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a2557970f3
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/35025 This PR fixes `F::interpolate` and `torch::nn::Upsample` implementation to match the Python API implementation. **This PR is BC-breaking in the following way:** There are changes to `UpsampleOptions` and `InterpolateFuncOptions`: - `size` is changed from `std::vector<int64_t>` to `c10::optional<std::vector<int64_t>>`. If you want to pass a list of `int64_t` to this argument, you must pass it as `std::vector<int64_t>`. - `scale_factor` is changed from `std::vector<double>` to `c10::optional<std::vector<double>>`. If you want to pass a list of `double` to this argument, you must pass it as `std::vector<double>`. **TODO**: cherry-pick this PR into v1.5 release branch. Test Plan: Imported from OSS Differential Revision: D20559892 Pulled By: yf225 fbshipit-source-id: ac18609e351a9f2931eaeced8966b9491b2995f7 |
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| .. | ||
| any.cpp | ||
| autograd.cpp | ||
| CMakeLists.txt | ||
| dataloader.cpp | ||
| dispatch.cpp | ||
| enum.cpp | ||
| expanding-array.cpp | ||
| functional.cpp | ||
| init.cpp | ||
| init_baseline.h | ||
| init_baseline.py | ||
| integration.cpp | ||
| jit.cpp | ||
| memory.cpp | ||
| misc.cpp | ||
| module.cpp | ||
| modulelist.cpp | ||
| modules.cpp | ||
| namespace.cpp | ||
| nn_utils.cpp | ||
| optim.cpp | ||
| optim_baseline.h | ||
| optim_baseline.py | ||
| ordered_dict.cpp | ||
| parallel.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 | ||
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.