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98 lines
4.1 KiB
Python
98 lines
4.1 KiB
Python
import sys
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import logging
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import math
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import os
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import time
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import numpy as np
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import torch
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import torch.nn.functional as F
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import torch.optim as optim
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from PIL import Image
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from pathlib import Path
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from torch.utils.tensorboard import SummaryWriter
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from torch.utils.data import Dataset, DataLoader
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from common.data import SRTrainDataset, SRTestDataset
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from common.utils import PSNR, cal_ssim, logger_info, _rgb2ycbcr, modcrop
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from common.validation import valid_steps
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from models import LoadCheckpoint
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torch.backends.cudnn.benchmark = True
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from datetime import datetime
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import argparse
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class ValOptions():
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def __init__(self):
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self.parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
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self.parser.add_argument('--model_path', type=str, default="../models/last.pth", help="Model path.")
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self.parser.add_argument('--datasets_dir', type=str, default="../data/", help="Path to datasets.")
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self.parser.add_argument('--val_datasets', type=str, default='Set5,Set14', help="Names of validation datasets.")
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self.parser.add_argument('--save_predictions', action='store_true', default=True, help='Save model predictions to exp_dir/val/dataset_name')
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self.parser.add_argument('--device', type=str, default='cuda', help='Device of the model')
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self.parser.add_argument('--color_model', type=str, default="RGB", help="Color model for train and test dataset.")
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def parse_args(self):
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args = self.parser.parse_args()
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args.datasets_dir = Path(args.datasets_dir).resolve()
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args.val_datasets = args.val_datasets.split(',')
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args.exp_dir = Path(args.model_path).resolve().parent.parent
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args.model_path = Path(args.model_path).resolve()
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args.model_name = args.model_path.stem
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args.valout_dir = Path(args.exp_dir).resolve() / 'val'
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if not args.valout_dir.exists():
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args.valout_dir.mkdir()
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args.current_iter = args.model_name.split('_')[-1]
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args.results_path = os.path.join(args.valout_dir, f'results_{args.model_name}_{args.device}.csv')
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# Tensorboard for monitoring
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writer = SummaryWriter(log_dir=args.valout_dir)
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logger_name = f'val_{args.model_path.stem}'
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logger_info(logger_name, os.path.join(args.valout_dir, logger_name + '.log'))
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logger = logging.getLogger(logger_name)
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args.writer = writer
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args.logger = logger
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return args
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def __repr__(self):
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config = self.parse_args()
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message = ''
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message += '----------------- Options ---------------\n'
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for k, v in sorted(vars(config).items()):
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comment = ''
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default = self.parser.get_default(k)
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if v != default:
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comment = '\t[default: %s]' % str(default)
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message += '{:>25}: {:<30}{}\n'.format(str(k), str(v), comment)
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message += '----------------- End -------------------'
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return message
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# TODO with unified save/load function any model file of net or lut can be tested with the same script.
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if __name__ == "__main__":
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script_start_time = datetime.now()
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config_inst = ValOptions()
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config = config_inst.parse_args()
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config.logger.info(config_inst)
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model = LoadCheckpoint(config.model_path)
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model = model.to(torch.device(config.device))
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print(model)
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test_datasets = {}
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for test_dataset_name in config.val_datasets:
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test_datasets[test_dataset_name] = SRTestDataset(
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hr_dir_path = Path(config.datasets_dir) / test_dataset_name / "HR",
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lr_dir_path = Path(config.datasets_dir) / test_dataset_name / "LR" / f"X{model.scale}",
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color_model=config.color_model
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)
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results = valid_steps(model=model, datasets=test_datasets, config=config, log_prefix=f"Model {config.model_name}")
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results.to_csv(config.results_path)
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print(config.exp_dir.stem, config.model_name)
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print(results)
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print()
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print(f"Results saved to {config.results_path}")
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total_script_time = datetime.now() - script_start_time
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config.logger.info(f"Completed after {total_script_time}") |