-
Notifications
You must be signed in to change notification settings - Fork 1
/
resnet50_scratch_dag.py
362 lines (355 loc) · 26.4 KB
/
resnet50_scratch_dag.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
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
import torch
import torch.nn as nn
class Resnet50_scratch_dag(nn.Module):
def __init__(self):
super(Resnet50_scratch_dag, self).__init__()
self.meta = {'mean': [131.0912, 103.8827, 91.4953],
'std': [1, 1, 1],
'imageSize': [224, 224, 3]}
self.conv1_7x7_s2 = nn.Conv2d(3, 64, kernel_size=[7, 7], stride=(2, 2), padding=(3, 3), bias=False)
self.conv1_7x7_s2_bn = nn.BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv1_relu_7x7_s2 = nn.ReLU()
self.pool1_3x3_s2 = nn.MaxPool2d(kernel_size=[3, 3], stride=[2, 2], padding=(0, 0), dilation=1, ceil_mode=True)
self.conv2_1_1x1_reduce = nn.Conv2d(64, 64, kernel_size=[1, 1], stride=(1, 1), bias=False)
self.conv2_1_1x1_reduce_bn = nn.BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv2_1_1x1_reduce_relu = nn.ReLU()
self.conv2_1_3x3 = nn.Conv2d(64, 64, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1), bias=False)
self.conv2_1_3x3_bn = nn.BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv2_1_3x3_relu = nn.ReLU()
self.conv2_1_1x1_increase = nn.Conv2d(64, 256, kernel_size=[1, 1], stride=(1, 1), bias=False)
self.conv2_1_1x1_increase_bn = nn.BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv2_1_1x1_proj = nn.Conv2d(64, 256, kernel_size=[1, 1], stride=(1, 1), bias=False)
self.conv2_1_1x1_proj_bn = nn.BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv2_1_relu = nn.ReLU()
self.conv2_2_1x1_reduce = nn.Conv2d(256, 64, kernel_size=[1, 1], stride=(1, 1), bias=False)
self.conv2_2_1x1_reduce_bn = nn.BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv2_2_1x1_reduce_relu = nn.ReLU()
self.conv2_2_3x3 = nn.Conv2d(64, 64, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1), bias=False)
self.conv2_2_3x3_bn = nn.BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv2_2_3x3_relu = nn.ReLU()
self.conv2_2_1x1_increase = nn.Conv2d(64, 256, kernel_size=[1, 1], stride=(1, 1), bias=False)
self.conv2_2_1x1_increase_bn = nn.BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv2_2_relu = nn.ReLU()
self.conv2_3_1x1_reduce = nn.Conv2d(256, 64, kernel_size=[1, 1], stride=(1, 1), bias=False)
self.conv2_3_1x1_reduce_bn = nn.BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv2_3_1x1_reduce_relu = nn.ReLU()
self.conv2_3_3x3 = nn.Conv2d(64, 64, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1), bias=False)
self.conv2_3_3x3_bn = nn.BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv2_3_3x3_relu = nn.ReLU()
self.conv2_3_1x1_increase = nn.Conv2d(64, 256, kernel_size=[1, 1], stride=(1, 1), bias=False)
self.conv2_3_1x1_increase_bn = nn.BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv2_3_relu = nn.ReLU()
