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fully-invertible-revnet's Issues

train.py does not work on my machine

Hi,

I ran train.py (as is, without any modifications) with Python 3.7.7, Torch 1.1.0 and Ubuntu 18.04.2 LTS. It gave the following error:

Traceback (most recent call last): File "train.py", line 176, in <module> logits_ac = ac(_zn.detach()) File "/home/harshay/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py", line 493, in __call__ result = self.forward(*input, **kwargs) File "/home/harshay/anaconda3/lib/python3.7/site-packages/torch/nn/modules/container.py", line 92, in forward input = module(input) File "/home/harshay/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py", line 493, in __call__ result = self.forward(*input, **kwargs) File "/home/harshay/anaconda3/lib/python3.7/site-packages/torch/nn/modules/linear.py", line 92, in forward return F.linear(input, self.weight, self.bias) File "/home/harshay/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py", line 1406, in linear ret = torch.addmm(bias, input, weight.t()) RuntimeError: size mismatch, m1: [128 x 3840], m2: [3072 x 512] at /opt/conda/conda-bld/pytorch_1556653215914/work/aten/src/THC/generic/THCTensorMathBlas.cu:268

Is there a way to fix this?

Thanks

Interpolation of nuisance variable

Hi,

Thanks for releasing this amazing library! I am trying to reproduce the Figure 7 in the paper. But when I interpolate the nuisance and compute the reverse, it seems model trained with cross entropy also behaves pretty well (the reverse sample is not dominated by nuisance). I notice that the logit is z[:,:10,2,2], do I just permute the rest of the elements to get figure 7?

Want to doublecheck if my implementation is correct.

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