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102 lines
3.5 KiB
102 lines
3.5 KiB
6 years ago
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#pragma once
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#include <algorithm>
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#include <vector>
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/operators/math/math_function.h"
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namespace paddle {
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namespace operators {
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template <typename DeviceContext, typename T>
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class ShuffleChannelOpKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto* input = ctx.Input<framework::Tensor>("X");
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auto* output = ctx.Output<framework::Tensor>("Out");
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auto group = ctx.Input<framework::Tensor>("group");
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auto input_dims = input->dims();
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auto num = input_dims[0];
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auto channel = input_dims[1];
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auto height = input_dims[2];
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auto weight = input_dims[3];
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auto feature_map_size = channel * height * weight;
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auto sp_sz = height * weight;
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int group_row = group;
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int group_column = channels / group_row;
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const T* input_data = input->data<T>();
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T* output_data = out->mutable_data<T>(ctx.GetPlace());
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for (int n = 0; n < num; ++n) {
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output_data_temp = output_data + n * feature_map_size;
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input_data_temp = input_data + n * feature_map_size;
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for (int i = 0; i < group_row; ++i) {
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for (int j = 0; j < group_column; ++j) {
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const auto* p_i = input_data_temp + (i * group_column + j) * sp_sz;
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auto* p_o = output_data_temp + (j * group_row + i) * sp_sz;
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memcpy(p_o, p_i, sizeof(Dtype) * sp_sz);
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}
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}
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}
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return;
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}
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};
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template <typename DeviceContext, typename T>
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class ShuffleChannelGradOpKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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auto* input = ctx.Input<framework::Tensor>("X");
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auto group = ctx.Input<framework::Tensor>("group");
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auto input_dims = input->dims();
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auto num = input_dims[0];
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auto channel = input_dims[1];
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auto height = input_dims[2];
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auto weight = input_dims[3];
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auto feature_map_size = channel * height * weight;
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auto sp_sz = height * weight;
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int group_row = group;
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int group_column = channels / group_row;
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auto* output_grad =
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ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
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auto* input_grad =
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ctx.Output<framework::Tensor>(framework::GradVarName("X"));
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T* input_grad_data = input_grad->mutable_data<T>(ctx.GetPlace());
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const T* output_grad_data = output_grad->data<T>();
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for (int n = 0; n < num; ++n) {
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output_grad_temp = output_grad_data + n * feature_map_size;
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input_grad_temp = input_grad_data + n * feature_map_size;
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for (int i = 0; i < group_row; ++i) {
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for (int j = 0; j < group_column; ++j) {
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const auto* p_i = output_grad_temp + (i * group_column + j) * sp_sz;
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auto* p_o = input_grad_temp + (j * group_row + i) * sp_sz;
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memcpy(p_o, p_i, sizeof(Dtype) * sp_sz);
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}
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}
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}
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return;
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}
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};
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} // namespace operators
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} // namespace paddle
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