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Unverified Commit 39fe34a2 authored by Vasilis Vryniotis's avatar Vasilis Vryniotis Committed by GitHub
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Eliminate runtime cyclic dependencies (#6476) 

* Move imports on constructor.

* Turn `_F` to a property.

* fix linter

* Fix mypy

* Make it class-wide attribute.

* Add tests based on code review

* Making changes from code-reviews.

* Remove the new tests.

* Clean up.

* Adding comments.

* Update the comment link.
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torchvision/prototype/features/_bounding_box.py
View file @ 39fe34a2
...@@ -63,25 +63,19 @@ class BoundingBox(_Feature): ...@@ -63,25 +63,19 @@ class BoundingBox(_Feature):
) )
def to_format(self, format: Union[str, BoundingBoxFormat]) -> BoundingBox: def to_format(self, format: Union[str, BoundingBoxFormat]) -> BoundingBox:
from torchvision.prototype.transforms import functional as _F
if isinstance(format, str): if isinstance(format, str):
format = BoundingBoxFormat.from_str(format.upper()) format = BoundingBoxFormat.from_str(format.upper())
return BoundingBox.new_like( return BoundingBox.new_like(
self, _F.convert_bounding_box_format(self, old_format=self.format, new_format=format), format=format self, self._F.convert_bounding_box_format(self, old_format=self.format, new_format=format), format=format
) )
def horizontal_flip(self) -> BoundingBox: def horizontal_flip(self) -> BoundingBox:
from torchvision.prototype.transforms import functional as _F output = self._F.horizontal_flip_bounding_box(self, format=self.format, image_size=self.image_size)
output = _F.horizontal_flip_bounding_box(self, format=self.format, image_size=self.image_size)
return BoundingBox.new_like(self, output) return BoundingBox.new_like(self, output)
def vertical_flip(self) -> BoundingBox: def vertical_flip(self) -> BoundingBox:
from torchvision.prototype.transforms import functional as _F output = self._F.vertical_flip_bounding_box(self, format=self.format, image_size=self.image_size)
output = _F.vertical_flip_bounding_box(self, format=self.format, image_size=self.image_size)
return BoundingBox.new_like(self, output) return BoundingBox.new_like(self, output)
def resize( # type: ignore[override] def resize( # type: ignore[override]
...@@ -91,22 +85,16 @@ class BoundingBox(_Feature): ...@@ -91,22 +85,16 @@ class BoundingBox(_Feature):
max_size: Optional[int] = None, max_size: Optional[int] = None,
antialias: bool = False, antialias: bool = False,
) -> BoundingBox: ) -> BoundingBox:
from torchvision.prototype.transforms import functional as _F output = self._F.resize_bounding_box(self, size, image_size=self.image_size, max_size=max_size)
output = _F.resize_bounding_box(self, size, image_size=self.image_size, max_size=max_size)
image_size = (size[0], size[0]) if len(size) == 1 else (size[0], size[1]) image_size = (size[0], size[0]) if len(size) == 1 else (size[0], size[1])
return BoundingBox.new_like(self, output, image_size=image_size, dtype=output.dtype) return BoundingBox.new_like(self, output, image_size=image_size, dtype=output.dtype)
def crop(self, top: int, left: int, height: int, width: int) -> BoundingBox: def crop(self, top: int, left: int, height: int, width: int) -> BoundingBox:
from torchvision.prototype.transforms import functional as _F output = self._F.crop_bounding_box(self, self.format, top, left)
output = _F.crop_bounding_box(self, self.format, top, left)
return BoundingBox.new_like(self, output, image_size=(height, width)) return BoundingBox.new_like(self, output, image_size=(height, width))
def center_crop(self, output_size: List[int]) -> BoundingBox: def center_crop(self, output_size: List[int]) -> BoundingBox:
from torchvision.prototype.transforms import functional as _F output = self._F.center_crop_bounding_box(
output = _F.center_crop_bounding_box(
self, format=self.format, output_size=output_size, image_size=self.image_size self, format=self.format, output_size=output_size, image_size=self.image_size
) )
image_size = (output_size[0], output_size[0]) if len(output_size) == 1 else (output_size[0], output_size[1]) image_size = (output_size[0], output_size[0]) if len(output_size) == 1 else (output_size[0], output_size[1])
