-
Notifications
You must be signed in to change notification settings - Fork 26
/
weights.py
380 lines (272 loc) · 30.9 KB
/
weights.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
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
import numpy as np
import torch
def load_weights(model, weights_file, dtype):
model_params = model.state_dict()
data_dict = np.load(weights_file, encoding='latin1').item()
if True:
model_params['conv1.weight'] = torch.from_numpy(data_dict['conv1']['weights']).type(dtype).permute(3,2,0,1)
#model_params['conv1.bias'] = torch.from_numpy(data_dict['conv1']['biases']).type(dtype)
model_params['bn1.weight'] = torch.from_numpy(data_dict['bn_conv1']['scale']).type(dtype)
model_params['bn1.bias'] = torch.from_numpy(data_dict['bn_conv1']['offset']).type(dtype)
model_params['layer1.0.downsample.0.weight'] = torch.from_numpy(data_dict['res2a_branch1']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer1.0.downsample.1.weight'] = torch.from_numpy(data_dict['bn2a_branch1']['scale']).type(dtype)
model_params['layer1.0.downsample.1.bias'] = torch.from_numpy(data_dict['bn2a_branch1']['offset']).type(dtype)
model_params['layer1.0.conv1.weight'] = torch.from_numpy(data_dict['res2a_branch2a']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer1.0.bn1.weight'] = torch.from_numpy(data_dict['bn2a_branch2a']['scale']).type(dtype)
model_params['layer1.0.bn1.bias'] = torch.from_numpy(data_dict['bn2a_branch2a']['offset']).type(dtype)
model_params['layer1.0.conv2.weight'] = torch.from_numpy(data_dict['res2a_branch2b']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer1.0.bn2.weight'] = torch.from_numpy(data_dict['bn2a_branch2b']['scale']).type(dtype)
model_params['layer1.0.bn2.bias'] = torch.from_numpy(data_dict['bn2a_branch2b']['offset']).type(dtype)
model_params['layer1.0.conv3.weight'] = torch.from_numpy(data_dict['res2a_branch2c']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer1.0.bn3.weight'] = torch.from_numpy(data_dict['bn2a_branch2c']['scale']).type(dtype)
model_params['layer1.0.bn3.bias'] = torch.from_numpy(data_dict['bn2a_branch2c']['offset']).type(dtype)
model_params['layer1.1.conv1.weight'] = torch.from_numpy(data_dict['res2b_branch2a']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer1.1.bn1.weight'] = torch.from_numpy(data_dict['bn2b_branch2a']['scale']).type(dtype)
model_params['layer1.1.bn1.bias'] = torch.from_numpy(data_dict['bn2b_branch2a']['offset']).type(dtype)
model_params['layer1.1.conv2.weight'] = torch.from_numpy(data_dict['res2b_branch2b']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer1.1.bn2.weight'] = torch.from_numpy(data_dict['bn2b_branch2b']['scale']).type(dtype)
model_params['layer1.1.bn2.bias'] = torch.from_numpy(data_dict['bn2b_branch2b']['offset']).type(dtype)
model_params['layer1.1.conv3.weight'] = torch.from_numpy(data_dict['res2b_branch2c']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer1.1.bn3.weight'] = torch.from_numpy(data_dict['bn2b_branch2c']['scale']).type(dtype)
model_params['layer1.1.bn3.bias'] = torch.from_numpy(data_dict['bn2b_branch2c']['offset']).type(dtype)
model_params['layer1.2.conv1.weight'] = torch.from_numpy(data_dict['res2c_branch2a']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer1.2.bn1.weight'] = torch.from_numpy(data_dict['bn2c_branch2a']['scale']).type(dtype)
model_params['layer1.2.bn1.bias'] = torch.from_numpy(data_dict['bn2c_branch2a']['offset']).type(dtype)
model_params['layer1.2.conv2.weight'] = torch.from_numpy(data_dict['res2c_branch2b']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer1.2.bn2.weight'] = torch.from_numpy(data_dict['bn2c_branch2b']['scale']).type(dtype)
