Convert base_linear_op to a dense linear operator in BlockDiagLinearOperator

linear_operator/operators/block_diag_linear_operator.py

@@ -9,6 +9,7 @@
 
 from linear_operator.operators._linear_operator import IndexType, LinearOperator
 from linear_operator.operators.block_linear_operator import BlockLinearOperator
+from linear_operator.operators.dense_linear_operator import DenseLinearOperator
 
 from linear_operator.utils.memoize import cached
 
@@ -49,6 +50,9 @@ class BlockDiagLinearOperator(BlockLinearOperator, metaclass=_MetaBlockDiagLinea
     """
 
     def __init__(self, base_linear_op, block_dim=-3):
+        if isinstance(base_linear_op, Tensor):
+            base_linear_op = DenseLinearOperator(base_linear_op)
+
         super().__init__(base_linear_op, block_dim)
         # block diagonal is restricted to have square diagonal blocks
         if self.base_linear_op.shape[-1] != self.base_linear_op.shape[-2]:

test/operators/test_block_diag_linear_operator.py

@@ -12,11 +12,19 @@ class TestBlockDiagLinearOperator(LinearOperatorTestCase, unittest.TestCase):
     seed = 0
     should_test_sample = True
 
+    # Whether to initialize `BlockDiagLinearOperator` from a tensor or a linear operator.
+    _initialize_from_tensor = False
+
     def create_linear_op(self):
         blocks = torch.randn(8, 4, 4)
         blocks = blocks.matmul(blocks.mT)
         blocks.add_(torch.eye(4, 4).unsqueeze_(0))
-        return BlockDiagLinearOperator(DenseLinearOperator(blocks))
+
+        return (
+            BlockDiagLinearOperator(blocks)
+            if self._initialize_from_tensor
+            else BlockDiagLinearOperator(DenseLinearOperator(blocks))
+        )
 
     def evaluate_linear_op(self, linear_op):
         blocks = linear_op.base_linear_op.tensor
@@ -26,6 +34,10 @@ def evaluate_linear_op(self, linear_op):
         return actual
 
 
+class TestBlockDiagLinearOperatorFromTensor(TestBlockDiagLinearOperator):
+    _initialize_from_tensor = True
+
+
 class TestBlockDiagLinearOperatorBatch(LinearOperatorTestCase, unittest.TestCase):
     seed = 0
     should_test_sample = True
@@ -75,7 +87,7 @@ def test_metaclass_constructor(self):
         base_operators = [torch.randn(k, n), torch.randn(b1, b2, k, n)]
         subtest_names = ["non-batched input", "batched input"]
         # repeats tests for both batched and non-batched tensors
-        for (base_op, test_name) in zip(base_operators, subtest_names):
+        for base_op, test_name in zip(base_operators, subtest_names):
             with self.subTest(test_name):
                 base_diag = DiagLinearOperator(base_op)
                 linear_op = BlockDiagLinearOperator(base_diag)