Delete demo.py

demo.py

@@ -1,89 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torchvision.transforms.functional as TF
-from PIL import Image
-import os
-import utils
-from skimage import img_as_ubyte
-from collections import OrderedDict
-from natsort import natsorted
-from glob import glob
-import cv2
-import argparse
-from model.CMFNet import CMFNet
-
-model = CMFNet()
-
-parser = argparse.ArgumentParser(description='Demo Image Restoration')
-parser.add_argument('--input_dir', default='./demo_samples/deraindrop', type=str, help='Input images folder')
-parser.add_argument('--result_dir', default='./demo_results', type=str, help='Directory for results')
-parser.add_argument('--weights', default='./pretrained_model/deraindrop_DeRainDrop_CMFNet.pth', type=str, help='Path to weights')
-
-args = parser.parse_args()
-
-def save_img(filepath, img):
-    cv2.imwrite(filepath, cv2.cvtColor(img, cv2.COLOR_RGB2BGR))
-
-def load_checkpoint(model, weights):
-    checkpoint = torch.load(weights)
-    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)
-
-inp_dir = args.input_dir
-out_dir = args.result_dir
-
-os.makedirs(out_dir, exist_ok=True)
-
-files = natsorted(glob(os.path.join(inp_dir, '*.jpg'))
-                  + glob(os.path.join(inp_dir, '*.JPG'))
-                  + glob(os.path.join(inp_dir, '*.png'))
-                  + glob(os.path.join(inp_dir, '*.PNG')))
-
-if len(files) == 0:
-    raise Exception(f"No files found at {inp_dir}")
-
-# Load corresponding model architecture and weights
-model = CMFNet()
-model.cuda()
-
-load_checkpoint(model, args.weights)
-model.eval()
-
-img_multiple_of = 8
-print('restoring images......')
-for file_ in 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 + img_multiple_of) // img_multiple_of) * img_multiple_of, (
-                (w + img_multiple_of) // img_multiple_of) * img_multiple_of
-    padh = H - h if h % img_multiple_of != 0 else 0
-    padw = W - w if w % img_multiple_of != 0 else 0
-    input_ = F.pad(input_, (0, padw, 0, padh), 'reflect')
-
-    with torch.no_grad():
-        restored = model(input_)
-    restored = restored[4]
-    restored = torch.clamp(restored, 0, 1)
-
-    # Un-pad the output
-    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 !')