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Remove unused pyre-ignore or pyre-fixme

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Reviewed By: bottler

Differential Revision: D47223471

fbshipit-source-id: 8bdabf2a69dd7aec7202141122a9c69220ba7ef1
This commit is contained in:
Emilien Garreau
2023-07-05 02:58:47 -07:00
committed by Facebook GitHub Bot
parent f68371d398
commit 4e7715ce66
21 changed files with 16 additions and 133 deletions
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15
pytorch3d/ops/cameras_alignment.py
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@@ -120,16 +120,12 @@ def corresponding_cameras_alignment(
# create a new cameras object and set the R and T accordingly
cameras_src_aligned = cameras_src.clone()
# pyre-fixme[6]: For 2nd param expected `Tensor` but got `Union[Tensor, Module]`.
cameras_src_aligned.R = torch.bmm(align_t_R.expand_as(cameras_src.R), cameras_src.R)
cameras_src_aligned.T = (
torch.bmm(
align_t_T[:, None].repeat(cameras_src.R.shape[0], 1, 1),
# pyre-fixme[6]: For 2nd param expected `Tensor` but got `Union[Tensor,
# Module]`.
cameras_src.R,
)[:, 0]
# pyre-fixme[29]: `Union[BoundMethod[typing.Callable(torch._C._TensorBase.__m...
+ cameras_src.T * align_t_s
)
@@ -177,7 +173,6 @@ def _align_camera_extrinsics(
R_A = (U V^T)^T
```
"""
# pyre-fixme[6]: For 1st param expected `Tensor` but got `Union[Tensor, Module]`.
RRcov = torch.bmm(cameras_src.R, cameras_tgt.R.transpose(2, 1)).mean(0)
U, _, V = torch.svd(RRcov)
align_t_R = V @ U.t()
@@ -207,17 +202,7 @@ def _align_camera_extrinsics(
T_A = mean(B) - mean(A) * s_A
```
"""
# 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.
# pyre-fixme[6]: For 1st param expected `Tensor` but got `Union[Tensor, Module]`.
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.
# pyre-fixme[6]: For 1st param expected `Tensor` but got `Union[Tensor, Module]`.
B = torch.bmm(cameras_src.R, cameras_tgt.T[:, :, None])[:, :, 0]
Amu = A.mean(0, keepdim=True)
Bmu = B.mean(0, keepdim=True)