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suppress errors in vision/fair/pytorch3d

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Differential Revision: D27934268

fbshipit-source-id: 51185fa493451012a9b2fd37379897d60596f73b
This commit is contained in:
Pyre Bot Jr 2021-04-21 23:27:13 -07:00 committed by Facebook GitHub Bot
parent eb04a488c5
commit 04d318d88f
8 changed files with 44 additions and 51 deletions
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2
pytorch3d/datasets/r2n2/utils.py
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@ -443,7 +443,7 @@ class BlenderCamera(CamerasBase):
def get_projection_transform(self, **kwargs) -> Transform3d: def get_projection_transform(self, **kwargs) -> Transform3d:
transform = Transform3d(device=self.device) transform = Transform3d(device=self.device)
transform._matrix = self.K.transpose(1, 2).contiguous() # pyre-ignore[16] transform._matrix = self.K.transpose(1, 2).contiguous()
return transform return transform

12
pytorch3d/ops/cameras_alignment.py
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@ -97,7 +97,6 @@ def corresponding_cameras_alignment(
cameras_src_aligned: `cameras_src` after applying the alignment transform. cameras_src_aligned: `cameras_src` after applying the alignment transform.
""" """
# pyre-fixme[16]: `CamerasBase` has no attribute `R`.
if cameras_src.R.shape[0] != cameras_tgt.R.shape[0]: if cameras_src.R.shape[0] != cameras_tgt.R.shape[0]:
raise ValueError( raise ValueError(
"cameras_src and cameras_tgt have to contain the same number of cameras!" "cameras_src and cameras_tgt have to contain the same number of cameras!"
@ -121,7 +120,6 @@ def corresponding_cameras_alignment(
torch.bmm( torch.bmm(
align_t_T[:, None].repeat(cameras_src.R.shape[0], 1, 1), cameras_src.R align_t_T[:, None].repeat(cameras_src.R.shape[0], 1, 1), cameras_src.R
)[:, 0] )[:, 0]
# pyre-fixme[16]: `CamerasBase` has no attribute `T`.
+ cameras_src.T * align_t_s + cameras_src.T * align_t_s
) )
@ -169,7 +167,6 @@ def _align_camera_extrinsics(
R_A = (U V^T)^T R_A = (U V^T)^T
``` ```
""" """
# pyre-fixme[16]: `CamerasBase` has no attribute `R`.
RRcov = torch.bmm(cameras_src.R, cameras_tgt.R.transpose(2, 1)).mean(0) RRcov = torch.bmm(cameras_src.R, cameras_tgt.R.transpose(2, 1)).mean(0)
U, _, V = torch.svd(RRcov) U, _, V = torch.svd(RRcov)
align_t_R = V @ U.t() align_t_R = V @ U.t()
@ -199,8 +196,15 @@ def _align_camera_extrinsics(
T_A = mean(B) - mean(A) * s_A T_A = mean(B) - mean(A) * s_A
``` ```
""" """
# pyre-fixme[16]: `CamerasBase` has no attribute `T`. # pyre-fixme[29]:
# `Union[BoundMethod[typing.Callable(torch.Tensor.__getitem__)[[Named(self,
# torch.Tensor), Named(item, typing.Any)], typing.Any], torch.Tensor],
# torch.Tensor, torch.nn.Module]` is not a function.
A = torch.bmm(cameras_src.R, cameras_src.T[:, :, None])[:, :, 0] A = torch.bmm(cameras_src.R, cameras_src.T[:, :, None])[:, :, 0]
# pyre-fixme[29]:
# `Union[BoundMethod[typing.Callable(torch.Tensor.__getitem__)[[Named(self,
# torch.Tensor), Named(item, typing.Any)], typing.Any], torch.Tensor],
# torch.Tensor, torch.nn.Module]` is not a function.
B = torch.bmm(cameras_src.R, cameras_tgt.T[:, :, None])[:, :, 0] B = torch.bmm(cameras_src.R, cameras_tgt.T[:, :, None])[:, :, 0]
Amu = A.mean(0, keepdim=True) Amu = A.mean(0, keepdim=True)
Bmu = B.mean(0, keepdim=True) Bmu = B.mean(0, keepdim=True)

