Add comprehensive Python testing infrastructure

.gitignore

@@ -128,6 +128,13 @@ dmypy.json
 # Pyre type checker
 .pyre/
 
+# IDE files
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+
 # Datasets
 data/
 logs/

pyproject.toml

@@ -0,0 +1,86 @@
+[tool.poetry]
+name = "hashnerf-pytorch"
+version = "0.1.0"
+description = "PyTorch implementation of HashNeRF"
+authors = ["HashNeRF Team <[email protected]>"]
+readme = "README.md"
+packages = [{include = "*.py"}]
+
+[tool.poetry.dependencies]
+python = "^3.8"
+torch = ">=1.10.0"
+torchvision = ">=0.11.0"
+numpy = ">=1.20.0"
+imageio = ">=2.9.0"
+matplotlib = ">=3.3.0"
+tqdm = ">=4.60.0"
+opencv-python = ">=4.5.0"
+
+[tool.poetry.group.dev.dependencies]
+pytest = "^7.4.0"
+pytest-cov = "^4.1.0"
+pytest-mock = "^3.11.0"
+
+# Scripts will be run with poetry run command
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.pytest.ini_options]
+testpaths = ["tests"]
+python_files = ["test_*.py", "*_test.py"]
+python_classes = ["Test*"]
+python_functions = ["test_*"]
+addopts = [
+    "--strict-markers",
+    "--strict-config",
+    "--verbose",
+    "--cov=.",
+    "--cov-report=term-missing",
+    "--cov-report=html:htmlcov",
+    "--cov-report=xml:coverage.xml",
+    "--cov-fail-under=80"
+]
+markers = [
+    "unit: Unit tests",
+    "integration: Integration tests", 
+    "slow: Slow running tests"
+]
+filterwarnings = [
+    "ignore::DeprecationWarning",
+    "ignore::PendingDeprecationWarning"
+]
+
+[tool.coverage.run]
+source = ["."]
+omit = [
+    "tests/*",
+    "scripts/*",
+    "configs/*",
+    "*/test_*",
+    "*/__pycache__/*",
+    "*/venv/*",
+    "*/.venv/*"
+]
+
+[tool.coverage.report]
+exclude_lines = [
+    "pragma: no cover",
+    "def __repr__",
+    "if __name__ == .__main__.:",
+    "raise AssertionError",
+    "raise NotImplementedError",
+    "if 0:",
+    "if False:",
+    "class .*\\bProtocol\\):",
+    "@(abc\\.)?abstractmethod"
+]
+show_missing = true
+precision = 2
+
+[tool.coverage.html]
+directory = "htmlcov"
+
+[tool.coverage.xml]
+output = "coverage.xml"

