-
Notifications
You must be signed in to change notification settings - Fork 331
/
symbol_10_560_25L_8scales_v1.py
executable file
·471 lines (391 loc) · 25.2 KB
/
symbol_10_560_25L_8scales_v1.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
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
# -*- coding: utf-8 -*-
"""
"""
import os
import sys
sys.path.append('..')
from config_farm import configuration_10_560_25L_8scales_v1 as cfg
from ChasingTrainFramework_GeneralOneClassDetection.loss_layer_farm.cross_entropy_with_hnm_for_one_class_detection import *
import mxnet
num_filters_list = [32, 64, 128, 256]
def loss_branch(input_data, prefix_name, mask=None, label=None, deploy_flag=False):
branch_conv1 = mxnet.symbol.Convolution(data=input_data,
kernel=(1, 1),
stride=(1, 1),
pad=(0, 0),
num_filter=num_filters_list[2],
name=prefix_name + '_1')
branch_relu1 = mxnet.symbol.Activation(data=branch_conv1, act_type='relu', name='relu_' + prefix_name + '_1')
branch_conv2_score = mxnet.symbol.Convolution(data=branch_relu1,
kernel=(1, 1),
stride=(1, 1),
pad=(0, 0),
num_filter=num_filters_list[2],
name=prefix_name + '_2_score')
branch_relu2_score = mxnet.symbol.Activation(data=branch_conv2_score, act_type='relu', name='relu_' + prefix_name + '_2_score')
branch_conv3_score = mxnet.symbol.Convolution(data=branch_relu2_score,
kernel=(1, 1),
stride=(1, 1),
pad=(0, 0),
num_filter=2,
name=prefix_name + '_3_score')
branch_conv2_bbox = mxnet.symbol.Convolution(data=branch_relu1,
kernel=(1, 1),
stride=(1, 1),
pad=(0, 0),
num_filter=num_filters_list[2],
name=prefix_name + '_2_bbox')
branch_relu2_bbox = mxnet.symbol.Activation(data=branch_conv2_bbox, act_type='relu', name='relu_' + prefix_name + '_2_bbox')
branch_conv3_bbox = mxnet.symbol.Convolution(data=branch_relu2_bbox,
kernel=(1, 1),
stride=(1, 1),
pad=(0, 0),
num_filter=4,
name=prefix_name + '_3_bbox')
if deploy_flag:
predict_score = mxnet.symbol.softmax(data=branch_conv3_score, axis=1)
predict_score = mxnet.symbol.slice_axis(predict_score, axis=1, begin=0, end=1)
predict_bbox = branch_conv3_bbox
return predict_score, predict_bbox
else:
mask_score = mxnet.symbol.slice_axis(mask, axis=1, begin=0, end=2)
label_score = mxnet.symbol.slice_axis(label, axis=1, begin=0, end=2)
loss_score = mxnet.symbol.Custom(pred=branch_conv3_score, label=label_score, mask=mask_score, hnm_ratio=cfg.param_hnm_ratio,
op_type='cross_entropy_with_hnm_for_one_class_detection', name=prefix_name + '_loss_score')
mask_bbox = mxnet.symbol.slice_axis(mask, axis=1, begin=2, end=6)
predict_bbox = branch_conv3_bbox * mask_bbox
label_bbox = mxnet.symbol.slice_axis(label, axis=1, begin=2, end=6) * mask_bbox
loss_bbox = mxnet.symbol.LinearRegressionOutput(data=predict_bbox, label=label_bbox, name=prefix_name + '_loss_bbox')
return loss_score, loss_bbox
def get_net_symbol(deploy_flag=False):
data_names = ['data']
label_names = ['mask_1', 'label_1',
'mask_2', 'label_2',
'mask_3', 'label_3',
'mask_4', 'label_4',
'mask_5', 'label_5',
'mask_6', 'label_6',
'mask_7', 'label_7',
'mask_8', 'label_8', ]
# batch data
data = mxnet.symbol.Variable(name='data', shape=(cfg.param_train_batch_size, cfg.param_num_image_channel, cfg.param_net_input_height, cfg.param_net_input_width))
label_1 = mxnet.symbol.Variable(name='label_1', shape=(cfg.param_train_batch_size, cfg.param_num_output_channels, cfg.param_feature_map_size_list[0], cfg.param_feature_map_size_list[0]))
mask_1 = mxnet.symbol.Variable(name='mask_1', shape=(cfg.param_train_batch_size, cfg.param_num_output_channels, cfg.param_feature_map_size_list[0], cfg.param_feature_map_size_list[0]))
label_2 = mxnet.symbol.Variable(name='label_2', shape=(cfg.param_train_batch_size, cfg.param_num_output_channels, cfg.param_feature_map_size_list[1], cfg.param_feature_map_size_list[1]))
