Test cleanups + add new tests for datapoints (#7828)

test/test_datapoints.py

@@ -2,22 +2,16 @@ from copy import deepcopy
 
 import pytest
 import torch
-from common_utils import assert_equal
+from common_utils import assert_equal, make_bounding_box, make_image, make_segmentation_mask, make_video
 from PIL import Image
 
 from torchvision import datapoints
-from common_utils import (
-    make_bounding_box,
-    make_detection_mask,
-    make_image,
-    make_image_tensor,
-    make_segmentation_mask,
-    make_video,
-)
 
 
 @pytest.fixture(autouse=True)
-def preserve_default_wrapping_behaviour():
+def restore_tensor_return_type():
+    # This is for security, as we should already be restoring the default manually in each test anyway
+    # (at least at the time of writing...)
     yield
     datapoints.set_return_type("Tensor")
 
@@ -74,8 +68,9 @@ def test_new_requires_grad(data, input_requires_grad, expected_requires_grad):
     assert datapoint.requires_grad is expected_requires_grad
 
 
-def test_isinstance():
-    assert isinstance(datapoints.Image(torch.rand(3, 16, 16)), torch.Tensor)
+@pytest.mark.parametrize("make_input", [make_image, make_bounding_box, make_segmentation_mask, make_video])
+def test_isinstance(make_input):
+    assert isinstance(make_input(), torch.Tensor)
 
 
 def test_wrapping_no_copy():
@@ -85,65 +80,71 @@ def test_wrapping_no_copy():
     assert image.data_ptr() == tensor.data_ptr()
 
 
-def test_to_wrapping():
-    image = datapoints.Image(torch.rand(3, 16, 16))
+@pytest.mark.parametrize("make_input", [make_image, make_bounding_box, make_segmentation_mask, make_video])
+def test_to_wrapping(make_input):
+    dp = make_input()
 
-    image_to = image.to(torch.float64)
+    dp_to = dp.to(torch.float64)
 
-    assert type(image_to) is datapoints.Image
-    assert image_to.dtype is torch.float64
+    assert type(dp_to) is type(dp)
+    assert dp_to.dtype is torch.float64
 
 
+@pytest.mark.parametrize("make_input", [make_image, make_bounding_box, make_segmentation_mask, make_video])
 @pytest.mark.parametrize("return_type", ["Tensor", "datapoint"])
-def test_to_datapoint_reference(return_type):
+def test_to_datapoint_reference(make_input, return_type):
     tensor = torch.rand((3, 16, 16), dtype=torch.float64)
-    image = datapoints.Image(tensor)
+    dp = make_input()
 
     with datapoints.set_return_type(return_type):
-        tensor_to = tensor.to(image)
+        tensor_to = tensor.to(dp)
 
-    assert type(tensor_to) is (datapoints.Image if return_type == "datapoint" else torch.Tensor)
-    assert tensor_to.dtype is torch.float64
+    assert type(tensor_to) is (type(dp) if return_type == "datapoint" else torch.Tensor)
+    assert tensor_to.dtype is dp.dtype
 
 
+@pytest.mark.parametrize("make_input", [make_image, make_bounding_box, make_segmentation_mask, make_video])
 @pytest.mark.parametrize("return_type", ["Tensor", "datapoint"])
-def test_clone_wrapping(return_type):
-    image = datapoints.Image(torch.rand(3, 16, 16))
+def test_clone_wrapping(make_input, return_type):
+    dp = make_input()
 
     with datapoints.set_return_type(return_type):
-        image_clone = image.clone()
+        dp_clone = dp.clone()
 
-    assert type(image_clone) is datapoints.Image
-    assert image_clone.data_ptr() != image.data_ptr()
+    assert type(dp_clone) is type(dp)
+    assert dp_clone.data_ptr() != dp.data_ptr()
 
 
+@pytest.mark.parametrize("make_input", [make_image, make_bounding_box, make_segmentation_mask, make_video])
 @pytest.mark.parametrize("return_type", ["Tensor", "datapoint"])
-def test_requires_grad__wrapping(return_type):
-    image = datapoints.Image(torch.rand(3, 16, 16))
+def test_requires_grad__wrapping(make_input, return_type):
+    dp = make_input(dtype=torch.float)
 
