feat: add comprehensive testing infrastructure with Poetry

.gitignore

@@ -8,3 +8,50 @@ dataset/*
 .ipynb_checkpoints
 code/chapter03_object_detection_introduction/tiny_detector_demo/checkpoint.pth.tar
 code/chapter03_object_detection_introduction/tiny_detector_demo/checkpoint_ssd300.pth.tar
+
+# Testing related
+.pytest_cache/
+.coverage
+htmlcov/
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.tox/
+.nox/
+
+# Claude settings
+.claude/*
+
+# Build artifacts
+dist/
+build/
+*.egg-info/
+*.egg
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.manifest
+*.spec
+
+# Virtual environments
+venv/
+ENV/
+env/
+.venv/
+.env
+
+# IDE files
+.vscode/
+*.swp
+*.swo
+*~
+.project
+.pydevproject
+.settings/
+
+# Package manager files
+# Note: Do NOT ignore poetry.lock or uv.lock
+__pycache__/
+*.py[cod]
+*$py.class

pyproject.toml

@@ -0,0 +1,82 @@
+[tool.poetry]
+name = "deep-learning-with-pytorch"
+version = "0.1.0"
+description = "Deep Learning with PyTorch - Tutorial and Examples"
+authors = ["Your Name <you@example.com>"]
+readme = "README.md"
+packages = [{include = "code"}]
+
+[tool.poetry.dependencies]
+python = "^3.8"
+torch = "^2.0.0"
+torchvision = "^0.15.0"
+
+[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*"]
+python_functions = ["test_*"]
+addopts = [
+    "-ra",
+    "--strict-markers",
+    "--strict-config",
+    "--cov=code",
+    "--cov-branch",
+    "--cov-report=term-missing:skip-covered",
+    "--cov-report=html:htmlcov",
+    "--cov-report=xml:coverage.xml",
+    "--cov-fail-under=0",
+]
+markers = [
+    "unit: Unit tests",
+    "integration: Integration tests",
+    "slow: Slow running tests",
+]
+
+[tool.coverage.run]
+source = ["code"]
+branch = true
+parallel = true
+omit = [
+    "*/tests/*",
+    "*/__pycache__/*",
+    "*/site-packages/*",
+    "*/dist-packages/*",
+    "*/.venv/*",
+    "*/venv/*",
+]
+
+[tool.coverage.report]
+exclude_lines = [
+    "pragma: no cover",
+    "def __repr__",
+    "def __str__",
+    "raise AssertionError",
+    "raise NotImplementedError",
+    "if __name__ == .__main__.:",
+    "if TYPE_CHECKING:",
+    "if typing.TYPE_CHECKING:",
+]
+precision = 2
+show_missing = true
+skip_covered = false
+
+[tool.coverage.html]
+directory = "htmlcov"
+
+[tool.coverage.xml]
+output = "coverage.xml"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

tests/conftest.py

@@ -0,0 +1,172 @@
+import os
+import tempfile
+import shutil
+from pathlib import Path
+from typing import Generator, Dict, Any
+
+import pytest
+import torch
+
+
+@pytest.fixture
+def temp_dir() -> Generator[Path, None, None]:
+    """
+    Create a temporary directory for test files.
+
+    Yields:
+        Path: Path to the temporary directory
+    """
+    temp_path = tempfile.mkdtemp()
+    yield Path(temp_path)
+    shutil.rmtree(temp_path)
+
+
+@pytest.fixture
+def sample_config() -> Dict[str, Any]:
+    """
+    Provide a sample configuration dictionary for testing.
+
+    Returns:
+        Dict[str, Any]: Sample configuration
+    """
+    return {
+        "model": {
+            "name": "test_model",
+            "num_classes": 10,
+            "input_size": 224,
+            "channels": 3,
+        },
+        "training": {
+            "batch_size": 32,
+            "learning_rate": 0.001,
+            "epochs": 10,
+            "device": "cpu",
+        },
+        "data": {
+            "train_path": "/path/to/train",
+            "val_path": "/path/to/val",
+            "test_path": "/path/to/test",
+        }
+    }
+
+
+@pytest.fixture
+def mock_dataset_path(temp_dir: Path) -> Path:
+    """
+    Create a mock dataset directory structure.
+
+    Args:
+        temp_dir: Temporary directory fixture
+
+    Returns:
+        Path: Path to the mock dataset
+    """
+    dataset_path = temp_dir / "mock_dataset"
+
+    # Create subdirectories
+    for split in ["train", "val", "test"]:
+        split_dir = dataset_path / split
+        split_dir.mkdir(parents=True, exist_ok=True)
+
+        # Create some mock class directories
+        for class_idx in range(3):
+            class_dir = split_dir / f"class_{class_idx}"
+            class_dir.mkdir(exist_ok=True)
+
+            # Create mock image files
+            for img_idx in range(2):
+                img_file = class_dir / f"image_{img_idx}.jpg"
+                img_file.touch()
+
+    return dataset_path
+
+
+@pytest.fixture
+def sample_tensor() -> torch.Tensor:
+    """
+    Create a sample tensor for testing.
+
+    Returns:
+        torch.Tensor: A sample 4D tensor (batch, channels, height, width)
+    """
+    return torch.randn(4, 3, 224, 224)
+
+
+@pytest.fixture
+def sample_labels() -> torch.Tensor:
+    """
+    Create sample labels for testing.
+
+    Returns:
+        torch.Tensor: A sample label tensor
+    """
+    return torch.randint(0, 10, (4,))
+
+
+@pytest.fixture
+def device() -> torch.device:
+    """
+    Get the appropriate device for testing.
+
+    Returns:
+        torch.device: CPU device for consistent testing
+    """
+    return torch.device("cpu")
+
+
+@pytest.fixture(autouse=True)
+def reset_random_seeds():
+    """
+    Reset random seeds before each test for reproducibility.
+    """
+    torch.manual_seed(42)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed_all(42)
+
+
+@pytest.fixture
+def mock_model_checkpoint(temp_dir: Path) -> Dict[str, Any]:
+    """
+    Create a mock model checkpoint.
+
+    Args:
+        temp_dir: Temporary directory fixture
+
+    Returns:
+        Dict[str, Any]: Mock checkpoint data
+    """
+    checkpoint = {
+        "epoch": 5,
+        "model_state_dict": {"layer1.weight": torch.randn(10, 10)},
+        "optimizer_state_dict": {"param_groups": [{"lr": 0.001}]},
+        "loss": 0.1234,
+        "accuracy": 0.95,
+    }
+
+    checkpoint_path = temp_dir / "checkpoint.pth"
+    torch.save(checkpoint, checkpoint_path)
+
+    return {
+        "path": checkpoint_path,
+        "data": checkpoint
+    }
+
+
+@pytest.fixture
+def capture_stdout(monkeypatch):
+    """
+    Capture stdout for testing print statements.
+
+    Args:
+        monkeypatch: pytest monkeypatch fixture
+
+    Returns:
+        list: List to collect stdout outputs
+    """
+    outputs = []
+
+    def mock_print(*args, **kwargs):
+        outputs.append(" ".join(map(str, args)))
+
+    monkeypatch.setattr("builtins.print", mock_print)
+    return outputs