mask_2 = mxnet.symbol.Variable(name='mask_2', shape=(cfg.param_train_batch_size, cfg.param_num_output_channels, cfg.param_feature_map_size_list[1], cfg.param_feature_map_size_list[1]))
label_3 = mxnet.symbol.Variable(name='label_3', shape=(cfg.param_train_batch_size, cfg.param_num_output_channels, cfg.param_feature_map_size_list[2], cfg.param_feature_map_size_list[2]))
mask_3 = mxnet.symbol.Variable(name='mask_3', shape=(cfg.param_train_batch_size, cfg.param_num_output_channels, cfg.param_feature_map_size_list[2], cfg.param_feature_map_size_list[2]))
label_4 = mxnet.symbol.Variable(name='label_4', shape=(cfg.param_train_batch_size, cfg.param_num_output_channels, cfg.param_feature_map_size_list[3], cfg.param_feature_map_size_list[3]))
mask_4 = mxnet.symbol.Variable(name='mask_4', shape=(cfg.param_train_batch_size, cfg.param_num_output_channels, cfg.param_feature_map_size_list[3], cfg.param_feature_map_size_list[3]))
label_5 = mxnet.symbol.Variable(name='label_5', shape=(cfg.param_train_batch_size, cfg.param_num_output_channels, cfg.param_feature_map_size_list[4], cfg.param_feature_map_size_list[4]))
mask_5 = mxnet.symbol.Variable(name='mask_5', shape=(cfg.param_train_batch_size, cfg.param_num_output_channels, cfg.param_feature_map_size_list[4], cfg.param_feature_map_size_list[4]))
label_6 = mxnet.symbol.Variable(name='label_6', shape=(cfg.param_train_batch_size, cfg.param_num_output_channels, cfg.param_feature_map_size_list[5], cfg.param_feature_map_size_list[5]))
mask_6 = mxnet.symbol.Variable(name='mask_6', shape=(cfg.param_train_batch_size, cfg.param_num_output_channels, cfg.param_feature_map_size_list[5], cfg.param_feature_map_size_list[5]))
label_7 = mxnet.symbol.Variable(name='label_7', shape=(cfg.param_train_batch_size, cfg.param_num_output_channels, cfg.param_feature_map_size_list[6], cfg.param_feature_map_size_list[6]))
mask_7 = mxnet.symbol.Variable(name='mask_7', shape=(cfg.param_train_batch_size, cfg.param_num_output_channels, cfg.param_feature_map_size_list[6], cfg.param_feature_map_size_list[6]))
label_8 = mxnet.symbol.Variable(name='label_8', shape=(cfg.param_train_batch_size, cfg.param_num_output_channels, cfg.param_feature_map_size_list[7], cfg.param_feature_map_size_list[7]))
mask_8 = mxnet.symbol.Variable(name='mask_8', shape=(cfg.param_train_batch_size, cfg.param_num_output_channels, cfg.param_feature_map_size_list[7], cfg.param_feature_map_size_list[7]))
data = (data - 127.5) / 127.5
# conv block 1 ---------------------------------------------------------------------------------------
conv1 = mxnet.symbol.Convolution(data=data,
kernel=(3, 3),
stride=(2, 2),
pad=(0, 0),
num_filter=num_filters_list[1],
name='conv1')
relu1 = mxnet.symbol.Activation(data=conv1, act_type='relu', name='relu_conv1')
# conv block 2 ----------------------------------------------------------------------------------------
conv2 = mxnet.symbol.Convolution(data=relu1,
kernel=(3, 3),
stride=(2, 2),
pad=(0, 0),
num_filter=num_filters_list[1],
name='conv2')
relu2 = mxnet.symbol.Activation(data=conv2, act_type='relu', name='relu_conv2')
# conv block 3 ----------------------------------------------------------------------------------------
conv3 = mxnet.symbol.Convolution(data=relu2,
kernel=(3, 3),
stride=(1, 1),
pad=(1, 1),
num_filter=num_filters_list[1],
name='conv3')
relu3 = mxnet.symbol.Activation(data=conv3, act_type='relu', name='relu_conv3')
# conv block 4 ----------------------------------------------------------------------------------------
conv4 = mxnet.symbol.Convolution(data=relu3,
kernel=(3, 3),
stride=(1, 1),
pad=(1, 1),
num_filter=num_filters_list[1],
name='conv4')
