forked from SegmentationBLWX/sssegmentation
-
Notifications
You must be signed in to change notification settings - Fork 1
/
mobilenet.py
260 lines (254 loc) · 13.2 KB
/
mobilenet.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
'''
Function:
Implementation of MobileNet
Author:
Zhenchao Jin
'''
import os
import torch
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
from .bricks import makedivisible, BuildNormalization, BuildActivation, AdptivePaddingConv2d, InvertedResidual, InvertedResidualV3
'''DEFAULT_MODEL_URLS'''
DEFAULT_MODEL_URLS = {
'mobilenetv2': 'https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth',
'mobilenetv3_small': 'https://download.openmmlab.com/pretrain/third_party/mobilenet_v3_small-47085aa1.pth',
'mobilenetv3_large': 'https://download.openmmlab.com/pretrain/third_party/mobilenet_v3_large-bc2c3fd3.pth',
}
'''AUTO_ASSERT_STRUCTURE_TYPES'''
AUTO_ASSERT_STRUCTURE_TYPES = {}
'''MobileNetV2'''
class MobileNetV2(nn.Module):
arch_settings = [[1, 16, 1], [6, 24, 2], [6, 32, 3], [6, 64, 4], [6, 96, 3], [6, 160, 3], [6, 320, 1]]
def __init__(self, structure_type, in_channels=3, widen_factor=1, outstride=8, out_indices=(1, 2, 4, 6), norm_cfg={'type': 'SyncBatchNorm'},
act_cfg={'type': 'ReLU6', 'inplace': True}, pretrained=True, pretrained_model_path=''):
super(MobileNetV2, self).__init__()
# set attributes
self.out_indices = out_indices
self.structure_type = structure_type
self.in_channels = in_channels
self.widen_factor = widen_factor
self.outstride = outstride
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)
# parse outstride
outstride_to_strides_and_dilations = {
8: ((1, 2, 2, 1, 1, 1, 1), (1, 1, 1, 2, 2, 4, 4)),
16: ((1, 2, 2, 2, 1, 1, 1), (1, 1, 1, 1, 1, 2, 2)),
32: ((1, 2, 2, 2, 1, 2, 1), (1, 1, 1, 1, 1, 1, 1)),
}
assert outstride in outstride_to_strides_and_dilations, 'unsupport outstride %s in MobileNetV2' % outstride
stride_list, dilation_list = outstride_to_strides_and_dilations[outstride]
# conv1
self.in_channels = makedivisible(32 * widen_factor, 8)
self.conv1 = nn.Sequential()
self.conv1.add_module('conv', nn.Conv2d(in_channels, self.in_channels, kernel_size=3, stride=2, padding=1, bias=False))
self.conv1.add_module('bn', BuildNormalization(placeholder=self.in_channels, norm_cfg=norm_cfg))
self.conv1.add_module('activation', BuildActivation(act_cfg))
# make layers
self.layers = []
for i, layer_cfg in enumerate(self.arch_settings):
expand_ratio, channel, num_blocks = layer_cfg
stride = stride_list[i]
dilation = dilation_list[i]
out_channels = makedivisible(channel * widen_factor, 8)
inverted_res_layer = self.makelayer(out_channels, num_blocks, stride, dilation, expand_ratio, norm_cfg, act_cfg)
layer_name = f'layer{i + 1}'
self.add_module(layer_name, inverted_res_layer)
self.layers.append(layer_name)
# load pretrained weights
if pretrained:
self.loadpretrainedweights(structure_type, pretrained_model_path)
'''loadpretrainedweights'''
def loadpretrainedweights(self, structure_type='mobilenetv2', pretrained_model_path=''):
if pretrained_model_path and os.path.exists(pretrained_model_path):
checkpoint = torch.load(pretrained_model_path, map_location='cpu')
else:
checkpoint = model_zoo.load_url(DEFAULT_MODEL_URLS[structure_type], map_location='cpu')
if 'state_dict' in checkpoint:
state_dict = checkpoint['state_dict']
else:
state_dict = checkpoint
keys = list(state_dict.keys())
for key in keys:
if key.startswith('backbone.'):
value = state_dict.pop(key)
key = '.'.join(key.split('.')[1:])
state_dict[key] = value
self.load_state_dict(state_dict, strict=False)
'''forward'''
def forward(self, x):