...@@ -122,9 +110,7 @@ class BoundingBox(_Feature): ...@@ -122,9 +110,7 @@ class BoundingBox(_Feature):
interpolation: InterpolationMode = InterpolationMode.BILINEAR, interpolation: InterpolationMode = InterpolationMode.BILINEAR,
antialias: bool = False, antialias: bool = False,
) -> BoundingBox: ) -> BoundingBox:
from torchvision.prototype.transforms import functional as _F output = self._F.resized_crop_bounding_box(self, self.format, top, left, height, width, size=size)
output = _F.resized_crop_bounding_box(self, self.format, top, left, height, width, size=size)
image_size = (size[0], size[0]) if len(size) == 1 else (size[0], size[1]) image_size = (size[0], size[0]) if len(size) == 1 else (size[0], size[1])
return BoundingBox.new_like(self, output, image_size=image_size, dtype=output.dtype) return BoundingBox.new_like(self, output, image_size=image_size, dtype=output.dtype)
...@@ -134,8 +120,6 @@ class BoundingBox(_Feature): ...@@ -134,8 +120,6 @@ class BoundingBox(_Feature):
fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None, fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None,
padding_mode: str = "constant", padding_mode: str = "constant",
) -> BoundingBox: ) -> BoundingBox:
from torchvision.prototype.transforms import functional as _F
if padding_mode not in ["constant"]: if padding_mode not in ["constant"]:
raise ValueError(f"Padding mode '{padding_mode}' is not supported with bounding boxes") raise ValueError(f"Padding mode '{padding_mode}' is not supported with bounding boxes")
...@@ -143,7 +127,7 @@ class BoundingBox(_Feature): ...@@ -143,7 +127,7 @@ class BoundingBox(_Feature):
if not isinstance(padding, int): if not isinstance(padding, int):
padding = list(padding) padding = list(padding)
output = _F.pad_bounding_box(self, padding, format=self.format) output = self._F.pad_bounding_box(self, padding, format=self.format)
# Update output image size: # Update output image size:
# TODO: remove the import below and make _parse_pad_padding available # TODO: remove the import below and make _parse_pad_padding available
...@@ -164,9 +148,7 @@ class BoundingBox(_Feature): ...@@ -164,9 +148,7 @@ class BoundingBox(_Feature):
fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None, fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None,
center: Optional[List[float]] = None, center: Optional[List[float]] = None,
) -> BoundingBox: ) -> BoundingBox:
from torchvision.prototype.transforms import functional as _F output = self._F.rotate_bounding_box(
output = _F.rotate_bounding_box(
self, format=self.format, image_size=self.image_size, angle=angle, expand=expand, center=center self, format=self.format, image_size=self.image_size, angle=angle, expand=expand, center=center
) )
image_size = self.image_size image_size = self.image_size
...@@ -174,7 +156,7 @@ class BoundingBox(_Feature): ...@@ -174,7 +156,7 @@ class BoundingBox(_Feature):
# The way we recompute image_size is not optimal due to redundant computations of # The way we recompute image_size is not optimal due to redundant computations of
# - rotation matrix (_get_inverse_affine_matrix) # - rotation matrix (_get_inverse_affine_matrix)
# - points dot matrix (_compute_output_size) # - points dot matrix (_compute_output_size)
# Alternatively, we could return new image size by _F.rotate_bounding_box # Alternatively, we could return new image size by self._F.rotate_bounding_box
height, width = image_size height, width = image_size
rotation_matrix = _get_inverse_affine_matrix([0.0, 0.0], angle, [0.0, 0.0], 1.0, [0.0, 0.0]) rotation_matrix = _get_inverse_affine_matrix([0.0, 0.0], angle, [0.0, 0.0], 1.0, [0.0, 0.0])
new_width, new_height = _compute_output_size(rotation_matrix, width, height) new_width, new_height = _compute_output_size(rotation_matrix, width, height)
...@@ -192,9 +174,7 @@ class BoundingBox(_Feature): ...@@ -192,9 +174,7 @@ class BoundingBox(_Feature):
fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None, fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None,
center: Optional[List[float]] = None, center: Optional[List[float]] = None,