self.conv3_1_1x1_reduce = nn.Conv2d(256, 128, kernel_size=[1, 1], stride=(2, 2), bias=False)
self.conv3_1_1x1_reduce_bn = nn.BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv3_1_1x1_reduce_relu = nn.ReLU()
self.conv3_1_3x3 = nn.Conv2d(128, 128, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1), bias=False)
self.conv3_1_3x3_bn = nn.BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv3_1_3x3_relu = nn.ReLU()
self.conv3_1_1x1_increase = nn.Conv2d(128, 512, kernel_size=[1, 1], stride=(1, 1), bias=False)
self.conv3_1_1x1_increase_bn = nn.BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv3_1_1x1_proj = nn.Conv2d(256, 512, kernel_size=[1, 1], stride=(2, 2), bias=False)
self.conv3_1_1x1_proj_bn = nn.BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv3_1_relu = nn.ReLU()
self.conv3_2_1x1_reduce = nn.Conv2d(512, 128, kernel_size=[1, 1], stride=(1, 1), bias=False)
self.conv3_2_1x1_reduce_bn = nn.BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv3_2_1x1_reduce_relu = nn.ReLU()
self.conv3_2_3x3 = nn.Conv2d(128, 128, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1), bias=False)
self.conv3_2_3x3_bn = nn.BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv3_2_3x3_relu = nn.ReLU()
self.conv3_2_1x1_increase = nn.Conv2d(128, 512, kernel_size=[1, 1], stride=(1, 1), bias=False)
self.conv3_2_1x1_increase_bn = nn.BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv3_2_relu = nn.ReLU()
self.conv3_3_1x1_reduce = nn.Conv2d(512, 128, kernel_size=[1, 1], stride=(1, 1), bias=False)
self.conv3_3_1x1_reduce_bn = nn.BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv3_3_1x1_reduce_relu = nn.ReLU()
self.conv3_3_3x3 = nn.Conv2d(128, 128, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1), bias=False)
self.conv3_3_3x3_bn = nn.BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv3_3_3x3_relu = nn.ReLU()
self.conv3_3_1x1_increase = nn.Conv2d(128, 512, kernel_size=[1, 1], stride=(1, 1), bias=False)
self.conv3_3_1x1_increase_bn = nn.BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv3_3_relu = nn.ReLU()
self.conv3_4_1x1_reduce = nn.Conv2d(512, 128, kernel_size=[1, 1], stride=(1, 1), bias=False)
self.conv3_4_1x1_reduce_bn = nn.BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv3_4_1x1_reduce_relu = nn.ReLU()
self.conv3_4_3x3 = nn.Conv2d(128, 128, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1), bias=False)
self.conv3_4_3x3_bn = nn.BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv3_4_3x3_relu = nn.ReLU()
self.conv3_4_1x1_increase = nn.Conv2d(128, 512, kernel_size=[1, 1], stride=(1, 1), bias=False)
self.conv3_4_1x1_increase_bn = nn.BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv3_4_relu = nn.ReLU()
self.conv4_1_1x1_reduce = nn.Conv2d(512, 256, kernel_size=[1, 1], stride=(2, 2), bias=False)
