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117 lines
3.9 KiB
117 lines
3.9 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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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#include "paddle/operators/math/maxouting.h"
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namespace paddle {
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namespace operators {
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namespace math {
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/*
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* All tensors are in NCHW format.
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* groups mustbe > 1
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*/
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template <typename MaxOutProcess, typename T>
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class MaxOutFunctor<platform::CPUPlace, MaxOutProcess, T> {
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public:
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void operator()(const platform::DeviceContext& context,
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const framework::Tensor& input,
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framework::Tensor * output,
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int groups,
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MaxOutProcess maxout_process) {
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const int batch_size = input.dims()[0];
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const int input_height = input.dims()[2];
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const int input_width = input.dims()[3];
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const int output_channels = output->dims()[1];
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int fea_size = input_height * input_width;
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// c_size mean output one batch size
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int c_size = fea_size * output_channels;
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const T* input_data = input.data<T>();
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T* output_data = output->mutable_data<T>(context.GetPlace());
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for (int i = 0; i < batch_size; ++i) {
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int new_bindex = c_size * i;
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for (int c = 0; c < output_channels; ++c) {
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int new_cindex = fea_size * c;
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for (int f = 0; f < fea_size; ++f) {
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T ele = maxout_process.initial();
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for (int ph = 0; ph < groups; ++ph) {
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maxout_process.compute(ele,
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input_data[(new_bindex+new_cindex) * groups+ph*fea_size+f]);
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}
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output_data[(new_bindex+new_cindex+f)] = ele;
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}
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}
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}
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}
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};
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template <class T>
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class MaxOutGradFunctor<platform::CPUPlace, T> {
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public:
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void operator()(const platform::DeviceContext& context,
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const framework::Tensor& input,
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framework::Tensor& input_grad,
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const framework::Tensor& output,
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const framework::Tensor& output_grad,
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int groups) {
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const int batch_size = input.dims()[0];
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const int input_height = input.dims()[2];
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const int input_width = input.dims()[3];
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const int output_channels = output.dims()[1];
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int fea_size = input_height * input_width;
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const T* input_data = input.data<T>();
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const T* output_data = output.data<T>();
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const T* output_grad_data = output_grad.data<T>();
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T* input_grad_data = input_grad.mutable_data<T>(context.GetPlace());
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for (int i = 0; i < batch_size; ++i) {
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int blen = fea_size * output_channels * i;
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for (int c = 0; c < output_channels; ++c) {
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int clen = fea_size * c;
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for (int f = 0; f < fea_size; ++f) {
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int input_idx = 0;
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bool stop = false;
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int output_idx = blen + clen + f;
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for (int g = 0; g < groups && !stop; ++g) {
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input_idx = (blen + clen) * groups + fea_size * g + f;
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input_grad_data[input_idx] = 0;
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if (input_data[input_idx] == output_data[output_idx]) {
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input_grad_data[input_idx] += output_grad_data[output_idx];
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stop = true;
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}
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}
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}
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}
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}
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}
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};
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template class MaxOutGradFunctor<platform::CPUPlace, float>;
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template class MaxOutGradFunctor<platform::CPUPlace, double>;
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template class MaxOutFunctor<platform::CPUPlace,
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math::MaxOut<float>, float>;
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template class MaxOutFunctor<platform::CPUPlace,
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math::MaxOut<double>, double>;
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} // namespace math
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} // namespace operators
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} // namespace paddle
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