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import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument('--cuda', type=int, default=0)
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parser.add_argument('--epochs', type=int, default=120, help='maximum number of epochs to train the total model.')
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parser.add_argument('--batch_size', type=int,default=8,help="Batch size to use per GPU")
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parser.add_argument('--lr', type=float, default=2e-4, help='learning rate of encoder.')
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parser.add_argument('--de_type', nargs='+', default=['denoise_15', 'denoise_25', 'denoise_50', 'derain', 'dehaze'],
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help='which type of degradations is training and testing for.')
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parser.add_argument('--patch_size', type=int, default=128, help='patchsize of input.')
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parser.add_argument('--num_workers', type=int, default=16, help='number of workers.')
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parser.add_argument('--data_file_dir', type=str, default='data_dir/', help='where clean images of denoising saves.')
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parser.add_argument('--denoise_dir', type=str, default='data/Train/Denoise/',
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help='where clean images of denoising saves.')
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parser.add_argument('--derain_dir', type=str, default='data/Train/Derain/',
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help='where training images of deraining saves.')
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parser.add_argument('--dehaze_dir', type=str, default='data/Train/Dehaze/',
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help='where training images of dehazing saves.')
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parser.add_argument('--output_path', type=str, default="output/", help='output save path')
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parser.add_argument('--ckpt_path', type=str, default="ckpt/Denoise/", help='checkpoint save path')
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parser.add_argument("--wblogger",type=str,default="promptir",help = "Determine to log to wandb or not and the project name")
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parser.add_argument("--ckpt_dir",type=str,default="train_ckpt",help = "Name of the Directory where the checkpoint is to be saved")
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parser.add_argument("--num_gpus",type=int,default= 4,help = "Number of GPUs to use for training")
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options = parser.parse_args()
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