allow decoding functions to accept the mode parameter as a string (#8627) [io] Allow decode_image() to support paths (#8624) [io] Automatically send video to CPU in io.write_video (#8537) [datasets] Better progress bar for file downloading (#8556) [datasets] Add Path type annotation for ImageFolder (#8526) [ops] Register nms and roi_align Autocast policy for PyTorch Intel GPU backend (#8541) [transforms] Use Sequence for parameters type checking in transforms.RandomErase (#8615) [transforms] Support v2.functional.gaussian_blur backprop (#8486) [transforms] Expose transforms.v2 utils for writing custom transforms. (#8670) [utils] Fix f-string in color error message (#8639) [packaging] Revamped and improved debuggability of setup.py build (#8535, #8581, #8581, #8582, #8590, #8533, #8528, #8659) [Documentation] Various documentation improvements (#8605, #8611, #8506, #8507, #8539, #8512, #8513, #8583, #8633) [tests] Various tests improvements (#8580, #8553, #8523, #8617, #8518, #8579, #8558, #8617, #8641) [code quality] Various code quality improvements (#8552, #8555, #8516, #8526, #8602, #8615, #8639, #8532) [ci] #8562, #8644, #8592, #8542, #8594, #8530, #8656

... (truncated)

Commits

Updates pytorch-lightning from 1.9.5 to 2.4.0

Release notes

Sourced from pytorch-lightning's releases.

Lightning v2.4

Lightning AI ⚡ is excited to announce the release of Lightning 2.4. This is mainly a compatibility upgrade for PyTorch 2.4 and Python 3.12, with a sprinkle of a few features and bug fixes.

Did you know? The Lightning philosophy extends beyond a boilerplate-free deep learning framework: We've been hard at work bringing you Lightning Studio. Code together, prototype, train, deploy, host AI web apps. All from your browser, with zero setup.

Changes

PyTorch Lightning

  • Made saving non-distributed checkpoints fully atomic (#20011)
  • Added dump_stats flag to AdvancedProfiler (#19703)
  • Added a flag verbose to the seed_everything() function (#20108)
  • Added support for PyTorch 2.4 (#20010)
  • Added support for Python 3.12 (20078)
  • The TQDMProgressBar now provides an option to retain prior training epoch bars (#19578)
  • Added the count of modules in train and eval mode to the printed ModelSummary table (#20159)
  • Triggering KeyboardInterrupt (Ctrl+C) during .fit(), .evaluate(), .test() or .predict() now terminates all processes launched by the Trainer and exits the program (#19976)
  • Changed the implementation of how seeds are chosen for dataloader workers when using seed_everything(..., workers=True) (#20055)
  • NumPy is no longer a required dependency (#20090)
  • Removed support for PyTorch 2.1 (#20009)
  • Removed support for Python 3.8 (#20071)
  • Avoid LightningCLI saving hyperparameters with class_path and init_args since this would be a breaking change (#20068)
  • Fixed an issue that would cause too many printouts of the seed info when using seed_everything() (#20108)
  • Fixed _LoggerConnector's _ResultMetric to move all registered keys to the device of the logged value if needed (#19814)
  • Fixed _optimizer_to_device logic for special 'step' key in optimizer state causing performance regression (#20019)
  • Fixed parameter counts in ModelSummary when model has distributed parameters (DTensor) (#20163)

Lightning Fabric

... (truncated)

Commits

Updates torchmetrics from 0.9.1 to 1.5.0

Release notes

Sourced from torchmetrics's releases.

Shape metrics are quantitative methods used to assess and compare the geometric properties of objects, often in datasets that represent shapes. One such metric is the Procrustes Disparity, which measures the sum of the squared differences between two datasets after applying a Procrustes transformation. This transformation involves scaling, rotating, and translating the datasets to achieve optimal alignment. The Procrustes Disparity is particularly useful when comparing datasets that are similar in structure but not perfectly aligned, allowing for more meaningful comparison by minimizing differences due to orientation or size.

[1.5.0] - 2024-10-18

Added

  • Added segmentation metric HausdorffDistance (#2122)
  • Added audio metric DNSMOS (#2525)
  • Added shape metric ProcrustesDistance (#2723)
  • Added MetricInputTransformer wrapper (#2392)
  • Added input_format argument to segmentation metrics (#2572)
  • Added multi-output support for MAE metric (#2605)
  • Added truncation argument to BERTScore (#2776)

Changed

  • Tracker higher is better integration (#2649)
  • Updated InfoLM class to dynamically set higher_is_better (#2674)

Deprecated

  • Deprecated num_outputs in R2Score (#2705)

Fixed

  • Fixed corner case in IoU metric for single empty prediction tensors (#2780)
  • Fixed PSNR calculation for integer type input images (#2788)

Key Contributors

@​Astraightrain, @​grahamannett, @​lgienapp, @​matsumotosan, @​quancs, @​SkafteNicki

New Contributors since 1.4.0

Full Changelog: Lightning-AI/torchmetrics@v1.4.0...v1.5.0

... (truncated)

Changelog

Sourced from torchmetrics's changelog.

