-
Notifications
You must be signed in to change notification settings - Fork 48
/
train_cls.py
executable file
·559 lines (439 loc) · 23 KB
/
train_cls.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
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
"""
Copyright (C) 2019 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
import os
import torch
import torch.nn as nn
import torch.optim as optim
import torch.utils.data
import torchvision
import numpy as np
from collections import OrderedDict
import time
from datetime import datetime
#from tensorboardX import SummaryWriter
import glob
from config import parse_config
from models import BaseNet, ROINet, TwoBranchNet, ContextNet
from utils.utils import inference, train_select, AverageMeter, get_gpu_memory, select_proposals
from utils.tube_utils import flatten_tubes, valid_tubes
from utils.solver import WarmupCosineLR, WarmupStepLR, get_params
from data.ava_cls import AVADataset, detection_collate, WIDTH, HEIGHT
from data.augmentations import TubeAugmentation, BaseTransform
from utils.eval_utils import ava_evaluation
from external.ActivityNet.Evaluation.get_ava_performance import read_labelmap
args = parse_config()
args.max_iter = 1 # only 1 step for classification pretraining
try:
import apex
from apex import amp
from apex.fp16_utils import *
except ImportError:
print ('Warning: If you want to use fp16, please apex with cuda support (https://github.com/NVIDIA/apex) and update pytorch to 1.0')
args.fp16 = False
pass
args.image_size = (WIDTH, HEIGHT)
label_dict = {}
if args.num_classes == 60:
label_map = os.path.join(args.data_root, 'label/ava_action_list_v2.1_for_activitynet_2018.pbtxt')
categories, class_whitelist = read_labelmap(open(label_map, 'r'))
classes = [(val['id'], val['name']) for val in categories]
id2class = {c[0]: c[1] for c in classes} # gt class id (1~80) --> class name
for i, c in enumerate(sorted(list(class_whitelist))):
label_dict[i] = c
else:
for i in range(80):
label_dict[i] = i+1
## set random seeds
np.random.seed(args.man_seed)
torch.manual_seed(args.man_seed)
if args.cuda:
torch.cuda.manual_seed_all(args.man_seed)
#args.device = torch.device("cuda:0" if args.cuda and torch.cuda.is_available() else "cpu")
gpu_count = torch.cuda.device_count()
torch.backends.cudnn.benchmark=True
def main():
args.exp_name = '{}-max{}-{}-{}'.format(args.name, args.max_iter, args.base_net, args.det_net)
args.save_root = os.path.join(args.save_root, args.exp_name+'/')
if not os.path.isdir(args.save_root):
os.makedirs(args.save_root)
log_name = args.save_root+"training-"+datetime.strftime(datetime.now(), '%Y%m%d-%H%M%S')+".log"
log_file = open(log_name, "w", 1)
log_file.write(args.exp_name+'\n')
################ DataLoader setup #################
print('Loading Dataset...')
augmentation = TubeAugmentation(args.image_size, args.means, args.stds, do_flip=args.do_flip, do_crop=args.do_crop, do_photometric=args.do_photometric, scale=args.scale_norm, do_erase=args.do_erase)
log_file.write("Data agumentation: "+ str(augmentation))
train_dataset = AVADataset(args.data_root, 'train', args.input_type, args.T, args.NUM_CHUNKS[args.max_iter], args.fps, augmentation, stride=1, num_classes=args.num_classes, foreground_only=True)
val_dataset = AVADataset(args.data_root, 'val', args.input_type, args.T, args.NUM_CHUNKS[args.max_iter], args.fps, BaseTransform(args.image_size, args.means, args.stds,args.scale_norm), stride=1, num_classes=args.num_classes, foreground_only=False)
if args.milestones[0] == -1:
args.milestones = [int(np.ceil(len(train_dataset) / args.batch_size) * args.max_epochs)]
train_dataloader = torch.utils.data.DataLoader(train_dataset, args.batch_size, num_workers=args.num_workers,
