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/* 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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Indicesou 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/detection_output_op.h"
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namespace paddle {
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namespace operators {
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class DetectionOutputOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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DetectionOutputOpMaker(OpProto* proto, OpAttrChecker* op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput("Loc",
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"(Tensor) The input tensor of detection_output operator."
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"The input predict locations"
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"The format of input tensor is kNCHW. Where K is priorbox point "
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"numbers,"
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"N is How many boxes are there on each point, "
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"C is 4, H and W both are 1.");
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AddInput("Conf",
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"(Tensor) The input tensor of detection_output operator."
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"The input priorbox confidence."
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"The format of input tensor is kNCHW. Where K is priorbox point "
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"numbers,"
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"N is How many boxes are there on each point, "
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"C is the number of classes, H and W both are 1.");
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AddInput("PriorBox",
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"(Tensor) The input tensor of detection_output operator."
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"The format of input tensor is the position and variance "
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"of the boxes");
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AddOutput("Out",
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"(Tensor) The output tensor of detection_output operator.");
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AddAttr<int>("background_label_id", "(int), The background class index.");
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AddAttr<int>("num_classes", "(int), The number of the classification.");
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AddAttr<float>("nms_threshold",
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"(float), The Non-maximum suppression threshold.");
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AddAttr<float>("confidence_threshold",
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"(float), The classification confidence threshold.");
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AddAttr<int>("top_k", "(int), The bbox number kept of the layer’s output.");
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AddAttr<int>("nms_top_k",
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"(int), The bbox number kept of the NMS’s output.");
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AddComment(R"DOC(
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detection output for SSD(single shot multibox detector)
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Apply the NMS to the output of network and compute the predict
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bounding box location. The output’s shape of this layer could
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be zero if there is no valid bounding box.
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)DOC");
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}
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};
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class DetectionOutputOp : 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("Loc"),
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"Input(X) of DetectionOutputOp"
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"should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("Conf"),
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"Input(X) of DetectionOutputOp"
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"should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("PriorBox"),
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"Input(X) of DetectionOutputOp"
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"should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Out"),
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"Output(Out) of DetectionOutputOp should not be null.");
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std::vector<int64_t> output_shape({1, 7});
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ctx->SetOutputDim("Out", framework::make_ddim(output_shape));
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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_WITHOUT_GRADIENT(detection_output, ops::DetectionOutputOp,
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ops::DetectionOutputOpMaker);
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REGISTER_OP_CPU_KERNEL(
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detection_output,
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ops::DetectionOutputKernel<paddle::platform::CPUDeviceContext, float>,
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ops::DetectionOutputKernel<paddle::platform::CPUDeviceContext, double>);
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