Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into optimize_hs_op
, test=developrevert-14398-imperative
JiabinYang
6 years ago
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/* Copyright (c) 2018 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/fluid/operators/yolov3_loss_op.h"
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#include "paddle/fluid/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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using framework::Tensor;
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class Yolov3LossOp : 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 Yolov3LossOp should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("GTBox"),
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"Input(GTBox) of Yolov3LossOp should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("GTLabel"),
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"Input(GTLabel) of Yolov3LossOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Loss"),
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"Output(Loss) of Yolov3LossOp should not be null.");
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auto dim_x = ctx->GetInputDim("X");
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auto dim_gtbox = ctx->GetInputDim("GTBox");
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auto dim_gtlabel = ctx->GetInputDim("GTLabel");
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auto anchors = ctx->Attrs().Get<std::vector<int>>("anchors");
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auto class_num = ctx->Attrs().Get<int>("class_num");
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PADDLE_ENFORCE_EQ(dim_x.size(), 4, "Input(X) should be a 4-D tensor.");
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PADDLE_ENFORCE_EQ(dim_x[2], dim_x[3],
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"Input(X) dim[3] and dim[4] should be euqal.");
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PADDLE_ENFORCE_EQ(dim_x[1], anchors.size() / 2 * (5 + class_num),
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"Input(X) dim[1] should be equal to (anchor_number * (5 "
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"+ class_num)).");
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PADDLE_ENFORCE_EQ(dim_gtbox.size(), 3,
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"Input(GTBox) should be a 3-D tensor");
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PADDLE_ENFORCE_EQ(dim_gtbox[2], 4, "Input(GTBox) dim[2] should be 5");
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PADDLE_ENFORCE_EQ(dim_gtlabel.size(), 2,
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"Input(GTBox) should be a 2-D tensor");
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PADDLE_ENFORCE_EQ(dim_gtlabel[0], dim_gtbox[0],
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"Input(GTBox) and Input(GTLabel) dim[0] should be same");
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PADDLE_ENFORCE_EQ(dim_gtlabel[1], dim_gtbox[1],
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"Input(GTBox) and Input(GTLabel) dim[1] should be same");
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PADDLE_ENFORCE_GT(anchors.size(), 0,
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"Attr(anchors) length should be greater then 0.");
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PADDLE_ENFORCE_EQ(anchors.size() % 2, 0,
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"Attr(anchors) length should be even integer.");
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PADDLE_ENFORCE_GT(class_num, 0,
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"Attr(class_num) should be an integer greater then 0.");
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std::vector<int64_t> dim_out({1});
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ctx->SetOutputDim("Loss", framework::make_ddim(dim_out));
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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<Tensor>("X")->type()),
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platform::CPUPlace());
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}
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};
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class Yolov3LossOpMaker : 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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"The input tensor of YOLO v3 loss operator, "
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"This is a 4-D tensor with shape of [N, C, H, W]."
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"H and W should be same, and the second dimention(C) stores"
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"box locations, confidence score and classification one-hot"
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"key of each anchor box");
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AddInput("GTBox",
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"The input tensor of ground truth boxes, "
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"This is a 3-D tensor with shape of [N, max_box_num, 5], "
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"max_box_num is the max number of boxes in each image, "
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"In the third dimention, stores x, y, w, h coordinates, "
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"x, y is the center cordinate of boxes and w, h is the "
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"width and height and x, y, w, h should be divided by "
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"input image height to scale to [0, 1].");
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AddInput("GTLabel",
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"The input tensor of ground truth label, "
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"This is a 2-D tensor with shape of [N, max_box_num], "
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"and each element shoudl be an integer to indicate the "
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"box class id.");
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AddOutput("Loss",
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"The output yolov3 loss tensor, "
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"This is a 1-D tensor with shape of [1]");
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AddAttr<int>("class_num", "The number of classes to predict.");
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AddAttr<std::vector<int>>("anchors",
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"The anchor width and height, "
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"it will be parsed pair by pair.");
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AddAttr<float>("ignore_thresh",
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"The ignore threshold to ignore confidence loss.");
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AddAttr<float>("loss_weight_xy", "The weight of x, y location loss.")
