def convert(conf, output, enable_micro=False):
if ModelKeys.quantize_stat in conf:
quantize_stat = conf[ModelKeys.quantize_stat]
else:
quantize_stat = False
for model_name, model_conf in conf["models"].items():
model_output = output + "/" + model_name + "/model"
org_model_dir = output + "/" + model_name + "/org_model"
util.mkdir_p(model_output)
util.mkdir_p(org_model_dir)
model_conf = normalize_model_config(model_conf)
model_file = util.download_or_get_model(
model_conf[ModelKeys.model_file_path], # noqa
model_conf[ModelKeys.model_sha256_checksum], # noqa
output + "/" + model_name + "/org_model")
model_conf[ModelKeys.model_file_path] = model_file
if ModelKeys.weight_file_path in model_conf:
weight_file = util.download_or_get_model(
model_conf[ModelKeys.weight_file_path],
model_conf[ModelKeys.weight_sha256_checksum], "/tmp/")
model_conf[ModelKeys.weight_file_path] = weight_file
# TODO: remove the following after quantize tool is made
if ModelKeys.quantize_range_file in model_conf:
range_file = util.download_or_get_model(
model_conf[ModelKeys.quantize_range_file],
"", model_output)
model_conf[ModelKeys.quantize_range_file] = range_file
mace_model = convert_model(model_conf, quantize_stat)
try:
visualizer = visualize_model.ModelVisualizer(model_name,
mace_model,
model_output)
visualizer.save_html()
except: # noqa
print("Failed to visualize model:", sys.exc_info())
model, params = merge_params(mace_model,
model_conf[ModelKeys.data_type])
if enable_micro:
micro_converter = MicroConverter(model_conf, copy.deepcopy(model),
copy.deepcopy(params), model_name)
micro_converter.gen_code()
micro_converter.package(model_output + "/" +
model_name + "_micro.tar.gz")
output_model_file = model_output + "/" + model_name + ".pb"
output_params_file = model_output + "/" + model_name + ".data"
with open(output_model_file, "wb") as f:
f.write(model.SerializeToString())
with open(output_params_file, "wb") as f:
f.write(bytearray(params))
with open(output_model_file + "_txt", "w") as f:
f.write(str(model))
def run_model_for_device(flags, args, dev, model_name, model_conf):
target_abi = flags.target_abi
install_dir = run_target.default_install_dir(target_abi) + "/" + model_name
sysdir = install_dir + "/interior"
dev.mkdir(sysdir)
runtime_list = []
for graph_name, graph_conf in model_conf[ModelKeys.subgraphs].items():
runtime = graph_conf[ModelKeys.runtime]
runtime_list.append(runtime)
mace_check(runtime != DeviceType.APU or target_abi == "arm64-v8a",
"APU runtime does only support arm64-v8a")
# install models to devices
workdir = flags.output + "/" + model_name
model_file = model_name + ".pb"
model_data_file = model_name + ".data"
model_path = workdir + "/model/" + model_file
model_data_path = workdir + "/model/" + model_data_file
if os.path.exists(model_path) and os.path.exists(model_data_path):
dev.install(Target(model_path), install_dir)
dev.install(Target(model_data_path), install_dir)
else:
MaceLogger.warning("No models exist in %s, use --model_file and"
" --model_data_file specified in args" % model_path)
if ModelKeys.check_tensors in model_conf:
model_conf[ModelKeys.output_tensors] = model_conf[
ModelKeys.check_tensors]
model_conf[ModelKeys.output_shapes] = model_conf[
ModelKeys.check_shapes]
model_file_path = ""
if not flags.gencode_model:
model_file_path = install_dir + "/" + model_file
model_data_file_path = ""
if not flags.gencode_param:
model_data_file_path = install_dir + "/" + model_data_file
