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Unverified Commit d4d20f01 authored by Philip Meier's avatar Philip Meier Committed by GitHub
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make type alias private (#7266)

parent e405f3c3
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Showing with 55 additions and 55 deletions
torchvision/transforms/v2/functional/_geometry.py
View file @ d4d20f01
......@@ -71,7 +71,7 @@ def horizontal_flip_video(video: torch.Tensor) -> torch.Tensor:
return horizontal_flip_image_tensor(video)
def horizontal_flip(inpt: datapoints.InputTypeJIT) -> datapoints.InputTypeJIT:
def horizontal_flip(inpt: datapoints._InputTypeJIT) -> datapoints._InputTypeJIT:
if not torch.jit.is_scripting():
_log_api_usage_once(horizontal_flip)
......@@ -120,7 +120,7 @@ def vertical_flip_video(video: torch.Tensor) -> torch.Tensor:
return vertical_flip_image_tensor(video)
def vertical_flip(inpt: datapoints.InputTypeJIT) -> datapoints.InputTypeJIT:
def vertical_flip(inpt: datapoints._InputTypeJIT) -> datapoints._InputTypeJIT:
if not torch.jit.is_scripting():
_log_api_usage_once(vertical_flip)
......@@ -255,12 +255,12 @@ def resize_video(
def resize(
inpt: datapoints.InputTypeJIT,
inpt: datapoints._InputTypeJIT,
size: List[int],
interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
max_size: Optional[int] = None,
antialias: Optional[Union[str, bool]] = "warn",
) -> datapoints.InputTypeJIT:
) -> datapoints._InputTypeJIT:
if not torch.jit.is_scripting():
_log_api_usage_once(resize)
if torch.jit.is_scripting() or is_simple_tensor(inpt):
......@@ -428,7 +428,7 @@ def _compute_affine_output_size(matrix: List[float], w: int, h: int) -> Tuple[in
def _apply_grid_transform(
img: torch.Tensor, grid: torch.Tensor, mode: str, fill: datapoints.FillTypeJIT
img: torch.Tensor, grid: torch.Tensor, mode: str, fill: datapoints._FillTypeJIT
) -> torch.Tensor:
# We are using context knowledge that grid should have float dtype
......@@ -470,7 +470,7 @@ def _assert_grid_transform_inputs(
image: torch.Tensor,
matrix: Optional[List[float]],
interpolation: str,
fill: datapoints.FillTypeJIT,
fill: datapoints._FillTypeJIT,
supported_interpolation_modes: List[str],
coeffs: Optional[List[float]] = None,
) -> None:
......@@ -533,7 +533,7 @@ def affine_image_tensor(
scale: float,
shear: List[float],
interpolation: Union[InterpolationMode, int] = InterpolationMode.NEAREST,
fill: datapoints.FillTypeJIT = None,
fill: datapoints._FillTypeJIT = None,
center: Optional[List[float]] = None,
) -> torch.Tensor:
interpolation = _check_interpolation(interpolation)
......@@ -585,7 +585,7 @@ def affine_image_pil(
scale: float,
shear: List[float],
interpolation: Union[InterpolationMode, int] = InterpolationMode.NEAREST,
fill: datapoints.FillTypeJIT = None,
fill: datapoints._FillTypeJIT = None,
center: Optional[List[float]] = None,
) -> PIL.Image.Image:
interpolation = _check_interpolation(interpolation)
......@@ -721,7 +721,7 @@ def affine_mask(
translate: List[float],
scale: float,
shear: List[float],
fill: datapoints.FillTypeJIT = None,
fill: datapoints._FillTypeJIT = None,
center: Optional[List[float]] = None,
) -> torch.Tensor:
if mask.ndim < 3:
......@@ -754,7 +754,7 @@ def affine_video(
