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255 lines
9.7 KiB
255 lines
9.7 KiB
/* Copyright (c) 2019 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/detection/sigmoid_focal_loss_op.h"
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#include <memory>
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#include <string>
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#include <vector>
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namespace paddle {
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namespace operators {
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using framework::Tensor;
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class SigmoidFocalLossOp : 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", "sigmoid_focal_loss");
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OP_INOUT_CHECK(ctx->HasInput("Label"), "Input", "Label",
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"sigmoid_focal_loss");
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OP_INOUT_CHECK(ctx->HasInput("FgNum"), "Input", "FgNum",
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"sigmoid_focal_loss");
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OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out",
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"sigmoid_focal_loss");
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auto x_dims = ctx->GetInputDim("X");
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auto labels_dims = ctx->GetInputDim("Label");
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auto fg_dims = ctx->GetInputDim("FgNum");
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int rank = x_dims.size();
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PADDLE_ENFORCE_EQ(
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rank, labels_dims.size(),
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platform::errors::InvalidArgument(
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"The rank of Input(X) should be equal to the rank of Input(Label), "
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"but received X rank is:%d, X shape is:[%s], "
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"Label rank is:%d, Label shape is:[%s].",
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rank, x_dims, labels_dims.size(), labels_dims));
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PADDLE_ENFORCE_EQ(
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fg_dims.size(), 1,
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platform::errors::InvalidArgument(
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"The rank of Input(FgNum) must be 1, but received FgNum rank is "
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":%d, FgNum shape is:[%s].",
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fg_dims.size(), fg_dims));
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bool check = true;
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if ((!ctx->IsRuntime()) && (framework::product(x_dims) <= 0 ||
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framework::product(labels_dims) <= 0)) {
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check = false;
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}
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if (check) {
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PADDLE_ENFORCE_EQ(
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framework::slice_ddim(x_dims, 0, rank - 1),
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framework::slice_ddim(labels_dims, 0, rank - 1),
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platform::errors::InvalidArgument(
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"Input(X) and Input(Label) should have the same shape "
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"except the last dimension, but received X shape is:[%s], "
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"Label shape is:[%s].",
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x_dims, labels_dims));
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}
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PADDLE_ENFORCE_EQ(
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labels_dims[rank - 1], 1UL,
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platform::errors::InvalidArgument(
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"The last dimension of Input(Label) should be 1, but received "
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"Label shape is:[%s].",
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labels_dims));
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ctx->ShareDim("X", /*->*/ "Out");
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ctx->ShareLoD("X", /*->*/ "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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OperatorWithKernel::IndicateVarDataType(ctx, "X"),
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ctx.device_context());
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}
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};
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class SigmoidFocalLossGradOp : 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", "sigmoid_focal_loss");
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OP_INOUT_CHECK(ctx->HasInput("Label"), "Input", "Label",
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"sigmoid_focal_loss");
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OP_INOUT_CHECK(ctx->HasInput("FgNum"), "Input", "FgNum",
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"sigmoid_focal_loss");
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OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Out")), "Input",
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"Out@GRAD", "sigmoid_focal_loss");
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OP_INOUT_CHECK(ctx->HasOutput(framework::GradVarName("X")), "Output",
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"X@GRAD", "sigmoid_focal_loss");
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auto x_dims = ctx->GetInputDim("X");
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auto labels_dims = ctx->GetInputDim("Label");
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auto fg_dims = ctx->GetInputDim("FgNum");
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auto dout_dims = ctx->GetInputDim(framework::GradVarName("Out"));
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int rank = x_dims.size();
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PADDLE_ENFORCE_EQ(
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rank, labels_dims.size(),
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platform::errors::InvalidArgument(
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"The rank of Input(X) should be equal to the rank of Input(Label), "
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"but received X rank is:%d, X shape is:[%s], "
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"Label rank is:%d, Label shape is:[%s].",
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rank, x_dims, labels_dims.size(), labels_dims));
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PADDLE_ENFORCE_EQ(
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fg_dims.size(), 1,
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platform::errors::InvalidArgument(
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"The rank of Input(FgNum) must be 1, but received FgNum rank is "
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":%d, FgNum shape is:[%s].",
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fg_dims.size(), fg_dims));
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bool check = true;
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if ((!ctx->IsRuntime()) && (framework::product(x_dims) <= 0 ||
