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191 lines
8.2 KiB
191 lines
8.2 KiB
/* 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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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/cross_entropy_op.h"
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namespace paddle {
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namespace operators {
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class CrossEntropyOp : 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 be not null.");
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PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) should be not null.");
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PADDLE_ENFORCE(ctx->HasOutput("Y"), "Output(Y) should be not null.");
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auto x_dims = ctx->GetInputDim("X");
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auto label_dims = ctx->GetInputDim("Label");
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int rank = x_dims.size();
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PADDLE_ENFORCE_EQ(rank, label_dims.size(),
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"Input(X) and Input(Label) shall have the same rank.");
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PADDLE_ENFORCE_EQ(framework::slice_ddim(x_dims, 0, rank - 1),
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framework::slice_ddim(label_dims, 0, rank - 1),
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"Input(X) and Input(Label) shall have the same shape "
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"except the last dimension.");
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if (ctx->Attrs().Get<bool>("soft_label")) {
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PADDLE_ENFORCE_EQ(x_dims[rank - 1], label_dims[rank - 1],
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"If Attr(soft_label) == true, the last dimension of "
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"Input(X) and Input(Label) should be equal.");
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} else {
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PADDLE_ENFORCE_EQ(label_dims[rank - 1], 1UL,
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"If Attr(softLabel) == false, the last dimension of "
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"Input(Label) should be 1.");
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}
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auto y_dims = x_dims;
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y_dims[rank - 1] = 1;
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ctx->SetOutputDim("Y", y_dims);
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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 cross_entropy
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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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framework::ToDataType(ctx.Input<Tensor>("X")->type()),
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ctx.device_context());
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}
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};
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class CrossEntropyGradientOp : 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 be not null.");
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PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) should be not null.");
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PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Y")),
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"Input(Y@GRAD) shoudl be not null.");
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PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
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"Output(X@GRAD) should be not null.");
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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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int rank = x_dims.size();
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PADDLE_ENFORCE_EQ(dy_dims.size(), rank,
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"Input(Y@Grad) and Input(X) should have the same rank.");
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PADDLE_ENFORCE_EQ(label_dims.size(), rank,
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"Input(Label) and Input(X) should have the same rank.");
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PADDLE_ENFORCE_EQ(framework::slice_ddim(x_dims, 0, rank - 1),
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framework::slice_ddim(label_dims, 0, rank - 1),
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"The Input(X) and Input(Label) should have the same "
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"shape except the last dimension.");
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PADDLE_ENFORCE_EQ(framework::slice_ddim(x_dims, 0, rank - 1),
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framework::slice_ddim(dy_dims, 0, rank - 1),
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"The Input(X) and Input(Y@Grad) should have the same "
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"shape except the last dimension.");
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PADDLE_ENFORCE_EQ(dy_dims[rank - 1], 1,
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"The last dimension of Input(Y@Grad) should be 1.");
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if (ctx->Attrs().Get<bool>("soft_label")) {
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PADDLE_ENFORCE_EQ(x_dims[rank - 1], label_dims[rank - 1],
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"When Attr(soft_label) == true, the last dimension of "
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"Input(X) and Input(Label) should be equal.");
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} else {
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PADDLE_ENFORCE_EQ(label_dims[rank - 1], 1,
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"When Attr(soft_label) == false, the last dimension of "
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"Input(Label) should be 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 cross_entropy
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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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framework::ToDataType(ctx.Input<Tensor>("X")->type()),
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ctx.device_context());
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}
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};
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class CrossEntropyOpMaker : 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 tensor whose last dimension "
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"size is equal to the number of classes. This input is a "
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"probability computed by the previous operator, which is almost "
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"always the result of a softmax operator.");
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AddInput(
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"Label",
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"(Tensor), the tensor which represents the ground truth. It has the "
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"same shape with 'X' except the last dimension. When soft_label is set "
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"to false, the last dimension size is 1; when soft_label is set to "
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"true, the last dimension size is equal to the number of classes.");
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AddOutput("Y",
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"(Tensor, default Tensor<float>), a tensor whose shape is same "
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"with 'X' except that the last dimension size is 1. It "
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"represents the cross entropy loss.");
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AddAttr<bool>("soft_label",
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"(bool, default false), a flag indicating whether to "
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"interpretate the given labels as soft labels.")
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.SetDefault(false);
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AddComment(R"DOC(
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CrossEntropy Operator.
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The input 'X' and 'Label' will first be logically flattened to 2-D matrixs.
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The matrix's second dimension(row length) is as same as the original last
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dimension, and the first dimension(column length) is the product of all other
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original dimensions. Then the softmax computation will take palce on each raw
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of flattened matrixs.
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It supports both standard cross-entropy and soft-label cross-entropy loss
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computation.
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1) One-hot cross-entropy:
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soft_label = false, Label[i, 0] indicates the class index for sample i:
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$Y[i] = -\log(X[i, Label[i]])$
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2) Soft-label cross-entropy:
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soft_label = true, Label[i, j] indicates the soft label of class j
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for sample i:
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$Y[i] = \sum_j{-Label[i, j] * log(X[i, j])}$
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Please make sure that in this case the summuation of each row of Label
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equals one.
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3) One-hot cross-entropy with vecterized Input(Label):
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As a special case of 2), when each row of Input(Label) has only one
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non-zero element (equals 1), soft-label cross-entropy degenerates to a
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one-hot cross-entropy with one-hot label representation.
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Both the input X and Label can carry the LoD (Level of Details) information,
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or not. But the output only shares the LoD information with input X.
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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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using CPUCtx = paddle::platform::CPUDeviceContext;
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REGISTER_OPERATOR(cross_entropy, ops::CrossEntropyOp, ops::CrossEntropyOpMaker,
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paddle::framework::DefaultGradOpDescMaker<true>);
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REGISTER_OPERATOR(cross_entropy_grad, ops::CrossEntropyGradientOp);
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REGISTER_OP_CPU_KERNEL(cross_entropy, ops::CrossEntropyOpKernel<CPUCtx, float>,
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ops::CrossEntropyOpKernel<CPUCtx, double>);
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REGISTER_OP_CPU_KERNEL(cross_entropy_grad,
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ops::CrossEntropyGradientOpKernel<CPUCtx, float>,
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ops::CrossEntropyGradientOpKernel<CPUCtx, double>);
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