Fix

Author: NicolasHug

test/common_utils.py

@@ -9,7 +9,6 @@
 import sys
 import tempfile
 import warnings
-from collections.abc import Sequence
 from subprocess import CalledProcessError, check_output, STDOUT
 
 import numpy as np
@@ -401,18 +400,10 @@ def make_image_pil(*args, **kwargs):
     return to_pil_image(make_image(*args, **kwargs))
 
 
-def make_keypoints(
-    canvas_size: tuple[int, int] = DEFAULT_SIZE, *, num_points: int | Sequence[int] = 4, dtype=None, device="cpu"
-) -> tv_tensors.KeyPoints:
-    """Make the KeyPoints for testing purposes"""
-    if isinstance(num_points, int):
-        num_points = [num_points]
-    single_coord_shape: tuple[int, ...] = tuple(num_points) + (1,)
-    y = torch.randint(0, canvas_size[0] - 1, single_coord_shape, dtype=dtype, device=device)
-    x = torch.randint(0, canvas_size[1] - 1, single_coord_shape, dtype=dtype, device=device)
-    points = torch.cat((x, y), dim=-1)
-    keypoints = tv_tensors.KeyPoints(points, canvas_size=canvas_size)
-    return keypoints
+def make_keypoints(canvas_size=DEFAULT_SIZE, *, num_points=4, dtype=None, device="cpu"):
+    y = torch.randint(0, canvas_size[0], size=(num_points, 1), dtype=dtype, device=device)
+    x = torch.randint(0, canvas_size[1], size=(num_points, 1), dtype=dtype, device=device)
+    return tv_tensors.KeyPoints(torch.cat((x, y), dim=-1), canvas_size=canvas_size)
 
 
 def make_bounding_boxes(

test/test_transforms_v2.py

@@ -6877,14 +6877,27 @@ def test_no_valid_input(self, query):
             query(["blah"])
 
     @pytest.mark.parametrize(
-        "boxes", [
-            tv_tensors.BoundingBoxes(torch.tensor([[1., 1., 2., 2.]]), format="XYXY", canvas_size=(4, 4)),  # [boxes0]
-            tv_tensors.BoundingBoxes(torch.tensor([[1., 1., 1., 1.]]), format="XYWH", canvas_size=(4, 4)),  # [boxes1]
-            tv_tensors.BoundingBoxes(torch.tensor([[1.5, 1.5, 1., 1.]]), format="CXCYWH", canvas_size=(4, 4)),  # [boxes2]
-            tv_tensors.BoundingBoxes(torch.tensor([[1.5, 1.5, 1., 1., 45]]), format="CXCYWHR", canvas_size=(4, 4)),  # [boxes3]
-            tv_tensors.BoundingBoxes(torch.tensor([[1., 1., 1., 1., 45.]]), format="XYWHR", canvas_size=(4, 4)),  # [boxes4]
-            tv_tensors.BoundingBoxes(torch.tensor([[1., 1., 1., 2., 2., 2., 2., 1.]]), format="XY" * 4, canvas_size=(4, 4)),  # [boxes5]
-        ]
+        "boxes",
+        [
+            tv_tensors.BoundingBoxes(
+                torch.tensor([[1.0, 1.0, 2.0, 2.0]]), format="XYXY", canvas_size=(4, 4)
+            ),  # [boxes0]
+            tv_tensors.BoundingBoxes(
+                torch.tensor([[1.0, 1.0, 1.0, 1.0]]), format="XYWH", canvas_size=(4, 4)
+            ),  # [boxes1]
+            tv_tensors.BoundingBoxes(
+                torch.tensor([[1.5, 1.5, 1.0, 1.0]]), format="CXCYWH", canvas_size=(4, 4)
+            ),  # [boxes2]
+            tv_tensors.BoundingBoxes(
+                torch.tensor([[1.5, 1.5, 1.0, 1.0, 45]]), format="CXCYWHR", canvas_size=(4, 4)
+            ),  # [boxes3]
+            tv_tensors.BoundingBoxes(
+                torch.tensor([[1.0, 1.0, 1.0, 1.0, 45.0]]), format="XYWHR", canvas_size=(4, 4)
+            ),  # [boxes4]
+            tv_tensors.BoundingBoxes(
+                torch.tensor([[1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 1.0]]), format="XY" * 4, canvas_size=(4, 4)
+            ),  # [boxes5]
+        ],
     )
     def test_convert_bounding_boxes_to_points(self, boxes: tv_tensors.BoundingBoxes):
         kp = F.convert_bounding_boxes_to_points(boxes)
@@ -6897,10 +6910,14 @@ def test_convert_bounding_boxes_to_points(self, boxes: tv_tensors.BoundingBoxes)
             # If we convert to XYXYXYXY format, we should get what we want.
             reconverted = kp.reshape(-1, 8)
             reconverted_bbox = F.convert_bounding_box_format(
-                tv_tensors.BoundingBoxes(reconverted, format=tv_tensors.BoundingBoxFormat.XYXYXYXY, canvas_size=kp.canvas_size),
-                new_format=boxes.format
+                tv_tensors.BoundingBoxes(
+                    reconverted, format=tv_tensors.BoundingBoxFormat.XYXYXYXY, canvas_size=kp.canvas_size
+                ),
+                new_format=boxes.format,
             )
-            assert ((reconverted_bbox - boxes).abs() < 1e-5).all(), (  # Rotational computations mean that we can't ensure exactitude.
+            assert (
+                (reconverted_bbox - boxes).abs() < 1e-5
+            ).all(), (  # Rotational computations mean that we can't ensure exactitude.
                 f"Invalid reconversion :\n\tGot:  {reconverted_bbox}\n\tFrom: {boxes}\n\t"
                 f"Diff: {reconverted_bbox - boxes}"
             )
@@ -6909,7 +6926,11 @@ def test_convert_bounding_boxes_to_points(self, boxes: tv_tensors.BoundingBoxes)
             # If we use A | C, we should get back the XYXY format of bounding box
             reconverted = torch.cat([kp[..., 0, :], kp[..., 2, :]], dim=-1)
             reconverted_bbox = F.convert_bounding_box_format(
-                tv_tensors.BoundingBoxes(reconverted, format=tv_tensors.BoundingBoxFormat.XYXY, canvas_size=kp.canvas_size),
+                tv_tensors.BoundingBoxes(
+                    reconverted, format=tv_tensors.BoundingBoxFormat.XYXY, canvas_size=kp.canvas_size
+                ),
                 new_format=boxes.format,
             )
-            assert (reconverted_bbox == boxes).all(), f"Invalid reconversion :\n\tGot:  {reconverted_bbox}\n\tFrom: {boxes}"
+            assert (
+                reconverted_bbox == boxes
+            ).all(), f"Invalid reconversion :\n\tGot:  {reconverted_bbox}\n\tFrom: {boxes}"