self.conv4_1_1x1_reduce_bn = nn.BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv4_1_1x1_reduce_relu = nn.ReLU()
self.conv4_1_3x3 = nn.Conv2d(256, 256, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1), bias=False)
self.conv4_1_3x3_bn = nn.BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv4_1_3x3_relu = nn.ReLU()
self.conv4_1_1x1_increase = nn.Conv2d(256, 1024, kernel_size=[1, 1], stride=(1, 1), bias=False)
self.conv4_1_1x1_increase_bn = nn.BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv4_1_1x1_proj = nn.Conv2d(512, 1024, kernel_size=[1, 1], stride=(2, 2), bias=False)
self.conv4_1_1x1_proj_bn = nn.BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv4_1_relu = nn.ReLU()
self.conv4_2_1x1_reduce = nn.Conv2d(1024, 256, kernel_size=[1, 1], stride=(1, 1), bias=False)
self.conv4_2_1x1_reduce_bn = nn.BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv4_2_1x1_reduce_relu = nn.ReLU()
self.conv4_2_3x3 = nn.Conv2d(256, 256, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1), bias=False)
self.conv4_2_3x3_bn = nn.BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv4_2_3x3_relu = nn.ReLU()
self.conv4_2_1x1_increase = nn.Conv2d(256, 1024, kernel_size=[1, 1], stride=(1, 1), bias=False)
self.conv4_2_1x1_increase_bn = nn.BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv4_2_relu = nn.ReLU()
self.conv4_3_1x1_reduce = nn.Conv2d(1024, 256, kernel_size=[1, 1], stride=(1, 1), bias=False)
self.conv4_3_1x1_reduce_bn = nn.BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv4_3_1x1_reduce_relu = nn.ReLU()
self.conv4_3_3x3 = nn.Conv2d(256, 256, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1), bias=False)
self.conv4_3_3x3_bn = nn.BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv4_3_3x3_relu = nn.ReLU()
self.conv4_3_1x1_increase = nn.Conv2d(256, 1024, kernel_size=[1, 1], stride=(1, 1), bias=False)
self.conv4_3_1x1_increase_bn = nn.BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv4_3_relu = nn.ReLU()
self.conv4_4_1x1_reduce = nn.Conv2d(1024, 256, kernel_size=[1, 1], stride=(1, 1), bias=False)
self.conv4_4_1x1_reduce_bn = nn.BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv4_4_1x1_reduce_relu = nn.ReLU()
self.conv4_4_3x3 = nn.Conv2d(256, 256, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1), bias=False)
self.conv4_4_3x3_bn = nn.BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv4_4_3x3_relu = nn.ReLU()
self.conv4_4_1x1_increase = nn.Conv2d(256, 1024, kernel_size=[1, 1], stride=(1, 1), bias=False)
self.conv4_4_1x1_increase_bn = nn.BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv4_4_relu = nn.ReLU()
self.conv4_5_1x1_reduce = nn.Conv2d(1024, 256, kernel_size=[1, 1], stride=(1, 1), bias=False)
self.conv4_5_1x1_reduce_bn = nn.BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv4_5_1x1_reduce_relu = nn.ReLU()
self.conv4_5_3x3 = nn.Conv2d(256, 256, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1), bias=False)