shuffle=True, collate_fn=detection_collate, pin_memory=True)
val_dataloader = torch.utils.data.DataLoader(val_dataset, args.batch_size, num_workers=args.num_workers,
shuffle=False, collate_fn=detection_collate, pin_memory=True)
log_file.write("Training size: " + str(len(train_dataset)) + "\n")
log_file.write("Validation size: " + str(len(val_dataset)) + "\n")
print('Training STEP on ', train_dataset.name)
################ define models #################
nets = OrderedDict()
# backbone network
nets['base_net'] = BaseNet(args)
# ROI pooling
nets['roi_net'] = ROINet(args.pool_mode, args.pool_size)
# detection network
for i in range(args.max_iter):
if args.det_net == "two_branch":
nets['det_net%d' % i] = TwoBranchNet(args, cls_only=True)
else:
raise NotImplementedError
if not args.no_context:
# context branch
nets['context_net'] = ContextNet(args)
for key in nets:
nets[key] = nets[key].cuda()
################ Training setup #################
params = get_params(nets, args)
if args.optimizer == 'sgd':
optimizer = optim.SGD(params, lr=args.det_lr, momentum=args.momentum, weight_decay=args.weight_decay)
elif args.optimizer == 'adam':
optimizer = optim.Adam(params, lr=args.det_lr)
else:
raise NotImplementedError
if args.scheduler == "cosine":
scheduler = WarmupCosineLR(optimizer, args.milestones, args.min_ratio, args.cycle_decay, args.warmup_iters)
else:
scheduler = WarmupStepLR(optimizer, args.milestones, args.warmup_iters)
# Initialize AMP if needed
if args.fp16:
models, optimizer = amp.initialize([net for _,net in nets.items()], optimizer, opt_level="O1")
for i, key in enumerate(nets):
nets[key] = models[i]
# DataParallel is used
nets['base_net'] = torch.nn.DataParallel(nets['base_net'])
if not args.no_context:
nets['context_net'] = torch.nn.DataParallel(nets['context_net'])
for i in range(args.max_iter):
nets['det_net%d' % i].to('cuda:%d' % ((i+1)%gpu_count))
nets['det_net%d' % i].set_device('cuda:%d' % ((i+1)%gpu_count))
############ Pretrain & Resume ###########
# resume trained model if needed
if args.resume_path is not None:
if args.resume_path.lower() == "best":
model_path = args.save_root+'/checkpoint_best.pth'
if not os.path.isfile(model_path):
model_path = None
elif args.resume_path.lower() == "auto":
# automatically get the latest model
model_paths = glob.glob(os.path.join(args.save_root, 'checkpoint_*.pth'))
best_path = os.path.join(args.save_root, 'checkpoint_best.pth')
if best_path in model_paths:
model_paths.remove(best_path)
if len(model_paths):
iters = [int(val.split('_')[-1].split('.')[0]) for val in model_paths]
model_path = model_paths[np.argmax(iters)]
else:
model_path = None
else:
model_path = args.resume_path
if not os.path.isfile(model_path):
raise ValueError("Resume model not found!", args.resume_path)
if model_path is not None:
print ("Resuming trained model from %s" % model_path)
checkpoint = torch.load(model_path, map_location='cpu')
nets['base_net'].load_state_dict(checkpoint['base_net'])
if not args.no_context and 'context_net' in checkpoint:
nets['context_net'].load_state_dict(checkpoint['context_net'])
for i in range(args.max_iter):
nets['det_net%d' % i].load_state_dict(checkpoint['det_net%d' % i])
optimizer.load_state_dict(checkpoint['optimizer'])
if 'scheduler' in checkpoint:
scheduler.load_state_dict(checkpoint['scheduler'])
args.start_iteration = checkpoint['iteration']
if checkpoint['iteration'] % int(np.ceil(len(train_dataset)/args.batch_size)) == 0:
args.start_epochs = checkpoint['epochs']
else:
args.start_epochs = checkpoint['epochs'] - 1
del checkpoint
torch.cuda.empty_cache()
######################################################
for arg in sorted(vars(args)):
print(arg, getattr(args, arg))
