pytorch/test/cpp/api
Pavel Belevich a31fd5ea68 C++ API parity: AdaptiveAvgPool2d
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/26818

Test Plan: Imported from OSS

Differential Revision: D17627822

Pulled By: pbelevich

fbshipit-source-id: 0e1dea1c3ff2650dbc7902ce704ac6b47588d0bb
2019-09-28 10:45:03 -07:00
..
any.cpp Re-organize C++ API torch::nn folder structure (#26262) 2019-09-17 10:07:29 -07:00
autograd.cpp Improve handling of mixed-type tensor operations (#22273) 2019-09-05 18:26:09 -07:00
CMakeLists.txt Re-organize C++ API torch::nn folder structure (#26262) 2019-09-17 10:07:29 -07:00
dataloader.cpp add sorting policy to ChunkDataset (#23053) 2019-07-29 12:34:02 -07:00
expanding-array.cpp Rewrite C++ API tests in gtest (#11953) 2018-09-21 21:28:16 -07:00
functional.cpp C++ API parity: AdaptiveAvgPool2d 2019-09-28 10:45:03 -07:00
init_baseline.h Kaiming Initialization (#14718) 2019-02-15 14:58:22 -08:00
init_baseline.py Kaiming Initialization (#14718) 2019-02-15 14:58:22 -08:00
init.cpp Fix torch::nn::init::orthogonal_ with CNNs (#18915) 2019-04-09 10:39:15 -07:00
integration.cpp Move isnan to C++ (#15722) 2019-01-08 10:42:33 -08:00
jit.cpp Add Pickler C++ API (#23241) 2019-08-12 14:43:31 -07:00
memory.cpp Hide c10::optional and nullopt in torch namespace (#12927) 2018-10-26 00:08:04 -07:00
misc.cpp C++ API parity: at::Tensor::grad 2019-09-18 09:20:38 -07:00
module.cpp Re-organize C++ API torch::nn folder structure (#26262) 2019-09-17 10:07:29 -07:00
modulelist.cpp Adding ModuleList to modules.h (#25346) 2019-08-29 10:49:22 -07:00
modules.cpp C++ API parity: AdaptiveAvgPool2d 2019-09-28 10:45:03 -07:00
optim_baseline.h Use torch:: instead of at:: in all C++ APIs (#13523) 2018-11-06 14:32:25 -08:00
optim_baseline.py Use torch:: instead of at:: in all C++ APIs (#13523) 2018-11-06 14:32:25 -08:00
optim.cpp Re-organize C++ API torch::nn folder structure (#26262) 2019-09-17 10:07:29 -07:00
ordered_dict.cpp C++ unregister_module function for Module (#26088) 2019-09-12 18:38:57 -07:00
parallel.cpp C++ API parity: at::Tensor::grad 2019-09-18 09:20:38 -07:00
README.md Rewrite C++ API tests in gtest (#11953) 2018-09-21 21:28:16 -07:00
rnn.cpp Make options.name_ private, and change all callsites to use options.name() (#26419) 2019-09-19 14:48:22 -07:00
sequential.cpp Re-organize C++ API torch::nn folder structure (#26262) 2019-09-17 10:07:29 -07:00
serialize.cpp Include iteration_ in SGD optimizer serialization (#26906) 2019-09-27 09:37:20 -07:00
static.cpp Re-organize C++ API torch::nn folder structure (#26262) 2019-09-17 10:07:29 -07:00
support.h Add TORCH_WARN_ONCE, and use it in Tensor.data<T>() (#25207) 2019-08-27 21:42:44 -07:00
tensor_cuda.cpp Deprecate tensor.data<T>(), and codemod tensor.data<T>() to tensor.data_ptr<T>() (#24886) 2019-08-21 20:11:24 -07:00
tensor_options_cuda.cpp Revert D15920763: Move TensorOptions to ATen/core 2019-08-13 12:07:18 -07:00
tensor_options.cpp Revert D15920763: Move TensorOptions to ATen/core 2019-08-13 12:07:18 -07:00
tensor.cpp Fix issues in torch::tensor constructor (#26890) 2019-09-27 12:07:50 -07:00
torch_include.cpp Add get/set_num_interop_threads into torch.h include (#20659) 2019-05-20 00:34:59 -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.