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suppress errors in vision/fair/pytorch3d

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Differential Revision: D37172764

fbshipit-source-id: a2ec367e56de2781a17f5e708eb5832ec9d7e6b4
This commit is contained in:
Pyre Bot Jr
2022-06-15 06:27:35 -07:00
committed by Facebook GitHub Bot
parent ea4f3260e4
commit 7978ffd1e4
61 changed files with 188 additions and 80 deletions
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12
pytorch3d/transforms/rotation_conversions.py
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@@ -50,6 +50,7 @@ def quaternion_to_matrix(quaternions: torch.Tensor) -> torch.Tensor:
Rotation matrices as tensor of shape (..., 3, 3).
"""
r, i, j, k = torch.unbind(quaternions, -1)
# pyre-fixme[58]: `/` is not supported for operand types `float` and `Tensor`.
two_s = 2.0 / (quaternions * quaternions).sum(-1)
o = torch.stack(
@@ -131,9 +132,17 @@ def matrix_to_quaternion(matrix: torch.Tensor) -> torch.Tensor:
# we produce the desired quaternion multiplied by each of r, i, j, k
quat_by_rijk = torch.stack(
[
# pyre-fixme[58]: `**` is not supported for operand types `Tensor` and
# `int`.
torch.stack([q_abs[..., 0] ** 2, m21 - m12, m02 - m20, m10 - m01], dim=-1),
# pyre-fixme[58]: `**` is not supported for operand types `Tensor` and
# `int`.
torch.stack([m21 - m12, q_abs[..., 1] ** 2, m10 + m01, m02 + m20], dim=-1),
# pyre-fixme[58]: `**` is not supported for operand types `Tensor` and
# `int`.
torch.stack([m02 - m20, m10 + m01, q_abs[..., 2] ** 2, m12 + m21], dim=-1),
# pyre-fixme[58]: `**` is not supported for operand types `Tensor` and
# `int`.
torch.stack([m10 - m01, m20 + m02, m21 + m12, q_abs[..., 3] ** 2], dim=-1),
],
dim=-2,
@@ -148,7 +157,7 @@ def matrix_to_quaternion(matrix: torch.Tensor) -> torch.Tensor:
# forall i; we pick the best-conditioned one (with the largest denominator)
return quat_candidates[
F.one_hot(q_abs.argmax(dim=-1), num_classes=4) > 0.5, : # pyre-ignore[16]
F.one_hot(q_abs.argmax(dim=-1), num_classes=4) > 0.5, :
].reshape(batch_dim + (4,))
@@ -314,6 +323,7 @@ def random_quaternions(
"""
if isinstance(device, str):
device = torch.device(device)
# pyre-fixme[6]: For 2nd param expected `dtype` but got `Optional[dtype]`.
o = torch.randn((n, 4), dtype=dtype, device=device)
s = (o * o).sum(1)
o = o / _copysign(torch.sqrt(s), o[:, 0])[:, None]