[Type Hints] datasets.ModelNet (#5701)

Added TypeHints

Co-authored-by: vsitaraman <vilayannur.sitaraman@hitachivantara.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Matthias Fey <matthias.fey@tu-dortmund.de>

CHANGELOG.md

@@ -43,7 +43,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Support `in_channels` with `tuple` in `GENConv` for bipartite message passing ([#5627](https://github.com/pyg-team/pytorch_geometric/pull/5627), [#5641](https://github.com/pyg-team/pytorch_geometric/pull/5641))
 - Handle cases of not having enough possible negative edges in `RandomLinkSplit` ([#5642](https://github.com/pyg-team/pytorch_geometric/pull/5642))
 - Fix `RGCN+pyg-lib` for `LongTensor` input ([#5610](https://github.com/pyg-team/pytorch_geometric/pull/5610))
-- Improved type hint support ([#5603](https://github.com/pyg-team/pytorch_geometric/pull/5603), [#5659](https://github.com/pyg-team/pytorch_geometric/pull/5659), [#5664](https://github.com/pyg-team/pytorch_geometric/pull/5664), [#5665](https://github.com/pyg-team/pytorch_geometric/pull/5665), [#5666](https://github.com/pyg-team/pytorch_geometric/pull/5666), [#5667](https://github.com/pyg-team/pytorch_geometric/pull/5667), [#5668](https://github.com/pyg-team/pytorch_geometric/pull/5668), [#5669](https://github.com/pyg-team/pytorch_geometric/pull/5669), [#5673](https://github.com/pyg-team/pytorch_geometric/pull/5673), [#5675](https://github.com/pyg-team/pytorch_geometric/pull/5675), [#5673](https://github.com/pyg-team/pytorch_geometric/pull/5676), [#5678](https://github.com/pyg-team/pytorch_geometric/pull/5678), [#5682](https://github.com/pyg-team/pytorch_geometric/pull/5682), [#5683](https://github.com/pyg-team/pytorch_geometric/pull/5683), [#5684](https://github.com/pyg-team/pytorch_geometric/pull/5684), [#5685](https://github.com/pyg-team/pytorch_geometric/pull/5685), [#5687](https://github.com/pyg-team/pytorch_geometric/pull/5687), [#5688](https://github.com/pyg-team/pytorch_geometric/pull/5688), [#5695](https://github.com/pyg-team/pytorch_geometric/pull/5695), [#5699](https://github.com/pyg-team/pytorch_geometric/pull/5699), [#5706](https://github.com/pyg-team/pytorch_geometric/pull/5706), [#5702](https://github.com/pyg-team/pytorch_geometric/pull/5702))
+- Improved type hint support ([#5603](https://github.com/pyg-team/pytorch_geometric/pull/5603), [#5659](https://github.com/pyg-team/pytorch_geometric/pull/5659), [#5664](https://github.com/pyg-team/pytorch_geometric/pull/5664), [#5665](https://github.com/pyg-team/pytorch_geometric/pull/5665), [#5666](https://github.com/pyg-team/pytorch_geometric/pull/5666), [#5667](https://github.com/pyg-team/pytorch_geometric/pull/5667), [#5668](https://github.com/pyg-team/pytorch_geometric/pull/5668), [#5669](https://github.com/pyg-team/pytorch_geometric/pull/5669), [#5673](https://github.com/pyg-team/pytorch_geometric/pull/5673), [#5675](https://github.com/pyg-team/pytorch_geometric/pull/5675), [#5673](https://github.com/pyg-team/pytorch_geometric/pull/5676), [#5678](https://github.com/pyg-team/pytorch_geometric/pull/5678), [#5682](https://github.com/pyg-team/pytorch_geometric/pull/5682), [#5683](https://github.com/pyg-team/pytorch_geometric/pull/5683), [#5684](https://github.com/pyg-team/pytorch_geometric/pull/5684), [#5685](https://github.com/pyg-team/pytorch_geometric/pull/5685), [#5687](https://github.com/pyg-team/pytorch_geometric/pull/5687), [#5688](https://github.com/pyg-team/pytorch_geometric/pull/5688), [#5695](https://github.com/pyg-team/pytorch_geometric/pull/5695), [#5699](https://github.com/pyg-team/pytorch_geometric/pull/5699), [#5701] (https://github.com/pyg-team/pytorch_geometric/pull/5701), [#5706](https://github.com/pyg-team/pytorch_geometric/pull/5706), [#5702](https://github.com/pyg-team/pytorch_geometric/pull/5702))
 - Avoid modifying `mode_kwargs` in `MultiAggregation` ([#5601](https://github.com/pyg-team/pytorch_geometric/pull/5601))
 - Changed `BatchNorm` to allow for batches of size one during training ([#5530](https://github.com/pyg-team/pytorch_geometric/pull/5530), [#5614](https://github.com/pyg-team/pytorch_geometric/pull/5614))
 - Integrated better temporal sampling support by requiring that local neighborhoods are sorted according to time ([#5516](https://github.com/pyg-team/pytorch_geometric/issues/5516), [#5602](https://github.com/pyg-team/pytorch_geometric/issues/5602))

torch_geometric/datasets/modelnet.py

@@ -2,10 +2,17 @@
 import os
 import os.path as osp
 import shutil
+from typing import Callable, Dict, List, Optional, Tuple
 
 import torch
-
-from torch_geometric.data import InMemoryDataset, download_url, extract_zip
+from torch import Tensor
+
+from torch_geometric.data import (
+    Data,
+    InMemoryDataset,
+    download_url,
+    extract_zip,
+)
 from torch_geometric.io import read_off
 
 
@@ -51,23 +58,30 @@ class ModelNet(InMemoryDataset):
         '40': 'http://modelnet.cs.princeton.edu/ModelNet40.zip'
     }
 
-    def __init__(self, root, name='10', train=True, transform=None,
-                 pre_transform=None, pre_filter=None):
+    def __init__(
+        self,
+        root: str,
+        name: str = '10',
+        train: bool = True,
+        transform: Optional[Callable] = None,
+        pre_transform: Optional[Callable] = None,
+        pre_filter: Optional[Callable] = None,
+    ):
         assert name in ['10', '40']
         self.name = name
         super().__init__(root, transform, pre_transform, pre_filter)
         path = self.processed_paths[0] if train else self.processed_paths[1]
         self.data, self.slices = torch.load(path)
 
     @property
-    def raw_file_names(self):
+    def raw_file_names(self) -> List[str]:
         return [
             'bathtub', 'bed', 'chair', 'desk', 'dresser', 'monitor',
             'night_stand', 'sofa', 'table', 'toilet'
         ]
 
     @property
-    def processed_file_names(self):
+    def processed_file_names(self) -> List[str]:
         return ['training.pt', 'test.pt']
 
     def download(self):
@@ -87,7 +101,7 @@ def process(self):
         torch.save(self.process_set('train'), self.processed_paths[0])
         torch.save(self.process_set('test'), self.processed_paths[1])
 
-    def process_set(self, dataset):
+    def process_set(self, dataset: str) -> Tuple[Data, Dict[str, Tensor]]:
         categories = glob.glob(osp.join(self.raw_dir, '*', ''))
         categories = sorted([x.split(os.sep)[-2] for x in categories])