Added more functional tests

test/test_prototype_transforms_functional.py

@@ -200,6 +200,30 @@ def horizontal_flip_bounding_box():
         yield SampleInput(bounding_box, format=bounding_box.format, image_size=bounding_box.image_size)
 
 
+@register_kernel_info_from_sample_inputs_fn
+def horizontal_flip_segmentation_mask():
+    for mask in make_segmentation_masks():
+        yield SampleInput(mask)
+
+
+@register_kernel_info_from_sample_inputs_fn
+def vertical_flip_image_tensor():
+    for image in make_images():
+        yield SampleInput(image)
+
+
+@register_kernel_info_from_sample_inputs_fn
+def vertical_flip_bounding_box():
+    for bounding_box in make_bounding_boxes(formats=[features.BoundingBoxFormat.XYXY]):
+        yield SampleInput(bounding_box, format=bounding_box.format, image_size=bounding_box.image_size)
+
+
+@register_kernel_info_from_sample_inputs_fn
+def vertical_flip_segmentation_mask():
+    for mask in make_segmentation_masks():
+        yield SampleInput(mask)
+
+
 @register_kernel_info_from_sample_inputs_fn
 def resize_image_tensor():
     for image, interpolation, max_size, antialias in itertools.product(
@@ -404,9 +428,17 @@ def crop_segmentation_mask():
 
 
 @register_kernel_info_from_sample_inputs_fn
-def vertical_flip_segmentation_mask():
-    for mask in make_segmentation_masks():
-        yield SampleInput(mask)
+def resized_crop_image_tensor():
+    for mask, top, left, height, width, size, antialias in itertools.product(
+        make_images(),
+        [-8, 9],
+        [-8, 9],
+        [12],
+        [12],
+        [(16, 18)],
+        [True, False],
+    ):
+        yield SampleInput(mask, top=top, left=left, height=height, width=width, size=size, antialias=antialias)
 
 
 @register_kernel_info_from_sample_inputs_fn
@@ -457,6 +489,19 @@ def pad_bounding_box():
         yield SampleInput(bounding_box, padding=padding, format=bounding_box.format)
 
 
+@register_kernel_info_from_sample_inputs_fn
+def perspective_image_tensor():
+    for image, perspective_coeffs, fill in itertools.product(
+        make_images(extra_dims=((), (4,))),
+        [
+            [1.2405, 0.1772, -6.9113, 0.0463, 1.251, -5.235, 0.00013, 0.0018],
+            [0.7366, -0.11724, 1.45775, -0.15012, 0.73406, 2.6019, -0.0072, -0.0063],
+        ],
+        [None, [128], [12.0]],  # fill
+    ):
+        yield SampleInput(image, perspective_coeffs=perspective_coeffs, fill=fill)
+
+
 @register_kernel_info_from_sample_inputs_fn
 def perspective_bounding_box():
     for bounding_box, perspective_coeffs in itertools.product(
@@ -488,6 +533,15 @@ def perspective_segmentation_mask():
         )
 
 
+@register_kernel_info_from_sample_inputs_fn
+def center_crop_image_tensor():
+    for mask, output_size in itertools.product(
+        make_images(sizes=((16, 16), (7, 33), (31, 9))),
+        [[4, 3], [42, 70], [4]],  # crop sizes < image sizes, crop_sizes > image sizes, single crop size
+    ):
+        yield SampleInput(mask, output_size)
+
+
 @register_kernel_info_from_sample_inputs_fn
 def center_crop_bounding_box():
     for bounding_box, output_size in itertools.product(make_bounding_boxes(), [(24, 12), [16, 18], [46, 48], [12]]):
@@ -1181,6 +1235,18 @@ def _compute_expected_mask(mask, top_, left_, height_, width_):
         torch.testing.assert_close(output_mask, expected_mask)
 
 
+@pytest.mark.parametrize("device", cpu_and_gpu())
+def test_correctness_horizontal_flip_segmentation_mask_on_fixed_input(device):
+    mask = torch.zeros((3, 3, 3), dtype=torch.long, device=device)
+    mask[:, :, 0] = 1
+
+    out_mask = F.horizontal_flip_segmentation_mask(mask)
+
+    expected_mask = torch.zeros((3, 3, 3), dtype=torch.long, device=device)
+    expected_mask[:, :, -1] = 1
+    torch.testing.assert_close(out_mask, expected_mask)
+
+
 @pytest.mark.parametrize("device", cpu_and_gpu())
 def test_correctness_vertical_flip_segmentation_mask_on_fixed_input(device):
     mask = torch.zeros((3, 3, 3), dtype=torch.long, device=device)