21d060ac2b
* Updated conversion of TF OD API 2.4 SSD models * Fixed issue when more Conv2D nodes were selected for weights permutation when converting TF OD API models * Code style fixes * Fixed code comments
313 lines
14 KiB
Python
313 lines
14 KiB
Python
# Copyright (C) 2018-2021 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import logging as log
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from collections import deque
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from re import match, compile
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import networkx as nx
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from mo.graph.graph import Node, Graph, set_edge_attribute_between_nodes, get_edge_attribute_between_nodes
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from mo.utils.error import Error
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from mo.utils.utils import refer_to_faq_msg
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def backward_bfs_for_operation(start_node: Node, op_names: list, skip_op_list: list = None):
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"""
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Find node with 'op' attribute equal to one of from 'op_name', searching in the backward direction.
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In case of branching algorithm goes into each branch, but if it can't find layer in one of them it returns
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empty list.
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:param start_node: Start node for BFS algorithm
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:param op_names: The list with names of operations to search
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:param skip_op_list: list of operations to be stopped at if they are met
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"""
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if skip_op_list is None:
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skip_op_list = []
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ret = []
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q = deque([start_node])
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while len(q) != 0:
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node = q.popleft()
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in_nodes_size = len(node.in_nodes())
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for id in range(in_nodes_size): # in_nodes() can return either list or dict
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pnode = node.in_node(id)
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if pnode.kind == 'op':
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if pnode.has_valid('op') and pnode.op in op_names:
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if pnode.id not in ret:
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ret.append(pnode.id)
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else:
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if pnode.op not in skip_op_list:
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q.append(pnode)
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elif pnode.kind == 'data' and pnode.value is None:
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q.append(pnode)
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return [Node(start_node.graph, x) for x in ret]
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def bfs_search(graph: Graph, start_nodes: list = list()):
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"""
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Performs breadth-first search over a graph and returns a list of nodes in the BFS order.
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:param graph: networkx graph to traverse.
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:param start_nodes: list of start nodes of the graph. If the list is empty then start from all nodes that do not
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have input nodes.
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:return: the list of nodes in the BFS order.
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"""
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result = list()
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if len(start_nodes) == 0:
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start_nodes = [node_name for node_name in graph.nodes() if len(graph.in_edges(node_name)) == 0]
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visited = set(start_nodes)
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d = deque(start_nodes)
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while len(d) != 0:
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cur_node_name = d.popleft()
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result.append(cur_node_name)
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for src_node, dst_node in graph.out_edges(cur_node_name):
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if dst_node not in visited:
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d.append(dst_node)
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visited.add(dst_node)
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return result
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def nodes_matching_name_pattern(graph: Graph, pattern: str):
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"""
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Returns list of node names of the graph that match regular expression.
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:param graph: graph to operate on.
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:param pattern: regular expression describing node name pattern.
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:return: list of matched node names.
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"""
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compiled_pattern = compile(pattern)
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return [node_name for node_name in list(graph.nodes()) if match(compiled_pattern, node_name)]
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def is_connected_component(graph: Graph, node_names: list):
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"""
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Checks that specified list of nodes forms a connected sub-graph. It ignores edges direction.
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The algorithm is the following. Run BFS from one of the nodes from the node_names list ignoring edges order and
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visiting only nodes from the node_names list. Prepare list of visited nodes. If this list is equal to the
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node_names list (we actually check that the node_names set is sub-set of 'visited' set that is equivalent) then the
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sub-graph is connected.
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:param graph: graph to operate on.
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:param node_names: list of node names to be checked.
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:return: Result of the check.
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"""
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if len(node_names) == 0:
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return True
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d = deque([node_names[0]])
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visited = set([node_names[0]])
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while len(d) != 0:
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cur_node_name = d.popleft()
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visited.add(cur_node_name)
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# find adjacent nodes from the list of node_names. Ignoring edges direction
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adj_nodes = [src_node for src_node, _ in graph.in_edges(cur_node_name) if src_node in node_names] + \
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[dst_node for _, dst_node in graph.out_edges(cur_node_name) if dst_node in node_names]
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for adj_node in adj_nodes:
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if adj_node not in visited:
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d.append(adj_node)
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visited.add(adj_node)
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return set(node_names).issubset(visited)
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def sub_graph_between_nodes(graph: Graph, start_nodes: list, end_nodes: list, detect_extra_start_node: callable=None,
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include_control_flow=True, allow_non_reachable_end_nodes=False):
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"""
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Finds nodes of the sub-graph between 'start_nodes' and 'end_nodes'. Input nodes for the sub-graph nodes are also
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added to the sub-graph. Constant inputs of the 'start_nodes' are also added to the sub-graph.
