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Enable __getitem__ for Cameras to return an instance of Cameras

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Summary:
Added a custom `__getitem__` method to `CamerasBase` which returns an instance of the appropriate camera instead of the `TensorAccessor` class.

Long term we should deprecate the `TensorAccessor` and the `__getitem__` method on `TensorProperties`

FB: In the next diff I will update the uses of `select_cameras` in implicitron.

Reviewed By: bottler

Differential Revision: D33185885

fbshipit-source-id: c31995d0eb126981e91ba61a6151d5404b263f67
This commit is contained in:
Nikhila Ravi 2021-12-21 05:45:32 -08:00 committed by Facebook GitHub Bot
parent cc3259ba93
commit 28ccdb7328
3 changed files with 224 additions and 13 deletions
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109
pytorch3d/renderer/cameras.py
View File

@ -75,6 +75,10 @@ class CamerasBase(TensorProperties):
boolean argument of the function.
"""
# Used in __getitem__ to index the relevant fields
# When creating a new camera, this should be set in the __init__
_FIELDS: Tuple = ()
def get_projection_transform(self):
"""
Calculate the projective transformation matrix.
@ -362,6 +366,55 @@ class CamerasBase(TensorProperties):
"""
return self.image_size if hasattr(self, "image_size") else None
def __getitem__(
self, index: Union[int, List[int], torch.LongTensor]
) -> "CamerasBase":
"""
Override for the __getitem__ method in TensorProperties which needs to be
refactored.
Args:
index: an int/list/long tensor used to index all the fields in the cameras given by
self._FIELDS.
Returns:
if `index` is an index int/list/long tensor return an instance of the current
cameras class with only the values at the selected index.
"""
kwargs = {}
if not isinstance(index, (int, list, torch.LongTensor)):
msg = "Invalid index type, expected int, List[int] or torch.LongTensor; got %r"
raise ValueError(msg % type(index))
if isinstance(index, int):
index = [index]
if max(index) >= len(self):
raise ValueError(f"Index {max(index)} is out of bounds for select cameras")
for field in self._FIELDS:
val = getattr(self, field, None)
if val is None:
continue
# e.g. "in_ndc" is set as attribute "_in_ndc" on the class
# but provided as "in_ndc" on initialization
if field.startswith("_"):
field = field[1:]
if isinstance(val, (str, bool)):
kwargs[field] = val
elif isinstance(val, torch.Tensor):
# In the init, all inputs will be converted to
# tensors before setting as attributes
kwargs[field] = val[index]
else:
raise ValueError(f"Field {field} type is not supported for indexing")
kwargs["device"] = self.device
return self.__class__(**kwargs)
############################################################
# Field of View Camera Classes #
@ -434,6 +487,18 @@ class FoVPerspectiveCameras(CamerasBase):
for rasterization.
"""
# For __getitem__
_FIELDS = (
"K",
"znear",
"zfar",
"aspect_ratio",
"fov",
"R",
"T",
"degrees",
)
def __init__(
self,
znear=1.0,
@ -590,7 +655,7 @@ class FoVPerspectiveCameras(CamerasBase):
xy_depth: torch.Tensor,
world_coordinates: bool = True,
scaled_depth_input: bool = False,
**kwargs
**kwargs,
) -> torch.Tensor:
""">!
FoV cameras further allow for passing depth in world units
@ -681,6 +746,20 @@ class FoVOrthographicCameras(CamerasBase):
The definition of the parameters follow the OpenGL orthographic camera.
"""
# For __getitem__
_FIELDS = (
"K",
"znear",
"zfar",
"R",
"T",
"max_y",
"min_y",
"max_x",
"min_x",
"scale_xyz",
)
def __init__(
self,
znear=1.0,
@ -819,7 +898,7 @@ class FoVOrthographicCameras(CamerasBase):
xy_depth: torch.Tensor,
world_coordinates: bool = True,
scaled_depth_input: bool = False,
**kwargs
**kwargs,
) -> torch.Tensor:
""">!
FoV cameras further allow for passing depth in world units
@ -907,6 +986,17 @@ class PerspectiveCameras(CamerasBase):
If parameters are specified in screen space, `in_ndc` must be set to False.
"""
# For __getitem__
_FIELDS = (
"K",
"R",
"T",
"focal_length",
"principal_point",
"_in_ndc", # arg is in_ndc but attribute set as _in_ndc
"image_size",
)
def __init__(
self,
focal_length=1.0,
@ -1007,7 +1097,7 @@ class PerspectiveCameras(CamerasBase):
xy_depth: torch.Tensor,
world_coordinates: bool = True,
from_ndc: bool = False,
**kwargs
**kwargs,
) -> torch.Tensor:
"""
Args:
@ -1126,6 +1216,17 @@ class OrthographicCameras(CamerasBase):
If parameters are specified in screen space, `in_ndc` must be set to False.
"""
# For __getitem__
_FIELDS = (
"K",
"R",
"T",
"focal_length",
"principal_point",
"_in_ndc",
"image_size",
)
def __init__(
self,
focal_length=1.0,
@ -1225,7 +1326,7 @@ class OrthographicCameras(CamerasBase):
xy_depth: torch.Tensor,
world_coordinates: bool = True,
from_ndc: bool = False,
**kwargs
**kwargs,
) -> torch.Tensor:
"""
Args:

