Fixes "make Image(some_pil_image)" from TODO list (pytorch#7217)

Added tests

test/test_prototype_datapoints.py

@@ -1,5 +1,7 @@
 import pytest
 import torch
+
+from PIL import Image
 from torchvision.prototype import datapoints
 
 
@@ -130,3 +132,24 @@ def test_wrap_like():
     assert type(label_new) is datapoints.Label
     assert label_new.data_ptr() == output.data_ptr()
     assert label_new.categories is label.categories
+
+
+@pytest.mark.parametrize("data", [torch.rand(3, 32, 32), Image.new("RGB", (32, 32), color=123)])
+def test_image_instance(data):
+    image = datapoints.Image(data)
+    assert isinstance(image, torch.Tensor)
+    assert image.ndim == 3 and image.shape[0] == 3
+
+
+@pytest.mark.parametrize("data", [torch.randint(0, 10, size=(1, 32, 32)), Image.new("L", (32, 32), color=2)])
+def test_mask_instance(data):
+    mask = datapoints.Mask(data)
+    assert isinstance(mask, torch.Tensor)
+    assert mask.ndim == 3 and mask.shape[0] == 1
+
+
+@pytest.mark.parametrize("data", [torch.randint(0, 32, size=(5, 4)), [[0, 0, 5, 5], [2, 2, 7, 7]]])
+def test_bbox_instance(data):
+    bboxes = datapoints.BoundingBox(data, format="XYXY", spatial_size=(32, 32))
+    assert isinstance(bboxes, torch.Tensor)
+    assert bboxes.ndim == 2 and bboxes.shape[1] == 4

torchvision/prototype/datapoints/_image.py

@@ -23,6 +23,11 @@ def __new__(
         device: Optional[Union[torch.device, str, int]] = None,
         requires_grad: Optional[bool] = None,
     ) -> Image:
+        if isinstance(data, PIL.Image.Image):
+            from ..transforms import functional as F
+
+            data = F.pil_to_tensor(data)
+
         tensor = cls._to_tensor(data, dtype=dtype, device=device, requires_grad=requires_grad)
         if tensor.ndim < 2:
             raise ValueError

torchvision/prototype/datapoints/_mask.py

@@ -2,6 +2,7 @@
 
 from typing import Any, List, Optional, Tuple, Union
 
+import PIL.Image
 import torch
 from torchvision.transforms import InterpolationMode
 
@@ -21,6 +22,11 @@ def __new__(
         device: Optional[Union[torch.device, str, int]] = None,
         requires_grad: Optional[bool] = None,
     ) -> Mask:
+        if isinstance(data, PIL.Image.Image):
+            from ..transforms import functional as F
+
+            data = F.pil_to_tensor(data)
+
         tensor = cls._to_tensor(data, dtype=dtype, device=device, requires_grad=requires_grad)
         return cls._wrap(tensor)