lut_reproduce/src/validate.py

import sys

import logging
import math
import os
import time
import numpy as np
import torch
import torch.nn.functional as F
import torch.optim as optim
from PIL import Image
from pathlib import Path
from torch.utils.tensorboard import SummaryWriter
from torch.utils.data import Dataset, DataLoader
from common.data import SRTrainDataset, SRTestDataset
from common.utils import PSNR, cal_ssim, logger_info, _rgb2ycbcr, modcrop
from common.validation import valid_steps
from models import LoadCheckpoint
torch.backends.cudnn.benchmark = True
from datetime import datetime
import argparse

class ValOptions():
    def __init__(self):
        self.parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
        self.parser.add_argument('--model_path', type=str, default="../models/last.pth", help="Model path.")
        self.parser.add_argument('--datasets_dir', type=str, default="../data/", help="Path to datasets.")
        self.parser.add_argument('--val_datasets', type=str, default='Set5,Set14', help="Names of validation datasets.")        
        self.parser.add_argument('--save_predictions', action='store_true', default=True, help='Save model predictions to exp_dir/val/dataset_name')
        self.parser.add_argument('--device', type=str, default='cuda', help='Device of the model')
        self.parser.add_argument('--color_model', type=str, default="RGB", help="Color model for train and test dataset.") 

    def parse_args(self):         
        args = self.parser.parse_args()
        args.datasets_dir = Path(args.datasets_dir).resolve()
        args.val_datasets = args.val_datasets.split(',')
        args.exp_dir = Path(args.model_path).resolve().parent.parent
        args.model_path = Path(args.model_path).resolve()
        args.model_name = args.model_path.stem
        args.valout_dir = Path(args.exp_dir).resolve() /  'val'
        if not args.valout_dir.exists():
            args.valout_dir.mkdir()
        args.current_iter = args.model_name.split('_')[-1]
        args.results_path = os.path.join(args.valout_dir, f'results_{args.model_name}_{args.device}.csv')
        # Tensorboard for monitoring
        writer = SummaryWriter(log_dir=args.valout_dir)
        logger_name = f'val_{args.model_path.stem}'
        logger_info(logger_name, os.path.join(args.valout_dir, logger_name + '.log'))
        logger = logging.getLogger(logger_name)
        args.writer = writer
        args.logger = logger

        return args

    def __repr__(self):
        config = self.parse_args() 
        message = ''
        message += '----------------- Options ---------------\n'
        for k, v in sorted(vars(config).items()):
            comment = ''
            default = self.parser.get_default(k)
            if v != default:
                comment = '\t[default: %s]' % str(default)
            message += '{:>25}: {:<30}{}\n'.format(str(k), str(v), comment)
        message += '----------------- End -------------------'
        return message


# TODO with unified save/load function any model file of net or lut can be tested with the same script.
if __name__ == "__main__":
    script_start_time = datetime.now()
    config_inst = ValOptions()
    config = config_inst.parse_args()
   
    config.logger.info(config_inst)

    model = LoadCheckpoint(config.model_path)
    model = model.to(torch.device(config.device))
    print(model)
      
    test_datasets = {}
    for test_dataset_name in config.val_datasets:
        test_datasets[test_dataset_name] = SRTestDataset(
            hr_dir_path = Path(config.datasets_dir) / test_dataset_name  / "HR",
            lr_dir_path = Path(config.datasets_dir) / test_dataset_name / "LR" / f"X{model.scale}",
            color_model=config.color_model
        )

    results = valid_steps(model=model, datasets=test_datasets, config=config, log_prefix=f"Model {config.model_name}")

    results.to_csv(config.results_path)
    print(config.exp_dir.stem, config.model_name)
    print(results)
    print()
    print(f"Results saved to {config.results_path}")

    total_script_time = datetime.now() - script_start_time
    config.logger.info(f"Completed after {total_script_time}")