2
pytorch3d/renderer/utils.py
View File

@ -155,7 +155,7 @@ class TensorProperties(nn.Module):
Returns:
if `index` is an index int/slice return a TensorAccessor class
with getattribute/setattribute methods which return/update the value
at the index in the original camera.
at the index in the original class.
"""
if isinstance(index, (int, slice)):
return TensorAccessor(class_object=self, index=index)

126
tests/test_cameras.py
View File

@ -783,18 +783,53 @@ class TestFoVPerspectiveProjection(TestCaseMixin, unittest.TestCase):
self.assertTrue(cam.znear.shape == (2,))
self.assertTrue(cam.zfar.shape == (2,))
# update znear element 1
cam[1].znear = 20.0
self.assertTrue(cam.znear[1] == 20.0)
# Get item and get value
c0 = cam[0]
self.assertTrue(c0.zfar == 100.0)
# Test to
new_cam = cam.to(device=device)
self.assertTrue(new_cam.device == device)
def test_getitem(self):
R_matrix = torch.randn((6, 3, 3))
cam = FoVPerspectiveCameras(znear=10.0, zfar=100.0, R=R_matrix)
# Check get item returns an instance of the same class
# with all the same keys
c0 = cam[0]
self.assertTrue(isinstance(c0, FoVPerspectiveCameras))
self.assertEqual(cam.__dict__.keys(), c0.__dict__.keys())
# Check all fields correct in get item with int index
self.assertEqual(len(c0), 1)
self.assertClose(c0.zfar, torch.tensor([100.0]))
self.assertClose(c0.znear, torch.tensor([10.0]))
self.assertClose(c0.R, R_matrix[0:1, ...])
self.assertEqual(c0.device, torch.device("cpu"))
# Check list(int) index
c012 = cam[[0, 1, 2]]
self.assertEqual(len(c012), 3)
self.assertClose(c012.zfar, torch.tensor([100.0] * 3))
self.assertClose(c012.znear, torch.tensor([10.0] * 3))
self.assertClose(c012.R, R_matrix[0:3, ...])
# Check torch.LongTensor index
index = torch.tensor([1, 3, 5], dtype=torch.int64)
c135 = cam[index]
self.assertEqual(len(c135), 3)
self.assertClose(c135.zfar, torch.tensor([100.0] * 3))
self.assertClose(c135.znear, torch.tensor([10.0] * 3))
self.assertClose(c135.R, R_matrix[[1, 3, 5], ...])
# Check errors with get item
with self.assertRaisesRegex(ValueError, "out of bounds"):
cam[6]
with self.assertRaisesRegex(ValueError, "Invalid index type"):
cam[slice(0, 1)]
with self.assertRaisesRegex(ValueError, "Invalid index type"):
index = torch.tensor([1, 3, 5], dtype=torch.float32)
cam[index]
def test_get_full_transform(self):
cam = FoVPerspectiveCameras()
T = torch.tensor([0.0, 0.0, 1.0]).view(1, -1)
@ -919,6 +954,30 @@ class TestFoVOrthographicProjection(TestCaseMixin, unittest.TestCase):
self.assertFalse(cam.is_perspective())
self.assertEqual(cam.get_znear(), 1.0)
def test_getitem(self):
R_matrix = torch.randn((6, 3, 3))
scale = torch.tensor([[1.0, 1.0, 1.0]], requires_grad=True)
cam = FoVOrthographicCameras(
znear=10.0, zfar=100.0, R=R_matrix, scale_xyz=scale
)