model_params['layer1.2.bn2.bias'] = torch.from_numpy(data_dict['bn2c_branch2b']['offset']).type(dtype)
model_params['layer1.2.conv3.weight'] = torch.from_numpy(data_dict['res2c_branch2c']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer1.2.bn3.weight'] = torch.from_numpy(data_dict['bn2c_branch2c']['scale']).type(dtype)
model_params['layer1.2.bn3.bias'] = torch.from_numpy(data_dict['bn2c_branch2c']['offset']).type(dtype)
model_params['layer2.0.downsample.0.weight'] = torch.from_numpy(data_dict['res3a_branch1']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer2.0.downsample.1.weight'] = torch.from_numpy(data_dict['bn3a_branch1']['scale']).type(dtype)
model_params['layer2.0.downsample.1.bias'] = torch.from_numpy(data_dict['bn3a_branch1']['offset']).type(dtype)
model_params['layer2.0.conv1.weight'] = torch.from_numpy(data_dict['res3a_branch2a']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer2.0.bn1.weight'] = torch.from_numpy(data_dict['bn3a_branch2a']['scale']).type(dtype)
model_params['layer2.0.bn1.bias'] = torch.from_numpy(data_dict['bn3a_branch2a']['offset']).type(dtype)
model_params['layer2.0.conv2.weight'] = torch.from_numpy(data_dict['res3a_branch2b']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer2.0.bn2.weight'] = torch.from_numpy(data_dict['bn3a_branch2b']['scale']).type(dtype)
model_params['layer2.0.bn2.bias'] = torch.from_numpy(data_dict['bn3a_branch2b']['offset']).type(dtype)
model_params['layer2.0.conv3.weight'] = torch.from_numpy(data_dict['res3a_branch2c']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer2.0.bn3.weight'] = torch.from_numpy(data_dict['bn3a_branch2c']['scale']).type(dtype)
model_params['layer2.0.bn3.bias'] = torch.from_numpy(data_dict['bn3a_branch2c']['offset']).type(dtype)
model_params['layer2.1.conv1.weight'] = torch.from_numpy(data_dict['res3b_branch2a']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer2.1.bn1.weight'] = torch.from_numpy(data_dict['bn3b_branch2a']['scale']).type(dtype)
model_params['layer2.1.bn1.bias'] = torch.from_numpy(data_dict['bn3b_branch2a']['offset']).type(dtype)
model_params['layer2.1.conv2.weight'] = torch.from_numpy(data_dict['res3b_branch2b']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer2.1.bn2.weight'] = torch.from_numpy(data_dict['bn3b_branch2b']['scale']).type(dtype)
model_params['layer2.1.bn2.bias'] = torch.from_numpy(data_dict['bn3b_branch2b']['offset']).type(dtype)
model_params['layer2.1.conv3.weight'] = torch.from_numpy(data_dict['res3b_branch2c']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer2.1.bn3.weight'] = torch.from_numpy(data_dict['bn3b_branch2c']['scale']).type(dtype)
model_params['layer2.1.bn3.bias'] = torch.from_numpy(data_dict['bn3b_branch2c']['offset']).type(dtype)
model_params['layer2.2.conv1.weight'] = torch.from_numpy(data_dict['res3c_branch2a']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer2.2.bn1.weight'] = torch.from_numpy(data_dict['bn3c_branch2a']['scale']).type(dtype)
model_params['layer2.2.bn1.bias'] = torch.from_numpy(data_dict['bn3c_branch2a']['offset']).type(dtype)
model_params['layer2.2.conv2.weight'] = torch.from_numpy(data_dict['res3c_branch2b']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer2.2.bn2.weight'] = torch.from_numpy(data_dict['bn3c_branch2b']['scale']).type(dtype)
model_params['layer2.2.bn2.bias'] = torch.from_numpy(data_dict['bn3c_branch2b']['offset']).type(dtype)
model_params['layer2.2.conv3.weight'] = torch.from_numpy(data_dict['res3c_branch2c']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer2.2.bn3.weight'] = torch.from_numpy(data_dict['bn3c_branch2c']['scale']).type(dtype)
model_params['layer2.2.bn3.bias'] = torch.from_numpy(data_dict['bn3c_branch2c']['offset']).type(dtype)
