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5099db08d4
Summary: Currently, the Python API doesn't serialize layers that don't have weights (such as `nn.ReLU` and `nn.MaxPool2d`e.g. in https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py#L80-L81). If one saves a model that contains weight-less layers in Python and tries to load it into C++, the C++ module loading code (`torch::load(...)`) will throw an error complaining that the expected layers are not found in the serialized file (e.g. https://github.com/pytorch/vision/pull/728#issuecomment-480974175). This PR solves the problem by ignoring layers that are not serializable (which currently only include `nn::Functional`) in the C++ module serialization code (`torch::save(...)` and `torch::load(...)`), and the user is expected to use `nn::Functional` to wrap the weight-less layers so that they can be ignored when serializing / deserializing. Pull Request resolved: https://github.com/pytorch/pytorch/pull/19740 Differential Revision: D15100575 Pulled By: yf225 fbshipit-source-id: 956481a2355d1de45341585abedda05e35d2ee8b |
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|---|---|---|
| .. | ||
| any.cpp | ||
| CMakeLists.txt | ||
| dataloader.cpp | ||
| expanding-array.cpp | ||
| init.cpp | ||
| init_baseline.h | ||
| init_baseline.py | ||
| integration.cpp | ||
| jit.cpp | ||
| memory.cpp | ||
| misc.cpp | ||
| module.cpp | ||
| modules.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.h | ||
| tensor.cpp | ||
| tensor_cuda.cpp | ||
| tensor_options.cpp | ||
| tensor_options_cuda.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.