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238 lines
9.3 KiB
238 lines
9.3 KiB
/* 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/teacher_student_sigmoid_loss_op.h"
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#include <memory>
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#include "paddle/fluid/operators/math/math_function.h"
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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class TeacherStudentSigmoidLossOp : 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",
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"teacher_student_sigmoid_loss");
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OP_INOUT_CHECK(ctx->HasInput("Label"), "Input", "Label",
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"teacher_student_sigmoid_loss");
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OP_INOUT_CHECK(ctx->HasOutput("Y"), "Output", "Y",
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"teacher_student_sigmoid_loss");
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auto x_dims = ctx->GetInputDim("X");
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auto label_dims = ctx->GetInputDim("Label");
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PADDLE_ENFORCE_EQ(x_dims.size(), 2UL,
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platform::errors::InvalidArgument(
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"Input(X)'s rank should be 2. But received: "
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"Input(X)'s rank is [%d]",
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x_dims.size()));
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PADDLE_ENFORCE_EQ(label_dims.size(), 2UL,
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platform::errors::InvalidArgument(
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"Input(Label)'s rank should be 2. But "
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"received Input(Label)'s rank is [%d]",
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label_dims.size()));
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if (ctx->IsRuntime()) {
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PADDLE_ENFORCE_EQ(
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x_dims[0], label_dims[0],
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platform::errors::InvalidArgument(
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"The 1st dimension of Input(X) and Input(Label) should "
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"be equal. The difference is [%d]: [%d]",
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x_dims[0], label_dims[0]));
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PADDLE_ENFORCE_EQ(label_dims[1], 1UL,
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platform::errors::InvalidArgument(
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"The 2nd dimension of "
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"Input(Label) should be 1. But received "
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"Input(Label)'s 2nd dim is [%d]",
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label_dims[1]));
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}
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ctx->SetOutputDim("Y", {x_dims[0], 1});
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ctx->ShareLoD("X", /*->*/ "Y");
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}
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protected:
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// Explicitly set that the data type of computation kernel of
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// teacher_student_sigmoid_loss
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// is determined by its input "X".
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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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template <typename T>
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class TeacherStudentSigmoidLossGradOpMaker
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: 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("teacher_student_sigmoid_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(framework::GradVarName("Y"), this->OutputGrad("Y"));
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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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class TeacherStudentSigmoidLossGradientOp
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: 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",
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"teacher_student_sigmoid_loss_grad");
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OP_INOUT_CHECK(ctx->HasInput("Label"), "Input", "X",
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"teacher_student_sigmoid_loss_grad");
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OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Y")), "Input",
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"Y@Grad", "teacher_student_sigmoid_loss_grad");
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OP_INOUT_CHECK(ctx->HasOutput(framework::GradVarName("X")), "Input",
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"X@Grad", "teacher_student_sigmoid_loss_grad");
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auto x_dims = ctx->GetInputDim("X");
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auto label_dims = ctx->GetInputDim("Label");
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auto dy_dims = ctx->GetInputDim(framework::GradVarName("Y"));
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PADDLE_ENFORCE_EQ(
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x_dims.size(), 2,
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platform::errors::InvalidArgument(
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"Input(X)'s rank should be 2. But received Input(X)'s rank is [%d]",
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x_dims.size()));
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PADDLE_ENFORCE_EQ(dy_dims.size(), 2,
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platform::errors::InvalidArgument(
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"Input(Y@Grad)'s rank should be 2. But received "
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"Input(Y@Grad)'s rank is [%d]",
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dy_dims.size()));
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PADDLE_ENFORCE_EQ(label_dims.size(), 2,
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platform::errors::InvalidArgument(
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"Input(Label)'s rank should be 2. But received "
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"Input(Y@Grad)'s rank is [%d]",
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label_dims.size()));
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if (ctx->IsRuntime()) {
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PADDLE_ENFORCE_EQ(
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x_dims[0], label_dims[0],
