Delete main_test_HWMNet

main_test_HWMNet

@@ -1,86 +0,0 @@
-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()