-
Notifications
You must be signed in to change notification settings - Fork 2
/
train_vqa_video.py
222 lines (168 loc) · 8.69 KB
/
train_vqa_video.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
'''
code modified from https://github.com/salesforce/BLIP, https://github.com/salesforce/ALPRO
'''
import argparse
import os
import ruamel.yaml as yaml
import numpy as np
import random
import time
import datetime
import json
from pathlib import Path
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
import torch.backends.cudnn as cudnn
import torch.distributed as dist
from models.blip_vqa import blip_vqa, blip_vqa_video
import utils
from utils import cosine_lr_schedule
from data import create_dataset, create_sampler, create_loader
from data.vqa_dataset import vqa_collate_fn
from data.utils import save_result
def train(model, data_loader, optimizer, epoch, device):
# train
model.train()
metric_logger = utils.MetricLogger(delimiter=" ")
metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
metric_logger.add_meter('loss', utils.SmoothedValue(window_size=1, fmt='{value:.4f}'))
header = 'Train Epoch: [{}]'.format(epoch)
print_freq = 50
for i,(image_b, question, answer, weights, n) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
if config['video_representation'] == 'single_frame':
# pick middle frame
num_frm = image_b.size()[1]
image = image_b[:,int(num_frm/2),:,:,:].to(device,non_blocking=True)
elif config['video_representation'] == 'concat_frame':
image = image_b.to(device,non_blocking=True) # (B, num_frm, C, H, W)
weights = weights.to(device,non_blocking=True)
loss = model(image, question, answer, train=True, n=n, weights=weights)
optimizer.zero_grad()
loss.backward()
optimizer.step()
metric_logger.update(loss=loss.item())
metric_logger.update(lr=optimizer.param_groups[0]["lr"])
# gather the stats from all processes
metric_logger.synchronize_between_processes()
print("Averaged stats:", metric_logger.global_avg())
return {k: "{:.3f}".format(meter.global_avg) for k, meter in metric_logger.meters.items()}
@torch.no_grad()
def evaluation(model, data_loader, device, config):
# test
model.eval()
metric_logger = utils.MetricLogger(delimiter=" ")
header = 'Generate VQA test result:'
print_freq = 50
result = []
if config['inference']=='rank':
answer_list = data_loader.dataset.answer_list
answer_candidates = model.tokenizer(answer_list, padding='longest', return_tensors='pt').to(device)
answer_candidates.input_ids[:,0] = model.tokenizer.bos_token_id
for n, (image_b, question, question_id) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
if config['video_representation'] == 'single_frame':
# pick middle frame
num_frm = image_b.size()[1]
image = image_b[:,int(num_frm/2),:,:,:].to(device,non_blocking=True)
elif config['video_representation'] == 'concat_frame':
image = image_b.to(device,non_blocking=True) # (B, num_frm, C, H, W)
if config['inference']=='generate':
answers = model(image, question, train=False, inference='generate')
for answer, ques_id in zip(answers, question_id):
ques_id = int(ques_id.item())
result.append({"question_id":ques_id, "answer":answer})
elif config['inference']=='rank':
answer_ids = model(image, question, answer_candidates, train=False, inference='rank', k_test=config['k_test'])
for ques_id, answer_id in zip(question_id, answer_ids):
result.append({"question_id":int(ques_id.item()), "answer":answer_list[answer_id]})
return result
def main(args, config):
utils.init_distributed_mode(args)
device = torch.device(args.device)
# fix the seed for reproducibility
seed = args.seed + utils.get_rank()
torch.manual_seed(seed)
np.random.seed(seed)
random.seed(seed)
cudnn.benchmark = True
#### Dataset ####
print("Creating vqa datasets")
train_dataset, test_dataset = create_dataset(config['dataset'], config)
print('train dataset size:',len(train_dataset))
print('test dataset size:',len(test_dataset))
if args.distributed:
num_tasks = utils.get_world_size()
global_rank = utils.get_rank()
samplers = create_sampler([train_dataset, test_dataset], [True, False], num_tasks, global_rank)
else:
samplers = [None, None]
train_loader, test_loader = create_loader([train_dataset, test_dataset],samplers,
batch_size=[config['batch_size_train'],config['batch_size_test']],
num_workers=[4,4],is_trains=[True, False],
collate_fns=[vqa_collate_fn,None])
#### Model ####
print("Creating model")
if config['video_representation'] == 'single_frame':
print('represent video as single frame by selecting middle frame')
model = blip_vqa(pretrained=config['pretrained'], image_size=config['image_size'],
vit=config['vit'], vit_grad_ckpt=config['vit_grad_ckpt'], vit_ckpt_layer=config['vit_ckpt_layer'])
elif config['video_representation'] == 'concat_frame':
print('represent video as concat frames')
model = blip_vqa_video(pretrained=config['pretrained'], image_size=config['image_size'],
vit=config['vit'], vit_grad_ckpt=config['vit_grad_ckpt'], vit_ckpt_layer=config['vit_ckpt_layer'])
model = model.to(device)
model_without_ddp = model
if args.distributed:
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
model_without_ddp = model.module
optimizer = torch.optim.AdamW(params=model.parameters(), lr=config['init_lr'], weight_decay=config['weight_decay'])
best = 0
best_epoch = 0
print("Start training")
start_time = time.time()
for epoch in range(0, config['max_epoch']):
if not args.evaluate:
if args.distributed:
train_loader.sampler.set_epoch(epoch)
cosine_lr_schedule(optimizer, epoch, config['max_epoch'], config['init_lr'], config['min_lr'])
train_stats = train(model, train_loader, optimizer, epoch, device)
else:
break
if utils.is_main_process():
log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
'epoch': epoch,
}
with open(os.path.join(args.output_dir, "log.txt"),"a") as f:
f.write(json.dumps(log_stats) + "\n")
save_obj = {
'model': model_without_ddp.state_dict(),
'optimizer': optimizer.state_dict(),
'config': config,
'epoch': epoch,
}
torch.save(save_obj, os.path.join(args.output_dir, 'checkpoint_%02d.pth'%epoch))
dist.barrier()
vqa_result = evaluation(model_without_ddp, test_loader, device, config)
result_file = save_result(vqa_result, args.result_dir, 'video_vqa_result')
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
print('Training time {}'.format(total_time_str))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--config', default='./configs/vqa.yaml')
parser.add_argument('--output_dir', default='output/VQA')
parser.add_argument('--evaluate', action='store_true')
parser.add_argument('--device', default='cuda')
parser.add_argument('--seed', default=42, type=int)
parser.add_argument('--world_size', default=1, type=int, help='number of distributed processes')
parser.add_argument('--dist_url', default='env:https://', help='url used to set up distributed training')
parser.add_argument('--distributed', default=True, type=bool)
args = parser.parse_args()
config = yaml.load(open(args.config, 'r'), Loader=yaml.Loader)
args.result_dir = os.path.join(args.output_dir, 'result')
Path(args.output_dir).mkdir(parents=True, exist_ok=True)
Path(args.result_dir).mkdir(parents=True, exist_ok=True)
yaml.dump(config, open(os.path.join(args.output_dir, 'config.yaml'), 'w'))
print(config)
main(args, config)