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.
256 lines
9.4 KiB
256 lines
9.4 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.
|
|
Indicesou 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/framework/data_layout.h"
|
|
#include "paddle/fluid/framework/eigen.h"
|
|
#include "paddle/fluid/framework/op_registry.h"
|
|
|
|
namespace paddle {
|
|
namespace operators {
|
|
|
|
class AffineChannelOpMaker : public framework::OpProtoAndCheckerMaker {
|
|
public:
|
|
void Make() override {
|
|
AddInput("X",
|
|
"(Tensor) Feature map input can be a 4D tensor with order NCHW "
|
|
"or NHWC. It also can be a 2D tensor and C is the second "
|
|
"dimension.");
|
|
AddInput("Scale",
|
|
"(Tensor) 1D input of shape (C), the c-th element "
|
|
"is the scale factor of the affine transformation "
|
|
"for the c-th channel of the input.");
|
|
AddInput("Bias",
|
|
"(Tensor) 1D input of shape (C), the c-th element "
|
|
"is the bias of the affine transformation for the "
|
|
"c-th channel of the input.");
|
|
AddAttr<std::string>(
|
|
"data_layout",
|
|
"(string, default NCHW) Only used in "
|
|
"An optional string from: \"NHWC\", \"NCHW\". "
|
|
"Defaults to \"NHWC\". Specify the data format of the output data, "
|
|
"the input will be transformed automatically. ")
|
|
.SetDefault("AnyLayout");
|
|
AddOutput("Out", "(Tensor) A tensor of the same shape and order with X.");
|
|
AddComment(R"DOC(
|
|
|
|
Applies a separate affine transformation to each channel of the input. Useful
|
|
for replacing spatial batch norm with its equivalent fixed transformation.
|
|
The input also can be 2D tensor and applies a affine transformation in second
|
|
dimension.
|
|
|
|
$$Out = Scale*X + Bias$$
|
|
|
|
)DOC");
|
|
}
|
|
};
|
|
|
|
class AffineChannelOp : public framework::OperatorWithKernel {
|
|
public:
|
|
using framework::OperatorWithKernel::OperatorWithKernel;
|
|
void InferShape(framework::InferShapeContext* ctx) const override {
|
|
PADDLE_ENFORCE(ctx->HasInput("X"),
|
|
"Input(X) of AffineChannelOp should not be null.");
|
|
PADDLE_ENFORCE(ctx->HasInput("Scale"),
|
|
"Input(Scale) of AffineChannelOp should not be null.");
|
|
PADDLE_ENFORCE(ctx->HasInput("Bias"),
|
|
"Input(Bias) of AffineChannelOp should not be null.");
|
|
PADDLE_ENFORCE(ctx->HasOutput("Out"),
|
|
"Output(Out) of AffineChannelOp should not be null.");
|
|
ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
|
|
ctx->ShareLoD("X", "Out");
|
|
}
|
|
};
|
|
|
|
class AffineChannelOpGrad : public framework::OperatorWithKernel {
|
|
public:
|
|
using framework::OperatorWithKernel::OperatorWithKernel;
|
|
void InferShape(framework::InferShapeContext* ctx) const override {
|
|
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
|
|
"Input(Out@GRAD) should not be null.");
|
|
if (ctx->HasOutput(framework::GradVarName("X"))) {
|
|
PADDLE_ENFORCE(ctx->HasInput("Scale"),
|
|
"Input(Scale) should not be null.");
|
|
ctx->SetOutputDim(framework::GradVarName("X"),
|
|
ctx->GetInputDim(framework::GradVarName("Out")));
|
|
}
|
|
if (ctx->HasOutput(framework::GradVarName("Scale"))) {
|
|
// Scale@GRAD and Bias@GRAD must exist at the same time.
