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suppress errors in vision/fair/pytorch3d

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Differential Revision: D25781183

fbshipit-source-id: e27808a4c2b94bba205756001cb909827182b592
This commit is contained in:
generatedunixname89002005307016 2021-01-05 05:27:11 -08:00 committed by Facebook GitHub Bot
parent 1af1a36bd6
commit 1f9cf91e1b
3 changed files with 23 additions and 0 deletions
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13
pytorch3d/ops/points_to_volumes.py
View File

@ -184,6 +184,7 @@ def add_points_features_to_volume_densities_features(
if volume_features is None:
# initialize features if not passed in
# pyre-fixme[16]: `Tensor` has no attribute `new_zeros`.
volume_features_flatten = volume_densities.new_zeros(ba, feature_dim, n_voxels)
else:
# otherwise just flatten
@ -227,6 +228,7 @@ def _check_points_to_volumes_inputs(
mask: Optional[torch.Tensor] = None,
):
# pyre-fixme[16]: `Tuple` has no attribute `values`.
max_grid_size = grid_sizes.max(dim=0).values
if torch.prod(max_grid_size) > volume_densities.shape[1]:
raise ValueError(
@ -321,9 +323,11 @@ def splat_points_to_volumes(
rXYZ = points_3d_indices - XYZ.type_as(points_3d) # remainder of floor
# split into separate coordinate vectors
# pyre-fixme[16]: `LongTensor` has no attribute `split`.
X, Y, Z = XYZ.split(1, dim=2)
# rX = remainder after floor = 1-"the weight of each vote into
# the X coordinate of the 8-neighborhood"
# pyre-fixme[16]: `Tensor` has no attribute `split`.
rX, rY, rZ = rXYZ.split(1, dim=2)
# get random indices for the purpose of adding out-of-bounds values
@ -344,6 +348,7 @@ def splat_points_to_volumes(
w = wX * wY * wZ
# valid - binary indicators of votes that fall into the volume
# pyre-fixme[16]: `int` has no attribute `long`.
valid = (
(0 <= X_)
* (X_ < grid_sizes_xyz[:, None, 0:1])
@ -362,20 +367,25 @@ def splat_points_to_volumes(
idx_valid = idx * valid + rand_idx * (1 - valid)
w_valid = w * valid.type_as(w)
if mask is not None:
# pyre-fixme[6]: Expected `_OtherTensor` for 1st param but got
# `int`.
w_valid = w_valid * mask.type_as(w)[:, :, None]
# scatter add casts the votes into the weight accumulator
# and the feature accumulator
# pyre-fixme[16]: `Tensor` has no attribute `scatter_add_`.
volume_densities.scatter_add_(1, idx_valid, w_valid)
# reshape idx_valid -> (minibatch, feature_dim, n_points)
idx_valid = idx_valid.view(ba, 1, n_points).expand_as(points_features)
# pyre-fixme[16]: `int` has no attribute `view`.
w_valid = w_valid.view(ba, 1, n_points)
# volume_features of shape (minibatch, feature_dim, n_voxels)
volume_features.scatter_add_(2, idx_valid, w_valid * points_features)
# divide each feature by the total weight of the votes
# pyre-fixme[20]: Argument `max` expected.
volume_features = volume_features / volume_densities.view(ba, 1, n_voxels).clamp(
min_weight
)
@ -453,6 +463,7 @@ def round_points_to_volumes(
# valid - binary indicators of votes that fall into the volume
grid_sizes = grid_sizes.type_as(XYZ)
# pyre-fixme[16]: `int` has no attribute `long`.
valid = (
(0 <= X)
* (X < grid_sizes_xyz[:, None, 0:1])
@ -474,6 +485,7 @@ def round_points_to_volumes(
# scatter add casts the votes into the weight accumulator
# and the feature accumulator
# pyre-fixme[16]: `Tensor` has no attribute `scatter_add_`.
volume_densities.scatter_add_(1, idx_valid, w_valid)
# reshape idx_valid -> (minibatch, feature_dim, n_points)
@ -484,6 +496,7 @@ def round_points_to_volumes(
volume_features.scatter_add_(2, idx_valid, w_valid * points_features)
# divide each feature by the total weight of the votes
# pyre-fixme[20]: Argument `max` expected.
volume_features = volume_features / volume_densities.view(ba, 1, n_voxels).clamp(
1.0
)

3
pytorch3d/renderer/implicit/raymarching.py
View File

@ -153,6 +153,7 @@ class AbsorptionOnlyRaymarcher(torch.nn.Module):
_check_density_bounds(rays_densities)
total_transmission = torch.prod(1 - rays_densities, dim=-1, keepdim=True)
opacities = 1.0 - total_transmission
# pyre-fixme[7]: Expected `Optional[torch.Tensor]` but got `float`.
return opacities
@ -176,6 +177,7 @@ def _check_density_bounds(
Checks whether the elements of `rays_densities` range within `bounds`.
If not issues a warning.
"""
# pyre-fixme[16]: `ByteTensor` has no attribute `any`.
if ((rays_densities > bounds[1]) | (rays_densities < bounds[0])).any():
warnings.warn(
"One or more elements of rays_densities are outside of valid"
@ -213,6 +215,7 @@ def _check_raymarcher_inputs(
rays_shape = rays_densities.shape[:-1]
# pyre-fixme[16]: `Optional` has no attribute `shape`.
if not z_can_be_none and rays_z.shape != rays_shape:
raise ValueError("rays_z have to be of the same shape as rays_densities.")

7
pytorch3d/structures/volumes.py
View File

@ -473,6 +473,7 @@ class Volumes(object):
)
def __len__(self) -> int:
# pyre-fixme[16]: `List` has no attribute `shape`.
return self._densities.shape[0]
def __getitem__(
@ -497,6 +498,7 @@ class Volumes(object):
raise IndexError(index)
new = self.__class__(
# pyre-fixme[16]: `Optional` has no attribute `__getitem__`.
features=self.features()[index] if self._features is not None else None,
densities=self.densities()[index],
)
@ -521,6 +523,8 @@ class Volumes(object):
Returns:
**densities**: The tensor of volume densities.
"""
# pyre-fixme[7]: Expected `Tensor` but got `Union[List[torch.Tensor],
# torch.Tensor]`.
return self._densities
def densities_list(self) -> List[torch.Tensor]:
@ -542,6 +546,7 @@ class Volumes(object):
"""
if self._features is None:
# No features provided so return None
# pyre-fixme[7]: Expected `List[torch.Tensor]` but got `None`.
return None
return self._features_densities_list(self.features())
@ -687,8 +692,10 @@ class Volumes(object):
other = self.clone()
if self.device != device:
other.device = device
# pyre-fixme[16]: `List` has no attribute `to`.
other._densities = self._densities.to(device)
if self._features is not None:
# pyre-fixme[16]: `Optional` has no attribute `to`.
other._features = self.features().to(device)
other._local_to_world_transform = (
self.get_local_to_world_coords_transform().to(device)