40
pytorch3d/renderer/cameras.py
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@ -478,17 +478,17 @@ class FoVPerspectiveCameras(CamerasBase):
[0, 0, 1, 0], [0, 0, 1, 0],
] ]
""" """
K = kwargs.get("K", self.K) # pyre-ignore[16] K = kwargs.get("K", self.K)
if K is not None: if K is not None:
if K.shape != (self._N, 4, 4): if K.shape != (self._N, 4, 4):
msg = "Expected K to have shape of (%r, 4, 4)" msg = "Expected K to have shape of (%r, 4, 4)"
raise ValueError(msg % (self._N)) raise ValueError(msg % (self._N))
else: else:
K = self.compute_projection_matrix( K = self.compute_projection_matrix(
kwargs.get("znear", self.znear), # pyre-ignore[16] kwargs.get("znear", self.znear),
kwargs.get("zfar", self.zfar), # pyre-ignore[16] kwargs.get("zfar", self.zfar),
kwargs.get("fov", self.fov), # pyre-ignore[16] kwargs.get("fov", self.fov),
kwargs.get("aspect_ratio", self.aspect_ratio), # pyre-ignore[16] kwargs.get("aspect_ratio", self.aspect_ratio),
kwargs.get("degrees", self.degrees), kwargs.get("degrees", self.degrees),
) )
@ -702,20 +702,20 @@ class FoVOrthographicCameras(CamerasBase):
[0, 0, 0, 1], [0, 0, 0, 1],
] ]
""" """
K = kwargs.get("K", self.K) # pyre-ignore[16] K = kwargs.get("K", self.K)
if K is not None: if K is not None:
if K.shape != (self._N, 4, 4): if K.shape != (self._N, 4, 4):
msg = "Expected K to have shape of (%r, 4, 4)" msg = "Expected K to have shape of (%r, 4, 4)"
raise ValueError(msg % (self._N)) raise ValueError(msg % (self._N))
else: else:
K = self.compute_projection_matrix( K = self.compute_projection_matrix(
kwargs.get("znear", self.znear), # pyre-ignore[16] kwargs.get("znear", self.znear),
kwargs.get("zfar", self.zfar), # pyre-ignore[16] kwargs.get("zfar", self.zfar),
kwargs.get("max_x", self.max_x), # pyre-ignore[16] kwargs.get("max_x", self.max_x),
kwargs.get("min_x", self.min_x), # pyre-ignore[16] kwargs.get("min_x", self.min_x),
kwargs.get("max_y", self.max_y), # pyre-ignore[16] kwargs.get("max_y", self.max_y),
kwargs.get("min_y", self.min_y), # pyre-ignore[16] kwargs.get("min_y", self.min_y),
kwargs.get("scale_xyz", self.scale_xyz), # pyre-ignore[16] kwargs.get("scale_xyz", self.scale_xyz),
) )
transform = Transform3d(device=self.device) transform = Transform3d(device=self.device)
@ -902,13 +902,12 @@ class PerspectiveCameras(CamerasBase):
[0, 0, 1, 0], [0, 0, 1, 0],
] ]
""" """
K = kwargs.get("K", self.K) # pyre-ignore[16] K = kwargs.get("K", self.K)
