Set up comprehensive Python testing infrastructure with Poetry

.gitignore

@@ -1,4 +1,69 @@
-__pycache__
-build
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
 *.so
-runs
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Testing
+.pytest_cache/
+.coverage
+htmlcov/
+coverage.xml
+*.cover
+.hypothesis/
+.tox/
+.nox/
+
+# Virtual environments
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# IDE
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+.DS_Store
+
+# Project specific
+runs/
+outputs/
+checkpoints/
+*.pth
+*.ckpt
+
+# Claude
+.claude/*
+
+# Poetry
+poetry.lock
+
+# Build artifacts
+*.o
+*.obj
+*.exp
+*.lib
+*.dll
+*.exe
+*.out
+*.app

pyproject.toml

@@ -0,0 +1,113 @@
+[tool.poetry]
+name = "dva-mvp"
+version = "0.1.0"
+description = "DVA-MVP: Diffusion Model for 3D Generation"
+authors = ["Your Name <you@example.com>"]
+readme = "README.md"
+packages = [{include = "dva"}, {include = "models"}, {include = "datasets"}, {include = "utils"}]
+
+[tool.poetry.dependencies]
+python = ">=3.9,<3.12"
+einops = "*"
+omegaconf = "*"
+opencv-python = "*"
+libigl = "*"
+trimesh = "4.2.0"
+pygltflib = "*"
+pymeshlab = "0.2"
+PyMCubes = "*"
+xatlas = "*"
+nvdiffrast = {git = "https://github.com/NVlabs/nvdiffrast/"}
+scikit-learn = "*"
+open-clip-torch = "*"
+triton = "2.1.0"
+rembg = "*"
+gradio = "*"
+tqdm = "*"
+transformers = "4.40.1"
+diffusers = "0.19.3"
+ninja = "*"
+imageio = "*"
+imageio-ffmpeg = "*"
+gradio-litmodel3d = "0.0.1"
+jaxtyping = "0.2.31"
+
+[tool.poetry.group.dev.dependencies]
+pytest = "^8.0.0"
+pytest-cov = "^5.0.0"
+pytest-mock = "^3.14.0"
+pillow = "^10.0.0"
+
+[tool.poetry.scripts]
+test = "pytest:main"
+tests = "pytest:main"
+
+[tool.pytest.ini_options]
+minversion = "8.0"
+testpaths = ["tests"]
+python_files = ["test_*.py", "*_test.py"]
+python_classes = ["Test*", "*Tests"]
+python_functions = ["test_*"]
+addopts = """
+    -v
+    --strict-markers
+    --strict-config
+    --cov=dva
+    --cov=models
+    --cov=datasets
+    --cov=utils
+    --cov-branch
+    --cov-report=term-missing:skip-covered
+    --cov-report=html:htmlcov
+    --cov-report=xml:coverage.xml
+"""
+markers = [
+    "unit: marks tests as unit tests (fast, isolated)",
+    "integration: marks tests as integration tests (may require external resources)",
+    "slow: marks tests as slow (deselect with '-m \"not slow\"')",
+]
+console_output_style = "progress"
+filterwarnings = [
+    "error",
+    "ignore::UserWarning",
+    "ignore::DeprecationWarning",
+]
+
+[tool.coverage.run]
+source = ["dva", "models", "datasets", "utils"]
+omit = [
+    "*/tests/*",
+    "*/test_*",
+    "*/__init__.py",
+    "*/setup.py",
+    "*/simple-knn/*",
+    "*/extensions/*",
+]
+
+[tool.coverage.report]
+exclude_lines = [
+    "pragma: no cover",
+    "def __repr__",
+    "if self.debug:",
+    "if settings.DEBUG",
+    "raise AssertionError",
+    "raise NotImplementedError",
+    "if 0:",
+    "if __name__ == .__main__.:",
+    "if TYPE_CHECKING:",
+    "class .*\\bProtocol\\):",
+    "@(abc\\.)?abstractmethod",
+]
+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 +1,166 @@
+"""Shared pytest fixtures and configuration for all tests."""
+
+import os
+import tempfile
+import shutil
+from pathlib import Path
+from typing import Generator, Dict, Any
+
+import pytest
+import numpy as np
+import torch
+from omegaconf import DictConfig, OmegaConf
+
+
+@pytest.fixture
+def temp_dir() -> Generator[Path, None, None]:
+    """Create a temporary directory for test files."""
+    temp_path = tempfile.mkdtemp()
+    yield Path(temp_path)
+    shutil.rmtree(temp_path)
+
+
+@pytest.fixture
+def sample_config() -> DictConfig:
+    """Create a sample configuration for testing."""
+    config = {
+        "model": {
+            "type": "test_model",
+            "hidden_dim": 128,
+            "num_layers": 4,
+        },
+        "training": {
