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223 lines
8.6 KiB
223 lines
8.6 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/roi_pool_op.h"
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#include <memory>
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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using LoDTensor = framework::LoDTensor;
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class ROIPoolOp : 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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OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "roi_pool");
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OP_INOUT_CHECK(ctx->HasInput("ROIs"), "Input", "ROIs", "roi_pool");
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OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "roi_pool");
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OP_INOUT_CHECK(ctx->HasOutput("Argmax"), "Output", "Argmax", "roi_pool");
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auto input_dims = ctx->GetInputDim("X");
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auto rois_dims = ctx->GetInputDim("ROIs");
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if (ctx->HasInput("RoisLod")) {
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auto rois_lod_dims = ctx->GetInputDim("RoisLod");
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PADDLE_ENFORCE(rois_lod_dims.size() == 1, "");
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}
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PADDLE_ENFORCE_EQ(input_dims.size(), 4,
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platform::errors::InvalidArgument(
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"The input data should be a four-dimensional "
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"tensor with [N,C,H,W], but received input data with "
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" %d dimension",
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input_dims.size()));
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PADDLE_ENFORCE_EQ(
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rois_dims.size(), 2,
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platform::errors::InvalidArgument(
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"ROIs should be a 2-D LoDTensor with shape (num_rois, 4)"
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"given as [[x1, y1, x2, y2], ...], but received ROIs is "
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"%d-dimensional LoDTensor",
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rois_dims.size()));
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PADDLE_ENFORCE_EQ(
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rois_dims[1], kROISize,
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platform::errors::InvalidArgument(
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"ROIs should be a 2-D LoDTensor with shape (num_rois, 4)"
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"given as [[x1, y1, x2, y2], ...]. But the second dimension of "
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"the received data is %d",
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rois_dims[1]));
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int pooled_height = ctx->Attrs().Get<int>("pooled_height");
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int pooled_width = ctx->Attrs().Get<int>("pooled_width");
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float spatial_scale = ctx->Attrs().Get<float>("spatial_scale");
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PADDLE_ENFORCE_GT(pooled_height, 0,
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platform::errors::OutOfRange(
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"The pooled output height must be greater than 0"
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"but received height is %d",
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pooled_height));
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PADDLE_ENFORCE_GT(pooled_width, 0,
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platform::errors::OutOfRange(
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"The pooled output width must be greater than 0"
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"but received width is %d",
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pooled_width));
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PADDLE_ENFORCE_GT(spatial_scale, 0.0f,
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platform::errors::OutOfRange(
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"The spatial scale must be greater than 0, "
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"but received spatial scale is %f",
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spatial_scale));
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auto out_dims = input_dims;
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out_dims[0] = rois_dims[0];
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out_dims[1] = input_dims[1];
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out_dims[2] = pooled_height;
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out_dims[3] = pooled_width;
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ctx->SetOutputDim("Out", out_dims);
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ctx->SetOutputDim("Argmax", out_dims);
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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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OperatorWithKernel::IndicateVarDataType(ctx, "X"),
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ctx.device_context());
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}
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};
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class ROIPoolGradOp : 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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OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Out")), "Input",
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framework::GradVarName("Out"), "roi_pool");
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OP_INOUT_CHECK(ctx->HasOutput(framework::GradVarName("X")), "Output",
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framework::GradVarName("X"), "roi_pool");
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ctx->SetOutputsDim(framework::GradVarName("X"), ctx->GetInputsDim("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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OperatorWithKernel::IndicateVarDataType(ctx, "X"),
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ctx.device_context());
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}
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};
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class ROIPoolOpMaker : 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), "
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"the input of ROIPoolOp. "
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"The format of input tensor is NCHW. Where N is batch size, "
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"C is the number of input channels, "
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"H is the height of the feature, and "
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"W is the width of the feature.");
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AddInput("ROIs",
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"(LoDTensor), "
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"ROIs (Regions of Interest) to pool over. "
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"should be a 2-D LoDTensor of shape (num_rois, 4)"
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"given as [[x1, y1, x2, y2], ...]. "
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"Where batch_id is the id of the data, "
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"(x1, y1) is the top left coordinates, and "
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"(x2, y2) is the bottom right coordinates.");
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AddInput("RoisLod", "(Tensor), The lod info of rois.").AsDispensable();
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AddOutput("Out",
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"(Tensor), "
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"The output of ROIPoolOp is a 4-D tensor with shape "
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"(num_rois, channels, pooled_h, pooled_w).");
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AddOutput("Argmax",
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"(Tensor), "
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"Argmaxes corresponding to indices in X used "
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"for gradient computation. Only output "
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"if arg \"is_test\" is false.")
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.AsIntermediate();
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AddAttr<float>("spatial_scale",
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"(float, default 1.0), "
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"Multiplicative spatial scale factor "
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"to translate ROI coords from their input scale "
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"to the scale used when pooling.")
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.SetDefault(1.0);
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AddAttr<int>("pooled_height",
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"(int, default 1), "
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"The pooled output height.")
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.SetDefault(1);
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AddAttr<int>("pooled_width",
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"(int, default 1), "
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"The pooled output width.")
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.SetDefault(1);
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AddComment(R"DOC(
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**ROIPool Operator**
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Region of interest pooling (also known as RoI pooling) is to perform
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is to perform max pooling on inputs of nonuniform sizes to obtain
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fixed-size feature maps (e.g. 7*7).
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The operator has three steps:
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1. Dividing each region proposal into equal-sized sections with
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the pooled_width and pooled_height
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2. Finding the largest value in each section
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3. Copying these max values to the output buffer
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ROI Pooling for Faster-RCNN. The link below is a further introduction:
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https://stackoverflow.com/questions/43430056/what-is-roi-layer-in-fast-rcnn
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)DOC");
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}
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};
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template <typename T>
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class ROIPoolGradMaker : public framework::SingleGradOpMaker<T> {
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public:
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using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
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protected:
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void Apply(GradOpPtr<T> op) const override {
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op->SetType("roi_pool_grad");
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op->SetInput("X", this->Input("X"));
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op->SetInput("ROIs", this->Input("ROIs"));
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op->SetInput("RoisLod", this->Input("RoisLod"));
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op->SetInput("Argmax", this->Output("Argmax"));
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op->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
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op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
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op->SetAttrMap(this->Attrs());
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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(roi_pool, ops::ROIPoolOp, ops::ROIPoolOpMaker,
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ops::ROIPoolGradMaker<paddle::framework::OpDesc>,
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ops::ROIPoolGradMaker<paddle::imperative::OpBase>);
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REGISTER_OPERATOR(roi_pool_grad, ops::ROIPoolGradOp);
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REGISTER_OP_CPU_KERNEL(
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roi_pool,
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ops::CPUROIPoolOpKernel<paddle::platform::CPUDeviceContext, float>,
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ops::CPUROIPoolOpKernel<paddle::platform::CPUDeviceContext, double>,
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ops::CPUROIPoolOpKernel<paddle::platform::CPUDeviceContext, int>);
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REGISTER_OP_CPU_KERNEL(
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roi_pool_grad,
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ops::CPUROIPoolGradOpKernel<paddle::platform::CPUDeviceContext, float>,
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ops::CPUROIPoolGradOpKernel<paddle::platform::CPUDeviceContext, double>,
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ops::CPUROIPoolGradOpKernel<paddle::platform::CPUDeviceContext, int>);
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