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393ad6582d
Summary: In TorchScript and C++ extensions we currently advocate a mix of `torch::` and `at::` namespace usage. In the C++ frontend I had instead exported all symbols from `at::` and some from `c10::` into the `torch::` namespace. This is far, far easier for users to understand, and also avoid bugs around creating tensors vs. variables. The same should from now on be true for the TorchScript C++ API (for running and loading models) and all C++ extensions. Note that since we're just talking about typedefs, this change does not break any existing code. Once this lands I will update stuff in `pytorch/tutorials` too. zdevito ezyang gchanan Pull Request resolved: https://github.com/pytorch/pytorch/pull/13523 Differential Revision: D12942787 Pulled By: goldsborough fbshipit-source-id: 76058936bd8707b33d9e5bbc2d0705fc3d820763
43 lines
903 B
C++
43 lines
903 B
C++
#pragma once
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#include <test/cpp/common/support.h>
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#include <gtest/gtest.h>
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#include <torch/nn/cloneable.h>
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#include <torch/types.h>
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#include <torch/utils.h>
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#include <string>
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#include <utility>
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namespace torch {
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namespace test {
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// Lets you use a container without making a new class,
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// for experimental implementations
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class SimpleContainer : public nn::Cloneable<SimpleContainer> {
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public:
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void reset() override {}
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template <typename ModuleHolder>
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ModuleHolder add(
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ModuleHolder module_holder,
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std::string name = std::string()) {
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return Module::register_module(std::move(name), module_holder);
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}
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};
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inline bool pointer_equal(at::Tensor first, at::Tensor second) {
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return first.data<float>() == second.data<float>();
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}
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struct SeedingFixture : public ::testing::Test {
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SeedingFixture() {
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torch::manual_seed(0);
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}
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};
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} // namespace test
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} // namespace torch
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