# Residual 1:
conv4 = conv2 + conv4
relu4 = mxnet.symbol.Activation(data=conv4, act_type='relu', name='relu_conv4')
# conv block 5 ----------------------------------------------------------------------------------------
conv5 = mxnet.symbol.Convolution(data=relu4,
kernel=(3, 3),
stride=(1, 1),
pad=(1, 1),
num_filter=num_filters_list[1],
name='conv5')
relu5 = mxnet.symbol.Activation(data=conv5, act_type='relu', name='relu_conv5')
# conv block 6 ----------------------------------------------------------------------------------------
conv6 = mxnet.symbol.Convolution(data=relu5,
kernel=(3, 3),
stride=(1, 1),
pad=(1, 1),
num_filter=num_filters_list[1],
name='conv6')
# Residual 2:
conv6 = conv4 + conv6
relu6 = mxnet.symbol.Activation(data=conv6, act_type='relu', name='relu_conv6')
# conv block 7 ----------------------------------------------------------------------------------------
conv7 = mxnet.symbol.Convolution(data=relu6,
kernel=(3, 3),
stride=(1, 1),
pad=(1, 1),
num_filter=num_filters_list[1],
name='conv7')
relu7 = mxnet.symbol.Activation(data=conv7, act_type='relu', name='relu_conv7')
# conv block 8 ----------------------------------------------------------------------------------------
conv8 = mxnet.symbol.Convolution(data=relu7,
kernel=(3, 3),
stride=(1, 1),
pad=(1, 1),
num_filter=num_filters_list[1],
name='conv8')
# Residual 3:
conv8 = conv6 + conv8
relu8 = mxnet.symbol.Activation(data=conv8, act_type='relu', name='relu_conv8')
# loss 1 RF:55 ----------------------------------------------------------------------------------------------------
# for scale [10,15]
if deploy_flag:
predict_score_1, predict_bbox_1 = loss_branch(relu8, 'conv8', deploy_flag=deploy_flag)
else:
loss_score_1, loss_bbox_1 = loss_branch(relu8, 'conv8', mask=mask_1, label=label_1)
# conv block 9 ----------------------------------------------------------------------------------------
conv9 = mxnet.symbol.Convolution(data=relu8,
kernel=(3, 3),
stride=(1, 1),
pad=(1, 1),
num_filter=num_filters_list[1],
name='conv9')
relu9 = mxnet.symbol.Activation(data=conv9, act_type='relu', name='relu_conv9')
# conv block 10 ----------------------------------------------------------------------------------------
conv10 = mxnet.symbol.Convolution(data=relu9,
kernel=(3, 3),
stride=(1, 1),
pad=(1, 1),
num_filter=num_filters_list[1],
name='conv10')
# Residual 4:
conv10 = conv8 + conv10
relu10 = mxnet.symbol.Activation(data=conv10, act_type='relu', name='relu_conv10')
# loss 2 RF:71 ----------------------------------------------------------------------------------------------------
# for scale [15,20]
if deploy_flag:
predict_score_2, predict_bbox_2 = loss_branch(relu10, 'conv10', deploy_flag=deploy_flag)
else:
loss_score_2, loss_bbox_2 = loss_branch(relu10, 'conv10', mask=mask_2, label=label_2)
# conv block 11 ----------------------------------------------------------------------------------------
conv11 = mxnet.symbol.Convolution(data=relu10,
kernel=(3, 3),
stride=(2, 2),
pad=(0, 0),
num_filter=num_filters_list[1],
name='conv11')
relu11 = mxnet.symbol.Activation(data=conv11, act_type='relu', name='relu_conv11')
# conv block 12 ----------------------------------------------------------------------------------------
conv12 = mxnet.symbol.Convolution(data=relu11,
kernel=(3, 3),
stride=(1, 1),
pad=(1, 1),
num_filter=num_filters_list[1],
name='conv12')
relu12 = mxnet.symbol.Activation(data=conv12, act_type='relu', name='relu_conv12')
# conv block 13 ----------------------------------------------------------------------------------------
conv13 = mxnet.symbol.Convolution(data=relu12,
kernel=(3, 3),
stride=(1, 1),
pad=(1, 1),
num_filter=num_filters_list[1],
name='conv13')
# Residual
conv13 = conv11 + conv13
