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Retrieval.py
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Retrieval.py
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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.model_retrieval import ALBEF
from models.vit import interpolate_pos_embed
from models.tokenization_bert import BertTokenizer
import utils
from dataset import create_dataset, create_sampler, create_loader
from scheduler import create_scheduler
from optim import create_optimizer
def train(model, data_loader, optimizer, tokenizer, epoch, warmup_steps, device, scheduler, config):
# 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
step_size = 100
warmup_iterations = warmup_steps*step_size
for i,(image, text, idx) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
image = image.to(device,non_blocking=True)
idx = idx.to(device,non_blocking=True)
text_input = tokenizer(text, padding='longest', max_length=30, return_tensors="pt").to(device)
if epoch>0 or not config['warm_up']:
alpha = config['alpha']
else:
alpha = config['alpha']*min(1,i/len(data_loader))
loss_ita, loss_itm = model(image, text_input,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"])
if epoch==0 and i%step_size==0 and i<=warmup_iterations:
scheduler.step(i//step_size)
# 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, tokenizer, device, config):
# test
model.eval()
metric_logger = utils.MetricLogger(delimiter=" ")
header = 'Evaluation:'
print('Computing features for evaluation...')
start_time = time.time()
texts = data_loader.dataset.text
num_text = len(texts)
text_bs = 256
text_feats = []
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=30, return_tensors="pt").to(device)
text_output = model.text_encoder(text_input.input_ids, attention_mask = text_input.attention_mask, mode='text')
text_feat = text_output.last_hidden_state
text_embed = F.normalize(model.text_proj(text_feat[:,0,:]))
text_embeds.append(text_embed)
text_feats.append(text_feat)
text_atts.append(text_input.attention_mask)
text_embeds = torch.cat(text_embeds,dim=0)
text_feats = torch.cat(text_feats,dim=0)
text_atts = torch.cat(text_atts,dim=0)
image_feats = []
image_embeds = []
for image, img_id in data_loader:
image = image.to(device)
image_feat = model.visual_encoder(image)
image_embed = model.vision_proj(image_feat[:,0,:])
image_embed = F.normalize(image_embed,dim=-1)
image_feats.append(image_feat)
image_embeds.append(image_embed)
image_feats = torch.cat(image_feats,dim=0)
image_embeds = torch.cat(image_embeds,dim=0)
sims_matrix = image_embeds @ text_embeds.t()
score_matrix_i2t = torch.full((len(data_loader.dataset.image),len(texts)),-100.0).to(device)
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 = image_feats[start+i].repeat(config['k_test'],1,1)
encoder_att = torch.ones(encoder_output.size()[:-1],dtype=torch.long).to(device)
output = model.text_encoder(encoder_embeds = text_feats[topk_idx],
attention_mask = text_atts[topk_idx],
encoder_hidden_states = encoder_output,
encoder_attention_mask = encoder_att,
return_dict = True,
mode = 'fusion'
)
score = model.itm_head(output.last_hidden_state[:,0,:])[:,1]
score_matrix_i2t[start+i,topk_idx] = score
sims_matrix = sims_matrix.t()
score_matrix_t2i = torch.full((len(texts),len(data_loader.dataset.image)),-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 = image_feats[topk_idx]
encoder_att = torch.ones(encoder_output.size()[:-1],dtype=torch.long).to(device)
output = model.text_encoder(encoder_embeds = text_feats[start+i].repeat(config['k_test'],1,1),
attention_mask = text_atts[start+i].repeat(config['k_test'],1),
encoder_hidden_states = encoder_output,
encoder_attention_mask = encoder_att,
return_dict = True,
mode = 'fusion'
)
score = model.itm_head(output.last_hidden_state[:,0,:])[:,1]
score_matrix_t2i[start+i,topk_idx] = score
if args.distributed:
dist.barrier()
torch.distributed.all_reduce(score_matrix_i2t, op=torch.distributed.ReduceOp.SUM)
torch.distributed.all_reduce(score_matrix_t2i, 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_i2t.cpu().numpy(), score_matrix_t2i.cpu().numpy()
@torch.no_grad()
def itm_eval(scores_i2t, scores_t2i, txt2img, img2txt):
#Images->Text
ranks = np.zeros(scores_i2t.shape[0])
for index,score in enumerate(scores_i2t):
inds = np.argsort(score)[::-1]
# Score
rank = 1e20
for i in img2txt[index]:
tmp = np.where(inds == i)[0][0]
