pytorch/test/cpp/api
Xinyu Li c9bb990707 [c++] Distance-agnostic triplet margin loss (#45377)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45377

This PR adds a C++ implementation of the TripletMarginWithDistanceLoss, for which the Python implementation was introduced in PR #43680.  It's based on PR #44072, but I'm resubmitting this to unlink it from Phabricator.

Test Plan: Imported from OSS

Reviewed By: izdeby

Differential Revision: D24003973

fbshipit-source-id: 2d9ada7260a6f27425ff2fdbbf623dad0fb79405
2020-09-30 12:37:35 -07:00
..
any.cpp
autograd.cpp Don't materialize output grads (#41821) 2020-08-11 04:27:07 -07:00
CMakeLists.txt C++ API TransformerEncoderLayer (#42633) 2020-08-07 11:49:42 -07:00
dataloader.cpp
dispatch.cpp [Codemod][GleanFbcode] Remove dead includes in caffe2/test (#39023) 2020-05-27 14:07:26 -07:00
enum.cpp
expanding-array.cpp
fft.cpp Add one dimensional FFTs to torch.fft namespace (#43011) 2020-09-19 23:32:22 -07:00
functional.cpp [c++] Distance-agnostic triplet margin loss (#45377) 2020-09-30 12:37:35 -07:00
init.cpp [Codemod][GleanFbcode] Remove dead includes in caffe2/test (#39023) 2020-05-27 14:07:26 -07:00
init_baseline.h
init_baseline.py
integration.cpp
jit.cpp
memory.cpp
misc.cpp Throw error if torch.set_deterministic(True) is called with nondeterministic CuBLAS config (#41377) 2020-08-05 12:42:24 -07:00
module.cpp
modulelist.cpp
modules.cpp [c++] Distance-agnostic triplet margin loss (#45377) 2020-09-30 12:37:35 -07:00
namespace.cpp
nn_utils.cpp [WIP] Fix cpp grad accessor API (#40887) 2020-07-16 09:11:12 -07:00
operations.cpp [Codemod][GleanFbcode] Remove dead includes in caffe2/test (#43953) 2020-09-01 21:48:28 -07:00
optim.cpp [WIP] Fix cpp grad accessor API (#40887) 2020-07-16 09:11:12 -07:00
optim_baseline.h Add AdamW to C++ frontend (#40009) 2020-06-18 15:28:12 -07:00
optim_baseline.py Add AdamW to C++ frontend (#40009) 2020-06-18 15:28:12 -07:00
ordered_dict.cpp
parallel.cpp [PyTorch] Modify data_parallel to work with small tensors (#37704) 2020-05-04 11:06:42 -07:00
parallel_benchmark.cpp [aten] Pass std::function<> to thread_pool by value, instead of const ref. (#37681) 2020-05-05 08:41:38 -07:00
parameterdict.cpp Python/C++ API Parity: Add impl and tests for ParameterDict (#40654) 2020-06-29 08:50:44 -07:00
parameterlist.cpp Impl for ParameterList (#41259) 2020-07-12 20:50:31 -07:00
README.md
rnn.cpp
sequential.cpp
serialize.cpp Add AdamW to C++ frontend (#40009) 2020-06-18 15:28:12 -07:00
static.cpp
support.cpp
support.h Changes warnings generated in cpp to show point of Python origination (#36052) 2020-04-25 21:18:58 -07:00
tensor.cpp Change to.dtype_layout to c10-full (#41169) 2020-07-10 16:04:34 -07:00
tensor_cuda.cpp
tensor_indexing.cpp
tensor_options.cpp
tensor_options_cuda.cpp
torch_include.cpp
transformer.cpp C++ APIs Transformer NN Module Top Layer (#44333) 2020-09-11 08:25:27 -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.