1）配置文件retrieval_coco.yaml
dataset: 'coco'image_root: '/export/share/datasets/vision/coco/images/'
ann_root: 'annotation'
dataset : coco
pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'
vit: 'base'
batch_size_train: 32
batch_size_test: 64
vit_grad_ckpt: True
vit_ckpt_layer: 4
init_lr: 1e-5
image_size: 384
queue_size: 57600
alpha: 0.4
k_test: 256
negative_all_rank: True
weight_decay: 0.05
min_lr: 0
max_epoch: 6
2）train_retrieval.py代码
parser = argparse.ArgumentParser()
parser.add_argument('--config', default='./configs/retrieval_flickr.yaml')
parser.add_argument('--output_dir', default='output/Retrieval_flickr')
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)
def main(args,config)
utils.init_distributed_mode(args)
device = torch.device(args.device)
seed = args.seed + utils.get_rank()
torch.manual_seed(seed)
np.random.seed(seed)
random.seed(seed)
cudnn.benchmark = True
print("Creating retrieval dataset")
train_dataset, val_dataset, test_dataset = create_dataset('retrieval_%s'%config['dataset'], 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])
print("创建模型")
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'])
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("开始训练")
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, config)
score_val_i2t, score_val_t2i, = evaluation(model_without_ddp, val_loader, device, config)
score_test_i2t, score_test_t2i = evaluation(model_without_ddp, test_loader, 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)
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_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()},
}
with open(os.path.join(args.output_dir, "evaluate.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,
def train(model, data_loader, optimizer, epoch, device, config):
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, caption, 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)
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"])
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):
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_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 = model.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] = model.tokenizer.enc_token_id
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.cpu())
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).to(device)
encoder_att = torch.ones(encoder_output.size()[:-1],dtype=torch.long).to(device)
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_i2t[start+i,topk_idx] = score + topk_sim
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].to(device)
def itm_eval(scores_i2t, scores_t2i, txt2img, img2txt):
ranks = np.zeros(scores_i2t.shape[0])
for index,score in enumerate(scores_i2t):
inds = np.argsort(score)[::-1]
rank = 1e20
for i in img2txt[index]:
tmp = np.where(inds == i)[0][0]
if tmp < rank:
rank = tmp
ranks[index] = rank
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)
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]
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