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android / platform / external / pytorch / 483ae8cb5d / . / test / cpp / api
tree: e211db59cfae70bb798d8bc15d5cea880f4f5370 [path history] [tgz]
  1. any.cpp
  2. cursor.cpp
  3. integration.cpp
  4. main.cpp
  5. misc.cpp
  6. module.cpp
  7. modules.cpp
  8. optim.cpp
  9. optim_baseline.h
  10. optim_baseline.py
  11. README.md
  12. rnn.cpp
  13. sequential.cpp
  14. serialization.cpp
  15. static.cpp
  16. tensor.cpp
  17. tensor_cuda.cpp
  18. tensor_options.cpp
  19. tensor_options_cuda.cpp
  20. util.h
test/cpp/api/README.md

C++ API Tests

In this folder live the tests for PyTorch's C++ API (formerly known as autogradpp). They use the Catch2 test framework.

CUDA Tests

The way we handle CUDA tests is by separating them into a separate TEST_CASE (e.g. we have optim and optim_cuda test cases in optim.cpp), and giving them the [cuda] tag. Then, inside main.cpp we detect at runtime whether CUDA is available. If not, we disable these CUDA tests by appending ~[cuda] to the test specifications. The ~ disables the tag.

One annoying aspect is that Catch only allows filtering on test cases and not sections. Ideally, one could have a section like LSTM inside the RNN test case, and give this section a [cuda] tag to only run it when CUDA is available. Instead, we have to create a whole separate RNN_cuda test case and put all these CUDA sections in there.

Integration Tests

Integration tests use the MNIST dataset. You must download it by running the following command from the PyTorch root folder:

$ python tools/download_mnist.py -d test/cpp/api/mnist