[fbsync] Remove performance workaround for mask resize (#6729)

Summary:
* Remove performance workaround for mask resize

* Fix linter

* bug fixes

* remove unnecessary import

* Fixing linter

Reviewed By: NicolasHug

Differential Revision: D40427473

fbshipit-source-id: e7e632069c62cc4e53c31571f0c177d68ee61c3e

Author: Yosua Michael Maranatha

test/prototype_transforms_kernel_infos.py

@@ -19,7 +19,6 @@
     make_video_loaders,
     mark_framework_limitation,
     TestMark,
-    VALID_EXTRA_DIMS,
 )
 from torchvision.prototype import features
 from torchvision.transforms.functional_tensor import _max_value as get_max_value
@@ -215,16 +214,6 @@ def sample_inputs_resize_image_tensor():
     ):
         yield ArgsKwargs(image_loader, size=[min(image_loader.image_size) + 1], interpolation=interpolation)
 
-    # We have a speed hack in place for nearest interpolation and single channel images (grayscale)
-    for image_loader in make_image_loaders(
-        sizes=["random"],
-        color_spaces=[features.ColorSpace.GRAY],
-        extra_dims=VALID_EXTRA_DIMS,
-    ):
-        yield ArgsKwargs(
-            image_loader, size=[min(image_loader.image_size) + 1], interpolation=F.InterpolationMode.NEAREST
-        )
-
     yield ArgsKwargs(make_image_loader(size=(11, 17)), size=20, max_size=25)
 
 

torchvision/prototype/transforms/functional/_geometry.py

@@ -14,12 +14,7 @@
     pil_to_tensor,
     to_pil_image,
 )
-from torchvision.transforms.functional_tensor import (
-    _cast_squeeze_in,
-    _cast_squeeze_out,
-    _parse_pad_padding,
-    interpolate,
-)
+from torchvision.transforms.functional_tensor import _parse_pad_padding
 
 from ._meta import (
     convert_format_bounding_box,
@@ -130,32 +125,12 @@ def resize_image_tensor(
     if image.numel() > 0:
         image = image.view(-1, num_channels, old_height, old_width)
 
-        # This is a perf hack to avoid slow channels_last upsample code path
-        # Related issue: https://github.com/pytorch/pytorch/issues/83840
-        # We are transforming (N, 1, H, W) into (N, 2, H, W) to force to take channels_first path
-        if image.shape[1] == 1 and interpolation == InterpolationMode.NEAREST:
-            # Below code is copied from _FT.resize
-            # This is due to the fact that we need to apply the hack on casted image and not before
-            # Otherwise, image will be copied while cast to float and interpolate will work on twice more data
-            image, need_cast, need_squeeze, out_dtype = _cast_squeeze_in(image, [torch.float32, torch.float64])
-
-            shape = (image.shape[0], 2, image.shape[2], image.shape[3])
-            image = image.expand(shape)
-
-            image = interpolate(
-                image, size=[new_height, new_width], mode=interpolation.value, align_corners=None, antialias=False
-            )
-
-            image = image[:, 0, ...]
-            image = _cast_squeeze_out(image, need_cast=need_cast, need_squeeze=need_squeeze, out_dtype=out_dtype)
-
-        else:
-            image = _FT.resize(
-                image,
-                size=[new_height, new_width],
-                interpolation=interpolation.value,
-                antialias=antialias,
-            )
+        image = _FT.resize(
+            image,
+            size=[new_height, new_width],
+            interpolation=interpolation.value,
+            antialias=antialias,
+        )
 
     return image.view(extra_dims + (num_channels, new_height, new_width))