x = self.conv1(x)
outs = []
for idx, layer_name in enumerate(self.layers):
layer = getattr(self, layer_name)
x = layer(x)
if idx in self.out_indices:
outs.append(x)
return tuple(outs)
'''makelayer'''
def makelayer(self, out_channels, num_blocks, stride, dilation, expand_ratio, norm_cfg=None, act_cfg=None):
if act_cfg is None: act_cfg = {'type': 'ReLU6', 'inplace': True}
layers = []
for i in range(num_blocks):
layers.append(
InvertedResidual(
self.in_channels, out_channels, stride=stride if i == 0 else 1, expand_ratio=expand_ratio,
dilation=dilation if i == 0 else 1, norm_cfg=norm_cfg, act_cfg=act_cfg
)
)
self.in_channels = out_channels
return nn.Sequential(*layers)
'''MobileNetV3'''
class MobileNetV3(nn.Module):
arch_settings = {
'small': [
[3, 16, 16, True, {'type': 'ReLU'}, 2], [3, 72, 24, False, {'type': 'ReLU'}, 2], [3, 88, 24, False, {'type': 'ReLU'}, 1],
[5, 96, 40, True, {'type': 'HardSwish'}, 2], [5, 240, 40, True, {'type': 'HardSwish'}, 1], [5, 240, 40, True, {'type': 'HardSwish'}, 1],
[5, 120, 48, True, {'type': 'HardSwish'}, 1], [5, 144, 48, True, {'type': 'HardSwish'}, 1], [5, 288, 96, True, {'type': 'HardSwish'}, 2],
[5, 576, 96, True, {'type': 'HardSwish'}, 1], [5, 576, 96, True, {'type': 'HardSwish'}, 1],
],
'large': [
[3, 16, 16, False, {'type': 'ReLU'}, 1], [3, 64, 24, False, {'type': 'ReLU'}, 2], [3, 72, 24, False, {'type': 'ReLU'}, 1],
[5, 72, 40, True, {'type': 'ReLU'}, 2], [5, 120, 40, True, {'type': 'ReLU'}, 1], [5, 120, 40, True, {'type': 'ReLU'}, 1],
[3, 240, 80, False, {'type': 'HardSwish'}, 2], [3, 200, 80, False, {'type': 'HardSwish'}, 1], [3, 184, 80, False, {'type': 'HardSwish'}, 1],
[3, 184, 80, False, {'type': 'HardSwish'}, 1], [3, 480, 112, True, {'type': 'HardSwish'}, 1], [3, 672, 112, True, {'type': 'HardSwish'}, 1],
[5, 672, 160, True, {'type': 'HardSwish'}, 2], [5, 960, 160, True, {'type': 'HardSwish'}, 1], [5, 960, 160, True, {'type': 'HardSwish'}, 1],
],
}
def __init__(self, structure_type, in_channels=3, arch_type='large', outstride=8, out_indices=(1, 3, 16), reduction_factor=1, norm_cfg={'type': 'SyncBatchNorm'},
act_cfg={'type': 'HardSwish'}, pretrained=True, pretrained_model_path=''):
super(MobileNetV3, self).__init__()
# set attributes
self.structure_type = structure_type
self.in_channels = in_channels
self.arch_type = arch_type
self.outstride = outstride
self.out_indices = out_indices
self.reduction_factor = reduction_factor
self.norm_cfg = norm_cfg
self.act_cfg = act_cfg
self.pretrained = pretrained
self.pretrained_model_path = pretrained_model_path
# assert
assert arch_type in self.arch_settings
assert isinstance(reduction_factor, int) and reduction_factor > 0
assert outstride in [8, 16, 32], 'unsupport outstride %s in MobileNetV3' % outstride
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 layers
self.layers = self.makelayers(in_channels, arch_type, reduction_factor, outstride, norm_cfg, act_cfg)
# load pretrained weights
if pretrained:
self.loadpretrainedweights(structure_type, pretrained_model_path)
'''loadpretrainedweights'''
def loadpretrainedweights(self, structure_type='mobilenetv3_small', pretrained_model_path=''):
if pretrained_model_path and os.path.exists(pretrained_model_path):
checkpoint = torch.load(pretrained_model_path, map_location='cpu')
else:
checkpoint = model_zoo.load_url(DEFAULT_MODEL_URLS[structure_type], map_location='cpu')
if 'state_dict' in checkpoint:
state_dict = checkpoint['state_dict']
else:
state_dict = checkpoint
keys = list(state_dict.keys())
for key in keys:
if key.startswith('backbone.'):
value = state_dict.pop(key)
key = '.'.join(key.split('.')[1:])
