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122 lines
4.5 KiB
122 lines
4.5 KiB
/* 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 <vector>
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#include "paddle/fluid/framework/eigen.h"
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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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#include "paddle/fluid/operators/math/pooling.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 DeviceContext, typename T1, typename T2>
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class MaxPoolWithIndexKernel : public framework::OpKernel<T1> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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const Tensor* in_x = context.Input<Tensor>("X");
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Tensor* out = context.Output<Tensor>("Out");
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Tensor* mask = context.Output<Tensor>("Mask");
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std::vector<int> ksize = context.Attr<std::vector<int>>("ksize");
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std::vector<int> strides = context.Attr<std::vector<int>>("strides");
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std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
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bool adaptive = context.Attr<bool>("adaptive");
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auto& dev_ctx = context.template device_context<DeviceContext>();
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if (context.Attr<bool>("global_pooling")) {
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for (size_t i = 0; i < ksize.size(); ++i) {
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paddings[i] = 0;
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ksize[i] = static_cast<int>(in_x->dims()[i + 2]);
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}
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}
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switch (ksize.size()) {
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case 2: {
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paddle::operators::math::MaxPool2dWithIndexFunctor<DeviceContext, T1,
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T2>
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pool2d_forward;
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pool2d_forward(dev_ctx, *in_x, ksize, strides, paddings, adaptive, out,
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mask);
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} break;
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case 3: {
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paddle::operators::math::MaxPool3dWithIndexFunctor<DeviceContext, T1,
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T2>
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pool3d_forward;
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pool3d_forward(dev_ctx, *in_x, ksize, strides, paddings, adaptive, out,
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mask);
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} break;
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default: {
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PADDLE_THROW(platform::errors::InvalidArgument(
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"Pool op only supports 2D and 3D input."));
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}
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}
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}
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};
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template <typename DeviceContext, typename T1, typename T2>
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class MaxPoolWithIndexGradKernel : public framework::OpKernel<T1> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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const Tensor* mask = context.Input<Tensor>("Mask");
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const Tensor* out_grad =
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context.Input<Tensor>(framework::GradVarName("Out"));
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Tensor* in_x_grad = context.Output<Tensor>(framework::GradVarName("X"));
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std::vector<int> ksize = context.Attr<std::vector<int>>("ksize");
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std::vector<int> strides = context.Attr<std::vector<int>>("strides");
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std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
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bool adaptive = context.Attr<bool>("adaptive");
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if (context.Attr<bool>("global_pooling")) {
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for (size_t i = 0; i < ksize.size(); ++i) {
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paddings[i] = 0;
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ksize[i] = static_cast<int>(in_x_grad->dims()[i + 2]);
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}
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}
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if (in_x_grad) {
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in_x_grad->mutable_data<T1>(context.GetPlace());
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auto& device_ctx = context.template device_context<DeviceContext>();
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math::set_constant(device_ctx, in_x_grad, 0);
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switch (ksize.size()) {
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case 2: {
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paddle::operators::math::MaxPool2dWithIndexGradFunctor<DeviceContext,
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T1, T2>
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pool2d_backward;
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pool2d_backward(device_ctx, *out_grad, *mask, ksize, strides,
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paddings, adaptive, in_x_grad);
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} break;
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case 3: {
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paddle::operators::math::MaxPool3dWithIndexGradFunctor<DeviceContext,
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T1, T2>
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pool3d_backward;
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pool3d_backward(device_ctx, *out_grad, *mask, ksize, strides,
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paddings, adaptive, in_x_grad);
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} break;
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default: {
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PADDLE_THROW(platform::errors::InvalidArgument(
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"Pool op only supports 2D and 3D input."));
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}
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}
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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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