) -> BoundingBox: ) -> BoundingBox:
from torchvision.prototype.transforms import functional as _F output = self._F.affine_bounding_box(
output = _F.affine_bounding_box(
self, self,
self.format, self.format,
self.image_size, self.image_size,
...@@ -212,9 +192,7 @@ class BoundingBox(_Feature): ...@@ -212,9 +192,7 @@ class BoundingBox(_Feature):
interpolation: InterpolationMode = InterpolationMode.BILINEAR, interpolation: InterpolationMode = InterpolationMode.BILINEAR,
fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None, fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None,
) -> BoundingBox: ) -> BoundingBox:
from torchvision.prototype.transforms import functional as _F output = self._F.perspective_bounding_box(self, self.format, perspective_coeffs)
output = _F.perspective_bounding_box(self, self.format, perspective_coeffs)
return BoundingBox.new_like(self, output, dtype=output.dtype) return BoundingBox.new_like(self, output, dtype=output.dtype)
def elastic( def elastic(
...@@ -223,7 +201,5 @@ class BoundingBox(_Feature): ...@@ -223,7 +201,5 @@ class BoundingBox(_Feature):
interpolation: InterpolationMode = InterpolationMode.BILINEAR, interpolation: InterpolationMode = InterpolationMode.BILINEAR,
fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None, fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None,
) -> BoundingBox: ) -> BoundingBox:
from torchvision.prototype.transforms import functional as _F output = self._F.elastic_bounding_box(self, self.format, displacement)
output = _F.elastic_bounding_box(self, self.format, displacement)
return BoundingBox.new_like(self, output, dtype=output.dtype) return BoundingBox.new_like(self, output, dtype=output.dtype)
torchvision/prototype/features/_encoded.py
View file @ 39fe34a2
...@@ -49,11 +49,7 @@ class EncodedImage(EncodedData): ...@@ -49,11 +49,7 @@ class EncodedImage(EncodedData):
def decode(self) -> Image: def decode(self) -> Image:
# TODO: this is useful for developing and debugging but we should remove or at least revisit this before we # TODO: this is useful for developing and debugging but we should remove or at least revisit this before we
# promote this out of the prototype state # promote this out of the prototype state
return Image(self._F.decode_image_with_pil(self))
# import at runtime to avoid cyclic imports
from torchvision.prototype.transforms.functional import decode_image_with_pil
return Image(decode_image_with_pil(self))
class EncodedVideo(EncodedData): class EncodedVideo(EncodedData):
......
torchvision/prototype/features/_feature.py
View file @ 39fe34a2
from __future__ import annotations from __future__ import annotations
from types import ModuleType
from typing import Any, Callable, cast, List, Mapping, Optional, Sequence, Tuple, Type, TypeVar, Union from typing import Any, Callable, cast, List, Mapping, Optional, Sequence, Tuple, Type, TypeVar, Union
import torch import torch
...@@ -10,6 +11,8 @@ F = TypeVar("F", bound="_Feature") ...@@ -10,6 +11,8 @@ F = TypeVar("F", bound="_Feature")
class _Feature(torch.Tensor): class _Feature(torch.Tensor):
__F: Optional[ModuleType] = None
def __new__( def __new__(
cls: Type[F], cls: Type[F],
data: Any, data: Any,
...@@ -92,6 +95,18 @@ class _Feature(torch.Tensor): ...@@ -92,6 +95,18 @@ class _Feature(torch.Tensor):
extra_repr = ", ".join(f"{key}={value}" for key, value in kwargs.items()) extra_repr = ", ".join(f"{key}={value}" for key, value in kwargs.items())
return f"{super().__repr__()[:-1]}, {extra_repr})" return f"{super().__repr__()[:-1]}, {extra_repr})"
@property
def _F(self) -> ModuleType:
# This implements a lazy import of the functional to get around the cyclic import. This import is deferred
# until the first time we need reference to the functional module and it's shared across all instances of
# the class. This approach avoids the DataLoader issue described at
# https://github.com/pytorch/vision/pull/6476#discussion_r953588621
if _Feature.__F is None:
from ..transforms import functional
_Feature.__F = functional
return _Feature.__F
def horizontal_flip(self) -> _Feature: def horizontal_flip(self) -> _Feature:
return self return self
......
