Merge pull request #359 from dingquanyu/update-kabsch_rotation

move the kabsch rotation step to gpu

openfold/utils/loss.py

@@ -37,7 +37,6 @@ from openfold.utils.tensor_utils import (
 import random
 from openfold.np import residue_constants as rc
 import logging 
-import procrustes 
 from openfold.utils.tensor_utils import tensor_tree_map
 import gc
 logger = logging.getLogger(__name__)
@@ -1716,9 +1715,8 @@ def kabsch_rotation(P, Q):
     Use procrustes package to calculate best rotation that minimises
     the RMSD betwee P and Q
 
-    The optimal rotation matrix was calculated using 
-    the rotational() function from procrustes package. Details can be found here:
-    https://procrustes.qcdevs.org/api/rotational.html#rotational
+    The optimal rotation matrix was calculated using Kabsch algorithm:
+    https://en.wikipedia.org/wiki/Kabsch_algorithm
 
     Args:
     P: [N * 3] Nres is the number of atoms and each row corresponds to the atom's x,y,z coordinates
@@ -1727,15 +1725,18 @@ def kabsch_rotation(P, Q):
     return:
     A 3*3 rotation matrix 
     """
-
     assert P.shape == torch.Size([Q.shape[0],Q.shape[1]])
-    rotation = procrustes.rotational(P.detach().cpu().float().numpy(),
-                                  Q.detach().cpu().float().numpy(),translate=False,scale=False)
-    # Rotation.t doesn't mean transpose, t only means get the matrix out of the procruste object
-    rotation = torch.tensor(rotation.t,dtype=torch.float)
-    assert rotation.shape == torch.Size([3,3])
-    return rotation.to(device=P.device, dtype=P.dtype)
 
+    # Firstly, compute SVD of P.T * Q 
+    u,_,vt = torch.linalg.svd(torch.matmul(P.to(torch.float32).T,Q.to(torch.float32)),driver='gesvd')
+    # Then construct s matrix
+    s = torch.eye(P.shape[1],device=P.device)
+    # correct the rotation matrix to ensure a right-handed coordinate
+    s[-1, -1] = torch.sign(torch.linalg.det(torch.matmul(u, vt)))
+    # finally compute the rotation matrix
+    r_opt = torch.matmul(torch.matmul(u, s), vt)
+    assert r_opt.shape == torch.Size([3,3])
+    return r_opt.to(device=P.device, dtype=P.dtype)
 
 def get_optimal_transform(
     src_atoms: torch.Tensor,