Hello, I’m a beginner to PyTorch, and I don’t understand why I’m getting the error in the title when I declare images, labels = dataiter.next(). Below is everything relevant
class DogsDataset(Dataset):
    def __init__(self, df_data, data_dir = './',  transform=None):
        super().__init__()
        self.df = df_data.values
        self.data_dir = data_dir
        # self.img_ext = img_ext
        self.transform = transform

    def __len__(self):
        return len(self.df)
    
    def __getitem__(self, index):
        img_name,label = self.df[index]
        img_path = os.path.join(self.data_dir, img_name)
        image = cv2.imread(img_path)
        if self.transform is not None:
            image = self.transform(image)
        return image, label

data_transform = transforms.Compose([
        transforms.ToPILImage(),
        transforms.RandomHorizontalFlip(),
        transforms.Resize(32),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406],
                             std=[0.229, 0.224, 0.225])])

#added
df_labels = pd.read_csv(LABELS_CSV_PATH)
train_data = DogsDataset(df_data=df_labels, data_dir=TRAIN_IMG_PATH, transform=data_transform)

# Set Batch Size
batch_size = 64

# Percentage of training set to use as validation
valid_size = 0.2

# obtain training indices that will be used for validation
num_train = len(train_data)
indices = list(range(num_train))
np.random.shuffle(indices)
split = int(np.floor(valid_size * num_train))
train_idx, valid_idx = indices[split:], indices[:split]

# Create Samplers
train_sampler = SubsetRandomSampler(train_idx)
valid_sampler = SubsetRandomSampler(valid_idx)

# prepare data loaders (combine dataset and sampler)
train_loader = DataLoader(train_data, batch_size=batch_size, sampler=train_sampler)
valid_loader = DataLoader(train_data, batch_size=batch_size, sampler=valid_sampler)

dataiter = iter(train_loader)
images, labels = dataiter.next()

TypeError                                 Traceback (most recent call last)
<ipython-input-10-a3862cdf9037> in <module>
      1 # obtain one batch of training images
      2 dataiter = iter(train_loader)
----> 3 images, labels = dataiter.next()
      4 print(images)

/opt/conda/lib/python3.6/site-packages/torch/utils/data/dataloader.py in __next__(self)
    558         if self.num_workers == 0:  # same-process loading
    559             indices = next(self.sample_iter)  # may raise StopIteration
--> 560             batch = self.collate_fn([self.dataset[i] for i in indices])
    561             if self.pin_memory:
    562                 batch = _utils.pin_memory.pin_memory_batch(batch)

/opt/conda/lib/python3.6/site-packages/torch/utils/data/dataloader.py in <listcomp>(.0)
    558         if self.num_workers == 0:  # same-process loading
    559             indices = next(self.sample_iter)  # may raise StopIteration
--> 560             batch = self.collate_fn([self.dataset[i] for i in indices])
    561             if self.pin_memory:
    562                 batch = _utils.pin_memory.pin_memory_batch(batch)

<ipython-input-6-ee5990aa6797> in __getitem__(self, index)
     16         image = cv2.imread(img_path)
     17         if self.transform is not None:
---> 18             image = self.transform(image)
     19         return image, label

/opt/conda/lib/python3.6/site-packages/torchvision/transforms/transforms.py in __call__(self, img)
     59     def __call__(self, img):
     60         for t in self.transforms:
---> 61             img = t(img)
     62         return img
     63 

/opt/conda/lib/python3.6/site-packages/torchvision/transforms/transforms.py in __call__(self, pic)
    125 
    126         """
--> 127         return F.to_pil_image(pic, self.mode)
    128 
    129     def __repr__(self):

/opt/conda/lib/python3.6/site-packages/torchvision/transforms/functional.py in to_pil_image(pic, mode)
    110     """
    111     if not(isinstance(pic, torch.Tensor) or isinstance(pic, np.ndarray)):
--> 112         raise TypeError('pic should be Tensor or ndarray. Got {}.'.format(type(pic)))
    113 
    114     elif isinstance(pic, torch.Tensor):

TypeError: pic should be Tensor or ndarray. Got <class 'NoneType'>.