Update main_test_SRMNet.py

main_test_SRMNet.py

@@ -1,10 +1,24 @@
+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 model.SRMNet import SRMNet
+from utils import util_calculate_psnr_ssim as util
+
 
 def save_img(filepath, img):
     cv2.imwrite(filepath, cv2.cvtColor(img, cv2.COLOR_RGB2BGR))
 
+
 def load_checkpoint(model, weights):
     checkpoint = torch.load(weights)
     try:
@@ -17,8 +31,62 @@ def load_checkpoint(model, weights):
             new_state_dict[name] = v
         model.load_state_dict(new_state_dict)
 
+
+def main():
+    parser = argparse.ArgumentParser(description='Demo Image Denoising')
+    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/AWGN_denoising_SRMNet.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 = sorted(glob.glob(os.path.join(inp_dir, '*.PNG')))
+
+    if len(files) == 0:
+        raise Exception(f"No files found at {inp_dir}")
+
+    # Load corresponding models architecture and weights
+    model = SRMNet()
+    model.cuda()
+
+    load_checkpoint(model, args.weights)
+    model.eval()
+
+    mul = 16
+    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 + 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 setup(args):
     save_dir = 'result/'
     folder = 'test/'
 
     return folder, save_dir
+
+if __name__ == '__main__':
+    main()