Class OpHintArgumentTracker
Aliases:
- Class
tf.contrib.lite.OpHint.OpHintArgumentTracker
- Class
tf.lite.OpHint.OpHintArgumentTracker
Defined in tensorflow/lite/python/op_hint.py
.
Conceptually tracks indices of arguments of "OpHint functions".
The inputs and arguments of these functions both use an instance of the class so they can have independent numbering.
__init__
__init__(
function_name,
unique_function_id,
node_name_prefix,
attr_name
)
Initialize ophint argument.
Args:
function_name
: Name of the function that this tracks arguments for.unique_function_id
: UUID of function that this tracks arguments for.node_name_prefix
: How identities that are created are named.attr_name
: Name of attribute to use to store the index for this hint. i.e. FUNCTION_INPUT_INDEX or FUNCTION_OUTPUT_INDEX
Methods
tf.lite.OpHint.OpHintArgumentTracker.add
add(
arg,
tag=None,
name=None,
aggregate=None,
index_override=None
)
Return a wrapped tensor of an input tensor as an argument.
Args:
arg
: A TensorFlow tensor that should be considered an argument.tag
: String tag to identify arguments that should be packed.name
: Name of argument. This is included in the Identity hint op names.aggregate
: Strategy to aggregate. Acceptable values are OpHint.AGGREGATE_FIRST, OpHint.AGGREGATE_LAST, and OpHint.AGGREGATE_STACK. Note, aggregate is only valid if tag is specified.index_override
: Specify what input/output index should this be in the final stub. i.e. add(arg0, index=1); add(arg1, index=0) wil make the final stub be as stub_func(inputs[arg1, arg0], outputs=[]) rather than the default call order based ordering.
Returns:
A tensor representing the wrapped argument.
Raises:
ValueError
: When indices are not consistent.