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| ''' | |
| * Copyright (c) 2022, salesforce.com, inc. | |
| * All rights reserved. | |
| * SPDX-License-Identifier: BSD-3-Clause | |
| * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause | |
| * By Junnan Li | |
| ''' | |
| 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 | |
| import utils | |
| from utils import cosine_lr_schedule | |
| from data import create_dataset, create_sampler, create_loader | |
| def train(model, data_loader, optimizer, epoch, device, 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 | |
| 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"]) | |
| # 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()} | |
| def evaluation(model, data_loader, 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_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) | |
| encoder_att = torch.ones(encoder_output.size()[:-1],dtype=torch.long).to(device) | |
| 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_t2i[start+i,topk_idx] = score + topk_sim | |
| 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() | |
| 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('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]) | |
| #### Model #### | |
| print("Creating model") | |
| 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("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, 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, | |
| '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_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) |