input_tensors_info = config_parser.find_input_tensors_info(
model_conf[ModelKeys.subgraphs], model_conf[ModelKeys.input_tensors])
output_tensors_info = config_parser.find_output_tensors_info(
model_conf[ModelKeys.subgraphs], model_conf[ModelKeys.output_tensors])
model_args = {
"model_name":
model_name,
"model_file":
model_file_path,
"model_data_file":
model_data_file_path,
"input_node":
",".join(model_conf[ModelKeys.input_tensors]),
"input_shape":
join_2d_array(input_tensors_info[ModelKeys.input_shapes]),
"output_node":
",".join(model_conf[ModelKeys.output_tensors]),
"output_shape":
join_2d_array(output_tensors_info[ModelKeys.output_shapes]),
"input_data_format":
",".join([
df.name for df in input_tensors_info[ModelKeys.input_data_formats]
]),
"output_data_format":
",".join([
df.name
for df in output_tensors_info[ModelKeys.output_data_formats]
])
}
opts = [
"--%s='%s'" % (arg_key, arg_val)
for arg_key, arg_val in model_args.items()
] + args
should_generate_data = (flags.validate or flags.tune
or "--benchmark" in opts)
if should_generate_data:
tmpdirname = tempfile.mkdtemp()
input_file_prefix = tmpdirname + "/" + model_name
if ModelKeys.validation_inputs_data in model_conf:
input_tensor = model_conf[ModelKeys.input_tensors]
input_data = model_conf[ModelKeys.validation_inputs_data]
mace_check(
len(input_tensor) == len(input_data),
"len(input_tensor) != len(validate_data")
for i in range(len(input_tensor)):
util.download_or_get_file(
model_conf[ModelKeys.validation_inputs_data][i], "",
util.formatted_file_name(input_file_prefix,
input_tensor[i]))
else:
generate_input_data(input_file_prefix,
model_conf[ModelKeys.input_tensors],
input_tensors_info[ModelKeys.input_shapes],
input_tensors_info[ModelKeys.input_ranges],
input_tensors_info[ModelKeys.input_data_types])
dev.install(Target(tmpdirname), install_dir + "/validate_in")
target_input_file = "%s/validate_in/%s" % (install_dir, model_name)
target_output_dir = "%s/validate_out" % install_dir
dev.mkdir(target_output_dir)
target_output_file = target_output_dir + "/" + model_name
opts += [
"--input_file=%s" % target_input_file,
"--output_file=%s" % target_output_file
]
# run
envs = flags.envs.split(" ") + ["MACE_INTERNAL_STORAGE_PATH=%s" % sysdir]
if flags.tune:
envs += [
"MACE_TUNING=1",
"MACE_RUN_PARAMETER_PATH=%s/interior/tune_params" % install_dir
]
opts += ["--round=0"]
mace_check(flags.vlog_level >= 0,
"vlog_level should be greater than zeror")
envs += ["MACE_CPP_MIN_VLOG_LEVEL=%s" % flags.vlog_level]
build_dir = flags.build_dir + "/" + target_abi
libs = []
if DeviceType.HEXAGON in runtime_list:
libs += ["third_party/nnlib/%s/libhexagon_controller.so" % target_abi]
elif runtime == DeviceType.HTA:
libs += ["third_party/hta/%s/libhta_hexagon_runtime.so" % target_abi]
elif DeviceType.APU in runtime_list:
apu_libs = get_apu_so_paths(dev)
libs += apu_libs
cpp_shared_lib_path = os.path.join(build_dir,
"install/lib/libc++_shared.so")
if os.path.exists(cpp_shared_lib_path):
libs.append(cpp_shared_lib_path)
target = Target(build_dir + "/install/bin/mace_run",
libs,
opts=opts,
envs=envs)
run_target.run_target(target_abi, install_dir, target, dev)
if DeviceType.GPU in runtime_list:
opencl_dir = workdir + "/opencl"
util.mkdir_p(opencl_dir)
dev.pull(
Target(install_dir + "/interior/mace_cl_compiled_program.bin"),
"%s/%s_compiled_opencl_kernel.%s.%s.bin" %
(opencl_dir, model_name, dev.info()["ro.product.model"].replace(