self.conv4_5_3x3_bn = nn.BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv4_5_3x3_relu = nn.ReLU()
self.conv4_5_1x1_increase = nn.Conv2d(256, 1024, kernel_size=[1, 1], stride=(1, 1), bias=False)
self.conv4_5_1x1_increase_bn = nn.BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv4_5_relu = nn.ReLU()
self.conv4_6_1x1_reduce = nn.Conv2d(1024, 256, kernel_size=[1, 1], stride=(1, 1), bias=False)
self.conv4_6_1x1_reduce_bn = nn.BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv4_6_1x1_reduce_relu = nn.ReLU()
self.conv4_6_3x3 = nn.Conv2d(256, 256, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1), bias=False)
self.conv4_6_3x3_bn = nn.BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv4_6_3x3_relu = nn.ReLU()
self.conv4_6_1x1_increase = nn.Conv2d(256, 1024, kernel_size=[1, 1], stride=(1, 1), bias=False)
self.conv4_6_1x1_increase_bn = nn.BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv4_6_relu = nn.ReLU()
self.conv5_1_1x1_reduce = nn.Conv2d(1024, 512, kernel_size=[1, 1], stride=(2, 2), bias=False)
self.conv5_1_1x1_reduce_bn = nn.BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv5_1_1x1_reduce_relu = nn.ReLU()
self.conv5_1_3x3 = nn.Conv2d(512, 512, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1), bias=False)
self.conv5_1_3x3_bn = nn.BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv5_1_3x3_relu = nn.ReLU()
self.conv5_1_1x1_increase = nn.Conv2d(512, 2048, kernel_size=[1, 1], stride=(1, 1), bias=False)
self.conv5_1_1x1_increase_bn = nn.BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv5_1_1x1_proj = nn.Conv2d(1024, 2048, kernel_size=[1, 1], stride=(2, 2), bias=False)
self.conv5_1_1x1_proj_bn = nn.BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv5_1_relu = nn.ReLU()
self.conv5_2_1x1_reduce = nn.Conv2d(2048, 512, kernel_size=[1, 1], stride=(1, 1), bias=False)
self.conv5_2_1x1_reduce_bn = nn.BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv5_2_1x1_reduce_relu = nn.ReLU()
self.conv5_2_3x3 = nn.Conv2d(512, 512, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1), bias=False)
self.conv5_2_3x3_bn = nn.BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv5_2_3x3_relu = nn.ReLU()
self.conv5_2_1x1_increase = nn.Conv2d(512, 2048, kernel_size=[1, 1], stride=(1, 1), bias=False)
self.conv5_2_1x1_increase_bn = nn.BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv5_2_relu = nn.ReLU()
self.conv5_3_1x1_reduce = nn.Conv2d(2048, 512, kernel_size=[1, 1], stride=(1, 1), bias=False)
self.conv5_3_1x1_reduce_bn = nn.BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv5_3_1x1_reduce_relu = nn.ReLU()
self.conv5_3_3x3 = nn.Conv2d(512, 512, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1), bias=False)
self.conv5_3_3x3_bn = nn.BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv5_3_3x3_relu = nn.ReLU()
