pytorch/test/cpp/api
Will Feng 65bfcde05e Use c10::variant-based enums for SmoothL1Loss module and functional
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/29536

Test Plan: Imported from OSS

Differential Revision: D18432272

Pulled By: yf225

fbshipit-source-id: fa355145962e93025b7de98b99b0a4fc82e8c871
2019-11-12 16:05:31 -08:00
..
any.cpp Separate libtorch tests from libtorch build. (#26927) 2019-10-02 08:04:52 -07:00
autograd.cpp Fix bugs in torch::tensor constructor (#28523) 2019-10-31 12:53:06 -07:00
CMakeLists.txt Use torch::variant for enums in C++ API 2019-10-16 22:40:57 -07:00
dataloader.cpp Fix bugs in torch::tensor constructor (#28523) 2019-10-31 12:53:06 -07:00
enum.cpp Use c10::variant-based enums for F::grid_sample 2019-11-12 16:05:26 -08:00
expanding-array.cpp Change C++ API test files to only include torch/torch.h (#27067) 2019-10-10 09:46:29 -07:00
functional.cpp Use c10::variant-based enums for SmoothL1Loss module and functional 2019-11-12 16:05:31 -08:00
init.cpp Use c10::variant-based enums for Nonlinearity and FanMode 2019-10-18 17:48:34 -07:00
init_baseline.h
init_baseline.py
integration.cpp Change C++ API test files to only include torch/torch.h (#27067) 2019-10-10 09:46:29 -07:00
jit.cpp Remove attempToRecoverType (#26767) 2019-10-16 11:07:13 -07:00
memory.cpp
misc.cpp Change C++ API test files to only include torch/torch.h (#27067) 2019-10-10 09:46:29 -07:00
module.cpp Allow passing undefined Tensor to Module::register_parameter (#27948) 2019-10-15 10:10:42 -07:00
modulelist.cpp C++ API: torch::nn::BatchNorm1d (#28176) 2019-10-29 17:29:42 -07:00
modules.cpp Use c10::variant-based enums for SmoothL1Loss module and functional 2019-11-12 16:05:31 -08:00
nn_utils.cpp Add C++ API clip_grad_value_ for nn:utils (#28736) 2019-10-31 19:11:54 -07:00
optim.cpp Re-organize C++ API torch::nn folder structure (#26262) 2019-09-17 10:07:29 -07:00
optim_baseline.h
optim_baseline.py
ordered_dict.cpp Change C++ API test files to only include torch/torch.h (#27067) 2019-10-10 09:46:29 -07:00
parallel.cpp Fix bugs in torch::tensor constructor (#28523) 2019-10-31 12:53:06 -07:00
README.md
rnn.cpp Change C++ API test files to only include torch/torch.h (#27067) 2019-10-10 09:46:29 -07:00
sequential.cpp C++ API: torch::nn::BatchNorm1d (#28176) 2019-10-29 17:29:42 -07:00
serialize.cpp Implement more of of the nn.Module API (#28828) 2019-11-06 22:58:25 -08:00
static.cpp Re-organize C++ API torch::nn folder structure (#26262) 2019-09-17 10:07:29 -07:00
support.h
tensor.cpp move type inference for arange into c++ (#27629) 2019-11-11 11:26:21 -08:00
tensor_cuda.cpp
tensor_options.cpp Change C++ API test files to only include torch/torch.h (#27067) 2019-10-10 09:46:29 -07:00
tensor_options_cuda.cpp Change C++ API test files to only include torch/torch.h (#27067) 2019-10-10 09:46:29 -07:00
torch_include.cpp Relax set_num_threads restriction in parallel native case (#27947) 2019-10-16 21:53:36 -07:00

C++ Frontend Tests

In this folder live the tests for PyTorch's C++ Frontend. They use the GoogleTest test framework.

CUDA Tests

To make a test runnable only on platforms with CUDA, you should suffix your test with _CUDA, e.g.

TEST(MyTestSuite, MyTestCase_CUDA) { }

To make it runnable only on platforms with at least two CUDA machines, suffix it with _MultiCUDA instead of _CUDA, e.g.

TEST(MyTestSuite, MyTestCase_MultiCUDA) { }

There is logic in main.cpp that detects the availability and number of CUDA devices and supplies the appropriate negative filters to GoogleTest.

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

The required paths will be referenced as test/cpp/api/mnist/... in the test code, so you must run the integration tests from the PyTorch root folder.