Inefficient usage of torch.randn() in vision/model_factory.py

[perfNerdJK]

torch.randn() used here first allocates space for self.batch_size in the host memory and then copies it to the device memory, wasting host-device bandwidth.

As self.batch_size contains random values, we can directly create self.batch_size on the device to bypass this non-sense host-device data copy. The proposed patch is as follows.

-torch.randn((self.batch_size, 3, 224, 224)).to(self.device)
+torch.randn((self.batch_size, 3, 224, 224), device=self.device)