Notebook 03: Getting Error in your own code file on test_dataloader line

[Shuaibmuneer]

Getting Error in your own code in 03_pytorch_computer_vision.ipynb in Section 3.3 Creating a training loop and training a model on batches of data at code:

### Testing
    # Setup variables for accumulatively adding up loss and accuracy 
  '''  test_loss, test_acc = 0, 0 
  model_0.eval()
  with torch.inference_mode():
    for X, y in test_dataloader:  # **### Here on this line Error come for Error please see down.** 
            # 1. Forward pass
      test_pred = model_0(X)
           
            # 2. Calculate loss (accumatively)
      test_loss += loss_fn(test_pred, y) # accumulatively add up the loss per epoch

            # 3. Calculate accuracy (preds need to be same as y_true)
      test_acc += accuracy_fn(y_true=y, y_pred=test_pred.argmax(dim=1))
        
        # Calculations on test metrics need to happen inside torch.inference_mode()
        # Divide total test loss by length of test dataloader (per batch)
      test_loss /= len(test_dataloader)

        # Divide total accuracy by length of test dataloader (per batch)
      test_acc /= len(test_dataloader) 

Error is : -

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
[<ipython-input-68-6d6049aa9139>](https://localhost:8080/#) in <module>
     45 
     46   with torch.inference_mode():
---> 47     for x_test,y_test in test_dataloader:
     48       # 1. Forward pass
     49      test_pred = model_0(x_test)

4 frames
[/usr/local/lib/python3.8/dist-packages/torchvision/datasets/mnist.py](https://localhost:8080/#) in __getitem__(self, index)
    143 
    144         if self.transform is not None:
--> 145             img = self.transform(img)
    146 
    147         if self.target_transform is not None:

TypeError: __init__() takes 1 positional argument but 2 were given

[mrdbourke]

Hi @Shuaibmuneer,

I just ran notebook 03 and went through that section and didn't get any errors.

See the notebook here: https://github.com/mrdbourke/pytorch-deep-learning/blob/main/03_pytorch_computer_vision.ipynb

I'd check your network creation in section 3 and make sure your forward() method is implemented correctly.

As well your test_dataloader in this code:

from torch.utils.data import DataLoader

# Setup the batch size hyperparameter
BATCH_SIZE = 32

# Turn datasets into iterables (batches)
train_dataloader = DataLoader(train_data, # dataset to turn into iterable
    batch_size=BATCH_SIZE, # how many samples per batch? 
    shuffle=True # shuffle data every epoch?
)

test_dataloader = DataLoader(test_data,
    batch_size=BATCH_SIZE,
    shuffle=False # don't necessarily have to shuffle the testing data
)

# Let's check out what we've created
print(f"Dataloaders: {train_dataloader, test_dataloader}") 
print(f"Length of train dataloader: {len(train_dataloader)} batches of {BATCH_SIZE}")
print(f"Length of test dataloader: {len(test_dataloader)} batches of {BATCH_SIZE}")