-    assert not image.requires_grad
+    assert not dp.requires_grad
 
     with datapoints.set_return_type(return_type):
-        image_requires_grad = image.requires_grad_(True)
+        dp_requires_grad = dp.requires_grad_(True)
 
-    assert type(image_requires_grad) is datapoints.Image
-    assert image.requires_grad
-    assert image_requires_grad.requires_grad
+    assert type(dp_requires_grad) is type(dp)
+    assert dp.requires_grad
+    assert dp_requires_grad.requires_grad
 
 
+@pytest.mark.parametrize("make_input", [make_image, make_bounding_box, make_segmentation_mask, make_video])
 @pytest.mark.parametrize("return_type", ["Tensor", "datapoint"])
-def test_detach_wrapping(return_type):
-    image = datapoints.Image(torch.rand(3, 16, 16), requires_grad=True)
+def test_detach_wrapping(make_input, return_type):
+    dp = make_input(dtype=torch.float).requires_grad_(True)
 
     with datapoints.set_return_type(return_type):
-        image_detached = image.detach()
+        dp_detached = dp.detach()
 
-    assert type(image_detached) is datapoints.Image
+    assert type(dp_detached) is type(dp)
 
 
 @pytest.mark.parametrize("return_type", ["Tensor", "datapoint"])
 def test_force_subclass_with_metadata(return_type):
     # Sanity checks for the ops in _FORCE_TORCHFUNCTION_SUBCLASS and datapoints with metadata
+    # Largely the same as above, we additionally check that the metadata is preserved
     format, canvas_size = "XYXY", (32, 32)
     bbox = datapoints.BoundingBoxes([[0, 0, 5, 5], [2, 2, 7, 7]], format=format, canvas_size=canvas_size)
 
@@ -165,19 +166,22 @@ def test_force_subclass_with_metadata(return_type):
     if return_type == "datapoint":
         assert bbox.format, bbox.canvas_size == (format, canvas_size)
         assert bbox.requires_grad
+    datapoints.set_return_type("tensor")
 
 
+@pytest.mark.parametrize("make_input", [make_image, make_bounding_box, make_segmentation_mask, make_video])
 @pytest.mark.parametrize("return_type", ["Tensor", "datapoint"])
-def test_other_op_no_wrapping(return_type):
-    image = datapoints.Image(torch.rand(3, 16, 16))
+def test_other_op_no_wrapping(make_input, return_type):
+    dp = make_input()
 
     with datapoints.set_return_type(return_type):
         # any operation besides the ones listed in _FORCE_TORCHFUNCTION_SUBCLASS will do here
-        output = image * 2
+        output = dp * 2
 
-    assert type(output) is (datapoints.Image if return_type == "datapoint" else torch.Tensor)
+    assert type(output) is (type(dp) if return_type == "datapoint" else torch.Tensor)
 
 
+@pytest.mark.parametrize("make_input", [make_image, make_bounding_box, make_segmentation_mask, make_video])
 @pytest.mark.parametrize(
     "op",
     [
@@ -186,146 +190,117 @@ def test_other_op_no_wrapping(return_type):
         lambda t: t.max(dim=-1),
     ],
 )
-def test_no_tensor_output_op_no_wrapping(op):
-    image = datapoints.Image(torch.rand(3, 16, 16))
+def test_no_tensor_output_op_no_wrapping(make_input, op):
+    dp = make_input()
 
-    output = op(image)
+    output = op(dp)
 
-    assert type(output) is not datapoints.Image
+    assert type(output) is not type(dp)
 
 
+@pytest.mark.parametrize("make_input", [make_image, make_bounding_box, make_segmentation_mask, make_video])
 @pytest.mark.parametrize("return_type", ["Tensor", "datapoint"])
-def test_inplace_op_no_wrapping(return_type):
-    image = datapoints.Image(torch.rand(3, 16, 16))
+def test_inplace_op_no_wrapping(make_input, return_type):
+    dp = make_input()
+    original_type = type(dp)
 
     with datapoints.set_return_type(return_type):
-        output = image.add_(0)
+        output = dp.add_(0)
 