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framework::product(labels_dims) <= 0)) {
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check = false;
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}
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if (check) {
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PADDLE_ENFORCE_EQ(
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framework::slice_ddim(x_dims, 0, rank - 1),
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framework::slice_ddim(labels_dims, 0, rank - 1),
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platform::errors::InvalidArgument(
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"Input(X) and Input(Label) should have the same shape "
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"except the last dimension, but received X shape is:[%s], "
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"Label shape is:[%s].",
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x_dims, labels_dims));
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PADDLE_ENFORCE_EQ(
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labels_dims[rank - 1], 1UL,
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platform::errors::InvalidArgument(
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"The last dimension of Input(Label) should be 1, but received "
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"Label shape is:[%s].",
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labels_dims));
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PADDLE_ENFORCE_EQ(framework::slice_ddim(x_dims, 0, rank),
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framework::slice_ddim(dout_dims, 0, rank),
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platform::errors::InvalidArgument(
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"Input(X) and Input(Out@Grad) should have the same "
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"shape, but received "
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"X shape is:[%s], Out@Grad shape is:[%s].",
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x_dims, dout_dims));
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}
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ctx->SetOutputDim(framework::GradVarName("X"), x_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 SigmoidFocalLossOpMaker : 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, default Tensor<float>), a 2-D tensor with shape [N, D], "
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"where N is the batch size and D is the number of classes "
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"(excluding background). This input is a tensor of logits "
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"computed by the previous operator.");
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AddInput("Label",
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"(Tensor, default Tensor<int>), a 2-D tensor with shape [N, 1]. "
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"This input is a tensor of probabilistic labels.");
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AddInput("FgNum",
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"(Tensor, default Tensor<int>), a 1-D tensor with shape [1]. "
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"This input is the number of foreground.");
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AddOutput(
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"Out",
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"(Tensor, default Tensor<float>), a 2-D tensor with shape [N, D]. "
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"This output is the focal loss.");
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AddAttr<float>(
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"gamma",
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"Hyper-parameter of sigmoid focal loss op, which is to balance the "
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"easy and hard examples. "
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"A float scalar with default value 2.0.")
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.SetDefault(2.0);
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AddAttr<float>(
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"alpha",
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"Hyper-parameter of sigmoid focal loss op, which is to balance the "
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"positive and negative examples. "
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"A float scalar with default value 0.5.")
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.SetDefault(0.25);
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AddComment(R"DOC(
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Sigmoid Focal Loss Operator.
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Focal loss is used to address the foreground-background class imbalance existed
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on the training phase of one-stage detectors. This operator computes the sigmoid
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value for each element in the input tensor, after which focal loss is measured.
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The focal loss is given as follows:
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$$Loss_j = (-Label_j * alpha * \pow(1 - \sigma(X_j), gamma) * \log(\sigma(X_j)) -
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(1 - Labels_j) * (1 - alpha) * \pow(\sigma(X_j), gamma) * \log(1 - \sigma(X_j)))
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/ FgNum, j = 1,...,K$$
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We know that $$\sigma(X_j) = \\frac{1}{1 + \exp(-X_j)}$$.
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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 SigmoidFocalLossGradOpMaker : 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("sigmoid_focal_loss_grad");
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op->SetInput("X", this->Input("X"));
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op->SetInput("Label", this->Input("Label"));
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op->SetInput("FgNum", this->Input("FgNum"));
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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(sigmoid_focal_loss, ops::SigmoidFocalLossOp,
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ops::SigmoidFocalLossOpMaker,
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ops::SigmoidFocalLossGradOpMaker<paddle::framework::OpDesc>,
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ops::SigmoidFocalLossGradOpMaker<paddle::imperative::OpBase>);
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REGISTER_OPERATOR(sigmoid_focal_loss_grad, ops::SigmoidFocalLossGradOp);
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REGISTER_OP_CPU_KERNEL(
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sigmoid_focal_loss,
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ops::SigmoidFocalLossKernel<paddle::platform::CPUDeviceContext, float>,
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ops::SigmoidFocalLossKernel<paddle::platform::CPUDeviceContext, double>);
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REGISTER_OP_CPU_KERNEL(
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sigmoid_focal_loss_grad,
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ops::SigmoidFocalLossGradKernel<paddle::platform::CPUDeviceContext, float>,
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ops::SigmoidFocalLossGradKernel<paddle::platform::CPUDeviceContext,
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double>);
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