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.SetDefault(1.0);
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AddAttr<float>("loss_weight_wh", "The weight of w, h location loss.")
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.SetDefault(1.0);
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AddAttr<float>(
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"loss_weight_conf_target",
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"The weight of confidence score loss in locations with target object.")
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.SetDefault(1.0);
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AddAttr<float>("loss_weight_conf_notarget",
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"The weight of confidence score loss in locations without "
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"target object.")
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.SetDefault(1.0);
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AddAttr<float>("loss_weight_class", "The weight of classification loss.")
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.SetDefault(1.0);
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AddComment(R"DOC(
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This operator generate yolov3 loss by given predict result and ground
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truth boxes.
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The output of previous network is in shape [N, C, H, W], while H and W
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should be the same, specify the grid size, each grid point predict given
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number boxes, this given number is specified by anchors, it should be
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half anchors length, which following will be represented as S. In the
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second dimention(the channel dimention), C should be S * (class_num + 5),
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class_num is the box categoriy number of source dataset(such as coco),
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so in the second dimention, stores 4 box location coordinates x, y, w, h
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and confidence score of the box and class one-hot key of each anchor box.
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While the 4 location coordinates if $$tx, ty, tw, th$$, the box predictions
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correspnd to:
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$$
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b_x = \sigma(t_x) + c_x
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b_y = \sigma(t_y) + c_y
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b_w = p_w e^{t_w}
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b_h = p_h e^{t_h}
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$$
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While $$c_x, c_y$$ is the left top corner of current grid and $$p_w, p_h$$
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is specified by anchors.
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As for confidence score, it is the logistic regression value of IoU between
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anchor boxes and ground truth boxes, the score of the anchor box which has
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the max IoU should be 1, and if the anchor box has IoU bigger then ignore
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thresh, the confidence score loss of this anchor box will be ignored.
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Therefore, the yolov3 loss consist of three major parts, box location loss,
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confidence score loss, and classification loss. The MSE loss is used for
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box location, and binary cross entropy loss is used for confidence score
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loss and classification loss.
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Final loss will be represented as follow.
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$$
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loss = \loss_weight_{xy} * loss_{xy} + \loss_weight_{wh} * loss_{wh}
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+ \loss_weight_{conf_target} * loss_{conf_target}
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+ \loss_weight_{conf_notarget} * loss_{conf_notarget}
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+ \loss_weight_{class} * loss_{class}
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$$
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)DOC");
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}
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};
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class Yolov3LossOpGrad : 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"), "Input(X) should not be null");
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PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Loss")),
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"Input(Loss@GRAD) should not be null");
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auto dim_x = ctx->GetInputDim("X");
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if (ctx->HasOutput(framework::GradVarName("X"))) {
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ctx->SetOutputDim(framework::GradVarName("X"), dim_x);
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}
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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<Tensor>("X")->type()),
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platform::CPUPlace());
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}
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};
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class Yolov3LossGradMaker : public framework::SingleGradOpDescMaker {
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public:
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using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
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protected:
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std::unique_ptr<framework::OpDesc> Apply() const override {
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auto* op = new framework::OpDesc();
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op->SetType("yolov3_loss_grad");
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op->SetInput("X", Input("X"));
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op->SetInput("GTBox", Input("GTBox"));
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op->SetInput("GTLabel", Input("GTLabel"));
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op->SetInput(framework::GradVarName("Loss"), OutputGrad("Loss"));
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op->SetAttrMap(Attrs());
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op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
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op->SetOutput(framework::GradVarName("GTBox"), {});
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op->SetOutput(framework::GradVarName("GTLabel"), {});
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return std::unique_ptr<framework::OpDesc>(op);
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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(yolov3_loss, ops::Yolov3LossOp, ops::Yolov3LossOpMaker,
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ops::Yolov3LossGradMaker);
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REGISTER_OPERATOR(yolov3_loss_grad, ops::Yolov3LossOpGrad);
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REGISTER_OP_CPU_KERNEL(yolov3_loss, ops::Yolov3LossKernel<float>,
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ops::Yolov3LossKernel<double>);
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REGISTER_OP_CPU_KERNEL(yolov3_loss_grad, ops::Yolov3LossGradKernel<float>,
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ops::Yolov3LossGradKernel<double>);
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