scale: float,
shear: List[float],
interpolation: Union[InterpolationMode, int] = InterpolationMode.NEAREST,
fill: datapoints.FillTypeJIT = None,
fill: datapoints._FillTypeJIT = None,
center: Optional[List[float]] = None,
) -> torch.Tensor:
return affine_image_tensor(
......@@ -770,15 +770,15 @@ def affine_video(
def affine(
inpt: datapoints.InputTypeJIT,
inpt: datapoints._InputTypeJIT,
angle: Union[int, float],
translate: List[float],
scale: float,
shear: List[float],
interpolation: Union[InterpolationMode, int] = InterpolationMode.NEAREST,
fill: datapoints.FillTypeJIT = None,
fill: datapoints._FillTypeJIT = None,
center: Optional[List[float]] = None,
) -> datapoints.InputTypeJIT:
) -> datapoints._InputTypeJIT:
if not torch.jit.is_scripting():
_log_api_usage_once(affine)
......@@ -822,7 +822,7 @@ def rotate_image_tensor(
interpolation: Union[InterpolationMode, int] = InterpolationMode.NEAREST,
expand: bool = False,
center: Optional[List[float]] = None,
fill: datapoints.FillTypeJIT = None,
fill: datapoints._FillTypeJIT = None,
) -> torch.Tensor:
interpolation = _check_interpolation(interpolation)
......@@ -867,7 +867,7 @@ def rotate_image_pil(
interpolation: Union[InterpolationMode, int] = InterpolationMode.NEAREST,
expand: bool = False,
center: Optional[List[float]] = None,
fill: datapoints.FillTypeJIT = None,
fill: datapoints._FillTypeJIT = None,
) -> PIL.Image.Image:
interpolation = _check_interpolation(interpolation)
......@@ -910,7 +910,7 @@ def rotate_mask(
angle: float,
expand: bool = False,
center: Optional[List[float]] = None,
fill: datapoints.FillTypeJIT = None,
fill: datapoints._FillTypeJIT = None,
) -> torch.Tensor:
if mask.ndim < 3:
mask = mask.unsqueeze(0)
......@@ -939,19 +939,19 @@ def rotate_video(
interpolation: Union[InterpolationMode, int] = InterpolationMode.NEAREST,
expand: bool = False,
center: Optional[List[float]] = None,
fill: datapoints.FillTypeJIT = None,
fill: datapoints._FillTypeJIT = None,
) -> torch.Tensor:
return rotate_image_tensor(video, angle, interpolation=interpolation, expand=expand, fill=fill, center=center)
def rotate(
inpt: datapoints.InputTypeJIT,
inpt: datapoints._InputTypeJIT,
angle: float,
interpolation: Union[InterpolationMode, int] = InterpolationMode.NEAREST,
expand: bool = False,
center: Optional[List[float]] = None,
fill: datapoints.FillTypeJIT = None,
) -> datapoints.InputTypeJIT:
fill: datapoints._FillTypeJIT = None,
) -> datapoints._InputTypeJIT:
if not torch.jit.is_scripting():
_log_api_usage_once(rotate)
......@@ -1156,11 +1156,11 @@ def pad_video(
def pad(
inpt: datapoints.InputTypeJIT,
inpt: datapoints._InputTypeJIT,
padding: List[int],
fill: Optional[Union[int, float, List[float]]] = None,
padding_mode: str = "constant",
) -> datapoints.InputTypeJIT:
) -> datapoints._InputTypeJIT:
if not torch.jit.is_scripting():
_log_api_usage_once(pad)
......@@ -1239,7 +1239,7 @@ def crop_video(video: torch.Tensor, top: int, left: int, height: int, width: int
return crop_image_tensor(video, top, left, height, width)
def crop(inpt: datapoints.InputTypeJIT, top: int, left: int, height: int, width: int) -> datapoints.InputTypeJIT:
def crop(inpt: datapoints._InputTypeJIT, top: int, left: int, height: int, width: int) -> datapoints._InputTypeJIT:
