AttributeError: 'DataModuleClass' object has no attribute 'training_dataset'

[Opss-0]

I am trying to learn PyTorch Lightning by writing a very simple DataModuleClass. After prepare_data(), and setup() I am trying to check these functions are working or not. So, I am trying to get the training and validation dataset from setup(). But I am getting an error

AttributeError: 'DataModuleClass' object has no attribute 'training_dataset'

Code

def prepare_data(self):
    x = np.random.uniform(0, 10, 10)
    e = np.random.normal(0, self.sigma, len(x))
    
    # Making target or labels
    y = x + e
    
    # Marging x and e for 2 features
    X = np.transpose(np.array([x, e]))

    # Converting numpy array to Tensor
    self.x_train_tensor = torch.from_numpy(X).float().to(device)
    self.y_train_tensor = torch.from_numpy(y).float().to(device)
    
    training_dataset = TensorDataset(self.x_train_tensor, self.y_train_tensor)

    self.training_dataset = training_dataset

def setup(self):
    data = self.training_dataset
    self.train_data, self.val_data = random_split(data, [8, 2])
    return self.train_data, self.val_data
    
    
def train_dataloader(self):
    return DataLoader(self.train_data)

def val_dataloader(self):
    return DataLoader(self.val_data)
    
obj = DataModuleClass()
print(obj.setup())  

Could you tell me why I am getting this error?

[kyleong]

I think you need to call the prepare_data method explicitly in order to work.

obj = DataModuleClass()
obj.prepare_data()
obj.setup()

or you call it inside the def setup(self) method.

def setup(self):
    self.prepare_data()
    data = self.training_dataset
    self.train_data, self.val_data = random_split(data, [8, 2])
    return self.train_data, self.val_data

LightningDataModule has a property has_prepared_data which letting you know if datamodule.prepare_data() has been called or not. Maybe you can make use of that.

To know more, you can look into the source code