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.
121 lines
5.1 KiB
121 lines
5.1 KiB
/* Copyright (c) 2016 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/squared_l2_distance_op.h"
|
|
|
|
namespace paddle {
|
|
namespace operators {
|
|
|
|
class SquaredL2DistanceOp : public framework::OperatorWithKernel {
|
|
public:
|
|
using framework::OperatorWithKernel::OperatorWithKernel;
|
|
|
|
void InferShape(framework::InferShapeContext* ctx) const override {
|
|
PADDLE_ENFORCE(ctx->HasInput("X"),
|
|
"Input(X) of SquaredL2DistanceOp should not be null.");
|
|
PADDLE_ENFORCE(ctx->HasInput("Y"),
|
|
"Input(Y) of SquaredL2DistanceOp should not be null.");
|
|
PADDLE_ENFORCE(
|
|
ctx->HasOutput("sub_result"),
|
|
"Output(sub_result) of SquaredL2DistanceOp should not be null.");
|
|
PADDLE_ENFORCE(ctx->HasOutput("Out"),
|
|
"Output(Out) of SquaredL2DistanceOp should not be null.");
|
|
|
|
auto x_dims = ctx->GetInputDim("X");
|
|
auto y_dims = ctx->GetInputDim("Y");
|
|
|
|
PADDLE_ENFORCE_EQ(framework::arity(x_dims), framework::arity(y_dims),
|
|
"Tensor rank of both SquaredL2DistanceOp's "
|
|
"inputs must be same.");
|
|
|
|
int rank = framework::arity(x_dims);
|
|
PADDLE_ENFORCE_GE(rank, 2, "Tensor rank should be at least equal to 2.");
|
|
PADDLE_ENFORCE_EQ(product(x_dims) / x_dims[0], product(y_dims) / y_dims[0],
|
|
"Product of dimensions expcet the first dimension of "
|
|
"input and target must be equal.");
|
|
PADDLE_ENFORCE(y_dims[0] == 1 || y_dims[0] == x_dims[0],
|
|
"First dimension of target must be equal to input "
|
|
"or to 1.");
|
|
|
|
ctx->SetOutputDim("sub_result", {x_dims[0], product(x_dims) / x_dims[0]});
|
|
ctx->SetOutputDim("Out", {x_dims[0], 1});
|
|
ctx->ShareLoD("X", /*->*/ "Out");
|
|
}
|
|
};
|
|
|
|
class SquaredL2DistanceOpMaker : public framework::OpProtoAndCheckerMaker {
|
|
public:
|
|
void Make() override {
|
|
AddInput("X", "(Tensor) Input of SquaredL2DistanceOp.");
|
|
AddInput("Y", "(Tensor) Target of SquaredL2DistanceOp.");
|
|
AddOutput("sub_result",
|
|
"(Tensor) Buffering subtraction result which "
|
|
"will be reused in backward.")
|
|
.AsIntermediate();
|
|
AddOutput("Out", "(Tensor) Squared l2 distance between input and target.");
|
|
AddComment(R"DOC(
|
|
SquaredL2Distance operator
|
|
|
|
This operator will cacluate the squared L2 distance for the input and
|
|
the target. Number of distance value will be equal to the first dimension
|
|
of input. First dimension of the target could be equal to the input or to 1.
|
|
If the first dimension of target is 1, the operator will broadcast target's
|
|
first dimension to input's first dimension. During backward propagation,
|
|
the user can decide whether to calculate the gradient of the input or
|
|
the target or both.
|
|
|
|
Both the input X and Y can carry the LoD (Level of Details) information.
|
|
However, the output only shares the LoD information with input X.
|
|
)DOC");
|
|
}
|
|
};
|
|
|
|
class SquaredL2DistanceGradOp : public framework::OperatorWithKernel {
|
|
public:
|
|
using framework::OperatorWithKernel::OperatorWithKernel;
|
|
|
|
void InferShape(framework::InferShapeContext* ctx) const override {
|
|
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
|
|
"Gradient of Out should not be null");
|
|
auto out_dims = ctx->GetInputDim(framework::GradVarName("Out"));
|
|
auto x_dims = ctx->GetInputDim("X");
|
|
auto y_dims = ctx->GetInputDim("Y");
|
|
PADDLE_ENFORCE_EQ(out_dims[0], x_dims[0],
|
|
"First dimension of output gradient and "
|
|
"input value must be equal.");
|
|
PADDLE_ENFORCE_EQ(out_dims[1], 1,
|
|
"Second dimension of output gradient "
|
|
"must be 1.");
|
|
auto x_grad_name = framework::GradVarName("X");
|
|
auto y_grad_name = framework::GradVarName("Y");
|
|
if (ctx->HasOutput(x_grad_name)) ctx->SetOutputDim(x_grad_name, x_dims);
|
|
if (ctx->HasOutput(y_grad_name)) ctx->SetOutputDim(y_grad_name, y_dims);
|
|
}
|
|
};
|
|
|
|
} // namespace operators
|
|
} // namespace paddle
|
|
|
|
namespace ops = paddle::operators;
|
|
REGISTER_OPERATOR(squared_l2_distance, ops::SquaredL2DistanceOp,
|
|
ops::SquaredL2DistanceOpMaker,
|
|
paddle::framework::DefaultGradOpDescMaker<true>);
|
|
REGISTER_OPERATOR(squared_l2_distance_grad, ops::SquaredL2DistanceGradOp);
|
|
REGISTER_OP_CPU_KERNEL(
|
|
squared_l2_distance,
|
|
ops::SquaredL2DistanceKernel<paddle::platform::CPUDeviceContext, float>);
|
|
REGISTER_OP_CPU_KERNEL(squared_l2_distance_grad,
|
|
ops::SquaredL2DistanceGradKernel<
|
|
paddle::platform::CPUDeviceContext, float>);
|