inference/inference_ridnet.py

import argparse
import cv2
import glob
import numpy as np
import os
import torch
from tqdm import tqdm

from basicsr.archs.ridnet_arch import RIDNet
from basicsr.utils.img_util import img2tensor, tensor2img

if __name__ == '__main__':
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    parser = argparse.ArgumentParser()
    parser.add_argument('--test_path', type=str, default='datasets/denoise/RNI15')
    parser.add_argument('--noise_g', type=int, default=25)
    parser.add_argument(
        '--model_path',
        type=str,
        default=  # noqa: E251
        'experiments/pretrained_models/RIDNet/RIDNet.pth')
    args = parser.parse_args()
    if args.test_path.endswith('/'):  # solve when path ends with /
        args.test_path = args.test_path[:-1]
    test_root = os.path.join(args.test_path, f'X{args.noise_g}')
    result_root = f'results/RIDNet/{os.path.basename(args.test_path)}'
    os.makedirs(result_root, exist_ok=True)

    # set up the RIDNet
    net = RIDNet(3, 64, 3).to(device)
    checkpoint = torch.load(args.model_path, map_location=lambda storage, loc: storage)
    net.load_state_dict(checkpoint)
    net.eval()

    # scan all the jpg and png images
    img_list = sorted(glob.glob(os.path.join(test_root, '*.[jp][pn]g')))
    pbar = tqdm(total=len(img_list), desc='')
    for idx, img_path in enumerate(img_list):
        img_name = os.path.basename(img_path).split('.')[0]
        pbar.update(1)
        pbar.set_description(f'{idx}: {img_name}')
        # read image
        img = cv2.imread(img_path, cv2.IMREAD_COLOR)
        img = img2tensor(img, bgr2rgb=True, float32=True).unsqueeze(0).to(device)
        # inference
        with torch.no_grad():
            output = net(img)
        # save image
        output = tensor2img(output, rgb2bgr=True, out_type=np.uint8, min_max=(0, 255))
        save_img_path = os.path.join(result_root, f'{img_name}_x{args.noise_g}_RIDNet.png')
        cv2.imwrite(save_img_path, output)