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115 lines
4.4 KiB
115 lines
4.4 KiB
/* 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/modified_huber_loss_op.h"
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namespace paddle {
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namespace operators {
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class ModifiedHuberLossOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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protected:
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void InferShape(framework::InferShapeContext* ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput("X"), "X must be initialized.");
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PADDLE_ENFORCE(ctx->HasInput("Y"), "Y must be initialized.");
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auto x_dims = ctx->GetInputDim("X");
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auto y_dims = ctx->GetInputDim("Y");
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PADDLE_ENFORCE_EQ(x_dims, y_dims, "The shape of X and Y must be the same.");
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PADDLE_ENFORCE_EQ(x_dims.size(), 2, "The tensor rank of X must be 2.");
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PADDLE_ENFORCE_EQ(x_dims[1], 1, "The 2nd dimension of X must be 1.");
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ctx->SetOutputDim("IntermediateVal", x_dims);
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ctx->SetOutputDim("Out", {x_dims[0], 1});
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}
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};
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class ModifiedHuberLossOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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ModifiedHuberLossOpMaker(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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"The input tensor of modified huber loss op."
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"X is 2-D tensor with shape [batch_size, 1].");
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AddInput("Y",
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"The target labels of modified huber loss op."
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"The shape of Y is same as X. Values of Y must be 0 or 1.");
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AddOutput("IntermediateVal",
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"Variable to save intermediate result which will be reused in "
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"backward processing.")
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.AsIntermediate();
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AddOutput("Out", "Classification loss for X.");
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AddComment(R"DOC(
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Modified huber loss is used in binary classification problem. The shape of
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input X and target Y are both [N, 1] and so is the shape of output loss.
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Since target Y is not differentiable, cacluating gradient for Y is illegal.
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The formulation of modified huber loss is:
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L(y, f(x)) = max(0, 1 - yf(x))^2 for yf(x) >= -1,
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-4yf(x) otherwise.
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Make sure the values of target label Y are in {0, 1} here. The operator will
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scale values of Y to {-1, +1} when computing losses and gradients.
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)DOC");
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}
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};
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class ModifiedHuberLossGradOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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protected:
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void InferShape(framework::InferShapeContext* ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput("X"), "X must be initialized.");
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PADDLE_ENFORCE(ctx->HasInput("Y"), "Y must be initialized.");
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PADDLE_ENFORCE(ctx->HasInput("IntermediateVal"),
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"Intermediate value must not be null.");
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PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
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"Input(Out@Grad) must not be null.");
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auto x_dims = ctx->GetInputDim("X");
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auto y_dims = ctx->GetInputDim("Y");
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auto intermediate_dims = ctx->GetInputDim("IntermediateVal");
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auto out_grad_dims = ctx->GetInputDim(framework::GradVarName("Out"));
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PADDLE_ENFORCE_EQ(
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intermediate_dims, x_dims,
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"The shape of X and intermediate value must be the same.");
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PADDLE_ENFORCE_EQ(out_grad_dims, x_dims,
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"The shape of Input(Out@Grad) and X must be the same.");
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if (ctx->HasOutput(framework::GradVarName("X"))) {
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ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
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}
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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(modified_huber_loss, ops::ModifiedHuberLossOp,
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ops::ModifiedHuberLossOpMaker, modified_huber_loss_grad,
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ops::ModifiedHuberLossGradOp);
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REGISTER_OP_CPU_KERNEL(
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modified_huber_loss,
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ops::ModifiedHuberLossKernel<paddle::platform::CPUPlace, float>);
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REGISTER_OP_CPU_KERNEL(modified_huber_loss_grad,
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ops::ModifiedHuberLossGradCPUKernel<float>);
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