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/* 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/pool_op.h"
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namespace paddle {
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namespace operators {
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int OutputSizePool(int input_size, int filter_size, int padding, 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 PoolOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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protected:
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void InferShape(const framework::InferShapeContext &ctx) const override {
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PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
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"X(Input) of Pooling should not be null.");
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PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
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"Out(Output) of Pooling should not be null.");
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auto in_x = ctx.Input<Tensor>("X");
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auto out = ctx.Output<Tensor>("Out");
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bool global_pooling = Attr<bool>("globalPooling");
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std::string pooling_type = Attr<std::string>("poolingType");
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std::vector<int> ksize = Attr<std::vector<int>>("ksize");
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std::vector<int> strides = Attr<std::vector<int>>("strides");
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std::vector<int> paddings = Attr<std::vector<int>>("paddings");
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PADDLE_ENFORCE(pooling_type == "max" || pooling_type == "avg",
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"pooling_type should be 'max' or 'avg'");
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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");
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if (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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ksize[i] = static_cast<int>(in_x->dims()[i + 2]);
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}
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PADDLE_ENFORCE(in_x->dims().size() == static_cast<size_t>(ksize.size() + 2),
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"Input size and Pooling size should be consistent.");
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PADDLE_ENFORCE(ksize.size() == 2 || ksize.size() == 3,
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"Pooling size should be 2 elements. or 3 elements.");
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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(OutputSizePool(in_x->dims()[i + 2], ksize[i],
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paddings[i], strides[i]));
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}
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out->Resize(framework::make_ddim(output_shape));
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}
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};
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class PoolOpGrad : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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protected:
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void InferShape(const framework::InferShapeContext &ctx) const override {
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PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
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"X(Input) of Pooling should not be null.");
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PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Out"),
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"Out(Output) of Pooling should not be null.");
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PADDLE_ENFORCE_NOT_NULL(ctx.Output<Tensor>(framework::GradVarName("X")),
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"Input@Grad of Pooling should not be null.");
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auto in = ctx.Input<Tensor>("X");
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auto d_in = ctx.Output<Tensor>(framework::GradVarName("X"));
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d_in->Resize(in->dims());
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}
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};
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class Pool2dOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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Pool2dOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput(
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"X",
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"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 and W is the height and width of feature.");
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AddOutput("Out",
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"The output tensor of pooling operator."
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"The format of output tensor is also NCHW.");
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AddAttr<std::string>("poolingType",
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"poolingType of pooling operator."
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"str constant equal to 'max' or 'avg'");
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AddAttr<std::vector<int>>(
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"ksize",
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"Pooling size(depth, height, width) of pooling operator."
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"If globalPooling = true, ksize is ignored and need not be specified.");
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AddAttr<bool>(
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"globalPooling",
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"whether to use the globalPooling."
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"int constant equal to false or true"
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"default false"
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"If globalPooling = true, ksize is ignored and need not be specified.")
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.SetDefault(false);
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AddAttr<std::vector<int>>("strides",
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"strides(height, width) of pooling operator."
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"default {1,1}")
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.SetDefault({1, 1})
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.AddCustomChecker(GreaterThanChecker_pool({0, 0}));
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AddAttr<std::vector<int>>("paddings",
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"paddings(height, width) of pooling operator."
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"default {0,0}")
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.SetDefault({0, 0})
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.AddCustomChecker(EqualGreaterThanChecker_pool({0, 0}));
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AddComment(R"DOC(
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The pooling2d operation calculates the output based on
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the input, poolingType and ksize, strides, paddings parameters.
