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Add multi-batch support for SE(3) unary operators #512

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merged 14 commits into from
May 4, 2023
Merged

Add multi-batch support for SE(3) unary operators #512

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0303a18
Add multi-batch SE3.rand(), SE3.randn() and SE3.identity()
fantaosha May 3, 2023
92cd9cf
Added multi-batch SE3.exp()
fantaosha May 3, 2023
2bb456e
refactor the code with size + (1,) -> *size, 1
fantaosha May 3, 2023
4d88c9f
refactor multi-batch SE3.Adjoint()
fantaosha May 3, 2023
765c84f
Add multi-batch SE3.hat()
fantaosha May 3, 2023
e2deea3
fixed a bug in SO3.normalize()
fantaosha May 3, 2023
e4c4be1
size + (3, 3) -> *size, 3, 3
fantaosha May 4, 2023
2873600
a minor refactor of the code
fantaosha May 4, 2023
8aa423e
added SO3._jlog_impl_helper()
fantaosha May 4, 2023
0c7f1da
impleted and tested multi-batch SE3.log()
fantaosha May 4, 2023
60bbe34
implemented and tested multi-batch SE3.inverse()
fantaosha May 4, 2023
289fa84
implemented and tested multi-batch SE3.vee()
fantaosha May 4, 2023
cdf2b93
tensor.new_ones() -> constant
fantaosha May 4, 2023
44b2323
replace tensors with constants
fantaosha May 4, 2023
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a minor refactor of the code
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@fantaosha
fantaosha committed May 4, 2023
commit 287360051dc477b81a51dec77ac6db2f6093f4c0
4 changes: 2 additions & 2 deletions theseus/labs/lie/functional/se3_impl.py
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Expand Up @@ -260,7 +260,7 @@ def _jexp_impl_helper(

jac[..., :3, 3:] = rotation.transpose(-1, -2) @ jac_temp_t

return jac
return jac, (None,)


def _jexp_impl(
Expand All @@ -278,7 +278,7 @@ def _jexp_impl(
theta_minus_sine_by_theta3_rot = torch.where(
near_zero, torch.zeros_like(theta), theta_minus_sine_by_theta3_t
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)
jac = _jexp_impl_helper(
jac, _ = _jexp_impl_helper(
tangent_vector,
ret[..., :3],
theta,
Expand Down
16 changes: 2 additions & 14 deletions theseus/labs/lie/functional/so3_impl.py
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Expand Up @@ -121,13 +121,7 @@ def rand(
) -> torch.Tensor:
# Reference:
# https://web.archive.org/web/20211105205926/http://planning.cs.uiuc.edu/node198.html
u = torch.rand(
3,
*size,
generator=generator,
dtype=dtype,
device=device,
)
u = torch.rand(3, *size, generator=generator, dtype=dtype, device=device)
u1 = u[0]
u2, u3 = u[1:3] * 2 * constants.PI

Expand Down Expand Up @@ -160,13 +154,7 @@ def randn(
) -> torch.Tensor:
ret = _exp_autograd_fn(
constants.PI
* torch.randn(
*size,
3,
generator=generator,
dtype=dtype,
device=device,
)
* torch.randn(*size, 3, generator=generator, dtype=dtype, device=device)
)
ret.requires_grad_(requires_grad)
return ret
Expand Down