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 main() -> None: config = config_parser.parse('config.json') db_worker = DBWorker(config) redis_client = redis.Redis(password=config['redis_pass']) try: redis_client.get('1') except redis.exceptions.ConnectionError: print('Ошибка подключения к Redis.') exit() args = parse_args() if args.s: # запуск start(config, db_worker, redis_client)
parser.add_argument("--backend", type=str, default="tensorflow", help="onnx backend framework") parser.add_argument("--build", action="store_true", help="if build before run") parser.add_argument('--output', type=str, default="build", help="output dir") parser.add_argument('--vlog_level', type=int, default="0", help="vlog level") return parser.parse_known_args() if __name__ == "__main__": flags, args = parse_args() conf = config_parser.parse(flags.config) if flags.build or flags.validate: micro_conf = config_parser.normalize_model_config( conf[ModelKeys.models][flags.model_name]) build_engine(flags.model_name, micro_conf[ModelKeys.data_type]) if flags.validate and flags.layers != "-1": run_layers_validate(flags, args, conf) else: run_model(flags, args, conf)
def convert_func(flags): configs = config_parser.parse(flags.config) print(configs) library_name = configs[YAMLKeyword.library_name] if not os.path.exists(BUILD_OUTPUT_DIR): os.makedirs(BUILD_OUTPUT_DIR) elif os.path.exists(os.path.join(BUILD_OUTPUT_DIR, library_name)): sh.rm("-rf", os.path.join(BUILD_OUTPUT_DIR, library_name)) os.makedirs(os.path.join(BUILD_OUTPUT_DIR, library_name)) if not os.path.exists(BUILD_DOWNLOADS_DIR): os.makedirs(BUILD_DOWNLOADS_DIR) model_output_dir = \ '%s/%s/%s' % (BUILD_OUTPUT_DIR, library_name, MODEL_OUTPUT_DIR_NAME) model_header_dir = \ '%s/%s/%s' % (BUILD_OUTPUT_DIR, library_name, MODEL_HEADER_DIR_PATH) # clear output dir if os.path.exists(model_output_dir): sh.rm("-rf", model_output_dir) os.makedirs(model_output_dir) if os.path.exists(model_header_dir): sh.rm("-rf", model_header_dir) if os.path.exists(MODEL_CODEGEN_DIR): sh.rm("-rf", MODEL_CODEGEN_DIR) if os.path.exists(ENGINE_CODEGEN_DIR): sh.rm("-rf", ENGINE_CODEGEN_DIR) if flags.quantize_stat: configs[YAMLKeyword.quantize_stat] = flags.quantize_stat if flags.model_data_format: model_data_format = flags.model_data_format else: model_data_format = configs.get(YAMLKeyword.model_data_format, "file") embed_model_data = model_data_format == ModelFormat.code if flags.model_graph_format: model_graph_format = flags.model_graph_format else: model_graph_format = configs.get(YAMLKeyword.model_graph_format, "file") embed_graph_def = model_graph_format == ModelFormat.code if flags.enable_micro: mace_check((not embed_model_data) and (not embed_graph_def), ModuleName.YAML_CONFIG, "You should specify file mode to convert micro model.") if embed_graph_def: os.makedirs(model_header_dir) sh_commands.gen_mace_engine_factory_source( configs[YAMLKeyword.models].keys(), embed_model_data) sh.cp("-f", glob.glob("mace/codegen/engine/*.h"), model_header_dir) convert.convert(configs, MODEL_CODEGEN_DIR, flags.enable_micro) for model_name, model_config in configs[YAMLKeyword.models].items(): if flags.enable_micro: data_type = model_config.get(YAMLKeyword.data_type, "") mace_check( data_type == FPDataType.fp32_fp32.value or data_type == FPDataType.bf16_fp32.value, ModuleName.YAML_CONFIG, "You should specify fp32_fp32 or bf16_fp32 data type " "to convert micro model.") model_codegen_dir = "%s/%s" % (MODEL_CODEGEN_DIR, model_name) encrypt.encrypt( model_name, "%s/model/%s.pb" % (model_codegen_dir, model_name), "%s/model/%s.data" % (model_codegen_dir, model_name), config_parser.parse_device_type(model_config[YAMLKeyword.runtime]), model_codegen_dir, bool(model_config.get(YAMLKeyword.obfuscate, 1)), model_graph_format == "code", model_data_format == "code") if model_graph_format == ModelFormat.file: sh.mv("-f", '%s/model/%s.pb' % (model_codegen_dir, model_name), model_output_dir) sh.mv("-f", '%s/model/%s.data' % (model_codegen_dir, model_name), model_output_dir) if flags.enable_micro: sh.mv( "-f", '%s/model/%s_micro.tar.gz' % (model_codegen_dir, model_name), model_output_dir) else: if not embed_model_data: sh.mv("-f", '%s/model/%s.data' % (model_codegen_dir, model_name), model_output_dir) sh.cp("-f", glob.glob("mace/codegen/models/*/code/*.h"), model_header_dir) MaceLogger.summary( StringFormatter.block("Model %s converted" % model_name)) if model_graph_format == ModelFormat.code: build_model_lib(configs, flags.address_sanitizer, flags.debug_mode) print_library_summary(configs)