self.conv5_3_1x1_increase = nn.Conv2d(512, 2048, kernel_size=[1, 1], stride=(1, 1), bias=False)
self.conv5_3_1x1_increase_bn = nn.BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.conv5_3_relu = nn.ReLU()
self.pool5_7x7_s1 = nn.AvgPool2d(kernel_size=[7, 7], stride=[1, 1], padding=0)
self.classifier = nn.Conv2d(2048, 8631, kernel_size=[1, 1], stride=(1, 1))
def forward(self, data):
conv1_7x7_s2 = self.conv1_7x7_s2(data)
conv1_7x7_s2_bn = self.conv1_7x7_s2_bn(conv1_7x7_s2)
conv1_7x7_s2_bnxx = self.conv1_relu_7x7_s2(conv1_7x7_s2_bn)
pool1_3x3_s2 = self.pool1_3x3_s2(conv1_7x7_s2_bnxx)
conv2_1_1x1_reduce = self.conv2_1_1x1_reduce(pool1_3x3_s2)
conv2_1_1x1_reduce_bn = self.conv2_1_1x1_reduce_bn(conv2_1_1x1_reduce)
conv2_1_1x1_reduce_bnxx = self.conv2_1_1x1_reduce_relu(conv2_1_1x1_reduce_bn)
conv2_1_3x3 = self.conv2_1_3x3(conv2_1_1x1_reduce_bnxx)
conv2_1_3x3_bn = self.conv2_1_3x3_bn(conv2_1_3x3)
conv2_1_3x3_bnxx = self.conv2_1_3x3_relu(conv2_1_3x3_bn)
conv2_1_1x1_increase = self.conv2_1_1x1_increase(conv2_1_3x3_bnxx)
conv2_1_1x1_increase_bn = self.conv2_1_1x1_increase_bn(conv2_1_1x1_increase)
conv2_1_1x1_proj = self.conv2_1_1x1_proj(pool1_3x3_s2)
conv2_1_1x1_proj_bn = self.conv2_1_1x1_proj_bn(conv2_1_1x1_proj)
conv2_1 = torch.add(conv2_1_1x1_proj_bn, 1, conv2_1_1x1_increase_bn)
conv2_1x = self.conv2_1_relu(conv2_1)
conv2_2_1x1_reduce = self.conv2_2_1x1_reduce(conv2_1x)
conv2_2_1x1_reduce_bn = self.conv2_2_1x1_reduce_bn(conv2_2_1x1_reduce)
conv2_2_1x1_reduce_bnxx = self.conv2_2_1x1_reduce_relu(conv2_2_1x1_reduce_bn)
conv2_2_3x3 = self.conv2_2_3x3(conv2_2_1x1_reduce_bnxx)
conv2_2_3x3_bn = self.conv2_2_3x3_bn(conv2_2_3x3)
conv2_2_3x3_bnxx = self.conv2_2_3x3_relu(conv2_2_3x3_bn)
conv2_2_1x1_increase = self.conv2_2_1x1_increase(conv2_2_3x3_bnxx)
conv2_2_1x1_increase_bn = self.conv2_2_1x1_increase_bn(conv2_2_1x1_increase)
conv2_2 = torch.add(conv2_1x, 1, conv2_2_1x1_increase_bn)
conv2_2x = self.conv2_2_relu(conv2_2)
conv2_3_1x1_reduce = self.conv2_3_1x1_reduce(conv2_2x)
conv2_3_1x1_reduce_bn = self.conv2_3_1x1_reduce_bn(conv2_3_1x1_reduce)
conv2_3_1x1_reduce_bnxx = self.conv2_3_1x1_reduce_relu(conv2_3_1x1_reduce_bn)
conv2_3_3x3 = self.conv2_3_3x3(conv2_3_1x1_reduce_bnxx)
conv2_3_3x3_bn = self.conv2_3_3x3_bn(conv2_3_3x3)
conv2_3_3x3_bnxx = self.conv2_3_3x3_relu(conv2_3_3x3_bn)
conv2_3_1x1_increase = self.conv2_3_1x1_increase(conv2_3_3x3_bnxx)
conv2_3_1x1_increase_bn = self.conv2_3_1x1_increase_bn(conv2_3_1x1_increase)
conv2_3 = torch.add(conv2_2x, 1, conv2_3_1x1_increase_bn)
conv2_3x = self.conv2_3_relu(conv2_3)
conv3_1_1x1_reduce = self.conv3_1_1x1_reduce(conv2_3x)
conv3_1_1x1_reduce_bn = self.conv3_1_1x1_reduce_bn(conv3_1_1x1_reduce)
conv3_1_1x1_reduce_bnxx = self.conv3_1_1x1_reduce_relu(conv3_1_1x1_reduce_bn)
conv3_1_3x3 = self.conv3_1_3x3(conv3_1_1x1_reduce_bnxx)
