GitLab

changed a small mistake.

First dimension is batch size, channel is the second

I have made some performance improvements to the model, and testing them now.  If results turn out good, I will submit a separate PR.

Using python2 style '/' will convert int type to float in python3 which will cause the following error when creating FloatTensor:

TypeError: torch.FloatTensor constructor received an invalid combination of arguments - got (float, int, int, int), but expected one of:
 * no arguments
 * (int ...)
      didn't match because some of the arguments have invalid types: (float, int, int, int)

    git ls-files | grep '\.py$' | xargs -n1 -P`nproc` autopep8 -i

* Add SqueezeNet 1.0 and 1.1 models
* Selectively avoid inplace in SqueezeNet
* Use Glorot uniform initialization in SqueezeNet
* Make all ReLU in SqueezeNet in-place
* Add pretrained SqueezeNet 1.0 and 1.1
* Minor fixes in SqueezeNet models

* Update VGG doco

Also add pre-trained ResNet-152 model.

  ResNet-152: Prec@1 78.312 Prec@5 94.046