log_file.write(str(arg)+': '+str(getattr(args, arg))+'\n')
for i in range(args.max_iter):
log_file.write(str(nets['det_net%d' % i])+'\n\n')
params_num = [sum(p.numel() for p in nets['base_net'].parameters() if p.requires_grad)]
if not args.no_context:
params_num.append(sum(p.numel() for p in nets['context_net'].parameters() if p.requires_grad))
for i in range(args.max_iter):
params_num.append(sum(p.numel() for p in nets['det_net%d' % i].parameters() if p.requires_grad))
log_file.write("Number of parameters: "+str(params_num)+'\n\n')
# Start training
train(args, nets, optimizer, scheduler, train_dataloader, val_dataloader, log_file)
def train(args, nets, optimizer, scheduler, train_dataloader, val_dataloader, log_file):
for _, net in nets.items():
net.train()
# loss counters
batch_time = AverageMeter(200)
losses = AverageMeter(200)
# writer = SummaryWriter(args.save_root+"summary"+datetime.strftime(datetime.now(), '%Y%m%d-%H%M%S'))
################ Training loop #################
best_mAP = 0. # best validation mAP so far
torch.cuda.synchronize()
t0 = time.perf_counter()
epochs = args.start_epochs
iteration = args.start_iteration
epoch_size = int(np.ceil(len(train_dataloader.dataset) / args.batch_size))
while epochs < args.max_epochs:
for _, (images, targets, tubes, infos) in enumerate(train_dataloader):
images = images.cuda()
# adjust learning rate
scheduler.step()
lr = optimizer.param_groups[-1]['lr']
# get conv features
conv_feat = nets['base_net'](images)
context_feat = None
if not args.no_context:
context_feat = nets['context_net'](conv_feat)
########### Forward pass for the two-branch header ############
optimizer.zero_grad()
# adaptively get the start chunk
max_chunks = 1
T_start = 0
T_length = args.T
selected_tubes = []
target_tubes = []
# use target boxes as tubes
for b in range(len(targets)):
cur_tubes = tubes[b]
selected_pos, selected_neg, ious = select_proposals(
targets[b][:,int(max_chunks/2)].reshape(targets[b].shape[0],1,-1),
cur_tubes[:, int(cur_tubes.shape[1]/2)].reshape(cur_tubes.shape[0],1,-1),
None,
0.75, 5, 'uniform', 3)
cur_selected_tubes = np.zeros((len(selected_pos)+len(selected_neg), cur_tubes.shape[1], 4), dtype=np.float32)
cur_target_tubes = np.zeros((len(selected_pos)+len(selected_neg), 1, 6+args.num_classes), dtype=np.float32) # only one frame for loss
row = 0
for ii,jj in selected_pos:
cur_selected_tubes[row] = cur_tubes[jj]
cur_target_tubes[row,:,:4] = targets[b][ii,int(max_chunks/2),:4]
cur_target_tubes[row,:,6:] = targets[b][ii,int(max_chunks/2),4:]
cur_target_tubes[row,:,4] = 1 # flag for classification
row += 1
for ii,jj in selected_neg:
cur_selected_tubes[row] = cur_tubes[jj]
cur_target_tubes[row,:,4] = 1 # flag for classification
row += 1
cur_target_tubes = np.concatenate([cur_target_tubes,
cur_target_tubes,
cur_target_tubes], axis=1)
selected_tubes.append(cur_selected_tubes)
target_tubes.append(cur_target_tubes)
# flatten list of tubes
flat_targets, tubes_nums = flatten_tubes(target_tubes, batch_idx=False)
flat_tubes, _ = flatten_tubes(selected_tubes, batch_idx=True) # add batch_idx for ROI pooling
flat_targets = torch.FloatTensor(flat_targets).to(conv_feat)
flat_tubes = torch.FloatTensor(flat_tubes).to(conv_feat) # gpu:0 for ROI pooling
# ROI Pooling
pooled_feat = nets['roi_net'](conv_feat[:, T_start:T_start+T_length].contiguous(), flat_tubes)
_,C,W,H = pooled_feat.size()
pooled_feat = pooled_feat.view(-1, T_length, pooled_feat.size(1), W, H)
temp_context_feat = None
if not args.no_context:
temp_context_feat = torch.zeros((pooled_feat.size(0),context_feat.size(1),T_length,1,1)).to(context_feat)
for p in range(pooled_feat.size(0)):
temp_context_feat[p] = context_feat[int(flat_tubes[p,0,0].item()/T_length),:,T_start:T_start+T_length].contiguous().clone()