-    assert type(output) is (datapoints.Image if return_type == "datapoint" else torch.Tensor)
-    assert type(image) is datapoints.Image
+    assert type(output) is (type(dp) if return_type == "datapoint" else torch.Tensor)
+    assert type(dp) is original_type
 
 
-def test_wrap_like():
-    image = datapoints.Image(torch.rand(3, 16, 16))
+@pytest.mark.parametrize("make_input", [make_image, make_bounding_box, make_segmentation_mask, make_video])
+def test_wrap_like(make_input):
+    dp = make_input()
 
     # any operation besides the ones listed in _FORCE_TORCHFUNCTION_SUBCLASS will do here
-    output = image * 2
+    output = dp * 2
 
-    image_new = datapoints.Image.wrap_like(image, output)
+    dp_new = type(dp).wrap_like(dp, output)
 
-    assert type(image_new) is datapoints.Image
-    assert image_new.data_ptr() == output.data_ptr()
+    assert type(dp_new) is type(dp)
+    assert dp_new.data_ptr() == output.data_ptr()
 
 
-@pytest.mark.parametrize(
-    "datapoint",
-    [
-        datapoints.Image(torch.rand(3, 16, 16)),
-        datapoints.Video(torch.rand(2, 3, 16, 16)),
-        datapoints.BoundingBoxes([0.0, 1.0, 2.0, 3.0], format=datapoints.BoundingBoxFormat.XYXY, canvas_size=(10, 10)),
-        datapoints.Mask(torch.randint(0, 256, (16, 16), dtype=torch.uint8)),
-    ],
-)
+@pytest.mark.parametrize("make_input", [make_image, make_bounding_box, make_segmentation_mask, make_video])
 @pytest.mark.parametrize("requires_grad", [False, True])
-def test_deepcopy(datapoint, requires_grad):
-    if requires_grad and not datapoint.dtype.is_floating_point:
-        return
+def test_deepcopy(make_input, requires_grad):
+    dp = make_input(dtype=torch.float)
 
-    datapoint.requires_grad_(requires_grad)
+    dp.requires_grad_(requires_grad)
 
-    datapoint_deepcopied = deepcopy(datapoint)
+    dp_deepcopied = deepcopy(dp)
 
-    assert datapoint_deepcopied is not datapoint
-    assert datapoint_deepcopied.data_ptr() != datapoint.data_ptr()
-    assert_equal(datapoint_deepcopied, datapoint)
+    assert dp_deepcopied is not dp
+    assert dp_deepcopied.data_ptr() != dp.data_ptr()
+    assert_equal(dp_deepcopied, dp)
 
-    assert type(datapoint_deepcopied) is type(datapoint)
-    assert datapoint_deepcopied.requires_grad is requires_grad
+    assert type(dp_deepcopied) is type(dp)
+    assert dp_deepcopied.requires_grad is requires_grad
 
