def convert_model(conf):
option = cvt.ConverterOption()
if ModelKeys.graph_optimize_options in conf:
option.transformer_option = conf[ModelKeys.graph_optimize_options]
if ModelKeys.winograd in conf:
option.winograd = conf[ModelKeys.winograd]
if ModelKeys.quantize in conf:
option.quantize = conf[ModelKeys.quantize]
if ModelKeys.quantize_large_weights in conf:
option.quantize_large_weights = conf[ModelKeys.quantize_large_weights]
if ModelKeys.quantize_range_file in conf:
option.quantize_range_file = conf[ModelKeys.quantize_range_file]
if ModelKeys.change_concat_ranges in conf:
option.change_concat_ranges = conf[ModelKeys.change_concat_ranges]
if ModelKeys.cl_mem_type in conf:
option.cl_mem_type = conf[ModelKeys.cl_mem_type]
if ModelKeys.runtime in conf:
option.device = conf[ModelKeys.runtime]
if option.device == DeviceType.CPU_GPU:
# when convert, cpu and gpu share the same model
option.device = DeviceType.CPU
# we don't need `value`, but to be consistent with legacy code
# used by `base_converter`
option.device = option.device.value
option.data_type = conf[ModelKeys.data_types]
for i in range(len(conf[ModelKeys.input_tensors])):
input_node = cvt.NodeInfo()
input_node.name = conf[ModelKeys.input_tensors][i]
input_node.shape = conf[ModelKeys.input_shapes][i]
input_node.data_type = conf[ModelKeys.input_data_types][i]
input_node.data_format = conf[ModelKeys.input_data_formats][i]
if (input_node.data_format == DataFormat.NCHW and len(
input_node.shape) == 4):
input_node.shape = transpose_shape(input_node.shape, [0, 2, 3, 1])
input_node.data_format = DataFormat.NHWC
input_node.range = conf[ModelKeys.input_ranges][i]
option.add_input_node(input_node)
for i in range(len(conf[ModelKeys.output_tensors])):
output_node = cvt.NodeInfo()
output_node.name = conf[ModelKeys.output_tensors][i]
output_node.shape = conf[ModelKeys.output_shapes][i]
output_node.data_type = conf[ModelKeys.output_data_types][i]
output_node.data_format = conf[ModelKeys.output_data_formats][i]
if output_node.data_format == DataFormat.NCHW and len(
output_node.shape) == 4:
output_node.shape = transpose_shape(output_node.shape,
[0, 2, 3, 1])
output_node.data_format = DataFormat.NHWC
option.add_output_node(output_node)
if ModelKeys.check_tensors in conf:
for i in range(len(conf[ModelKeys.check_tensors])):
check_node = cvt.NodeInfo()
check_node.name = conf[ModelKeys.check_tensors][i]
check_node.shape = conf[ModelKeys.check_shapes][i]
option.add_check_node(check_node)
else:
option.check_nodes = option.output_nodes
option.build()
print("Transform model to one that can better run on device")
platform = conf[ModelKeys.platform]
if platform == Platform.TENSORFLOW:
from transform import tensorflow_converter
converter = tensorflow_converter.TensorflowConverter(
option, conf["model_file_path"])
elif platform == Platform.CAFFE:
from transform import caffe_converter
converter = caffe_converter.CaffeConverter(option,
conf["model_file_path"],
conf["weight_file_path"])
elif platform == Platform.ONNX:
from transform import onnx_converter
converter = onnx_converter.OnnxConverter(option,
conf["model_file_path"])
else:
mace_check(False, "Mace do not support platorm %s yet." % platform)
output_graph_def = converter.run()
mace_transformer = transformer.Transformer(
option, output_graph_def)
output_graph_def, quantize_activation_info = mace_transformer.run()
runtime = conf[ModelKeys.runtime]
if runtime in [DeviceType.HEXAGON,
DeviceType.HTA]:
from transform import hexagon_converter
converter = hexagon_converter.HexagonConverter(
option, output_graph_def, quantize_activation_info)
output_graph_def = converter.run()
elif runtime == DeviceType.APU:
mace_check(platform == Platform.TENSORFLOW,
"apu only support model from tensorflow")
from transform import apu_converter
converter = apu_converter.ApuConverter(
option, output_graph_def, quantize_activation_info)
output_graph_def = converter.run()
return output_graph_def
def convert_model(conf):
print(conf)
platform = conf["platform"]
mace_check(platform in ['tensorflow', 'caffe', 'onnx'],
"platform not supported")
runtime = conf["runtime"]
mace_check(runtime in ['cpu', 'gpu', 'dsp', 'hta', 'apu', 'cpu+gpu'],
"runtime not supported")
option = cvt.ConverterOption()
if "graph_optimize_options" in conf:
option.transformer_option = conf["graph_optimize_options"].split(',')
option.winograd = conf.get("winograd", 0)
option.quantize = bool(conf.get("quantize", 0))
