Update app.py

app.py

@@ -22,10 +22,10 @@ pretrain_model_url = {
 
 
 # download weights
-if not os.path.exists('./pretrained_models/SAFMN_L_Real_LSDIR_x2-v2.pth'):
+if not os.path.exists('pretrained_models/SAFMN_L_Real_LSDIR_x2-v2.pth'):
 	load_file_from_url(url=pretrain_model_url['safmn_x2'], model_dir='./pretrained_models/', progress=True, file_name=None)
 
-if not os.path.exists('./pretrained_models/SAFMN_L_Real_LSDIR_x4-v2.pth'):
+if not os.path.exists('pretrained_models/SAFMN_L_Real_LSDIR_x4-v2.pth'):
 	load_file_from_url(url=pretrain_model_url['safmn_x4'], model_dir='./pretrained_models/', progress=True, file_name=None)
 
 
@@ -34,9 +34,9 @@ device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 def set_safmn(upscale):
 	model = SAFMN(dim=128, n_blocks=16, ffn_scale=2.0, upscaling_factor=upscale)
 	if upscale == 2:
-		model_path = './pretrained_models/SAFMN_L_Real_LSDIR_x2.pth'
+		model_path = 'pretrained_models/SAFMN_L_Real_LSDIR_x2-v2.pth'
 	elif upscale == 4:
-		model_path = './pretrained_models/SAFMN_L_Real_LSDIR_x4-v2.pth'
+		model_path = 'pretrained_models/SAFMN_L_Real_LSDIR_x4-v2.pth'
 	else:
 		raise NotImplementedError('Only support x2/x4 upscaling!')
 
@@ -104,17 +104,6 @@ def patch2img(outs, idxes, sr_size, scale=4, crop_size=512):
     return (preds / count_mt).to(outs.device)
 
 
-def load_img(filename, norm=True):
-    img = np.array(Image.open(filename).convert("RGB"))
-    h, w = img.shape[:2]
-    
-    if norm:
-        img = img.astype(np.float32) / 255.
-        
-    return img
-
-
-
 def inference(image, upscale, large_input_flag, color_fix):
 	if upscale is None or not isinstance(upscale, (int, float)) or upscale == 3.:
 		upscale = 2