/
channel_shuffle_op.cc
100 lines (85 loc) · 3.72 KB
/
channel_shuffle_op.cc
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
// Copyright (c) 2021 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/framework/infershape_utils.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/op_version_registry.h"
#include "paddle/phi/core/infermeta_utils.h"
#include "paddle/phi/infermeta/backward.h"
#include "paddle/phi/infermeta/unary.h"
namespace paddle {
namespace operators {
class ChannelShuffleOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
};
class ChannelShuffleOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X",
"(Tensor, default Tensor<float>), "
"the input feature data of ChannelShuffleOp, the layout is "
"[N, C, H, W] or [N, H, W, C].");
AddOutput("Out",
"(Tensor, default Tensor<float>), the output of "
"ChannelShuffleOp. The layout is also [N, C, "
"H, W] or [N, H, W, C].");
AddAttr<int>("groups", "number of groups to divide channels in.");
AddAttr<std::string>(
"data_format",
"An optional string from: \"NHWC\", \"NCHW\". "
"Defaults to \"NHWC\", Specify the data format of the input data.")
.SetDefault("NCHW");
AddComment(R"DOC(
Channel Shuffle operator
This operator divides channels in a tensor of shape :math:`(*, C, H, W)`
into :math:`g` groups and rearranges them as :math:`(*, C/g, g, H, W)`
while keeping the original tensor shape.
Please refer to the paper:
`ShuffleNet: An Extremely Efficient Convolutional Neural Network for
Mobile Devices <https://arxiv.org/abs/1707.01083>`_
by Zhang et. al (2017) for more details.
)DOC");
}
};
class ChannelShuffleGradOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
};
template <typename T>
class ChannelShuffleGradOpMaker : public framework::SingleGradOpMaker<T> {
public:
using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
protected:
void Apply(GradOpPtr<T> op) const override {
op->SetType("channel_shuffle_grad");
op->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
op->SetAttrMap(this->Attrs());
op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
DECLARE_INFER_SHAPE_FUNCTOR(channel_shuffle, ChannelShuffleInferShapeFunctor,
PD_INFER_META(phi::ChannelShuffleInferMeta));
REGISTER_OPERATOR(channel_shuffle, ops::ChannelShuffleOp,
ops::ChannelShuffleOpMaker,
ops::ChannelShuffleGradOpMaker<paddle::framework::OpDesc>,
ops::ChannelShuffleGradOpMaker<paddle::imperative::OpBase>,
ChannelShuffleInferShapeFunctor);
DECLARE_INFER_SHAPE_FUNCTOR(channel_shuffle_grad,
ChannelShuffleGradInferShapeFunctor,
PD_INFER_META(phi::ChannelShuffleGradInferMeta));
REGISTER_OPERATOR(channel_shuffle_grad, ops::ChannelShuffleGradOp,
ChannelShuffleGradInferShapeFunctor);