[fbsync] Set allow_redefinition = True for mypy (#4531)

Summary:
* Allow redefinition for mypy

* appease mypy

Reviewed By: NicolasHug

Differential Revision: D31505572

fbshipit-source-id: 718742dccd5e4d087ac3f9ec5f9a0b71ba91313c

Co-authored-by: Philip Meier <[email protected]>

Author: 2 people

mypy.ini

@@ -3,6 +3,7 @@
 files = torchvision
 show_error_codes = True
 pretty = True
+allow_redefinition = True
 
 [mypy-torchvision.io._video_opt.*]
 

torchvision/datasets/celeba.py

@@ -1,7 +1,6 @@
 import csv
 import os
 from collections import namedtuple
-from functools import partial
 from typing import Any, Callable, List, Optional, Union, Tuple
 
 import PIL
@@ -115,15 +114,14 @@ def _load_csv(
         filename: str,
         header: Optional[int] = None,
     ) -> CSV:
-        data, indices, headers = [], [], []
-
-        fn = partial(os.path.join, self.root, self.base_folder)
-        with open(fn(filename)) as csv_file:
+        with open(os.path.join(self.root, self.base_folder, filename)) as csv_file:
             data = list(csv.reader(csv_file, delimiter=" ", skipinitialspace=True))
 
         if header is not None:
             headers = data[header]
             data = data[header + 1 :]
+        else:
+            headers = []
 
         indices = [row[0] for row in data]
         data = [row[1:] for row in data]

torchvision/datasets/imagenet.py

@@ -119,7 +119,7 @@ def parse_devkit_archive(root: str, file: Optional[str] = None) -> None:
     """
     import scipy.io as sio
 
-    def parse_meta_mat(devkit_root: str) -> Tuple[Dict[int, str], Dict[str, str]]:
+    def parse_meta_mat(devkit_root: str) -> Tuple[Dict[int, str], Dict[str, Tuple[str, ...]]]:
         metafile = os.path.join(devkit_root, "data", "meta.mat")
         meta = sio.loadmat(metafile, squeeze_me=True)["synsets"]
         nums_children = list(zip(*meta))[4]

torchvision/prototype/datasets/_builtin/caltech.py

@@ -117,11 +117,11 @@ def _make_datapipe(
         images_dp, anns_dp = resource_dps
 
         images_dp = TarArchiveReader(images_dp)
-        images_dp = Filter(images_dp, self._is_not_background_image)
+        images_dp: IterDataPipe = Filter(images_dp, self._is_not_background_image)
         images_dp = Shuffler(images_dp, buffer_size=INFINITE_BUFFER_SIZE)
 
         anns_dp = TarArchiveReader(anns_dp)
-        anns_dp = Filter(anns_dp, self._is_ann)
+        anns_dp: IterDataPipe = Filter(anns_dp, self._is_ann)
 
         dp = KeyZipper(
             images_dp,
@@ -136,7 +136,7 @@ def _make_datapipe(
     def generate_categories_file(self, root: Union[str, pathlib.Path]) -> None:
         dp = self.resources(self.default_config)[0].to_datapipe(pathlib.Path(root) / self.name)
         dp = TarArchiveReader(dp)
-        dp = Filter(dp, self._is_not_background_image)
+        dp: IterDataPipe = Filter(dp, self._is_not_background_image)
         dir_names = {pathlib.Path(path).parent.name for path, _ in dp}
         create_categories_file(HERE, self.name, sorted(dir_names))
 
@@ -185,7 +185,7 @@ def _make_datapipe(
     ) -> IterDataPipe[Dict[str, Any]]:
         dp = resource_dps[0]
         dp = TarArchiveReader(dp)
-        dp = Filter(dp, self._is_not_rogue_file)
+        dp: IterDataPipe = Filter(dp, self._is_not_rogue_file)
         dp = Shuffler(dp, buffer_size=INFINITE_BUFFER_SIZE)
         return Mapper(dp, self._collate_and_decode_sample, fn_kwargs=dict(decoder=decoder))
 

torchvision/prototype/datasets/_folder.py

@@ -49,8 +49,8 @@ def from_data_folder(
     root = pathlib.Path(root).expanduser().resolve()
     categories = sorted(entry.name for entry in os.scandir(root) if entry.is_dir())
     masks: Union[List[str], str] = [f"*.{ext}" for ext in valid_extensions] if valid_extensions is not None else ""
-    dp: IterDataPipe = FileLister(str(root), recursive=recursive, masks=masks)
-    dp = Filter(dp, _is_not_top_level_file, fn_kwargs=dict(root=root))
+    dp = FileLister(str(root), recursive=recursive, masks=masks)
+    dp: IterDataPipe = Filter(dp, _is_not_top_level_file, fn_kwargs=dict(root=root))
     dp = Shuffler(dp, buffer_size=INFINITE_BUFFER_SIZE)
     dp = FileLoader(dp)
     return (