[1.5.0] - 2024-10-18

Added

  • Added segmentation metric HausdorffDistance (#2122)
  • Added audio metric DNSMOS (#2525)
  • Added shape metric ProcrustesDistance (#2723
  • Added MetricInputTransformer wrapper (#2392)
  • Added input_format argument to segmentation metrics (#2572)
  • Added multi-output support for MAE metric (#2605)
  • Added truncation argument to BERTScore (#2776)

Changed

  • Tracker higher is better integration (#2649)
  • Updated InfoLM class to dynamically set higher_is_better (#2674)

Deprecated

  • Deprecated num_outputs in R2Score (#2705)

Fixed

  • Fixed corner case in IoU metric for single empty prediction tensors (#2780)
  • Fixed PSNR calculation for integer type input images (#2788)

[1.4.3] - 2024-10-10

Fixed

  • Fixed for Pearson changes inputs (#2765)
  • Fixed bug in PESQ metric where NoUtterancesError prevented calculating on a batch of data (#2753)
  • Fixed corner case in MatthewsCorrCoef (#2743)

[1.4.2] - 2022-09-12

Added

  • Re-adding Chrf implementation (#2701)

Fixed

  • Fixed wrong aggregation in segmentation.MeanIoU (#2698)
  • Fixed handling zero division error in binary IoU (Jaccard index) calculation (#2726)
  • Corrected the padding related calculation errors in SSIM (#2721)
  • Fixed compatibility of audio domain with new scipy (#2733)
  • Fixed how prefix/postfix works in MultitaskWrapper (#2722)

... (truncated)

Commits
  • a8e8337 releasing 1.5.0
  • 0fd1f96 Fix overflow for PSNR metric when used with uint8 input (#2788)
  • 6377aa5 docs: adding PESQ example to gallery (#2755)
  • a8cbae7 Fix corner case when single empty pred is provided to detection IOU metric ...
  • 801cec8 Improve CI stability for clustering examples (#2785)
  • b1f4db2 build(deps): bump sphinx-gallery from 0.17.1 to 0.18.0 in /requirements (#2784)
  • 8187e51 build(deps): update cloudpickle requirement from <=3.0.0,>1.3 to >1.3,<=3.1.0...
  • 1335c7b New segmentation metric: Hausdorff Distance (#2122)
  • 13f7b94 fix(lint): emergency bump docformatter (#2779)
  • 0990ecf Add possibility to control the truncation in bert score (#2776)
  • Additional commits viewable in compare view

Updates torchvision from 0.19.0 to 0.20.0

Release notes

Sourced from torchvision's releases.

Torchvision 0.20 release

Highlights

Encoding / Decoding images

Torchvision is further extending its encoding/decoding capabilities. For this version, we added a WEBP decoder, and a batch JPEG decoder on CUDA GPUs, which can lead to 10X speed-ups over CPU decoding.

We have also improved the UX of our decoding APIs to be more user-friendly. The main entry point is now torchvision.io.decode_image(), and it can take as input either a path (as str or pathlib.Path), or a tensor containing the raw encoded data.

Read more on the docs!

We also added support for HEIC and AVIF decoding, but these are currently only available when building from source. We are working on making those available directly in the upcoming releases. Stay tuned!

Detailed changes

Bug Fixes

[datasets] Update URL of SBDataset train_noval (#8551) [datasets] EuroSAT: fix SSL certificate issues (#8563) [io] Check average_rate availability in video reader (#8548)

New Features

[io] Add batch JPEG GPU decoding (decode_jpeg()) (#8496) [io] Add WEBP image decoder: decode_image(), decode_webp() (#8527, #8612, #8610) [io] Add HEIC and AVIF decoders, only available when building from source (#8597, #8596, #8647, #8613, #8621)

