centre random augmentation needs to be done for all batch samples sep…

…arately

alphafold3_pytorch/alphafold3.py

@@ -2120,53 +2120,63 @@ class CentreRandomAugmentation(Module):
     def __init__(self, trans_scale: float = 1.0):
         super().__init__()
         self.trans_scale = trans_scale
+        self.register_buffer('dummy', torch.tensor(0), persistent = False)
+
+    @property
+    def device(self):
+        return self.dummy.device
 
     @typecheck
     def forward(self, coords: Float['b n 3']) -> Float['b n 3']:
         """
         coords: coordinates to be augmented
         """
+        batch_size = coords.shape[0]
+
         # Center the coordinates
         centered_coords = coords - coords.mean(dim=1, keepdim=True)
 
         # Generate random rotation matrix
-        rotation_matrix = self._random_rotation_matrix(coords.device)
+        rotation_matrix = self._random_rotation_matrix(batch_size)
 
         # Generate random translation vector
-        translation_vector = self._random_translation_vector(coords.device)
+        translation_vector = self._random_translation_vector(batch_size)
+        translation_vector = rearrange(translation_vector, 'b c -> b 1 c')
 
         # Apply rotation and translation
-        augmented_coords = torch.einsum('bni,ij->bnj', centered_coords, rotation_matrix) + translation_vector
+        augmented_coords = einsum(centered_coords, rotation_matrix, 'b n i, b i j -> b n j') + translation_vector
 
         return augmented_coords
 
     @typecheck
-    def _random_rotation_matrix(self, device: torch.device) -> Float['3 3']:
+    def _random_rotation_matrix(self, batch_size: int) -> Float['b 3 3']:
         # Generate random rotation angles
-        angles = torch.rand(3, device=device) * 2 * torch.pi
+        angles = torch.rand((batch_size, 3), device = self.device) * 2 * torch.pi
 
         # Compute sine and cosine of angles
         sin_angles = torch.sin(angles)
         cos_angles = torch.cos(angles)
 
         # Construct rotation matrix
-        rotation_matrix = torch.eye(3, device=device)
-        rotation_matrix[0, 0] = cos_angles[0] * cos_angles[1]
-        rotation_matrix[0, 1] = cos_angles[0] * sin_angles[1] * sin_angles[2] - sin_angles[0] * cos_angles[2]
-        rotation_matrix[0, 2] = cos_angles[0] * sin_angles[1] * cos_angles[2] + sin_angles[0] * sin_angles[2]
-        rotation_matrix[1, 0] = sin_angles[0] * cos_angles[1]
-        rotation_matrix[1, 1] = sin_angles[0] * sin_angles[1] * sin_angles[2] + cos_angles[0] * cos_angles[2]
-        rotation_matrix[1, 2] = sin_angles[0] * sin_angles[1] * cos_angles[2] - cos_angles[0] * sin_angles[2]
-        rotation_matrix[2, 0] = -sin_angles[1]
-        rotation_matrix[2, 1] = cos_angles[1] * sin_angles[2]
-        rotation_matrix[2, 2] = cos_angles[1] * cos_angles[2]
+        eye = torch.eye(3, device = self.device)
+        rotation_matrix = repeat(eye, 'i j -> b i j', b = batch_size).clone()
+
+        rotation_matrix[:, 0, 0] = cos_angles[:, 0] * cos_angles[:, 1]
+        rotation_matrix[:, 0, 1] = cos_angles[:, 0] * sin_angles[:, 1] * sin_angles[:, 2] - sin_angles[:, 0] * cos_angles[:, 2]
+        rotation_matrix[:, 0, 2] = cos_angles[:, 0] * sin_angles[:, 1] * cos_angles[:, 2] + sin_angles[:, 0] * sin_angles[:, 2]
+        rotation_matrix[:, 1, 0] = sin_angles[:, 0] * cos_angles[:, 1]
+        rotation_matrix[:, 1, 1] = sin_angles[:, 0] * sin_angles[:, 1] * sin_angles[:, 2] + cos_angles[:, 0] * cos_angles[:, 2]
+        rotation_matrix[:, 1, 2] = sin_angles[:, 0] * sin_angles[:, 1] * cos_angles[:, 2] - cos_angles[:, 0] * sin_angles[:, 2]
+        rotation_matrix[:, 2, 0] = -sin_angles[:, 1]
+        rotation_matrix[:, 2, 1] = cos_angles[:, 1] * sin_angles[:, 2]
+        rotation_matrix[:, 2, 2] = cos_angles[:, 1] * cos_angles[:, 2]
 
         return rotation_matrix
 
     @typecheck
-    def _random_translation_vector(self, device: torch.device) -> Float['3']:
+    def _random_translation_vector(self, batch_size: int) -> Float['b 3']:
         # Generate random translation vector
-        translation_vector = torch.randn(3, device=device) * self.trans_scale
+        translation_vector = torch.randn((batch_size, 3), device = self.device) * self.trans_scale
         return translation_vector
 
 # input embedder

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "alphafold3-pytorch"
-version = "0.0.24"
+version = "0.0.25"
 description = "Alphafold 3 - Pytorch"
 authors = [
     { name = "Phil Wang", email = "[email protected]" }