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:param graph: graph to operate on.
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:param start_nodes: list of nodes names that specifies start nodes.
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:param end_nodes: list of nodes names that specifies end nodes.
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:param detect_extra_start_node: callable function to add additional nodes to the list of start nodes instead of
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traversing the graph further. The list of additional start nodes is returned of the function is not None.
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:param include_control_flow: flag to specify whether to follow the control flow edges or not
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:param allow_non_reachable_end_nodes: do not fail if the end nodes are not reachable from the start nodes
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:return: list of nodes of the identified sub-graph or None if the sub-graph cannot be extracted.
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"""
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sub_graph_nodes = list()
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visited = set(start_nodes)
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d = deque(start_nodes)
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extra_start_nodes = []
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nx.set_node_attributes(G=graph, name='prev', values=None)
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while len(d) != 0:
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cur_node_id = d.popleft()
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sub_graph_nodes.append(cur_node_id)
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if cur_node_id not in end_nodes: # do not add output nodes of the end_nodes
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for _, dst_node_name, attrs in graph.out_edges(cur_node_id, data=True):
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if dst_node_name not in visited and (include_control_flow or not attrs.get('control_flow_edge', False)):
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d.append(dst_node_name)
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visited.add(dst_node_name)
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graph.node[dst_node_name]['prev'] = cur_node_id
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for src_node_name, _, attrs in graph.in_edges(cur_node_id, data=True):
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# add input nodes for the non-start_nodes
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if cur_node_id not in start_nodes and src_node_name not in visited and\
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(include_control_flow or not attrs.get('control_flow_edge', False)):
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if detect_extra_start_node is not None and detect_extra_start_node(Node(graph, cur_node_id)):
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extra_start_nodes.append(cur_node_id)
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else:
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d.append(src_node_name)
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graph.node[src_node_name]['prev'] = cur_node_id
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visited.add(src_node_name)
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# use forward dfs to check that all end nodes are reachable from at least one of input nodes
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forward_visited = set()
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for start_node in start_nodes:
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graph.dfs(start_node, forward_visited)
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for end_node in end_nodes:
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if not allow_non_reachable_end_nodes and end_node not in forward_visited:
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raise Error('End node "{}" is not reachable from start nodes: {}. '.format(end_node, start_nodes) +
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refer_to_faq_msg(74))
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for node_id in sub_graph_nodes:
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# sub-graph should not contain Placeholder nodes
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if graph.node[node_id].get('op', '') == 'Parameter':
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path = list()
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cur_node = node_id
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while cur_node and 'prev' in graph.node[cur_node]:
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path.append(str(cur_node))
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cur_node = graph.node[cur_node]['prev']
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log.debug("The path from input node is the following: {}".format('\n'.join(path)))
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raise Error('The matched sub-graph contains network input node "{}". '.format(node_id) +
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refer_to_faq_msg(75))
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if detect_extra_start_node is None:
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return sub_graph_nodes
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else:
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return sub_graph_nodes, extra_start_nodes
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def invert_sub_graph_between_nodes(graph: Graph, start_nodes: list, end_nodes: list, detect_extra_start_node: callable=None):
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"""
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Finds nodes of the sub-graph between 'start_nodes' and 'end_nodes'. But doing it from start_nodes stepping
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backward by in edges.
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Input nodes for the sub-graph nodes are also added to the sub-graph. Constant inputs of the 'start_nodes'
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are also added to the sub-graph.
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:param graph: graph to operate on.
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:param start_nodes: list of nodes names that specifies start nodes.
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:param end_nodes: list of nodes names that specifies end nodes.
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:return: list of nodes of the identified sub-graph or None if the sub-graph cannot be extracted.
