240 lines
10 KiB
Python
240 lines
10 KiB
Python
# Copyright (C) 2018-2021 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import argparse
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import logging as log
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import os
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from operator import itemgetter
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import networkx as nx
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from extensions.back.RemoveUselessConvert import RemoveUselessConvert
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from extensions.back.ResultRename import ResultRename
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from extensions.back.op_versioning import OpVersioning
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from extensions.ops.Cast import Cast
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from mo.back.ie_ir_ver_2.emitter import port_renumber, serialize_constants, generate_ie_ir, serialize_mean_image
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from mo.graph.graph import Node, Graph
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from mo.middle.passes import tensor_names, convert_data_type
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from mo.middle.passes.convert_data_type import data_type_str_to_np
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from mo.middle.passes.infer import type_infer
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from mo.middle.pattern_match import for_graph_and_each_sub_graph_recursively
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from mo.utils.error import Error
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def determined_sort(outputs: list):
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op_order = []
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data_order = []
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stack = list(outputs)
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visited = set()
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while len(stack) != 0:
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node = stack.pop(0)
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node_id = node.id
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visited.add(node_id)
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has_child = False
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in_names = [n for n, d in node.get_inputs()]
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for in_node_name in in_names:
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if in_node_name not in visited:
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stack.insert(0, node)
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stack.insert(0, Node(node.graph, in_node_name))
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has_child = True
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break
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if not has_child:
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if node.kind == 'op':
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op_order.append(node_id)
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if node.kind == 'data':
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data_order.append(node_id)
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return op_order, data_order
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def get_fw_tensor_debug_info(node: Node):
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while not node.has_valid('fw_tensor_debug_info') and not node.has_valid('output_sort_order') \
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and len(node.in_nodes()):
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try:
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node = node.in_node()
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except Exception as e:
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log.warning('Was not able to determine tensor debug info for node {}'.format(node.name))
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return "dummy_node_name"
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if node.has_valid('output_sort_order'):
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return node.soft_get('output_sort_order')
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return node.soft_get('fw_tensor_debug_info')
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def get_sorted_outputs(graph: Graph):
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outputs = []
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outputs_for_sort = {}
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for node in graph.nodes():
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if len(graph.out_edges(node)) == 0:
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outputs.append(Node(graph, node))
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if len(outputs) == 1:
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return outputs
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for node in outputs:
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debug_info = get_fw_tensor_debug_info(node)
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if isinstance(debug_info, str):
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outputs_for_sort[node.id] = debug_info
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elif isinstance(debug_info, list):
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outputs_for_sort[node.id] = debug_info[0][0] + '_' + str(debug_info[0][1])
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else:
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raise Error('Unsupported type of the variable with debug information used to sort output nodes')
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if len(outputs_for_sort) != len(set(outputs_for_sort.values())):
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log.warning('There are at least two output nodes with the same key used to sort the outputs. This means that '
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'IRs with different order of nodes may be generated between Model Optimizer runs. The dictionary '
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'with outputs is: {}'.format(outputs_for_sort))
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return [Node(graph, key) for key, value in sorted(outputs_for_sort.items(), key=itemgetter(1))]
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def collect_sub_graphs(graph: Graph):
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""" Go over all nodes and sub_graphs in the graph recursively; returns all found sub-graphs. """
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result = []
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for node in graph.nodes():
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node = Node(graph, node)
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if node.has_valid('sub_graphs'):
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for sub_graph in node.sub_graphs:
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result.append(node[sub_graph])
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result += collect_sub_graphs(node[sub_graph])
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return result
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def relabel_nodes_inplace_safe(graph: Graph, new_labels: dict):
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""" Safely relabels graph in-place without graph copy.
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Safety in this place means that it is guaranteed that
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there won't be collisions during relabeling process.