' ', ''), dev.info()["ro.board.platform"]))
if flags.tune:
dev.pull(
Target(install_dir + "/interior/tune_params"),
"%s/%s_tuned_opencl_parameter.%s.%s.bin" %
(opencl_dir, model_name,
dev.info()["ro.product.model"].replace(
' ', ''), dev.info()["ro.board.platform"]))
if flags.validate:
validate_model_file = util.download_or_get_model(
model_conf[ModelKeys.model_file_path],
model_conf[ModelKeys.model_sha256_checksum], tmpdirname)
validate_weight_file = ""
if ModelKeys.weight_file_path in model_conf:
validate_weight_file = util.download_or_get_model(
model_conf[ModelKeys.weight_file_path],
model_conf[ModelKeys.weight_sha256_checksum], tmpdirname)
dev.pull(Target(target_output_dir), tmpdirname + "/validate_out")
output_file_prefix = tmpdirname + "/validate_out/" + model_name
validate.validate(model_conf[ModelKeys.platform], validate_model_file,
validate_weight_file, input_file_prefix,
output_file_prefix,
input_tensors_info[ModelKeys.input_shapes],
output_tensors_info[ModelKeys.output_shapes],
input_tensors_info[ModelKeys.input_data_formats],
output_tensors_info[ModelKeys.output_data_formats],
input_tensors_info[ModelKeys.input_tensors],
output_tensors_info[ModelKeys.output_tensors],
flags.validate_threshold,
input_tensors_info[ModelKeys.input_data_types],
flags.backend, "", "")
if should_generate_data:
shutil.rmtree(tmpdirname)
def run_model_with_conf(flags, args, model_name, model_conf):
target_abi = "host"
dev = device.HostDevice("host", target_abi)
install_dir = "/tmp/micro_run/" + model_name
if ModelKeys.check_tensors in model_conf:
model_conf[ModelKeys.output_tensors] = model_conf[
ModelKeys.check_tensors]
model_conf[ModelKeys.output_shapes] = model_conf[
ModelKeys.check_shapes]
model_args = {
"model_name":
model_name,
"input_node":
",".join(model_conf[ModelKeys.input_tensors]),
"input_shape":
join_2d_array(model_conf[ModelKeys.input_shapes]),
"output_node":
",".join(model_conf[ModelKeys.output_tensors]),
"output_shape":
join_2d_array(model_conf[ModelKeys.output_shapes]),
"input_data_format":
",".join([df.name for df in model_conf[ModelKeys.input_data_formats]]),
"output_data_format":
",".join([df.name for df in model_conf[ModelKeys.output_data_formats]])
}
opts = [
"--%s=%s" % (arg_key, arg_val)
for arg_key, arg_val in model_args.items()
] + args
# generate data start
tmp_dir_name = tempfile.mkdtemp()
input_file_prefix = tmp_dir_name + "/" + model_name
if ModelKeys.validation_inputs_data in model_conf:
input_tensor = model_conf[ModelKeys.input_tensors]
input_data = model_conf[ModelKeys.validation_inputs_data]
mace_check(
len(input_tensor) == len(input_data),
"len(input_tensor) != len(validate_data")
for i in range(len(input_tensor)):
util.download_or_get_file(
model_conf[ModelKeys.validation_inputs_data][i], "",
util.formatted_file_name(input_file_prefix, input_tensor[i]))
else:
generate_input_data(input_file_prefix,
model_conf[ModelKeys.input_tensors],
model_conf[ModelKeys.input_shapes],
model_conf[ModelKeys.input_ranges],
model_conf[ModelKeys.input_data_types])
dev.install(Target(tmp_dir_name), install_dir + "/validate_in")
target_input_file = "%s/validate_in/%s" % (install_dir, model_name)
target_output_dir = "%s/validate_out" % install_dir
dev.mkdir(target_output_dir)
target_output_file = target_output_dir + "/" + model_name
opts += [
"--input_file=%s" % target_input_file,
"--output_file=%s" % target_output_file
]
# generate data end
envs = []
if flags.vlog_level > 0:
