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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
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
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
operations.cpp	[Codemod][GleanFbcode] Remove dead includes in caffe2/test (#43953 )	2020-09-01 21:48:28 -07:00
optim.cpp
optim_baseline.h
optim_baseline.py
ordered_dict.cpp
parallel.cpp
parallel_benchmark.cpp
parameterdict.cpp
parameterlist.cpp
README.md
rnn.cpp
sequential.cpp
serialize.cpp
static.cpp
support.cpp
support.h
tensor.cpp
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

README.md

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.