File size: 22,895 Bytes
7734d5b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse
import os
import sys
import json

class opts(object):
  def __init__(self):
    self.parser = argparse.ArgumentParser()
    # basic experiment setting
    self.parser.add_argument('task', default='',
                             help='ctdet | ddd | multi_pose '
                             '| tracking or combined with ,')
    self.parser.add_argument('--dataset', default='coco',
                             help='see lib/dataset/dataset_facotry for ' + 
                            'available datasets')
    self.parser.add_argument('--test_dataset', default='',
                             help='coco | kitti | coco_hp | pascal')
    self.parser.add_argument('--exp_id', default='default')
    self.parser.add_argument('--test', action='store_true')
    self.parser.add_argument('--debug', type=int, default=0,
                             help='level of visualization.'
                                  '1: only show the final detection results'
                                  '2: show the network output features'
                                  '3: use matplot to display' # useful when lunching training with ipython notebook
                                  '4: save all visualizations to disk')
    self.parser.add_argument('--no_pause', action='store_true')
    self.parser.add_argument('--demo', default='', 
                             help='path to image/ image folders/ video. '
                                  'or "webcam"')
    self.parser.add_argument('--load_model', default='',
                             help='path to pretrained model')
    self.parser.add_argument('--resume', action='store_true',
                             help='resume an experiment. '
                                  'Reloaded the optimizer parameter and '
                                  'set load_model to model_last.pth '
                                  'in the exp dir if load_model is empty.') 

    # system
    self.parser.add_argument('--gpus', default='0', 
                             help='-1 for CPU, use comma for multiple gpus')
    self.parser.add_argument('--num_workers', type=int, default=4,
                             help='dataloader threads. 0 for single-thread.')
    self.parser.add_argument('--not_cuda_benchmark', action='store_true',
                             help='disable when the input size is not fixed.')
    self.parser.add_argument('--seed', type=int, default=317, 
                             help='random seed') # from CornerNet
    self.parser.add_argument('--not_set_cuda_env', action='store_true',
                             help='used when training in slurm clusters.')

    # log
    self.parser.add_argument('--print_iter', type=int, default=0, 
                             help='disable progress bar and print to screen.')
    self.parser.add_argument('--save_all', action='store_true',
                             help='save model to disk every 5 epochs.')
    self.parser.add_argument('--vis_thresh', type=float, default=0.3,
                             help='visualization threshold.')
    self.parser.add_argument('--debugger_theme', default='white', 
                             choices=['white', 'black'])
    self.parser.add_argument('--eval_val', action='store_true')
    self.parser.add_argument('--save_imgs', default='', help='')
    self.parser.add_argument('--save_img_suffix', default='', help='')
    self.parser.add_argument('--skip_first', type=int, default=-1, help='')
    self.parser.add_argument('--save_video', action='store_true')
    self.parser.add_argument('--save_framerate', type=int, default=30)
    self.parser.add_argument('--resize_video', action='store_true')
    self.parser.add_argument('--video_h', type=int, default=512, help='')
    self.parser.add_argument('--video_w', type=int, default=512, help='')
    self.parser.add_argument('--transpose_video', action='store_true')
    self.parser.add_argument('--show_track_color', action='store_true')
    self.parser.add_argument('--not_show_bbox', action='store_true')
    self.parser.add_argument('--not_show_number', action='store_true')
    self.parser.add_argument('--qualitative', action='store_true')
    self.parser.add_argument('--tango_color', action='store_true')

