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)
 
 
@@ -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
@@ -183,7 +184,7 @@ 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,
@@ -226,7 +227,7 @@ 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]]:

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))