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"""
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# Relabel nodes in two stages
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intermediate_map = {node: graph.unique_id('__relabel__{}__'.format(str(i))) for i, node in enumerate(graph.nodes())}
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final_map = {dst: new_labels[src] for src, dst in intermediate_map.items()}
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assert len(set(intermediate_map.keys()).intersection(set(intermediate_map.values()))) == 0
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assert len(set(final_map.keys()).intersection(set(final_map.values()))) == 0
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nx.relabel_nodes(graph, intermediate_map, copy=False)
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nx.relabel_nodes(graph, final_map, copy=False)
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def convert_const_node_value_type(const_node: Node, np_data_type):
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assert const_node.type == 'Const'
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log.warning('Converting type of Const node "{}" to "{}"'.format(const_node.name, np_data_type))
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const_node.value = const_node.value.astype(np_data_type)
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const_node.data_type = np_data_type
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const_node.infer(const_node)
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const_node.type_infer(const_node)
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# if the Const node has an input data node then need to update it also
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if len(const_node.in_nodes()) == 1:
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input_data = const_node.in_node(0)
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assert input_data.kind == 'data'
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input_data.value = input_data.value.astype(const_node.data_type)
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input_data.data_type = const_node.data_type
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def convert_inputs_of_specific_ops(graph: Graph):
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type_port = {'Broadcast': {1: 'int64', 2: 'int64'},
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'ConvolutionBackpropData': {2: 'int64'},
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'Deconvolution': {2: 'int64'},
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'Gather': {2: 'int64'},
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'GroupConvolutionBackpropData': {2: 'int64'},
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'Interpolate': {1: 'int64'},
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'LRN': {1: 'int64'},
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'NonMaxSuppression': {2: 'int64'},
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'NormalizeL2': {1: 'int64'},
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'OneHot': {1: 'int64'},
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'Pad': {1: 'int64', 2: 'int64'},
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'PriorBox': {0: 'int64', 1: 'int64'},
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'PriorBoxClustered': {0: 'int64', 1: 'int64'},
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'ReduceLogicalAnd': {1: 'int64'},
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'ReduceLogicalOr': {1: 'int64'},
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'ReduceMax': {1: 'int64'},
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'ReduceMean': {1: 'int64'},
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'ReduceMin': {1: 'int64'},
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'ReduceProd': {1: 'int64'},
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'ReduceSum': {1: 'int64'},
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'Reshape': {1: 'int64'},
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'Squeeze': {1: 'int64'},
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'StridedSlice': {1: 'int64', 2: 'int64', 3: 'int64'},
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'Split': {1: 'int64'},
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'Tile': {1: 'int64'},
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'Transpose': {1: 'int64'},
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'Unsqueeze': {1: 'int64'},
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'VariadicSplit': {1: 'int64', 2: 'int64'},
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}
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for node in graph.get_op_nodes():
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if node.soft_get('type') in type_port:
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ports_to_update = type_port[node.soft_get('type')]
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for port_id, precision in ports_to_update.items():
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if port_id in node.in_ports() and not node.in_port(port_id).disconnected():
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log.debug('Converting value for the input port "{}" of op "{}" to "{}".'
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''.format(port_id, node.soft_get('name', node.id), precision))
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in_port = node.in_port(port_id)
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np_type = data_type_str_to_np(precision)
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if in_port.get_source().node.type == 'Const':
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convert_const_node_value_type(node.in_port(port_id).get_source().node, np_type)
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else:
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in_port.get_connection().insert_node(Cast(graph, {'dst_type': np_type}).create_node())
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def prepare_emit_ir(graph: Graph, data_type: str, output_dir: str, output_model_name: str,
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mean_data: [list, None] = None, input_names: list = None, meta_info: dict = None,
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use_temporary_path=False, used_by_ir_reader=False):
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if input_names is None:
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input_names = []
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if meta_info is None:
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meta_info = {}
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graph.strict_mode = False
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# temporary disable new FP16 generation
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# if not used_by_ir_reader:
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if True:
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# convert Parameter data types
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convert_data_type.convert_parameters_data_type(graph, data_type)
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# convert blobs (usually weights and biases)
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for sub_graph in [graph] + collect_sub_graphs(graph):
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convert_data_type.convert_blobs(sub_graph, data_type)
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# restore data type for specific inputs/outputs of specific ops to the data types required by nGraph
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for_graph_and_each_sub_graph_recursively(graph, convert_inputs_of_specific_ops)
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for_graph_and_each_sub_graph_recursively(graph, OpVersioning().find_and_replace_pattern)
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# do not run the type inference in sub-graphs. It will be called automatically as part of the type inference of
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# the TensorIterator nodes
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type_infer(graph)
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for_graph_and_each_sub_graph_recursively(graph, RemoveUselessConvert().find_and_replace_pattern)
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ResultRename().find_and_replace_pattern(graph)
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for sub_graph in [graph] + collect_sub_graphs(graph):
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op_order, data_order = determined_sort(get_sorted_outputs(sub_graph))
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mapping = {v: u for u, v in enumerate(op_order)}
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mapping.update({v: u for u, v in enumerate(data_order, start=len(sub_graph))})
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relabel_nodes_inplace_safe(sub_graph, mapping)
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port_renumber(sub_graph)
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tensor_names.propagate_op_name_to_tensor(graph)
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ir_path_suffix = "_tmp" if use_temporary_path else ""
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bin_file = os.path.join(output_dir, '{}{}.bin'.format(output_model_name, ir_path_suffix))
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serialize_constants(graph, bin_file)
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mean_offset = None
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mean_size = None
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if mean_data:
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mean_offset, mean_size = serialize_mean_image(bin_file, mean_data=mean_data)
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generate_ie_ir(graph=graph,
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file_name=os.path.join(output_dir, '{}{}.xml'.format(output_model_name, ir_path_suffix)),
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input_names=input_names,
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mean_offset=mean_offset,
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mean_size=mean_size,
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meta_info=meta_info)
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tensor_names.output_tensor_names_map(graph, os.path.join(output_dir, '{}{}.mapping'.format(output_model_name, ir_path_suffix)))
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def get_ir_version(argv: argparse.Namespace):
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"""
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Determine IR version based on command line arguments and the default version.
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:param argv: the parsed command line arguments
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:return: the IR version
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"""
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return 11
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