Create main_test_HWMNet.py

main_test_HWMNet.py

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+import argparse
+import cv2
+import glob
+import numpy as np
+from collections import OrderedDict
+from skimage import img_as_ubyte
+import os
+import torch
+import requests
+from PIL import Image
+import torchvision.transforms.functional as TF
+import torch.nn.functional as F
+from natsort import natsorted
+from model.HWMNet import HWMNet 
+
+def main():
+    parser = argparse.ArgumentParser(description='Demo Low-light Image enhancement')
+    parser.add_argument('--input_dir', default='test/', type=str, help='Input images')
+    parser.add_argument('--result_dir', default='result/', type=str, help='Directory for results')
+    parser.add_argument('--weights',
+                        default='experiments/pretrained_models/LOL_enhancement_HWMNet.pth', type=str,
+                        help='Path to weights')
+
+    args = parser.parse_args()
+
+    inp_dir = args.input_dir
+    out_dir = args.result_dir
+
+    os.makedirs(out_dir, exist_ok=True)
+
+    files = natsorted(glob.glob(os.path.join(inp_dir, '*')))
+
+    if len(files) == 0:
+        raise Exception(f"No files found at {inp_dir}")
+
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+    # Load corresponding models architecture and weights
+    model = HWMNet(in_chn=3, wf=96, depth=4)
+    model = model.to(device)
+    model.eval()
+    load_checkpoint(model, args.weights)
+    
+
+    mul = 16
+    for file_ in files:
+        img = Image.open(file_).convert('RGB')
+        input_ = TF.to_tensor(img).unsqueeze(0).to(device)
+
+        # 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)
+
+
+def save_img(filepath, img):
+    cv2.imwrite(filepath, cv2.cvtColor(img, cv2.COLOR_RGB2BGR))
+
+
+def load_checkpoint(model, weights):
+    checkpoint = torch.load(weights, map_location=torch.device('cpu'))
+    try:
+        model.load_state_dict(checkpoint["state_dict"])
+    except:
+        state_dict = checkpoint["state_dict"]
+        new_state_dict = OrderedDict()
+        for k, v in state_dict.items():
+            name = k[7:]  # remove `module.`
+            new_state_dict[name] = v
+        model.load_state_dict(new_state_dict)
+
+
+if __name__ == '__main__':
+    main()