You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
209 lines
8.0 KiB
209 lines
8.0 KiB
/* Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
|
|
|
|
Licensed under the Apache License, Version 2.0 (the "License");
|
|
you may not use this file except in compliance with the License.
|
|
You may obtain a copy of the License at
|
|
|
|
http://www.apache.org/licenses/LICENSE-2.0
|
|
|
|
Unless required by applicable law or agreed to in writing, software
|
|
distributed under the License is distributed on an "AS IS" BASIS,
|
|
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
See the License for the specific language governing permissions and
|
|
limitations under the License. */
|
|
|
|
#include "paddle/fluid/operators/detection/sigmoid_focal_loss_op.h"
|
|
#include <memory>
|
|
#include <string>
|
|
#include <vector>
|
|
|
|
namespace paddle {
|
|
namespace operators {
|
|
|
|
using framework::Tensor;
|
|
|
|
class SigmoidFocalLossOp : public framework::OperatorWithKernel {
|
|
public:
|
|
using framework::OperatorWithKernel::OperatorWithKernel;
|
|
|
|
void InferShape(framework::InferShapeContext* ctx) const override {
|
|
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null.");
|
|
PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) should not be null.");
|
|
PADDLE_ENFORCE(ctx->HasInput("FgNum"), "Input(FgNum) should not be null.");
|
|
PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) should not be null.");
|
|
|
|
auto x_dims = ctx->GetInputDim("X");
|
|
auto labels_dims = ctx->GetInputDim("Label");
|
|
auto fg_dims = ctx->GetInputDim("FgNum");
|
|
|
|
int rank = x_dims.size();
|
|
PADDLE_ENFORCE_EQ(rank, labels_dims.size(),
|
|
"Input(X) and Input(Label) shall have the same rank.");
|
|
PADDLE_ENFORCE_EQ(fg_dims.size(), 1, "The rank of Input(FgNum) must be 1.");
|
|
bool check = true;
|
|
if ((!ctx->IsRuntime()) && (framework::product(x_dims) <= 0 ||
|
|
framework::product(labels_dims) <= 0)) {
|
|
check = false;
|
|
}
|
|
|
|
if (check) {
|
|
PADDLE_ENFORCE_EQ(framework::slice_ddim(x_dims, 0, rank - 1),
|
|
framework::slice_ddim(labels_dims, 0, rank - 1),
|
|
"Input(X) and Input(Label) shall have the same shape "
|
|
"except the last dimension.");
|
|
}
|
|
|
|
PADDLE_ENFORCE_EQ(labels_dims[rank - 1], 1UL,
|
|
"The last dimension of input(Label) should be 1.");
|
|
|
|
ctx->ShareDim("X", /*->*/ "Out");
|
|
ctx->ShareLoD("X", /*->*/ "Out");
|
|
}
|
|
|
|
protected:
|
|
framework::OpKernelType GetExpectedKernelType(
|
|
const framework::ExecutionContext& ctx) const override {
|
|
return framework::OpKernelType(ctx.Input<framework::LoDTensor>("X")->type(),
|
|
ctx.device_context());
|
|
}
|
|
};
|
|
|
|
class SigmoidFocalLossGradOp : public framework::OperatorWithKernel {
|
|
public:
|
|
using framework::OperatorWithKernel::OperatorWithKernel;
|
|
|
|
void InferShape(framework::InferShapeContext* ctx) const override {
|
|
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null.");
|
|
PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) should not be null.");
|
|
PADDLE_ENFORCE(ctx->HasInput("FgNum"), "Input(FgNum) should not be null.");
|
|
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
|
|
"Input(Out@GRAD) should not be null.");
|
|
PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
|
|
"Output(X@GRAD) should not be null.");
|
|
|
|
auto x_dims = ctx->GetInputDim("X");
|
|
auto labels_dims = ctx->GetInputDim("Label");
|
|
auto fg_dims = ctx->GetInputDim("FgNum");
|
|
auto dout_dims = ctx->GetInputDim(framework::GradVarName("Out"));
|
|
|
|
int rank = x_dims.size();
|
|
PADDLE_ENFORCE_EQ(rank, labels_dims.size(),
|
|
"Input(X) and Input(Label) shall have the same rank.");
|
|
PADDLE_ENFORCE_EQ(fg_dims.size(), 1, "The rank of Input(FgNum) must be 1.");
|
|
bool check = true;
|
|
if ((!ctx->IsRuntime()) && (framework::product(x_dims) <= 0 ||
|
|
framework::product(labels_dims) <= 0)) {
|
|
check = false;
|
|
}
|
|
|
|
if (check) {
|
|
PADDLE_ENFORCE_EQ(framework::slice_ddim(x_dims, 0, rank - 1),