+            "batch_size": 32,
+            "learning_rate": 1e-4,
+            "num_epochs": 10,
+        },
+        "data": {
+            "dataset": "test_dataset",
+            "num_workers": 4,
+        },
+    }
+    return OmegaConf.create(config)
+
+
+@pytest.fixture
+def mock_tensor_data() -> Dict[str, torch.Tensor]:
+    """Create mock tensor data for testing."""
+    return {
+        "input": torch.randn(4, 3, 256, 256),
+        "target": torch.randn(4, 128, 128, 128),
+        "mask": torch.ones(4, 1, 256, 256),
+    }
+
+
+@pytest.fixture
+def mock_numpy_data() -> Dict[str, np.ndarray]:
+    """Create mock numpy data for testing."""
+    return {
+        "points": np.random.randn(1000, 3).astype(np.float32),
+        "colors": np.random.randint(0, 255, (1000, 3), dtype=np.uint8),
+        "normals": np.random.randn(1000, 3).astype(np.float32),
+    }
+
+
+@pytest.fixture
+def sample_image_path(temp_dir: Path) -> Path:
+    """Create a sample image file for testing."""
+    image_path = temp_dir / "test_image.png"
+    # Create a simple 10x10 white image
+    import numpy as np
+    from PIL import Image
+
+    img_array = np.ones((10, 10, 3), dtype=np.uint8) * 255
+    img = Image.fromarray(img_array)
+    img.save(image_path)
+    return image_path
+
+
+@pytest.fixture
+def sample_mesh_data() -> Dict[str, Any]:
+    """Create sample mesh data for testing."""
+    vertices = np.array([
+        [0, 0, 0],
+        [1, 0, 0],
+        [0, 1, 0],
+        [0, 0, 1],
+    ], dtype=np.float32)
+
+    faces = np.array([
+        [0, 1, 2],
+        [0, 1, 3],
+        [0, 2, 3],
+        [1, 2, 3],
+    ], dtype=np.int32)
+
+    return {
+        "vertices": vertices,
+        "faces": faces,
+        "vertex_colors": np.random.rand(4, 3).astype(np.float32),
+    }
+
+
+@pytest.fixture
+def device() -> torch.device:
+    """Get the appropriate device for testing."""
+    return torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+@pytest.fixture(autouse=True)
+def reset_random_seeds():
+    """Reset random seeds before each test for reproducibility."""
+    np.random.seed(42)
+    torch.manual_seed(42)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed_all(42)
+        torch.backends.cudnn.deterministic = True
+        torch.backends.cudnn.benchmark = False
+
+
+@pytest.fixture
+def mock_model_weights(temp_dir: Path) -> Path:
+    """Create mock model weights file."""
+    weights_path = temp_dir / "model_weights.pth"
+    torch.save({
+        "model_state_dict": {"layer1.weight": torch.randn(10, 10)},
+        "optimizer_state_dict": {"param_groups": []},
+        "epoch": 5,
+        "loss": 0.123,
+    }, weights_path)
+    return weights_path
+
+
+@pytest.fixture
+def environment_variables() -> Generator[Dict[str, str], None, None]:
+    """Temporarily set environment variables for testing."""
+    original_env = os.environ.copy()
+    test_env = {
+        "TEST_MODE": "true",
+        "LOG_LEVEL": "DEBUG",
+    }
+    os.environ.update(test_env)
+    yield test_env
+    # Restore original environment
+    os.environ.clear()
+    os.environ.update(original_env)
+
+
+# Custom markers configuration
+def pytest_configure(config):
+    """Configure custom markers."""
+    config.addinivalue_line(
+        "markers", "gpu: marks tests that require GPU (deselect with '-m \"not gpu\"')"
+    )
+    config.addinivalue_line(
+        "markers", "network: marks tests that require network access"
+    )
+
+
+# Hook to skip GPU tests if CUDA is not available
+def pytest_collection_modifyitems(config, items):
+    """Modify test collection to skip GPU tests when appropriate."""
+    if not torch.cuda.is_available():
+        skip_gpu = pytest.mark.skip(reason="GPU not available")
+        for item in items:
+            if "gpu" in item.keywords:
+                item.add_marker(skip_gpu)