conv3_1_3x3_bn = self.conv3_1_3x3_bn(conv3_1_3x3)
conv3_1_3x3_bnxx = self.conv3_1_3x3_relu(conv3_1_3x3_bn)
conv3_1_1x1_increase = self.conv3_1_1x1_increase(conv3_1_3x3_bnxx)
conv3_1_1x1_increase_bn = self.conv3_1_1x1_increase_bn(conv3_1_1x1_increase)
conv3_1_1x1_proj = self.conv3_1_1x1_proj(conv2_3x)
conv3_1_1x1_proj_bn = self.conv3_1_1x1_proj_bn(conv3_1_1x1_proj)
conv3_1 = torch.add(conv3_1_1x1_proj_bn, 1, conv3_1_1x1_increase_bn)
conv3_1x = self.conv3_1_relu(conv3_1)
conv3_2_1x1_reduce = self.conv3_2_1x1_reduce(conv3_1x)
conv3_2_1x1_reduce_bn = self.conv3_2_1x1_reduce_bn(conv3_2_1x1_reduce)
conv3_2_1x1_reduce_bnxx = self.conv3_2_1x1_reduce_relu(conv3_2_1x1_reduce_bn)
conv3_2_3x3 = self.conv3_2_3x3(conv3_2_1x1_reduce_bnxx)
conv3_2_3x3_bn = self.conv3_2_3x3_bn(conv3_2_3x3)
conv3_2_3x3_bnxx = self.conv3_2_3x3_relu(conv3_2_3x3_bn)
conv3_2_1x1_increase = self.conv3_2_1x1_increase(conv3_2_3x3_bnxx)
conv3_2_1x1_increase_bn = self.conv3_2_1x1_increase_bn(conv3_2_1x1_increase)
conv3_2 = torch.add(conv3_1x, 1, conv3_2_1x1_increase_bn)
conv3_2x = self.conv3_2_relu(conv3_2)
conv3_3_1x1_reduce = self.conv3_3_1x1_reduce(conv3_2x)
conv3_3_1x1_reduce_bn = self.conv3_3_1x1_reduce_bn(conv3_3_1x1_reduce)
conv3_3_1x1_reduce_bnxx = self.conv3_3_1x1_reduce_relu(conv3_3_1x1_reduce_bn)
conv3_3_3x3 = self.conv3_3_3x3(conv3_3_1x1_reduce_bnxx)
conv3_3_3x3_bn = self.conv3_3_3x3_bn(conv3_3_3x3)
conv3_3_3x3_bnxx = self.conv3_3_3x3_relu(conv3_3_3x3_bn)
conv3_3_1x1_increase = self.conv3_3_1x1_increase(conv3_3_3x3_bnxx)
conv3_3_1x1_increase_bn = self.conv3_3_1x1_increase_bn(conv3_3_1x1_increase)
conv3_3 = torch.add(conv3_2x, 1, conv3_3_1x1_increase_bn)
conv3_3x = self.conv3_3_relu(conv3_3)
conv3_4_1x1_reduce = self.conv3_4_1x1_reduce(conv3_3x)
conv3_4_1x1_reduce_bn = self.conv3_4_1x1_reduce_bn(conv3_4_1x1_reduce)
conv3_4_1x1_reduce_bnxx = self.conv3_4_1x1_reduce_relu(conv3_4_1x1_reduce_bn)
conv3_4_3x3 = self.conv3_4_3x3(conv3_4_1x1_reduce_bnxx)
conv3_4_3x3_bn = self.conv3_4_3x3_bn(conv3_4_3x3)
conv3_4_3x3_bnxx = self.conv3_4_3x3_relu(conv3_4_3x3_bn)
conv3_4_1x1_increase = self.conv3_4_1x1_increase(conv3_4_3x3_bnxx)
conv3_4_1x1_increase_bn = self.conv3_4_1x1_increase_bn(conv3_4_1x1_increase)
conv3_4 = torch.add(conv3_3x, 1, conv3_4_1x1_increase_bn)
conv3_4x = self.conv3_4_relu(conv3_4)
conv4_1_1x1_reduce = self.conv4_1_1x1_reduce(conv3_4x)
conv4_1_1x1_reduce_bn = self.conv4_1_1x1_reduce_bn(conv4_1_1x1_reduce)
conv4_1_1x1_reduce_bnxx = self.conv4_1_1x1_reduce_relu(conv4_1_1x1_reduce_bn)
conv4_1_3x3 = self.conv4_1_3x3(conv4_1_1x1_reduce_bnxx)
conv4_1_3x3_bn = self.conv4_1_3x3_bn(conv4_1_3x3)
conv4_1_3x3_bnxx = self.conv4_1_3x3_relu(conv4_1_3x3_bn)
conv4_1_1x1_increase = self.conv4_1_1x1_increase(conv4_1_3x3_bnxx)
conv4_1_1x1_increase_bn = self.conv4_1_1x1_increase_bn(conv4_1_1x1_increase)
conv4_1_1x1_proj = self.conv4_1_1x1_proj(conv3_4x)
