def normalize_model_config(conf, model_output=None, org_model_dir=None):
conf = normalize_graph_config(conf, model_output, org_model_dir)
if ModelKeys.subgraphs in conf:
nor_subgraphs = {}
if isinstance(conf[ModelKeys.subgraphs], list):
nor_subgraph = normalize_graph_config(conf[ModelKeys.subgraphs][0],
model_output, org_model_dir)
conf[ModelKeys.input_tensors] = \
nor_subgraph[ModelKeys.input_tensors]
conf[ModelKeys.output_tensors] = \
nor_subgraph[ModelKeys.output_tensors]
set_default_config_value(nor_subgraph, conf)
nor_subgraphs[ModelKeys.default_graph] = nor_subgraph
else:
for graph_name, subgraph in conf[ModelKeys.subgraphs].items():
nor_subgraph = normalize_graph_config(subgraph, model_output,
org_model_dir)
set_default_config_value(nor_subgraph, conf)
nor_subgraphs[graph_name] = nor_subgraph
conf[ModelKeys.subgraphs] = nor_subgraphs
model_base_conf = copy.deepcopy(conf)
del model_base_conf[ModelKeys.subgraphs]
subgraphs = conf[ModelKeys.subgraphs]
for net_name, subgraph in subgraphs.items():
net_conf = copy.deepcopy(model_base_conf)
net_conf.update(subgraph)
subgraphs[net_name] = net_conf
MaceLogger.summary(conf)
return conf
def normalize_model_config(conf):
conf = copy.deepcopy(conf)
if ModelKeys.subgraphs in conf:
subgraph = conf[ModelKeys.subgraphs][0]
del conf[ModelKeys.subgraphs]
conf.update(subgraph)
conf[ModelKeys.platform] = parse_platform(conf[ModelKeys.platform])
conf[ModelKeys.runtime] = parse_device_type(conf[ModelKeys.runtime])
if ModelKeys.quantize in conf and conf[ModelKeys.quantize] == 1:
conf[ModelKeys.data_type] = mace_pb2.DT_FLOAT
else:
if ModelKeys.data_type in conf:
conf[ModelKeys.data_type] = parse_internal_data_type(
conf[ModelKeys.data_type])
else:
conf[ModelKeys.data_type] = mace_pb2.DT_HALF
# parse input
conf[ModelKeys.input_tensors] = to_list(conf[ModelKeys.input_tensors])
conf[ModelKeys.input_tensors] = [
str(i) for i in conf[ModelKeys.input_tensors]
]
input_count = len(conf[ModelKeys.input_tensors])
conf[ModelKeys.input_shapes] = [
parse_int_array(shape)
for shape in to_list(conf[ModelKeys.input_shapes])
]
mace_check(
len(conf[ModelKeys.input_shapes]) == input_count,
"input node count and shape count do not match")
input_data_types = [
parse_data_type(dt)
for dt in to_list(conf.get(ModelKeys.input_data_types, ["float32"]))
]
if len(input_data_types) == 1 and input_count > 1:
input_data_types = [input_data_types[0]] * input_count
mace_check(
len(input_data_types) == input_count,
"the number of input_data_types should be "
"the same as input tensors")
conf[ModelKeys.input_data_types] = input_data_types
input_data_formats = [
parse_data_format(df)
for df in to_list(conf.get(ModelKeys.input_data_formats, ["NHWC"]))
]
if len(input_data_formats) == 1 and input_count > 1:
input_data_formats = [input_data_formats[0]] * input_count
mace_check(
len(input_data_formats) == input_count,
"the number of input_data_formats should be "
"the same as input tensors")
conf[ModelKeys.input_data_formats] = input_data_formats
input_ranges = [
parse_float_array(r)
for r in to_list(conf.get(ModelKeys.input_ranges, ["-1.0,1.0"]))
]
if len(input_ranges) == 1 and input_count > 1:
input_ranges = [input_ranges[0]] * input_count
mace_check(
len(input_ranges) == input_count,
"the number of input_ranges should be "
"the same as input tensors")
conf[ModelKeys.input_ranges] = input_ranges
# parse output
conf[ModelKeys.output_tensors] = to_list(conf[ModelKeys.output_tensors])
conf[ModelKeys.output_tensors] = [
str(i) for i in conf[ModelKeys.output_tensors]
]
output_count = len(conf[ModelKeys.output_tensors])
conf[ModelKeys.output_shapes] = [
parse_int_array(shape)
for shape in to_list(conf[ModelKeys.output_shapes])
]
mace_check(
len(conf[ModelKeys.output_tensors]) == output_count,
"output node count and shape count do not match")
output_data_types = [
parse_data_type(dt)
for dt in to_list(conf.get(ModelKeys.output_data_types, ["float32"]))
]
if len(output_data_types) == 1 and output_count > 1:
output_data_types = [output_data_types[0]] * output_count
mace_check(
len(output_data_types) == output_count,
"the number of output_data_types should be "
"the same as output tensors")
conf[ModelKeys.output_data_types] = output_data_types
output_data_formats = [
parse_data_format(df)
for df in to_list(conf.get(ModelKeys.output_data_formats, ["NHWC"]))
]
if len(output_data_formats) == 1 and output_count > 1:
output_data_formats = [output_data_formats[0]] * output_count
mace_check(
len(output_data_formats) == output_count,
"the number of output_data_formats should be "
"the same as output tensors")
conf[ModelKeys.output_data_formats] = output_data_formats
if ModelKeys.check_tensors in conf:
conf[ModelKeys.check_tensors] = to_list(conf[ModelKeys.check_tensors])
conf[ModelKeys.check_shapes] = [
parse_int_array(shape)
for shape in to_list(conf[ModelKeys.check_shapes])
]
mace_check(
len(conf[ModelKeys.check_tensors]) == len(
conf[ModelKeys.check_shapes]),
"check tensors count and shape count do not match.")
MaceLogger.summary(conf)
return conf