|
|
PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("Bias")),
|
|
"Output(Scale@GRAD) should not be null.");
|
|
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null.");
|
|
ctx->SetOutputDim(framework::GradVarName("Scale"),
|
|
ctx->GetInputDim("Scale"));
|
|
ctx->SetOutputDim(framework::GradVarName("Bias"),
|
|
ctx->GetInputDim("Scale"));
|
|
}
|
|
}
|
|
};
|
|
|
|
template <typename T>
|
|
using EigenArrayMap =
|
|
Eigen::Map<Eigen::Array<T, Eigen::Dynamic, Eigen::Dynamic>>;
|
|
template <typename T>
|
|
using ConstEigenArrayMap =
|
|
Eigen::Map<const Eigen::Array<T, Eigen::Dynamic, Eigen::Dynamic>>;
|
|
template <typename T>
|
|
using EigenVectorArrayMap = Eigen::Map<Eigen::Array<T, Eigen::Dynamic, 1>>;
|
|
template <typename T>
|
|
using ConstEigenVectorArrayMap =
|
|
Eigen::Map<const Eigen::Array<T, Eigen::Dynamic, 1>>;
|
|
|
|
template <typename DeviceContext, typename T>
|
|
class AffineChannelKernel : public framework::OpKernel<T> {
|
|
public:
|
|
void Compute(const framework::ExecutionContext& ctx) const override {
|
|
auto* x = ctx.Input<framework::Tensor>("X");
|
|
auto* scale = ctx.Input<framework::Tensor>("Scale");
|
|
auto* bias = ctx.Input<framework::Tensor>("Bias");
|
|
|
|
auto* y = ctx.Output<framework::Tensor>("Out");
|
|
y->mutable_data<T>(ctx.GetPlace());
|
|
|
|
const framework::DataLayout layout =
|
|
framework::StringToDataLayout(ctx.Attr<std::string>("data_layout"));
|
|
|
|
auto dims = x->dims();
|
|
int N = dims[0];
|
|
int C = layout == framework::DataLayout::kNCHW ? dims[1]
|
|
: dims[dims.size() - 1];
|
|
int HxW = x->numel() / N / C;
|
|
|
|
auto* scale_d = scale->data<T>();
|
|
auto* bias_d = bias->data<T>();
|
|
ConstEigenVectorArrayMap<T> a_e(scale_d, C);
|
|
ConstEigenVectorArrayMap<T> b_e(bias_d, C);
|
|
|
|
auto* x_d = x->data<T>();
|
|
auto* y_d = y->data<T>();
|
|
if (layout == framework::DataLayout::kNCHW) {
|
|
int stride = C * HxW;
|
|
for (int i = 0; i < N; i++) {
|
|
ConstEigenArrayMap<T> x_e(x_d, HxW, C);
|
|
EigenArrayMap<T> y_e(y_d, HxW, C);
|
|
y_e = (x_e.rowwise() * a_e.transpose()).rowwise() + b_e.transpose();
|
|
x_d += stride;
|
|
y_d += stride;
|
|
}
|
|
} else {
|
|
int num = N * HxW;
|
|
ConstEigenArrayMap<T> x_e(x_d, C, num);
|
|
EigenArrayMap<T> y_e(y_d, C, num);
|
|
y_e = (x_e.colwise() * a_e).colwise() + b_e;
|
|
}
|
|
}
|
|
};
|
|
|
|
template <typename DeviceContext, typename T>
|
|
class AffineChannelGradKernel : public framework::OpKernel<T> {
|
|
public:
|
|
void Compute(const framework::ExecutionContext& ctx) const override {
|
|
auto* x = ctx.Input<framework::Tensor>("X");
|
|
auto* scale = ctx.Input<framework::Tensor>("Scale");
|
|
auto* dy = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
|
|
|
|
auto* dx = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
|
|
auto* dscale =
