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292 lines
12 KiB
292 lines
12 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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#include "paddle/fluid/operators/pool_with_index_op.h"
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namespace paddle {
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namespace operators {
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inline int MaxPoolOutputSize(int input_size, int filter_size, int padding,
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int stride) {
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int output_size = (input_size - filter_size + 2 * padding) / stride + 1;
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return output_size;
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}
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class MaxPoolWithIndexOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext *ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput("X"),
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"Input(X) of Pooling should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Out"),
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"Output(Out) of Pooling should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Mask"),
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"Output(Mask) of Pooling should not be null.");
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auto in_x_dims = ctx->GetInputDim("X");
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std::vector<int> ksize = ctx->Attrs().Get<std::vector<int>>("ksize");
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std::vector<int> strides = ctx->Attrs().Get<std::vector<int>>("strides");
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std::vector<int> paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
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PADDLE_ENFORCE(in_x_dims.size() == 4 || in_x_dims.size() == 5,
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"Pooling intput should be 4-D or 5-D tensor.");
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if (ctx->Attrs().Get<bool>("global_pooling")) {
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ksize.resize(static_cast<size_t>(in_x_dims.size()) - 2);
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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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PADDLE_ENFORCE(in_x_dims.size() - ksize.size() == 2U,
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"Input size and pooling size should be consistent.");
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PADDLE_ENFORCE_EQ(ksize.size(), strides.size(),
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"Strides size and pooling size should be the same.");
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PADDLE_ENFORCE_EQ(ksize.size(), paddings.size(),
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"Paddings size and pooling size should be the same.");
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std::vector<int64_t> output_shape({in_x_dims[0], in_x_dims[1]});
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for (size_t i = 0; i < ksize.size(); ++i) {
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output_shape.push_back(MaxPoolOutputSize(in_x_dims[i + 2], ksize[i],
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paddings[i], strides[i]));
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}
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ctx->SetOutputDim("Out", framework::make_ddim(output_shape));
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ctx->SetOutputDim("Mask", framework::make_ddim(output_shape));
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}
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protected:
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext &ctx) const override {
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return framework::OpKernelType(
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framework::ToDataType(ctx.Input<framework::Tensor>("X")->type()),
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ctx.device_context());
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}
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};
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class MaxPoolWithIndexOpGrad : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext *ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput("Mask"), "Input(Mask) must not be null.");
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PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must not be null.");
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PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
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"Input(X@GRAD) should not be null.");
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ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
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}
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protected:
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext &ctx) const override {
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return framework::OpKernelType(
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framework::ToDataType(ctx.Input<framework::Tensor>("X")->type()),
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ctx.device_context());
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}
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};
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class MaxPool2dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput(
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"X",
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"(Tensor) The input tensor of pooling operator. "
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"The format of input tensor is NCHW, where N is batch size, C is the "
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"number of channels, H is the height of the image, "
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"and W is the width of the image.");
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AddOutput("Out",
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"(Tensor) The output tensor of pooling operator. "
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"The format of output tensor is also NCHW, "
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"where N is batch size, C is "
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"the number of channels, H is the height of the image "
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"and W is the width of the image.");
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AddOutput("Mask",
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"(Tensor) The Mask tensor of pooling operator."
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"The format of output tensor is also NCHW, "
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"where N is batch size, C is the number of channels, "
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"H is the height of the image, "
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"and W is the width of the image. "
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"It represents the index in the current feature map.");
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AddAttr<std::vector<int>>("ksize",
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"(vector<int>) The pooling window size(height, "
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"width) of pooling operator. "
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"If global_pooling = true, ksize and paddings "
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"will be ignored."); // TODO(Chengduo): Add
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// checker. (Currently,
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// TypedAttrChecker don't support vector type.)
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AddAttr<bool>(
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"global_pooling",
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"(bool, default:false) Whether to use the global pooling. "
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"If global_pooling = true, ksize and paddings will be ignored.")
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.SetDefault(false);
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AddAttr<std::vector<int>>("strides",
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"(vector<int>, default {1, 1}), strides(height, "
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"width) of pooling operator.")
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.SetDefault({1, 1}); // TODO(Chengduo): Add checker. (Currently,
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// TypedAttrChecker don't support vector type.)
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AddAttr<std::vector<int>>(
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"paddings",
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"(vector<int>, default:{0, 0}), paddings(height, width) of pooling "
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"operator. "
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"If global_pooling = true, paddings and will be ignored.")
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.SetDefault({0, 0}); // TODO(Chengduo): Add checker. (Currently,
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// TypedAttrChecker don't support vector type.)
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AddComment(R"DOC(
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MaxPool2d Operator.
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The maxPooling2d with index operation calculates the output and the mask
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based on the input, ksize, strides, and paddings parameters. Input(X) and
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output(Out, Mask) are in NCHW format, where N is batch size, C is the
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number of channels, H is the height of the feature,
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and W is the width of the feature.
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Parameters(ksize, strides, paddings) are two elements.
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These two elements represent height and width, respectively.
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The input(X) size and output(Out, Mask) size may be different.
