out = self.conv1(x)
      out = self.norm1(out)
      out = self.relu(out)

      out = self.conv2(out)
      out = self.norm2(out)
      out = self.relu(out)

      out = self.conv3(out)
      out = self.norm3(out)



training of  conv and norm is false
track_running_stats of norm is True

When I use pycharm to debug the code, from conv1 to conv3, the gpu memory not increase. But I exec norm3, the gpu memory increase.
If training of  conv and norm is True, I test that I exec every sentence, the gpu memory increase.
When I exec every sentence， that create new out, why the gpu memory not increase?
But why I exec norm3 the gpu memory increase?