Train Pytorch Autoencoder with custom dataset

Question

I am new to Pytorch. I was able to build an autoencoder model and train it using the MINST dataset.

However, I need to train the model using a custom dataset.
I am getting the error 'ToTensor' object is not iterable when i try to train with the custom dataset.

Below is a code of my dataset class

class AutoEncoderDataSet(Dataset):
    def __init__(self, in_dir, transform):
        self._transforms = transform
        self.img_paths = []

        files = os.listdir(in_dir)
        for file in files:
            self.img_paths.append(os.path.join(in_dir, file))


    def __getitem__(self, index):
        img, img_trans = Image.open(self.img_paths[index]),  Image.open(self.img_paths[index])
        x, y = transform(img), transform(img_trans)

        return x, y

    def __len__(self):
        return len(self.img_paths)

Here is how I am generating the dataloader

transform = transforms.Compose([torchvision.transforms.ToTensor()])
train_dataset = AutoEncoderDataSet('./datasets/train/', transform)

batch_size = 512
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True

When I try to train using the data generated with the custom dataset class, I am getting the error mentioned above.

Below is code for training the model

epochs = 2048

for epoch in range(epochs):
    loss = 0
    for batch_features, _ in train_loader:
        # reshape mini-batch data to [N, 784] matrix
        # load it to the active device
        batch_features = batch_features.view(-1, 250*250).to(device)
        
        # reset the gradients back to zero
        # PyTorch accumulates gradients on subsequent backward passes
        optimizer.zero_grad()
        
        # compute ecoder output
        outputs = model(batch_features)
        
        # compute training reconstruction loss
        train_loss = criterion(outputs, batch_features)
        
        # compute accumulated gradients
        train_loss.backward()
        
        # perform parameter update based on current gradients
        optimizer.step()
        
        # add the mini-batch training loss to epoch loss
        loss += train_loss.item()
    
    # compute the epoch training loss
    loss = loss / len(train_loader)
    
    # display the epoch training loss
    print("epoch : {}/{}, recon loss = {:.8f}".format(epoch + 1, epochs, loss))

And this is the error I am getting

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_11164/1462449221.py in <module>
      3 for epoch in range(epochs):
      4     loss = 0
----> 5     for batch_features, _ in test_loader:
      6         # reshape mini-batch data to [N, 784] matrix
      7         # load it to the active device

~\AppData\Local\Programs\Python\Python38\lib\site-packages\torch\utils\data\dataloader.py in __next__(self)
    519             if self._sampler_iter is None:
    520                 self._reset()
--> 521             data = self._next_data()
    522             self._num_yielded += 1
    523             if self._dataset_kind == _DatasetKind.Iterable and \

~\AppData\Local\Programs\Python\Python38\lib\site-packages\torch\utils\data\dataloader.py in _next_data(self)
    559     def _next_data(self):
    560         index = self._next_index()  # may raise StopIteration
--> 561         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration
    562         if self._pin_memory:
    563             data = _utils.pin_memory.pin_memory(data)

~\AppData\Local\Programs\Python\Python38\lib\site-packages\torch\utils\data\_utils\fetch.py in fetch(self, possibly_batched_index)
     47     def fetch(self, possibly_batched_index):
     48         if self.auto_collation:
---> 49             data = [self.dataset[idx] for idx in possibly_batched_index]
     50         else:
     51             data = self.dataset[possibly_batched_index]

~\AppData\Local\Programs\Python\Python38\lib\site-packages\torch\utils\data\_utils\fetch.py in <listcomp>(.0)
     47     def fetch(self, possibly_batched_index):
     48         if self.auto_collation:
---> 49             data = [self.dataset[idx] for idx in possibly_batched_index]
     50         else:
     51             data = self.dataset[possibly_batched_index]

~\AppData\Local\Programs\Python\Python38\lib\site-packages\torchvision\datasets\folder.py in __getitem__(self, index)
    232         sample = self.loader(path)
    233         if self.transform is not None:
--> 234             sample = self.transform(sample)
    235         if self.target_transform is not None:
    236             target = self.target_transform(target)

~\AppData\Local\Programs\Python\Python38\lib\site-packages\torchvision\transforms\transforms.py in __call__(self, img)
     58 
     59     def __call__(self, img):
---> 60         for t in self.transforms:
     61             img = t(img)
     62         return img

TypeError: 'ToTensor' object is not iterable

Any suggestions would be greatly appreciated.