Initialization of Transform3D with a custom matrix.

Summary:
Allows to initialize a Transform3D object with a batch of user-defined transformation matrices:
```
t = Transform3D(matrix=torch.randn(2, 4, 4))
```

Reviewed By: nikhilaravi

Differential Revision: D20693475

fbshipit-source-id: dccc49b2ca4c19a034844c63463953ba8f52c1bc

Author: davnov134

pytorch3d/transforms/transform3d.py

@@ -134,8 +134,37 @@ class Transform3d:
 
     """
 
-    def __init__(self, dtype=torch.float32, device="cpu"):
-        self._matrix = torch.eye(4, dtype=dtype, device=device).view(1, 4, 4)
+    def __init__(
+        self,
+        dtype: torch.dtype = torch.float32,
+        device="cpu",
+        matrix: Optional[torch.Tensor] = None,
+    ):
+        """
+        Args:
+            dtype: The data type of the transformation matrix.
+                to be used if `matrix = None`.
+            device: The device for storing the implemented transformation.
+                If `matrix != None`, uses the device of input `matrix`.
+            matrix: A tensor of shape (4, 4) or of shape (minibatch, 4, 4)
+                representing the 4x4 3D transformation matrix.
+                If `None`, initializes with identity using
+                the specified `device` and `dtype`.
+        """
+
+        if matrix is None:
+            self._matrix = torch.eye(4, dtype=dtype, device=device).view(1, 4, 4)
+        else:
+            if matrix.ndim not in (2, 3):
+                raise ValueError('"matrix" has to be a 2- or a 3-dimensional tensor.')
+            if matrix.shape[-2] != 4 or matrix.shape[-1] != 4:
+                raise ValueError(
+                    '"matrix" has to be a tensor of shape (minibatch, 4, 4)'
+                )
+            # set the device from matrix
+            device = matrix.device
+            self._matrix = matrix.view(-1, 4, 4)
+
         self._transforms = []  # store transforms to compose
         self._lu = None
         self.device = device

tests/test_transforms.py

@@ -57,6 +57,19 @@ def test_clone(self):
             matrix2 = t_pair[1].get_matrix()
             self.assertTrue(torch.allclose(matrix1, matrix2))
 
+    def test_init_with_custom_matrix(self):
+        for matrix in (torch.randn(10, 4, 4), torch.randn(4, 4)):
+            t = Transform3d(matrix=matrix)
+            self.assertTrue(t.device == matrix.device)
+            self.assertTrue(t._matrix.dtype == matrix.dtype)
+            self.assertTrue(torch.allclose(t._matrix, matrix.view(t._matrix.shape)))
+
+    def test_init_with_custom_matrix_errors(self):
+        bad_shapes = [[10, 5, 4], [3, 4], [10, 4, 4, 1], [10, 4, 4, 2], [4, 4, 4, 3]]
+        for bad_shape in bad_shapes:
+            matrix = torch.randn(*bad_shape).float()
+            self.assertRaises(ValueError, Transform3d, matrix=matrix)
+
     def test_translate(self):
         t = Transform3d().translate(1, 2, 3)
         points = torch.tensor([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.5, 0.5, 0.0]]).view(