feat: Set up comprehensive Python testing infrastructure with Poetry

.gitignore

@@ -152,6 +152,9 @@ dmypy.json
 # Cython debug symbols
 cython_debug/
 
+# Claude settings
+.claude/*
+
 # PyCharm
 #  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
 #  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore

pyproject.toml

@@ -0,0 +1,172 @@
+[tool.poetry]
+name = "unireplknet"
+version = "0.1.0"
+description = "Universal Representation Learning with Large Kernel Networks"
+authors = ["UniRepLKNet Team"]
+readme = "README.md"
+packages = [
+    { include = "Audio" },
+    { include = "Image" },
+    { include = "Point" },
+    { include = "Time-Series", from = "." },
+    { include = "Video" },
+    { include = "detection" },
+    { include = "segmentation" }
+]
+
+[tool.poetry.dependencies]
+python = "^3.8.1"
+# Core ML/DL Dependencies
+torch = ">=1.2"
+torchvision = "*"
+torchaudio = "*"
+deepspeed = "*"
+timm = "0.4.12"
+triton = {version = "1.0.0", optional = true}
+tensorboard = "2.9.0"
+tensorboardX = "1.8"
+
+# Data Processing
+pandas = "*"
+numpy = "*"
+scipy = "*"
+einops = "*"
+scikit-learn = "*"
+
+# Computer Vision
+opencv-python = "*"
+pillow = "*"
+av = "*"
+decord = {version = "*", optional = true}
+
+# Visualization
+matplotlib = "*"
+
+[tool.poetry.group.dev.dependencies]
+pytest = "^7.4.0"
+pytest-cov = "^4.1.0"
+pytest-mock = "^3.11.1"
+black = "^23.7.0"
+isort = "^5.12.0"
+flake8 = "^6.1.0"
+mypy = "^1.4.1"
+pre-commit = "^3.3.3"
+
+[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",
+    "--cov=.",
+    "--cov-branch",
+    "--cov-report=term-missing",
+    "--cov-report=html:htmlcov",
+    "--cov-report=xml:coverage.xml",
+    "--cov-fail-under=0",
+    "-vv",
+    "--tb=short",
+    "--maxfail=1"
+]
+markers = [
+    "unit: Unit tests (fast, isolated tests)",
+    "integration: Integration tests (may require external resources)",
+    "slow: Slow tests (use sparingly)"
+]
+filterwarnings = [
+    "ignore::DeprecationWarning",
+    "ignore::PendingDeprecationWarning"
+]
+
+[tool.coverage.run]
+source = ["."]
+omit = [
+    "*/tests/*",
+    "*/test_*.py",
+    "*/__pycache__/*",
+    "*/site-packages/*",
+    "setup.py",
+    "*/migrations/*",
+    "*/__init__.py",
+    "*/conftest.py",
+    "*/venv/*",
+    "*/env/*",
+    ".venv/*"
+]
+
+[tool.coverage.report]
+precision = 2
+show_missing = true
+skip_covered = false
+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"
+]
+
+[tool.coverage.html]
+directory = "htmlcov"
+
+[tool.coverage.xml]
+output = "coverage.xml"
+
+[tool.isort]
+profile = "black"
+multi_line_output = 3
+include_trailing_comma = true
+force_grid_wrap = 0
+use_parentheses = true
+ensure_newline_before_comments = true
+line_length = 88
+skip_gitignore = true
+
+[tool.black]
+line-length = 88
+target-version = ['py38', 'py39', 'py310', 'py311']
+include = '\.pyi?$'
+extend-exclude = '''
+/(
+  # directories
+  \.eggs
+  | \.git
+  | \.hg
+  | \.mypy_cache
+  | \.tox
+  | \.venv
+  | build
+  | dist
+)/
+'''
+
+[tool.mypy]
+python_version = "3.8"
+warn_return_any = true
+warn_unused_configs = true
+disallow_untyped_defs = false
+ignore_missing_imports = true
+exclude = [
+    "tests/",
+    "venv/",
+    ".venv/",
+    "build/",
+    "dist/"
+]
+
+[build-system]
+requires = ["poetry-core>=1.0.0"]
+build-backend = "poetry.core.masonry.api"

