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
|
/**
* Copyright (c) 2016-present, Facebook, Inc.
*
* 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 "sigmoid_cross_entropy_loss_op.h"
namespace caffe2 {
REGISTER_CPU_OPERATOR(
SigmoidCrossEntropyLoss,
SigmoidCrossEntropyLossOp<float, CPUContext>);
REGISTER_CPU_OPERATOR(
SigmoidCrossEntropyLossGradient,
SigmoidCrossEntropyLossGradientOp<float, CPUContext>);
OPERATOR_SCHEMA(SigmoidCrossEntropyLoss)
.NumInputs(2)
.NumOutputs(1)
.SetDoc(R"DOC(
Compute sigmoid activations followed by averaged binary cross entropy loss. The
target values may be in {-1, 0, 1}, where -1 indicates that the corresponding
sample should be ignored and {0, 1} correspond to the binary classes 0 and 1. By
default the loss is divided by the number of targets > -1 and then multiplied by
the `scale` op argument. The divisive normalization may be disable by setting
the op argument `normalize` to 0 (the multiplication by `scale` still takes
effect).
This op fuses sigmoid and cross entropy for numerical stability in both forward
and gradient computation.
)DOC")
.Arg(
"scale",
"(float) default 1.0; multiply the loss by this scale factor.")
.Arg(
"normalize",
"(int) default 1; if true, divide the loss by the number of targets > "
"-1.")
.Input(
0,
"X",
"Tensor of predicted logits (shape must be at least 1D).")
.Input(
1,
"targets",
"Tensor of targets of type int and same shape as logits X.")
.Output(
0,
"loss",
"Scalar loss.");
OPERATOR_SCHEMA(SigmoidCrossEntropyLossGradient)
.NumInputs(3)
.NumOutputs(1)
.Input(
0,
"X",
"See SigmoidCrossEntropyLoss.")
.Input(
1,
"targets",
"See SigmoidCrossEntropyLoss.")
.Input(
2,
"d_loss",
"Gradient of forward output 0 (loss).")
.Output(
0,
"dX",
"Gradient of forward input 0 (X).");
class GetSigmoidCrossEntropyLossGradient : public GradientMakerBase {
using GradientMakerBase::GradientMakerBase;
vector<OperatorDef> GetGradientDefs() override {
return SingleGradientDef(
"SigmoidCrossEntropyLossGradient",
"",
vector<string>{I(0), I(1), GO(0)},
vector<string>{GI(0)});
}
};
REGISTER_GRADIENT(SigmoidCrossEntropyLoss, GetSigmoidCrossEntropyLossGradient);
} // namespace caffe2
|
