-
-
Notifications
You must be signed in to change notification settings - Fork 106
/
bisenetv1.py
195 lines (183 loc) · 8.67 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
'''
Function:
Implementation of BiSeNetV1
Author:
Zhenchao Jin
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
from ...utils import loadpretrainedweights
from .bricks import BuildNormalization, BuildActivation
'''DEFAULT_MODEL_URLS'''
DEFAULT_MODEL_URLS = {}
'''AUTO_ASSERT_STRUCTURE_TYPES'''
AUTO_ASSERT_STRUCTURE_TYPES = {}
'''SpatialPath'''
class SpatialPath(nn.Module):
def __init__(self, in_channels=3, num_channels_list=(64, 64, 64, 128), norm_cfg=None, act_cfg=None):
super(SpatialPath, self).__init__()
assert len(num_channels_list) == 4
self.layers = []
for idx in range(len(num_channels_list)):
layer_name = f'layer{idx + 1}'
self.layers.append(layer_name)
if idx == 0:
conv = nn.Sequential(
nn.Conv2d(in_channels, num_channels_list[idx], kernel_size=7, stride=2, padding=3, bias=False),
BuildNormalization(placeholder=num_channels_list[idx], norm_cfg=norm_cfg),
BuildActivation(act_cfg),
)
elif idx == len(num_channels_list) - 1:
conv = nn.Sequential(
nn.Conv2d(num_channels_list[idx - 1], num_channels_list[idx], kernel_size=1, stride=1, padding=0, bias=False),
BuildNormalization(placeholder=num_channels_list[idx], norm_cfg=norm_cfg),
BuildActivation(act_cfg),
)
else:
conv = nn.Sequential(
nn.Conv2d(num_channels_list[idx - 1], num_channels_list[idx], kernel_size=3, stride=2, padding=1, bias=False),
BuildNormalization(placeholder=num_channels_list[idx], norm_cfg=norm_cfg),
BuildActivation(act_cfg),
)
self.add_module(layer_name, conv)
'''forward'''
def forward(self, x):
for idx, layer_name in enumerate(self.layers):
layer_stage = getattr(self, layer_name)
x = layer_stage(x)
return x
'''AttentionRefinementModule'''
class AttentionRefinementModule(nn.Module):
def __init__(self, in_channels, out_channels, norm_cfg=None, act_cfg=None):
super(AttentionRefinementModule, self).__init__()
self.conv_layer = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False),
BuildNormalization(placeholder=out_channels, norm_cfg=norm_cfg),
BuildActivation(act_cfg),
)
self.atten_conv_layer = nn.Sequential(
nn.AdaptiveAvgPool2d((1, 1)),
nn.Conv2d(out_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=False),
BuildNormalization(placeholder=out_channels, norm_cfg=norm_cfg),
nn.Sigmoid(),
)
'''forward'''
def forward(self, x):
x = self.conv_layer(x)
x_atten = self.atten_conv_layer(x)
x_out = x * x_atten
return x_out
'''ContextPath'''
class ContextPath(nn.Module):
def __init__(self, backbone_cfg, context_channels_list=(128, 256, 512), norm_cfg=None, act_cfg=None):
super(ContextPath, self).__init__()
assert len(context_channels_list) == 3
if 'norm_cfg' not in backbone_cfg: backbone_cfg['norm_cfg'] = norm_cfg
self.backbone_net = self.buildbackbone(backbone_cfg)
self.arm16 = AttentionRefinementModule(context_channels_list[1], context_channels_list[0], norm_cfg=norm_cfg, act_cfg=act_cfg)
self.arm32 = AttentionRefinementModule(context_channels_list[2], context_channels_list[0], norm_cfg=norm_cfg, act_cfg=act_cfg)
self.conv_head32 = nn.Sequential(
nn.Conv2d(context_channels_list[0], context_channels_list[0], kernel_size=3, stride=1, padding=1, bias=False),
BuildNormalization(placeholder=context_channels_list[0], norm_cfg=norm_cfg),
BuildActivation(act_cfg),
)
self.conv_head16 = nn.Sequential(
nn.Conv2d(context_channels_list[0], context_channels_list[0], kernel_size=3, stride=1, padding=1, bias=False),
BuildNormalization(placeholder=context_channels_list[0], norm_cfg=norm_cfg),
BuildActivation(act_cfg),
)
self.gap_conv = nn.Sequential(
nn.AdaptiveAvgPool2d((1, 1)),