    # model
    self.parser.add_argument('--arch', default='dla_34', 
                             help='model architecture. Currently tested'
                                  'res_18 | res_101 | resdcn_18 | resdcn_101 |'
                                  'dlav0_34 | dla_34 | hourglass')
    self.parser.add_argument('--dla_node', default='dcn') 
    self.parser.add_argument('--head_conv', type=int, default=-1,
                             help='conv layer channels for output head'
                                  '0 for no conv layer'
                                  '-1 for default setting: '
                                  '64 for resnets and 256 for dla.')
    self.parser.add_argument('--num_head_conv', type=int, default=1)
    self.parser.add_argument('--head_kernel', type=int, default=3, help='')
    self.parser.add_argument('--down_ratio', type=int, default=4,
                             help='output stride. Currently only supports 4.')
    self.parser.add_argument('--not_idaup', action='store_true')
    self.parser.add_argument('--num_classes', type=int, default=-1)
    self.parser.add_argument('--num_layers', type=int, default=101)
    self.parser.add_argument('--backbone', default='dla34')
    self.parser.add_argument('--neck', default='dlaup')
    self.parser.add_argument('--msra_outchannel', type=int, default=256)
    self.parser.add_argument('--efficient_level', type=int, default=0)
    self.parser.add_argument('--prior_bias', type=float, default=-4.6) # -2.19
    self.parser.add_argument('--embedding', action='store_true')
    self.parser.add_argument('--box_nms', type=float, default=-1)
    self.parser.add_argument('--inference', action='store_true')
    self.parser.add_argument('--clip_len', type=int, default=1, help='number of images used in trades'
                                                                     'including the current image')
    self.parser.add_argument('--no_repeat', action='store_true', default=True)
    self.parser.add_argument('--seg', action='store_true', default=False)
    self.parser.add_argument('--seg_feat_channel', default=8, type=int, help='.')
    self.parser.add_argument('--deform_kernel_size', type=int, default=3)
    self.parser.add_argument('--trades', action='store_true', help='Track to Detect and Segment:'
                                                                   'An Online Multi Object Tracker')

    # input
    self.parser.add_argument('--input_res', type=int, default=-1, 
                             help='input height and width. -1 for default from '
                             'dataset. Will be overriden by input_h | input_w')
    self.parser.add_argument('--input_h', type=int, default=-1, 
                             help='input height. -1 for default from dataset.')
    self.parser.add_argument('--input_w', type=int, default=-1, 
                             help='input width. -1 for default from dataset.')
    self.parser.add_argument('--dataset_version', default='')

    # train
    self.parser.add_argument('--optim', default='adam')
    self.parser.add_argument('--lr', type=float, default=1.25e-4, 
                             help='learning rate for batch size 32.')
    self.parser.add_argument('--lr_step', type=str, default='60',
                             help='drop learning rate by 10.')
    self.parser.add_argument('--save_point', type=str, default='90',
                             help='when to save the model to disk.')
    self.parser.add_argument('--num_epochs', type=int, default=70,
                             help='total training epochs.')
    self.parser.add_argument('--batch_size', type=int, default=32,
                             help='batch size')
    self.parser.add_argument('--master_batch_size', type=int, default=-1,
                             help='batch size on the master gpu.')
    self.parser.add_argument('--num_iters', type=int, default=-1,
                             help='default: #samples / batch_size.')
    self.parser.add_argument('--val_intervals', type=int, default=10000,
                             help='number of epochs to run validation.')
    self.parser.add_argument('--trainval', action='store_true',
                             help='include validation in training and '
                                  'test on test set')
    self.parser.add_argument('--ltrb', action='store_true',
                             help='')          
    self.parser.add_argument('--ltrb_weight', type=float, default=0.1,
                             help='')
    self.parser.add_argument('--reset_hm', action='store_true')
    self.parser.add_argument('--reuse_hm', action='store_true')
    self.parser.add_argument('--use_kpt_center', action='store_true')
    self.parser.add_argument('--add_05', action='store_true')
    self.parser.add_argument('--dense_reg', type=int, default=1, help='')

    # test
    self.parser.add_argument('--flip_test', action='store_true',
                             help='flip data augmentation.')
    self.parser.add_argument('--test_scales', type=str, default='1',
                             help='multi scale test augmentation.')
    self.parser.add_argument('--nms', action='store_true',
                             help='run nms in testing.')
    self.parser.add_argument('--K', type=int, default=100,
                             help='max number of output objects.') 
    self.parser.add_argument('--not_prefetch_test', action='store_true',
                             help='not use parallal data pre-processing.')
    self.parser.add_argument('--fix_short', type=int, default=-1)
    self.parser.add_argument('--keep_res', action='store_true',
                             help='keep the original resolution'
                                  ' during validation.')
    self.parser.add_argument('--map_argoverse_id', action='store_true',
                             help='if trained on nuscenes and eval on kitti')
    self.parser.add_argument('--out_thresh', type=float, default=-1,
                             help='')
    self.parser.add_argument('--depth_scale', type=float, default=1,
                             help='')
    self.parser.add_argument('--save_results', action='store_true')
    self.parser.add_argument('--load_results', default='')
    self.parser.add_argument('--use_loaded_results', action='store_true')
    self.parser.add_argument('--ignore_loaded_cats', default='')
    self.parser.add_argument('--model_output_list', action='store_true',
                             help='Used when convert to onnx')
    self.parser.add_argument('--non_block_test', action='store_true')
    self.parser.add_argument('--vis_gt_bev', default='', help='')
    self.parser.add_argument('--kitti_split', default='3dop',
                             help='different validation split for kitti: '
                                  '3dop | subcnn')
    self.parser.add_argument('--test_focal_length', type=int, default=-1)

