TypeError: 'NoneType' object is not callable

[talhaanwarch]

Here the augmentation function.

def rand_augmentation():
    aug=transforms.Compose([
        transforms.RandomResizedCrop(248, scale=(0.08, 1.0), interpolation=Image.BICUBIC),
        transforms.RandomHorizontalFlip(1),
        transforms.RandomVerticalFlip(1),
        transforms.RandomRotation(degrees=30),
        transforms.ColorJitter(brightness=0.4,contrast=0.4,saturation=0.4),
        transforms.RandomPerspective(distortion_scale=0.1), 
        transforms.RandomAffine(degrees=10),
        transforms.ToTensor(),
        transforms.RandomErasing(p=0.5), 
        transforms.Normalize((0.5, ), (0.5, )),
                          ])
    return aug.transforms.insert(0, RandAugment(4, 3))

Here is data loader

def load_data(df,batchsize=8):
     data =SiameseNetworkDataset(df,image_D='2D',transform=(0,rand_augmentation()))
    loader = DataLoader(data,shuffle=True,num_workers=0,batch_size=batchsize)
    return loader

here is data loader

def __getitem__(self,index):
  
        if self.transform[0]==2:
            img0 = self.transform[1](image=np.array(img0))['image']   
            img1 = self.transform[1](image=np.array(img1))['image']  
        else:
            img0=self.transform[1](img0) 
            img1=self.transform[1](img1) 

        return img0, img1 ,label

If I return aug only instead of aug.transforms.insert(0, RandAugment(4, 3)), there is no error.
Error

TypeError                                 Traceback (most recent call last)
<timed exec> in <module>

D:\Datasets\Image dataset\Xray\SIAMESE-classifier\src\cross_vals.py in kfoldcv(model, data, epochs, n_splits, lr, batchsize, skip_tuning, aug)
     70 
     71         #train on all train images
---> 72         model=train_dl(train_loader,epochs,model,"cuda",criterion,opt)
     73         train_features,train_labels=get_features(train,model)
     74          #now get embeddings of test data

D:\Datasets\Image dataset\Xray\SIAMESE-classifier\src\dl_training.py in train_dl(loader, epochs, model, device, criterion, opt)
    120     model=model.to(device)
    121     for _epoch in range(epochs):
--> 122         for batch in loader:
    123             img1,img2,label=batch
    124             img1_emb,img2_emb=model(img1.to(device)),model(img2.to(device))

C:\Anaconda3\lib\site-packages\torch\utils\data\dataloader.py in __next__(self)
    433         if self._sampler_iter is None:
    434             self._reset()
--> 435         data = self._next_data()
    436         self._num_yielded += 1
    437         if self._dataset_kind == _DatasetKind.Iterable and \

C:\Anaconda3\lib\site-packages\torch\utils\data\dataloader.py in _next_data(self)
    473     def _next_data(self):
    474         index = self._next_index()  # may raise StopIteration
--> 475         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration
    476         if self._pin_memory:
    477             data = _utils.pin_memory.pin_memory(data)

C:\Anaconda3\lib\site-packages\torch\utils\data\_utils\fetch.py in fetch(self, possibly_batched_index)
     42     def fetch(self, possibly_batched_index):
     43         if self.auto_collation:
---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]
     45         else:
     46             data = self.dataset[possibly_batched_index]

C:\Anaconda3\lib\site-packages\torch\utils\data\_utils\fetch.py in <listcomp>(.0)
     42     def fetch(self, possibly_batched_index):
     43         if self.auto_collation:
---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]
     45         else:
     46             data = self.dataset[possibly_batched_index]

D:\Datasets\Image dataset\Xray\SIAMESE-classifier\src\dataloader.py in __getitem__(self, index)
     49             img1 = self.transform[1](image=np.array(img1))['image']
     50         else:
---> 51             img0=self.transform[1](img0)
     52             img1=self.transform[1](img1)
     53 

TypeError: 'NoneType' object is not callable