def _min_matrix_dim(self):
"""Minimum of domain/range dimension, if statically available, else None."""
domain_dim = tensor_shape.dimension_value(self.domain_dimension)
range_dim = tensor_shape.dimension_value(self.range_dimension)
if domain_dim is None or range_dim is None:
return None
return min(domain_dim, range_dim)
def _set_diag_operators(self, diag_update, is_diag_update_positive):
"""Set attributes self._diag_update and self._diag_operator."""
if diag_update is not None:
self._diag_operator = linear_operator_diag.LinearOperatorDiag(
self._diag_update, is_positive_definite=is_diag_update_positive)
else:
if tensor_shape.dimension_value(tensor_shape.TensorShape(self.u.shape)[-1]) is not None:
r = tensor_shape.dimension_value(tensor_shape.TensorShape(self.u.shape)[-1])
else:
r = array_ops.shape(self.u)[-1]
self._diag_operator = linear_operator_identity.LinearOperatorIdentity(
num_rows=r, dtype=self.dtype)
def _range_dimension_tensor(self, shape=None):
# `shape` may be passed in if this can be pre-computed in a
# more efficient manner, e.g. without excessive Tensor conversions.
dim_value = tensor_shape.dimension_value(self.range_dimension)
if dim_value is not None:
return ops.convert_to_tensor(dim_value)
else:
shape = self.shape_tensor() if shape is None else shape
return shape[-2]
def _static_check_for_same_dimensions(operators):
"""ValueError if operators determined to have different dimensions."""
if len(operators) < 2:
return
domain_dimensions = [
(op.name, tensor_shape.dimension_value(op.domain_dimension))
for op in operators
if tensor_shape.dimension_value(op.domain_dimension) is not None]
if len(set(value for name, value in domain_dimensions)) > 1:
raise ValueError(f"All `operators` must have the same `domain_dimension`. "
f"Received: {domain_dimensions}.")
range_dimensions = [
(op.name, tensor_shape.dimension_value(op.range_dimension))
for op in operators
if tensor_shape.dimension_value(op.range_dimension) is not None]
if len(set(value for name, value in range_dimensions)) > 1:
raise ValueError(f"All operators must have the same `range_dimension`. "
f"Received: {range_dimensions}.")
def _static_check_for_same_dimensions(operators):
"""ValueError if operators determined to have different dimensions."""
if len(operators) < 2:
return
domain_dimensions = [
(op.name, tensor_shape.dimension_value(op.domain_dimension))
for op in operators
if tensor_shape.dimension_value(op.domain_dimension) is not None
]
if len(set(value for name, value in domain_dimensions)) > 1:
raise ValueError(
"Operators must have the same domain dimension. Found: %s" %
domain_dimensions)
range_dimensions = [
(op.name, tensor_shape.dimension_value(op.range_dimension))
for op in operators
if tensor_shape.dimension_value(op.range_dimension) is not None
]
if len(set(value for name, value in range_dimensions)) > 1:
raise ValueError(
"Operators must have the same range dimension. Found: %s" %
range_dimensions)
def _to_dense(self):
"""Generic and often inefficient implementation. Override often."""
if self.batch_shape.is_fully_defined():
batch_shape = self.batch_shape
else:
batch_shape = self.batch_shape_tensor()
dim_value = tensor_shape.dimension_value(self.domain_dimension)
if dim_value is not None:
n = dim_value
else:
n = self.domain_dimension_tensor()
eye = linalg_ops.eye(num_rows=n, batch_shape=batch_shape, dtype=self.dtype)
return self.matmul(eye)
def _convert_dimension_to_tensor(value, dtype=None):
dtype = dtype or np.int32
if dtype not in (np.int32, np.int64):
raise TypeConversionError(value, dtype)
return convert_to_tensor(tensor_shape.dimension_value(value), dtype=dtype)
