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train_retrieval_video.py
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train_retrieval_video.py
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'''
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
import torch.backends.cudnn as cudnn
import torch.distributed as dist
from torch.utils.data import DataLoader
from models.blip_retrieval import blip_retrieval, blip_retrieval_video
from models.blip import blip_decoder
from models.blip_itm import blip_itm
import utils
from utils import cosine_lr_schedule
from data import create_dataset, create_sampler, create_loader
def select_frame(filterer, images, text):
itm_output = filterer(images, [text for i in range(images.size()[0])], match_head='itm')
# print(itm_output.size())
itm_score = torch.nn.functional.softmax(itm_output,dim=1)[:,1].detach().cpu().numpy()
# pick max score frame:
idx = np.argmax(itm_score)
return images[idx]
def train(model, data_loader, optimizer, epoch, device, config, filterer):
# 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_itm', utils.SmoothedValue(window_size=1, fmt='{value:.4f}'))
metric_logger.add_meter('loss_ita', utils.SmoothedValue(window_size=1, fmt='{value:.4f}'))
header = 'Train Epoch: [{}]'.format(epoch)
print_freq = 50
for i,(image_b, caption, idx) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
if config['video_representation'] == 'single_frame':
assert filterer is not None
image_b = image_b.to(device,non_blocking=True) # (B, num_frm, C, H, W)
# select one frame using a pretrained BLIP filterer:
picked_frms = []
for j in range(image_b.size()[0]):
picked_frms.append(select_frame(filterer,image_b[j],caption[j]))
image = torch.stack(picked_frms).to(device,non_blocking=True) # (B, C, H, W)
elif config['video_representation'] == 'concat_frame':
image = image_b.to(device,non_blocking=True) # (B, num_frm, C, H, W)
idx = idx.to(device,non_blocking=True)
if epoch>0:
alpha = config['alpha']
else:
alpha = config['alpha']*min(1,i/len(data_loader))
loss_ita, loss_itm = model(image, caption, alpha=alpha, idx=idx)
loss = loss_ita + loss_itm
optimizer.zero_grad()
loss.backward()
optimizer.step()
metric_logger.update(loss_itm=loss_itm.item())
metric_logger.update(loss_ita=loss_ita.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: "{:.5f}".format(meter.global_avg) for k, meter in metric_logger.meters.items()}
@torch.no_grad()
def evaluation(model, data_loader, tokenizer, device, config):
# test
model.eval()
metric_logger = utils.MetricLogger(delimiter=" ")
header = 'Evaluation:'
start_time = time.time()
texts = data_loader.dataset.text
print('Computing text features for evaluation...')
num_text = len(texts)
text_bs = 256
text_ids = []
text_embeds = []
text_atts = []
for i in range(0, num_text, text_bs):
text = texts[i: min(num_text, i+text_bs)]
text_input = tokenizer(text, padding='max_length', truncation=True, max_length=35, return_tensors="pt").to(device)
text_output = model.text_encoder(text_input.input_ids, attention_mask = text_input.attention_mask, mode='text')
text_embed = F.normalize(model.text_proj(text_output.last_hidden_state[:,0,:]))
text_embeds.append(text_embed)
text_ids.append(text_input.input_ids)
text_atts.append(text_input.attention_mask)
text_embeds = torch.cat(text_embeds,dim=0)
text_ids = torch.cat(text_ids,dim=0)
text_atts = torch.cat(text_atts,dim=0)
text_ids[:,0] = tokenizer.additional_special_tokens_ids[0]
print('Computing video features for evaluation...')
