mirror of
https://github.com/MLSysBook/TinyTorch.git
synced 2026-06-02 21:30:52 -05:00
4a0ce4759f
- Add comprehensive mock-based tests for Activations module (tests/test_activations.py): * TestReLUActivation: 7 test methods covering positive/negative values, mixed inputs, 2D processing * TestSigmoidActivation: 6 test methods covering zero input, symmetry, extreme values, 2D processing * TestTanhActivation: 6 test methods covering antisymmetry, extreme values, mathematical properties * TestSoftmaxActivation: 6 test methods covering probability distribution, numerical stability, batch processing * TestActivationIntegration: 3 test methods covering chaining, consistency, shape preservation * TestActivationEdgeCases: 3 test methods covering empty input, small values, inf/nan handling * Total: 514 lines with MockTensor class avoiding cross-module dependencies - Add comprehensive mock-based tests for Networks module (tests/test_networks.py): * TestSequentialNetwork: 8 test methods covering initialization, layer addition, forward pass, batch processing * TestMLPNetwork: 6 test methods covering basic/parameter initialization, network structure, forward pass * TestNetworkIntegration: 3 test methods covering composition, equivalence, complex architectures * TestNetworkEdgeCases: 4 test methods covering incompatible layers, edge sizes, empty networks * TestNetworkPerformance: 2 test methods covering call efficiency and scalability * Total: 552 lines with MockTensor and MockLayer classes for isolated testing - Add comprehensive mock-based tests for CNN module (tests/test_cnn.py): * TestConv2DNaive: 6 test methods covering basic convolution, edge detection, different sizes, kernels * TestConv2DLayer: 7 test methods covering initialization, forward pass, batch processing, consistency * TestFlattenFunction: 6 test methods covering 2D/3D tensors, shape preservation, batch dimensions * TestCNNIntegration: 4 test methods covering conv-to-flatten pipeline, multiple layers, feature extraction * TestCNNEdgeCases: 4 test methods covering minimal input, large kernels, numerical stability * TestCNNPerformance: 4 test methods covering consistency, scalability, efficiency * TestCNNMathematicalProperties: 3 test methods covering linearity, translation invariance, bijection * Total: 521 lines with MockTensor class for isolated CNN testing - Add comprehensive mock-based tests for DataLoader module (tests/test_dataloader.py): * TestDatasetInterface: 6 test methods covering abstract methods, MockDataset functionality, configurations * TestDataLoaderBasic: 4 test methods covering initialization, length calculation, iteration * TestDataLoaderShuffling: 3 test methods covering shuffle/no-shuffle behavior, consistency * TestDataLoaderEdgeCases: 5 test methods covering empty datasets, single samples, edge cases * TestDataLoaderIntegration: 3 test methods covering SimpleDataset, custom datasets, different data types * TestDataLoaderPerformance: 3 test methods covering memory efficiency, iteration speed, scalability * TestDataLoaderRobustness: 3 test methods covering invalid inputs, error handling, consistency * Total: 585 lines with MockTensor and MockDataset classes for isolated testing - All mock-based tests follow established patterns: * Simple, visible mocks instead of complex mocking frameworks * Test interface contracts and behavior, not implementation details * Avoid dependency cascade where tests fail due to other module bugs * Focus on mathematical correctness and architectural patterns * Educational value with clear test structure and comprehensive coverage - Complete mock-based testing implementation: 2,172 lines across 4 modules - Total testing architecture: 6,200+ lines across inline and mock-based tests - Ready for production-quality module isolation and validation