nn.Conv2d(context_channels_list[2], context_channels_list[0], kernel_size=1, stride=1, padding=0, bias=False),
BuildNormalization(placeholder=context_channels_list[0], norm_cfg=norm_cfg),
BuildActivation(act_cfg),
)
'''forward'''
def forward(self, x):
x_4, x_8, x_16, x_32 = self.backbone_net(x)
x_gap = self.gap_conv(x_32)
x_32_arm = self.arm32(x_32)
x_32_sum = x_32_arm + x_gap
x_32_up = F.interpolate(input=x_32_sum, size=x_16.shape[2:], mode='nearest')
x_32_up = self.conv_head32(x_32_up)
x_16_arm = self.arm16(x_16)
x_16_sum = x_16_arm + x_32_up
x_16_up = F.interpolate(input=x_16_sum, size=x_8.shape[2:], mode='nearest')
x_16_up = self.conv_head16(x_16_up)
return x_16_up, x_32_up
'''buildbackbone'''
def buildbackbone(self, cfg):
from .resnet import ResNet
supported_backbones = {
'ResNet': ResNet,
}
backbone_type = cfg.pop('type')
assert backbone_type, f'unsupport backbone type {backbone_type}'
return supported_backbones[backbone_type](**cfg)
'''FeatureFusionModule'''
class FeatureFusionModule(nn.Module):
def __init__(self, in_channels, out_channels, norm_cfg=None, act_cfg=None):
super(FeatureFusionModule, self).__init__()
self.conv1 = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=False),
BuildNormalization(placeholder=out_channels, norm_cfg=norm_cfg),
BuildActivation(act_cfg),
)
self.gap = nn.AdaptiveAvgPool2d((1, 1))
self.conv_atten = nn.Sequential(
nn.Conv2d(out_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=False),
BuildNormalization(placeholder=out_channels, norm_cfg=norm_cfg),
BuildActivation(act_cfg),
nn.Sigmoid(),
)
'''forward'''
def forward(self, x_sp, x_cp):
x_concat = torch.cat([x_sp, x_cp], dim=1)
x_fuse = self.conv1(x_concat)
x_atten = self.gap(x_fuse)
x_atten = self.conv_atten(x_atten)
x_atten = x_fuse * x_atten
x_out = x_atten + x_fuse
return x_out
'''BiSeNetV1'''
class BiSeNetV1(nn.Module):
def __init__(self, structure_type, backbone_cfg=None, in_channels=3, spatial_channels_list=(64, 64, 64, 128),
context_channels_list=(128, 256, 512), out_indices=(0, 1, 2), out_channels=256, norm_cfg={'type': 'SyncBatchNorm'},
act_cfg={'type': 'ReLU', 'inplace': True}, pretrained=False, pretrained_model_path=''):
super(BiSeNetV1, self).__init__()
assert (len(spatial_channels_list) == 4) and (len(context_channels_list) == 3)
# set attributes
self.structure_type = structure_type
self.backbone_cfg = backbone_cfg
self.in_channels = in_channels
self.spatial_channels_list = spatial_channels_list
self.context_channels_list = context_channels_list
self.out_indices = out_indices
self.out_channels = out_channels
self.norm_cfg = norm_cfg
self.act_cfg = act_cfg
self.pretrained = pretrained
self.pretrained_model_path = pretrained_model_path
# assert
if structure_type in AUTO_ASSERT_STRUCTURE_TYPES:
for key, value in AUTO_ASSERT_STRUCTURE_TYPES[structure_type].items():
assert hasattr(self, key) and (getattr(self, key) == value)
# set modules
self.context_path = ContextPath(backbone_cfg, context_channels_list, norm_cfg=norm_cfg, act_cfg=act_cfg)
self.spatial_path = SpatialPath(in_channels, spatial_channels_list, norm_cfg=norm_cfg, act_cfg=act_cfg)
self.ffm = FeatureFusionModule(context_channels_list[1], out_channels, norm_cfg=norm_cfg, act_cfg=act_cfg)
# load pretrained weights
if pretrained:
state_dict = loadpretrainedweights(
structure_type=structure_type, pretrained_model_path=pretrained_model_path, default_model_urls=DEFAULT_MODEL_URLS
)
self.load_state_dict(state_dict, strict=False)
'''forward'''
def forward(self, x):
x_context8, x_context16 = self.context_path(x)
x_spatial = self.spatial_path(x)
x_fuse = self.ffm(x_spatial, x_context8)
outs = [x_context8, x_context16, x_fuse]
outs = [outs[i] for i in self.out_indices]
return tuple(outs)