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"""
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sub_graph_nodes = list()
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visited = set(start_nodes)
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d = deque(start_nodes)
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extra_start_nodes = []
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nx.set_node_attributes(G=graph, name='prev', values=None)
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while len(d) != 0:
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cur_node_name = d.popleft()
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sub_graph_nodes.append(cur_node_name)
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if cur_node_name not in start_nodes and \
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detect_extra_start_node is not None and detect_extra_start_node(Node(graph, cur_node_name)):
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extra_start_nodes.append(cur_node_name)
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else:
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if cur_node_name not in end_nodes: # do not add output nodes of the end_nodes
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for src_node_name, _ in graph.in_edges(cur_node_name):
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if src_node_name not in visited:
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d.append(src_node_name)
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visited.add(src_node_name)
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graph.node[cur_node_name]['prev'] = src_node_name
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for node_name in sub_graph_nodes:
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# sub-graph should not contain Input nodes
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if graph.node[node_name].get('op', '') == 'Parameter':
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path = list()
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cur_node = node_name
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while cur_node and 'prev' in graph.node[cur_node]:
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path.append(str(cur_node))
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cur_node = graph.node[cur_node]['prev']
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log.debug("The path from input node is the following: {}".format('\n'.join(path)))
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raise Error('The matched sub-graph contains network input node "{}". '.format(node_name) +
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refer_to_faq_msg(75))
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if detect_extra_start_node is None:
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return sub_graph_nodes
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else:
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return sub_graph_nodes, extra_start_nodes
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def node_neighbourhood(node_name: str, depth: int, next_node_fn):
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"""
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Find neighbourhood of the node..
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:param node_name: name of the node to find neighbourhood for.
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:param depth: maximum depth of search nodes.
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:param next_node_fn: callable that accepts node name and should return list of adjacent nodes.
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:return: list of names of nodes in the neighbourhood.
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"""
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dist = dict()
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dist[node_name] = 0
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deq = deque([node_name])
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while len(deq) != 0:
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cur_node_name = deq.popleft()
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cur_dist = dist[cur_node_name]
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if cur_dist < depth:
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for next_node_name in next_node_fn(cur_node_name):
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next_dist = dist.setdefault(next_node_name, depth + 1)
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if next_dist > cur_dist + 1:
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dist[next_node_name] = cur_dist + 1
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deq.append(next_node_name)
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return list(dist.keys())
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def node_incoming_neighbourhood(graph: Graph, node_name: str, depth: int):
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"""
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Find input neighbourhood of the node.
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:param graph: graph to operate on.
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:param node_name: name of the node to find neighbourhood for.
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:param depth: maximum depth of input nodes.
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:return: list of names of nodes in the neighbourhood.
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"""
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return node_neighbourhood(node_name, depth, lambda node_name: [u for u, v in graph.in_edges([node_name])])
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def node_outcoming_neighbourhood(graph: Graph, node_name: str, depth: int):
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"""
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Find output neighbourhood of the node.
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:param graph: graph to operate on.
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:param node_name: name of the node to find neighbourhood for.
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:param depth: maximum depth of output nodes.
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:return: list of names of nodes in the neighbourhood.
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"""
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return node_neighbourhood(node_name, depth, lambda node_name: [v for u, v in graph.out_edges([node_name])])
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def scope_output_nodes(graph: Graph, scope: str, scope_delimiter: str='/'):
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"""
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The function returns nodes producing output of the sub-graph defined by scope (name prefix). The node is considered
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output of the scope if it is in this scope and it's output is outside of the scope.
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:param graph: graph to operate on.
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:param scope: string with scope (prefix of the node name).
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:param scope_delimiter: delimiter between scope parts.
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:return: list of Node objects which are outputs of the scope.
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"""
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if scope[-1] != scope_delimiter:
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scope += scope_delimiter
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result = set()
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for node_id in graph.nodes():
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if node_id.startswith(scope):
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for _, out_node_name in graph.out_edges(node_id):
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if not out_node_name.startswith(scope):
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result.add(node_id)
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break
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return [Node(graph, node_id) for node_id in result]
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def clear_tensor_names_info(nodes: list):
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"""
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Clears tensor names information from 'fw_tensor_debug_info' attribute for all edges outgoing from
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given nodes.
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This method is used in cases when transformation adds postprocessing and the result does not
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correspond to the original tensor.
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This method should only be used during the front phase.
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:param nodes: list of Node objects.
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"""
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for node in nodes:
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for out_idx in node.out_nodes():
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out_node = node.out_node(out_idx)
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fw_info_list = get_edge_attribute_between_nodes(node, out_node, 'fw_tensor_debug_info')
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new_fw_info = []
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for fw_info in fw_info_list:
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if fw_info is not None and len(fw_info) >= 2:
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new_fw_info.append((fw_info[0], fw_info[1], None))
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set_edge_attribute_between_nodes(node, out_node, 'fw_tensor_debug_info', new_fw_info)
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