# tf.contrib.receptive_field.get_compute_order

tf.contrib.receptive_field.get_compute_order(
graph_def,
input_node_name='',
input_node_size=None
)


Computes order of computation for a given CNN graph.

Optionally, the function may also compute the input and output feature map resolutions at each node. In this case, input_node_name and input_node_size must be set. Note that if a node's op type is unknown, the input and output resolutions are ignored and set to None.

#### Args:

• graph_def: GraphDef object.
• input_node_name: Name of node with fixed input resolution (optional). This is usually the node name for the input image in a CNN.
• input_node_size: 2D list of integers, fixed input resolution to use (optional). This is usually the input resolution used for the input image in a CNN (common examples are: [224, 224], [299, 299], [321, 321]).

#### Returns:

• node_info: Default dict keyed by node name, mapping to a named tuple with the following fields:
• order: Integer denoting topological order;
• node: NodeDef for the given node;
• input_size: 2D list of integers, denoting the input spatial resolution to the node;
• output_size: 2D list of integers, denoting the output spatial resolution of the node.
• name_to_node: Dict keyed by node name, each entry containing the node's NodeDef.