torchvision/prototype/features/_image.py
View file @ 39fe34a2
...@@ -103,14 +103,12 @@ class Image(_Feature): ...@@ -103,14 +103,12 @@ class Image(_Feature):
return ColorSpace.OTHER return ColorSpace.OTHER
def to_color_space(self, color_space: Union[str, ColorSpace], copy: bool = True) -> Image: def to_color_space(self, color_space: Union[str, ColorSpace], copy: bool = True) -> Image:
from torchvision.prototype.transforms import functional as _F
if isinstance(color_space, str): if isinstance(color_space, str):
color_space = ColorSpace.from_str(color_space.upper()) color_space = ColorSpace.from_str(color_space.upper())
return Image.new_like( return Image.new_like(
self, self,
_F.convert_color_space_image_tensor( self._F.convert_color_space_image_tensor(
self, old_color_space=self.color_space, new_color_space=color_space, copy=copy self, old_color_space=self.color_space, new_color_space=color_space, copy=copy
), ),
color_space=color_space, color_space=color_space,
...@@ -127,15 +125,11 @@ class Image(_Feature): ...@@ -127,15 +125,11 @@ class Image(_Feature):
return Image.new_like(self, draw_bounding_boxes(self, bounding_box.to_format("xyxy").view(-1, 4), **kwargs)) return Image.new_like(self, draw_bounding_boxes(self, bounding_box.to_format("xyxy").view(-1, 4), **kwargs))
def horizontal_flip(self) -> Image: def horizontal_flip(self) -> Image:
from torchvision.prototype.transforms import functional as _F output = self._F.horizontal_flip_image_tensor(self)
output = _F.horizontal_flip_image_tensor(self)
return Image.new_like(self, output) return Image.new_like(self, output)
def vertical_flip(self) -> Image: def vertical_flip(self) -> Image:
from torchvision.prototype.transforms import functional as _F output = self._F.vertical_flip_image_tensor(self)
output = _F.vertical_flip_image_tensor(self)
return Image.new_like(self, output) return Image.new_like(self, output)
def resize( # type: ignore[override] def resize( # type: ignore[override]
...@@ -145,21 +139,17 @@ class Image(_Feature): ...@@ -145,21 +139,17 @@ class Image(_Feature):
max_size: Optional[int] = None, max_size: Optional[int] = None,
antialias: bool = False, antialias: bool = False,
) -> Image: ) -> Image:
from torchvision.prototype.transforms import functional as _F output = self._F.resize_image_tensor(
self, size, interpolation=interpolation, max_size=max_size, antialias=antialias
output = _F.resize_image_tensor(self, size, interpolation=interpolation, max_size=max_size, antialias=antialias) )
return Image.new_like(self, output) return Image.new_like(self, output)
def crop(self, top: int, left: int, height: int, width: int) -> Image: def crop(self, top: int, left: int, height: int, width: int) -> Image:
from torchvision.prototype.transforms import functional as _F output = self._F.crop_image_tensor(self, top, left, height, width)
output = _F.crop_image_tensor(self, top, left, height, width)
return Image.new_like(self, output) return Image.new_like(self, output)
def center_crop(self, output_size: List[int]) -> Image: def center_crop(self, output_size: List[int]) -> Image:
from torchvision.prototype.transforms import functional as _F output = self._F.center_crop_image_tensor(self, output_size=output_size)
output = _F.center_crop_image_tensor(self, output_size=output_size)
return Image.new_like(self, output) return Image.new_like(self, output)
def resized_crop( def resized_crop(
...@@ -172,9 +162,7 @@ class Image(_Feature): ...@@ -172,9 +162,7 @@ class Image(_Feature):
interpolation: InterpolationMode = InterpolationMode.BILINEAR, interpolation: InterpolationMode = InterpolationMode.BILINEAR,
antialias: bool = False, antialias: bool = False,
) -> Image: ) -> Image:
from torchvision.prototype.transforms import functional as _F output = self._F.resized_crop_image_tensor(
output = _F.resized_crop_image_tensor(
self, top, left, height, width, size=list(size), interpolation=interpolation, antialias=antialias self, top, left, height, width, size=list(size), interpolation=interpolation, antialias=antialias