conv4_1_1x1_proj_bn = self.conv4_1_1x1_proj_bn(conv4_1_1x1_proj)
conv4_1 = torch.add(conv4_1_1x1_proj_bn, 1, conv4_1_1x1_increase_bn)
conv4_1x = self.conv4_1_relu(conv4_1)
conv4_2_1x1_reduce = self.conv4_2_1x1_reduce(conv4_1x)
conv4_2_1x1_reduce_bn = self.conv4_2_1x1_reduce_bn(conv4_2_1x1_reduce)
conv4_2_1x1_reduce_bnxx = self.conv4_2_1x1_reduce_relu(conv4_2_1x1_reduce_bn)
conv4_2_3x3 = self.conv4_2_3x3(conv4_2_1x1_reduce_bnxx)
conv4_2_3x3_bn = self.conv4_2_3x3_bn(conv4_2_3x3)
conv4_2_3x3_bnxx = self.conv4_2_3x3_relu(conv4_2_3x3_bn)
conv4_2_1x1_increase = self.conv4_2_1x1_increase(conv4_2_3x3_bnxx)
conv4_2_1x1_increase_bn = self.conv4_2_1x1_increase_bn(conv4_2_1x1_increase)
conv4_2 = torch.add(conv4_1x, 1, conv4_2_1x1_increase_bn)
conv4_2x = self.conv4_2_relu(conv4_2)
conv4_3_1x1_reduce = self.conv4_3_1x1_reduce(conv4_2x)
conv4_3_1x1_reduce_bn = self.conv4_3_1x1_reduce_bn(conv4_3_1x1_reduce)
conv4_3_1x1_reduce_bnxx = self.conv4_3_1x1_reduce_relu(conv4_3_1x1_reduce_bn)
conv4_3_3x3 = self.conv4_3_3x3(conv4_3_1x1_reduce_bnxx)
conv4_3_3x3_bn = self.conv4_3_3x3_bn(conv4_3_3x3)
conv4_3_3x3_bnxx = self.conv4_3_3x3_relu(conv4_3_3x3_bn)
conv4_3_1x1_increase = self.conv4_3_1x1_increase(conv4_3_3x3_bnxx)
conv4_3_1x1_increase_bn = self.conv4_3_1x1_increase_bn(conv4_3_1x1_increase)
conv4_3 = torch.add(conv4_2x, 1, conv4_3_1x1_increase_bn)
conv4_3x = self.conv4_3_relu(conv4_3)
conv4_4_1x1_reduce = self.conv4_4_1x1_reduce(conv4_3x)
conv4_4_1x1_reduce_bn = self.conv4_4_1x1_reduce_bn(conv4_4_1x1_reduce)
conv4_4_1x1_reduce_bnxx = self.conv4_4_1x1_reduce_relu(conv4_4_1x1_reduce_bn)
conv4_4_3x3 = self.conv4_4_3x3(conv4_4_1x1_reduce_bnxx)
conv4_4_3x3_bn = self.conv4_4_3x3_bn(conv4_4_3x3)
conv4_4_3x3_bnxx = self.conv4_4_3x3_relu(conv4_4_3x3_bn)
conv4_4_1x1_increase = self.conv4_4_1x1_increase(conv4_4_3x3_bnxx)
conv4_4_1x1_increase_bn = self.conv4_4_1x1_increase_bn(conv4_4_1x1_increase)
conv4_4 = torch.add(conv4_3x, 1, conv4_4_1x1_increase_bn)
conv4_4x = self.conv4_4_relu(conv4_4)
conv4_5_1x1_reduce = self.conv4_5_1x1_reduce(conv4_4x)
conv4_5_1x1_reduce_bn = self.conv4_5_1x1_reduce_bn(conv4_5_1x1_reduce)
conv4_5_1x1_reduce_bnxx = self.conv4_5_1x1_reduce_relu(conv4_5_1x1_reduce_bn)
conv4_5_3x3 = self.conv4_5_3x3(conv4_5_1x1_reduce_bnxx)
conv4_5_3x3_bn = self.conv4_5_3x3_bn(conv4_5_3x3)
conv4_5_3x3_bnxx = self.conv4_5_3x3_relu(conv4_5_3x3_bn)
conv4_5_1x1_increase = self.conv4_5_1x1_increase(conv4_5_3x3_bnxx)
conv4_5_1x1_increase_bn = self.conv4_5_1x1_increase_bn(conv4_5_1x1_increase)
conv4_5 = torch.add(conv4_4x, 1, conv4_5_1x1_increase_bn)
conv4_5x = self.conv4_5_relu(conv4_5)
conv4_6_1x1_reduce = self.conv4_6_1x1_reduce(conv4_5x)
conv4_6_1x1_reduce_bn = self.conv4_6_1x1_reduce_bn(conv4_6_1x1_reduce)
conv4_6_1x1_reduce_bnxx = self.conv4_6_1x1_reduce_relu(conv4_6_1x1_reduce_bn)
conv4_6_3x3 = self.conv4_6_3x3(conv4_6_1x1_reduce_bnxx)
