I use gpu to train ResNet and save the parameters. Then I load the parameters and use ResNet on the cpu to do inference. I find that the time cost is high, about 70 ms/image. If I run this net on GPU, the time cost will be 11 ms/image. The Pytorch version is 1.0.1.
The parameters in net look like the following:

The code of saving parameters looks like:
torch.save(net.state_dict(), path)
The code of  inference looks like:
net.load_state_dict(torch.load(path, map_location=‘cpu’))
Is my speed of inference on the CPU  normal? How can speed up the inference.