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Fixing device selection (cuda or cpu) #10

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Fixing device selection (cuda or cpu) #10

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518901e
Fixing device selection (cuda or cpu)
ArashJavan Jul 2, 2020
dad56d8
bug fix, using cuda if needed
ArashJavan Jul 4, 2020
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4 changes: 2 additions & 2 deletions liegroups/torch/_base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -112,11 +112,11 @@ def is_valid_matrix(cls, mat):

# Determinants of each matrix in the batch should be 1
det_check = utils.isclose(mat.__class__(
np.linalg.det(mat.detach().cpu().numpy())), 1.)
np.linalg.det(mat.detach().cpu().numpy())), 1.).to(mat.device)

# The transpose of each matrix in the batch should be its inverse
inv_check = utils.isclose(mat.transpose(2, 1).bmm(mat),
torch.eye(cls.dim, dtype=mat.dtype)).sum(dim=1).sum(dim=1) \
torch.eye(cls.dim, dtype=mat.dtype, device=mat.device)).sum(dim=1).sum(dim=1) \
== cls.dim * cls.dim

return shape_check & det_check & inv_check
Expand Down
14 changes: 7 additions & 7 deletions liegroups/torch/so3.py
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Original file line number Diff line number Diff line change
Expand Up @@ -31,7 +31,7 @@ def exp(cls, phi):

if len(small_angle_inds) > 0:
mat[small_angle_inds] = \
torch.eye(cls.dim, dtype=phi.dtype).expand_as(mat[small_angle_inds]) + \
torch.eye(cls.dim, dtype=phi.dtype, device=phi.device).expand_as(mat[small_angle_inds]) + \
cls.wedge(phi[small_angle_inds])

# Otherwise...
Expand All @@ -47,7 +47,7 @@ def exp(cls, phi):
c = angle.cos().unsqueeze_(dim=1).unsqueeze_(
dim=2).expand_as(mat[large_angle_inds])

A = c * torch.eye(cls.dim, dtype=phi.dtype).unsqueeze_(dim=0).expand_as(
A = c * torch.eye(cls.dim, dtype=phi.dtype, device=phi.device).unsqueeze_(dim=0).expand_as(
mat[large_angle_inds])
B = (1. - c) * utils.outer(axis, axis)
C = s * cls.wedge(axis)
Expand Down Expand Up @@ -132,7 +132,7 @@ def inv_left_jacobian(cls, phi):
small_angle_inds = small_angle_mask.nonzero().squeeze_(dim=1)
if len(small_angle_inds) > 0:
jac[small_angle_inds] = \
torch.eye(cls.dof, dtype=phi.dtype).expand_as(jac[small_angle_inds]) - \
torch.eye(cls.dof, dtype=phi.dtype, device=phi.device).expand_as(jac[small_angle_inds]) - \
0.5 * cls.wedge(phi[small_angle_inds])

# Otherwise...
Expand All @@ -153,7 +153,7 @@ def inv_left_jacobian(cls, phi):
dim=2).expand_as(jac[large_angle_inds])

A = hacha * \
torch.eye(cls.dof, dtype=phi.dtype).unsqueeze_(
torch.eye(cls.dof, dtype=phi.dtype, device=phi.device).unsqueeze_(
dim=0).expand_as(jac[large_angle_inds])
B = (1. - hacha) * utils.outer(axis, axis)
C = -ha * cls.wedge(axis)
Expand All @@ -179,7 +179,7 @@ def left_jacobian(cls, phi):
small_angle_inds = small_angle_mask.nonzero().squeeze_(dim=1)
if len(small_angle_inds) > 0:
jac[small_angle_inds] = \
torch.eye(cls.dof, dtype=phi.dtype).expand_as(jac[small_angle_inds]) + \
torch.eye(cls.dof, dtype=phi.dtype, device=phi.device).expand_as(jac[small_angle_inds]) + \
0.5 * cls.wedge(phi[small_angle_inds])

# Otherwise...
Expand All @@ -195,7 +195,7 @@ def left_jacobian(cls, phi):

A = (s / angle).unsqueeze_(dim=1).unsqueeze_(
dim=2).expand_as(jac[large_angle_inds]) * \
torch.eye(cls.dof, dtype=phi.dtype).unsqueeze_(dim=0).expand_as(
torch.eye(cls.dof, dtype=phi.dtype, device=phi.device).unsqueeze_(dim=0).expand_as(
jac[large_angle_inds])
B = (1. - s / angle).unsqueeze_(dim=1).unsqueeze_(
dim=2).expand_as(jac[large_angle_inds]) * \
Expand Down Expand Up @@ -228,7 +228,7 @@ def log(self):
if len(small_angle_inds) > 0:
phi[small_angle_inds, :] = \
self.vee(mat[small_angle_inds] -
torch.eye(self.dim, dtype=mat.dtype).expand_as(mat[small_angle_inds]))
torch.eye(self.dim, dtype=mat.dtype, device=mat.device).expand_as(mat[small_angle_inds]))

# Otherwise...
large_angle_mask = small_angle_mask.logical_not()
Expand Down
2 changes: 1 addition & 1 deletion liegroups/torch/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -44,6 +44,6 @@ def trace(mat):
mat = mat.unsqueeze(dim=0)

# Element-wise multiply by identity and take the sum
tr = (torch.eye(mat.shape[1], dtype=mat.dtype) * mat).sum(dim=1).sum(dim=1)
tr = (torch.eye(mat.shape[1], dtype=mat.dtype, device=mat.device) * mat).sum(dim=1).sum(dim=1)

return tr.view(mat.shape[0])