 
+@pytest.mark.parametrize("make_input", [make_image, make_bounding_box, make_segmentation_mask, make_video])
 @pytest.mark.parametrize("return_type", ["Tensor", "datapoint"])
-def test_operations(return_type):
-    datapoints.set_return_type(return_type)
+@pytest.mark.parametrize(
+    "op",
+    (
+        lambda dp: dp + torch.rand(*dp.shape),
+        lambda dp: torch.rand(*dp.shape) + dp,
+        lambda dp: dp * torch.rand(*dp.shape),
+        lambda dp: torch.rand(*dp.shape) * dp,
+        lambda dp: dp + 3,
+        lambda dp: 3 + dp,
+        lambda dp: dp + dp,
+        lambda dp: dp.sum(),
+        lambda dp: dp.reshape(-1),
+        lambda dp: dp.int(),
+        lambda dp: torch.stack([dp, dp]),
+        lambda dp: torch.chunk(dp, 2)[0],
+        lambda dp: torch.unbind(dp)[0],
+    ),
+)
+def test_usual_operations(make_input, return_type, op):
+
+    dp = make_input()
+    with datapoints.set_return_type(return_type):
+        out = op(dp)
+    assert type(out) is (type(dp) if return_type == "datapoint" else torch.Tensor)
+    if isinstance(dp, datapoints.BoundingBoxes) and return_type == "datapoint":
+        assert hasattr(out, "format")
+        assert hasattr(out, "canvas_size")
+
+
+def test_subclasses():
+    img = make_image()
+    masks = make_segmentation_mask()
+
+    with pytest.raises(TypeError, match="unsupported operand"):
+        img + masks
+
+
+def test_set_return_type():
+    img = make_image()
+
+    assert type(img + 3) is torch.Tensor
+
+    with datapoints.set_return_type("datapoint"):
+        assert type(img + 3) is datapoints.Image
+    assert type(img + 3) is torch.Tensor
+
+    datapoints.set_return_type("datapoint")
+    assert type(img + 3) is datapoints.Image
+
+    with datapoints.set_return_type("tensor"):
+        assert type(img + 3) is torch.Tensor
+        with datapoints.set_return_type("datapoint"):
+            assert type(img + 3) is datapoints.Image
+            datapoints.set_return_type("tensor")
+            assert type(img + 3) is torch.Tensor
+        assert type(img + 3) is torch.Tensor
+    # Exiting a context manager will restore the return type as it was prior to entering it,
+    # regardless of whether the "global" datapoints.set_return_type() was called within the context manager.
+    assert type(img + 3) is datapoints.Image
 
-    img = datapoints.Image(torch.rand(3, 10, 10))
-    t = torch.rand(3, 10, 10)
-    mask = datapoints.Mask(torch.rand(1, 10, 10))
-
-    for out in (
-        [
-            img + t,
-            t + img,
-            img * t,
-            t * img,
-            img + 3,
-            3 + img,
-            img * 3,
-            3 * img,
-            img + img,
-            img.sum(),
-            img.reshape(-1),
-            img.float(),
-            torch.stack([img, img]),
-        ]
-        + list(torch.chunk(img, 2))
-        + list(torch.unbind(img))
-    ):
-        assert type(out) is (datapoints.Image if return_type == "datapoint" else torch.Tensor)
-
-    for out in (
-        [
-            mask + t,
-            t + mask,
-            mask * t,
-            t * mask,
-            mask + 3,
-            3 + mask,
-            mask * 3,
-            3 * mask,
-            mask + mask,
-            mask.sum(),
-            mask.reshape(-1),
-            mask.float(),
-            torch.stack([mask, mask]),
-        ]
-        + list(torch.chunk(mask, 2))
-        + list(torch.unbind(mask))
-    ):
-        assert type(out) is (datapoints.Mask if return_type == "datapoint" else torch.Tensor)
-
-    with pytest.raises(TypeError, match="unsupported operand type"):
-        img + mask
-
-    with pytest.raises(TypeError, match="unsupported operand type"):
-        img * mask
-
-    bboxes = datapoints.BoundingBoxes(
-        [[17, 16, 344, 495], [0, 10, 0, 10]], format=datapoints.BoundingBoxFormat.XYXY, canvas_size=(1000, 1000)
-    )
-    t = torch.rand(2, 4)
-
-    for out in (
-        [
-            bboxes + t,
-            t + bboxes,
-            bboxes * t,
-            t * bboxes,
-            bboxes + 3,
-            3 + bboxes,
-            bboxes * 3,
-            3 * bboxes,
-            bboxes + bboxes,
-            bboxes.sum(),
-            bboxes.reshape(-1),
-            bboxes.float(),
-            torch.stack([bboxes, bboxes]),
-        ]
-        + list(torch.chunk(bboxes, 2))
-        + list(torch.unbind(bboxes))
-    ):
-        if return_type == "Tensor":
-            assert type(out) is torch.Tensor
-        else:
-            assert isinstance(out, datapoints.BoundingBoxes)
-            assert hasattr(out, "format")
-            assert hasattr(out, "canvas_size")
+    datapoints.set_return_type("tensor")