if K is not None: if K is not None:
if K.shape != (self._N, 4, 4): if K.shape != (self._N, 4, 4):
msg = "Expected K to have shape of (%r, 4, 4)" msg = "Expected K to have shape of (%r, 4, 4)"
raise ValueError(msg % (self._N)) raise ValueError(msg % (self._N))
else: else:
# pyre-ignore[16]
image_size = kwargs.get("image_size", self.image_size) image_size = kwargs.get("image_size", self.image_size)
# if imwidth > 0, parameters are in screen space # if imwidth > 0, parameters are in screen space
image_size = image_size if image_size[0][0] > 0 else None image_size = image_size if image_size[0][0] > 0 else None
@ -916,8 +915,8 @@ class PerspectiveCameras(CamerasBase):
K = _get_sfm_calibration_matrix( K = _get_sfm_calibration_matrix(
self._N, self._N,
self.device, self.device,
kwargs.get("focal_length", self.focal_length), # pyre-ignore[16] kwargs.get("focal_length", self.focal_length),
kwargs.get("principal_point", self.principal_point), # pyre-ignore[16] kwargs.get("principal_point", self.principal_point),
orthographic=False, orthographic=False,
image_size=image_size, image_size=image_size,
) )
@ -1067,13 +1066,12 @@ class OrthographicCameras(CamerasBase):
[0, 0, 0, 1], [0, 0, 0, 1],
] ]
""" """
K = kwargs.get("K", self.K) # pyre-ignore[16] K = kwargs.get("K", self.K)
if K is not None: if K is not None:
if K.shape != (self._N, 4, 4): if K.shape != (self._N, 4, 4):
msg = "Expected K to have shape of (%r, 4, 4)" msg = "Expected K to have shape of (%r, 4, 4)"
raise ValueError(msg % (self._N)) raise ValueError(msg % (self._N))
else: else:
# pyre-ignore[16]
image_size = kwargs.get("image_size", self.image_size) image_size = kwargs.get("image_size", self.image_size)
# if imwidth > 0, parameters are in screen space # if imwidth > 0, parameters are in screen space
image_size = image_size if image_size[0][0] > 0 else None image_size = image_size if image_size[0][0] > 0 else None
@ -1081,8 +1079,8 @@ class OrthographicCameras(CamerasBase):
K = _get_sfm_calibration_matrix( K = _get_sfm_calibration_matrix(
self._N, self._N,
self.device, self.device,
kwargs.get("focal_length", self.focal_length), # pyre-ignore[16] kwargs.get("focal_length", self.focal_length),
kwargs.get("principal_point", self.principal_point), # pyre-ignore[16] kwargs.get("principal_point", self.principal_point),
orthographic=True, orthographic=True,
image_size=image_size, image_size=image_size,
) )

