I am working on training a CNN with transfer learning on resnet50. My script was working until I made some modifications. I manually split my dataset into train and validation folders. And I setup weights on the training dataset since it was unbalanced. I am receiving an error when I run the training. Here is my code and the error:
train_data_dir = '/home/ec2-user/SageMaker/data/frame_150/train'
val_data_dir = '/home/ec2-user/SageMaker/data/frame_150/val'
batch_size = 100
data_transforms = {
    'train': transforms.Compose([transforms.ToTensor(),
                                 transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])]),
    'val': transforms.Compose([transforms.ToTensor(),
                                 transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])  
}
image_datasets = {'train': datasets.ImageFolder(train_data_dir, data_transforms['train']),
                  'val': datasets.ImageFolder(val_data_dir, data_transforms['val'])
               }
def make_weights_for_balanced_classes(images, nclasses):                        
    count = [0] * nclasses                                                      
    for item in images:                                                         
        count[item[1]] += 1                                                     
    weight_per_class = [0.] * nclasses                                      
    N = float(sum(count))                                                   
    for i in range(nclasses):                                                   
        weight_per_class[i] = N/float(count[i])                                 
    weight = [0] * len(images)                                              
    for idx, val in enumerate(images):                                          
        weight[idx] = weight_per_class[val[1]]                                  
    return weight  

weights = make_weights_for_balanced_classes(image_datasets['train'].imgs, len(image_datasets['train'].classes))                                                                
weights = torch.DoubleTensor(weights)                                       
sampler = torch.utils.data.sampler.WeightedRandomSampler(weights, len(weights)) 

dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=batch_size, sampler=sampler, num_workers=4) for x in ['train', 'val']}

dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
class_names = image_datasets['train'].classes
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
    since = time.time()

    best_model_wts = copy.deepcopy(model.state_dict())
    best_acc = 0.0

    for epoch in range(num_epochs):
        print('Epoch {}/{}'.format(epoch, num_epochs - 1))
        print('-' * 10)

        # Each epoch has a training and validation phase
        for phase in ['train', 'val']:
            if phase == 'train':
                model.train()  # Set model to training mode
            else:
                model.eval()   # Set model to evaluate mode

            running_loss = 0.0
            running_corrects = 0

            # Iterate over data.
            for inputs, labels in dataloaders[phase]:
                inputs = inputs.to(device)
                labels = labels.to(device)

                # zero the parameter gradients
                optimizer.zero_grad()

                # forward
                # track history if only in train
                with torch.set_grad_enabled(phase == 'train'):
                    outputs = model(inputs)
                    _, preds = torch.max(outputs, 1)                    
                    loss = criterion(outputs, labels)

                    # backward + optimize only if in training phase
                    if phase == 'train':
                        loss.backward()
                        optimizer.step()

                # statistics
                running_loss += loss.item() * inputs.size(0)
                running_corrects += torch.sum(preds == labels.data)
            if phase == 'train':
                scheduler.step()

            epoch_loss = running_loss / dataset_sizes[phase]
            epoch_acc = running_corrects.double() / dataset_sizes[phase]

            print('{} Loss: {:.4f} Acc: {:.4f}'.format(
                phase, epoch_loss, epoch_acc))

            # deep copy the model
            if phase == 'val' and epoch_acc > best_acc:
                best_acc = epoch_acc
                best_model_wts = copy.deepcopy(model.state_dict())

        print()

    time_elapsed = time.time() - since
    print('Training complete in {:.0f}m {:.0f}s'.format(
        time_elapsed // 60, time_elapsed % 60))
    print('Best val Acc: {:4f}'.format(best_acc))

    # load best model weights
    model.load_state_dict(best_model_wts)
    return model

model_ft = models.resnet50(pretrained=True)
num_ftrs = model_ft.fc.in_features
# Here the size of each output sample is set to 2.
# Alternatively, it can be generalized to nn.Linear(num_ftrs, len(class_names)).
model_ft.fc = nn.Linear(num_ftrs, 2)
model_ft = model_ft.to(device)
criterion = nn.CrossEntropyLoss()
# Observe that all parameters are being optimized
optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)
# Decay LR by a factor of 0.1 every 7 epochs
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)

model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler, num_epochs=6)

ERROR:
---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-29-c677c5f760af> in <module>()
      1 model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,
----> 2                        num_epochs=6)

<ipython-input-26-6f4f613da14c> in train_model(model, criterion, optimizer, scheduler, num_epochs)
     20 
     21             # Iterate over data.
---> 22             for inputs, labels in dataloaders[phase]:
     23                 inputs = inputs.to(device)
     24                 labels = labels.to(device)

~/.local/lib/python3.6/site-packages/torch/utils/data/dataloader.py in __next__(self)
    343 
    344     def __next__(self):
--> 345         data = self._next_data()
    346         self._num_yielded += 1
    347         if self._dataset_kind == _DatasetKind.Iterable and \

~/.local/lib/python3.6/site-packages/torch/utils/data/dataloader.py in _next_data(self)
    854             else:
    855                 del self._task_info[idx]
--> 856                 return self._process_data(data)
    857 
    858     def _try_put_index(self):

~/.local/lib/python3.6/site-packages/torch/utils/data/dataloader.py in _process_data(self, data)
    879         self._try_put_index()
    880         if isinstance(data, ExceptionWrapper):
--> 881             data.reraise()
    882         return data
    883 

~/.local/lib/python3.6/site-packages/torch/_utils.py in reraise(self)
    392             # (https://bugs.python.org/issue2651), so we work around it.
    393             msg = KeyErrorMessage(msg)
--> 394         raise self.exc_type(msg)

IndexError: Caught IndexError in DataLoader worker process 0.
Original Traceback (most recent call last):
  File "/home/ec2-user/.local/lib/python3.6/site-packages/torch/utils/data/_utils/worker.py", line 178, in _worker_loop
    data = fetcher.fetch(index)
  File "/home/ec2-user/.local/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/home/ec2-user/.local/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py", line 44, in <listcomp>
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/home/ec2-user/.local/lib/python3.6/site-packages/torchvision/datasets/folder.py", line 137, in __getitem__
    path, target = self.samples[index]
IndexError: list index out of range