model_params['layer2.3.conv1.weight'] = torch.from_numpy(data_dict['res3d_branch2a']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer2.3.bn1.weight'] = torch.from_numpy(data_dict['bn3d_branch2a']['scale']).type(dtype)
model_params['layer2.3.bn1.bias'] = torch.from_numpy(data_dict['bn3d_branch2a']['offset']).type(dtype)
model_params['layer2.3.conv2.weight'] = torch.from_numpy(data_dict['res3d_branch2b']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer2.3.bn2.weight'] = torch.from_numpy(data_dict['bn3d_branch2b']['scale']).type(dtype)
model_params['layer2.3.bn2.bias'] = torch.from_numpy(data_dict['bn3d_branch2b']['offset']).type(dtype)
model_params['layer2.3.conv3.weight'] = torch.from_numpy(data_dict['res3d_branch2c']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer2.3.bn3.weight'] = torch.from_numpy(data_dict['bn3d_branch2c']['scale']).type(dtype)
model_params['layer2.3.bn3.bias'] = torch.from_numpy(data_dict['bn3d_branch2c']['offset']).type(dtype)
model_params['layer3.0.downsample.0.weight'] = torch.from_numpy(data_dict['res4a_branch1']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer3.0.downsample.1.weight'] = torch.from_numpy(data_dict['bn4a_branch1']['scale']).type(dtype)
model_params['layer3.0.downsample.1.bias'] = torch.from_numpy(data_dict['bn4a_branch1']['offset']).type(dtype)
model_params['layer3.0.conv1.weight'] = torch.from_numpy(data_dict['res4a_branch2a']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer3.0.bn1.weight'] = torch.from_numpy(data_dict['bn4a_branch2a']['scale']).type(dtype)
model_params['layer3.0.bn1.bias'] = torch.from_numpy(data_dict['bn4a_branch2a']['offset']).type(dtype)
model_params['layer3.0.conv2.weight'] = torch.from_numpy(data_dict['res4a_branch2b']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer3.0.bn2.weight'] = torch.from_numpy(data_dict['bn4a_branch2b']['scale']).type(dtype)
model_params['layer3.0.bn2.bias'] = torch.from_numpy(data_dict['bn4a_branch2b']['offset']).type(dtype)
model_params['layer3.0.conv3.weight'] = torch.from_numpy(data_dict['res4a_branch2c']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer3.0.bn3.weight'] = torch.from_numpy(data_dict['bn4a_branch2c']['scale']).type(dtype)
model_params['layer3.0.bn3.bias'] = torch.from_numpy(data_dict['bn4a_branch2c']['offset']).type(dtype)
model_params['layer3.1.conv1.weight'] = torch.from_numpy(data_dict['res4b_branch2a']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer3.1.bn1.weight'] = torch.from_numpy(data_dict['bn4b_branch2a']['scale']).type(dtype)
model_params['layer3.1.bn1.bias'] = torch.from_numpy(data_dict['bn4b_branch2a']['offset']).type(dtype)
model_params['layer3.1.conv2.weight'] = torch.from_numpy(data_dict['res4b_branch2b']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer3.1.bn2.weight'] = torch.from_numpy(data_dict['bn4b_branch2b']['scale']).type(dtype)
model_params['layer3.1.bn2.bias'] = torch.from_numpy(data_dict['bn4b_branch2b']['offset']).type(dtype)
model_params['layer3.1.conv3.weight'] = torch.from_numpy(data_dict['res4b_branch2c']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer3.1.bn3.weight'] = torch.from_numpy(data_dict['bn4b_branch2c']['scale']).type(dtype)
model_params['layer3.1.bn3.bias'] = torch.from_numpy(data_dict['bn4b_branch2c']['offset']).type(dtype)
model_params['layer3.2.conv1.weight'] = torch.from_numpy(data_dict['res4c_branch2a']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer3.2.bn1.weight'] = torch.from_numpy(data_dict['bn4c_branch2a']['scale']).type(dtype)
model_params['layer3.2.bn1.bias'] = torch.from_numpy(data_dict['bn4c_branch2a']['offset']).type(dtype)
