Update app.py

app.py

@@ -93,8 +93,8 @@ size = 256
 means = [0.485, 0.456, 0.406]
 stds = [0.229, 0.224, 0.225]
 
-t_stds = torch.tensor(stds).cpu()[:,None,None]
-t_means = torch.tensor(means).cpu()[:,None,None]
+t_stds = torch.tensor(stds).cpu().half().float()[:,None,None]
+t_means = torch.tensor(means).cpu().half().float()[:,None,None]
 
 def makeEven(_x):
   return int(_x) if (_x % 2 == 0) else int(_x+1)
@@ -107,7 +107,7 @@ def tensor2im(var):
      return var.mul(t_stds).add(t_means).mul(255.).clamp(0,255).permute(1,2,0)
 
 def proc_pil_img(input_image, model):
-    transformed_image = img_transforms(input_image)[None,...].cpu()()
+    transformed_image = img_transforms(input_image)[None,...].cpu().half().float()
             
     with torch.no_grad():
         result_image = model(transformed_image)[0]
@@ -118,9 +118,9 @@ def proc_pil_img(input_image, model):
     
     
  
-modelv4 = torch.jit.load(modelarcanev4).eval().cpu()
-modelv3 = torch.jit.load(modelarcanev3).eval().cpu()
-modelv2 = torch.jit.load(modelarcanev2).eval().cpu()
+modelv4 = torch.jit.load(modelarcanev4,map_location='cpu').eval().cpu().half().float()
+modelv3 = torch.jit.load(modelarcanev3,map_location='cpu').eval().cpu().half().float()
+modelv2 = torch.jit.load(modelarcanev2,map_location='cpu').eval().cpu().half().float()
 
 def process(im, version):
     if version == 'version 0.4':