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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
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@nikhilaravi @facebook-github-bot
nikhilaravi authored and facebook-github-bot committed Dec 21, 2021
1 parent cc3259b commit 28ccdb7
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Showing 3 changed files with 224 additions and 13 deletions.
109 changes: 105 additions & 4 deletions pytorch3d/renderer/cameras.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -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.
Expand Down Expand Up @@ -362,6 +366,55 @@ def get_image_size(self):
"""
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 #
Expand Down Expand Up @@ -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,
Expand Down Expand Up @@ -590,7 +655,7 @@ def unproject_points(
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
Expand Down Expand Up @@ -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,
Expand Down Expand Up @@ -819,7 +898,7 @@ def unproject_points(
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
Expand Down Expand Up @@ -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,
Expand Down Expand Up @@ -1007,7 +1097,7 @@ def unproject_points(
xy_depth: torch.Tensor,
world_coordinates: bool = True,
from_ndc: bool = False,
**kwargs
**kwargs,
) -> torch.Tensor:
"""
Args:
Expand Down Expand Up @@ -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,
Expand Down Expand Up @@ -1225,7 +1326,7 @@ def unproject_points(
xy_depth: torch.Tensor,
world_coordinates: bool = True,
from_ndc: bool = False,
**kwargs
**kwargs,
) -> torch.Tensor:
"""
Args:
Expand Down
2 changes: 1 addition & 1 deletion pytorch3d/renderer/utils.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -155,7 +155,7 @@ def __getitem__(self, index: Union[int, slice]) -> TensorAccessor:
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)
Expand Down
126 changes: 118 additions & 8 deletions tests/test_cameras.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -783,18 +783,53 @@ def test_camera_class_init(self):
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)
Expand Down Expand Up @@ -919,6 +954,30 @@ def test_perspective_type(self):
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 #
Expand Down Expand Up @@ -976,6 +1035,30 @@ def test_perspective_type(self):
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 #
Expand Down Expand Up @@ -1027,3 +1110,30 @@ def test_perspective_type(self):
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)

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