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 test_get_sorted_outputs_fine_situation(self):
nodes = {'A': {'type': 'Identity', 'kind': 'op'},
'B': {'type': 'Identity', 'kind': 'op'},
'C': {'type': 'Identity', 'kind': 'op'},
'D': {'type': 'Identity', 'kind': 'op'},
'E': {'type': 'Identity', 'kind': 'op'},
'F': {'type': 'Identity', 'kind': 'op'},
'G': {'type': 'Identity', 'kind': 'op'},
'H': {'type': 'Identity', 'kind': 'op'},
'Ad': {'value': None, 'kind': 'data'},
'Bd': {'value': None, 'kind': 'data'},
'Cd': {'value': None, 'kind': 'data', 'fw_tensor_debug_info': [('C', 0)]},
'Dd': {'value': None, 'kind': 'data'},
'Ed': {'value': None, 'kind': 'data', 'fw_tensor_debug_info': [('E', 0)]},
'Fd': {'value': None, 'kind': 'data', 'fw_tensor_debug_info': [('F', 0)]},
'Gd': {'value': None, 'kind': 'data', 'fw_tensor_debug_info': [('G', 0)]},
'Hd': {'value': None, 'kind': 'data', 'fw_tensor_debug_info': [('H', 0)]}
}
edges = [
('A', 'Ad', {'out': 0}),
('Ad', 'B', {'in': 0}),
('B', 'Bd', {'out': 0}),
('Bd', 'C', {'in': 0}),
('C', 'Cd', {'out': 0}),
('Cd', 'D', {'in': 0}),
('D', 'Dd', {'out': 0}),
('Dd', 'E', {'in': 0}),
('E', 'Ed', {'out': 0}),
('Cd', 'F', {'in': 0}),
('F', 'Fd', {'out': 0}),
('Fd', 'G', {'in': 0}),
('G', 'Gd', {'out': 0}),
('Cd', 'H', {'in': 0}),
('H', 'Hd', {'out': 0})
]
graph = build_graph_with_edge_attrs(nodes, edges)
self.assertListEqual([node.id for node in get_sorted_outputs(graph)], ['Ed', 'Gd', 'Hd'])