commit

lpips/__init__.py

@@ -4,9 +4,8 @@
 from __future__ import print_function
 
 import numpy as np
-from skimage.measure import compare_ssim
 import torch
-from torch.autograd import Variable
+# from torch.autograd import Variable
 
 from lpips.trainer import *
 from lpips.lpips import *
@@ -51,6 +50,7 @@ def psnr(p0, p1, peak=255.):
  return 10*np.log10(peak**2/np.mean((1.*p0-1.*p1)**2))
 
 def dssim(p0, p1, range=255.):
+ from skimage.measure import compare_ssim
  return (1 - compare_ssim(p0, p1, data_range=range, multichannel=True)) / 2.
 
 def rgb2lab(in_img,mean_cent=False):

lpips/lpips.py

@@ -6,7 +6,6 @@
 import torch.nn.init as init
 from torch.autograd import Variable
 import numpy as np
-from IPython import embed
 from . import pretrained_networks as pn
 import torch.nn
 

lpips/pretrained_networks.py

@@ -1,7 +1,6 @@
 from collections import namedtuple
 import torch
 from torchvision import models as tv
-from IPython import embed
 
 class squeezenet(torch.nn.Module):
  def __init__(self, requires_grad=False, pretrained=True):

lpips/trainer.py

@@ -8,7 +8,6 @@
 from torch.autograd import Variable
 from scipy.ndimage import zoom
 from tqdm import tqdm
-from IPython import embed
 import lpips
 
 class Trainer():

lpips_loss.py

@@ -19,8 +19,9 @@
 ref = lpips.im2tensor(lpips.load_image(opt.ref_path))
 pred = Variable(lpips.im2tensor(lpips.load_image(opt.pred_path)), requires_grad=True)
 if(opt.use_gpu):
- ref = ref.cuda()
- pred = pred.cuda()
+ with torch.no_grad():
+ ref = ref.cuda()
+ pred = pred.cuda()
 
 optimizer = torch.optim.Adam([pred,], lr=1e-3, betas=(0.9, 0.999))