d863391871
Summary:
Added a way to `dynamic_cast` an `nn::Module` and get a pointer to it. `nn::Module::is<T>` just checked if the return value of the `dynamic_cast` was nullptr, so I got rid of `is<T>` since it's equivalent to `as<T> != nullptr`(or just `as<T>` due to boolean conversion).
We're now at
```
if (auto* conv = module.as<nn::Conv2d>()) {
conv->weight.data().normal_(0.0, 0.02);
} else if (auto* bn = module.as<nn::BatchNorm>()) {
bn->weight.data().normal_(1.0, 0.02);
bn->bias.data().fill_(0);
}
```
ezyang apaszke ebetica
Closes https://github.com/pytorch/pytorch/pull/9149
Differential Revision: D8735954
Pulled By: goldsborough
fbshipit-source-id: e2b8f6f0cea16a621f8bc0807a33cc7651d25154
|
||
|---|---|---|
| .. | ||
| any.cpp | ||
| cursor.cpp | ||
| integration.cpp | ||
| main.cpp | ||
| misc.cpp | ||
| module.cpp | ||
| modules.cpp | ||
| optim.cpp | ||
| optim_baseline.h | ||
| optim_baseline.py | ||
| README.md | ||
| rnn.cpp | ||
| sequential.cpp | ||
| serialization.cpp | ||
| static.cpp | ||
| tensor.cpp | ||
| tensor_cuda.cpp | ||
| tensor_options.cpp | ||
| tensor_options_cuda.cpp | ||
| util.h | ||
C++ API Tests
In this folder live the tests for PyTorch's C++ API (formerly known as autogradpp). They use the Catch2 test framework.
CUDA Tests
The way we handle CUDA tests is by separating them into a separate TEST_CASE
(e.g. we have optim and optim_cuda test cases in optim.cpp), and giving
them the [cuda] tag. Then, inside main.cpp we detect at runtime whether
CUDA is available. If not, we disable these CUDA tests by appending ~[cuda]
to the test specifications. The ~ disables the tag.
One annoying aspect is that Catch only allows filtering on test cases and not
sections. Ideally, one could have a section like LSTM inside the RNN test
case, and give this section a [cuda] tag to only run it when CUDA is
available. Instead, we have to create a whole separate RNN_cuda test case and
put all these CUDA sections in there.
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