def convert_func(flags): configs = config_parser.parse(flags.config) print(configs) library_name = configs[YAMLKeyword.library_name] if not os.path.exists(BUILD_OUTPUT_DIR): os.makedirs(BUILD_OUTPUT_DIR) elif os.path.exists(os.path.join(BUILD_OUTPUT_DIR, library_name)): sh.rm("-rf", os.path.join(BUILD_OUTPUT_DIR, library_name)) os.makedirs(os.path.join(BUILD_OUTPUT_DIR, library_name)) if not os.path.exists(BUILD_DOWNLOADS_DIR): os.makedirs(BUILD_DOWNLOADS_DIR) model_output_dir = \ '%s/%s/%s' % (BUILD_OUTPUT_DIR, library_name, MODEL_OUTPUT_DIR_NAME) model_header_dir = \ '%s/%s/%s' % (BUILD_OUTPUT_DIR, library_name, MODEL_HEADER_DIR_PATH) # clear output dir if os.path.exists(model_output_dir): sh.rm("-rf", model_output_dir) os.makedirs(model_output_dir) if os.path.exists(model_header_dir): sh.rm("-rf", model_header_dir) if os.path.exists(MODEL_CODEGEN_DIR): sh.rm("-rf", MODEL_CODEGEN_DIR) if os.path.exists(ENGINE_CODEGEN_DIR): sh.rm("-rf", ENGINE_CODEGEN_DIR) if flags.model_data_format: model_data_format = flags.model_data_format else: model_data_format = configs.get(YAMLKeyword.model_data_format, "file") embed_model_data = model_data_format == ModelFormat.code if flags.model_graph_format: model_graph_format = flags.model_graph_format else: model_graph_format = configs.get(YAMLKeyword.model_graph_format, "file") if model_graph_format == ModelFormat.code: os.makedirs(model_header_dir) sh_commands.gen_mace_engine_factory_source( configs[YAMLKeyword.models].keys(), embed_model_data) sh.cp("-f", glob.glob("mace/codegen/engine/*.h"), model_header_dir) convert.convert(configs, MODEL_CODEGEN_DIR) for model_name, model_config in configs[YAMLKeyword.models].items(): model_codegen_dir = "%s/%s" % (MODEL_CODEGEN_DIR, model_name) encrypt.encrypt( model_name, "%s/model/%s.pb" % (model_codegen_dir, model_name), "%s/model/%s.data" % (model_codegen_dir, model_name), config_parser.parse_device_type(model_config[YAMLKeyword.runtime]), model_codegen_dir, bool(model_config.get(YAMLKeyword.obfuscate, 1)), model_graph_format == "code", model_data_format == "code") if model_graph_format == ModelFormat.file: sh.mv("-f", '%s/model/%s.pb' % (model_codegen_dir, model_name), model_output_dir) sh.mv("-f", '%s/model/%s.data' % (model_codegen_dir, model_name), model_output_dir) else: if not embed_model_data: sh.mv("-f", '%s/model/%s.data' % (model_codegen_dir, model_name), model_output_dir) sh.cp("-f", glob.glob("mace/codegen/models/*/code/*.h"), model_header_dir) MaceLogger.summary( StringFormatter.block("Model %s converted" % model_name)) if model_graph_format == ModelFormat.code: build_model_lib(configs, flags.address_sanitizer, flags.debug_mode) print_library_summary(configs)
# # graph = tf.Graph() # with tf.compat.v1.Session(graph=graph) as sess: # loader = tf.compat.v1.train.import_meta_graph(trained_checkpoint_prefix + # '.meta') # loader.restore(sess, trained_checkpoint_prefix) # # builder = tf.compat.v1.saved_model.builder.SavedModelBuilder(export_directory) # builder.add_meta_graph_and_variables(sess, # [tf.saved_model.TRAINING, # tf.saved_model.SERVING], # strip_default_attrs=True) # builder.save() model_config_file = "cfg/darknet.cfg" parsed_config = config_parser.parse(model_config_file) detection_model = Detector(parsed_config, 1) detection_model = detection_model.build_model((416, 416, 3), 1) detection_model.layers[0].trainable = False checkpoint = tf.train.Checkpoint(model=detection_model) latest_checkpoint_path = tf.train.latest_checkpoint("/home/karan/Checkpoint/") checkpoint.restore(latest_checkpoint_path) # shape = (1, 416, 416, 3) # sample_image = np.random.randint(0, 255, size=(1, 416, 416, 3), dtype=np.uint8) # input_image = np.array(sample_image / 255, dtype=np.float32) # model = tf.keras.models.load_model("/home/karan/Checkpoint/") # model.build_model(shape[1:], shape[0])