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6ebfbdf4c6
Summary:
Previously, we were not able to assign names to `nn::Sequential`'s submodules. This PR adds this feature to match the Python API. Example use:
```cpp
Sequential sequential(named_submodule({
{"linear", Linear(10, 3)},
{"conv2d", Conv2d(1, 2, 3)},
{"dropout", Dropout(0.5)},
{"batchnorm", BatchNorm(5)},
{"embedding", Embedding(4, 10)},
{"lstm", LSTM(4, 5)}
}));
```
It also enables loading parameters of Python `nn.Sequential` module with custom submodules names into C++ frontend, unblocking https://github.com/pytorch/vision/pull/728#issuecomment-466661344.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17552
Differential Revision: D14246834
Pulled By: yf225
fbshipit-source-id: 3030b5c5d68f6dd5d3e37ac4b4f98dc6d6d9ba72
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|---|---|---|
| .. | ||
| any.cpp | ||
| CMakeLists.txt | ||
| dataloader.cpp | ||
| expanding-array.cpp | ||
| init_baseline.h | ||
| init_baseline.py | ||
| init.cpp | ||
| integration.cpp | ||
| jit.cpp | ||
| memory.cpp | ||
| misc.cpp | ||
| module.cpp | ||
| modules.cpp | ||
| optim_baseline.h | ||
| optim_baseline.py | ||
| optim.cpp | ||
| ordered_dict.cpp | ||
| parallel.cpp | ||
| README.md | ||
| rnn.cpp | ||
| sequential.cpp | ||
| serialize.cpp | ||
| static.cpp | ||
| support.h | ||
| tensor_cuda.cpp | ||
| tensor_options_cuda.cpp | ||
| tensor_options.cpp | ||
| tensor.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.