torchvision/transforms/v2/functional/_geometry.py

@@ -2678,7 +2678,9 @@ def five_crop_video(
     return five_crop_image(video, size)
 
 
-def ten_crop(inpt: torch.Tensor, size: list[int], vertical_flip: bool = False) -> tuple[
+def ten_crop(
+    inpt: torch.Tensor, size: list[int], vertical_flip: bool = False
+) -> tuple[
     torch.Tensor,
     torch.Tensor,
     torch.Tensor,
@@ -2702,7 +2704,9 @@ def ten_crop(inpt: torch.Tensor, size: list[int], vertical_flip: bool = False) -
 
 @_register_five_ten_crop_kernel_internal(ten_crop, torch.Tensor)
 @_register_five_ten_crop_kernel_internal(ten_crop, tv_tensors.Image)
-def ten_crop_image(image: torch.Tensor, size: list[int], vertical_flip: bool = False) -> tuple[
+def ten_crop_image(
+    image: torch.Tensor, size: list[int], vertical_flip: bool = False
+) -> tuple[
     torch.Tensor,
     torch.Tensor,
     torch.Tensor,
@@ -2727,7 +2731,9 @@ def ten_crop_image(image: torch.Tensor, size: list[int], vertical_flip: bool = F
 
 
 @_register_five_ten_crop_kernel_internal(ten_crop, PIL.Image.Image)
-def _ten_crop_image_pil(image: PIL.Image.Image, size: list[int], vertical_flip: bool = False) -> tuple[
+def _ten_crop_image_pil(
+    image: PIL.Image.Image, size: list[int], vertical_flip: bool = False
+) -> tuple[
     PIL.Image.Image,
     PIL.Image.Image,
     PIL.Image.Image,
@@ -2752,7 +2758,9 @@ def _ten_crop_image_pil(image: PIL.Image.Image, size: list[int], vertical_flip:
 
 
 @_register_five_ten_crop_kernel_internal(ten_crop, tv_tensors.Video)
-def ten_crop_video(video: torch.Tensor, size: list[int], vertical_flip: bool = False) -> tuple[
+def ten_crop_video(
+    video: torch.Tensor, size: list[int], vertical_flip: bool = False
+) -> tuple[
     torch.Tensor,
     torch.Tensor,
     torch.Tensor,

torchvision/transforms/v2/functional/_misc.py

@@ -344,7 +344,7 @@ def sanitize_keypoints(
 
     .. note::
 
-        Points that touch the edge of the canvas are removed, unlike for :func:`sanitize_bounding_boxes`.  
+        Points that touch the edge of the canvas are removed, unlike for :func:`sanitize_bounding_boxes`.
 
     Raises:
         ValueError: If the keypoints are not passed as a two dimensional tensor.