Merge branch 'main' into patch-1

Author: NicolasHug

test/test_transforms_v2.py

@@ -519,34 +519,6 @@ def test__transform(self, mocker):
  assert isinstance(output_masks, tv_tensors.Mask)
 
 
-class TestScaleJitter:
- def test__get_params(self):
- canvas_size = (24, 32)
- target_size = (16, 12)
- scale_range = (0.5, 1.5)
-
- transform = transforms.ScaleJitter(target_size=target_size, scale_range=scale_range)
-
- sample = make_image(canvas_size)
-
- n_samples = 5
- for _ in range(n_samples):
-
- params = transform._get_params([sample])
-
- assert "size" in params
- size = params["size"]
-
- assert isinstance(size, tuple) and len(size) == 2
- height, width = size
-
- r_min = min(target_size[1] / canvas_size[0], target_size[0] / canvas_size[1]) * scale_range[0]
- r_max = min(target_size[1] / canvas_size[0], target_size[0] / canvas_size[1]) * scale_range[1]
-
- assert int(canvas_size[0] * r_min) <= height <= int(canvas_size[0] * r_max)
- assert int(canvas_size[1] * r_min) <= width <= int(canvas_size[1] * r_max)
-
-
 class TestRandomShortestSize:
  @pytest.mark.parametrize("min_size,max_size", [([5, 9], 20), ([5, 9], None)])
  def test__get_params(self, min_size, max_size):

test/test_transforms_v2_refactored.py

@@ -4788,3 +4788,41 @@ def test_transform(self, make_input, dtype, device):
  ),
  make_input(dtype=dtype, device=device),
  )
+
+
+class TestScaleJitter:
+ # Tests are light because this largely relies on the already tested `resize` kernels.
+
+ INPUT_SIZE = (17, 11)
+ TARGET_SIZE = (12, 13)
+
+ @pytest.mark.parametrize(
+ "make_input",
+ [make_image_tensor, make_image_pil, make_image, make_bounding_boxes, make_segmentation_mask, make_video],
+ )
+ @pytest.mark.parametrize("device", cpu_and_cuda())
+ def test_transform(self, make_input, device):
+ if make_input is make_image_pil and device != "cpu":
+ pytest.skip("PIL image tests with parametrization device!='cpu' will degenerate to that anyway.")
+
+ check_transform(transforms.ScaleJitter(self.TARGET_SIZE), make_input(self.INPUT_SIZE, device=device))
+
+ def test__get_params(self):
+ input_size = self.INPUT_SIZE
+ target_size = self.TARGET_SIZE
+ scale_range = (0.5, 1.5)
+
+ transform = transforms.ScaleJitter(target_size=target_size, scale_range=scale_range)
+ params = transform._get_params([make_image(input_size)])
+
+ assert "size" in params
+ size = params["size"]
+
+ assert isinstance(size, tuple) and len(size) == 2
+ height, width = size
+
+ r_min = min(target_size[1] / input_size[0], target_size[0] / input_size[1]) * scale_range[0]
+ r_max = min(target_size[1] / input_size[0], target_size[0] / input_size[1]) * scale_range[1]
+
+ assert int(input_size[0] * r_min) <= height <= int(input_size[0] * r_max)
+ assert int(input_size[1] * r_min) <= width <= int(input_size[1] * r_max)