Update app.py

app.py

@@ -86,9 +86,8 @@ def predict(net,  inputs_val, shapes_val, hypar, device):
    
     ds_val = net(inputs_val_v)[0] # list of 6 results
 
-    print(ds_val)
     print(ds_val[0])
-    pred_val = ds_val[1][0,:,:,:] # B x 1 x H x W    # we want the first one which is the most accurate prediction
+    pred_val = ds_val[5][0,:,:,:] # B x 1 x H x W    # we want the first one which is the most accurate prediction
 
     ## recover the prediction spatial size to the orignal image size
     pred_val = torch.squeeze(F.upsample(torch.unsqueeze(pred_val,0),(shapes_val[0][0],shapes_val[0][1]),mode='bilinear'))