model_params['layer3.2.conv2.weight'] = torch.from_numpy(data_dict['res4c_branch2b']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer3.2.bn2.weight'] = torch.from_numpy(data_dict['bn4c_branch2b']['scale']).type(dtype)
model_params['layer3.2.bn2.bias'] = torch.from_numpy(data_dict['bn4c_branch2b']['offset']).type(dtype)
model_params['layer3.2.conv3.weight'] = torch.from_numpy(data_dict['res4c_branch2c']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer3.2.bn3.weight'] = torch.from_numpy(data_dict['bn4c_branch2c']['scale']).type(dtype)
model_params['layer3.2.bn3.bias'] = torch.from_numpy(data_dict['bn4c_branch2c']['offset']).type(dtype)
model_params['layer3.3.conv1.weight'] = torch.from_numpy(data_dict['res4d_branch2a']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer3.3.bn1.weight'] = torch.from_numpy(data_dict['bn4d_branch2a']['scale']).type(dtype)
model_params['layer3.3.bn1.bias'] = torch.from_numpy(data_dict['bn4d_branch2a']['offset']).type(dtype)
model_params['layer3.3.conv2.weight'] = torch.from_numpy(data_dict['res4d_branch2b']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer3.3.bn2.weight'] = torch.from_numpy(data_dict['bn4d_branch2b']['scale']).type(dtype)
model_params['layer3.3.bn2.bias'] = torch.from_numpy(data_dict['bn4d_branch2b']['offset']).type(dtype)
model_params['layer3.3.conv3.weight'] = torch.from_numpy(data_dict['res4d_branch2c']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer3.3.bn3.weight'] = torch.from_numpy(data_dict['bn4d_branch2c']['scale']).type(dtype)
model_params['layer3.3.bn3.bias'] = torch.from_numpy(data_dict['bn4d_branch2c']['offset']).type(dtype)
model_params['layer3.4.conv1.weight'] = torch.from_numpy(data_dict['res4e_branch2a']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer3.4.bn1.weight'] = torch.from_numpy(data_dict['bn4e_branch2a']['scale']).type(dtype)
model_params['layer3.4.bn1.bias'] = torch.from_numpy(data_dict['bn4e_branch2a']['offset']).type(dtype)
model_params['layer3.4.conv2.weight'] = torch.from_numpy(data_dict['res4e_branch2b']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer3.4.bn2.weight'] = torch.from_numpy(data_dict['bn4e_branch2b']['scale']).type(dtype)
model_params['layer3.4.bn2.bias'] = torch.from_numpy(data_dict['bn4e_branch2b']['offset']).type(dtype)
model_params['layer3.4.conv3.weight'] = torch.from_numpy(data_dict['res4e_branch2c']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer3.4.bn3.weight'] = torch.from_numpy(data_dict['bn4e_branch2c']['scale']).type(dtype)
model_params['layer3.4.bn3.bias'] = torch.from_numpy(data_dict['bn4e_branch2c']['offset']).type(dtype)
model_params['layer3.5.conv1.weight'] = torch.from_numpy(data_dict['res4f_branch2a']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer3.5.bn1.weight'] = torch.from_numpy(data_dict['bn4f_branch2a']['scale']).type(dtype)
model_params['layer3.5.bn1.bias'] = torch.from_numpy(data_dict['bn4f_branch2a']['offset']).type(dtype)
model_params['layer3.5.conv2.weight'] = torch.from_numpy(data_dict['res4f_branch2b']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer3.5.bn2.weight'] = torch.from_numpy(data_dict['bn4f_branch2b']['scale']).type(dtype)
model_params['layer3.5.bn2.bias'] = torch.from_numpy(data_dict['bn4f_branch2b']['offset']).type(dtype)
model_params['layer3.5.conv3.weight'] = torch.from_numpy(data_dict['res4f_branch2c']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer3.5.bn3.weight'] = torch.from_numpy(data_dict['bn4f_branch2c']['scale']).type(dtype)
model_params['layer3.5.bn3.bias'] = torch.from_numpy(data_dict['bn4f_branch2c']['offset']).type(dtype)