video_feats = []
video_embeds = []
for video, video_id in data_loader:
B,N,C,W,H = video.size()
video = video.view(-1,C,W,H)
video = video.to(device,non_blocking=True)
video_feat = model.visual_encoder(video)
video_embed = model.vision_proj(video_feat[:,0,:])
video_embed = video_embed.view(B,N,-1).mean(dim=1)
video_embed = F.normalize(video_embed,dim=-1)
video_feat = video_feat.view(B,-1,video_feat.shape[-1])
video_feats.append(video_feat.cpu())
video_embeds.append(video_embed)
video_feats = torch.cat(video_feats,dim=0)
video_embeds = torch.cat(video_embeds,dim=0)
sims_matrix = video_embeds @ text_embeds.t()
score_matrix_v2t = torch.full((len(texts),len(texts)),-100.0).to(device)
print('Done computing embedding')
num_tasks = utils.get_world_size()
rank = utils.get_rank()
step = sims_matrix.size(0)//num_tasks + 1
start = rank*step
end = min(sims_matrix.size(0),start+step)
for i,sims in enumerate(metric_logger.log_every(sims_matrix[start:end], 50, header)):
topk_sim, topk_idx = sims.topk(k=config['k_test'], dim=0)
encoder_output = video_feats[start+i].repeat(config['k_test'],1,1).to(device,non_blocking=True)
encoder_att = torch.ones(encoder_output.size()[:-1],dtype=torch.long).to(device,non_blocking=True)
output = model.text_encoder(text_ids[topk_idx],
attention_mask = text_atts[topk_idx],
encoder_hidden_states = encoder_output,
encoder_attention_mask = encoder_att,
return_dict = True,
)
score = model.itm_head(output.last_hidden_state[:,0,:])[:,1]
score_matrix_v2t[start+i,topk_idx] = score + topk_sim
sims_matrix = sims_matrix.t()
score_matrix_t2v = torch.full((len(texts),len(texts)),-100.0).to(device)
step = sims_matrix.size(0)//num_tasks + 1
start = rank*step
end = min(sims_matrix.size(0),start+step)
for i,sims in enumerate(metric_logger.log_every(sims_matrix[start:end], 50, header)):
topk_sim, topk_idx = sims.topk(k=config['k_test'], dim=0)
encoder_output = video_feats[topk_idx].to(device,non_blocking=True)
encoder_att = torch.ones(encoder_output.size()[:-1],dtype=torch.long).to(device,non_blocking=True)
output = model.text_encoder(text_ids[start+i].repeat(config['k_test'],1),
attention_mask = text_atts[start+i].repeat(config['k_test'],1),
encoder_hidden_states = encoder_output,
encoder_attention_mask = encoder_att,
return_dict = True,
)
score = model.itm_head(output.last_hidden_state[:,0,:])[:,1]
score_matrix_t2v[start+i,topk_idx] = score + topk_sim
if args.distributed:
dist.barrier()
torch.distributed.all_reduce(score_matrix_v2t, op=torch.distributed.ReduceOp.SUM)
torch.distributed.all_reduce(score_matrix_t2v, op=torch.distributed.ReduceOp.SUM)
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
print('Evaluation time {}'.format(total_time_str))
return score_matrix_v2t.cpu().numpy(), score_matrix_t2v.cpu().numpy()
@torch.no_grad()
def itm_eval(scores_v2t, scores_t2v, txt2vmg, vid2txt):
#Video->Text
ranks = np.zeros(scores_v2t.shape[0])
for index,score in enumerate(scores_v2t):
inds = np.argsort(score)[::-1]
ranks[index] = np.where(inds == vid2txt[index])[0][0]
# Compute metrics
tr1 = 100.0 * len(np.where(ranks < 1)[0]) / len(ranks)
tr5 = 100.0 * len(np.where(ranks < 5)[0]) / len(ranks)
tr10 = 100.0 * len(np.where(ranks < 10)[0]) / len(ranks)
#Text->Video
ranks = np.zeros(scores_t2v.shape[0])
for index,score in enumerate(scores_t2v):
inds = np.argsort(score)[::-1]
ranks[index] = np.where(inds == txt2vmg[index])[0][0]
mdR = np.median(ranks+1)
# Compute metrics
vr1 = 100.0 * len(np.where(ranks < 1)[0]) / len(ranks)
vr5 = 100.0 * len(np.where(ranks < 5)[0]) / len(ranks)
vr10 = 100.0 * len(np.where(ranks < 10)[0]) / len(ranks)
tr_mean = (tr1 + tr5 + tr10) / 3
vr_mean = (vr1 + vr5 + vr10) / 3
r_mean = (tr_mean + vr_mean) / 2
eval_result = {'txt_r1': tr1,
'txt_r5': tr5,
'txt_r10': tr10,
'txt_r_mean': tr_mean,
'vid_r1': vr1,
'vid_r5': vr5,
'vid_r10': vr10,
'vid_r_mean': vr_mean,
'vid_mdR': mdR,
'r_mean': r_mean}
return eval_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
#### Model ####
print("Creating model")
if config['video_representation'] == 'single_frame':
print('represent video as single frame by selecting the best matched frame to caption')
model = blip_retrieval(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'],
queue_size=config['queue_size'], negative_all_rank=config['negative_all_rank'])
