def is_var(node):
if not is_tensor(node):
return False
if node.op.type.startswith('Variable'):
return True
if ((resource_variable_ops.is_resource_variable(node)
or utils.is_reference_variable(node))):
return True
if node.dtype == tf.resource and node.op.type == 'VarHandleOp':
return True
return False
def var_to_tensors(var):
if resource_variable_ops.is_resource_variable(var):
if tf.control_flow_v2_enabled():
# TODO(b/143690035): Note that the "captures" property relies on an
# API which might change.
captures = layer_collection.graph.captures
return [h for vh, h in captures if vh is var.handle]
else:
return [var.handle]
if utils.is_reference_variable(var):
return [tf_ops.internal_convert_to_tensor(var, as_ref=True)]
raise ValueError('%s is not a recognized variable type.' % str(var))
def is_var(node):
if not is_tensor(node):
return False
if node.op.type.startswith('Variable'):
return True
if ((resource_variable_ops.is_resource_variable(node) or
utils.is_reference_variable(node))):
return True
# TODO(b/143690035): Note that the Placeholder type handles the Control Flow
# V2 case, but this could stop working in the future if the implementation of
# Control Flow V2 changes.
if node.dtype == tf.resource and (node.op.type == 'VarHandleOp'
or node.op.type == 'Placeholder'):
return True
return False
def var_to_tensor(var):
if resource_variable_ops.is_resource_variable(var):
return var.handle
if utils.is_reference_variable(var):
return tf_ops.internal_convert_to_tensor(var, as_ref=True)
raise ValueError('%s is not a recognized variable type.' % str(var))