model_params['layer4.0.downsample.0.weight'] = torch.from_numpy(data_dict['res5a_branch1']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer4.0.downsample.1.weight'] = torch.from_numpy(data_dict['bn5a_branch1']['scale']).type(dtype)
model_params['layer4.0.downsample.1.bias'] = torch.from_numpy(data_dict['bn5a_branch1']['offset']).type(dtype)
model_params['layer4.0.conv1.weight'] = torch.from_numpy(data_dict['res5a_branch2a']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer4.0.bn1.weight'] = torch.from_numpy(data_dict['bn5a_branch2a']['scale']).type(dtype)
model_params['layer4.0.bn1.bias'] = torch.from_numpy(data_dict['bn5a_branch2a']['offset']).type(dtype)
model_params['layer4.0.conv2.weight'] = torch.from_numpy(data_dict['res5a_branch2b']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer4.0.bn2.weight'] = torch.from_numpy(data_dict['bn5a_branch2b']['scale']).type(dtype)
model_params['layer4.0.bn2.bias'] = torch.from_numpy(data_dict['bn5a_branch2b']['offset']).type(dtype)
model_params['layer4.0.conv3.weight'] = torch.from_numpy(data_dict['res5a_branch2c']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer4.0.bn3.weight'] = torch.from_numpy(data_dict['bn5a_branch2c']['scale']).type(dtype)
model_params['layer4.0.bn3.bias'] = torch.from_numpy(data_dict['bn5a_branch2c']['offset']).type(dtype)
model_params['layer4.1.conv1.weight'] = torch.from_numpy(data_dict['res5b_branch2a']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer4.1.bn1.weight'] = torch.from_numpy(data_dict['bn5b_branch2a']['scale']).type(dtype)
model_params['layer4.1.bn1.bias'] = torch.from_numpy(data_dict['bn5b_branch2a']['offset']).type(dtype)
model_params['layer4.1.conv2.weight'] = torch.from_numpy(data_dict['res5b_branch2b']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer4.1.bn2.weight'] = torch.from_numpy(data_dict['bn5b_branch2b']['scale']).type(dtype)
model_params['layer4.1.bn2.bias'] = torch.from_numpy(data_dict['bn5b_branch2b']['offset']).type(dtype)
model_params['layer4.1.conv3.weight'] = torch.from_numpy(data_dict['res5b_branch2c']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer4.1.bn3.weight'] = torch.from_numpy(data_dict['bn5b_branch2c']['scale']).type(dtype)
model_params['layer4.1.bn3.bias'] = torch.from_numpy(data_dict['bn5b_branch2c']['offset']).type(dtype)
model_params['layer4.2.conv1.weight'] = torch.from_numpy(data_dict['res5c_branch2a']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer4.2.bn1.weight'] = torch.from_numpy(data_dict['bn5c_branch2a']['scale']).type(dtype)
model_params['layer4.2.bn1.bias'] = torch.from_numpy(data_dict['bn5c_branch2a']['offset']).type(dtype)
model_params['layer4.2.conv2.weight'] = torch.from_numpy(data_dict['res5c_branch2b']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer4.2.bn2.weight'] = torch.from_numpy(data_dict['bn5c_branch2b']['scale']).type(dtype)
model_params['layer4.2.bn2.bias'] = torch.from_numpy(data_dict['bn5c_branch2b']['offset']).type(dtype)
model_params['layer4.2.conv3.weight'] = torch.from_numpy(data_dict['res5c_branch2c']['weights']).type(dtype).permute(3,2,0,1)
model_params['layer4.2.bn3.weight'] = torch.from_numpy(data_dict['bn5c_branch2c']['scale']).type(dtype)
model_params['layer4.2.bn3.bias'] = torch.from_numpy(data_dict['bn5c_branch2c']['offset']).type(dtype)
model_params['conv2.weight'] = torch.from_numpy(data_dict['layer1']['weights']).type(dtype).permute(3,2,0,1)
#model_params['conv2.bias'] = torch.from_numpy(data_dict['layer1']['biases']).type(dtype)
model_params['bn2.weight'] = torch.from_numpy(data_dict['layer1_BN']['scale']).type(dtype)
model_params['bn2.bias'] = torch.from_numpy(data_dict['layer1_BN']['offset']).type(dtype)
# set True to enable weight import, or set False to initialize by yourself
if True:
