mobilenet v2结构

发表于 | 阅读次数: 
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
MobileNetV2(
  (features): Sequential(
    (0): Sequential(
      (0): Conv2d(5, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True)
      (2): ReLU(inplace)
    )
    (1): InvertedResidual(
      (conv): Sequential(
        (0): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU6(inplace)
        (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
        (4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True)
        (5): ReLU6(inplace)
        (6): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (7): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (2): InvertedResidual(
      (conv): Sequential(
        (0): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU6(inplace)
        (3): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=96, bias=False)
        (4): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True)
        (5): ReLU6(inplace)
        (6): Conv2d(96, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (7): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (3): InvertedResidual(
      (conv): Sequential(
        (0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU6(inplace)
        (3): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
        (4): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True)
        (5): ReLU6(inplace)
        (6): Conv2d(144, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (7): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (4): InvertedResidual(
      (conv): Sequential(
        (0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU6(inplace)
        (3): Conv2d(144, 144, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=144, bias=False)
        (4): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True)
        (5): ReLU6(inplace)
        (6): Conv2d(144, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (7): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (5): InvertedResidual(
      (conv): Sequential(
        (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU6(inplace)
        (3): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)
        (4): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True)
        (5): ReLU6(inplace)
        (6): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (7): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (6): InvertedResidual(
      (conv): Sequential(
        (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU6(inplace)
        (3): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)
        (4): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True)
        (5): ReLU6(inplace)
        (6): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (7): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (7): InvertedResidual(
      (conv): Sequential(
        (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU6(inplace)
        (3): Conv2d(192, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=192, bias=False)
        (4): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True)
        (5): ReLU6(inplace)
        (6): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (7): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (8): InvertedResidual(
      (conv): Sequential(
        (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU6(inplace)
        (3): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)
        (4): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True)
        (5): ReLU6(inplace)
        (6): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (7): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (9): InvertedResidual(
      (conv): Sequential(
        (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU6(inplace)
        (3): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)
        (4): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True)
        (5): ReLU6(inplace)
        (6): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (7): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (10): InvertedResidual(
      (conv): Sequential(
        (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU6(inplace)
        (3): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)
        (4): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True)
        (5): ReLU6(inplace)
        (6): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (7): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (11): InvertedResidual(
      (conv): Sequential(
        (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU6(inplace)
        (3): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)
        (4): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True)
        (5): ReLU6(inplace)
        (6): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (7): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (12): InvertedResidual(
      (conv): Sequential(
        (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU6(inplace)
        (3): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)
        (4): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True)
        (5): ReLU6(inplace)
        (6): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (7): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (13): InvertedResidual(
      (conv): Sequential(
        (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU6(inplace)
        (3): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)
        (4): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True)
        (5): ReLU6(inplace)
        (6): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (7): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (14): InvertedResidual(
      (conv): Sequential(
        (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU6(inplace)
        (3): Conv2d(576, 576, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=576, bias=False)
        (4): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True)
        (5): ReLU6(inplace)
        (6): Conv2d(576, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (7): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (15): InvertedResidual(
      (conv): Sequential(
        (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU6(inplace)
        (3): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)
        (4): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True)
        (5): ReLU6(inplace)
        (6): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (7): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (16): InvertedResidual(
      (conv): Sequential(
        (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU6(inplace)
        (3): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)
        (4): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True)
        (5): ReLU6(inplace)
        (6): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (7): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (17): InvertedResidual(
      (conv): Sequential(
        (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU6(inplace)
        (3): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)
        (4): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True)
        (5): ReLU6(inplace)
        (6): Conv2d(960, 320, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (7): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (18): Sequential(
      (0): Conv2d(320, 1280, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (1): BatchNorm2d(1280, eps=1e-05, momentum=0.1, affine=True)
      (2): ReLU(inplace)
    )
    (19): AvgPool2d(kernel_size=7, stride=7, padding=0, ceil_mode=False, count_include_pad=True)
  )
  (classifier): Sequential(
    (0): Dropout(p=0.5)
    (1): Linear(in_features=1280, out_features=32, bias=True)
  )
)
请作者喝一杯咖啡☕️