forked from PaddlePaddle/PaddleClas
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathtriplet.py
157 lines (137 loc) · 6.1 KB
/
triplet.py
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
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
# Copyright (c) 2018 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle
import paddle.nn as nn
class TripletLossV2(nn.Layer):
"""Triplet loss with hard positive/negative mining.
paper : [Facenet: A unified embedding for face recognition and clustering](https://arxiv.org/pdf/1503.03832.pdf)
code reference: https://github.com/okzhili/Cartoon-face-recognition/blob/master/loss/triplet_loss.py
Args:
margin (float): margin for triplet.
"""
def __init__(self,
margin=0.5,
normalize_feature=True,
feature_from="features"):
super(TripletLossV2, self).__init__()
self.margin = margin
self.feature_from = feature_from
self.ranking_loss = paddle.nn.loss.MarginRankingLoss(margin=margin)
self.normalize_feature = normalize_feature
def forward(self, input, target):
"""
Args:
inputs: feature matrix with shape (batch_size, feat_dim)
target: ground truth labels with shape (num_classes)
"""
inputs = input[self.feature_from]
if self.normalize_feature:
inputs = 1. * inputs / (paddle.expand_as(
paddle.norm(
inputs, p=2, axis=-1, keepdim=True), inputs) + 1e-12)
bs = inputs.shape[0]
# compute distance
dist = paddle.pow(inputs, 2).sum(axis=1, keepdim=True).expand([bs, bs])
dist = dist + dist.t()
dist = paddle.addmm(
input=dist, x=inputs, y=inputs.t(), alpha=-2.0, beta=1.0)
dist = paddle.clip(dist, min=1e-12).sqrt()
# hard negative mining
is_pos = paddle.expand(target, (
bs, bs)).equal(paddle.expand(target, (bs, bs)).t())
is_neg = paddle.expand(target, (
bs, bs)).not_equal(paddle.expand(target, (bs, bs)).t())
# `dist_ap` means distance(anchor, positive)
## both `dist_ap` and `relative_p_inds` with shape [N, 1]
'''
dist_ap, relative_p_inds = paddle.max(
paddle.reshape(dist[is_pos], (bs, -1)), axis=1, keepdim=True)
# `dist_an` means distance(anchor, negative)
# both `dist_an` and `relative_n_inds` with shape [N, 1]
dist_an, relative_n_inds = paddle.min(
paddle.reshape(dist[is_neg], (bs, -1)), axis=1, keepdim=True)
'''
dist_ap = paddle.max(paddle.reshape(
paddle.masked_select(dist, is_pos), (bs, -1)),
axis=1,
keepdim=True)
# `dist_an` means distance(anchor, negative)
# both `dist_an` and `relative_n_inds` with shape [N, 1]
dist_an = paddle.min(paddle.reshape(
paddle.masked_select(dist, is_neg), (bs, -1)),
axis=1,
keepdim=True)
# shape [N]
dist_ap = paddle.squeeze(dist_ap, axis=1)
dist_an = paddle.squeeze(dist_an, axis=1)
# Compute ranking hinge loss
y = paddle.ones_like(dist_an)
loss = self.ranking_loss(dist_an, dist_ap, y)
return {"TripletLossV2": loss}
class TripletLoss(nn.Layer):
"""Triplet loss with hard positive/negative mining.
Reference:
Hermans et al. In Defense of the Triplet Loss for Person Re-Identification. arXiv:1703.07737.
Code imported from https://github.com/Cysu/open-reid/blob/master/reid/loss/triplet.py.
Args:
margin (float): margin for triplet.
"""
def __init__(self, margin=1.0):
super(TripletLoss, self).__init__()
self.margin = margin
self.ranking_loss = paddle.nn.loss.MarginRankingLoss(margin=margin)
def forward(self, input, target):
"""
Args:
inputs: feature matrix with shape (batch_size, feat_dim)
target: ground truth labels with shape (num_classes)
"""
inputs = input["features"]
bs = inputs.shape[0]
# Compute pairwise distance, replace by the official when merged
dist = paddle.pow(inputs, 2).sum(axis=1, keepdim=True).expand([bs, bs])
dist = dist + dist.t()
dist = paddle.addmm(
input=dist, x=inputs, y=inputs.t(), alpha=-2.0, beta=1.0)
dist = paddle.clip(dist, min=1e-12).sqrt()
mask = paddle.equal(
target.expand([bs, bs]), target.expand([bs, bs]).t())
mask_numpy_idx = mask.numpy()
dist_ap, dist_an = [], []
for i in range(bs):
# dist_ap_i = paddle.to_tensor(dist[i].numpy()[mask_numpy_idx[i]].max(),dtype='float64').unsqueeze(0)
# dist_ap_i.stop_gradient = False
# dist_ap.append(dist_ap_i)
dist_ap.append(
max([
dist[i][j] if mask_numpy_idx[i][j] == True else float(
"-inf") for j in range(bs)
]).unsqueeze(0))
# dist_an_i = paddle.to_tensor(dist[i].numpy()[mask_numpy_idx[i] == False].min(), dtype='float64').unsqueeze(0)
# dist_an_i.stop_gradient = False
# dist_an.append(dist_an_i)
dist_an.append(
min([
dist[i][k] if mask_numpy_idx[i][k] == False else float(
"inf") for k in range(bs)
]).unsqueeze(0))
dist_ap = paddle.concat(dist_ap, axis=0)
dist_an = paddle.concat(dist_an, axis=0)
# Compute ranking hinge loss
y = paddle.ones_like(dist_an)
loss = self.ranking_loss(dist_an, dist_ap, y)
return {"TripletLoss": loss}