model_params['up1.conv1_1.weight'] = torch.from_numpy(data_dict['layer2x_br1_ConvA']['weights']).type(dtype).permute(3,2,0,1)
model_params['up1.conv1_1.bias'] = torch.from_numpy(data_dict['layer2x_br1_ConvA']['biases']).type(dtype)
model_params['up1.conv1_2.weight'] = torch.from_numpy(data_dict['layer2x_br1_ConvB']['weights']).type(dtype).permute(3,2,0,1)
model_params['up1.conv1_2.bias'] = torch.from_numpy(data_dict['layer2x_br1_ConvB']['biases']).type(dtype)
model_params['up1.conv1_3.weight'] = torch.from_numpy(data_dict['layer2x_br1_ConvC']['weights']).type(dtype).permute(3,2,0,1)
model_params['up1.conv1_3.bias'] = torch.from_numpy(data_dict['layer2x_br1_ConvC']['biases']).type(dtype)
model_params['up1.conv1_4.weight'] = torch.from_numpy(data_dict['layer2x_br1_ConvD']['weights']).type(dtype).permute(3,2,0,1)
model_params['up1.conv1_4.bias'] = torch.from_numpy(data_dict['layer2x_br1_ConvD']['biases']).type(dtype)
model_params['up1.bn1_1.weight'] = torch.from_numpy(data_dict['layer2x_br1_BN']['scale']).type(dtype)
model_params['up1.bn1_1.bias'] = torch.from_numpy(data_dict['layer2x_br1_BN']['offset']).type(dtype)
model_params['up1.conv2_1.weight'] = torch.from_numpy(data_dict['layer2x_br2_ConvA']['weights']).type(dtype).permute(3,2,0,1)
model_params['up1.conv2_1.bias'] = torch.from_numpy(data_dict['layer2x_br2_ConvA']['biases']).type(dtype)
model_params['up1.conv2_2.weight'] = torch.from_numpy(data_dict['layer2x_br2_ConvB']['weights']).type(dtype).permute(3,2,0,1)
model_params['up1.conv2_2.bias'] = torch.from_numpy(data_dict['layer2x_br2_ConvB']['biases']).type(dtype)
model_params['up1.conv2_3.weight'] = torch.from_numpy(data_dict['layer2x_br2_ConvC']['weights']).type(dtype).permute(3,2,0,1)
model_params['up1.conv2_3.bias'] = torch.from_numpy(data_dict['layer2x_br2_ConvC']['biases']).type(dtype)
model_params['up1.conv2_4.weight'] = torch.from_numpy(data_dict['layer2x_br2_ConvD']['weights']).type(dtype).permute(3,2,0,1)
model_params['up1.conv2_4.bias'] = torch.from_numpy(data_dict['layer2x_br2_ConvD']['biases']).type(dtype)
model_params['up1.bn1_2.weight'] = torch.from_numpy(data_dict['layer2x_br2_BN']['scale']).type(dtype)
model_params['up1.bn1_2.bias'] = torch.from_numpy(data_dict['layer2x_br2_BN']['offset']).type(dtype)
model_params['up1.conv3.weight'] = torch.from_numpy(data_dict['layer2x_Conv']['weights']).type(dtype).permute(3,2,0,1)
model_params['up1.conv3.bias'] = torch.from_numpy(data_dict['layer2x_Conv']['biases']).type(dtype)
model_params['up1.bn2.weight'] = torch.from_numpy(data_dict['layer2x_BN']['scale']).type(dtype)
model_params['up1.bn2.bias'] = torch.from_numpy(data_dict['layer2x_BN']['offset']).type(dtype)
model_params['up2.conv1_1.weight'] = torch.from_numpy(data_dict['layer4x_br1_ConvA']['weights']).type(dtype).permute(3,2,0,1)
model_params['up2.conv1_1.bias'] = torch.from_numpy(data_dict['layer4x_br1_ConvA']['biases']).type(dtype)
model_params['up2.conv1_2.weight'] = torch.from_numpy(data_dict['layer4x_br1_ConvB']['weights']).type(dtype).permute(3,2,0,1)
model_params['up2.conv1_2.bias'] = torch.from_numpy(data_dict['layer4x_br1_ConvB']['biases']).type(dtype)
model_params['up2.conv1_3.weight'] = torch.from_numpy(data_dict['layer4x_br1_ConvC']['weights']).type(dtype).permute(3,2,0,1)
model_params['up2.conv1_3.bias'] = torch.from_numpy(data_dict['layer4x_br1_ConvC']['biases']).type(dtype)
model_params['up2.conv1_4.weight'] = torch.from_numpy(data_dict['layer4x_br1_ConvD']['weights']).type(dtype).permute(3,2,0,1)
model_params['up2.conv1_4.bias'] = torch.from_numpy(data_dict['layer4x_br1_ConvD']['biases']).type(dtype)
