90 lines
3.7 KiB
Python
90 lines
3.7 KiB
Python
"""
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Copyright (C) 2018-2020 Intel Corporation
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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from copy import copy
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from extensions.back.ConvolutionNormalizer import ConvolutionNormalizer, ConvolutionWithGroupsResolver
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from extensions.back.PackBinaryWeights import PackBinaryWeights
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from extensions.back.SpecialNodesFinalization import RemoveConstOps, CreateConstNodesReplacement
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from extensions.back.StridedSliceMasksNormalizer import StridedSliceMasksNormalizer
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from extensions.back.blob_normalizer import BlobNormalizer
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from extensions.back.kaldi_remove_memory_output import KaldiRemoveMemoryOutputBackReplacementPattern
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from mo.graph.graph import Graph
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from mo.middle.passes.convert_data_type import data_type_str_to_precision
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from mo.middle.pattern_match import for_graph_and_each_sub_graph_recursively
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from mo.pipeline.common import prepare_emit_ir
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from mo.utils.class_registration import apply_replacements_list
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from mo.utils.ir_engine.ir_engine import IREngine
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from mo.utils.ir_reader.layer_to_class import copy_graph_with_ops, collect_extenders, collect_ops
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from mo.utils.utils import get_mo_root_dir
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def restore_graph_from_ir(path_to_xml: str, path_to_bin: str = None) -> (Graph, dict):
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"""
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Function to make valid graph and metadata for MO back stage from IR.
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:param path_to_xml:
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:param path_to_bin:
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:return: (restored graph, meta data)
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"""
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ir = IREngine(path_to_xml, path_to_bin)
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assert ir.graph.graph.get('ir_version') >= 10, 'IR version {} is not supported, ' \
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'please generate actual IR for your model and use it.'.format(ir.graph.graph.get('ir_version'))
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path = get_mo_root_dir()
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collect_ops(path)
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collect_extenders(path)
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# Create a new copy of graph with correct attributes (shape & type infer, backend attrs etc.)
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new_graph = copy_graph_with_ops(ir.graph)
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return new_graph, copy(ir.meta_data)
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def save_restored_graph(graph: Graph, path: str, meta_data, name=None):
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"""
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Function to apply all necessary transforms from back stage to prepare and save restored graph and metadata.
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:param graph: Graph to save
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:param path: Path to saved IR
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:param meta_data: Namespace with converting parameters restored from IR
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:param name: Name for saved IR
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:return:
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"""
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if name is None:
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name = graph.name
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precision = data_type_str_to_precision(graph.graph['cmd_params'].data_type)
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assert precision in ['FP16', 'FP32'], 'Cannot define precision for restored model!'
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# List items order matters, do not change it.
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transformation_list = [
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ConvolutionWithGroupsResolver,
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StridedSliceMasksNormalizer,
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PackBinaryWeights,
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BlobNormalizer,
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ConvolutionNormalizer,
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KaldiRemoveMemoryOutputBackReplacementPattern,
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]
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# We need to run some specific passes from MO back stage.
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apply_replacements_list(graph, transformation_list)
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# Transformations with enabled=False should be run manually.
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for_graph_and_each_sub_graph_recursively(graph, RemoveConstOps().find_and_replace_pattern)
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for_graph_and_each_sub_graph_recursively(graph, CreateConstNodesReplacement().find_and_replace_pattern)
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prepare_emit_ir(graph, precision, path, name, meta_info=meta_data)
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