def save_restored_graph(graph: Graph, path: str, meta_data, name=None):
"""
Function to apply all necessary transforms from back stage to prepare and save restored graph and metadata.
:param graph: Graph to save
:param path: Path to saved IR
:param meta_data: Namespace with converting parameters restored from IR
:param name: Name for saved IR
:return:
"""
if name is None:
name = graph.name
precision = data_type_str_to_precision(graph.graph['cmd_params'].data_type)
assert precision in ['FP16', 'FP32'], 'Cannot define precision for restored model!'
# List items order matters, do not change it.
transformation_list = [
ConvolutionWithGroupsResolver,
StridedSliceMasksNormalizer,
PackBinaryWeights,
BlobNormalizer,
ConvolutionNormalizer,
KaldiRemoveMemoryOutputBackReplacementPattern,
]
# We need to run some specific passes from MO back stage.
apply_replacements_list(graph, transformation_list)
# Transformations with enabled=False should be run manually.
for_graph_and_each_sub_graph_recursively(graph, RemoveConstOps().find_and_replace_pattern)
for_graph_and_each_sub_graph_recursively(graph, CreateConstNodesReplacement().find_and_replace_pattern)
prepare_emit_ir(graph, precision, path, name, meta_info=meta_data)
def save_restored_graph(graph: Graph, path: str, meta_data, name=None):
"""
Function to apply all necessary transforms from back stage to prepare and save restored graph and metadata.
:param graph: Graph to save
:param path: Path to saved IR
:param meta_data: Namespace with converting parameters restored from IR
:param name: Name for saved IR
:return:
"""
if name is None:
name = graph.name
if 'data_type' not in meta_data:
log.debug(
'Provided `meta_data` does not contain `data_type` parameter. Set `data_type`'
' parameter value to `FP32`.')
# Set data_type to FP32. All restored constants will be saved in provided data type.
data_type = 'FP32'
# We need to specify this attribute to pass graph transformations. This information will not be saved into IR.
# All constants and placeholders will be saved with same types as restored from IR
graph.graph['cmd_params'].data_type = data_type
else:
data_type = data_type_str_to_precision(
graph.graph['cmd_params'].data_type)
assert data_type in ['FP16', 'FP32'], '`data_type` value {} is not supported by MO,' \
' cannot save graph'.format(data_type)
# List items order matters, do not change it.
transformation_list = [
ConvolutionWithGroupsResolver,
StridedSliceMasksNormalizer,
PackBinaryWeights,
BlobNormalizer,
ConvolutionNormalizer,
MarkNodesWithShapeValues,
]
# We need to run some specific passes from MO back stage.
apply_replacements_list(graph, transformation_list)
# Transformations with enabled=False should be run manually.
for_graph_and_each_sub_graph_recursively(
graph,
RemoveConstOps().find_and_replace_pattern)
for_graph_and_each_sub_graph_recursively(
graph,
CreateConstNodesReplacement().find_and_replace_pattern)
prepare_emit_ir(graph,
data_type,
path,
name,
meta_info=meta_data,
used_by_ir_reader=True)