_,_,_,_, cur_loss_global_cls, _, _ = nets['det_net0'](pooled_feat, context_feat=temp_context_feat, tubes=flat_tubes, targets=flat_targets)
cur_loss_global_cls = cur_loss_global_cls.mean()
########### Gradient updates ############
losses.update(cur_loss_global_cls.item())
if args.fp16:
with amp.scale_loss(cur_loss_global_cls, optimizer) as scaled_loss:
scaled_loss.backward()
else:
cur_loss_global_cls.backward()
optimizer.step()
############### Print logs and save models ############
iteration += 1
if iteration % args.print_step == 0 and iteration>0:
gpu_memory = get_gpu_memory()
torch.cuda.synchronize()
t1 = time.perf_counter()
batch_time.update(t1 - t0)
print_line = 'Epoch {}/{}({}) Iteration {:06d} lr {:.2e} ' \
'loss {:.3f}({:.3f}) Timer {:0.3f}({:0.3f}) GPU usage: {}'.format(
epochs+1, args.max_epochs, epoch_size, iteration, lr,
losses.val, losses.avg, batch_time.val, batch_time.avg, gpu_memory)
torch.cuda.synchronize()
t0 = time.perf_counter()
log_file.write(print_line+'\n')
print(print_line)
if (iteration % args.save_step == 0) and iteration>0:
print('Saving state, iter:', iteration)
save_name = args.save_root+'checkpoint_'+str(iteration) + '.pth'
save_dict = {
'epochs': epochs+1,
'iteration': iteration,
'base_net': nets['base_net'].state_dict(),
'context_net': nets['context_net'].state_dict(),
'optimizer': optimizer.state_dict(),
'scheduler': scheduler.state_dict(),
'val_mAP': best_mAP,
'cfg': args}
for i in range(args.max_iter):
save_dict['det_net%d' % i] = nets['det_net%d' % i].state_dict()
torch.save(save_dict, save_name)
# only keep the latest model
if os.path.isfile(args.save_root+'checkpoint_'+str(iteration-args.save_step) + '.pth'):
os.remove(args.save_root+'checkpoint_'+str(iteration-args.save_step) + '.pth')
print (args.save_root+'checkpoint_'+str(iteration-args.save_step) + '.pth removed!')
# For consistency when resuming from the middle of an epoch
if iteration % epoch_size == 0 and iteration > 0:
break
##### Validation at the end of each epoch #####
if True:
torch.cuda.synchronize()
tvs = time.perf_counter()
for _, net in nets.items():
net.eval() # switch net to evaluation mode
print('Validating at ', iteration)
all_metrics = validate(args, val_dataloader, nets, iteration, iou_thresh=args.iou_thresh)
prt_str = ''
for i in range(args.max_iter):
prt_str += 'Iter '+str(i+1)+': MEANAP =>'+str(all_metrics[i]['PascalBoxes_Precision/[email protected]'])+'\n'
print(prt_str)
log_file.write(prt_str)
log_file.write("Best MEANAP so far => {}\n".format(best_mAP))
for i in class_whitelist:
log_file.write("({}) {}: {}\n".format(i,id2class[i],
all_metrics[-1]["PascalBoxes_PerformanceByCategory/[email protected]/{}".format(id2class[i])]))
# writer.add_scalar('mAP', all_metrics[-1]['PascalBoxes_Precision/[email protected]'], iteration)
# for key, ap in all_metrics[-1].items():
# writer.add_scalar(key, ap, iteration)
if all_metrics[-1]['PascalBoxes_Precision/[email protected]'] > best_mAP:
best_mAP = all_metrics[-1]['PascalBoxes_Precision/[email protected]']
print('Saving current best model, iter:', iteration)
save_name = args.save_root+'checkpoint_best.pth'
save_dict = {
'epochs': epochs+1,
'iteration': iteration,
'base_net': nets['base_net'].state_dict(),
'context_net': nets['context_net'].state_dict(),
'optimizer': optimizer.state_dict(),
'scheduler': scheduler.state_dict(),
'val_mAP': best_mAP,
'cfg': args}
for i in range(args.max_iter):
save_dict['det_net%d' % i] = nets['det_net%d' % i].state_dict()
torch.save(save_dict, save_name)
for _, net in nets.items():
net.train() # switch net to training mode
torch.cuda.synchronize()
t0 = time.perf_counter()
prt_str2 = '\nValidation TIME::: {:0.3f}\n\n'.format(t0-tvs)