Improvements

[io] Add support for decoding 16bits png (#8524) [io] Allow decoding functions to accept the mode parameter as a string (#8627) [io] Allow decode_image() to support paths (#8624) [io] Automatically send video to CPU in io.write_video (#8537) [datasets] Better progress bar for file downloading (#8556) [datasets] Add Path type annotation for ImageFolder (#8526) [ops] Register nms and roi_align Autocast policy for PyTorch Intel GPU backend (#8541) [transforms] Use Sequence for parameters type checking in transforms.RandomErase (#8615) [transforms] Support v2.functional.gaussian_blur backprop (#8486) [transforms] Expose transforms.v2 utils for writing custom transforms. (#8670) [utils] Fix f-string in color error message (#8639) [packaging] Revamped and improved debuggability of setup.py build (#8535, #8581, #8581, #8582, #8590, #8533, #8528, #8659) [Documentation] Various documentation improvements (#8605, #8611, #8506, #8507, #8539, #8512, #8513, #8583, #8633) [tests] Various tests improvements (#8580, #8553, #8523, #8617, #8518, #8579, #8558, #8617, #8641) [code quality] Various code quality improvements (#8552, #8555, #8516, #8526, #8602, #8615, #8639, #8532) [ci] #8562, #8644, #8592, #8542, #8594, #8530, #8656

... (truncated)

Commits

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Bump the requirements-txt group across 2 directories with 3 updates
c8ce7ec 
Bumps the requirements-txt group with 3 updates in the /demo-notebooks/guided-demos directory: [pytorch-lightning](https://github.com/Lightning-AI/lightning), [torchmetrics](https://github.com/Lightning-AI/torchmetrics) and [torchvision](https://github.com/pytorch/vision).
Bumps the requirements-txt group with 3 updates in the /tests/e2e directory: [pytorch-lightning](https://github.com/Lightning-AI/lightning), [torchmetrics](https://github.com/Lightning-AI/torchmetrics) and [torchvision](https://github.com/pytorch/vision).


Updates `pytorch-lightning` from 1.9.5 to 2.4.0
- [Release notes](https://github.com/Lightning-AI/lightning/releases)
- [Commits](Lightning-AI/pytorch-lightning@1.9.5...2.4.0)

Updates `torchmetrics` from 0.9.1 to 1.5.0
- [Release notes](https://github.com/Lightning-AI/torchmetrics/releases)
- [Changelog](https://github.com/Lightning-AI/torchmetrics/blob/master/CHANGELOG.md)
- [Commits](Lightning-AI/torchmetrics@v0.9.1...v1.5.0)

Updates `torchvision` from 0.12.0 to 0.20.0
- [Release notes](https://github.com/pytorch/vision/releases)
- [Commits](pytorch/vision@v0.12.0...v0.20.0)

Updates `pytorch-lightning` from 1.9.5 to 2.4.0
- [Release notes](https://github.com/Lightning-AI/lightning/releases)
- [Commits](Lightning-AI/pytorch-lightning@1.9.5...2.4.0)

Updates `torchmetrics` from 0.9.1 to 1.5.0
- [Release notes](https://github.com/Lightning-AI/torchmetrics/releases)
- [Changelog](https://github.com/Lightning-AI/torchmetrics/blob/master/CHANGELOG.md)
- [Commits](Lightning-AI/torchmetrics@v0.9.1...v1.5.0)

Updates `torchvision` from 0.19.0 to 0.20.0
- [Release notes](https://github.com/pytorch/vision/releases)
- [Commits](pytorch/vision@v0.12.0...v0.20.0)

---
updated-dependencies:
- dependency-name: pytorch-lightning
  dependency-type: direct:production
  update-type: version-update:semver-major
  dependency-group: requirements-txt
- dependency-name: torchmetrics
  dependency-type: direct:production
  update-type: version-update:semver-major
  dependency-group: requirements-txt
- dependency-name: torchvision
  dependency-type: direct:production
  update-type: version-update:semver-minor
  dependency-group: requirements-txt
- dependency-name: pytorch-lightning
  dependency-type: direct:production
  update-type: version-update:semver-major
  dependency-group: requirements-txt
- dependency-name: torchmetrics
  dependency-type: direct:production
  update-type: version-update:semver-major
  dependency-group: requirements-txt
- dependency-name: torchvision
  dependency-type: direct:production
  update-type: version-update:semver-minor
  dependency-group: requirements-txt
...

Signed-off-by: dependabot[bot] <[email protected]>
@dependabot dependabot bot added the test-guided-notebooks Run PR check to verify Guided notebooks label Oct 21, 2024
@dependabot dependabot bot mentioned this pull request Oct 21, 2024
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@dependabot dependabot bot deleted the dependabot/pip/demo-notebooks/guided-demos/requirements-txt-34b69c6113 branch October 22, 2024 15:57
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