Update app.py

app.py

@@ -105,7 +105,7 @@ def patch2img(outs, idxes, sr_size, scale=4, crop_size=512):
     return (preds / count_mt).to(outs.device)
 
 
-def load_img (filename, norm=True,):
+def load_img (filename, norm=True):
     img = np.array(Image.open(filename).convert("RGB"))
     h, w = img.shape[:2]
     
@@ -116,20 +116,21 @@ def load_img (filename, norm=True,):
 
 
 def inference(image, upscale, large_input_flag, color_fix):
+	model = set_safmn(upscale)
+
+    img = np.array(image)
+    img = img.astype(np.float32) / 255.
+    
+    # img2tensor
+    y = torch.from_numpy(np.transpose(img[:, :, [2, 1, 0]], (2, 0, 1))).float()
+    y = y.unsqueeze(0).to(device)
+
 	upscale = int(upscale) # convert type to int
 	if upscale > 4: 
 		upscale = 4 
 	if 0 < upscale < 3:
 		upscale = 2
 
-	model = set_safmn(upscale)
-    img = np.array(image)
-    img = img / 255.
-    img = img.astype(np.float32)
-
-    # img2tensor
-    y = torch.tensor(img).permute(2,0,1).unsqueeze(0).to(device)
-
 	# inference
 	if large_input_flag:
 		patches, idx, size = img2patch(y, scale=upscale)