conv4_6_3x3_bn = self.conv4_6_3x3_bn(conv4_6_3x3)
conv4_6_3x3_bnxx = self.conv4_6_3x3_relu(conv4_6_3x3_bn)
conv4_6_1x1_increase = self.conv4_6_1x1_increase(conv4_6_3x3_bnxx)
conv4_6_1x1_increase_bn = self.conv4_6_1x1_increase_bn(conv4_6_1x1_increase)
conv4_6 = torch.add(conv4_5x, 1, conv4_6_1x1_increase_bn)
conv4_6x = self.conv4_6_relu(conv4_6)
conv5_1_1x1_reduce = self.conv5_1_1x1_reduce(conv4_6x)
conv5_1_1x1_reduce_bn = self.conv5_1_1x1_reduce_bn(conv5_1_1x1_reduce)
conv5_1_1x1_reduce_bnxx = self.conv5_1_1x1_reduce_relu(conv5_1_1x1_reduce_bn)
conv5_1_3x3 = self.conv5_1_3x3(conv5_1_1x1_reduce_bnxx)
conv5_1_3x3_bn = self.conv5_1_3x3_bn(conv5_1_3x3)
conv5_1_3x3_bnxx = self.conv5_1_3x3_relu(conv5_1_3x3_bn)
conv5_1_1x1_increase = self.conv5_1_1x1_increase(conv5_1_3x3_bnxx)
conv5_1_1x1_increase_bn = self.conv5_1_1x1_increase_bn(conv5_1_1x1_increase)
conv5_1_1x1_proj = self.conv5_1_1x1_proj(conv4_6x)
conv5_1_1x1_proj_bn = self.conv5_1_1x1_proj_bn(conv5_1_1x1_proj)
conv5_1 = torch.add(conv5_1_1x1_proj_bn, 1, conv5_1_1x1_increase_bn)
conv5_1x = self.conv5_1_relu(conv5_1)
conv5_2_1x1_reduce = self.conv5_2_1x1_reduce(conv5_1x)
conv5_2_1x1_reduce_bn = self.conv5_2_1x1_reduce_bn(conv5_2_1x1_reduce)
conv5_2_1x1_reduce_bnxx = self.conv5_2_1x1_reduce_relu(conv5_2_1x1_reduce_bn)
conv5_2_3x3 = self.conv5_2_3x3(conv5_2_1x1_reduce_bnxx)
conv5_2_3x3_bn = self.conv5_2_3x3_bn(conv5_2_3x3)
conv5_2_3x3_bnxx = self.conv5_2_3x3_relu(conv5_2_3x3_bn)
conv5_2_1x1_increase = self.conv5_2_1x1_increase(conv5_2_3x3_bnxx)
conv5_2_1x1_increase_bn = self.conv5_2_1x1_increase_bn(conv5_2_1x1_increase)
conv5_2 = torch.add(conv5_1x, 1, conv5_2_1x1_increase_bn)
conv5_2x = self.conv5_2_relu(conv5_2)
conv5_3_1x1_reduce = self.conv5_3_1x1_reduce(conv5_2x)
conv5_3_1x1_reduce_bn = self.conv5_3_1x1_reduce_bn(conv5_3_1x1_reduce)
conv5_3_1x1_reduce_bnxx = self.conv5_3_1x1_reduce_relu(conv5_3_1x1_reduce_bn)
conv5_3_3x3 = self.conv5_3_3x3(conv5_3_1x1_reduce_bnxx)
conv5_3_3x3_bn = self.conv5_3_3x3_bn(conv5_3_3x3)
conv5_3_3x3_bnxx = self.conv5_3_3x3_relu(conv5_3_3x3_bn)
conv5_3_1x1_increase = self.conv5_3_1x1_increase(conv5_3_3x3_bnxx)
conv5_3_1x1_increase_bn = self.conv5_3_1x1_increase_bn(conv5_3_1x1_increase)
conv5_3 = torch.add(conv5_2x, 1, conv5_3_1x1_increase_bn)
conv5_3x = self.conv5_3_relu(conv5_3)
pool5_7x7_s1 = self.pool5_7x7_s1(conv5_3x)
classifier_preflatten = self.classifier(pool5_7x7_s1)
classifier = classifier_preflatten.view(classifier_preflatten.size(0), -1)
# return classifier, pool5_7x7_s1
return classifier
def resnet50_scratch_dag(weights_path=None, **kwargs):
"""
load imported model instance
Args:
weights_path (str): If set, loads model weights from the given path
"""
model = Resnet50_scratch_dag()
if weights_path:
state_dict = torch.load(weights_path)
model.load_state_dict(state_dict)
return model