4
pytorch3d/renderer/implicit/raysampling.py
View File

@ -113,7 +113,7 @@ class GridRaysampler(torch.nn.Module):
containing the 2D image coordinates of each ray. containing the 2D image coordinates of each ray.
""" """
batch_size = cameras.R.shape[0] # pyre-ignore batch_size = cameras.R.shape[0]
device = cameras.device device = cameras.device
@ -229,7 +229,7 @@ class MonteCarloRaysampler(torch.nn.Module):
containing the 2D image coordinates of each ray. containing the 2D image coordinates of each ray.
""" """
batch_size = cameras.R.shape[0] # pyre-ignore batch_size = cameras.R.shape[0]
device = cameras.device device = cameras.device

10
pytorch3d/renderer/lighting.py
View File

@ -183,7 +183,6 @@ class DirectionalLights(TensorProperties):
direction=direction, direction=direction,
) )
_validate_light_properties(self) _validate_light_properties(self)
# pyre-fixme[16]: `DirectionalLights` has no attribute `direction`.
if self.direction.shape[-1] != 3: if self.direction.shape[-1] != 3:
msg = "Expected direction to have shape (N, 3); got %r" msg = "Expected direction to have shape (N, 3); got %r"
raise ValueError(msg % repr(self.direction.shape)) raise ValueError(msg % repr(self.direction.shape))
@ -198,9 +197,7 @@ class DirectionalLights(TensorProperties):
# need to know the light type. # need to know the light type.
return diffuse( return diffuse(
normals=normals, normals=normals,
# pyre-fixme[16]: `DirectionalLights` has no attribute `diffuse_color`.
color=self.diffuse_color, color=self.diffuse_color,
# pyre-fixme[16]: `DirectionalLights` has no attribute `direction`.
direction=self.direction, direction=self.direction,
) )
@ -208,9 +205,7 @@ class DirectionalLights(TensorProperties):
return specular( return specular(
points=points, points=points,
normals=normals, normals=normals,
# pyre-fixme[16]: `DirectionalLights` has no attribute `specular_color`.
color=self.specular_color, color=self.specular_color,
# pyre-fixme[16]: `DirectionalLights` has no attribute `direction`.
direction=self.direction, direction=self.direction,
camera_position=camera_position, camera_position=camera_position,
shininess=shininess, shininess=shininess,
@ -249,7 +244,6 @@ class PointLights(TensorProperties):
location=location, location=location,
) )
_validate_light_properties(self) _validate_light_properties(self)
# pyre-fixme[16]: `PointLights` has no attribute `location`.
if self.location.shape[-1] != 3: if self.location.shape[-1] != 3:
msg = "Expected location to have shape (N, 3); got %r" msg = "Expected location to have shape (N, 3); got %r"
raise ValueError(msg % repr(self.location.shape)) raise ValueError(msg % repr(self.location.shape))
@ -259,18 +253,14 @@ class PointLights(TensorProperties):
return super().clone(other) return super().clone(other)
def diffuse(self, normals, points) -> torch.Tensor: def diffuse(self, normals, points) -> torch.Tensor:
# pyre-fixme[16]: `PointLights` has no attribute `location`.
direction = self.location - points direction = self.location - points
# pyre-fixme[16]: `PointLights` has no attribute `diffuse_color`.
return diffuse(normals=normals, color=self.diffuse_color, direction=direction) return diffuse(normals=normals, color=self.diffuse_color, direction=direction)
def specular(self, normals, points, camera_position, shininess) -> torch.Tensor: def specular(self, normals, points, camera_position, shininess) -> torch.Tensor:
# pyre-fixme[16]: `PointLights` has no attribute `location`.
direction = self.location - points direction = self.location - points
return specular( return specular(
points=points, points=points,
normals=normals, normals=normals,
# pyre-fixme[16]: `PointLights` has no attribute `specular_color`.
color=self.specular_color, color=self.specular_color,
direction=direction, direction=direction,
camera_position=camera_position, camera_position=camera_position,

1
pytorch3d/renderer/points/pulsar/renderer.py
View File

@ -632,7 +632,6 @@ class Renderer(torch.nn.Module):
enabled. enabled.
""" """
# The device tracker is registered as buffer. # The device tracker is registered as buffer.
# pyre-fixme[16]: `Renderer` has no attribute `device_tracker`.
self._renderer.device_tracker = self.device_tracker self._renderer.device_tracker = self.device_tracker
( (
pos_vec, pos_vec,