    # dataset
    self.parser.add_argument('--not_rand_crop', action='store_true',
                             help='not use the random crop data augmentation'
                                  'from CornerNet.')
    self.parser.add_argument('--not_max_crop', action='store_true',
                             help='used when the training dataset has'
                                  'inbalanced aspect ratios.')
    self.parser.add_argument('--shift', type=float, default=0,
                             help='when not using random crop, 0.1'
                                  'apply shift augmentation.')
    self.parser.add_argument('--scale', type=float, default=0,
                             help='when not using random crop, 0.4'
                                  'apply scale augmentation.')
    self.parser.add_argument('--aug_rot', type=float, default=0, 
                             help='probability of applying '
                                  'rotation augmentation.')
    self.parser.add_argument('--rotate', type=float, default=0,
                             help='when not using random crop'
                                  'apply rotation augmentation.')
    self.parser.add_argument('--flip', type=float, default=0.5,
                             help='probability of applying flip augmentation.')
    self.parser.add_argument('--no_color_aug', action='store_true',
                             help='not use the color augmenation '
                                  'from CornerNet')

    # Tracking
    self.parser.add_argument('--tracking', action='store_true')
    self.parser.add_argument('--pre_hm', action='store_true')
    self.parser.add_argument('--same_aug_pre', action='store_true')
    self.parser.add_argument('--zero_pre_hm', action='store_true')
    self.parser.add_argument('--hm_disturb', type=float, default=0)
    self.parser.add_argument('--lost_disturb', type=float, default=0)
    self.parser.add_argument('--fp_disturb', type=float, default=0)
    self.parser.add_argument('--pre_thresh', type=float, default=-1)
    self.parser.add_argument('--track_thresh', type=float, default=0.3)
    self.parser.add_argument('--match_thresh', type=float, default=0.8)
    self.parser.add_argument('--track_buffer', type=int, default=30)   
    self.parser.add_argument('--new_thresh', type=float, default=0.0)
    self.parser.add_argument('--max_frame_dist', type=int, default=3)
    self.parser.add_argument('--ltrb_amodal', action='store_true')
    self.parser.add_argument('--ltrb_amodal_weight', type=float, default=0.1)
    self.parser.add_argument('--window_size', type=int, default=20)
    self.parser.add_argument('--public_det', action='store_true')
    self.parser.add_argument('--no_pre_img', action='store_true')
    self.parser.add_argument('--zero_tracking', action='store_true')
    self.parser.add_argument('--hungarian', action='store_true')
    self.parser.add_argument('--max_age', type=int, default=-1)


    # loss
    self.parser.add_argument('--tracking_weight', type=float, default=1)
    self.parser.add_argument('--reg_loss', default='l1',
                             help='regression loss: sl1 | l1 | l2')
    self.parser.add_argument('--hm_weight', type=float, default=1,
                             help='loss weight for keypoint heatmaps.')
    self.parser.add_argument('--off_weight', type=float, default=1,
                             help='loss weight for keypoint local offsets.')
    self.parser.add_argument('--wh_weight', type=float, default=0.1,
                             help='loss weight for bounding box size.')
    self.parser.add_argument('--hp_weight', type=float, default=1,
                             help='loss weight for human pose offset.')
    self.parser.add_argument('--hm_hp_weight', type=float, default=1,
                             help='loss weight for human keypoint heatmap.')
    self.parser.add_argument('--amodel_offset_weight', type=float, default=1,
                             help='Please forgive the typo.')
    self.parser.add_argument('--dep_weight', type=float, default=1,
                             help='loss weight for depth.')
    self.parser.add_argument('--dim_weight', type=float, default=1,
                             help='loss weight for 3d bounding box size.')
    self.parser.add_argument('--rot_weight', type=float, default=1,
                             help='loss weight for orientation.')
    self.parser.add_argument('--nuscenes_att', action='store_true')
    self.parser.add_argument('--nuscenes_att_weight', type=float, default=1)
    self.parser.add_argument('--velocity', action='store_true')
    self.parser.add_argument('--velocity_weight', type=float, default=1)
    self.parser.add_argument('--nID', type=int, default=-1)