envs += ["MACE_CPP_MIN_VLOG_LEVEL=%s" % flags.vlog_level]
target = Target("build/micro/host/tools/micro_run_static", [],
opts=opts,
envs=envs)
run_target.run_target(target_abi, install_dir, target, device_ids="host")
if flags.validate:
validate_model_file = util.download_or_get_model(
model_conf[ModelKeys.model_file_path],
model_conf[ModelKeys.model_sha256_checksum], tmp_dir_name)
validate_weight_file = ""
if ModelKeys.weight_file_path in model_conf:
validate_weight_file = util.download_or_get_model(
model_conf[ModelKeys.weight_file_path],
model_conf[ModelKeys.weight_sha256_checksum], tmp_dir_name)
dev.pull(Target(target_output_dir), tmp_dir_name + "/validate_out")
output_file_prefix = tmp_dir_name + "/validate_out/" + model_name
validate.validate(
model_conf[ModelKeys.platform], validate_model_file,
validate_weight_file, input_file_prefix, output_file_prefix,
model_conf[ModelKeys.input_shapes],
model_conf[ModelKeys.output_shapes],
model_conf[ModelKeys.input_data_formats],
model_conf[ModelKeys.output_data_formats],
model_conf[ModelKeys.input_tensors],
model_conf[ModelKeys.output_tensors], flags.validate_threshold,
model_conf[ModelKeys.input_data_types], flags.backend, "", "")
shutil.rmtree(tmp_dir_name)
def normalize_graph_config(conf, model_output, org_model_dir):
conf = copy.deepcopy(conf)
if ModelKeys.platform in conf:
conf[ModelKeys.platform] = parse_platform(conf[ModelKeys.platform])
if ModelKeys.model_file_path in conf and org_model_dir is not None:
model_file = util.download_or_get_model(
conf[ModelKeys.model_file_path],
conf[ModelKeys.model_sha256_checksum], org_model_dir)
conf[ModelKeys.model_file_path] = model_file
if ModelKeys.weight_file_path in conf:
weight_file = util.download_or_get_model(
conf[ModelKeys.weight_file_path],
conf[ModelKeys.weight_sha256_checksum], "/tmp/")
conf[ModelKeys.weight_file_path] = weight_file
if ModelKeys.runtime in conf:
conf[ModelKeys.runtime] = parse_device_type(conf[ModelKeys.runtime])
if ModelKeys.data_type in conf:
conf[ModelKeys.data_type] = parse_internal_data_type(
conf[ModelKeys.data_type])
# TODO: remove the following after quantize tool is made
if ModelKeys.quantize_range_file in conf and model_output is not None:
range_file = util.download_or_get_model(
conf[ModelKeys.quantize_range_file], "", model_output)
conf[ModelKeys.quantize_range_file] = range_file
input_count = 0
input_data_formats = []
input_ranges = []
if ModelKeys.input_tensors in conf:
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])
input_data_formats = [
parse_data_format(df)
for df in to_list(conf.get(ModelKeys.input_data_formats, ["NHWC"]))
]
input_ranges = [
parse_float_array(r)
for r in to_list(conf.get(ModelKeys.input_ranges, ["-1.0,1.0"]))
]
normalize_input_data_types(conf, input_count)
if ModelKeys.input_shapes in conf:
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")
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
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
output_count = 0
output_data_types = []
output_data_formats = []
if ModelKeys.output_tensors in conf:
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])
output_data_formats = [
parse_data_format(df) for df in to_list(
conf.get(ModelKeys.output_data_formats, ["NHWC"]))
]
normalize_output_data_types(conf, output_count)
if ModelKeys.output_shapes in conf:
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")
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.")
return conf
