def prepare_emit_ir(graph: nx.MultiDiGraph, data_type: str, output_dir: str, output_model_name: str,
mean_data: [list, None] = None, input_names: list = [], meta_info: dict = dict()):
for sub_graph in [graph] + collect_sub_graphs(graph):
create_const_nodes(sub_graph, start_data_nodes_are_not_allowed=(sub_graph == graph))
op_order, data_order = determined_sort(get_sorted_outputs(sub_graph))
mapping = {v: u for u, v in enumerate(op_order)}
mapping.update({v: u for u, v in enumerate(data_order, start=len(sub_graph))})
relabel_nodes_inplace_safe(sub_graph, mapping)
port_renumber(sub_graph)
convert_data_type.convert(sub_graph, data_type)
tensor_names.propagate_op_name_to_tensor(graph)
weights = np.array([])
bin_file = os.path.join(output_dir, '{}.bin'.format(output_model_name))
if(data_type == "FP16"):
weights = serialize_constants(weights,graph,data_type=np.float16)
elif(data_type == "FP32"):
weights = serialize_constants(weights,graph,data_type=np.float32)
mean_offset = None
mean_size = None
if mean_data:
mean_offset, mean_size = serialize_mean_image(bin_file, mean_data=mean_data)
xml_string = generate_ie_ir(graph=graph,
file_name=os.path.join(output_dir, '{}.xml'.format(output_model_name)),
input_names=input_names,
mean_offset=mean_offset,
mean_size=mean_size,
meta_info=meta_info)
# tensor_names.output_tensor_names_map(graph, os.path.join(output_dir, '{}.mapping'.format(output_model_name)))
return weights, xml_string
def prepare_emit_ir(graph: Graph, data_type: str, output_dir: str, output_model_name: str,
mean_data: [list, None] = None, input_names: list = None, meta_info: dict = None):
if input_names is None:
input_names = []
if meta_info is None:
meta_info = {}
graph.strict_mode = False
# convert Parameter data types
convert_data_type.convert_parameters_data_type(graph, data_type)
# convert blobs (usually weights and biases)
for sub_graph in [graph] + collect_sub_graphs(graph):
convert_data_type.convert_blobs(sub_graph, data_type)
# restore data type for specific inputs/outputs of specific ops to the data types required by nGraph
if not graph.graph['cmd_params'].generate_deprecated_IR_V7:
for_graph_and_each_sub_graph_recursively(graph, convert_inputs_of_specific_ops)
if graph.graph['cmd_params'].generate_experimental_IR_V10:
for_graph_and_each_sub_graph_recursively(graph, OpVersioning().find_and_replace_pattern)
# do not run the type inference in sub-graphs. It will be called automatically as part of the type inference of
# the TensorIterator nodes
type_infer(graph)
RemoveUselessConvert().find_and_replace_pattern(graph)
for sub_graph in [graph] + collect_sub_graphs(graph):
op_order, data_order = determined_sort(get_sorted_outputs(sub_graph))
mapping = {v: u for u, v in enumerate(op_order)}
mapping.update({v: u for u, v in enumerate(data_order, start=len(sub_graph))})
relabel_nodes_inplace_safe(sub_graph, mapping)
port_renumber(sub_graph)
tensor_names.propagate_op_name_to_tensor(graph)
bin_file = os.path.join(output_dir, '{}.bin'.format(output_model_name))
serialize_constants(graph, bin_file)
mean_offset = None
mean_size = None
if mean_data:
mean_offset, mean_size = serialize_mean_image(bin_file, mean_data=mean_data)
generate_ie_ir(graph=graph,
file_name=os.path.join(output_dir, '{}.xml'.format(output_model_name)),
input_names=input_names,
mean_offset=mean_offset,
mean_size=mean_size,
meta_info=meta_info)
tensor_names.output_tensor_names_map(graph, os.path.join(output_dir, '{}.mapping'.format(output_model_name)))
def prepare_emit_ir(graph: Graph,
data_type: str,
output_dir: str,
output_model_name: str,
mean_data: [list, None] = None,
input_names: list = [],
meta_info: dict = dict()):
graph.strict_mode = False
for sub_graph in [graph] + collect_sub_graphs(graph):
op_order, data_order = determined_sort(get_sorted_outputs(sub_graph))
mapping = {v: u for u, v in enumerate(op_order)}
mapping.update(
{v: u
for u, v in enumerate(data_order, start=len(sub_graph))})
relabel_nodes_inplace_safe(sub_graph, mapping)
port_renumber(sub_graph)
convert_data_type.convert(sub_graph, data_type)
tensor_names.propagate_op_name_to_tensor(graph)
bin_file = os.path.join(output_dir, '{}.bin'.format(output_model_name))
serialize_constants(graph, bin_file)
mean_offset = None
mean_size = None
if mean_data:
mean_offset, mean_size = serialize_mean_image(bin_file,
mean_data=mean_data)
generate_ie_ir(graph=graph,
file_name=os.path.join(output_dir,
'{}.xml'.format(output_model_name)),
input_names=input_names,
mean_offset=mean_offset,
mean_size=mean_size,
meta_info=meta_info)
tensor_names.output_tensor_names_map(
graph, os.path.join(output_dir,
'{}.mapping'.format(output_model_name)))
