diff --git a/train_face_deblur.py b/train_face_deblur.py
index ba23490..dfc61f1 100644
--- a/train_face_deblur.py
+++ b/train_face_deblur.py
@@ -151,20 +151,20 @@ criterionCAE1 = nn.SmoothL1Loss()
 
 target= torch.FloatTensor(opt.batchSize, outputChannelSize, opt.imageSize, opt.imageSize)
 input = torch.FloatTensor(opt.batchSize, inputChannelSize, opt.imageSize, opt.imageSize)
-target_128= torch.FloatTensor(opt.batchSize, outputChannelSize, (opt.imageSize/4), (opt.imageSize/4))
-input_128 = torch.FloatTensor(opt.batchSize, inputChannelSize, (opt.imageSize/4), (opt.imageSize/4))
-target_256= torch.FloatTensor(opt.batchSize, outputChannelSize, (opt.imageSize/2), (opt.imageSize/2))
-input_256 = torch.FloatTensor(opt.batchSize, inputChannelSize, (opt.imageSize/2), (opt.imageSize/2))
+target_128= torch.FloatTensor(opt.batchSize, outputChannelSize, (opt.imageSize//4), (opt.imageSize//4))
+input_128 = torch.FloatTensor(opt.batchSize, inputChannelSize, (opt.imageSize//4), (opt.imageSize//4))
+target_256= torch.FloatTensor(opt.batchSize, outputChannelSize, (opt.imageSize//2), (opt.imageSize//2))
+input_256 = torch.FloatTensor(opt.batchSize, inputChannelSize, (opt.imageSize//2), (opt.imageSize//2))
 
 
 
 
 val_target= torch.FloatTensor(opt.valBatchSize, outputChannelSize, opt.imageSize, opt.imageSize)
 val_input = torch.FloatTensor(opt.valBatchSize, inputChannelSize, opt.imageSize, opt.imageSize)
-val_target_128= torch.FloatTensor(opt.batchSize, outputChannelSize, (opt.imageSize/4), (opt.imageSize/4))
-val_input_128 = torch.FloatTensor(opt.batchSize, inputChannelSize, (opt.imageSize/4), (opt.imageSize/4))
-val_target_256= torch.FloatTensor(opt.batchSize, outputChannelSize, (opt.imageSize/2), (opt.imageSize/2))
-val_input_256 = torch.FloatTensor(opt.batchSize, inputChannelSize, (opt.imageSize/2), (opt.imageSize/2))
+val_target_128= torch.FloatTensor(opt.batchSize, outputChannelSize, (opt.imageSize//4), (opt.imageSize//4))
+val_input_128 = torch.FloatTensor(opt.batchSize, inputChannelSize, (opt.imageSize//4), (opt.imageSize//4))
+val_target_256= torch.FloatTensor(opt.batchSize, outputChannelSize, (opt.imageSize//2), (opt.imageSize//2))
+val_input_256 = torch.FloatTensor(opt.batchSize, inputChannelSize, (opt.imageSize//2), (opt.imageSize//2))
 label_d = torch.FloatTensor(opt.batchSize)