def run_models_for_device(flags, args, dev):
conf = config_parser.parse(flags.config)
for name, model_conf in conf["models"].items():
if not flags.model_name or name == flags.model_name:
MaceLogger.info("Run model %s" % name)
model_conf = config_parser.normalize_model_config(model_conf)
run_model_for_device(flags, args, dev, name, model_conf)
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 run_models(flags, args):
if flags.device_conf:
device_conf = config_parser.parse_device_info(flags.device_conf)
device.ArmLinuxDevice.set_devices(device_conf)
run_devices = device.choose_devices(flags.target_abi, flags.target_socs)
MaceLogger.info("Run on devices: %s" % run_devices)
for device_id in run_devices:
dev = device.create_device(flags.target_abi, device_id)
run_models_for_device(flags, args, dev)
def get_version(flags):
device_id = get_cur_device_id(flags)
if device_id is not None:
android_device = device.create_device(flags.target_abi, device_id)
target_props = android_device.info()
target_soc = target_props["ro.board.platform"]
android_ver = (int)(target_props["ro.build.version.release"])
apu_version = get_apu_version(True, android_ver, target_soc)
else:
apu_version = 4
MaceLogger.warning("Can not get unique device ID, MACE select the"
" latest apu version: %s" % apu_version)
sys.exit(apu_version)
def generate_input_data(input_file, input_node, input_shape, input_ranges,
input_data_type):
np.random.seed()
for i in range(len(input_node)):
data = np.random.random(input_shape[i]) * (
input_ranges[i][1] - input_ranges[i][0]) + input_ranges[i][0]
input_file_name = util.formatted_file_name(input_file, input_node[i])
MaceLogger.info('Generate input file: %s' % input_file_name)
if input_data_type[i] == mace_pb2.DT_FLOAT:
np_data_type = np.float32
elif input_data_type[i] == mace_pb2.DT_INT32:
np_data_type = np.int32
data.astype(np_data_type).tofile(input_file_name)
def get_cur_device_id(flags):
run_devices = device.choose_devices(flags.target_abi, "all")
run_device = None
device_num = len(run_devices)
if device_num == 0: # for CI
MaceLogger.warning("No Android devices are plugged in, "
"you need to copy `apu` so files by yourself.")
elif device_num > 1: # for CI
MaceLogger.warning("More than one Android devices are plugged in, "
"you need to copy `apu` so files by yourself.")
else:
run_device = run_devices[0]
return run_device
def run_models(flags, args):
if flags.device_conf:
device_conf = config_parser.parse_device_info(flags.device_conf)
device.ArmLinuxDevice.set_devices(device_conf)
run_devices = device.choose_devices(flags.target_abi, flags.target_socs)
MaceLogger.info("Run on devices: %s" % run_devices)
for device_id in run_devices:
dev = device.create_device(flags.target_abi, device_id)
if len(flags.devices_to_run) > 0 and \
device_id not in flags.devices_to_run.split(','):
continue
run_models_for_device(flags, args, dev)
def merge_opencl_binaries(opencl_binaries, output_file):
platform_info_key = 'mace_opencl_precompiled_platform_info_key'
kvs = {}
for binary in opencl_binaries:
if not os.path.exists(binary):
MaceLogger.warning("OpenCL bin %s not found" % binary)
continue
with open(binary, "rb") as f:
binary_array = np.fromfile(f, dtype=np.uint8)
idx = 0
size, = struct.unpack("Q", binary_array[idx:idx + 8])
idx += 8
for _ in range(size):
key_size, = struct.unpack("i", binary_array[idx:idx + 4])
idx += 4
key, = struct.unpack(
str(key_size) + "s", binary_array[idx:idx + key_size])
idx += key_size
value_size, = struct.unpack("i", binary_array[idx:idx + 4])
idx += 4
if key == platform_info_key and key in kvs:
mace_check(
(kvs[key] == binary_array[idx:idx + value_size]).all(),
"There exists more than one OpenCL version for models:"
" %s vs %s " %
(kvs[key], binary_array[idx:idx + value_size]))
else:
kvs[key] = binary_array[idx:idx + value_size]
idx += value_size
output_byte_array = bytearray()
data_size = len(kvs)
output_byte_array.extend(struct.pack("Q", data_size))
for key, value in kvs.items():
key_size = len(key)
output_byte_array.extend(struct.pack("i", key_size))
output_byte_array.extend(struct.pack(str(key_size) + "s", key))
value_size = len(value)
output_byte_array.extend(struct.pack("i", value_size))
output_byte_array.extend(value)
np.array(output_byte_array).tofile(output_file)
def merge_opencl_parameters(params_files, output_file):
kvs = {}
for params in params_files:
if not os.path.exists(params):
MaceLogger.warning("Tune param %s not found" % params)
continue
with open(params, "rb") as f:
binary_array = np.fromfile(f, dtype=np.uint8)
idx = 0
size, = struct.unpack("Q", binary_array[idx:idx + 8])
idx += 8
for _ in range(size):
key_size, = struct.unpack("i", binary_array[idx:idx + 4])
idx += 4
key, = struct.unpack(
str(key_size) + "s", binary_array[idx:idx + key_size])
idx += key_size
value_size, = struct.unpack("i", binary_array[idx:idx + 4])
idx += 4
kvs[key] = binary_array[idx:idx + value_size]
idx += value_size
output_byte_array = bytearray()
data_size = len(kvs)
output_byte_array.extend(struct.pack("Q", data_size))
for key, value in kvs.items():
key_size = len(key)
output_byte_array.extend(struct.pack("i", key_size))
output_byte_array.extend(struct.pack(str(key_size) + "s", key))
value_size = len(value)
output_byte_array.extend(struct.pack("i", value_size))
output_byte_array.extend(value)
np.array(output_byte_array).tofile(output_file)
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 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
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from utils import device
from utils.util import MaceLogger
cwd = os.path.dirname(__file__)
# TODO: Remove bazel deps
try:
device.execute("bazel version")
except: # noqa
MaceLogger.warning("No bazel, use cmake.")