# Check get item returns an instance of the same class
# with all the same keys
c0 = cam[0]
self.assertTrue(isinstance(c0, FoVOrthographicCameras))
self.assertEqual(cam.__dict__.keys(), c0.__dict__.keys())
# Check torch.LongTensor index
index = torch.tensor([1, 3, 5], dtype=torch.int64)
c135 = cam[index]
self.assertEqual(len(c135), 3)
self.assertClose(c135.zfar, torch.tensor([100.0] * 3))
self.assertClose(c135.znear, torch.tensor([10.0] * 3))
self.assertClose(c135.min_x, torch.tensor([-1.0] * 3))
self.assertClose(c135.max_x, torch.tensor([1.0] * 3))
self.assertClose(c135.R, R_matrix[[1, 3, 5], ...])
self.assertClose(c135.scale_xyz, scale.expand(3, -1))
############################################################
# Orthographic Camera #
@ -976,6 +1035,30 @@ class TestOrthographicProjection(TestCaseMixin, unittest.TestCase):
self.assertFalse(cam.is_perspective())
self.assertIsNone(cam.get_znear())
def test_getitem(self):
R_matrix = torch.randn((6, 3, 3))
principal_point = torch.randn((6, 2, 1))
focal_length = 5.0
cam = OrthographicCameras(
R=R_matrix,
focal_length=focal_length,
principal_point=principal_point,
)
# Check get item returns an instance of the same class
# with all the same keys
c0 = cam[0]
self.assertTrue(isinstance(c0, OrthographicCameras))
self.assertEqual(cam.__dict__.keys(), c0.__dict__.keys())
# Check torch.LongTensor index
index = torch.tensor([1, 3, 5], dtype=torch.int64)
c135 = cam[index]
self.assertEqual(len(c135), 3)
self.assertClose(c135.focal_length, torch.tensor([5.0] * 3))
self.assertClose(c135.R, R_matrix[[1, 3, 5], ...])
self.assertClose(c135.principal_point, principal_point[[1, 3, 5], ...])
############################################################
# Perspective Camera #
@ -1027,3 +1110,30 @@ class TestPerspectiveProjection(TestCaseMixin, unittest.TestCase):
cam = PerspectiveCameras(focal_length=5.0, principal_point=((2.5, 2.5),))
self.assertTrue(cam.is_perspective())
self.assertIsNone(cam.get_znear())
def test_getitem(self):
R_matrix = torch.randn((6, 3, 3))
principal_point = torch.randn((6, 2, 1))
focal_length = 5.0
cam = PerspectiveCameras(
R=R_matrix,
focal_length=focal_length,
principal_point=principal_point,
)
# Check get item returns an instance of the same class
# with all the same keys
c0 = cam[0]
self.assertTrue(isinstance(c0, PerspectiveCameras))
self.assertEqual(cam.__dict__.keys(), c0.__dict__.keys())
# Check torch.LongTensor index
index = torch.tensor([1, 3, 5], dtype=torch.int64)
c135 = cam[index]
self.assertEqual(len(c135), 3)
self.assertClose(c135.focal_length, torch.tensor([5.0] * 3))
self.assertClose(c135.R, R_matrix[[1, 3, 5], ...])
self.assertClose(c135.principal_point, principal_point[[1, 3, 5], ...])
# Check in_ndc is handled correctly
self.assertEqual(cam._in_ndc, c0._in_ndc)