-
Notifications
You must be signed in to change notification settings - Fork 1.7k
/
isanet.py
200 lines (172 loc) · 8.34 KB
/
isanet.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
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
# Copyright (c) 2020 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.
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddleseg.models import layers
from paddleseg.cvlibs import manager
from paddleseg.utils import utils
@manager.MODELS.add_component
class ISANet(nn.Layer):
"""Interlaced Sparse Self-Attention for Semantic Segmentation.
The original article refers to Lang Huang, et al. "Interlaced Sparse Self-Attention for Semantic Segmentation"
(https://arxiv.org/abs/1907.12273).
Args:
num_classes (int): The unique number of target classes.
backbone (Paddle.nn.Layer): A backbone network.
backbone_indices (tuple): The values in the tuple indicate the indices of output of backbone.
isa_channels (int): The channels of ISA Module.
down_factor (tuple): Divide the height and width dimension to (Ph, PW) groups.
enable_auxiliary_loss (bool, optional): A bool value indicates whether adding auxiliary loss. Default: True.
align_corners (bool): An argument of F.interpolate. It should be set to False when the output size of feature
is even, e.g. 1024x512, otherwise it is True, e.g. 769x769. Default: False.
pretrained (str, optional): The path or url of pretrained model. Default: None.
"""
def __init__(self,
num_classes,
backbone,
backbone_indices=(2, 3),
isa_channels=256,
down_factor=(8, 8),
enable_auxiliary_loss=True,
align_corners=False,
pretrained=None):
super().__init__()
self.backbone = backbone
self.backbone_indices = backbone_indices
in_channels = [self.backbone.feat_channels[i] for i in backbone_indices]
self.head = ISAHead(num_classes, in_channels, isa_channels, down_factor,
enable_auxiliary_loss)
self.align_corners = align_corners
self.pretrained = pretrained
self.init_weight()
def forward(self, x):
feats = self.backbone(x)
feats = [feats[i] for i in self.backbone_indices]
logit_list = self.head(feats)
logit_list = [
F.interpolate(logit,
x.shape[2:],
mode='bilinear',
align_corners=self.align_corners,
align_mode=1) for logit in logit_list
]
return logit_list
def init_weight(self):
if self.pretrained is not None:
utils.load_entire_model(self, self.pretrained)
class ISAHead(nn.Layer):
"""
The ISAHead.
Args:
num_classes (int): The unique number of target classes.
in_channels (tuple): The number of input channels.
isa_channels (int): The channels of ISA Module.
down_factor (tuple): Divide the height and width dimension to (Ph, PW) groups.
enable_auxiliary_loss (bool, optional): A bool value indicates whether adding auxiliary loss. Default: True.
"""
def __init__(self, num_classes, in_channels, isa_channels, down_factor,
enable_auxiliary_loss):
super(ISAHead, self).__init__()
self.in_channels = in_channels[-1]
inter_channels = self.in_channels // 4
self.inter_channels = inter_channels
self.down_factor = down_factor
self.enable_auxiliary_loss = enable_auxiliary_loss
self.in_conv = layers.ConvBNReLU(self.in_channels,
inter_channels,
3,
bias_attr=False)
self.global_relation = SelfAttentionBlock(inter_channels, isa_channels)
self.local_relation = SelfAttentionBlock(inter_channels, isa_channels)
self.out_conv = layers.ConvBNReLU(inter_channels * 2,
inter_channels,
1,
bias_attr=False)
self.cls = nn.Sequential(nn.Dropout2D(p=0.1),
nn.Conv2D(inter_channels, num_classes, 1))
self.aux = nn.Sequential(
layers.ConvBNReLU(in_channels=1024,
out_channels=256,
kernel_size=3,
bias_attr=False), nn.Dropout2D(p=0.1),
nn.Conv2D(256, num_classes, 1))
def forward(self, feat_list):
C3, C4 = feat_list
x = self.in_conv(C4)
x_shape = paddle.shape(x)
P_h, P_w = self.down_factor
Q_h, Q_w = paddle.ceil(x_shape[2:3] / P_h).astype('int32'), paddle.ceil(
x_shape[3:4] / P_w).astype('int32')
pad_h, pad_w = (Q_h * P_h - x_shape[2:3]).astype('int32'), (
Q_w * P_w - x_shape[3:4]).astype('int32')
if pad_h > 0 or pad_w > 0:
padding = paddle.concat([
pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2
],
axis=0)
feat = F.pad(x, padding)
else:
feat = x
feat = feat.reshape([0, x_shape[1], Q_h, P_h, Q_w, P_w])
feat = feat.transpose([0, 3, 5, 1, 2,
4]).reshape([-1, self.inter_channels, Q_h, Q_w])
feat = self.global_relation(feat)
feat = feat.reshape([x_shape[0], P_h, P_w, x_shape[1], Q_h, Q_w])
feat = feat.transpose([0, 4, 5, 3, 1,
2]).reshape([-1, self.inter_channels, P_h, P_w])
feat = self.local_relation(feat)
feat = feat.reshape([x_shape[0], Q_h, Q_w, x_shape[1], P_h, P_w])
feat = feat.transpose([0, 3, 1, 4, 2, 5]).reshape(
[0, self.inter_channels, P_h * Q_h, P_w * Q_w])
if pad_h > 0 or pad_w > 0:
feat = paddle.slice(
feat,
axes=[2, 3],
starts=[pad_h // 2, pad_w // 2],
ends=[pad_h // 2 + x_shape[2], pad_w // 2 + x_shape[3]])
feat = self.out_conv(paddle.concat([feat, x], axis=1))
output = self.cls(feat)
if self.enable_auxiliary_loss:
auxout = self.aux(C3)
return [output, auxout]
else:
return [output]
class SelfAttentionBlock(layers.AttentionBlock):
"""General self-attention block/non-local block.
Args:
in_channels (int): Input channels of key/query feature.
channels (int): Output channels of key/query transform.
"""
def __init__(self, in_channels, channels):
super(SelfAttentionBlock, self).__init__(key_in_channels=in_channels,
query_in_channels=in_channels,
channels=channels,
out_channels=in_channels,
share_key_query=False,
query_downsample=None,
key_downsample=None,
key_query_num_convs=2,
key_query_norm=True,
value_out_num_convs=1,
value_out_norm=False,
matmul_norm=True,
with_out=False)
self.output_project = self.build_project(in_channels,
in_channels,
num_convs=1,
use_conv_module=True)
def forward(self, x):
context = super(SelfAttentionBlock, self).forward(x, x)
return self.output_project(context)