model_params['up2.bn1_1.weight'] = torch.from_numpy(data_dict['layer4x_br1_BN']['scale']).type(dtype)
model_params['up2.bn1_1.bias'] = torch.from_numpy(data_dict['layer4x_br1_BN']['offset']).type(dtype)
model_params['up2.conv2_1.weight'] = torch.from_numpy(data_dict['layer4x_br2_ConvA']['weights']).type(dtype).permute(3,2,0,1)
model_params['up2.conv2_1.bias'] = torch.from_numpy(data_dict['layer4x_br2_ConvA']['biases']).type(dtype)
model_params['up2.conv2_2.weight'] = torch.from_numpy(data_dict['layer4x_br2_ConvB']['weights']).type(dtype).permute(3,2,0,1)
model_params['up2.conv2_2.bias'] = torch.from_numpy(data_dict['layer4x_br2_ConvB']['biases']).type(dtype)
model_params['up2.conv2_3.weight'] = torch.from_numpy(data_dict['layer4x_br2_ConvC']['weights']).type(dtype).permute(3,2,0,1)
model_params['up2.conv2_3.bias'] = torch.from_numpy(data_dict['layer4x_br2_ConvC']['biases']).type(dtype)
model_params['up2.conv2_4.weight'] = torch.from_numpy(data_dict['layer4x_br2_ConvD']['weights']).type(dtype).permute(3,2,0,1)
model_params['up2.conv2_4.bias'] = torch.from_numpy(data_dict['layer4x_br2_ConvD']['biases']).type(dtype)
model_params['up2.bn1_2.weight'] = torch.from_numpy(data_dict['layer4x_br2_BN']['scale']).type(dtype)
model_params['up2.bn1_2.bias'] = torch.from_numpy(data_dict['layer4x_br2_BN']['offset']).type(dtype)
model_params['up2.conv3.weight'] = torch.from_numpy(data_dict['layer4x_Conv']['weights']).type(dtype).permute(3,2,0,1)
model_params['up2.conv3.bias'] = torch.from_numpy(data_dict['layer4x_Conv']['biases']).type(dtype)
model_params['up2.bn2.weight'] = torch.from_numpy(data_dict['layer4x_BN']['scale']).type(dtype)
model_params['up2.bn2.bias'] = torch.from_numpy(data_dict['layer4x_BN']['offset']).type(dtype)
model_params['up3.conv1_1.weight'] = torch.from_numpy(data_dict['layer8x_br1_ConvA']['weights']).type(dtype).permute(3,2,0,1)
model_params['up3.conv1_1.bias'] = torch.from_numpy(data_dict['layer8x_br1_ConvA']['biases']).type(dtype)
model_params['up3.conv1_2.weight'] = torch.from_numpy(data_dict['layer8x_br1_ConvB']['weights']).type(dtype).permute(3,2,0,1)
model_params['up3.conv1_2.bias'] = torch.from_numpy(data_dict['layer8x_br1_ConvB']['biases']).type(dtype)
model_params['up3.conv1_3.weight'] = torch.from_numpy(data_dict['layer8x_br1_ConvC']['weights']).type(dtype).permute(3,2,0,1)
model_params['up3.conv1_3.bias'] = torch.from_numpy(data_dict['layer8x_br1_ConvC']['biases']).type(dtype)
model_params['up3.conv1_4.weight'] = torch.from_numpy(data_dict['layer8x_br1_ConvD']['weights']).type(dtype).permute(3,2,0,1)
model_params['up3.conv1_4.bias'] = torch.from_numpy(data_dict['layer8x_br1_ConvD']['biases']).type(dtype)
model_params['up3.bn1_1.weight'] = torch.from_numpy(data_dict['layer8x_br1_BN']['scale']).type(dtype)
model_params['up3.bn1_1.bias'] = torch.from_numpy(data_dict['layer8x_br1_BN']['offset']).type(dtype)
model_params['up3.conv2_1.weight'] = torch.from_numpy(data_dict['layer8x_br2_ConvA']['weights']).type(dtype).permute(3,2,0,1)
model_params['up3.conv2_1.bias'] = torch.from_numpy(data_dict['layer8x_br2_ConvA']['biases']).type(dtype)
model_params['up3.conv2_2.weight'] = torch.from_numpy(data_dict['layer8x_br2_ConvB']['weights']).type(dtype).permute(3,2,0,1)
model_params['up3.conv2_2.bias'] = torch.from_numpy(data_dict['layer8x_br2_ConvB']['biases']).type(dtype)
model_params['up3.conv2_3.weight'] = torch.from_numpy(data_dict['layer8x_br2_ConvC']['weights']).type(dtype).permute(3,2,0,1)
model_params['up3.conv2_3.bias'] = torch.from_numpy(data_dict['layer8x_br2_ConvC']['biases']).type(dtype)
model_params['up3.conv2_4.weight'] = torch.from_numpy(data_dict['layer8x_br2_ConvD']['weights']).type(dtype).permute(3,2,0,1)
