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suppress errors in vision/fair/pytorch3d
Differential Revision: D31496551 fbshipit-source-id: 705fd88f319875db3f7938a2946c48a51ea225f5
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@ -117,7 +117,6 @@ def marching_cubes_naive(
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volume_data_batch = volume_data_batch.detach().cpu()
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batched_verts, batched_faces = [], []
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D, H, W = volume_data_batch.shape[1:]
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# pyre-ignore [16]
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volume_size_xyz = volume_data_batch.new_tensor([W, H, D])[None]
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if return_local_coords:
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@ -435,7 +435,6 @@ def _add_points_features_to_volume_densities_features_python(
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if volume_features is None:
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# initialize features if not passed in
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# pyre-fixme[16]: `Tensor` has no attribute `new_zeros`.
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volume_features_flatten = volume_densities.new_zeros(ba, feature_dim, n_voxels)
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else:
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# otherwise just flatten
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@ -478,7 +477,6 @@ def _check_points_to_volumes_inputs(
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mask: Optional[torch.Tensor] = None,
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):
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# pyre-fixme[16]: `Tuple` has no attribute `values`.
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max_grid_size = grid_sizes.max(dim=0).values
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if torch.prod(max_grid_size) > volume_densities.shape[1]:
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raise ValueError(
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@ -134,7 +134,6 @@ def sample_farthest_points_naive(
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# Initialize closest distances to inf, shape: (P,)
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# This will be updated at each iteration to track the closest distance of the
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# remaining points to any of the selected points
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# pyre-fixme[16]: `torch.Tensor` has no attribute new_full.
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closest_dists = points.new_full(
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(lengths[n],), float("inf"), dtype=torch.float32
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)
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@ -67,7 +67,7 @@ def hard_rgb_blend(
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if isinstance(background_color_, torch.Tensor):
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background_color = background_color_.to(device)
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else:
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background_color = colors.new_tensor(background_color_) # pyre-fixme[16]
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background_color = colors.new_tensor(background_color_)
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# Find out how much background_color needs to be expanded to be used for masked_scatter.
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num_background_pixels = is_background.sum()
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@ -217,7 +217,6 @@ def softmax_rgb_blend(
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znear = znear[:, None, None, None]
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z_inv = (zfar - fragments.zbuf) / (zfar - znear) * mask
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# pyre-fixme[16]: `Tuple` has no attribute `values`.
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z_inv_max = torch.max(z_inv, dim=-1).values[..., None].clamp(min=eps)
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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(
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.reshape(batch_size, n_rays_per_image * 2, 2),
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torch.cat(
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(
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xy_grid.new_ones(batch_size, n_rays_per_image, 1), # pyre-ignore
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xy_grid.new_ones(batch_size, n_rays_per_image, 1),
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2.0 * xy_grid.new_ones(batch_size, n_rays_per_image, 1),
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),
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dim=1,
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@ -475,7 +475,6 @@ def rasterize_meshes_python( # noqa: C901
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)
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# Calculate all face bounding boxes.
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# pyre-fixme[16]: `Tuple` has no attribute `values`.
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x_mins = torch.min(faces_verts[:, :, 0], dim=1, keepdim=True).values
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x_maxs = torch.max(faces_verts[:, :, 0], dim=1, keepdim=True).values
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y_mins = torch.min(faces_verts[:, :, 1], dim=1, keepdim=True).values
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@ -1134,7 +1134,6 @@ class TexturesUV(TexturesBase):
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)
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merging_plan = pack_unique_rectangles(heights_and_widths)
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C = maps[0].shape[-1]
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# pyre-fixme[16]: `Tensor` has no attribute `new_zeros`.
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single_map = maps[0].new_zeros((*merging_plan.total_size, C))
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verts_uvs = self.verts_uvs_list()
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verts_uvs_merged = []
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@ -52,7 +52,6 @@ def list_to_padded(
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# replace empty 1D tensors with empty tensors with a correct number of dimensions
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x = [
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# pyre-fixme[16]: `Tensor` has no attribute `new_zeros`.
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(y.new_zeros([0] * element_ndim) if (y.ndim == 1 and y.nelement() == 0) else y)
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for y in x
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]
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@ -142,7 +142,6 @@ def matrix_to_quaternion(matrix: torch.Tensor) -> torch.Tensor:
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# We floor here at 0.1 but the exact level is not important; if q_abs is small,
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# the candidate won't be picked.
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# pyre-ignore [16]: `torch.Tensor` has no attribute `new_tensor`.
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quat_candidates = quat_by_rijk / (2.0 * q_abs[..., None].max(q_abs.new_tensor(0.1)))
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# 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(
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# make ray line endpoints
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min_max_ray_depth = torch.stack(
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[
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ray_bundle_flat.lengths.min(dim=1).values, # pyre-ignore[16]
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ray_bundle_flat.lengths.min(dim=1).values,
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ray_bundle_flat.lengths.max(dim=1).values,
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],
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dim=-1,
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