Data Preparation using LightningDataModule

[ParthaPratimBanik]

Have to implement the following things by using torch.utils.data.Dataset:

• def __init__(...): Setup data directory
• def __len__(...): Return the length of sample
• def __getitem__(...): Return the sample (x, y)
  
  Have to Implement the following things by using LightningDataModule, for the inj_cora dataset:
• def __init__(...): setup data directory
• def preapre_data(...): download data in local
• def setup(...): load data from local
• def train_dataloaders(...): setup training data for training
• Must be equal or close to load_data() result for inj_cora dataset
  
  Dataset preparations depend on the following functions and variables:

1. to_dense_adj(...)
2. num_neigh + num_layers
3. NeighborLoader(...)
   
   The above factors are dependent on models. So, have to do the following things:

• Point out models with to_dense_adj(...) and/or num_neigh + num_layers and NeighborLoader(...)

Have to do the following things, based on the above implementations:

• Comparing the output of the following datasets with the load_data() output:
  • inj_amazon
  • inj_flickr
  • weibo
  • reddit
  • disney
  • books
  • enron