pytorch/test/cpp/api
jon-tow f3df6b8ede Add C++ torch::nn::functional::affine_grid (#27263)
Summary:
Adds`torch::nn::functional::affine_grid` functional support for the C++ API.

Issue: https://github.com/pytorch/pytorch/issues/25883, https://github.com/pytorch/pytorch/issues/27196

Reviewer: yf225
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27263

Differential Revision: D17802350

Pulled By: yf225

fbshipit-source-id: e823ee53da4a4cc6a1650d2dfc09b0ef6a74e249
2019-10-09 23:17:49 -07:00
..
any.cpp Separate libtorch tests from libtorch build. (#26927) 2019-10-02 08:04:52 -07:00
autograd.cpp Improve handling of mixed-type tensor operations (#22273) 2019-09-05 18:26:09 -07:00
CMakeLists.txt Add clip_grad_norm_ to c++ api (#26140) 2019-10-04 13:50:36 -07:00
dataloader.cpp
expanding-array.cpp
functional.cpp Add C++ torch::nn::functional::affine_grid (#27263) 2019-10-09 23:17:49 -07:00
init.cpp Add temporary torch::k{name} enum declarations (#27051) 2019-09-30 13:38:29 -07:00
init_baseline.h
init_baseline.py
integration.cpp
jit.cpp Add Pickler C++ API (#23241) 2019-08-12 14:43:31 -07:00
memory.cpp
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 Implement torch.nn.Embedding / EmbeddingBag in PyTorch C++ API (#26358) 2019-10-08 22:13:39 -07:00
modules.cpp C++ API parity: PReLU 2019-10-09 16:31:54 -07:00
nn_utils.cpp Add clip_grad_norm_ to c++ api (#26140) 2019-10-04 13:50:36 -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 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
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 Implement torch.nn.Embedding / EmbeddingBag in PyTorch C++ API (#26358) 2019-10-08 22:13:39 -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.cpp C++ API parity: TensorTest.BackwardNonScalarOutputs 2019-10-03 15:36:35 -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.cpp Revert D15920763: Move TensorOptions to ATen/core 2019-08-13 12:07:18 -07:00
tensor_options_cuda.cpp Revert D15920763: Move TensorOptions to ATen/core 2019-08-13 12:07:18 -07:00
torch_include.cpp

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.