|
|
ctx.Output<framework::Tensor>(framework::GradVarName("Scale"));
|
|
auto* dbias = ctx.Output<framework::Tensor>(framework::GradVarName("Bias"));
|
|
|
|
const framework::DataLayout layout =
|
|
framework::StringToDataLayout(ctx.Attr<std::string>("data_layout"));
|
|
|
|
auto dims = x->dims();
|
|
int N = dims[0];
|
|
int C = layout == framework::DataLayout::kNCHW ? dims[1]
|
|
: dims[dims.size() - 1];
|
|
int HxW = x->numel() / N / C;
|
|
|
|
auto* x_d = x->data<T>();
|
|
auto* dy_d = dy->data<T>();
|
|
auto* scale_d = scale->data<T>();
|
|
ConstEigenVectorArrayMap<T> scale_e(scale_d, C);
|
|
|
|
T* dx_d = dx ? dx->mutable_data<T>(ctx.GetPlace()) : nullptr;
|
|
T* dscale_d = dscale ? dscale->mutable_data<T>(ctx.GetPlace()) : nullptr;
|
|
T* dbias_d = dbias ? dbias->mutable_data<T>(ctx.GetPlace()) : nullptr;
|
|
EigenVectorArrayMap<T> dscale_e(dscale_d, C);
|
|
EigenVectorArrayMap<T> dbias_e(dbias_d, C);
|
|
|
|
if (layout == framework::DataLayout::kNCHW) {
|
|
// compute dx
|
|
int stride = C * HxW;
|
|
if (dx) {
|
|
for (int i = 0; i < N; i++) {
|
|
ConstEigenArrayMap<T> dy_e(dy_d, HxW, C);
|
|
EigenArrayMap<T> dx_e(dx_d, HxW, C);
|
|
dx_e = dy_e.rowwise() * scale_e.transpose();
|
|
dy_d += stride;
|
|
dx_d += stride;
|
|
}
|
|
}
|
|
// compute dscale and dbias
|
|
if (dscale && dbias) {
|
|
dy_d = dy->data<T>();
|
|
for (int i = 0; i < N; i++) {
|
|
ConstEigenArrayMap<T> x_e(x_d, HxW, C);
|
|
ConstEigenArrayMap<T> dy_e(dy_d, HxW, C);
|
|
if (i == 0) {
|
|
dscale_e = (x_e * dy_e).colwise().sum();
|
|
} else {
|
|
dscale_e += (x_e * dy_e).colwise().sum();
|
|
}
|
|
if (i == 0) {
|
|
dbias_e = dy_e.colwise().sum();
|
|
} else {
|
|
dbias_e += dy_e.colwise().sum();
|
|
}
|
|
x_d += stride;
|
|
dy_d += stride;
|
|
}
|
|
}
|
|
} else {
|
|
int num = N * HxW;
|
|
ConstEigenArrayMap<T> dy_e(dy_d, C, num);
|
|
// compute dx
|
|
if (dx) {
|
|
EigenArrayMap<T> dx_e(dx_d, C, num);
|
|
dx_e = dy_e.colwise() * scale_e;
|
|
}
|
|
// compute dscale and dbias
|
|
if (dscale && dbias) {
|
|
ConstEigenArrayMap<T> x_e(x_d, C, num);
|
|
dscale_e = (x_e * dy_e).rowwise().sum();
|
|
dbias_e = dy_e.rowwise().sum();
|
|
}
|
|
}
|
|
}
|
|
};
|
|
|
|
} // namespace operators
|
|
} // namespace paddle
|
|
|
|
namespace ops = paddle::operators;
|
|
using CPU = paddle::platform::CPUDeviceContext;
|
|
|
|
REGISTER_OPERATOR(affine_channel, ops::AffineChannelOp,
|
|
ops::AffineChannelOpMaker,
|
|
paddle::framework::DefaultGradOpDescMaker<true>);
|
|
REGISTER_OPERATOR(affine_channel_grad, ops::AffineChannelOpGrad);
|
|
|
|
REGISTER_OP_CPU_KERNEL(affine_channel, ops::AffineChannelKernel<CPU, float>,
|
|
ops::AffineChannelKernel<CPU, double>);
|
|
REGISTER_OP_CPU_KERNEL(affine_channel_grad,
|
|
ops::AffineChannelGradKernel<CPU, float>,
|
|
ops::AffineChannelGradKernel<CPU, double>);
|