|
|
framework::slice_ddim(labels_dims, 0, rank - 1),
|
|
"Input(X) and Input(Label) shall have the same shape.");
|
|
|
|
PADDLE_ENFORCE_EQ(labels_dims[rank - 1], 1UL,
|
|
"The last dimension of input(Label) should be 1.");
|
|
|
|
PADDLE_ENFORCE_EQ(
|
|
framework::slice_ddim(x_dims, 0, rank),
|
|
framework::slice_ddim(dout_dims, 0, rank),
|
|
"Input(X) and Input(Out@Grad) shall have the same shape.");
|
|
}
|
|
|
|
ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
|
|
}
|
|
|
|
protected:
|
|
framework::OpKernelType GetExpectedKernelType(
|
|
const framework::ExecutionContext& ctx) const override {
|
|
return framework::OpKernelType(ctx.Input<framework::LoDTensor>("X")->type(),
|
|
ctx.device_context());
|
|
}
|
|
};
|
|
|
|
class SigmoidFocalLossOpMaker : public framework::OpProtoAndCheckerMaker {
|
|
public:
|
|
void Make() override {
|
|
AddInput("X",
|
|
"(Tensor, default Tensor<float>), a 2-D tensor with shape [N, D], "
|
|
"where N is the batch size and D is the number of classes "
|
|
"(excluding background). This input is a tensor of logits "
|
|
"computed by the previous operator.");
|
|
AddInput("Label",
|
|
"(Tensor, default Tensor<int>), a 2-D tensor with shape [N, 1]. "
|
|
"This input is a tensor of probabilistic labels.");
|
|
AddInput("FgNum",
|
|
"(Tensor, default Tensor<int>), a 1-D tensor with shape [1]. "
|
|
"This input is the number of foreground.");
|
|
AddOutput(
|
|
"Out",
|
|
"(Tensor, default Tensor<float>), a 2-D tensor with shape [N, D]. "
|
|
"This output is the focal loss.");
|
|
AddAttr<float>(
|
|
"gamma",
|
|
"Hyper-parameter of sigmoid focal loss op, which is to balance the "
|
|
"easy and hard examples. "
|
|
"A float scalar with default value 2.0.")
|
|
.SetDefault(2.0);
|
|
AddAttr<float>(
|
|
"alpha",
|
|
"Hyper-parameter of sigmoid focal loss op, which is to balance the "
|
|
"positive and negative examples. "
|
|
"A float scalar with default value 0.5.")
|
|
.SetDefault(0.25);
|
|
AddComment(R"DOC(
|
|
Sigmoid Focal Loss Operator.
|
|
|
|
Focal loss is used to address the foreground-background class imbalance existed
|
|
on the training phase of one-stage detectors. This operator computes the sigmoid
|
|
value for each element in the input tensor, after which focal loss is measured.
|
|
|
|
The focal loss is given as follows:
|
|
|
|
$$Loss_j = (-Label_j * alpha * \pow(1 - \sigma(X_j), gamma) * \log(\sigma(X_j)) -
|
|
(1 - Labels_j) * (1 - alpha) * \pow(\sigma(X_j), gamma) * \log(1 - \sigma(X_j)))
|
|
/ FgNum, j = 1,...,K$$
|
|
|
|
We know that $$\sigma(X_j) = \\frac{1}{1 + \exp(-X_j)}$$.
|
|
|
|
)DOC");
|
|
}
|
|
};
|
|
|
|
class SigmoidFocalLossGradOpDescMaker
|
|
: public framework::SingleGradOpDescMaker {
|
|
public:
|
|
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
|
|
|
|
protected:
|
|
std::unique_ptr<framework::OpDesc> Apply() const override {
|
|
std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
|
|
op->SetType("sigmoid_focal_loss_grad");
|
|
op->SetInput("X", Input("X"));
|
|
op->SetInput("Label", Input("Label"));
|
|
op->SetInput("FgNum", Input("FgNum"));
|
|
op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
|
|
op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
|
|
op->SetAttrMap(Attrs());
|
|
return op;
|
|
}
|
|
};
|
|
|
|
} // namespace operators
|
|
} // namespace paddle
|
|
|
|
namespace ops = paddle::operators;
|
|
REGISTER_OPERATOR(sigmoid_focal_loss, ops::SigmoidFocalLossOp,
|
|
ops::SigmoidFocalLossOpMaker,
|
|
ops::SigmoidFocalLossGradOpDescMaker);
|
|
REGISTER_OPERATOR(sigmoid_focal_loss_grad, ops::SigmoidFocalLossGradOp);
|
|
REGISTER_OP_CPU_KERNEL(
|
|
sigmoid_focal_loss,
|
|
ops::SigmoidFocalLossKernel<paddle::platform::CPUDeviceContext, float>,
|
|
ops::SigmoidFocalLossKernel<paddle::platform::CPUDeviceContext, double>);
|
|
REGISTER_OP_CPU_KERNEL(
|
|
sigmoid_focal_loss_grad,
|
|
ops::SigmoidFocalLossGradKernel<paddle::platform::CPUDeviceContext, float>,
|
|
ops::SigmoidFocalLossGradKernel<paddle::platform::CPUDeviceContext,
|
|
double>);
|