12
pytorch3d/renderer/points/pulsar/unified.py
View File

@ -189,17 +189,11 @@ class PulsarPointsRenderer(nn.Module):
if orthogonal_projection: if orthogonal_projection:
focal_length = torch.zeros((1,), dtype=torch.float32) focal_length = torch.zeros((1,), dtype=torch.float32)
if isinstance(cameras, FoVOrthographicCameras): if isinstance(cameras, FoVOrthographicCameras):
# pyre-fixme[16]: `FoVOrthographicCameras` has no attribute `znear`.
znear = kwargs.get("znear", cameras.znear)[cloud_idx] znear = kwargs.get("znear", cameras.znear)[cloud_idx]
# pyre-fixme[16]: `FoVOrthographicCameras` has no attribute `zfar`.
zfar = kwargs.get("zfar", cameras.zfar)[cloud_idx] zfar = kwargs.get("zfar", cameras.zfar)[cloud_idx]
# pyre-fixme[16]: `FoVOrthographicCameras` has no attribute `max_y`.
max_y = kwargs.get("max_y", cameras.max_y)[cloud_idx] max_y = kwargs.get("max_y", cameras.max_y)[cloud_idx]
# pyre-fixme[16]: `FoVOrthographicCameras` has no attribute `min_y`.
min_y = kwargs.get("min_y", cameras.min_y)[cloud_idx] min_y = kwargs.get("min_y", cameras.min_y)[cloud_idx]
# pyre-fixme[16]: `FoVOrthographicCameras` has no attribute `max_x`.
max_x = kwargs.get("max_x", cameras.max_x)[cloud_idx] max_x = kwargs.get("max_x", cameras.max_x)[cloud_idx]
# pyre-fixme[16]: `FoVOrthographicCameras` has no attribute `min_x`.
min_x = kwargs.get("min_x", cameras.min_x)[cloud_idx] min_x = kwargs.get("min_x", cameras.min_x)[cloud_idx]
if max_y != -min_y: if max_y != -min_y:
raise ValueError( raise ValueError(
@ -212,9 +206,7 @@ class PulsarPointsRenderer(nn.Module):
f"Max is {max_x} and min is {min_x}." f"Max is {max_x} and min is {min_x}."
) )
if not torch.all( if not torch.all(
# pyre-fixme[16]: `FoVOrthographicCameras` has no attribute `scale_xyz`. kwargs.get("scale_xyz", cameras.scale_xyz)[cloud_idx] == 1.0
kwargs.get("scale_xyz", cameras.scale_xyz)[cloud_idx]
== 1.0
): ):
raise ValueError( raise ValueError(
"The orthographic camera scale must be ((1.0, 1.0, 1.0),). " "The orthographic camera scale must be ((1.0, 1.0, 1.0),). "
@ -297,7 +289,6 @@ class PulsarPointsRenderer(nn.Module):
torch.zeros((1,), dtype=torch.float32), torch.zeros((1,), dtype=torch.float32),
) )
else: else:
# pyre-fixme[16]: `PerspectiveCameras` has no attribute `focal_length`.
focal_length_conf = kwargs.get("focal_length", cameras.focal_length)[ focal_length_conf = kwargs.get("focal_length", cameras.focal_length)[
cloud_idx cloud_idx
] ]
@ -336,7 +327,6 @@ class PulsarPointsRenderer(nn.Module):
) )
sensor_width = focal_length / focal_length_px * 2.0 sensor_width = focal_length / focal_length_px * 2.0
principal_point_x = ( principal_point_x = (
# pyre-fixme[16]: `PerspectiveCameras` has no attribute `principal_point`.
kwargs.get("principal_point", cameras.principal_point)[cloud_idx][0] kwargs.get("principal_point", cameras.principal_point)[cloud_idx][0]
* 0.5 * 0.5
* self.renderer._renderer.width * self.renderer._renderer.width

14
pytorch3d/vis/plotly_vis.py
View File

@ -470,10 +470,22 @@ def _add_struct_from_batch(
struct = None struct = None
if isinstance(batched_struct, CamerasBase): if isinstance(batched_struct, CamerasBase):
# we can't index directly into camera batches # we can't index directly into camera batches
R, T = batched_struct.R, batched_struct.T # pyre-ignore[16] R, T = batched_struct.R, batched_struct.T
# pyre-fixme[6]: Expected `Sized` for 1st param but got `Union[torch.Tensor,
# torch.nn.Module]`.
r_idx = min(scene_num, len(R) - 1) r_idx = min(scene_num, len(R) - 1)
# pyre-fixme[6]: Expected `Sized` for 1st param but got `Union[torch.Tensor,
# torch.nn.Module]`.
t_idx = min(scene_num, len(T) - 1) t_idx = min(scene_num, len(T) - 1)
# pyre-fixme[29]:
# `Union[BoundMethod[typing.Callable(torch.Tensor.__getitem__)[[Named(self,
# torch.Tensor), Named(item, typing.Any)], typing.Any], torch.Tensor],
# torch.Tensor, torch.nn.Module]` is not a function.
R = R[r_idx].unsqueeze(0) R = R[r_idx].unsqueeze(0)
# pyre-fixme[29]:
# `Union[BoundMethod[typing.Callable(torch.Tensor.__getitem__)[[Named(self,
# torch.Tensor), Named(item, typing.Any)], typing.Any], torch.Tensor],
# torch.Tensor, torch.nn.Module]` is not a function.
T = T[t_idx].unsqueeze(0) T = T[t_idx].unsqueeze(0)
struct = CamerasBase(device=batched_struct.device, R=R, T=T) struct = CamerasBase(device=batched_struct.device, R=R, T=T)
else: # batched meshes and pointclouds are indexable else: # batched meshes and pointclouds are indexable