relu13 = mxnet.symbol.Activation(data=conv13, act_type='relu', name='relu_conv13')
# loss 3 RF:111 ----------------------------------------------------------------------------------------------------
# for scale [20,40]
if deploy_flag:
predict_score_3, predict_bbox_3 = loss_branch(relu13, 'conv13', deploy_flag=deploy_flag)
else:
loss_score_3, loss_bbox_3 = loss_branch(relu13, 'conv13', mask=mask_3, label=label_3)
# conv block 14 ----------------------------------------------------------------------------------------
conv14 = mxnet.symbol.Convolution(data=relu13,
kernel=(3, 3),
stride=(1, 1),
pad=(1, 1),
num_filter=num_filters_list[1],
name='conv14')
relu14 = mxnet.symbol.Activation(data=conv14, act_type='relu', name='relu_conv14')
# conv block 15 ----------------------------------------------------------------------------------------
conv15 = mxnet.symbol.Convolution(data=relu14,
kernel=(3, 3),
stride=(1, 1),
pad=(1, 1),
num_filter=num_filters_list[1],
name='conv15')
# Residual
conv15 = conv13 + conv15
relu15 = mxnet.symbol.Activation(data=conv15, act_type='relu', name='relu_conv15')
# loss 4 RF:143 ----------------------------------------------------------------------------------------------------
# for scale [40,70]
if deploy_flag:
predict_score_4, predict_bbox_4 = loss_branch(relu15, 'conv15', deploy_flag=deploy_flag)
else:
loss_score_4, loss_bbox_4 = loss_branch(relu13, 'conv15', mask=mask_4, label=label_4)
# conv block 16 ----------------------------------------------------------------------------------------
conv16 = mxnet.symbol.Convolution(data=relu15,
kernel=(3, 3),
stride=(2, 2),
pad=(0, 0),
num_filter=num_filters_list[2],
name='conv16')
relu16 = mxnet.symbol.Activation(data=conv16, act_type='relu', name='relu_conv16')
# conv block 17 ----------------------------------------------------------------------------------------
conv17 = mxnet.symbol.Convolution(data=relu16,
kernel=(3, 3),
stride=(1, 1),
pad=(1, 1),
num_filter=num_filters_list[2],
name='conv17')
relu17 = mxnet.symbol.Activation(data=conv17, act_type='relu', name='relu_conv17')
# conv block 18 ----------------------------------------------------------------------------------------
conv18 = mxnet.symbol.Convolution(data=relu17,
kernel=(3, 3),
stride=(1, 1),
pad=(1, 1),
num_filter=num_filters_list[2],
name='conv18')
# Residual
conv18 = conv16 + conv18
relu18 = mxnet.symbol.Activation(data=conv18, act_type='relu', name='relu_conv18')
# loss 5 RF:223 ----------------------------------------------------------------------------------------------------
# for scale [70,110]
if deploy_flag:
predict_score_5, predict_bbox_5 = loss_branch(relu18, 'conv18', deploy_flag=deploy_flag)
else:
loss_score_5, loss_bbox_5 = loss_branch(relu18, 'conv18', mask=mask_5, label=label_5)
# conv block 19 ----------------------------------------------------------------------------------------
conv19 = mxnet.symbol.Convolution(data=relu18,
kernel=(3, 3),
stride=(2, 2),
pad=(0, 0),
num_filter=num_filters_list[2],
name='conv19')
relu19 = mxnet.symbol.Activation(data=conv19, act_type='relu', name='relu_conv19')
# conv block 20 ----------------------------------------------------------------------------------------
conv20 = mxnet.symbol.Convolution(data=relu19,
kernel=(3, 3),
stride=(1, 1),
pad=(1, 1),
num_filter=num_filters_list[2],
name='conv20')
relu20 = mxnet.symbol.Activation(data=conv20, act_type='relu', name='relu_conv20')
# conv block 21 ----------------------------------------------------------------------------------------
conv21 = mxnet.symbol.Convolution(data=relu20,
kernel=(3, 3),
stride=(1, 1),
pad=(1, 1),
num_filter=num_filters_list[2],
name='conv21')
# Residual
conv21 = conv19 + conv21
