chore: Set up Python testing infrastructure with Poetry and pytest

.gitignore

@@ -131,3 +131,41 @@ dmypy.json
 # Datasets
 data/
 logs/
+
+# Claude settings
+.claude/*
+
+# Poetry
+poetry.lock
+
+# IDE files
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+
+# OS files
+.DS_Store
+Thumbs.db
+
+# Testing artifacts
+.pytest_cache/
+.coverage
+htmlcov/
+coverage.xml
+*.cover
+.hypothesis/
+.tox/
+.nox/
+
+# Build artifacts
+build/
+dist/
+*.egg-info/
+.eggs/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg
+MANIFEST

pyproject.toml

@@ -0,0 +1,97 @@
+[tool.poetry]
+name = "hashnerf-pytorch"
+version = "0.1.0"
+description = "A pure PyTorch implementation of Instant-NGP for Neural Radiance Fields"
+authors = ["Your Name <your.email@example.com>"]
+readme = "README.md"
+packages = [{include = "*.py"}]
+
+[tool.poetry.dependencies]
+python = "^3.8"
+torch = "^2.0.0"
+numpy = "^1.21.0"
+imageio = "^2.31.0"
+matplotlib = "^3.5.0"
+opencv-python = "^4.8.0"
+tqdm = "^4.65.0"
+kornia = "^0.7.0"
+pyvista = {version = "^0.42.0", optional = true}
+pillow = "^10.0.0"
+
+[tool.poetry.extras]
+scannet = ["pyvista"]
+
+[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"
+python_classes = "Test*"
+python_functions = "test_*"
+addopts = [
+    "--strict-markers",
+    "--verbose",
+    "--cov=.",
+    "--cov-config=pyproject.toml",
+    "--cov-report=term-missing",
+    "--cov-report=html",
+    "--cov-report=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/*",
+    "*/test_*",
+    "*/__pycache__/*",
+    "*/site-packages/*",
+    "setup.py",
+    "*/migrations/*",
+    "*/config/*",
+    "*/scripts/*",
+]
+
+[tool.coverage.report]
+precision = 2
+show_missing = true
+skip_covered = false
+fail_under = 80
+exclude_lines = [
+    "pragma: no cover",
+    "def __repr__",
+    "def __str__",
+    "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"]
+build-backend = "poetry.core.masonry.api"

