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43c0b50c2e
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15084 Codemod generated with clangr shard mode, 25 files per diff, motivation: https://github.com/pytorch/pytorch/pull/12407 Reviewed By: ezyang Differential Revision: D13419711 fbshipit-source-id: dd2b740c3f13d8087085bafc5571aaf908d1af42
40 lines
1.0 KiB
C++
40 lines
1.0 KiB
C++
#ifndef CAFFE2_OPERATORS_NORMALIZE_L1_OP_H_
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#define CAFFE2_OPERATORS_NORMALIZE_L1_OP_H_
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#include "caffe2/core/context.h"
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#include "caffe2/core/operator.h"
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#include "caffe2/utils/math.h"
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namespace caffe2 {
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template <typename T, class Context>
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class NormalizeL1Op final : public Operator<Context> {
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public:
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USE_OPERATOR_CONTEXT_FUNCTIONS;
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USE_SIMPLE_CTOR_DTOR(NormalizeL1Op)
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bool RunOnDevice() override {
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const auto& x = Input(0);
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const auto* xData = x.template data<T>();
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auto* y = Output(0, x.sizes(), at::dtype<T>());
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auto* yData = y->template mutable_data<T>();
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const auto canonical_axis = x.canonical_axis_index(
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this->template GetSingleArgument<int>("axis", -1));
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const int m = x.dim32(canonical_axis);
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const int n = x.numel() / m;
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const int sf = x.size_from_dim(canonical_axis + 1);
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DoNormalize(xData, yData, m, n, sf);
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return true;
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}
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private:
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void
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DoNormalize(const T* xData, T* yData, const int m, const int n, const int sf);
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};
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} // namespace caffe2
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#endif // CAFFE2_OPERATORS_NORMALIZE_L1_OP_H_
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