relu21 = mxnet.symbol.Activation(data=conv21, act_type='relu', name='relu_conv21')
# loss 6 RF:383 ----------------------------------------------------------------------------------------------------
# for scale [110,190]
if deploy_flag:
predict_score_6, predict_bbox_6 = loss_branch(relu21, 'conv21', deploy_flag=deploy_flag)
else:
loss_score_6, loss_bbox_6 = loss_branch(relu21, 'conv21', mask=mask_6, label=label_6)
# conv block 22 ----------------------------------------------------------------------------------------
conv22 = mxnet.symbol.Convolution(data=relu21,
kernel=(3, 3),
stride=(1, 1),
pad=(1, 1),
num_filter=num_filters_list[2],
name='conv22')
relu22 = mxnet.symbol.Activation(data=conv22, act_type='relu', name='relu_conv22')
# conv block 23 ----------------------------------------------------------------------------------------
conv23 = mxnet.symbol.Convolution(data=relu22,
kernel=(3, 3),
stride=(1, 1),
pad=(1, 1),
num_filter=num_filters_list[2],
name='conv23')
# Residual
conv23 = conv21 + conv23
relu23 = mxnet.symbol.Activation(data=conv23, act_type='relu', name='relu_conv23')
# loss 7 RF:511 ----------------------------------------------------------------------------------------------------
# for scale [190,290]
if deploy_flag:
predict_score_7, predict_bbox_7 = loss_branch(relu23, 'conv23', deploy_flag=deploy_flag)
else:
loss_score_7, loss_bbox_7 = loss_branch(relu23, 'conv23', mask=mask_7, label=label_7)
# conv block 24 ----------------------------------------------------------------------------------------
conv24 = mxnet.symbol.Convolution(data=relu23,
kernel=(3, 3),
stride=(1, 1),
pad=(1, 1),
num_filter=num_filters_list[2],
name='conv24')
relu24 = mxnet.symbol.Activation(data=conv24, act_type='relu', name='relu_conv24')
# conv block 25 ----------------------------------------------------------------------------------------
conv25 = mxnet.symbol.Convolution(data=relu24,
kernel=(3, 3),
stride=(1, 1),
pad=(1, 1),
num_filter=num_filters_list[2],
name='conv25')
# Residual
conv25 = conv23 + conv25
relu25 = mxnet.symbol.Activation(data=conv25, act_type='relu', name='relu_conv25')
# loss 8 RF:639 ----------------------------------------------------------------------------------------------------
# for scale [290,410]
if deploy_flag:
predict_score_8, predict_bbox_8 = loss_branch(relu25, 'conv25', deploy_flag=deploy_flag)
else:
loss_score_8, loss_bbox_8 = loss_branch(relu25, 'conv25', mask=mask_8, label=label_8)
if deploy_flag:
net = mxnet.symbol.Group([predict_score_1, predict_bbox_1,
predict_score_2, predict_bbox_2,
predict_score_3, predict_bbox_3,
predict_score_4, predict_bbox_4,
predict_score_5, predict_bbox_5,
predict_score_6, predict_bbox_6,
predict_score_7, predict_bbox_7,
predict_score_8, predict_bbox_8])
return net
else:
net = mxnet.symbol.Group([loss_score_1, loss_bbox_1,
loss_score_2, loss_bbox_2,
loss_score_3, loss_bbox_3,
loss_score_4, loss_bbox_4,
loss_score_5, loss_bbox_5,
loss_score_6, loss_bbox_6,
loss_score_7, loss_bbox_7,
loss_score_8, loss_bbox_8])
return net, data_names, label_names
def run_get_net_symbol_for_train():
my_symbol, _, __ = get_net_symbol()
shape = {'data': (cfg.param_train_batch_size, cfg.param_num_image_channel, cfg.param_net_input_height, cfg.param_net_input_width)}
print(mxnet.viz.print_summary(my_symbol, shape=shape))
arg_names = my_symbol.list_arguments()
aux_names = my_symbol.list_auxiliary_states()
arg_shapes, out_shapes, _ = my_symbol.infer_shape()
print(arg_names)
print(aux_names)
print(my_symbol.list_outputs())
print(out_shapes)
if __name__ == '__main__':
run_get_net_symbol_for_train()
deploy_net = get_net_symbol(deploy_flag=True)
deploy_net.save(os.path.basename(__file__).replace('.py', '_deploy.json'))