) )
return Image.new_like(self, output) return Image.new_like(self, output)
...@@ -185,19 +173,15 @@ class Image(_Feature): ...@@ -185,19 +173,15 @@ class Image(_Feature):
fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None, fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None,
padding_mode: str = "constant", padding_mode: str = "constant",
) -> Image: ) -> Image:
from torchvision.prototype.transforms import functional as _F
# This cast does Sequence[int] -> List[int] and is required to make mypy happy # This cast does Sequence[int] -> List[int] and is required to make mypy happy
if not isinstance(padding, int): if not isinstance(padding, int):
padding = list(padding) padding = list(padding)
# PyTorch's pad supports only scalars on fill. So we need to overwrite the colour # PyTorch's pad supports only scalars on fill. So we need to overwrite the colour
if isinstance(fill, (int, float)) or fill is None: if isinstance(fill, (int, float)) or fill is None:
output = _F.pad_image_tensor(self, padding, fill=fill, padding_mode=padding_mode) output = self._F.pad_image_tensor(self, padding, fill=fill, padding_mode=padding_mode)
else: else:
from torchvision.prototype.transforms.functional._geometry import _pad_with_vector_fill output = self._F._geometry._pad_with_vector_fill(self, padding, fill=fill, padding_mode=padding_mode)
output = _pad_with_vector_fill(self, padding, fill=fill, padding_mode=padding_mode)
return Image.new_like(self, output) return Image.new_like(self, output)
...@@ -209,11 +193,9 @@ class Image(_Feature): ...@@ -209,11 +193,9 @@ class Image(_Feature):
fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None, fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None,
center: Optional[List[float]] = None, center: Optional[List[float]] = None,
) -> Image: ) -> Image:
from torchvision.prototype.transforms.functional import _geometry as _F fill = self._F._geometry._convert_fill_arg(fill)
fill = _F._convert_fill_arg(fill)
output = _F.rotate_image_tensor( output = self._F._geometry.rotate_image_tensor(
self, angle, interpolation=interpolation, expand=expand, fill=fill, center=center self, angle, interpolation=interpolation, expand=expand, fill=fill, center=center
) )
return Image.new_like(self, output) return Image.new_like(self, output)
...@@ -228,11 +210,9 @@ class Image(_Feature): ...@@ -228,11 +210,9 @@ class Image(_Feature):
fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None, fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None,
center: Optional[List[float]] = None, center: Optional[List[float]] = None,
) -> Image: ) -> Image:
from torchvision.prototype.transforms.functional import _geometry as _F fill = self._F._geometry._convert_fill_arg(fill)
fill = _F._convert_fill_arg(fill) output = self._F._geometry.affine_image_tensor(
output = _F.affine_image_tensor(
self, self,
angle, angle,
translate=translate, translate=translate,
...@@ -250,11 +230,11 @@ class Image(_Feature): ...@@ -250,11 +230,11 @@ class Image(_Feature):
interpolation: InterpolationMode = InterpolationMode.BILINEAR, interpolation: InterpolationMode = InterpolationMode.BILINEAR,
fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None, fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None,
) -> Image: ) -> Image:
from torchvision.prototype.transforms.functional import _geometry as _F fill = self._F._geometry._convert_fill_arg(fill)
fill = _F._convert_fill_arg(fill)
output = _F.perspective_image_tensor(self, perspective_coeffs, interpolation=interpolation, fill=fill) output = self._F._geometry.perspective_image_tensor(
self, perspective_coeffs, interpolation=interpolation, fill=fill
)
return Image.new_like(self, output) return Image.new_like(self, output)
def elastic( def elastic(
...@@ -263,81 +243,55 @@ class Image(_Feature): ...@@ -263,81 +243,55 @@ class Image(_Feature):
interpolation: InterpolationMode = InterpolationMode.BILINEAR, interpolation: InterpolationMode = InterpolationMode.BILINEAR,
fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None, fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None,
) -> Image: ) -> Image:
from torchvision.prototype.transforms.functional import _geometry as _F fill = self._F._geometry._convert_fill_arg(fill)
fill = _F._convert_fill_arg(fill) output = self._F._geometry.elastic_image_tensor(self, displacement, interpolation=interpolation, fill=fill)
output = _F.elastic_image_tensor(self, displacement, interpolation=interpolation, fill=fill)
return Image.new_like(self, output) return Image.new_like(self, output)
def adjust_brightness(self, brightness_factor: float) -> Image: def adjust_brightness(self, brightness_factor: float) -> Image:
from torchvision.prototype.transforms import functional as _F output = self._F.adjust_brightness_image_tensor(self, brightness_factor=brightness_factor)
output = _F.adjust_brightness_image_tensor(self, brightness_factor=brightness_factor)
return Image.new_like(self, output) return Image.new_like(self, output)
def adjust_saturation(self, saturation_factor: float) -> Image: def adjust_saturation(self, saturation_factor: float) -> Image:
from torchvision.prototype.transforms import functional as _F output = self._F.adjust_saturation_image_tensor(self, saturation_factor=saturation_factor)
output = _F.adjust_saturation_image_tensor(self, saturation_factor=saturation_factor)
return Image.new_like(self, output) return Image.new_like(self, output)
def adjust_contrast(self, contrast_factor: float) -> Image: def adjust_contrast(self, contrast_factor: float) -> Image:
from torchvision.prototype.transforms import functional as _F output = self._F.adjust_contrast_image_tensor(self, contrast_factor=contrast_factor)
output = _F.adjust_contrast_image_tensor(self, contrast_factor=contrast_factor)
return Image.new_like(self, output) return Image.new_like(self, output)
def adjust_sharpness(self, sharpness_factor: float) -> Image: def adjust_sharpness(self, sharpness_factor: float) -> Image:
from torchvision.prototype.transforms import functional as _F output = self._F.adjust_sharpness_image_tensor(self, sharpness_factor=sharpness_factor)
output = _F.adjust_sharpness_image_tensor(self, sharpness_factor=sharpness_factor)
return Image.new_like(self, output) return Image.new_like(self, output)
def adjust_hue(self, hue_factor: float) -> Image: def adjust_hue(self, hue_factor: float) -> Image:
from torchvision.prototype.transforms import functional as _F output = self._F.adjust_hue_image_tensor(self, hue_factor=hue_factor)
output = _F.adjust_hue_image_tensor(self, hue_factor=hue_factor)
return Image.new_like(self, output) return Image.new_like(self, output)
def adjust_gamma(self, gamma: float, gain: float = 1) -> Image: def adjust_gamma(self, gamma: float, gain: float = 1) -> Image:
from torchvision.prototype.transforms import functional as _F output = self._F.adjust_gamma_image_tensor(self, gamma=gamma, gain=gain)
output = _F.adjust_gamma_image_tensor(self, gamma=gamma, gain=gain)
return Image.new_like(self, output) return Image.new_like(self, output)
def posterize(self, bits: int) -> Image: def posterize(self, bits: int) -> Image:
from torchvision.prototype.transforms import functional as _F output = self._F.posterize_image_tensor(self, bits=bits)
output = _F.posterize_image_tensor(self, bits=bits)
return Image.new_like(self, output) return Image.new_like(self, output)
def solarize(self, threshold: float) -> Image: def solarize(self, threshold: float) -> Image:
from torchvision.prototype.transforms import functional as _F output = self._F.solarize_image_tensor(self, threshold=threshold)
output = _F.solarize_image_tensor(self, threshold=threshold)
return Image.new_like(self, output) return Image.new_like(self, output)
def autocontrast(self) -> Image: def autocontrast(self) -> Image:
from torchvision.prototype.transforms import functional as _F output = self._F.autocontrast_image_tensor(self)
output = _F.autocontrast_image_tensor(self)
return Image.new_like(self, output) return Image.new_like(self, output)
