v1.4.3

docsource/source/index.rst

@@ -231,6 +231,8 @@ Bits of code were adapted from SciPy. Documentation is generated, distributed an
 
 Changelog
 ==========
+Version 1.4.3: 
+    - Fix normalization bug in :func:`~roma.utils.quat_composition()` (thanks jamiesalter for reporting).
 Version 1.4.2:
     - Fix for :func:`~roma.utils.quat_action()` to support arbitrary devices and types.
     - Added conversion functions between Rigid and RigidUnitQuat.

roma/utils.py

@@ -195,6 +195,17 @@ def quat_inverse(quat):
     """
     return quat_conjugation(quat) / torch.sum(quat**2, dim=-1, keepdim=True)
 
+def quat_normalize(quat):
+    """
+    Returns a normalized, unit norm, copy of a batch of quaternions.
+
+    Args:
+        quat (...x4 tensor, XYZW convention): batch of quaternions.
+    Returns:
+        batch of quaternions (...x4 tensor, XYZW convention).        
+    """
+    return quat / torch.linalg.norm(quat, dim=-1, keepdim=True)
+
 def quat_product(p, q):
     """
     Returns the product of two quaternions.
@@ -235,7 +246,7 @@ def quat_composition(sequence, normalize = False):
     for q in sequence[1:]:
         res = quat_product(res, q)
     if normalize:
-        q = q / torch.norm(q, dim=-1, keepdim=True)
+        res = quat_normalize(res)
     return res
 
 def quat_action(q, v, is_normalized=False):
@@ -380,7 +391,7 @@ def unitquat_slerp_fast(q0, q1, steps, shortest_arc=True):
     # Interpolation
     q = alpha * q0.reshape((1,) * steps.dim() + q0.shape) + beta * q1.reshape((1,) * steps.dim() + q1.shape)
     # Normalization of the output
-    q /= torch.norm(q, dim=-1, keepdim=True)
+    q = quat_normalize(q)
     return q.reshape(steps.shape + batch_shape + (4,))
 
 def rotvec_slerp(rotvec0, rotvec1, steps):

setup.py

@@ -5,7 +5,7 @@
 
 setuptools.setup(
     name="roma",
-    version="1.4.2",
+    version="1.4.3",
     author="Romain Brégier",
     author_email="[email protected]",
     description="A lightweight library to deal with 3D rotations in PyTorch.",

test/test_utils.py

@@ -112,6 +112,8 @@ def test_quat(self):
             q_id = roma.rotvec_to_unitquat(torch.zeros(1,3))
             self.assertTrue(is_close(q_id, roma.quat_product(q, iq)))
             self.assertTrue(is_close(q_id, roma.quat_product(iq, q)))
+            nq = roma.quat_normalize(q)
+            self.assertTrue(is_close(torch.linalg.norm(nq, dim=-1), torch.ones(batch_size, dtype=dtype)))
 
     def test_quat_action(self):
         batch_size=100