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import argparse |
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import numpy as np |
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import random |
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from pathlib import Path |
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import torch |
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import torch.nn as nn |
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import torch.nn.functional as F |
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import torch.backends.cudnn as cudnn |
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import torch.distributed as dist |
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from torch.cuda.amp import GradScaler, autocast |
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from models.FFLIP import FLIP |
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from models import utils |
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from eval.pretrain_eval import evaluation, itm_eval |
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from data import create_dataset, create_sampler, create_loader |
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def main(args): |
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utils.init_distributed_mode(args) |
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device = torch.device(args.device) |
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seed = args.seed + utils.get_rank() |
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torch.manual_seed(seed) |
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np.random.seed(seed) |
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random.seed(seed) |
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cudnn.benchmark = True |
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print("Creating dataset") |
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train_dataset, test_dataset = create_dataset(args, 'facecaption') |
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if args.distributed: |
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num_tasks = utils.get_world_size() |
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global_rank = utils.get_rank() |
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samplers = create_sampler([train_dataset], [True], num_tasks, global_rank) + [None] |
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else: |
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samplers = [None, None] |
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train_loader, test_loader = create_loader([train_dataset, test_dataset], samplers, |
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batch_size=[80] + [80], |
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num_workers=[8, 8], |
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is_trains=[True, False], |
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collate_fns=[None, None]) |
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print("Creating model") |
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model = FLIP(pretrained=args.pretrained, vit='base', queue_size=61440) |
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model = model.to(device) |
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model_without_ddp = model |
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if args.distributed: |
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model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu]) |
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model_without_ddp = model.module |
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print("Start evaluation") |
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score_test_i2t, score_test_t2i = evaluation(args, model_without_ddp, test_loader, device) |
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if utils.is_main_process(): |
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test_result = itm_eval(score_test_i2t, score_test_t2i, test_loader.dataset.txt2img, |
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test_loader.dataset.img2txt) |
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print(test_result) |
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if args.distributed: |
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dist.barrier() |
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if __name__ == '__main__': |
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parser = argparse.ArgumentParser() |
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parser.add_argument('--output_dir', default='./outputs') |
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parser.add_argument('--img_root', default='./FaceCaption/images') |
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parser.add_argument('--ann_root', default='.FaceCaption/caption') |
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parser.add_argument('--pretrained', default='./FaceCaption-15M-base.pth') |
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parser.add_argument('--device', default='cuda') |
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parser.add_argument('--seed', default=42, type=int) |
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parser.add_argument('--world_size', default=1, type=int, help='number of distributed processes') |
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parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training') |
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parser.add_argument('--distributed', default=False, type=bool, help='whether to use distributed mode to training') |
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args = parser.parse_args() |
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main(args) |
