-
Notifications
You must be signed in to change notification settings - Fork 1.7k
/
Copy pathbisenetv1.py
263 lines (226 loc) · 9.47 KB
/
bisenetv1.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
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
# 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.
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddleseg.cvlibs import manager
from paddleseg.models import layers
from paddleseg.utils import utils
@manager.MODELS.add_component
class BiseNetV1(nn.Layer):
"""
The BiSeNetV1 implementation based on PaddlePaddle.
The original article refers to
Yu, Changqian, et al. "BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation"
(https://paperswithcode.com/paper/bisenet-bilateral-segmentation-network-for)
Args:
num_classes (int): The unique number of target classes.
backbone (paddle.nn.Layer): Backbone network, currently support Resnet18_vd/Resnet34_vd/Resnet50_vd/Resnet101_vd.
pretrained (str, optional): The path or url of pretrained model. Default: None.
"""
def __init__(self,
num_classes,
backbone,
conv_channel=128,
pretrained=None):
super().__init__()
self.backbone = backbone
self.spatial_path = SpatialPath(3, 128)
self.global_context = nn.Sequential(
nn.AdaptiveAvgPool2D(1),
layers.ConvBNReLU(512, conv_channel, 1, bias_attr=False),
)
self.arms = nn.LayerList([
AttentionRefinement(512, conv_channel),
AttentionRefinement(256, conv_channel),
])
self.refines = nn.LayerList([
layers.ConvBNReLU(conv_channel,
conv_channel,
3,
stride=1,
padding=1,
bias_attr=False),
layers.ConvBNReLU(conv_channel,
conv_channel,
3,
stride=1,
padding=1,
bias_attr=False),
])
self.heads = nn.LayerList([
BiSeNetHead(conv_channel, num_classes, 8, True),
BiSeNetHead(conv_channel, num_classes, 8, True),
BiSeNetHead(conv_channel * 2, num_classes, 8, False),
])
self.ffm = FeatureFusion(conv_channel * 2, conv_channel * 2, 1)
self.pretrained = pretrained
def init_weight(self):
if self.pretrained is not None:
utils.load_entire_model(self, self.pretrained)
def forward(self, x):
spatial_out = self.spatial_path(x)
context_blocks = self.backbone(x)
context_blocks.reverse()
global_context = self.global_context(context_blocks[0])
global_context = F.interpolate(global_context,
size=context_blocks[0].shape[2:],
mode='bilinear',
align_corners=True)
last_fm = global_context
pred_out = []
for i, (fm, arm, refine) in enumerate(
zip(context_blocks[:2], self.arms, self.refines)):
fm = arm(fm)
fm += last_fm
last_fm = F.interpolate(fm,
size=context_blocks[i + 1].shape[2:],
mode='bilinear',
align_corners=True)
last_fm = refine(last_fm)
pred_out.append(last_fm)
context_out = last_fm
concate_fm = self.ffm(spatial_out, context_out)
pred_out.append(concate_fm)
output = []
if self.training:
for i, head in enumerate(self.heads):
out = head(pred_out[i])
output.append(out)
else:
out = self.heads[-1](pred_out[-1])
output.append(out)
return output
class SpatialPath(nn.Layer):
"""
SpatialPath module of BiseNetV1 model
Args:
in_channels (int): The number of input channels in spatial path module.
out_channels (int): The number of output channels in spatial path module.
"""
def __init__(self, in_channels, out_channels, inner_channel=64):
super().__init__()
self.conv_7x7 = layers.ConvBNReLU(in_channels,
inner_channel,
7,
stride=2,
padding=3,
bias_attr=False)
self.conv_3x3_1 = layers.ConvBNReLU(inner_channel,
inner_channel,
3,
stride=2,
padding=1,
bias_attr=False)
self.conv_3x3_2 = layers.ConvBNReLU(inner_channel,
inner_channel,
3,
stride=2,
padding=1,
bias_attr=False)
self.conv_1x1 = layers.ConvBNReLU(inner_channel,
out_channels,
1,
bias_attr=False)
def forward(self, x):
x = self.conv_7x7(x)
x = self.conv_3x3_1(x)
x = self.conv_3x3_2(x)
x = self.conv_1x1(x)
return x
class BiSeNetHead(nn.Layer):
"""
BiSeNet head of BiseNetV1 model
Args:
in_channels (int): The number of input channels in spatial path module.
out_channels (int): The number of output channels in spatial path module.
scale (int, float): The scale factor of interpolation.
"""
def __init__(self, in_channels, out_channels, scale, is_aux=False):
super().__init__()
inner_channel = 128 if is_aux else 64
self.conv_3x3 = layers.ConvBNReLU(in_channels,
inner_channel,
3,
stride=1,
padding=1,
bias_attr=False)
self.conv_1x1 = nn.Conv2D(inner_channel, out_channels, 1)
self.scale = scale
def forward(self, x):
x = self.conv_3x3(x)
x = self.conv_1x1(x)
if self.scale > 1:
x = F.interpolate(x,
scale_factor=self.scale,
mode='bilinear',
align_corners=True)
return x
class AttentionRefinement(nn.Layer):
"""
AttentionRefinement module of BiseNetV1 model
Args:
in_channels (int): The number of input channels in spatial path module.
out_channels (int): The number of output channels in spatial path module.
"""
def __init__(self, in_channels, out_channels):
super().__init__()
self.conv_3x3 = layers.ConvBNReLU(in_channels,
out_channels,
3,
stride=1,
padding=1,
bias_attr=False)
self.channel_attention = nn.Sequential(
nn.AdaptiveAvgPool2D(1),
layers.ConvBNReLU(out_channels, out_channels, 1, bias_attr=False),
nn.Sigmoid(),
)
def forward(self, x):
x = self.conv_3x3(x)
se = self.channel_attention(x)
x = x * se
return x
class FeatureFusion(nn.Layer):
"""
AttentionRefinement module of BiseNetV1 model
Args:
in_channels (int): The number of input channels in spatial path module.
out_channels (int): The number of output channels in spatial path module.
reduction (int): A factor shrinks convolutional channels. Default: 1.
"""
def __init__(self, in_channels, out_channels, reduction=1):
super().__init__()
self.conv_1x1 = layers.ConvBNReLU(in_channels,
out_channels,
1,
bias_attr=False)
self.channel_attention = nn.Sequential(
nn.AdaptiveAvgPool2D(1),
layers.ConvBNReLU(out_channels,
out_channels // reduction,
1,
bias_attr=False),
layers.ConvBNReLU(out_channels // reduction,
out_channels,
1,
bias_attr=False),
nn.Sigmoid(),
)
def forward(self, x1, x2):
fm = paddle.concat([x1, x2], axis=1)
fm = self.conv_1x1(fm)
fm_se = self.channel_attention(fm)
output = fm + fm * fm_se
return output