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Example:
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Input:
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X shape: $(N, C, H_{in}, W_{in})$
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Output:
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Out shape: $(N, C, H_{out}, W_{out})$
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Mask shape: $(N, C, H_{out}, W_{out})$
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Where
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$$
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H_{out} = \frac{(H_{in} - ksize[0] + 2 * paddings[0])}{strides[0]} + 1 \\
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W_{out} = \frac{(W_{in} - ksize[1] + 2 * paddings[1])}{strides[1]} + 1
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$$
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)DOC");
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}
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};
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class MaxPool3dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("X",
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"(Tensor) The input tensor of pooling operator. "
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"The format of input tensor is NCDHW, where N is batch size, C is "
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"the number of channels, and D, H and W are the depth, height and "
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"width of "
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"the image, respectively");
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AddOutput("Out",
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"(Tensor) The output tensor of pooling operator. "
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"The format of output tensor is also NCDHW, "
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"where N is the batch size, C is the number of channels, "
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"and D, H and W are the depth, height and "
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"width of the image, respectively.");
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AddOutput("Mask",
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"(Tensor) The Mask tensor of pooling operator. "
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"The format of output tensor is also NCDHW, "
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"where N is the batch size, C is the number of channels, and "
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"D, H and W are the depth, height and width "
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"of the image, respectively. "
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"It represents the index in the current feature map.");
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AddAttr<std::vector<int>>("ksize",
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"(vector<int>) The pooling window size(depth, "
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"height, width) of pooling operator. "
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"If global_pooling = true, ksize and paddings "
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"will be ignored."); // TODO(Chengduo): Add
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// checker. (Currently,
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// TypedAttrChecker don't support vector type.)
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AddAttr<bool>(
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"global_pooling",
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"(bool, default false) Whether to use the global pooling. "
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"If global_pooling = true, ksize and paddings will be ignored.")
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.SetDefault(false);
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AddAttr<std::vector<int>>("strides",
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"(vector<int>, default {1,1,1}), strides(depth, "
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"height, width) of pooling operator.")
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.SetDefault({1, 1, 1}); // TODO(Chengduo): Add checker. (Currently,
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// TypedAttrChecker don't support vector type.)
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AddAttr<std::vector<int>>(
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"paddings",
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"(vector, default {0,0,0}), paddings(depth, "
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"height, width) of pooling operator. "
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"If global_pooling = true, paddings and ksize will be ignored.")
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.SetDefault({0, 0, 0}); // TODO(Chengduo): Add checker. (Currently,
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// TypedAttrChecker don't support vector type.)
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AddComment(R"DOC(
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MaxPool3d Operator.
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The maxpooling3d with index operation calculates the output and the mask
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based on the input and ksize, strides, paddings parameters.
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Input(X) and output(Out, Mask) are in NCDHW format, where N is batch
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size, C is the number of channels, and D, H and W are the depth, height and
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width of the feature, respectively.
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Parameters(ksize, strides, paddings) are three elements.
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These three elements represent depth, height and width, respectively.
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The input(X) size and output(Out, Mask) size may be different.
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Example:
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Input:
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X shape: $(N, C, D_{in}, H_{in}, W_{in})$
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Output:
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Out shape: $(N, C, D_{out}, H_{out}, W_{out})$
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Mask shape: $(N, C, D_{out}, H_{out}, W_{out})$
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Where
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$$
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D_{out} = \frac{(D_{in} - ksize[0] + 2 * paddings[0])}{strides[0]} + 1 \\
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H_{out} = \frac{(H_{in} - ksize[1] + 2 * paddings[1])}{strides[1]} + 1 \\
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W_{out} = \frac{(W_{in} - ksize[2] + 2 * paddings[2])}{strides[2]} + 1
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$$
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)DOC");
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OPERATOR(max_pool2d_with_index, ops::MaxPoolWithIndexOp,
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ops::MaxPool2dWithIndexOpMaker,
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paddle::framework::DefaultGradOpDescMaker<true>);
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REGISTER_OPERATOR(max_pool2d_with_index_grad, ops::MaxPoolWithIndexOpGrad);
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REGISTER_OP_CPU_KERNEL(
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max_pool2d_with_index,
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ops::MaxPoolWithIndexKernel<paddle::platform::CPUDeviceContext, float, int>,
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ops::MaxPoolWithIndexKernel<paddle::platform::CPUDeviceContext, double,
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int>);
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REGISTER_OP_CPU_KERNEL(
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max_pool2d_with_index_grad,
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ops::MaxPoolWithIndexGradKernel<paddle::platform::CPUDeviceContext, float,
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int>,
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ops::MaxPoolWithIndexGradKernel<paddle::platform::CPUDeviceContext, double,
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int>);
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REGISTER_OPERATOR(max_pool3d_with_index, ops::MaxPoolWithIndexOp,
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ops::MaxPool3dWithIndexOpMaker,
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paddle::framework::DefaultGradOpDescMaker<true>);
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REGISTER_OPERATOR(max_pool3d_with_index_grad, ops::MaxPoolWithIndexOpGrad);
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REGISTER_OP_CPU_KERNEL(
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max_pool3d_with_index,
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ops::MaxPoolWithIndexKernel<paddle::platform::CPUDeviceContext, float, int>,
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ops::MaxPoolWithIndexKernel<paddle::platform::CPUDeviceContext, double,
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int>);
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REGISTER_OP_CPU_KERNEL(
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max_pool3d_with_index_grad,
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ops::MaxPoolWithIndexGradKernel<paddle::platform::CPUDeviceContext, float,
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int>,
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ops::MaxPoolWithIndexGradKernel<paddle::platform::CPUDeviceContext, double,
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int>);
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