feat: Add comprehensive Python testing infrastructure with Poetry

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+Pipfile.lock
+
+# poetry
+poetry.lock
+
+# PEP 582
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# IDE
+.idea/
+.vscode/
+*.swp
+*.swo
+*~
+
+# Claude specific
+.claude/*
+
+# OS
+.DS_Store
+Thumbs.db
+
+# Model and data files
+*.pth
+*.pt
+*.h5
+*.hdf5
+*.pkl
+*.pickle
+checkpoints/
+data/
+models/
+weights/
+
+# Temporary files
+tmp/
+temp/
+*.tmp

pyproject.toml

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+[tool.poetry]
+name = "pytorch-pruning"
+version = "0.1.0"
+description = "PyTorch model pruning and fine-tuning"
+authors = ["Your Name <[email protected]>"]
+readme = "README.md"
+packages = [{include = "*.py"}]
+
+[tool.poetry.dependencies]
+python = "^3.8"
+torch = "^2.0.0"
+torchvision = "^0.15.0"
+opencv-python = "^4.8.0"
+numpy = "^1.24.0"
+
+[tool.poetry.group.dev.dependencies]
+pytest = "^7.4.0"
+pytest-cov = "^4.1.0"
+pytest-mock = "^3.11.1"
+
+[tool.poetry.scripts]
+test = "pytest:main"
+tests = "pytest:main"
+
+[tool.pytest.ini_options]
+minversion = "7.0"
+testpaths = ["tests"]
+python_files = ["test_*.py", "*_test.py"]
+python_classes = ["Test*", "*Tests"]
+python_functions = ["test_*"]
+addopts = [
+    "-ra",
+    "--strict-markers",
+    "--strict-config",
+    "--cov=.",
+    "--cov-branch",
+    "--cov-report=term-missing:skip-covered",
+    "--cov-report=html:htmlcov",
+    "--cov-report=xml:coverage.xml",
+    "--cov-fail-under=0",
+    "-vv"
+]
+markers = [
+    "unit: Unit tests",
+    "integration: Integration tests",
+    "slow: Slow tests"
+]
+
+[tool.coverage.run]
+source = ["."]
+omit = [
+    "*/tests/*",
+    "*/test_*",
+    "*/__pycache__/*",
+    "*/venv/*",
+    "*/env/*",
+    "*/.venv/*",
+    "setup.py",
+    "*/conftest.py"
+]
+
+[tool.coverage.report]
+precision = 2
+show_missing = true
+skip_covered = false
+exclude_lines = [
+    "pragma: no cover",
+    "def __repr__",
+    "raise AssertionError",
+    "raise NotImplementedError",
+    "if __name__ == .__main__.:",
+    "if TYPE_CHECKING:",
+    "class .*\\bProtocol\\):",
+    "@(abc\\.)?abstractmethod"
+]
+
+[tool.coverage.html]
+directory = "htmlcov"
+
+[tool.coverage.xml]
+output = "coverage.xml"
+
+[build-system]
+requires = ["poetry-core>=1.0.0"]
+build-backend = "poetry.core.masonry.api"

tests/conftest.py

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+import pytest
+import torch
+import tempfile
+import shutil
+import os
+from pathlib import Path
+import numpy as np
+from unittest.mock import Mock, patch
+
+@pytest.fixture
+def temp_dir():
+    """Create a temporary directory for test files."""
+    temp_dir = tempfile.mkdtemp()
+    yield Path(temp_dir)
+    shutil.rmtree(temp_dir)
+
+@pytest.fixture
+def mock_model():
+    """Create a mock PyTorch model for testing."""
+    model = Mock()
+    model.parameters = Mock(return_value=iter([
+        torch.nn.Parameter(torch.randn(3, 3)),
+        torch.nn.Parameter(torch.randn(10))
+    ]))
+    model.state_dict = Mock(return_value={
+        'conv1.weight': torch.randn(64, 3, 3, 3),
+        'conv1.bias': torch.randn(64),
+        'fc.weight': torch.randn(10, 512),
+        'fc.bias': torch.randn(10)
+    })
+    model.eval = Mock(return_value=model)
+    model.train = Mock(return_value=model)
+    return model
+
+@pytest.fixture
+def sample_tensor():
+    """Create a sample tensor for testing."""
+    return torch.randn(1, 3, 224, 224)
+
+@pytest.fixture
+def sample_batch():
+    """Create a sample batch of data for testing."""
+    batch_size = 4
+    images = torch.randn(batch_size, 3, 224, 224)
+    labels = torch.randint(0, 2, (batch_size,))
+    return images, labels
+
+@pytest.fixture
+def mock_dataset():
+    """Create a mock dataset for testing."""
+    dataset = Mock()
+    dataset.__len__ = Mock(return_value=100)
+    dataset.__getitem__ = Mock(side_effect=lambda idx: (
+        torch.randn(3, 224, 224),
+        torch.randint(0, 2, (1,)).item()
+    ))
+    return dataset
+
+@pytest.fixture
+def mock_dataloader(mock_dataset):
+    """Create a mock dataloader for testing."""
+    dataloader = Mock()
+    dataloader.__iter__ = Mock(return_value=iter([
+        (torch.randn(4, 3, 224, 224), torch.randint(0, 2, (4,)))
+        for _ in range(5)
+    ]))
+    dataloader.__len__ = Mock(return_value=5)
+    dataloader.dataset = mock_dataset
+    return dataloader
+
+@pytest.fixture
+def device():
+    """Get the appropriate device for testing."""
+    return torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+@pytest.fixture
+def random_seed():
+    """Set random seeds for reproducibility."""
+    seed = 42
+    torch.manual_seed(seed)
+    np.random.seed(seed)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed(seed)
+        torch.cuda.manual_seed_all(seed)
+    yield seed
+
+@pytest.fixture
+def mock_vgg_model():
+    """Create a mock VGG model for testing."""
+    with patch('torchvision.models.vgg16') as mock_vgg:
+        mock_model = Mock()
+        mock_model.features = torch.nn.Sequential(
+            torch.nn.Conv2d(3, 64, 3),
+            torch.nn.ReLU()
+        )
+        mock_model.classifier = torch.nn.Sequential(
+            torch.nn.Linear(25088, 4096),
+            torch.nn.ReLU(),
+            torch.nn.Linear(4096, 2)
+        )
+        mock_vgg.return_value = mock_model
+        yield mock_model
+
+@pytest.fixture
+def sample_checkpoint(temp_dir):
+    """Create a sample checkpoint file."""
+    checkpoint_path = temp_dir / 'checkpoint.pth'
+    checkpoint_data = {
+        'epoch': 10,
+        'model_state_dict': {
+            'conv1.weight': torch.randn(64, 3, 3, 3),
+            'conv1.bias': torch.randn(64)
+        },
+        'optimizer_state_dict': {},
+        'loss': 0.5
+    }
+    torch.save(checkpoint_data, checkpoint_path)
+    return checkpoint_path
+
+@pytest.fixture(autouse=True)
+def cleanup_cuda_memory():
+    """Clean up CUDA memory after each test."""
+    yield
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+
+@pytest.fixture
+def capture_stdout():
+    """Capture stdout for testing print statements."""
+    import io
+    import sys
+    captured = io.StringIO()
+    old_stdout = sys.stdout
+    sys.stdout = captured
+    yield captured
+    sys.stdout = old_stdout