if not torch.jit.is_scripting():
_log_api_usage_once(crop)
......@@ -1308,7 +1308,7 @@ def perspective_image_tensor(
startpoints: Optional[List[List[int]]],
endpoints: Optional[List[List[int]]],
interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
fill: datapoints.FillTypeJIT = None,
fill: datapoints._FillTypeJIT = None,
coefficients: Optional[List[float]] = None,
) -> torch.Tensor:
perspective_coeffs = _perspective_coefficients(startpoints, endpoints, coefficients)
......@@ -1355,7 +1355,7 @@ def perspective_image_pil(
startpoints: Optional[List[List[int]]],
endpoints: Optional[List[List[int]]],
interpolation: Union[InterpolationMode, int] = InterpolationMode.BICUBIC,
fill: datapoints.FillTypeJIT = None,
fill: datapoints._FillTypeJIT = None,
coefficients: Optional[List[float]] = None,
) -> PIL.Image.Image:
perspective_coeffs = _perspective_coefficients(startpoints, endpoints, coefficients)
......@@ -1461,7 +1461,7 @@ def perspective_mask(
mask: torch.Tensor,
startpoints: Optional[List[List[int]]],
endpoints: Optional[List[List[int]]],
fill: datapoints.FillTypeJIT = None,
fill: datapoints._FillTypeJIT = None,
coefficients: Optional[List[float]] = None,
) -> torch.Tensor:
if mask.ndim < 3:
......@@ -1485,7 +1485,7 @@ def perspective_video(
startpoints: Optional[List[List[int]]],
endpoints: Optional[List[List[int]]],
interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
fill: datapoints.FillTypeJIT = None,
fill: datapoints._FillTypeJIT = None,
coefficients: Optional[List[float]] = None,
) -> torch.Tensor:
return perspective_image_tensor(
......@@ -1494,13 +1494,13 @@ def perspective_video(
def perspective(
inpt: datapoints.InputTypeJIT,
inpt: datapoints._InputTypeJIT,
startpoints: Optional[List[List[int]]],
endpoints: Optional[List[List[int]]],
interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
fill: datapoints.FillTypeJIT = None,
fill: datapoints._FillTypeJIT = None,
coefficients: Optional[List[float]] = None,
) -> datapoints.InputTypeJIT:
) -> datapoints._InputTypeJIT:
if not torch.jit.is_scripting():
_log_api_usage_once(perspective)
if torch.jit.is_scripting() or is_simple_tensor(inpt):
......@@ -1526,7 +1526,7 @@ def elastic_image_tensor(
image: torch.Tensor,
displacement: torch.Tensor,
interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
fill: datapoints.FillTypeJIT = None,
fill: datapoints._FillTypeJIT = None,
) -> torch.Tensor:
interpolation = _check_interpolation(interpolation)
......@@ -1583,7 +1583,7 @@ def elastic_image_pil(
image: PIL.Image.Image,
displacement: torch.Tensor,
interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
fill: datapoints.FillTypeJIT = None,
fill: datapoints._FillTypeJIT = None,
) -> PIL.Image.Image:
t_img = pil_to_tensor(image)
output = elastic_image_tensor(t_img, displacement, interpolation=interpolation, fill=fill)
......@@ -1656,7 +1656,7 @@ def elastic_bounding_box(
def elastic_mask(
mask: torch.Tensor,
displacement: torch.Tensor,
fill: datapoints.FillTypeJIT = None,
fill: datapoints._FillTypeJIT = None,
) -> torch.Tensor:
if mask.ndim < 3:
mask = mask.unsqueeze(0)
......@@ -1676,17 +1676,17 @@ def elastic_video(
video: torch.Tensor,