print(prt_str2)
log_file.write(prt_str2)
epochs += 1
log_file.close()
# writer.close()
def validate(args, val_dataloader, nets, iteration=0, iou_thresh=0.5):
"""
Test the model on validation set
"""
# write results to files for evaluation
output_files = []
fouts = []
for i in range(args.max_iter):
output_file = args.save_root+'val_result-'+str(iteration)+'-iter'+str(i+1)+'.csv'
output_files.append(output_file)
f = open(output_file, 'w')
fouts.append(f)
gt_file = args.save_root+'val_gt.csv'
fout = open(gt_file, 'w')
with torch.no_grad(): # for evaluation
for num, (images, targets, tubes, infos) in enumerate(val_dataloader):
if (num+1) % 100 == 0:
print ("%d / %d" % (num+1, len(val_dataloader.dataset)/args.batch_size))
for b in range(len(infos)):
for n in range(len(infos[b]['boxes'])):
mid = int(len(infos[b]['boxes'][n])/2)
box = infos[b]['boxes'][n][mid]
labels = infos[b]['labels'][n][mid]
for label in labels:
fout.write('{0},{1:04},{2:.4},{3:.4},{4:.4},{5:.4},{6}\n'.format(
infos[b]['video_name'],
infos[b]['fid'],
box[0], box[1], box[2], box[3],
label))
_, _, channels, height, width = images.size()
images = images.cuda()
# get conv features
conv_feat = nets['base_net'](images)
context_feat = None
if not args.no_context:
context_feat = nets['context_net'](conv_feat)
############## Inference ##############
T_start = 0
T_length = args.T
selected_tubes = []
# use target boxes as tubes
for b in range(len(targets)):
cur_tubes = tubes[b]
selected_tubes.append(cur_tubes)
# flatten list of tubes
flat_tubes, tubes_nums = flatten_tubes(selected_tubes, batch_idx=True) # add batch_idx for ROI pooling
flat_tubes = torch.FloatTensor(flat_tubes).to(conv_feat) # gpu:0 for ROI pooling
# ROI Pooling
pooled_feat = nets['roi_net'](conv_feat[:, T_start:T_start+T_length].contiguous(), flat_tubes)
_,C,W,H = pooled_feat.size()
pooled_feat = pooled_feat.view(-1, T_length, pooled_feat.size(1), W, H)
temp_context_feat = None
if not args.no_context:
temp_context_feat = torch.zeros((pooled_feat.size(0),context_feat.size(1),T_length,1,1)).to(context_feat)
for p in range(pooled_feat.size(0)):
temp_context_feat[p] = context_feat[int(flat_tubes[p,0,0].item()/T_length),:,T_start:T_start+T_length].contiguous().clone()
global_prob, _,_,_, _,_,_ = nets['det_net0'](pooled_feat, context_feat=temp_context_feat, tubes=None, targets=None)
#################### Evaluation #################
# loop for each batch
tubes_count = 0
for b in range(len(tubes_nums)):
info = infos[b]
seq_start = tubes_count
tubes_count = tubes_count + tubes_nums[b]
cur_pred_prob = global_prob[seq_start:seq_start+tubes_nums[b]]
cur_pred_tubes = flat_tubes[seq_start:seq_start+tubes_nums[b]][:,int(flat_tubes.shape[1]/2),1:]
# loop for each foreground class
for cl_ind in range(args.num_classes):
scores = cur_pred_prob[:, cl_ind].squeeze().view(-1)
c_mask = scores.gt(args.conf_thresh) # greater than minmum threshold
scores = scores[c_mask]
if len(scores) == 0:
continue
boxes = cur_pred_tubes.clone()
try:
l_mask = c_mask.unsqueeze(1).expand_as(boxes)
except:
pdb.set_trace()
boxes = boxes[l_mask].view(-1, 4)
scores = scores.cpu().numpy()
boxes = boxes.cpu().numpy()
boxes[:, ::2] /= width
boxes[:, 1::2] /= height
# write to files
for n in range(boxes.shape[0]):
fouts[i].write('{0},{1:04},{2:.4},{3:.4},{4:.4},{5:.4},{6},{7:.4}\n'.format(
info['video_name'],
info['fid'],
boxes[n,0],boxes[n,1],boxes[n,2],boxes[n,3],
label_dict[cl_ind],
scores[n]))
fout.close()
all_metrics = []
for i in range(args.max_iter):
fouts[i].close()
metrics = ava_evaluation(os.path.join(args.data_root, 'label/'), output_files[i], gt_file)
all_metrics.append(metrics)
return all_metrics
if __name__ == '__main__':
main()