forgot to update no transform for the celebAHQ dataloader

dataloader/load.py

@@ -75,12 +75,12 @@ class UnconditionalDataset_CelebAHQ(Dataset):
         """
         Args:
             fpath (string):   Path to the folder where images are stored
-            img_size (int): size of output image img_size=height=width
-            ext (string): type of images used(eg .png)
+            img_size (int):   Size of output image img_size=height=width
+            ext (string):     Type of images used(eg .png)
             transform (Bool): Image augmentation for diffusion model
-            skip_first_n: skips the first n values. Usefull for datasets that are sorted by increasing Likeliehood 
+            skip_first_n:     Skips the first n values. Usefull for datasets that are sorted by increasing Likeliehood 
             train (Bool):     Choose dataset to be either train set or test set. frac(float) required 
-            frac (float): value within (0,1] (seeded)random shuffles dataset, then divides into train and test set. 
+            frac (float):     Value within (0,1] (seeded)random shuffles dataset, then divides into train and test set. 
         """
         # they provide a fixed train and validation split
         if train:
@@ -96,7 +96,6 @@ class UnconditionalDataset_CelebAHQ(Dataset):
         self.df = df[df["Filepath"].str.endswith(ext)]
             
         if transform: 
-            # for training
             intermediate_size = 137
             theta = np.pi/4 -np.arccos(intermediate_size/(np.sqrt(2)*img_size)) #Check dataloading.ipynb in analysis-depot for more details
              
@@ -107,7 +106,6 @@ class UnconditionalDataset_CelebAHQ(Dataset):
                                      transforms.RandomHorizontalFlip(p=0.5),
                                      transforms.Normalize(mean=(0.5,0.5,0.5),std=(0.5,0.5,0.5))])
 
-            
             transform_flip =  transforms.Compose([transforms.ToTensor(),
                               transforms.Resize(img_size, antialias=True),
                               transforms.RandomHorizontalFlip(p=0.5),
@@ -115,9 +113,7 @@ class UnconditionalDataset_CelebAHQ(Dataset):
 
             self.transform =  transforms.RandomChoice([transform_rotate_flip,transform_flip])                                                   
         else :
-            # for evaluation 
             self.transform =  transforms.Compose([transforms.ToTensor(),
-                                transforms.Lambda(lambda x: (x * 255).type(torch.uint8)),
                                 transforms.Resize(img_size)])
             
     def __len__(self):