def equalize(self) -> Image: def equalize(self) -> Image:
from torchvision.prototype.transforms import functional as _F output = self._F.equalize_image_tensor(self)
output = _F.equalize_image_tensor(self)
return Image.new_like(self, output) return Image.new_like(self, output)
def invert(self) -> Image: def invert(self) -> Image:
from torchvision.prototype.transforms import functional as _F output = self._F.invert_image_tensor(self)
output = _F.invert_image_tensor(self)
return Image.new_like(self, output) return Image.new_like(self, output)
def gaussian_blur(self, kernel_size: List[int], sigma: Optional[List[float]] = None) -> Image: def gaussian_blur(self, kernel_size: List[int], sigma: Optional[List[float]] = None) -> Image:
from torchvision.prototype.transforms import functional as _F output = self._F.gaussian_blur_image_tensor(self, kernel_size=kernel_size, sigma=sigma)
output = _F.gaussian_blur_image_tensor(self, kernel_size=kernel_size, sigma=sigma)
return Image.new_like(self, output) return Image.new_like(self, output)
torchvision/prototype/features/_segmentation_mask.py
View file @ 39fe34a2
...@@ -10,15 +10,11 @@ from ._feature import _Feature ...@@ -10,15 +10,11 @@ from ._feature import _Feature
class SegmentationMask(_Feature): class SegmentationMask(_Feature):
def horizontal_flip(self) -> SegmentationMask: def horizontal_flip(self) -> SegmentationMask:
from torchvision.prototype.transforms import functional as _F output = self._F.horizontal_flip_segmentation_mask(self)
output = _F.horizontal_flip_segmentation_mask(self)
return SegmentationMask.new_like(self, output) return SegmentationMask.new_like(self, output)
def vertical_flip(self) -> SegmentationMask: def vertical_flip(self) -> SegmentationMask:
from torchvision.prototype.transforms import functional as _F output = self._F.vertical_flip_segmentation_mask(self)
output = _F.vertical_flip_segmentation_mask(self)
return SegmentationMask.new_like(self, output) return SegmentationMask.new_like(self, output)
def resize( # type: ignore[override] def resize( # type: ignore[override]
...@@ -28,21 +24,15 @@ class SegmentationMask(_Feature): ...@@ -28,21 +24,15 @@ class SegmentationMask(_Feature):
max_size: Optional[int] = None, max_size: Optional[int] = None,
antialias: bool = False, antialias: bool = False,
) -> SegmentationMask: ) -> SegmentationMask:
from torchvision.prototype.transforms import functional as _F output = self._F.resize_segmentation_mask(self, size, max_size=max_size)
output = _F.resize_segmentation_mask(self, size, max_size=max_size)
return SegmentationMask.new_like(self, output) return SegmentationMask.new_like(self, output)
def crop(self, top: int, left: int, height: int, width: int) -> SegmentationMask: def crop(self, top: int, left: int, height: int, width: int) -> SegmentationMask:
from torchvision.prototype.transforms import functional as _F output = self._F.crop_segmentation_mask(self, top, left, height, width)
output = _F.crop_segmentation_mask(self, top, left, height, width)
return SegmentationMask.new_like(self, output) return SegmentationMask.new_like(self, output)
def center_crop(self, output_size: List[int]) -> SegmentationMask: def center_crop(self, output_size: List[int]) -> SegmentationMask:
from torchvision.prototype.transforms import functional as _F output = self._F.center_crop_segmentation_mask(self, output_size=output_size)
output = _F.center_crop_segmentation_mask(self, output_size=output_size)
return SegmentationMask.new_like(self, output) return SegmentationMask.new_like(self, output)
def resized_crop( def resized_crop(
...@@ -55,9 +45,7 @@ class SegmentationMask(_Feature): ...@@ -55,9 +45,7 @@ class SegmentationMask(_Feature):
interpolation: InterpolationMode = InterpolationMode.NEAREST, interpolation: InterpolationMode = InterpolationMode.NEAREST,
antialias: bool = False, antialias: bool = False,
) -> SegmentationMask: ) -> SegmentationMask:
