Inherit dataset class from task class.

(Optional) Define class attribute data_path, if not defined, default to data root + name of dataset class.

Define list_data:

• Input:

  • self.path - the dataset path

• Output:

  • return X1, X2, ..., Xn, Y1, Y2, ..., Ym, lists of inputs and outputs

    Typically return X, Y

• Inout:

  • self.dict - universal recording (across experiments)

    Typically used for mapping between class name and class index

• Effects:

  List all the data in self.path. Do any preprocessing on raw data. This function will be called in __init__ iff do split. The returned data type should be reasonable to store in csv files.

  Typically X, Y are paths to the actual data

Define read_data:

• Input:

  • x1, x2, ..., xn, y1, y2, ..., ym - a tuple of input and output corresponding to the output of list_data.

• Output:

  • x1, x2, ..., xn, y1, y2, ..., ym - data to pass to model and loss function

• Effects:

  Defines how to read actual data from listed data. Do any online preprocessing. This function will be called in __getitem__. The returned data type should be torch.Tensor.

(Optional) Define n_inputs if it is not 1.

n_inputs and n_outputs (inferred) must be consistent between data and model

Define any parameter to model as a class attribute.

Define model

• __init__(self, params from data, **kwargs)
• forward(self, x1, x2, ..., xn)

(Optional) Define loss function