    # custom dataset
    self.parser.add_argument('--custom_dataset_img_path', default='')
    self.parser.add_argument('--custom_dataset_ann_path', default='')

  def parse(self, args=''):
    if args == '':
      opt = self.parser.parse_args()
    else:
      opt = self.parser.parse_args(args)
  
    if opt.test_dataset == '':
      opt.test_dataset = opt.dataset
    
    opt.gpus_str = opt.gpus
    opt.gpus = [int(gpu) for gpu in opt.gpus.split(',')]
    opt.gpus = [i for i in range(len(opt.gpus))] if opt.gpus[0] >=0 else [-1]
    opt.lr_step = [int(i) for i in opt.lr_step.split(',')]
    opt.save_point = [int(i) for i in opt.save_point.split(',')]
    opt.test_scales = [float(i) for i in opt.test_scales.split(',')]
    opt.save_imgs = [i for i in opt.save_imgs.split(',')] \
      if opt.save_imgs != '' else []
    opt.ignore_loaded_cats = \
      [int(i) for i in opt.ignore_loaded_cats.split(',')] \
      if opt.ignore_loaded_cats != '' else []

    opt.num_workers = max(opt.num_workers, 2 * len(opt.gpus))
    opt.pre_img = False
    if 'tracking' in opt.task:
      print('Running tracking')
      opt.tracking = True
#       opt.out_thresh = max(opt.track_thresh, opt.out_thresh)
#       opt.pre_thresh = max(opt.track_thresh, opt.pre_thresh)
#       opt.new_thresh = max(opt.track_thresh, opt.new_thresh)
      opt.pre_img = not opt.no_pre_img
      print('Using tracking threshold for out threshold!', opt.track_thresh)
      # if 'ddd' in opt.task:
      opt.show_track_color = True
      if opt.dataset in ['mot', 'mots', 'youtube_vis']:
        opt.overlap_thresh = 0.05
      elif opt.dataset == 'nuscenes':
        opt.window_size = 7
        opt.overlap_thresh = -1
      else:
        opt.overlap_thresh = 0.05

    opt.fix_res = not opt.keep_res
    print('Fix size testing.' if opt.fix_res else 'Keep resolution testing.')

    if opt.head_conv == -1: # init default head_conv
      opt.head_conv = 256 if 'dla' in opt.arch else 64

    opt.pad = 127 if 'hourglass' in opt.arch else 31
    opt.num_stacks = 2 if opt.arch == 'hourglass' else 1

    if opt.master_batch_size == -1:
      opt.master_batch_size = opt.batch_size // len(opt.gpus)
    rest_batch_size = (opt.batch_size - opt.master_batch_size)
    opt.chunk_sizes = [opt.master_batch_size]
    for i in range(len(opt.gpus) - 1):
      slave_chunk_size = rest_batch_size // (len(opt.gpus) - 1)
      if i < rest_batch_size % (len(opt.gpus) - 1):
        slave_chunk_size += 1
      opt.chunk_sizes.append(slave_chunk_size)
    print('training chunk_sizes:', opt.chunk_sizes)

    if opt.debug > 0:
      opt.num_workers = 0
      opt.batch_size = 1
      opt.gpus = [opt.gpus[0]]
      opt.master_batch_size = -1

