AttributeError: 'Datamodule' object has no attribute '_log_hyperparams'

DataModule
1

Hello,

Does anyone knows this error? I’ve been checking DataModule documentation but so far, this parameter doesn’t seem to be describe anyware

Here the error

image
image1069×545 35.8 KB

This is my code:

class MRIDatamodule(pl.LightningDataModule):
  def __init__(self,Paths,im_size=256,batch_size=75,factor=1):
    self.batch_size=batch_size
    self.transform=ImageTransform(img_size=img_size)
    self.factor=factor
    self.TrainDir,self.ValtDir,self.TestDir=self.preparedata(Paths)
    #self._log_hyperparams =None
    self.prepare_data_per_node=True

  def preparedata(self,Paths,balance=True):
    DataObject=getDir(Paths)
    df=DataObject.df
    
if balance:
      drop_indices = np.random.choice(df[df["Group"]=="D3"].index, DataObject.D3-(DataObject.D1+DataObject.D2), replace=False)
      df_subset = df.drop(drop_indices).reset_index(drop=True)
      print("subset is {}".format(df_subset.shape))
    else:
      df_subset=df.copy()

    TrainDir,tmp=train_test_split(df_subset, test_size=0.20,shuffle=True)
    TestDir,ValtDir=train_test_split(tmp, test_size=0.50,shuffle=True)
    
    TrainDir=TrainDir.reset_index(drop=True)
    ValtDir=ValtDir.reset_index(drop=True)
    TestDir=TestDir.reset_index(drop=True)
    print("Train is {}".format(TrainDir.shape))
    return TrainDir,ValtDir,TestDir 

  def prepare_data(self):
    """
    Empty prepare_data method left in intentionally. 
    https://pytorch-lightning.readthedocs.io/en/latest/data/datamodule.html#prepare-data
    """
    pass             


  def setup(self, stage=None):
    if stage == "fit" or stage is None:
      self.Train_dataset = MRIDataset(self.TrainDir,self.transform,factor=self.factor)
      self.Val_dataset = MRIDataset(self.ValtDir, self.transform,factor=self.factor)
    
    if stage=="test":
      self.Test_dataset = MRIDataset(self.TestDir, self.transform, phase="test")
  
  def train_dataloader(self):
    return DataLoader(self.Train_dataset,shuffle=True,batch_size=self.batch_size)
    
  def val_dataloader(self):
    return DataLoader(self.Val_dataset,batch_size=self.batch_size)
    
  def test_dataloader(self):
    return DataLoader(self.Test_dataset,batch_size=self.batch_size)
2

Is it because you forgot to call
super().__init__()
in your DataModule?

3

I think that was the problem. Thanks so much!

1 Like