if tmp < rank:
rank = tmp
ranks[index] = rank
# 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->Images
ranks = np.zeros(scores_t2i.shape[0])
for index,score in enumerate(scores_t2i):
inds = np.argsort(score)[::-1]
ranks[index] = np.where(inds == txt2img[index])[0][0]
# Compute metrics
ir1 = 100.0 * len(np.where(ranks < 1)[0]) / len(ranks)
ir5 = 100.0 * len(np.where(ranks < 5)[0]) / len(ranks)
ir10 = 100.0 * len(np.where(ranks < 10)[0]) / len(ranks)
tr_mean = (tr1 + tr5 + tr10) / 3
ir_mean = (ir1 + ir5 + ir10) / 3
r_mean = (tr_mean + ir_mean) / 2
eval_result = {'txt_r1': tr1,
'txt_r5': tr5,
'txt_r10': tr10,
'txt_r_mean': tr_mean,
'img_r1': ir1,
'img_r5': ir5,
'img_r10': ir10,
'img_r_mean': ir_mean,
'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
#### Dataset ####
print("Creating retrieval dataset")
train_dataset, val_dataset, test_dataset = create_dataset('re', config)
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])
tokenizer = BertTokenizer.from_pretrained(args.text_encoder)
#### Model ####
print("Creating model")
model = ALBEF(config=config, text_encoder=args.text_encoder, tokenizer=tokenizer)
if args.checkpoint:
checkpoint = torch.load(args.checkpoint, map_location='cpu')
state_dict = checkpoint['model']
# reshape positional embedding to accomodate for image resolution change
pos_embed_reshaped = interpolate_pos_embed(state_dict['visual_encoder.pos_embed'],model.visual_encoder)
state_dict['visual_encoder.pos_embed'] = pos_embed_reshaped
m_pos_embed_reshaped = interpolate_pos_embed(state_dict['visual_encoder_m.pos_embed'],model.visual_encoder_m)
state_dict['visual_encoder_m.pos_embed'] = m_pos_embed_reshaped
for key in list(state_dict.keys()):
if 'bert' in key:
encoder_key = key.replace('bert.','')
state_dict[encoder_key] = state_dict[key]
del state_dict[key]
msg = model.load_state_dict(state_dict,strict=False)
print('load checkpoint from %s'%args.checkpoint)
print(msg)
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
arg_opt = utils.AttrDict(config['optimizer'])
optimizer = create_optimizer(arg_opt, model)
arg_sche = utils.AttrDict(config['schedular'])
lr_scheduler, _ = create_scheduler(arg_sche, optimizer)
max_epoch = config['schedular']['epochs']
warmup_steps = config['schedular']['warmup_epochs']
best = 0
best_epoch = 0
print("Start training")
start_time = time.time()
for epoch in range(0, max_epoch):
if not args.evaluate:
if args.distributed:
train_loader.sampler.set_epoch(epoch)
train_stats = train(model, train_loader, optimizer, tokenizer, epoch, warmup_steps, device, lr_scheduler, config)
score_val_i2t, score_val_t2i, = evaluation(model_without_ddp, val_loader, tokenizer, device, config)
score_test_i2t, score_test_t2i = evaluation(model_without_ddp, test_loader, tokenizer, device, config)
if utils.is_main_process():
val_result = itm_eval(score_val_i2t, score_val_t2i, val_loader.dataset.txt2img, val_loader.dataset.img2txt)
print(val_result)
test_result = itm_eval(score_test_i2t, score_test_t2i, test_loader.dataset.txt2img, test_loader.dataset.img2txt)
print(test_result)
if args.evaluate:
log_stats = {**{f'val_{k}': v for k, v in val_result.items()},
**{f'test_{k}': v for k, v in test_result.items()},
'epoch': epoch,
}
with open(os.path.join(args.output_dir, "log.txt"),"a") as f:
f.write(json.dumps(log_stats) + "\n")
else:
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,
}
with open(os.path.join(args.output_dir, "log.txt"),"a") as f:
f.write(json.dumps(log_stats) + "\n")
if val_result['r_mean']>best:
save_obj = {
'model': model_without_ddp.state_dict(),
'optimizer': optimizer.state_dict(),
'lr_scheduler': lr_scheduler.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
if args.evaluate:
break
lr_scheduler.step(epoch+warmup_steps+1)
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 utils.is_main_process():
with open(os.path.join(args.output_dir, "log.txt"),"a") as f:
f.write("best epoch: %d"%best_epoch)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--config', default='./configs/Retrieval_flickr.yaml')
parser.add_argument('--output_dir', default='output/Retrieval_flickr')
parser.add_argument('--checkpoint', default='')
parser.add_argument('--text_encoder', default='bert-base-uncased')
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://', 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)
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)