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83 lines
2.9 KiB
83 lines
2.9 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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#pragma once
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#include "paddle/framework/eigen.h"
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#include "paddle/framework/op_registry.h"
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#include "paddle/operators/math/softmax_function.h"
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#include "paddle/operators/math/utils.h"
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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template <typename T, int MajorType = Eigen::RowMajor,
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typename IndexType = Eigen::DenseIndex>
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using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
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template <typename T>
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class SoftmaxWithCrossEntropyKernel : public framework::OpKernel {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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PADDLE_ENFORCE(platform::is_cpu_place(context.GetPlace()),
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"This kernel only runs on CPU.");
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// Calculate ths softmax outputs.
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const Tensor* logits = context.Input<Tensor>("Logits");
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Tensor* softmax = context.Output<Tensor>("Softmax");
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softmax->mutable_data<T>(context.GetPlace());
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math::SoftmaxFunctor<platform::CPUPlace, T>()(logits, softmax, context);
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// Calculate the cross entropy loss based on hard labels.
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T* softmax_out = softmax->data<T>();
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const int* label_data = context.Input<Tensor>("Label")->data<int>();
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Tensor* loss = context.Output<Tensor>("Out");
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loss->mutable_data<T>(context.GetPlace());
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T* loss_data = loss->data<T>();
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const int batch_size = logits->dims()[0];
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const int class_num = logits->dims()[1];
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for (int i = 0; i < batch_size; ++i) {
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int index = i * class_num + label_data[i];
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loss_data[i] = -math::tolerable_value(std::log(softmax_out[index]));
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}
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}
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};
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template <typename T>
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class SoftmaxWithCrossEntropyGradKernel : public framework::OpKernel {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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Tensor* logit_grad =
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context.Output<Tensor>(framework::GradVarName("Logits"));
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logit_grad->ShareDataWith<T>(*context.Input<Tensor>("Softmax"));
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T* logit_grad_data = logit_grad->data<T>();
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const int batch_size = logit_grad->dims()[0];
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const int class_num = logit_grad->dims()[1];
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const int* label_data = context.Input<Tensor>("Label")->data<int>();
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for (int i = 0; i < batch_size; ++i) {
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int index = i * class_num + label_data[i];
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logit_grad_data[index] -= 1.;
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}
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}
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};
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} // namespace operators
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} // namespace paddle
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