state_dict[key] = value
self.load_state_dict(state_dict, strict=False)
'''make layers'''
def makelayers(self, in_channels, arch_type, reduction_factor, outstride, norm_cfg=None, act_cfg=None):
layers, act_cfg_default = [], act_cfg.copy()
# build the first layer
in_channels_first_layer, in_channels = in_channels, 16
layer = nn.Sequential()
layer.add_module('conv', AdptivePaddingConv2d(in_channels_first_layer, in_channels, kernel_size=3, stride=2, padding=1, bias=False))
layer.add_module('bn', BuildNormalization(placeholder=in_channels, norm_cfg=norm_cfg))
layer.add_module('activation', BuildActivation(act_cfg_default))
self.add_module('layer0', layer)
layers.append('layer0')
# build the middle layers
layer_setting = self.arch_settings[arch_type]
for i, params in enumerate(layer_setting):
(kernel_size, mid_channels, out_channels, with_se, act_cfg, stride) = params
if (arch_type == 'large' and i >= 12) or (arch_type == 'small' and i >= 8):
mid_channels = mid_channels // reduction_factor
out_channels = out_channels // reduction_factor
se_cfg = None
if with_se:
se_cfg = {
'channels': mid_channels, 'ratio': 4, 'act_cfgs': ({'type': 'ReLU'}, {'type': 'HardSigmoid', 'bias': 3.0, 'divisor': 6.0})
}
layer = InvertedResidualV3(
in_channels=in_channels, out_channels=out_channels, mid_channels=mid_channels, kernel_size=kernel_size,
stride=stride, se_cfg=se_cfg, with_expand_conv=(in_channels != mid_channels), norm_cfg=norm_cfg, act_cfg=act_cfg,
)
in_channels = out_channels
layer_name = 'layer{}'.format(i + 1)
self.add_module(layer_name, layer)
layers.append(layer_name)
# build the last layer
out_channels = 576 if arch_type == 'small' else 960
layer = nn.Sequential()
layer.add_module('conv', nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, dilation={8: 4, 16: 2, 32: 1}[outstride], padding=0, bias=False))
layer.add_module('bn', BuildNormalization(placeholder=out_channels, norm_cfg=norm_cfg))
layer.add_module('activation', BuildActivation(act_cfg_default))
layer_name = 'layer{}'.format(len(layer_setting) + 1)
self.add_module(layer_name, layer)
layers.append(layer_name)
# convert backbone MobileNetV3 to a semantic segmentation version
if outstride == 32: return layers
if arch_type == 'small':
self.layer4.depthwise_conv[0].stride = (1, 1)
if outstride == 8:
self.layer9.depthwise_conv[0].stride = (1, 1)
for i in range(4, len(layers)):
layer = getattr(self, layers[i])
if isinstance(layer, InvertedResidualV3): modified_module = layer.depthwise_conv[0]
else: modified_module = layer[0]
if i < 9 or (outstride == 16):
modified_module.dilation = (2, 2)
pad = 2
else:
modified_module.dilation = (4, 4)
pad = 4
if not isinstance(modified_module, AdptivePaddingConv2d):
pad *= (modified_module.kernel_size[0] - 1) // 2
modified_module.padding = (pad, pad)
else:
self.layer7.depthwise_conv[0].stride = (1, 1)
if outstride == 8:
self.layer13.depthwise_conv[0].stride = (1, 1)
for i in range(7, len(layers)):
layer = getattr(self, layers[i])
if isinstance(layer, InvertedResidualV3): modified_module = layer.depthwise_conv[0]
else: modified_module = layer[0]
if i < 13 or (outstride == 16):
modified_module.dilation = (2, 2)
pad = 2
else:
modified_module.dilation = (4, 4)
pad = 4
if not isinstance(modified_module, AdptivePaddingConv2d):
pad *= (modified_module.kernel_size[0] - 1) // 2
modified_module.padding = (pad, pad)
# return layers
return layers
'''forward'''
def forward(self, x):
outs = []
for idx, layer_name in enumerate(self.layers):
layer = getattr(self, layer_name)
x = layer(x)
if idx in self.out_indices:
outs.append(x)
return tuple(outs)