tests/conftest.py

@@ -0,0 +1,191 @@
+"""Shared pytest fixtures and configuration for UniRepLKNet tests."""
+
+import os
+import tempfile
+from pathlib import Path
+from typing import Generator, Dict, Any
+from unittest.mock import MagicMock
+
+import pytest
+import torch
+import numpy as np
+
+
+@pytest.fixture
+def temp_dir() -> Generator[Path, None, None]:
+    """Create a temporary directory for test files."""
+    with tempfile.TemporaryDirectory() as tmpdir:
+        yield Path(tmpdir)
+
+
+@pytest.fixture
+def mock_config() -> Dict[str, Any]:
+    """Provide a mock configuration dictionary for testing."""
+    return {
+        "model": {
+            "name": "unireplknet",
+            "num_classes": 10,
+            "input_size": 224,
+            "patch_size": 16,
+            "embed_dim": 768,
+            "depth": 12,
+            "num_heads": 12,
+        },
+        "training": {
+            "batch_size": 32,
+            "learning_rate": 0.001,
+            "epochs": 100,
+            "warmup_epochs": 5,
+            "weight_decay": 0.05,
+        },
+        "data": {
+            "dataset": "test_dataset",
+            "data_path": "/tmp/test_data",
+            "num_workers": 4,
+            "pin_memory": True,
+        },
+        "augmentation": {
+            "mixup": 0.8,
+            "cutmix": 1.0,
+            "cutmix_minmax": None,
+            "mixup_prob": 1.0,
+            "mixup_switch_prob": 0.5,
+            "mixup_mode": "batch",
+        },
+    }
+
+
+@pytest.fixture
+def sample_image_tensor() -> torch.Tensor:
+    """Create a sample image tensor for testing."""
+    return torch.randn(1, 3, 224, 224)
+
+
+@pytest.fixture
+def sample_batch() -> Dict[str, torch.Tensor]:
+    """Create a sample batch of data for testing."""
+    batch_size = 4
+    return {
+        "images": torch.randn(batch_size, 3, 224, 224),
+        "labels": torch.randint(0, 10, (batch_size,)),
+        "masks": torch.ones(batch_size, 224, 224),
+    }
+
+
+@pytest.fixture
+def sample_audio_tensor() -> torch.Tensor:
+    """Create a sample audio tensor for testing."""
+    # 1 second of audio at 16kHz sample rate
+    return torch.randn(1, 16000)
+
+
+@pytest.fixture
+def sample_video_tensor() -> torch.Tensor:
+    """Create a sample video tensor for testing."""
+    # batch_size=1, num_frames=8, channels=3, height=224, width=224
+    return torch.randn(1, 8, 3, 224, 224)
+
+
+@pytest.fixture
+def sample_point_cloud() -> torch.Tensor:
+    """Create a sample point cloud tensor for testing."""
+    # num_points=1024, dimensions=3 (x, y, z)
+    return torch.randn(1024, 3)
+
+
+@pytest.fixture
+def sample_time_series() -> np.ndarray:
+    """Create a sample time series data for testing."""
+    # 100 time steps with 5 features
+    return np.random.randn(100, 5)
+
+
+@pytest.fixture
+def mock_model() -> MagicMock:
+    """Create a mock model for testing."""
+    model = MagicMock()
+    model.forward.return_value = torch.randn(1, 10)  # Mock output
+    model.parameters.return_value = [torch.randn(10, 10)]
+    model.state_dict.return_value = {"layer1.weight": torch.randn(10, 10)}
+    return model
+
+
+@pytest.fixture
+def mock_dataset() -> MagicMock:
+    """Create a mock dataset for testing."""
+    dataset = MagicMock()
+    dataset.__len__.return_value = 100
+    dataset.__getitem__.return_value = (torch.randn(3, 224, 224), 0)
+    return dataset
+
+
+@pytest.fixture
+def mock_dataloader(mock_dataset) -> MagicMock:
+    """Create a mock dataloader for testing."""
+    dataloader = MagicMock()
+    dataloader.__iter__.return_value = iter([
+        (torch.randn(32, 3, 224, 224), torch.randint(0, 10, (32,)))
+        for _ in range(3)
+    ])
+    dataloader.dataset = mock_dataset
+    return dataloader
+
+
+@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
+def random_seed() -> int:
+    """Set and return a fixed random seed for reproducible tests."""
+    seed = 42
+    torch.manual_seed(seed)
+    np.random.seed(seed)
+    return seed
+
+
+@pytest.fixture(autouse=True)
+def cleanup_gpu_memory():
+    """Automatically clean up GPU memory after each test."""
+    yield
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+
+
+@pytest.fixture
+def checkpoint_path(temp_dir: Path) -> Path:
+    """Create a path for saving checkpoints during tests."""
+    checkpoint_dir = temp_dir / "checkpoints"
+    checkpoint_dir.mkdir(exist_ok=True)
+    return checkpoint_dir / "test_checkpoint.pth"
+
+
+@pytest.fixture
+def sample_checkpoint(checkpoint_path: Path, mock_model) -> Path:
+    """Create a sample checkpoint file for testing."""
+    checkpoint = {
+        "epoch": 10,
+        "model_state_dict": mock_model.state_dict(),
+        "optimizer_state_dict": {"param_groups": [{"lr": 0.001}]},
+        "loss": 0.5,
+        "accuracy": 0.85,
+    }
+    torch.save(checkpoint, checkpoint_path)
+    return checkpoint_path
+
+
+@pytest.fixture
+def env_setup(monkeypatch):
+    """Set up environment variables for testing."""
+    monkeypatch.setenv("CUDA_VISIBLE_DEVICES", "0")
+    monkeypatch.setenv("OMP_NUM_THREADS", "1")
+    monkeypatch.setenv("TORCH_HOME", "/tmp/torch_cache")
+
+
+@pytest.fixture
+def capture_logs(caplog):
+    """Fixture to capture and return logs during tests."""
+    with caplog.at_level("DEBUG"):
+        yield caplog