tests/__init__.py

@@ -0,0 +1 @@
+# HashNeRF Testing Package

tests/conftest.py

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+"""Shared pytest fixtures for HashNeRF testing."""
+
+import os
+import tempfile
+import pytest
+import torch
+import numpy as np
+from pathlib import Path
+
+
+@pytest.fixture
+def temp_dir():
+    """Create a temporary directory for testing."""
+    with tempfile.TemporaryDirectory() as tmpdir:
+        yield Path(tmpdir)
+
+
+@pytest.fixture
+def device():
+    """Get the appropriate device for testing."""
+    return torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+@pytest.fixture
+def sample_config():
+    """Sample configuration for testing."""
+    return {
+        'datadir': './data/nerf_synthetic/lego',
+        'dataset_type': 'blender',
+        'white_bkgd': True,
+        'half_res': True,
+        'testskip': 8,
+        'N_samples': 64,
+        'N_importance': 64,
+        'use_viewdirs': True,
+        'raw_noise_std': 0.0,
+        'lrate': 5e-4,
+        'lrate_decay': 250,
+        'chunk': 1024*32,
+        'netchunk': 1024*64,
+        'no_batching': True,
+        'no_reload': False,
+        'ft_path': None,
+        'N_iters': 200000,
+        'i_print': 100,
+        'i_img': 500,
+        'i_weights': 10000,
+        'i_testset': 50000,
+        'i_video': 50000,
+        'N_rand': 1024,
+        'precrop_iters': 0,
+        'precrop_frac': 0.5,
+        'multires': 10,
+        'multires_views': 4,
+        'netdepth': 8,
+        'netwidth': 256,
+        'netdepth_fine': 8,
+        'netwidth_fine': 256,
+        'perturb': 1.0,
+        'lindisp': False,
+        'i_embed': 0,
+        'render_only': False,
+        'render_test': False,
+        'render_factor': 0,
+        'expname': 'test',
+        'basedir': './logs',
+        'factor': 8,
+        'no_ndc': True,
+        'spherify': False,
+        'llffhold': 8,
+        'bound': 1.5,
+        'finest_res': 512,
+        'log2_hashmap_size': 19,
+        'lrate_hash': 1e-2,
+        'hash_levels': 16,
+        'hash_features': 2
+    }
+
+
+@pytest.fixture
+def sample_bounding_box():
+    """Sample bounding box for testing."""
+    return torch.tensor([[-1.0, -1.0, -1.0], [1.0, 1.0, 1.0]], dtype=torch.float32)
+
+
+@pytest.fixture
+def sample_rays():
+    """Sample ray data for testing."""
+    batch_size = 1024
+    rays_o = torch.randn(batch_size, 3)
+    rays_d = torch.randn(batch_size, 3)
+    rays_d = rays_d / torch.norm(rays_d, dim=-1, keepdim=True)  # normalize
+    return rays_o, rays_d
+
+
+@pytest.fixture
+def sample_points():
+    """Sample 3D points for testing."""
+    return torch.randn(1000, 3)
+
+
+@pytest.fixture
+def sample_image_data():
+    """Sample image data for testing."""
+    height, width = 100, 100
+    images = np.random.rand(10, height, width, 3).astype(np.float32)
+    poses = np.random.rand(10, 4, 4).astype(np.float32)
+    hwf = [height, width, 100.0]  # focal length
+    return images, poses, hwf
+
+
+@pytest.fixture
+def mock_config_file(temp_dir):
+    """Create a mock config file for testing."""
+    config_content = """expname = test_experiment
+basedir = ./logs
+datadir = ./data/nerf_synthetic/lego
+dataset_type = blender
+white_bkgd = True
+half_res = True
+testskip = 8
+"""
+    config_file = temp_dir / "test_config.txt"
+    config_file.write_text(config_content)
+    return str(config_file)
+
+
+@pytest.fixture
+def mock_nerf_model():
+    """Mock NeRF model for testing."""
+    import torch.nn as nn
+
+    class MockNeRF(nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.linear = nn.Linear(63, 4)  # 63 = 3*21 for position encoding
+
+        def forward(self, x):
+            return self.linear(x)
+
+    return MockNeRF()
+
+
+@pytest.fixture(autouse=True)
+def set_random_seeds():
+    """Set random seeds for reproducible tests."""
+    torch.manual_seed(42)
+    np.random.seed(42)
+
+
+@pytest.fixture
+def suppress_warnings():
+    """Suppress common warnings during testing."""
+    import warnings
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        yield
+
+
+@pytest.fixture(scope="session")
+def test_data_dir():
+    """Directory for test data files."""
+    return Path(__file__).parent / "test_data"

tests/integration/__init__.py

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+# Integration Tests Package

tests/integration/test_sample_integration.py

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+"""Sample integration test to validate integration testing structure."""
+
+import pytest
+import torch
+
+
+@pytest.mark.integration
+def test_torch_cuda_integration():
+    """Test PyTorch CUDA integration (if available)."""
+    if torch.cuda.is_available():
+        device = torch.device("cuda")
+        tensor = torch.tensor([1.0, 2.0, 3.0]).to(device)
+        result = tensor * 2
+        expected = torch.tensor([2.0, 4.0, 6.0]).to(device)
+        assert torch.allclose(result, expected)
+    else:
+        pytest.skip("CUDA not available")
+
+
+@pytest.mark.integration
+def test_model_device_integration(device, mock_nerf_model):
+    """Test model and device integration."""
+    model = mock_nerf_model.to(device)
+    input_tensor = torch.randn(5, 63).to(device)
+
+    with torch.no_grad():
+        output = model(input_tensor)
+
+    assert output.device == device
+    assert output.shape == (5, 4)