remove unused file

data/base_dataset.py

@@ -29,6 +29,7 @@ def __init__(self, opt):
         self.opt = opt
         self.root = opt.dataroot
         self.current_epoch = 0
+
     @staticmethod
     def modify_commandline_options(parser, is_train):
         """Add new dataset-specific options, and rewrite default values for existing options.
@@ -112,10 +113,9 @@ def get_transform(opt, params=None, grayscale=False, method=Image.BICUBIC, conve
     if 'trim' in opt.preprocess:
         transform_list.append(transforms.Lambda(lambda img: __trim(img, opt.crop_size)))
 
-    #if opt.preprocess == 'none':
+    # if opt.preprocess == 'none':
     transform_list.append(transforms.Lambda(lambda img: __make_power_2(img, base=4, method=method)))
 
-
     if not opt.no_flip:
         if params is None or 'flip' not in params:
             transform_list.append(transforms.RandomHorizontalFlip())
@@ -138,7 +138,6 @@ def __make_power_2(img, base, method=Image.BICUBIC):
     if h == oh and w == ow:
         return img
 
-    #__print_size_warning(ow, oh, w, h)
     return img.resize((w, h), method)
 
 

util/util.py

@@ -139,6 +139,7 @@ def mkdir(path):
     if not os.path.exists(path):
         os.makedirs(path)
 
+
 def correct_resize_label(t, size):
     device = t.device
     t = t.detach().cpu()
@@ -158,9 +159,8 @@ def correct_resize(t, size, mode=Image.BICUBIC):
     t = t.detach().cpu()
     resized = []
     for i in range(t.size(0)):
-        one_t = t[i:i+1]
+        one_t = t[i:i + 1]
         one_image = Image.fromarray(tensor2im(one_t)).resize(size, Image.BICUBIC)
         resized_t = torchvision.transforms.functional.to_tensor(one_image) * 2 - 1.0
         resized.append(resized_t)
     return torch.stack(resized, dim=0).to(device)
-