67 lines
2.3 KiB
Python
67 lines
2.3 KiB
Python
# This script iterates through all the data with no worker threads and performs whatever transformations are prescribed.
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# The idea is to find bad/corrupt images.
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import math
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import argparse
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import random
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import torch
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import options.options as option
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from utils import util
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from data import create_dataloader, create_dataset
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from time import time
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from tqdm import tqdm
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from skimage import io
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def main():
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#### options
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parser = argparse.ArgumentParser()
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parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../../options/train_mi1_spsr_switched2.yml')
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parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none',
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help='job launcher')
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parser.add_argument('--local_rank', type=int, default=0)
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args = parser.parse_args()
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opt = option.parse(args.opt, is_train=True)
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#### distributed training settings
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opt['dist'] = False
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rank = -1
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# convert to NoneDict, which returns None for missing keys
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opt = option.dict_to_nonedict(opt)
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#### random seed
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seed = opt['train']['manual_seed']
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if seed is None:
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seed = random.randint(1, 10000)
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util.set_random_seed(seed)
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torch.backends.cudnn.benchmark = True
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# torch.backends.cudnn.deterministic = True
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#### create train and val dataloader
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for phase, dataset_opt in opt['datasets'].items():
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if phase == 'train':
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train_set = create_dataset(dataset_opt)
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train_size = int(math.ceil(len(train_set) / dataset_opt['batch_size']))
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total_iters = int(opt['train']['niter'])
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total_epochs = int(math.ceil(total_iters / train_size))
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dataset_opt['n_workers'] = 0 # Force num_workers=0 to make dataloader work in process.
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train_loader = create_dataloader(train_set, dataset_opt, opt, None)
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if rank <= 0:
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print('Number of train images: {:,d}, iters: {:,d}'.format(
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len(train_set), train_size))
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assert train_loader is not None
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tq_ldr = tqdm(train_set.paths_GT)
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for path in tq_ldr:
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try:
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_ = io.imread(path)
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# Do stuff with img
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except Exception as e:
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print("Error with %s" % (path,))
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print(e)
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if __name__ == '__main__':
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main()
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