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
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
|
/**
* 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_focal_loss_op.h"
namespace caffe2 {
REGISTER_CPU_OPERATOR(SigmoidFocalLoss, SigmoidFocalLossOp<float, CPUContext>);
REGISTER_CPU_OPERATOR(
SigmoidFocalLossGradient,
SigmoidFocalLossGradientOp<float, CPUContext>);
OPERATOR_SCHEMA(SigmoidFocalLoss)
.NumInputs(3)
.NumOutputs(1)
.SetDoc(R"DOC(
The binary form of Focal Loss designed for use in RetinaNet-like models.
The input is assumed to be unnormalized scores (sometimes called 'logits')
arranged in a 4D tensor with shape (N, C, H, W), where N is the number of
elements in the batch, H and W are the height and width, and C = num_anchors *
num_classes defines num_anchors 'groups' of logits, each of length
num_classes. For the binary form of Focal Loss, num_classes does not include
the background category. (So, for COCO, num_classes = 80, not 81.)
The binary form of focal loss is:
FL(p_t) = -alpha * (1 - p_t)**gamma * log(p_t),
where p = sigmoid(x), p_t = p or 1 - p depending on if the label is 1 or 0,
respectively.
See: https://arxiv.org/abs/1708.02002 for details.
)DOC")
.Arg(
"scale",
"(float) default 1.0; multiply the loss by this scale factor.")
.Arg(
"alpha",
"(float) default 0.25; Focal Loss's alpha hyper-parameter.")
.Arg(
"gamma",
"(float) default 1.0; Focal Loss's gamma hyper-parameter.")
.Arg(
"num_classes",
"(int) default 80; number of classes (excluding background).")
.Input(
0,
"logits",
"4D tensor of sigmoid inputs (called 'scores' or 'logits') with shape "
"(N, C, H, W), where C = num_anchors * num_classes.")
.Input(
1,
"labels",
"4D tensor of labels with shape (N, num_anchors, H, W). Each entry is "
"a class label in [0, num_classes - 1] (inclusive). The label "
"identifies the one class that should have a sigmoid target of 1.")
.Input(
2,
"normalizer",
"Scalar; the loss is normalized by 1 / max(1, normalizer)."
)
.Output(
0,
"loss",
"Scalar loss.");
OPERATOR_SCHEMA(SigmoidFocalLossGradient)
.NumInputs(4)
.NumOutputs(1)
.Input(
0,
"logits",
"See SigmoidFocalLoss.")
.Input(
1,
"labels",
"See SigmoidFocalLoss.")
.Input(
2,
"normalizer",
"See SigmoidFocalLoss.")
.Input(
3,
"d_loss",
"Gradient of forward output 0 (loss)")
.Output(
0,
"d_logits",
"Gradient of forward input 0 (logits)");
class GetSigmoidFocalLossGradient : public GradientMakerBase {
using GradientMakerBase::GradientMakerBase;
vector<OperatorDef> GetGradientDefs() override {
vector<string> blob_names{
{I(0), I(1), I(2), GO(0)},
};
return SingleGradientDef(
"SigmoidFocalLossGradient", "", blob_names, vector<string>{GI(0)});
}
};
REGISTER_GRADIENT(SigmoidFocalLoss, GetSigmoidFocalLossGradient);
} // namespace caffe2
|