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)DOC");
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}
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private:
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struct GreaterThanChecker_pool {
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public:
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explicit GreaterThanChecker_pool(std::vector<int> lower_bound)
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: lower_bound_(lower_bound) {}
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void operator()(std::vector<int> &value) const {
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PADDLE_ENFORCE(value.size() == lower_bound_.size(), "equal check fails.");
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for (size_t i = 0; i < value.size(); ++i) {
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PADDLE_ENFORCE(value[i] > lower_bound_[i], "larger_than check fails.");
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}
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}
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private:
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std::vector<int> lower_bound_;
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};
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struct EqualGreaterThanChecker_pool {
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public:
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explicit EqualGreaterThanChecker_pool(std::vector<int> lower_bound)
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: lower_bound_(lower_bound) {}
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void operator()(std::vector<int> &value) const {
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PADDLE_ENFORCE(value.size() == lower_bound_.size(), "equal check fails.");
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for (size_t i = 0; i < value.size(); ++i) {
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PADDLE_ENFORCE(value[i] >= lower_bound_[i], "larger_than check fails.");
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}
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}
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private:
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std::vector<int> lower_bound_;
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};
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};
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class Pool3dOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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Pool3dOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput("X",
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"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 "
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"number of channels, D, H and W is the depth, height and width of "
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"feature.");
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AddOutput("Out",
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"The output tensor of pooling operator."
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"The format of output tensor is also NCDHW.");
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AddAttr<std::string>("poolingType",
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"poolingType of pooling operator."
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"str constant equal to 'max' or 'avg'");
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AddAttr<std::vector<int>>(
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"ksize",
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"pooling size(depth, height, width) of pooling operator."
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"If globalPooling = true, ksize is ignored and need not be specified.");
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AddAttr<bool>(
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"globalPooling",
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"whether to use the globalPooling."
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"int constant equal to false or true"
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"default false"
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"If globalPooling = true, ksize is ignored and need not be specified.")
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.SetDefault(false);
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AddAttr<std::vector<int>>(
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"strides",
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"strides(depth, height, width) of pooling operator."
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"default {1,1,1}")
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.SetDefault({1, 1, 1})
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.AddCustomChecker(GreaterThanChecker_pool({0, 0, 0}));
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AddAttr<std::vector<int>>(
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"paddings",
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"paddings(depth, height, width) of pooling operator."
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"default {0,0,0}")
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.SetDefault({0, 0, 0})
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.AddCustomChecker(EqualGreaterThanChecker_pool({0, 0, 0}));
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AddComment(R"DOC(
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The pooling3d operation calculates the output based on
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the input, poolingType and ksize, strides, paddings parameters.
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)DOC");
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}
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private:
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struct GreaterThanChecker_pool {
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public:
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explicit GreaterThanChecker_pool(std::vector<int> lower_bound)
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: lower_bound_(lower_bound) {}
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void operator()(std::vector<int> &value) const {
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PADDLE_ENFORCE(value.size() == lower_bound_.size(), "equal check fails.");
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for (size_t i = 0; i < value.size(); ++i) {
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PADDLE_ENFORCE(value[i] > lower_bound_[i], "larger_than check fails.");
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}
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}
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private:
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std::vector<int> lower_bound_;
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};
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struct EqualGreaterThanChecker_pool {
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public:
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explicit EqualGreaterThanChecker_pool(std::vector<int> lower_bound)
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: lower_bound_(lower_bound) {}
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void operator()(std::vector<int> &value) const {
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PADDLE_ENFORCE(value.size() == lower_bound_.size(), "equal check fails.");
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for (size_t i = 0; i < value.size(); ++i) {
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PADDLE_ENFORCE(value[i] >= lower_bound_[i], "larger_than check fails.");
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}
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}
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private:
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std::vector<int> lower_bound_;
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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_OP(pool2d, ops::PoolOp, ops::Pool2dOpMaker, pool2d_grad,
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ops::PoolOpGrad);
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REGISTER_OP_CPU_KERNEL(pool2d,
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ops::PoolKernel<paddle::platform::CPUPlace, float>);
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REGISTER_OP_CPU_KERNEL(pool2d_grad,
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ops::PoolGradKernel<paddle::platform::CPUPlace, float>)
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REGISTER_OP(pool3d, ops::PoolOp, ops::Pool3dOpMaker, pool3d_grad,
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ops::PoolOpGrad);
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REGISTER_OP_CPU_KERNEL(pool3d,
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ops::PoolKernel<paddle::platform::CPUPlace, float>);
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REGISTER_OP_CPU_KERNEL(pool3d_grad,
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ops::PoolGradKernel<paddle::platform::CPUPlace, float>);
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