remove fn_kwargs from Filter and Mapper datapipes

torchvision/prototype/datasets/_builtin/caltech.py

@@ -1,3 +1,4 @@
+import functools
 import io
 import pathlib
 import re
@@ -132,7 +133,7 @@ def _make_datapipe(
             buffer_size=INFINITE_BUFFER_SIZE,
             keep_key=True,
         )
-        return Mapper(dp, self._collate_and_decode_sample, fn_kwargs=dict(decoder=decoder))
+        return Mapper(dp, functools.partial(self._collate_and_decode_sample, decoder=decoder))
 
     def _generate_categories(self, root: pathlib.Path) -> List[str]:
         dp = self.resources(self.default_config)[0].load(pathlib.Path(root) / self.name)
@@ -185,7 +186,7 @@ def _make_datapipe(
         dp = Filter(dp, self._is_not_rogue_file)
         dp = hint_sharding(dp)
         dp = hint_shuffling(dp)
-        return Mapper(dp, self._collate_and_decode_sample, fn_kwargs=dict(decoder=decoder))
+        return Mapper(dp, functools.partial(self._collate_and_decode_sample, decoder=decoder))
 
     def _generate_categories(self, root: pathlib.Path) -> List[str]:
         dp = self.resources(self.default_config)[0].load(pathlib.Path(root) / self.name)

torchvision/prototype/datasets/_builtin/celeba.py

@@ -1,4 +1,5 @@
 import csv
+import functools
 import io
 from typing import Any, Callable, Dict, List, Optional, Tuple, Iterator, Sequence
 
@@ -26,7 +27,6 @@
     hint_shuffling,
 )
 
-
 csv.register_dialect("celeba", delimiter=" ", skipinitialspace=True)
 
 
@@ -155,7 +155,7 @@ def _make_datapipe(
         splits_dp, images_dp, identities_dp, attributes_dp, bboxes_dp, landmarks_dp = resource_dps
 
         splits_dp = CelebACSVParser(splits_dp, fieldnames=("image_id", "split_id"))
-        splits_dp = Filter(splits_dp, self._filter_split, fn_kwargs=dict(split=config.split))
+        splits_dp = Filter(splits_dp, functools.partial(self._filter_split, split=config.split))
         splits_dp = hint_sharding(splits_dp)
         splits_dp = hint_shuffling(splits_dp)
 
@@ -181,4 +181,4 @@ def _make_datapipe(
             keep_key=True,
         )
         dp = IterKeyZipper(dp, anns_dp, key_fn=getitem(0), buffer_size=INFINITE_BUFFER_SIZE)
-        return Mapper(dp, self._collate_and_decode_sample, fn_kwargs=dict(decoder=decoder))
+        return Mapper(dp, functools.partial(self._collate_and_decode_sample, decoder=decoder))

torchvision/prototype/datasets/_builtin/cifar.py

@@ -89,7 +89,7 @@ def _make_datapipe(
         dp = CifarFileReader(dp, labels_key=self._LABELS_KEY)
         dp = hint_sharding(dp)
         dp = hint_shuffling(dp)
-        return Mapper(dp, self._collate_and_decode, fn_kwargs=dict(decoder=decoder))
+        return Mapper(dp, functools.partial(self._collate_and_decode, decoder=decoder))
 
     def _generate_categories(self, root: pathlib.Path) -> List[str]:
         dp = self.resources(self.default_config)[0].load(pathlib.Path(root) / self.name)

torchvision/prototype/datasets/_builtin/coco.py

@@ -1,3 +1,4 @@
+import functools
 import io
 import pathlib
 import re
@@ -183,12 +184,16 @@ def _make_datapipe(
         if config.annotations is None:
             dp = hint_sharding(images_dp)
             dp = hint_shuffling(dp)
-            return Mapper(dp, self._collate_and_decode_image, fn_kwargs=dict(decoder=decoder))
+            return Mapper(dp, functools.partial(self._collate_and_decode_image, decoder=decoder))
 
         meta_dp = Filter(
             meta_dp,
-            self._filter_meta_files,
-            fn_kwargs=dict(split=config.split, year=config.year, annotations=config.annotations),
+            functools.partial(
+                self._filter_meta_files,
+                split=config.split,
+                year=config.year,
+                annotations=config.annotations,
+            ),
         )
         meta_dp = JsonParser(meta_dp)
         meta_dp = Mapper(meta_dp, getitem(1))
@@ -226,7 +231,7 @@ def _make_datapipe(
             buffer_size=INFINITE_BUFFER_SIZE,
         )
         return Mapper(
-            dp, self._collate_and_decode_sample, fn_kwargs=dict(annotations=config.annotations, decoder=decoder)
+            dp, functools.partial(self._collate_and_decode_sample, annotations=config.annotations, decoder=decoder)
         )
 
     def _generate_categories(self, root: pathlib.Path) -> Tuple[Tuple[str, str]]:
@@ -235,7 +240,8 @@ def _generate_categories(self, root: pathlib.Path) -> Tuple[Tuple[str, str]]:
 
         dp = resources[1].load(pathlib.Path(root) / self.name)
         dp = Filter(
-            dp, self._filter_meta_files, fn_kwargs=dict(split=config.split, year=config.year, annotations="instances")
+            dp,
+            functools.partial(self._filter_meta_files, split=config.split, year=config.year, annotations="instances"),
         )
         dp = JsonParser(dp)
 

torchvision/prototype/datasets/_builtin/imagenet.py

@@ -1,3 +1,4 @@
+import functools
 import io
 import pathlib
 import re
@@ -165,7 +166,7 @@ def _make_datapipe(
             dp = hint_shuffling(dp)
             dp = Mapper(dp, self._collate_test_data)
 
