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149 lines
6.2 KiB
149 lines
6.2 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/sigmoid_cross_entropy_with_logits_op.h"
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namespace paddle {
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namespace operators {
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using framework::Tensor;
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class SigmoidCrossEntropyWithLogitsOp : 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("Out"), "Output(Out) should be not null.");
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auto x_dims = ctx->GetInputDim("X");
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auto labels_dims = ctx->GetInputDim("Label");
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PADDLE_ENFORCE_EQ(x_dims.size(), 2, "Input(X)'s rank should be 2.");
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PADDLE_ENFORCE_EQ(labels_dims.size(), 2,
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"Input(Label)'s rank should be 2.");
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PADDLE_ENFORCE_EQ(x_dims[0], labels_dims[0],
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"The 1st dimension of Input(X) and Input(Label) should "
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"be equal.");
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PADDLE_ENFORCE_EQ(x_dims[1], labels_dims[1],
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"The 2nd dimension of Input(X) and Input(Label) should "
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"be equal.");
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ctx->SetOutputDim("Out", x_dims);
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ctx->ShareLoD("X", /*->*/ "Out");
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}
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};
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class SigmoidCrossEntropyWithLogitsGradOp
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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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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("Out")),
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"Input(Out@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 labels_dims = ctx->GetInputDim("Label");
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auto dout_dims = ctx->GetInputDim(framework::GradVarName("Out"));
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PADDLE_ENFORCE_EQ(x_dims.size(), 2, "Input(X)'s rank should be 2.");
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PADDLE_ENFORCE_EQ(labels_dims.size(), 2,
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"Input(Label)'s rank should be 2.");
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PADDLE_ENFORCE_EQ(dout_dims.size(), 2,
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"Input(Out@Grad)'s rank should be 2.");
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PADDLE_ENFORCE_EQ(x_dims[0], labels_dims[0],
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"The 1st dimension of Input(X) and Input(Label) should "
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"be equal.");
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PADDLE_ENFORCE_EQ(x_dims[1], labels_dims[1],
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"The 2nd dimension of Input(X) and Input(Label) should "
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"be equal.");
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PADDLE_ENFORCE_EQ(x_dims[0], dout_dims[0],
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"The 1st dimension of Input(X) and Input(Out@Grad) "
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"should be equal.");
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PADDLE_ENFORCE_EQ(x_dims[1], dout_dims[1],
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"The 2nd dimension of Input(X) and Input(Out@Grad) "
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"should be equal.");
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ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
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}
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};
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class SigmoidCrossEntropyWithLogitsOpMaker
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: public framework::OpProtoAndCheckerMaker {
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public:
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SigmoidCrossEntropyWithLogitsOpMaker(OpProto* proto,
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OpAttrChecker* op_checker)
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: framework::OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput("X",
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"(Tensor, default Tensor<float>), a 2-D tensor with shape N x D, "
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"where N is the batch size and D is the number of classes. "
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"This input is a tensor of logits computed by the previous "
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" operator. Logits are unscaled log probabilities given as "
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"log(p/(1-p)).");
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AddInput("Label",
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"(Tensor, default Tensor<float>), a 2-D tensor of the same type "
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"and shape as X. This input is a tensor of probabalistic labels "
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"for each logit");
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AddOutput("Out",
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"(Tensor, default Tensor<float>), a 2-D tensor with shape N x D "
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" of elementwise logistic losses.");
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AddComment(R"DOC(
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SigmoidCrossEntropyWithLogits Operator.
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This measures the element-wise probability error in classification tasks
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in which each class is independent. This can be thought of as predicting labels
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for a data-point, where labels are not mutually exclusive.
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For example, a news article can be about politics, technology or sports
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at the same time or none of these.
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The logistic loss is given as follows:
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$$loss = -Labels * \log(\sigma(X)) - (1 - Labels) * \log(1 - \sigma(X))$$
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We know that $$\sigma(X) = (1 / (1 + \exp(-X)))$$. By substituting this we get:
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$$loss = X - X * Labels + \log(1 + \exp(-X))$$
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For stability and to prevent overflow of $$\exp(-X)$$ when X < 0,
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we reformulate the loss as follows:
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$$loss = \max(X, 0) - X * Labels + \log(1 + \exp(-|X|))$$
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Both the input `X` and `Labels` can carry the LoD (Level of Details) information.
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However the output only shares the LoD 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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REGISTER_OP(sigmoid_cross_entropy_with_logits,
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ops::SigmoidCrossEntropyWithLogitsOp,
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ops::SigmoidCrossEntropyWithLogitsOpMaker,
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sigmoid_cross_entropy_with_logits_grad,
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ops::SigmoidCrossEntropyWithLogitsGradOp);
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REGISTER_OP_CPU_KERNEL(sigmoid_cross_entropy_with_logits,
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ops::SigmoidCrossEntropyWithLogitsKernel<
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paddle::platform::CPUDeviceContext, float>);
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REGISTER_OP_CPU_KERNEL(sigmoid_cross_entropy_with_logits_grad,
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ops::SigmoidCrossEntropyWithLogitsGradKernel<
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paddle::platform::CPUDeviceContext, float>);
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