parallel bugfix

README.md

@@ -184,4 +184,4 @@ If you find this repository useful for your research, please use the following.
 
 ## Acknowledgements
 
-This repository borrows partially from the [pytorch-CycleGAN-and-pix2pix](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) repository. The average precision (AP) code is borrowed from the [py-faster-rcnn](https://github.com/rbgirshick/py-faster-rcnn/blob/master/lib/datasets/voc_eval.py) repository. [Angjoo Kanazawa](https://github.com/akanazawa), [Connelly Barnes](http://www.connellybarnes.com/work/), [Gaurav Mittal](https://github.com/g1910), [wilhelmhb](https://github.com/wilhelmhb), [Filippo Mameli](https://github.com/mameli), [SuperShinyEyes](https://github.com/SuperShinyEyes) helped to improve the codebase.
+This repository borrows partially from the [pytorch-CycleGAN-and-pix2pix](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) repository. The average precision (AP) code is borrowed from the [py-faster-rcnn](https://github.com/rbgirshick/py-faster-rcnn/blob/master/lib/datasets/voc_eval.py) repository. [Angjoo Kanazawa](https://github.com/akanazawa), [Connelly Barnes](http://www.connellybarnes.com/work/), [Gaurav Mittal](https://github.com/g1910), [wilhelmhb](https://github.com/wilhelmhb), [Filippo Mameli](https://github.com/mameli), [SuperShinyEyes](https://github.com/SuperShinyEyes), [Minyoung Huh](http://people.csail.mit.edu/minhuh/) helped to improve the codebase.

lpips/lpips.py

@@ -8,6 +8,7 @@
 import numpy as np
 from IPython import embed
 from . import pretrained_networks as pn
+import torch.nn
 
 import lpips
 
@@ -64,6 +65,7 @@ def __init__(self, pretrained=True, net='alex', version='0.1', lpips=True, spati
  self.lin5 = NetLinLayer(self.chns[5], use_dropout=use_dropout)
  self.lin6 = NetLinLayer(self.chns[6], use_dropout=use_dropout)
  self.lins+=[self.lin5,self.lin6]
+ self.lins = nn.ModuleList(self.lins)
 
  if(pretrained):
  if(model_path is None):
@@ -112,6 +114,7 @@ def forward(self, in0, in1, retPerLayer=False, normalize=False):
  else:
  return val
 
+
 class ScalingLayer(nn.Module):
  def __init__(self):
  super(ScalingLayer, self).__init__()