-        return Mapper(dp, self._collate_and_decode_sample, fn_kwargs=dict(decoder=decoder))
+        return Mapper(dp, functools.partial(self._collate_and_decode_sample, decoder=decoder))
 
     # Although the WordNet IDs (wnids) are unique, the corresponding categories are not. For example, both n02012849
     # and n03126707 are labeled 'crane' while the first means the bird and the latter means the construction equipment

torchvision/prototype/datasets/_builtin/mnist.py

@@ -136,7 +136,7 @@ def _make_datapipe(
         dp = Zipper(images_dp, labels_dp)
         dp = hint_sharding(dp)
         dp = hint_shuffling(dp)
-        return Mapper(dp, self._collate_and_decode, fn_kwargs=dict(config=config, decoder=decoder))
+        return Mapper(dp, functools.partial(self._collate_and_decode, config=config, decoder=decoder))
 
 
 class MNIST(_MNISTBase):

torchvision/prototype/datasets/_builtin/sbd.py

@@ -1,3 +1,4 @@
+import functools
 import io
 import pathlib
 import re
@@ -152,7 +153,7 @@ def _make_datapipe(
                 ref_key_fn=path_accessor("stem"),
                 buffer_size=INFINITE_BUFFER_SIZE,
             )
-        return Mapper(dp, self._collate_and_decode_sample, fn_kwargs=dict(config=config, decoder=decoder))
+        return Mapper(dp, functools.partial(self._collate_and_decode_sample, config=config, decoder=decoder))
 
     def _generate_categories(self, root: pathlib.Path) -> Tuple[str, ...]:
         dp = self.resources(self.default_config)[0].load(pathlib.Path(root) / self.name)

torchvision/prototype/datasets/_builtin/semeion.py

@@ -1,3 +1,4 @@
+import functools
 import io
 from typing import Any, Callable, Dict, List, Optional, Tuple
 
@@ -65,5 +66,5 @@ def _make_datapipe(
         dp = CSVParser(dp, delimiter=" ")
         dp = hint_sharding(dp)
         dp = hint_shuffling(dp)
-        dp = Mapper(dp, self._collate_and_decode_sample, fn_kwargs=dict(decoder=decoder))
+        dp = Mapper(dp, functools.partial(self._collate_and_decode_sample, decoder=decoder))
         return dp

torchvision/prototype/datasets/_builtin/voc.py

@@ -127,7 +127,7 @@ def _make_datapipe(
             buffer_size=INFINITE_BUFFER_SIZE,
         )
 
-        split_dp = Filter(split_dp, self._is_in_folder, fn_kwargs=dict(name=self._SPLIT_FOLDER[config.task]))
+        split_dp = Filter(split_dp, functools.partial(self._is_in_folder, name=self._SPLIT_FOLDER[config.task]))
         split_dp = Filter(split_dp, path_comparator("name", f"{config.split}.txt"))
         split_dp = LineReader(split_dp, decode=True)
         split_dp = hint_sharding(split_dp)
@@ -142,4 +142,4 @@ def _make_datapipe(
                 ref_key_fn=path_accessor("stem"),
                 buffer_size=INFINITE_BUFFER_SIZE,
             )
-        return Mapper(dp, self._collate_and_decode_sample, fn_kwargs=dict(config=config, decoder=decoder))
+        return Mapper(dp, functools.partial(self._collate_and_decode_sample, config=config, decoder=decoder))

torchvision/prototype/datasets/_folder.py

@@ -1,3 +1,4 @@
+import functools
 import io
 import os
 import os.path
@@ -50,12 +51,12 @@ def from_data_folder(
     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 = FileLister(str(root), recursive=recursive, masks=masks)
-    dp: IterDataPipe = Filter(dp, _is_not_top_level_file, fn_kwargs=dict(root=root))
+    dp: IterDataPipe = Filter(dp, functools.partial(_is_not_top_level_file, root=root))
     dp = hint_sharding(dp)
     dp = Shuffler(dp, buffer_size=INFINITE_BUFFER_SIZE)
     dp = FileLoader(dp)
     return (
-        Mapper(dp, _collate_and_decode_data, fn_kwargs=dict(root=root, categories=categories, decoder=decoder)),
+        Mapper(dp, functools.partial(_collate_and_decode_data, root=root, categories=categories, decoder=decoder)),
         categories,
     )