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127 lines
4.7 KiB
127 lines
4.7 KiB
7 years ago
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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/roi_pool_op.h"
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namespace paddle {
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namespace operators {
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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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PADDLE_ENFORCE(ctx->HasInput("X"),
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"Input(X) of RoiPoolOp should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("Rois"),
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"Input(Rois) of RoiPoolOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Out"),
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"Output(Out) of RoiPoolOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Argmax"),
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"Output(Argmax) of RoiPoolOp should not be null.");
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auto input_dims = ctx->GetInputDim("X");
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// Initialize the output's dims to maximum,
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// and re-set to real dims by the value of Rois at kernel
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ctx->SetOutputDim("Out", input_dims);
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}
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protected:
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framework::OpKernelType GetKernelType(
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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 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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PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
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"The gradient of Out should not be null.");
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PADDLE_ENFORCE(ctx->HasOutputs(framework::GradVarName("X")),
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"The gradient of X should not be null.");
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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 GetKernelType(
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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 RoiPoolOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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RoiPoolOpMaker(framework::OpProto* proto,
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framework::OpAttrChecker* op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput("X",
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"(Tensor), "
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"the input of RoiPoolOp.");
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AddInput("Rois",
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"(Tensor), "
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"RoIs (Regions of Interest) to pool over. "
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"Should be a 2-D tensor of shape (num_rois, 5)"
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"given as [[batch_id, x1, y1, x2, y2], …].");
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AddOutput("Out",
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"(Tensor), "
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"RoI pooled output 4-D tensor of 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.").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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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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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OP(roi_pool, ops::RoiPoolOp, ops::RoiPoolOpMaker,
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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::CPUPlace, float>);
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REGISTER_OP_CPU_KERNEL(
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roi_pool_grad,
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ops::CPURoiPoolGradOpKernel<paddle::platform::CPUPlace, float>);
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