displacement: torch.Tensor,
interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
fill: datapoints.FillTypeJIT = None,
fill: datapoints._FillTypeJIT = None,
) -> torch.Tensor:
return elastic_image_tensor(video, displacement, interpolation=interpolation, fill=fill)
def elastic(
inpt: datapoints.InputTypeJIT,
inpt: datapoints._InputTypeJIT,
displacement: torch.Tensor,
interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
fill: datapoints.FillTypeJIT = None,
) -> datapoints.InputTypeJIT:
fill: datapoints._FillTypeJIT = None,
) -> datapoints._InputTypeJIT:
if not torch.jit.is_scripting():
_log_api_usage_once(elastic)
......@@ -1802,7 +1802,7 @@ def center_crop_video(video: torch.Tensor, output_size: List[int]) -> torch.Tens
return center_crop_image_tensor(video, output_size)
def center_crop(inpt: datapoints.InputTypeJIT, output_size: List[int]) -> datapoints.InputTypeJIT:
def center_crop(inpt: datapoints._InputTypeJIT, output_size: List[int]) -> datapoints._InputTypeJIT:
if not torch.jit.is_scripting():
_log_api_usage_once(center_crop)
......@@ -1888,7 +1888,7 @@ def resized_crop_video(
def resized_crop(
inpt: datapoints.InputTypeJIT,
inpt: datapoints._InputTypeJIT,
top: int,
left: int,
height: int,
......@@ -1896,7 +1896,7 @@ def resized_crop(
size: List[int],
interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
antialias: Optional[Union[str, bool]] = "warn",
) -> datapoints.InputTypeJIT:
) -> datapoints._InputTypeJIT:
if not torch.jit.is_scripting():
_log_api_usage_once(resized_crop)
......@@ -1972,7 +1972,7 @@ def five_crop_video(
return five_crop_image_tensor(video, size)
ImageOrVideoTypeJIT = Union[datapoints.ImageTypeJIT, datapoints.VideoTypeJIT]
ImageOrVideoTypeJIT = Union[datapoints._ImageTypeJIT, datapoints._VideoTypeJIT]
def five_crop(
......@@ -2069,7 +2069,7 @@ def ten_crop_video(
def ten_crop(
inpt: Union[datapoints.ImageTypeJIT, datapoints.VideoTypeJIT], size: List[int], vertical_flip: bool = False
inpt: Union[datapoints._ImageTypeJIT, datapoints._VideoTypeJIT], size: List[int], vertical_flip: bool = False
) -> Tuple[
ImageOrVideoTypeJIT,
ImageOrVideoTypeJIT,
......
torchvision/transforms/v2/functional/_meta.py
View file @ d4d20f01
......@@ -27,7 +27,7 @@ def get_dimensions_image_tensor(image: torch.Tensor) -> List[int]:
get_dimensions_image_pil = _FP.get_dimensions
def get_dimensions(inpt: Union[datapoints.ImageTypeJIT, datapoints.VideoTypeJIT]) -> List[int]:
def get_dimensions(inpt: Union[datapoints._ImageTypeJIT, datapoints._VideoTypeJIT]) -> List[int]:
if not torch.jit.is_scripting():
_log_api_usage_once(get_dimensions)
......@@ -64,7 +64,7 @@ def get_num_channels_video(video: torch.Tensor) -> int:
return get_num_channels_image_tensor(video)
def get_num_channels(inpt: Union[datapoints.ImageTypeJIT, datapoints.VideoTypeJIT]) -> int:
def get_num_channels(inpt: Union[datapoints._ImageTypeJIT, datapoints._VideoTypeJIT]) -> int:
if not torch.jit.is_scripting():
_log_api_usage_once(get_num_channels)
......@@ -114,7 +114,7 @@ def get_spatial_size_bounding_box(bounding_box: datapoints.BoundingBox) -> List[
return list(bounding_box.spatial_size)
def get_spatial_size(inpt: datapoints.InputTypeJIT) -> List[int]:
def get_spatial_size(inpt: datapoints._InputTypeJIT) -> List[int]:
if not torch.jit.is_scripting():
_log_api_usage_once(get_spatial_size)
......@@ -135,7 +135,7 @@ def get_num_frames_video(video: torch.Tensor) -> int:
return video.shape[-4]
def get_num_frames(inpt: datapoints.VideoTypeJIT) -> int:
def get_num_frames(inpt: datapoints._VideoTypeJIT) -> int:
if not torch.jit.is_scripting():
_log_api_usage_once(get_num_frames)
......@@ -208,11 +208,11 @@ def _convert_format_bounding_box(
def convert_format_bounding_box(
inpt: datapoints.InputTypeJIT,
inpt: datapoints._InputTypeJIT,
old_format: Optional[BoundingBoxFormat] = None,
new_format: Optional[BoundingBoxFormat] = None,
inplace: bool = False,
) -> datapoints.InputTypeJIT:
) -> datapoints._InputTypeJIT:
# This being a kernel / dispatcher hybrid, we need an option to pass `old_format` explicitly for simple tensor
# inputs as well as extract it from `datapoints.BoundingBox` inputs. However, putting a default value on
# `old_format` means we also need to put one on `new_format` to have syntactically correct Python. Here we mimic the
......@@ -259,10 +259,10 @@ def _clamp_bounding_box(
def clamp_bounding_box(
inpt: datapoints.InputTypeJIT,
inpt: datapoints._InputTypeJIT,
format: Optional[BoundingBoxFormat] = None,
spatial_size: Optional[Tuple[int, int]] = None,
) -> datapoints.InputTypeJIT:
) -> datapoints._InputTypeJIT:
if not torch.jit.is_scripting():
_log_api_usage_once(clamp_bounding_box)
......@@ -355,7 +355,7 @@ def convert_dtype_video(video: torch.Tensor, dtype: torch.dtype = torch.float) -
def convert_dtype(
inpt: Union[datapoints.ImageTypeJIT, datapoints.VideoTypeJIT], dtype: torch.dtype = torch.float
inpt: Union[datapoints._ImageTypeJIT, datapoints._VideoTypeJIT], dtype: torch.dtype = torch.float
) -> torch.Tensor:
if not torch.jit.is_scripting():
_log_api_usage_once(convert_dtype)
......
torchvision/transforms/v2/functional/_misc.py
View file @ d4d20f01
......@@ -53,7 +53,7 @@ def normalize_video(video: torch.Tensor, mean: List[float], std: List[float], in
def normalize(
inpt: Union[datapoints.TensorImageTypeJIT, datapoints.TensorVideoTypeJIT],
inpt: Union[datapoints._TensorImageTypeJIT, datapoints._TensorVideoTypeJIT],
mean: List[float],
std: List[float],
inplace: bool = False,
......@@ -166,8 +166,8 @@ def gaussian_blur_video(
def gaussian_blur(
inpt: datapoints.InputTypeJIT, kernel_size: List[int], sigma: Optional[List[float]] = None
) -> datapoints.InputTypeJIT:
inpt: datapoints._InputTypeJIT, kernel_size: List[int], sigma: Optional[List[float]] = None
) -> datapoints._InputTypeJIT:
if not torch.jit.is_scripting():
_log_api_usage_once(gaussian_blur)
......
torchvision/transforms/v2/functional/_temporal.py
View file @ d4d20f01
......@@ -14,7 +14,7 @@ def uniform_temporal_subsample_video(video: torch.Tensor, num_samples: int) -> t
return torch.index_select(video, -4, indices)
def uniform_temporal_subsample(inpt: datapoints.VideoTypeJIT, num_samples: int) -> datapoints.VideoTypeJIT:
def uniform_temporal_subsample(inpt: datapoints._VideoTypeJIT, num_samples: int) -> datapoints._VideoTypeJIT:
if not torch.jit.is_scripting():
_log_api_usage_once(uniform_temporal_subsample)
......
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