device.execute("bash tools/cmake/cmake-generate-proto-py-host.sh")
else:
try:
device.execute("bazel build //mace/proto:mace_py")
device.execute("cp -f bazel-genfiles/mace/proto/mace_pb2.py %s" % cwd)
device.execute("bazel build //mace/proto:micro_mem_py")
device.execute("cp -f bazel-genfiles/mace/proto/micro_mem_pb2.py %s" %
cwd)
device.execute("bazel build //third_party/caffe:caffe_py")
device.execute(
"cp -f bazel-genfiles/third_party/caffe/caffe_pb2.py %s" % cwd)
except: # noqa
MaceLogger.error("Failed in proto files' building")
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from utils import device
from utils.util import MaceLogger
cwd = os.path.dirname(__file__)
# TODO: Remove bazel deps
try:
device.execute("bazel build //mace/proto:mace_py")
device.execute("cp -f bazel-genfiles/mace/proto/mace_pb2.py %s" % cwd)
device.execute("bazel build //third_party/caffe:caffe_py")
device.execute("cp -f bazel-genfiles/third_party/caffe/caffe_pb2.py %s" %
cwd)
except: # noqa
MaceLogger.warning("No bazel, use cmake.")
default="arm64-v8a",
help="Target ABI: only support arm64-v8a")
parser.add_argument("--target_soc",
type=str,
default="all",
help="serialno for adb connection")
parser.add_argument("--apu_path",
type=str,
default="",
help="path for storing apu so files on device")
return parser.parse_known_args()
if __name__ == "__main__":
flags, args = parse_args()
run_devices = device.choose_devices(flags.target_abi, flags.target_soc)
device_num = len(run_devices)
if device_num == 0: # for CI
MaceLogger.warning("No Android devices are plugged in, "
"you need to copy `apu` so files by yourself.")
elif device_num > 1: # for CI
MaceLogger.warning("More than one Android devices are plugged in, "
"you need to copy `apu` so files by yourself.")
else:
device_id = run_devices[0]
android_device = device.create_device(flags.target_abi, device_id)
apu_so_paths = get_apu_so_paths(android_device)
for apu_so_path in apu_so_paths:
device.execute("cp -f %s %s" % (apu_so_path, flags.apu_path), True)
f.write(source)
if __name__ == '__main__':
flags = parse_args()
codegen_dir = "mace/codegen/models"
device.execute("rm -rf %s/*" % codegen_dir)
models = []
if flags.config:
conf = config_parser.parse(flags.config)
for name, model_conf in conf["models"].items():
model_conf = config_parser.normalize_model_config(model_conf)
if not flags.model_name or name == flags.model_name:
MaceLogger.info("Encrypt model %s" % name)
encrypt(name, "build/%s/model/%s.pb" % (name, name),
"build/%s/model/%s.data" % (name, name),
model_conf[ModelKeys.runtime],
codegen_dir + "/" + name, not flags.no_obfuscate,
flags.gencode_model, flags.gencode_param)
models.append(name)
os.rename("%s/%s/%s.pb" % (codegen_dir, name, name),
"build/%s/model/%s.pb" % (name, name))
os.rename("%s/%s/%s.data" % (codegen_dir, name, name),
"build/%s/model/%s.data" % (name, name))
else:
device_type = config_parser.parse_device_type(flags.device)
encrypt(flags.model_name, flags.model_file, flags.params_file,
device_type, codegen_dir, not flags.no_obfuscate,
flags.gencode_model, flags.gencode_param)