model_params['up3.conv2_4.bias'] = torch.from_numpy(data_dict['layer8x_br2_ConvD']['biases']).type(dtype)
model_params['up3.bn1_2.weight'] = torch.from_numpy(data_dict['layer8x_br2_BN']['scale']).type(dtype)
model_params['up3.bn1_2.bias'] = torch.from_numpy(data_dict['layer8x_br2_BN']['offset']).type(dtype)
model_params['up3.conv3.weight'] = torch.from_numpy(data_dict['layer8x_Conv']['weights']).type(dtype).permute(3,2,0,1)
model_params['up3.conv3.bias'] = torch.from_numpy(data_dict['layer8x_Conv']['biases']).type(dtype)
model_params['up3.bn2.weight'] = torch.from_numpy(data_dict['layer8x_BN']['scale']).type(dtype)
model_params['up3.bn2.bias'] = torch.from_numpy(data_dict['layer8x_BN']['offset']).type(dtype)
model_params['up4.conv1_1.weight'] = torch.from_numpy(data_dict['layer16x_br1_ConvA']['weights']).type(dtype).permute(3,2,0,1)
model_params['up4.conv1_1.bias'] = torch.from_numpy(data_dict['layer16x_br1_ConvA']['biases']).type(dtype)
model_params['up4.conv1_2.weight'] = torch.from_numpy(data_dict['layer16x_br1_ConvB']['weights']).type(dtype).permute(3,2,0,1)
model_params['up4.conv1_2.bias'] = torch.from_numpy(data_dict['layer16x_br1_ConvB']['biases']).type(dtype)
model_params['up4.conv1_3.weight'] = torch.from_numpy(data_dict['layer16x_br1_ConvC']['weights']).type(dtype).permute(3,2,0,1)
model_params['up4.conv1_3.bias'] = torch.from_numpy(data_dict['layer16x_br1_ConvC']['biases']).type(dtype)
model_params['up4.conv1_4.weight'] = torch.from_numpy(data_dict['layer16x_br1_ConvD']['weights']).type(dtype).permute(3,2,0,1)
model_params['up4.conv1_4.bias'] = torch.from_numpy(data_dict['layer16x_br1_ConvD']['biases']).type(dtype)
model_params['up4.bn1_1.weight'] = torch.from_numpy(data_dict['layer16x_br1_BN']['scale']).type(dtype)
model_params['up4.bn1_1.bias'] = torch.from_numpy(data_dict['layer16x_br1_BN']['offset']).type(dtype)
model_params['up4.conv2_1.weight'] = torch.from_numpy(data_dict['layer16x_br2_ConvA']['weights']).type(dtype).permute(3,2,0,1)
model_params['up4.conv2_1.bias'] = torch.from_numpy(data_dict['layer16x_br2_ConvA']['biases']).type(dtype)
model_params['up4.conv2_2.weight'] = torch.from_numpy(data_dict['layer16x_br2_ConvB']['weights']).type(dtype).permute(3,2,0,1)
model_params['up4.conv2_2.bias'] = torch.from_numpy(data_dict['layer16x_br2_ConvB']['biases']).type(dtype)
model_params['up4.conv2_3.weight'] = torch.from_numpy(data_dict['layer16x_br2_ConvC']['weights']).type(dtype).permute(3,2,0,1)
model_params['up4.conv2_3.bias'] = torch.from_numpy(data_dict['layer16x_br2_ConvC']['biases']).type(dtype)
model_params['up4.conv2_4.weight'] = torch.from_numpy(data_dict['layer16x_br2_ConvD']['weights']).type(dtype).permute(3,2,0,1)
model_params['up4.conv2_4.bias'] = torch.from_numpy(data_dict['layer16x_br2_ConvD']['biases']).type(dtype)
model_params['up4.bn1_2.weight'] = torch.from_numpy(data_dict['layer16x_br2_BN']['scale']).type(dtype)
model_params['up4.bn1_2.bias'] = torch.from_numpy(data_dict['layer16x_br2_BN']['offset']).type(dtype)
model_params['up4.conv3.weight'] = torch.from_numpy(data_dict['layer16x_Conv']['weights']).type(dtype).permute(3,2,0,1)
model_params['up4.conv3.bias'] = torch.from_numpy(data_dict['layer16x_Conv']['biases']).type(dtype)
model_params['up4.bn2.weight'] = torch.from_numpy(data_dict['layer16x_BN']['scale']).type(dtype)
model_params['up4.bn2.bias'] = torch.from_numpy(data_dict['layer16x_BN']['offset']).type(dtype)
model_params['conv3.weight'] = torch.from_numpy(data_dict['ConvPred']['weights']).type(dtype).permute(3,2,0,1)
model_params['conv3.bias'] = torch.from_numpy(data_dict['ConvPred']['biases']).type(dtype)
print('Up proj weights loaded!!!!!!!!!!!')
return model_params