Replacing Sequencies with Lists.

Author: datumbox

torchvision/prototype/transforms/functional/_geometry.py

@@ -1,6 +1,6 @@
 import numbers
 import warnings
-from typing import List, Optional, Sequence, Tuple, Union
+from typing import List, Optional, Tuple, Union
 
 import PIL.Image
 import torch
@@ -117,14 +117,12 @@ def resize_image_tensor(
 @torch.jit.unused
 def resize_image_pil(
     img: PIL.Image.Image,
-    size: Union[Sequence[int], int],
+    size: Union[List[int], int],
     interpolation: InterpolationMode = InterpolationMode.BILINEAR,
     max_size: Optional[int] = None,
 ) -> PIL.Image.Image:
     if isinstance(size, int):
         size = [size, size]
-    # Explicitly cast size to list otherwise mypy issue: incompatible type "Sequence[int]"; expected "List[int]"
-    size: List[int] = list(size)
     size = _compute_resized_output_size(img.size[::-1], size=size, max_size=max_size)
     return _FP.resize(img, size, interpolation=pil_modes_mapping[interpolation])
 
@@ -261,7 +259,7 @@ def affine_image_pil(
     scale: float,
     shear: List[float],
     interpolation: InterpolationMode = InterpolationMode.NEAREST,
-    fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None,
+    fill: Optional[Union[int, float, List[int], List[float]]] = None,
     center: Optional[List[float]] = None,
 ) -> PIL.Image.Image:
     angle, translate, shear, center = _affine_parse_args(angle, translate, scale, shear, interpolation, center)
@@ -403,15 +401,15 @@ def affine_mask(
     return output
 
 
-def _convert_fill_arg(fill: Optional[Union[int, float, Sequence[int], Sequence[float]]]) -> Optional[List[float]]:
+def _convert_fill_arg(fill: Optional[Union[int, float, List[int], List[float]]]) -> Optional[List[float]]:
     # Fill = 0 is not equivalent to None, https://github.com/pytorch/vision/issues/6517
     # So, we can't reassign fill to 0
     # if fill is None:
     #     fill = 0
     if fill is None:
         return fill
 
-    # This cast does Sequence -> List[float] to please mypy and torch.jit.script
+    # This cast does List -> List[float] to please mypy and torch.jit.script
     if not isinstance(fill, (int, float)):
         fill = [float(v) for v in list(fill)]
     else:
@@ -427,7 +425,7 @@ def affine(
     scale: float,
     shear: List[float],
     interpolation: InterpolationMode = InterpolationMode.NEAREST,
-    fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None,
+    fill: Optional[Union[int, float, List[int], List[float]]] = None,
     center: Optional[List[float]] = None,
 ) -> features.DType:
     if isinstance(inpt, torch.Tensor) and (torch.jit.is_scripting() or not isinstance(inpt, features._Feature)):
@@ -503,7 +501,7 @@ def rotate_image_pil(
     angle: float,
     interpolation: InterpolationMode = InterpolationMode.NEAREST,
     expand: bool = False,
-    fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None,
+    fill: Optional[Union[int, float, List[int], List[float]]] = None,
     center: Optional[List[float]] = None,
 ) -> PIL.Image.Image:
     if center is not None and expand:
@@ -570,7 +568,7 @@ def rotate(
     angle: float,
     interpolation: InterpolationMode = InterpolationMode.NEAREST,
     expand: bool = False,
-    fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None,
+    fill: Optional[Union[int, float, List[int], List[float]]] = None,
     center: Optional[List[float]] = None,
 ) -> features.DType:
     if isinstance(inpt, torch.Tensor) and (torch.jit.is_scripting() or not isinstance(inpt, features._Feature)):
@@ -611,7 +609,7 @@ def pad_image_tensor(
 def _pad_with_vector_fill(
     img: torch.Tensor,
     padding: Union[int, List[int]],
-    fill: Sequence[float] = [0.0],
+    fill: List[float],
     padding_mode: str = "constant",
 ) -> torch.Tensor:
     if padding_mode != "constant":
@@ -677,15 +675,11 @@ def pad_bounding_box(
 
 def pad(
     inpt: features.DType,
-    padding: Union[int, Sequence[int]],
-    fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None,
+    padding: Union[int, List[int]],
+    fill: Optional[Union[int, float, List[int], List[float]]] = None,
     padding_mode: str = "constant",
 ) -> features.DType:
     if isinstance(inpt, torch.Tensor) and (torch.jit.is_scripting() or not isinstance(inpt, features._Feature)):
-        # This cast does Sequence[int] -> List[int] and is required to make mypy happy
-        if not isinstance(padding, int):
-            padding = list(padding)
-
         # TODO: PyTorch's pad supports only scalars on fill. So we need to overwrite the colour
         if isinstance(fill, (int, float)) or fill is None:
             return pad_image_tensor(inpt, padding, fill=fill, padding_mode=padding_mode)
@@ -746,7 +740,7 @@ def perspective_image_pil(
     img: PIL.Image.Image,
     perspective_coeffs: List[float],
     interpolation: InterpolationMode = InterpolationMode.BICUBIC,
-    fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None,
+    fill: Optional[Union[int, float, List[int], List[float]]] = None,
 ) -> PIL.Image.Image:
     return _FP.perspective(img, perspective_coeffs, interpolation=pil_modes_mapping[interpolation], fill=fill)
 
@@ -858,7 +852,7 @@ def perspective(
     startpoints: List[List[int]],
     endpoints: List[List[int]],
     interpolation: InterpolationMode = InterpolationMode.BILINEAR,
-    fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None,
+    fill: Optional[Union[int, float, List[int], List[float]]] = None,
 ) -> features.DType:
     perspective_coeffs = _get_perspective_coeffs(startpoints, endpoints)
 
@@ -885,7 +879,7 @@ def elastic_image_pil(
     img: PIL.Image.Image,
     displacement: torch.Tensor,
     interpolation: InterpolationMode = InterpolationMode.BILINEAR,
-    fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None,
+    fill: Optional[Union[int, float, List[int], List[float]]] = None,
 ) -> PIL.Image.Image:
     t_img = pil_to_tensor(img)
     fill = _convert_fill_arg(fill)
@@ -953,7 +947,7 @@ def elastic(
     inpt: features.DType,
     displacement: torch.Tensor,
     interpolation: InterpolationMode = InterpolationMode.BILINEAR,
-    fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None,
+    fill: Optional[Union[int, float, List[int], List[float]]] = None,
 ) -> features.DType:
     if isinstance(inpt, torch.Tensor) and (torch.jit.is_scripting() or not isinstance(inpt, features._Feature)):
         fill = _convert_fill_arg(fill)