insert original dataloader for training on Landscapes (LHQ256).

dataloader/load.py

@@ -20,69 +20,53 @@ class UnconditionalDataset(Dataset):
                             """
 
         ### Create DataFrame
-        #file_list = []
-        #for root, dirs, files in os.walk(fpath, topdown=False):
-        #    for name in sorted(files):
-        #        file_list.append(os.path.join(root, name))
-        #
-        #df = pd.DataFrame({"Filepath":file_list},)
-        #self.df = df[df["Filepath"].str.endswith(ext)] 
+        file_list = []
+        for root, dirs, files in os.walk(fpath, topdown=False):
+            for name in sorted(files):
+                file_list.append(os.path.join(root, name))
+
+        df = pd.DataFrame({"Filepath":file_list},)
+        self.df = df[df["Filepath"].str.endswith(ext)] 
         
         
             
         if skip_first_n:
             self.df = self.df[skip_first_n:]
         
-        print(fpath) 
         if train: 
-            fpath = os.path.join(fpath, 'train') 
+            df_train = self.df.sample(frac=frac,random_state=2)
+            self.df = df_train
         else:
-            fpath = os.path.join(fpath, 'valid')
-
-        file_list =[]
-        for root, dirs, files in os.walk(fpath, topdown=False):
-            for name in sorted(files):
-                file_list.append(os.path.join(root, name))
-        df = pd.DataFrame({"Filepath":file_list},)
-        self.df = df[df["Filepath"].str.endswith(ext)]
+            df_train = self.df.sample(frac=frac,random_state=2)
+            df_test = df.drop(df_train.index)
+            self.df = df_test
             
         if transform: 
-            # for training
             intermediate_size = 150
             theta = np.pi/4 -np.arccos(intermediate_size/(np.sqrt(2)*img_size)) #Check dataloading.ipynb in analysis-depot for more details
             
-            transform_rotate =  transforms.Compose([transforms.ToTensor(),
+            transform_rotate =  transforms.Compose([transforms.ToTensor(),transforms.Normalize(mean=(0.5,0.5,0.5),std=(0.5,0.5,0.5)),
                                 transforms.Resize(intermediate_size,antialias=True),
                                 transforms.RandomRotation(theta/np.pi*180,interpolation=transforms.InterpolationMode.BILINEAR),
-                                transforms.CenterCrop(img_size),transforms.RandomHorizontalFlip(p=0.5),
-                                transforms.Normalize(mean=(0.5,0.5,0.5),std=(0.5,0.5,0.5))])
+                                transforms.CenterCrop(img_size),transforms.RandomHorizontalFlip(p=0.5)])
             
-            transform_randomcrop  =  transforms.Compose([transforms.ToTensor(),
-                                     transforms.Resize(intermediate_size, antialias=True),
-                                     transforms.RandomCrop(img_size),transforms.RandomHorizontalFlip(p=0.5),
-                                     transforms.Normalize(mean=(0.5,0.5,0.5),std=(0.5,0.5,0.5))])
+            transform_randomcrop  =  transforms.Compose([transforms.ToTensor(),transforms.Normalize(mean=(0.5,0.5,0.5),std=(0.5,0.5,0.5)),
+                                transforms.Resize(intermediate_size),transforms.RandomCrop(img_size),transforms.RandomHorizontalFlip(p=0.5)])
 
             self.transform =  transforms.RandomChoice([transform_rotate,transform_randomcrop])
         else : 
-            # for evaluation 
             self.transform =  transforms.Compose([transforms.ToTensor(),
-                                transforms.Lambda(lambda x: (x * 255).type(torch.uint8)),
                                 transforms.Resize(img_size)])
             
-        if train==False:
-            # for testing
-            self.transform = transforms.Compose([transforms.ToTensor(),
-                                                 transforms.Resize(intermediate_size, antialias=True),
-                                                 transforms.Normalize(mean=(0.5,0.5,0.5),std=(0.5,0.5,0.5))])
- 
     def __len__(self):
         return len(self.df)
     
     def __getitem__(self,idx):
-        path =  self.df.iloc[idx].Filepath
+        path =  self.df.iloc[idx].Filepaths
         img = Image.open(path)
         return self.transform(img),0
         
     def tensor2PIL(self,img):
         back2pil = transforms.Compose([transforms.Normalize(mean=(-1,-1,-1),std=(2,2,2)),transforms.ToPILImage()])
         return back2pil(img)
+        
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