Create main_test_SRMNet.py

main_test_SRMNet.py

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+import torch
+import torchvision.transforms.functional as TF
+import torch.nn.functional as F
+from PIL import Image
+import os
+from skimage import img_as_ubyte
+from tqdm import tqdm
+from natsort import natsorted
+from glob import glob
+from utils.image_utils import save_img
+from utils.model_utils import load_checkpoint
+import argparse
+from model_arch.SRMNet_SWFF import SRMNet_SWFF
+from model_arch.SRMNet import SRMNet
+
+tasks = ['Deblurring_motionblur',
+         'Dehaze_realworld',
+         'Denoise_gaussian',
+         'Denoise_realworld',
+         'Deraining_raindrop',
+         'Deraining_rainstreak',
+         'LLEnhancement',
+         'Retouching']
+
+def main():
+    parser = argparse.ArgumentParser(description='Quick demo Image Restoration')
+    parser.add_argument('--input_dir', default='test/', type=str, help='Input images root')
+    parser.add_argument('--result_dir', default='result/', type=str, help='Results images root')
+    parser.add_argument('--weights_root', default='pretrained_model', type=str, help='Weights root')
+    parser.add_argument('--task', default='Retouching', type=str, help='Restoration task (Above task list)')
+
+    args = parser.parse_args()
+
+    # Prepare testing data
+    inp_dir = os.path.join(args.input_dir, args.task)
+    files = natsorted(glob.glob(os.path.join(inp_dir, '*')))
+    if len(files) == 0:
+        raise Exception("\nNo images in {} \nPlease enter the following tasks: \n\n{}".format(inp_dir, '\n'.join(tasks)))
+
+    out_dir = os.path.join(args.result_dir, args.task)
+    os.makedirs(out_dir, exist_ok=True)
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    # Build model
+    model = define_model(args)
+    model.eval()
+    model = model.to(device)
+
+    print('restoring images......')
+
+    mul = 16
+
+    for i, file_ in enumerate(tqdm(files)):
+        img = Image.open(file_).convert('RGB')
+        input_ = TF.to_tensor(img).unsqueeze(0).cuda()
+
+        # Pad the input if not_multiple_of 8
+        h, w = input_.shape[2], input_.shape[3]
+        H, W = ((h + mul) // mul) * mul, ((w + mul) // mul) * mul
+        padh = H - h if h % mul != 0 else 0
+        padw = W - w if w % mul != 0 else 0
+        input_ = F.pad(input_, (0, padw, 0, padh), 'reflect')
+        with torch.no_grad():
+            restored = model(input_)
+
+        restored = torch.clamp(restored, 0, 1)
+        restored = restored[:, :, :h, :w]
+        restored = restored.permute(0, 2, 3, 1).cpu().detach().numpy()
+        restored = img_as_ubyte(restored[0])
+
+        f = os.path.splitext(os.path.split(file_)[-1])[0]
+        save_img((os.path.join(out_dir, f + '.png')), restored)
+
+    print(f"Files saved at {out_dir}")
+    print('finish !')
+
+
+def define_model(args):
+    # Enhance models
+    if args.task in ['LLEnhancement', 'Retouching']:
+        model = SRMNet(in_chn=3, wf=96, depth=4)
+        weight_path = os.path.join(args.weights_root, args.task + '.pth')
+        load_checkpoint(model, weight_path)
+
+    # Restored models
+    else:
+        model = SRMNet_SWFF(in_chn=3, wf=96, depth=4)
+        weight_path = os.path.join(args.weights_root, args.task + '.pth')
+        load_checkpoint(model, weight_path)
+
+    return model
+
+
+if __name__ == '__main__':
+    main()