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102 lines
4.1 KiB
102 lines
4.1 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/softmax_with_cross_entropy_op.h"
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namespace paddle {
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namespace operators {
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class SoftmaxWithCrossEntropyOpMaker
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: public framework::OpProtoAndCheckerMaker {
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public:
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SoftmaxWithCrossEntropyOpMaker(framework::OpProto* proto,
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framework::OpAttrChecker* op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput("Logits",
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"The unscaled log probabilities which is a 2-D tensor<float> with"
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"shape [N x K]. N is the batch_size, and K is the class number.")
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.NotInGradient();
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AddInput("Label", "The ground truth. A 1-D tensor<int> with shape N.");
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AddOutput("Softmax",
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"Store the outputs of softmax function, "
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"which will be used in backward calculation.")
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.AsIntermediate();
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AddOutput("Out", "A 1-D tensor<float> with shape N.");
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AddComment(R"DOC(
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Cross entropy loss with softmax are used as the output layer extensively. This
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operator computes the softmax normalized values for each row of the input
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tensor, after which cross-entropy loss is then computed. This provides a more
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numerically stable gradient.
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Because this operators performs a softmax on logits internally, it expects
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unscaled logits. Please do not call this op with the output of softmax operator,
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which will produce incorrect results.
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This operators expects mutually exclusive hard labels, each sample in a batch
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is in exactly one class with probabilities 1. Each sample in the batch with one
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and only one label.
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)DOC");
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}
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};
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class SoftmaxWithCrossEntropyOpGrad : 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(const framework::InferShapeContext& ctx) const override {
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PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
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"Input(Out@Grad) should not be null");
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PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Softmax"),
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"Input(Softmax) should be not null.");
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PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Label"),
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"Input(Lable) should be not null.");
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ctx.Output<framework::LoDTensor>(framework::GradVarName("Logits"))
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->Resize(ctx.Input<Tensor>("Softmax")->dims());
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}
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};
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class SoftmaxWithCrossEntropyOp : 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(const framework::InferShapeContext& ctx) const override {
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const Tensor* logits = ctx.Input<Tensor>("Logits");
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PADDLE_ENFORCE(
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logits->dims().size() == 2UL,
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"The input of softmax_with_cross_entropy should be a 2-d tensor.");
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PADDLE_ENFORCE(ctx.Input<Tensor>("Label")->dims().size() == 1UL,
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"The label should be a 1-d tensor.");
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ctx.Output<framework::LoDTensor>("Softmax")->Resize(logits->dims());
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ctx.Output<framework::LoDTensor>("Out")->Resize({logits->dims()[0], 1});
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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(softmax_with_cross_entropy, ops::SoftmaxWithCrossEntropyOp,
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ops::SoftmaxWithCrossEntropyOpMaker,
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softmax_with_cross_entropy_grad,
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ops::SoftmaxWithCrossEntropyOpGrad);
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REGISTER_OP_CPU_KERNEL(softmax_with_cross_entropy,
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ops::SoftmaxWithCrossEntropyKernel<float>);
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REGISTER_OP_CPU_KERNEL(softmax_with_cross_entropy_grad,
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ops::SoftmaxWithCrossEntropyGradKernel<float>);
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