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platform::errors::InvalidArgument(
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"The 1st dimension of Input(X) and Input(Label) should "
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"be equal. The difference is [%d]: [%d]",
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x_dims[0], label_dims[0]));
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PADDLE_ENFORCE_EQ(
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x_dims[0], dy_dims[0],
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platform::errors::InvalidArgument(
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"The 1st dimension of Input(X) and Input(Y@Grad) should "
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"be equal. The difference is [%d]: [%d]",
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x_dims[0], dy_dims[0]));
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PADDLE_ENFORCE_EQ(
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dy_dims[1], 1,
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platform::errors::InvalidArgument(
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"The 2nd dimension of Input(Y@Grad) should be 1. "
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"But received Input(Y@Grad)'s 2nd dimension is [%d]",
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dy_dims[1]));
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PADDLE_ENFORCE_EQ(
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label_dims[1], 1,
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platform::errors::InvalidArgument(
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"When Attr(soft_label) == false, the 2nd dimension of "
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"Input(Label) should be 1. But received Input(Label)'s 2nd "
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"dimemsion "
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"is [%d]",
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label_dims[1]));
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}
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ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
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ctx->ShareLoD("X", framework::GradVarName("X"));
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}
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protected:
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// Explicitly set that the data type of computation kernel of
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// teacher_student_sigmoid_loss
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// is determined by its input "X".
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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 TeacherStudentSigmoidLossOpMaker
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: 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 x 1],"
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" where N is the batch size and D is the output. "
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"This input is a probability computed by the previous operator, "
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"which is almost always the result of a softmax operator.");
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AddInput("Label",
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"(Tensor), the ground truth which is a 2-D tensor. "
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"Label is a Tensor<float> with shape [N x 1]. ");
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AddOutput("Y",
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"(Tensor, default Tensor<float>), a 2-D tensor with shape "
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"[N x 1]. The teacher student sigmoid loss.");
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AddAttr<float>(
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"soft_max_up_bound",
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"fp32, if input > soft_max_up_bound, input will be bound, default 15.0")
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.SetDefault(15.0);
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AddAttr<float>("soft_max_lower_bound",
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"fp32, if input < soft_max_lower_bound, input will be "
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"bound, default -15.0")
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.SetDefault(-15.0);
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AddComment(R"DOC(
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TeacherStudentSigmoidLoss Operator.
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It's similarity to SigmoidCrossEntropyWithLogits Operator. The difference is that
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we add another label(z') to original.
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loss = max(x, 0) - x * z + log(1 + exp(-abs(x))) + max(x, 0) - x * z' + log(1 + exp(-abs(x)))
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z is click or not
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z' is teacher value
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label = {-2, -1, [0, 2]}
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when z' is not exist, clk = 0 : label = -2;
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when z' is not exist, clk = 1 : label = -1;
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when z' is exist , clk = 0 : label = 0 + z';
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when z' is exist , clk = 1 : label = 1 + z';
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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_OPERATOR(
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teacher_student_sigmoid_loss, ops::TeacherStudentSigmoidLossOp,
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ops::TeacherStudentSigmoidLossOpMaker,
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ops::TeacherStudentSigmoidLossGradOpMaker<paddle::framework::OpDesc>,
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ops::TeacherStudentSigmoidLossGradOpMaker<paddle::imperative::OpBase>);
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REGISTER_OPERATOR(teacher_student_sigmoid_loss_grad,
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ops::TeacherStudentSigmoidLossGradientOp);
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REGISTER_OP_CPU_KERNEL(teacher_student_sigmoid_loss,
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ops::TeacherStudentSigmoidLossOpKernel<float>,
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ops::TeacherStudentSigmoidLossOpKernel<double>);
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REGISTER_OP_CPU_KERNEL(teacher_student_sigmoid_loss_grad,
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ops::TeacherStudentSigmoidLossGradOpKernel<float>,
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ops::TeacherStudentSigmoidLossGradOpKernel<double>);
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