from torchvision.prototype.transforms import functional as _F output = self._F.resized_crop_segmentation_mask(self, top, left, height, width, size=size)
output = _F.resized_crop_segmentation_mask(self, top, left, height, width, size=size)
return SegmentationMask.new_like(self, output) return SegmentationMask.new_like(self, output)
def pad( def pad(
...@@ -66,13 +54,11 @@ class SegmentationMask(_Feature): ...@@ -66,13 +54,11 @@ class SegmentationMask(_Feature):
fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None, fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None,
padding_mode: str = "constant", padding_mode: str = "constant",
) -> SegmentationMask: ) -> SegmentationMask:
from torchvision.prototype.transforms import functional as _F
# This cast does Sequence[int] -> List[int] and is required to make mypy happy # This cast does Sequence[int] -> List[int] and is required to make mypy happy
if not isinstance(padding, int): if not isinstance(padding, int):
padding = list(padding) padding = list(padding)
output = _F.pad_segmentation_mask(self, padding, padding_mode=padding_mode) output = self._F.pad_segmentation_mask(self, padding, padding_mode=padding_mode)
return SegmentationMask.new_like(self, output) return SegmentationMask.new_like(self, output)
def rotate( def rotate(
...@@ -83,9 +69,7 @@ class SegmentationMask(_Feature): ...@@ -83,9 +69,7 @@ class SegmentationMask(_Feature):
fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None, fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None,
center: Optional[List[float]] = None, center: Optional[List[float]] = None,
) -> SegmentationMask: ) -> SegmentationMask:
from torchvision.prototype.transforms import functional as _F output = self._F.rotate_segmentation_mask(self, angle, expand=expand, center=center)
output = _F.rotate_segmentation_mask(self, angle, expand=expand, center=center)
return SegmentationMask.new_like(self, output) return SegmentationMask.new_like(self, output)
def affine( def affine(
...@@ -98,9 +82,7 @@ class SegmentationMask(_Feature): ...@@ -98,9 +82,7 @@ class SegmentationMask(_Feature):
fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None, fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None,
center: Optional[List[float]] = None, center: Optional[List[float]] = None,
) -> SegmentationMask: ) -> SegmentationMask:
from torchvision.prototype.transforms import functional as _F output = self._F.affine_segmentation_mask(
output = _F.affine_segmentation_mask(
self, self,
angle, angle,
translate=translate, translate=translate,
...@@ -116,9 +98,7 @@ class SegmentationMask(_Feature): ...@@ -116,9 +98,7 @@ class SegmentationMask(_Feature):
interpolation: InterpolationMode = InterpolationMode.NEAREST, interpolation: InterpolationMode = InterpolationMode.NEAREST,
fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None, fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None,
) -> SegmentationMask: ) -> SegmentationMask:
from torchvision.prototype.transforms import functional as _F output = self._F.perspective_segmentation_mask(self, perspective_coeffs)
output = _F.perspective_segmentation_mask(self, perspective_coeffs)
return SegmentationMask.new_like(self, output) return SegmentationMask.new_like(self, output)
def elastic( def elastic(
...@@ -127,7 +107,5 @@ class SegmentationMask(_Feature): ...@@ -127,7 +107,5 @@ class SegmentationMask(_Feature):
interpolation: InterpolationMode = InterpolationMode.NEAREST, interpolation: InterpolationMode = InterpolationMode.NEAREST,
fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None, fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None,
) -> SegmentationMask: ) -> SegmentationMask:
from torchvision.prototype.transforms import functional as _F output = self._F.elastic_segmentation_mask(self, displacement)
output = _F.elastic_segmentation_mask(self, displacement)
return SegmentationMask.new_like(self, output, dtype=output.dtype) return SegmentationMask.new_like(self, output, dtype=output.dtype)
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