elif config['video_representation'] == 'concat_frame':
print('represent video as concat frames')
model = blip_retrieval_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'],
queue_size=config['queue_size'], negative_all_rank=config['negative_all_rank'])
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'])
#### Dataset ####
print("Creating retrieval dataset")
train_dataset, val_dataset, test_dataset = create_dataset(config['dataset'], config)
print('train dataset size:',len(train_dataset))
print('val dataset size:',len(val_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], [True], num_tasks, global_rank) + [None, None]
else:
samplers = [None, None, None]
train_loader, val_loader, test_loader = create_loader([train_dataset, val_dataset, test_dataset],samplers,
batch_size=[config['batch_size_train']]+[config['batch_size_test']]*2,
num_workers=[4,4,4],
is_trains=[True, False, False],
collate_fns=[None,None,None])
#### main loop ####
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)
if config['video_representation'] == 'single_frame':
# set up filter model
filterer = blip_itm(pretrained=config["filterer_model_ckpt"], image_size=config["image_size"], vit=config["vit"])
filterer.eval()
filterer.to(device)
else:
filterer = None
# train
cosine_lr_schedule(optimizer, epoch, config['max_epoch'], config['init_lr'], config['min_lr'])
train_stats = train(model, train_loader, optimizer, epoch, device, config, filterer)
# release before evaluation
if not filterer:
del filterer
if "eval_only_once" in config and epoch != config['max_epoch']-1:
if config['eval_only_once']:
print('skip eval...')
continue
if ('skip_val' not in config) or (not config['skip_val']):
score_val_v2t, score_val_t2v = evaluation(model_without_ddp, val_loader, model_without_ddp.tokenizer, device, config)
score_test_v2t, score_test_t2v = evaluation(model_without_ddp, test_loader, model_without_ddp.tokenizer, device, config)
if utils.is_main_process():
if ('skip_val' not in config) or (not config['skip_val']):
val_result = itm_eval(score_val_v2t, score_val_t2v, val_loader.dataset.txt2video, val_loader.dataset.video2txt)
print(val_result)
if val_result['r_mean']>best:
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_best.pth'))
best = val_result['r_mean']
best_epoch = epoch
test_result = itm_eval(score_test_v2t, score_test_t2v, test_loader.dataset.txt2video, test_loader.dataset.video2txt)
print(test_result)
else:
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_best.pth'))
test_result = itm_eval(score_test_v2t, score_test_t2v, test_loader.dataset.txt2video, test_loader.dataset.video2txt)
print(test_result)
best = test_result['r_mean']
best_epoch = epoch
if args.evaluate:
if ('skip_val' not in config) or (not config['skip_val']):
log_stats = {**{f'val_{k}': v for k, v in val_result.items()},
**{f'test_{k}': v for k, v in test_result.items()},
}
else:
log_stats = {**{f'test_{k}': v for k, v in test_result.items()}}
with open(os.path.join(args.output_dir, "evaluate.txt"),"a") as f:
f.write(json.dumps(log_stats) + "\n")
else:
if ('skip_val' not in config) or (not config['skip_val']):
log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
**{f'val_{k}': v for k, v in val_result.items()},
**{f'test_{k}': v for k, v in test_result.items()},
'epoch': epoch,
'best_epoch': best_epoch,
}
else:
log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
**{f'test_{k}': v for k, v in test_result.items()},
'epoch': epoch,
'best_epoch': best_epoch,
}
with open(os.path.join(args.output_dir, "log.txt"),"a") as f:
f.write(json.dumps(log_stats) + "\n")
if args.evaluate:
break
dist.barrier()
torch.cuda.empty_cache()
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/retrieval_msrvtt.yaml')
parser.add_argument('--output_dir', default='output/Retrieval_msrvtt')
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)
parser.add_argument('--evaluate', action='store_true')
args = parser.parse_args()
config = yaml.load(open(args.config, 'r'), Loader=yaml.Loader)
# train with single frame or concat frame, default 'single_frame'
if 'video_representation' not in config:
config['video_representation'] = 'single_frame'
print(config)
Path(args.output_dir).mkdir(parents=True, exist_ok=True)
yaml.dump(config, open(os.path.join(args.output_dir, 'config.yaml'), 'w'))
main(args, config)