option.quantize_large_weights = bool(conf.get("quantize_large_weights", 0))
option.quantize_range_file = conf.get("quantize_range_file", "")
option.change_concat_ranges = bool(conf.get("change_concat_ranges", 0))
option.cl_mem_type = conf.get("cl_mem_type", "image")
option.device = device_type_map[conf.get("runtime", "cpu")]
option.data_type = parse_data_type(conf.get("data_type", "fp16_fp32"),
option.quantize)
input_tensors = to_list(conf["input_tensors"])
input_shapes = [
parse_int_array_from_str(shape)
for shape in to_list(conf["input_shapes"])
]
mace_check(
len(input_tensors) == len(input_shapes),
"input node count and shape count do not match")
input_count = len(input_tensors)
input_data_types = [
data_type_map[dt] for dt in to_list(
conf.get("input_data_types", ["float32"] * input_count))
]
input_data_formats = [
data_format_map[df] for df in to_list(
conf.get("input_data_formats", ["NHWC"] * input_count))
]
input_ranges = [
parse_float_array_from_str(r)
for r in to_list(conf.get("input_ranges", ["-1.0,1.0"] * input_count))
]
for i in range(len(input_tensors)):
input_node = cvt.NodeInfo()
input_node.name = input_tensors[i]
input_node.shape = input_shapes[i]
input_node.data_type = input_data_types[i]
input_node.data_format = input_data_formats[i]
if (input_node.data_format == cvt.DataFormat.NCHW
and len(input_node.shape) == 4):
input_node.shape = transpose_shape(input_node.shape, [0, 2, 3, 1])
input_node.data_format = cvt.DataFormat.NHWC
input_node.range = input_ranges[i]
option.add_input_node(input_node)
output_tensors = to_list(conf["output_tensors"])
output_shapes = [
parse_int_array_from_str(shape)
for shape in to_list(conf["output_shapes"])
]
mace_check(
len(output_tensors) == len(output_shapes),
"output node count and shape count do not match")
output_count = len(output_tensors)
output_data_types = [
data_type_map[dt] for dt in to_list(
conf.get("output_data_types", ["float32"] * output_count))
]
output_data_formats = [
data_format_map[df] for df in to_list(
conf.get("output_data_formats", ["NHWC"] * output_count))
]
for i in range(len(output_tensors)):
output_node = cvt.NodeInfo()
output_node.name = output_tensors[i]
output_node.shape = output_shapes[i]
output_node.data_type = output_data_types[i]
output_node.data_format = output_data_formats[i]
if output_node.data_format == cvt.DataFormat.NCHW and len(
output_node.shape) == 4:
output_node.shape = transpose_shape(output_node.shape,
[0, 2, 3, 1])
output_node.data_format = cvt.DataFormat.NHWC
option.add_output_node(output_node)
if "check_tensors" in conf:
check_tensors = to_list(conf["check_tensors"])
check_tensors_shapes = [
parse_int_array_from_str(shape)
for shape in to_list(conf["check_shapes"])
]
mace_check(
len(check_tensors) == len(check_tensors_shapes),
"check tensors count and shape count do not match.")
for i in range(len(check_tensors)):
check_node = cvt.NodeInfo()
check_node.name = check_tensors[i]
check_node.shape = check_tensors_shapes[i]
option.add_check_node(check_node)
else:
option.check_nodes = option.output_nodes
option.build()
print("Transform model to one that can better run on device")
if platform == 'tensorflow':
from transform import tensorflow_converter
converter = tensorflow_converter.TensorflowConverter(
option, conf["model_file_path"])
elif platform == 'caffe':
from transform import caffe_converter
converter = caffe_converter.CaffeConverter(option,
conf["model_file_path"],
conf["weight_file_path"])
elif platform == 'onnx':
from transform import onnx_converter
converter = onnx_converter.OnnxConverter(option,
conf["model_file_path"])
else:
mace_check(False, "Mace do not support platorm %s yet." % platform)
output_graph_def = converter.run()
mace_transformer = transformer.Transformer(option, output_graph_def)
output_graph_def, quantize_activation_info = mace_transformer.run()
if option.device in [
cvt.DeviceType.HEXAGON.value, cvt.DeviceType.HTA.value
]:
from transform import hexagon_converter
converter = hexagon_converter.HexagonConverter(
option, output_graph_def, quantize_activation_info)
output_graph_def = converter.run()
elif runtime == 'apu':
mace_check(platform == "tensorflow",
"apu only support model from tensorflow")
from transform import apu_converter
converter = apu_converter.ApuConverter(option, output_graph_def,
quantize_activation_info)
output_graph_def = converter.run()
return output_graph_def