Retrieval_BLIP.py

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 blip_original import create_dataset, create_loader, create_sampler

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()}


@torch.no_grad()
def evaluation(model, data_loader, device, config, args):
    # 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=90,
                                     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)

        if args.dataset_name == 'iu_xray':
            image_embeds0 = model.visual_encoder(image[:, 0])
            image_embeds1 = model.visual_encoder(image[:, 1])
            image_feat = torch.cat((image_embeds0, image_embeds1), dim=2)
            image_feat = model.iu_proj(image_feat)
        else:
            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()


@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

    tokenizer = None
    #### Dataset ####
    print("Creating retrieval dataset")
    train_dataset, val_dataset, test_dataset = create_dataset('retrieval_%s' % args.dataset_name, args, config)
    print('number of training samples: %d' % len(train_dataset))
    print('number of validation samples: %d' % len(val_dataset))
    print('number of test samples: %d' % 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=[args.batch_size] * 3,
                                                          num_workers=[4, 4, 4],
                                                          is_trains=[True, False, False],
                                                          collate_fns=[None, None, None])

    model = blip_retrieval(pretrained=args.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'], tokenizer=tokenizer, args=args)

    model = model.to(device)


    optimizer = torch.optim.AdamW(params=model.parameters(), lr=config['init_lr'], weight_decay=config['weight_decay'])

    best = 0
    best_epoch = 0

    start_epoch = 0
    if args.checkpoint:
        checkpoint = torch.load(args.checkpoint, map_location='cpu')
        state_dict = checkpoint['model']
        model.load_state_dict(state_dict)

        optimizer.load_state_dict(checkpoint['optimizer'])
        start_epoch = checkpoint['epoch'] + 1
        print('resume checkpoint from %s' % args.checkpoint)

    model_without_ddp = model
    if args.distributed:
        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
        model_without_ddp = model.module

    print("Start training")
    start_time = time.time()
    for epoch in range(start_epoch, args.epochs):
        if not args.evaluate:
            if args.distributed:
                train_loader.sampler.set_epoch(epoch)

            cosine_lr_schedule(optimizer, epoch, args.epochs, config['init_lr'], config['min_lr'])

            train_stats = train(model, train_loader, optimizer, epoch, device, config)
        else:
            if args.distributed:
                train_loader.sampler.set_epoch(epoch)


        score_val_i2t, score_val_t2i, = evaluation(model_without_ddp, val_loader, device, config, args)
        score_test_i2t, score_test_t2i = evaluation(model_without_ddp, test_loader, device, config, args)




        if utils.is_main_process():

            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_now.pth'))

            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 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


            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.record_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.record_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.yaml')
    parser.add_argument('--output_dir', default='output/Retrieval')
    parser.add_argument('--pretrained', default='')
    parser.add_argument('--checkpoint', default='')
    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)

    parser.add_argument('--threshold', type=int, default=3, help='the cut off frequency for the words.')


    parser.add_argument('--image_dir', type=str,
                        default='./dataset/iu_xray/images&./dataset/MIMIC-CXR/mimic_cxr/images',
                        help='the path to the directory containing the data.')
    parser.add_argument('--ann_path', type=str,
                        default='./dataset/iu_xray/annotation.json&./dataset/MIMIC-CXR/mimic_cxr/annotation.json',
                        help='the path to the directory containing the data.')
    parser.add_argument('--knowledge_path', type=str,
                        default='./dataset/KG/iu_train_kg_AO.json&./dataset/KG/mimic_train_kg_AO.json',
                        help='the path to the directory containing the data.')


    parser.add_argument('--dataset_name', type=str, default='iu_xray', choices=['iu_xray', 'mimic_cxr'],
                        help='the dataset to be used.')
    parser.add_argument('--batch_size', type=int, default=32, help='the number of samples for a batch')
    parser.add_argument('--epochs', type=int, default=30, help='the number of training epochs.')
    parser.add_argument('--record_dir', type=str, default='records/',
                        help='the patch to save the results of experiments')
    parser.add_argument('--bert', type=str, default='base', choices=['base', 'sci', 'cli'],
                        help='the dataset to be used.')
    parser.add_argument('--task', type=str, default='retrieval',
                        choices=['pretrain', 'retrieval', 'generation', 'diagnosis', 'vqa'],
                        help='the dataset to be used.')


    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)