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https://github.com/zebrajr/pytorch.git
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ae44a6b5e3
Summary: I noticed that `Sequential::clone()` does not work. This is because `Sequential` does not use `reset()` which is normally where modules have to initialize and register its submodules. Further, this is because of the way `Sequential` allows its modules to be passed in the constructor, which doesn't work with `reset()` (since it does "late" initialization). I've added some better error messages inside `Cloneable::clone()` which makes this kind of mistake clearer for other users, and tests for `Sequential::clone()`. I also had to give `AnyModule` a deep `clone()` method. ebetica ezyang Pull Request resolved: https://github.com/pytorch/pytorch/pull/9372 Differential Revision: D8865189 Pulled By: goldsborough fbshipit-source-id: b81586e0d3157cd3c4265b19ac8dd87c5d8dcf94
30 lines
703 B
C++
30 lines
703 B
C++
#pragma once
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#include <torch/nn/cloneable.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(torch::Tensor first, torch::Tensor second) {
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return first.data().data<float>() == second.data().data<float>();
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}
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} // namespace test
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} // namespace torch
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