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Partially Fixed Issue #4
- updated dataset.py for all models
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:
- [x] Point out models with `to_dense_adj(...)` and/or `num_neigh` + `num_layers` and `NeighborLoader(...)`
Have to implement the following things by usingtorch.utils.data.Dataset
:def __init__(...)
: Setup data directorydef __len__(...)
: Return the length of sampledef __getitem__(...)
: Return the sample (x, y)Have to Implement the following things by using
LightningDataModule
, for theinj_cora
dataset:def __init__(...)
: setup data directorydef preapre_data(...)
: download data in localdef setup(...)
: load data from localdef train_dataloaders(...)
: setup training data for trainingload_data()
result forinj_cora
datasetDataset preparations depend on the following functions and variables:
to_dense_adj(...)
num_neigh
+num_layers
NeighborLoader(...)
The above factors are dependent on models. So, have to do the following things:
to_dense_adj(...)
and/ornum_neigh
+num_layers
andNeighborLoader(...)
Have to do the following things, based on the above implementations:
load_data()
output:inj_amazon
inj_flickr
weibo
reddit
disney
books
enron
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