tests/conftest.py

@@ -0,0 +1,222 @@
+import pytest
+import tempfile
+import shutil
+import os
+import json
+import numpy as np
+import torch
+from pathlib import Path
+from unittest.mock import Mock, MagicMock
+
+
+@pytest.fixture
+def temp_dir():
+    """Create a temporary directory for test files."""
+    temp_dir = tempfile.mkdtemp()
+    yield temp_dir
+    shutil.rmtree(temp_dir)
+
+
+@pytest.fixture
+def temp_file(temp_dir):
+    """Create a temporary file path."""
+    def _temp_file(filename="test_file.txt"):
+        return os.path.join(temp_dir, filename)
+    return _temp_file
+
+
+@pytest.fixture
+def mock_config():
+    """Create a mock configuration dictionary."""
+    return {
+        "expname": "test_experiment",
+        "basedir": "./logs",
+        "datadir": "./data/test",
+        "N_rand": 1024,
+        "N_samples": 64,
+        "N_importance": 128,
+        "perturb": 1.0,
+        "use_viewdirs": True,
+        "i_embed": 0,
+        "multires": 10,
+        "multires_views": 4,
+        "raw_noise_std": 0.0,
+        "render_only": False,
+        "render_test": False,
+        "render_factor": 0,
+        "precrop_iters": 0,
+        "precrop_frac": 0.5,
+        "dataset_type": "blender",
+        "testskip": 8,
+        "shape": "greek",
+        "white_bkgd": False,
+        "half_res": False,
+        "factor": 8,
+        "no_ndc": True,
+        "lindisp": False,
+        "spherify": False,
+        "llffhold": 8,
+        "i_print": 100,
+        "i_img": 500,
+        "i_weights": 10000,
+        "i_testset": 50000,
+        "i_video": 50000,
+        "N_iters": 200000,
+        "finest_res": 512,
+        "log2_hashmap_size": 19,
+        "sparse_loss_weight": 0.0,
+        "tv_loss_weight": 0.0,
+        "lrate": 0.01,
+        "lrate_decay": 10,
+        "chunk": 1024*32,
+        "netchunk": 1024*64,
+        "no_batching": False,
+        "no_reload": False,
+        "ft_path": None,
+        "random_seed": None
+    }
+
+
+@pytest.fixture
+def sample_images():
+    """Create sample image tensors for testing."""
+    batch_size = 4
+    height, width = 100, 100
+    channels = 3
+    images = torch.rand(batch_size, height, width, channels)
+    return images
+
+
+@pytest.fixture
+def sample_poses():
+    """Create sample camera pose matrices."""
+    num_poses = 10
+    poses = torch.eye(4).unsqueeze(0).repeat(num_poses, 1, 1)
+    poses[:, :3, 3] = torch.randn(num_poses, 3)
+    return poses
+
+
+@pytest.fixture
+def sample_rays():
+    """Create sample ray origins and directions."""
+    num_rays = 1000
+    rays_o = torch.randn(num_rays, 3)
+    rays_d = torch.randn(num_rays, 3)
+    rays_d = rays_d / rays_d.norm(dim=-1, keepdim=True)
+    return rays_o, rays_d
+
+
+@pytest.fixture
+def mock_model():
+    """Create a mock neural network model."""
+    model = Mock()
+    model.forward = MagicMock(return_value=torch.randn(100, 4))
+    model.parameters = MagicMock(return_value=[torch.randn(10, 10)])
+    return model
+
+
+@pytest.fixture
+def sample_training_data():
+    """Create sample training data for NeRF."""
+    return {
+        "images": torch.rand(100, 100, 100, 3),
+        "poses": torch.eye(4).unsqueeze(0).repeat(100, 1, 1),
+        "render_poses": torch.eye(4).unsqueeze(0).repeat(40, 1, 1),
+        "hwf": [100, 100, 50.0],
+        "i_split": [[0, 80], [80, 90], [90, 100]]
+    }
+
+
+@pytest.fixture
+def mock_hash_encoding():
+    """Create a mock hash encoding module."""
+    mock = Mock()
+    mock.n_levels = 16
+    mock.n_features_per_level = 2
+    mock.forward = MagicMock(return_value=torch.randn(1000, 32))
+    return mock
+
+
+@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 seed for reproducibility."""
+    seed = 42
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed(seed)
+    return seed
+
+
+@pytest.fixture
+def sample_config_file(temp_dir):
+    """Create a sample configuration file."""
+    config_path = os.path.join(temp_dir, "test_config.txt")
+    config_content = """
+expname = test_experiment
+basedir = ./logs
+datadir = ./data/nerf_synthetic/chair
+
+dataset_type = blender
+no_batching = True
+
+use_viewdirs = True
+finest_res = 512
+log2_hashmap_size = 19
+
+N_samples = 64
+N_importance = 64
+
+perturb = 1.
+raw_noise_std = 0.
+
+render_only = False
+render_test = False
+
+chunk = 32768
+netchunk = 65536
+
+lrate = 0.01
+lrate_decay = 10
+
+N_iters = 30000
+i_testset = 2500
+i_video = 10000
+i_print = 100
+"""
+    with open(config_path, 'w') as f:
+        f.write(config_content)
+    return config_path
+
+
+@pytest.fixture
+def mock_dataloader():
+    """Create a mock data loader."""
+    loader = Mock()
+    loader.__iter__ = MagicMock(return_value=iter([
+        (torch.randn(32, 3), torch.randn(32, 3), torch.randn(32))
+        for _ in range(10)
+    ]))
+    loader.__len__ = MagicMock(return_value=10)
+    return loader
+
+
+@pytest.fixture(autouse=True)
+def cleanup_gpu():
+    """Clean up GPU memory after each test."""
+    yield
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+
+
+@pytest.fixture
+def capture_logs(caplog):
+    """Fixture to capture log messages during tests."""
+    with caplog.at_level("DEBUG"):
+        yield caplog