    # log dirs
    opt.root_dir = os.path.join(os.path.dirname(__file__), '..', '..')
    opt.data_dir = os.path.join(opt.root_dir, 'data')
    opt.exp_dir = os.path.join(opt.root_dir, 'exp', opt.task)
    opt.save_dir = os.path.join(opt.exp_dir, opt.exp_id)
    opt.debug_dir = os.path.join(opt.save_dir, 'debug')
    
    if opt.resume and opt.load_model == '':
      opt.load_model = os.path.join(opt.save_dir, 'model_last.pth')
    return opt


  def update_dataset_info_and_set_heads(self, opt, dataset):
    opt.num_classes = dataset.num_categories \
                      if opt.num_classes < 0 else opt.num_classes
    # input_h(w): opt.input_h overrides opt.input_res overrides dataset default
    input_h, input_w = dataset.default_resolution
    input_h = opt.input_res if opt.input_res > 0 else input_h
    input_w = opt.input_res if opt.input_res > 0 else input_w
    opt.input_h = opt.input_h if opt.input_h > 0 else input_h
    opt.input_w = opt.input_w if opt.input_w > 0 else input_w
    opt.output_h = opt.input_h // opt.down_ratio
    opt.output_w = opt.input_w // opt.down_ratio
    opt.input_res = max(opt.input_h, opt.input_w)
    opt.output_res = max(opt.output_h, opt.output_w)
  
    opt.heads = {'hm': opt.num_classes, 'reg': 2, 'wh': 2}

    if not opt.trades:
        if 'tracking' in opt.task:
          opt.heads.update({'tracking': 2})

    if 'ddd' in opt.task:
      opt.heads.update({'dep': 1, 'rot': 8, 'dim': 3, 'amodel_offset': 2})
    
    if 'multi_pose' in opt.task:
      opt.heads.update({
        'hps': dataset.num_joints * 2, 'hm_hp': dataset.num_joints,
        'hp_offset': 2})

    if opt.ltrb:
      opt.heads.update({'ltrb': 4})
    if opt.ltrb_amodal:
      opt.heads.update({'ltrb_amodal': 4})
    if opt.nuscenes_att:
      opt.heads.update({'nuscenes_att': 8})
    if opt.velocity:
      opt.heads.update({'velocity': 3})

    if opt.embedding:
        opt.heads.update({'embedding': 128})
    if opt.seg:
        opt.heads.update({'conv_weight': 2*opt.seg_feat_channel**2 + 5*opt.seg_feat_channel + 1})
        opt.heads.update({'seg_feat': opt.seg_feat_channel})
    weight_dict = {'hm': opt.hm_weight, 'wh': opt.wh_weight,
                   'reg': opt.off_weight, 'hps': opt.hp_weight,
                   'hm_hp': opt.hm_hp_weight, 'hp_offset': opt.off_weight,
                   'dep': opt.dep_weight, 'rot': opt.rot_weight,
                   'dim': opt.dim_weight,
                   'amodel_offset': opt.amodel_offset_weight,
                   'ltrb': opt.ltrb_weight,
                   'tracking': opt.tracking_weight,
                   'ltrb_amodal': opt.ltrb_amodal_weight,
                   'nuscenes_att': opt.nuscenes_att_weight,
                   'velocity': opt.velocity_weight,
                   'embedding': 1.0,
                   'conv_weight': 1.0,
                   'seg_feat':1.0}
    opt.weights = {head: weight_dict[head] for head in opt.heads}
    if opt.trades:
        opt.weights['cost_volume'] = 1.0
    if opt.seg:
        opt.weights['mask_loss'] = 1.0
    for head in opt.weights:
      if opt.weights[head] == 0:
        del opt.heads[head]
    opt.head_conv = {head: [opt.head_conv \
      for i in range(opt.num_head_conv if head != 'reg' else 1)] for head in opt.heads}
    
    print('input h w:', opt.input_h, opt.input_w)
    print('heads', opt.heads)
    print('weights', opt.weights)
    print('head conv', opt.head_conv)

    return opt

  def init(self, args=''):
    # only used in demo
    default_dataset_info = {
      'ctdet': 'coco', 'multi_pose': 'coco_hp', 'ddd': 'nuscenes',
      'tracking,ctdet': 'coco', 'tracking,multi_pose': 'coco_hp', 
      'tracking,ddd': 'nuscenes'
    }
    opt = self.parse()
    from dataset.dataset_factory import dataset_factory
    train_dataset = default_dataset_info[opt.task] \
      if opt.task in default_dataset_info else 'coco'
    if opt.dataset != 'coco':
        dataset = dataset_factory[opt.dataset]
    else:
        dataset = dataset_factory[train_dataset]
    opt = self.update_dataset_info_and_set_heads(opt, dataset)
    return opt