suppress errors in vision/fair/pytorch3d

Differential Revision: D31496551

fbshipit-source-id: 705fd88f319875db3f7938a2946c48a51ea225f5
This commit is contained in:
Pyre Bot Jr 2021-10-07 21:56:41 -07:00 committed by Facebook GitHub Bot
parent 6dfa326922
commit 6d36c1e2b0
10 changed files with 3 additions and 12 deletions

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@ -117,7 +117,6 @@ def marching_cubes_naive(
volume_data_batch = volume_data_batch.detach().cpu()
batched_verts, batched_faces = [], []
D, H, W = volume_data_batch.shape[1:]
# pyre-ignore [16]
volume_size_xyz = volume_data_batch.new_tensor([W, H, D])[None]
if return_local_coords:

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@ -435,7 +435,6 @@ def _add_points_features_to_volume_densities_features_python(
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
@ -478,7 +477,6 @@ 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(

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@ -134,7 +134,6 @@ def sample_farthest_points_naive(
# Initialize closest distances to inf, shape: (P,)
# This will be updated at each iteration to track the closest distance of the
# remaining points to any of the selected points
# pyre-fixme[16]: `torch.Tensor` has no attribute new_full.
closest_dists = points.new_full(
(lengths[n],), float("inf"), dtype=torch.float32
)

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@ -67,7 +67,7 @@ def hard_rgb_blend(
if isinstance(background_color_, torch.Tensor):
background_color = background_color_.to(device)
else:
background_color = colors.new_tensor(background_color_) # pyre-fixme[16]
background_color = colors.new_tensor(background_color_)
# Find out how much background_color needs to be expanded to be used for masked_scatter.
num_background_pixels = is_background.sum()
@ -217,7 +217,6 @@ def softmax_rgb_blend(
znear = znear[:, None, None, None]
z_inv = (zfar - fragments.zbuf) / (zfar - znear) * mask
# pyre-fixme[16]: `Tuple` has no attribute `values`.
z_inv_max = torch.max(z_inv, dim=-1).values[..., None].clamp(min=eps)
weights_num = prob_map * torch.exp((z_inv - z_inv_max) / blend_params.gamma)

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@ -298,7 +298,7 @@ def _xy_to_ray_bundle(
.reshape(batch_size, n_rays_per_image * 2, 2),
torch.cat(
(
xy_grid.new_ones(batch_size, n_rays_per_image, 1), # pyre-ignore
xy_grid.new_ones(batch_size, n_rays_per_image, 1),
2.0 * xy_grid.new_ones(batch_size, n_rays_per_image, 1),
),
dim=1,

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@ -475,7 +475,6 @@ def rasterize_meshes_python( # noqa: C901
)
# Calculate all face bounding boxes.
# pyre-fixme[16]: `Tuple` has no attribute `values`.
x_mins = torch.min(faces_verts[:, :, 0], dim=1, keepdim=True).values
x_maxs = torch.max(faces_verts[:, :, 0], dim=1, keepdim=True).values
y_mins = torch.min(faces_verts[:, :, 1], dim=1, keepdim=True).values

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@ -1134,7 +1134,6 @@ class TexturesUV(TexturesBase):
)
merging_plan = pack_unique_rectangles(heights_and_widths)
C = maps[0].shape[-1]
# pyre-fixme[16]: `Tensor` has no attribute `new_zeros`.
single_map = maps[0].new_zeros((*merging_plan.total_size, C))
verts_uvs = self.verts_uvs_list()
verts_uvs_merged = []

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@ -52,7 +52,6 @@ def list_to_padded(
# replace empty 1D tensors with empty tensors with a correct number of dimensions
x = [
# pyre-fixme[16]: `Tensor` has no attribute `new_zeros`.
(y.new_zeros([0] * element_ndim) if (y.ndim == 1 and y.nelement() == 0) else y)
for y in x
]

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@ -142,7 +142,6 @@ def matrix_to_quaternion(matrix: torch.Tensor) -> torch.Tensor:
# We floor here at 0.1 but the exact level is not important; if q_abs is small,
# the candidate won't be picked.
# pyre-ignore [16]: `torch.Tensor` has no attribute `new_tensor`.
quat_candidates = quat_by_rijk / (2.0 * q_abs[..., None].max(q_abs.new_tensor(0.1)))
# if not for numerical problems, quat_candidates[i] should be same (up to a sign),

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@ -791,7 +791,7 @@ def _add_ray_bundle_trace(
# make ray line endpoints
min_max_ray_depth = torch.stack(
[
ray_bundle_flat.lengths.min(dim=1).values, # pyre-ignore[16]
ray_bundle_flat.lengths.min(dim=1).values,
ray_bundle_flat.lengths.max(dim=1).values,
],
dim=-1,