def serving_encryption(): inference_model_to_serving( dirname="./uci_housing_model", params_filename=None, serving_server="encrypt_server", serving_client="encrypt_client", encryption=True)
def run(FLAGS, cfg): # build detector trainer = Trainer(cfg, mode='test') # load weights if cfg.architecture in ['DeepSORT', 'ByteTrack']: trainer.load_weights_sde(cfg.det_weights, cfg.reid_weights) else: trainer.load_weights(cfg.weights) # export model trainer.export(FLAGS.output_dir) if FLAGS.export_serving_model: from paddle_serving_client.io import inference_model_to_serving model_name = os.path.splitext(os.path.split(cfg.filename)[-1])[0] inference_model_to_serving( dirname="{}/{}".format(FLAGS.output_dir, model_name), serving_server="{}/{}/serving_server".format(FLAGS.output_dir, model_name), serving_client="{}/{}/serving_client".format(FLAGS.output_dir, model_name), model_filename="model.pdmodel", params_filename="model.pdiparams")
def serving_encryption(): inference_model_to_serving(dirname="./DarkNet53/ppcls_model/", model_filename="__model__", params_filename="./__params__", serving_server="encrypt_server", serving_client="encrypt_client", encryption=True)
def do_export(model_dir): feed_names, fetch_names = serving_io.inference_model_to_serving( dirname=model_dir, serving_server="transformer_server", serving_client="transformer_client", model_filename="transformer.pdmodel", params_filename="transformer.pdiparams") print("model feed_names : %s" % feed_names) print("model fetch_names : %s" % fetch_names)
def run(FLAGS, cfg): # build detector trainer = Trainer(cfg, mode='test') # load weights trainer.load_weights(cfg.weights, 'resume') # export model trainer.export(FLAGS.output_dir) if FLAGS.export_serving_model: from paddle_serving_client.io import inference_model_to_serving model_name = os.path.splitext(os.path.split(cfg.filename)[-1])[0] inference_model_to_serving( dirname="{}/{}".format(FLAGS.output_dir, model_name), serving_server="{}/{}/serving_server".format( FLAGS.output_dir, model_name), serving_client="{}/{}/serving_client".format( FLAGS.output_dir, model_name), model_filename="model.pdmodel", params_filename="model.pdiparams")
parser = argparse.ArgumentParser() parser.add_argument( "--inference_model_dir", type=str, default="./export/", help="The directory of the inference model.") parser.add_argument( "--model_file", type=str, default='inference.pdmodel', help="The inference model file name.") parser.add_argument( "--params_file", type=str, default='inference.pdiparams', help="The input inference parameters file name.") return parser.parse_args() if __name__ == '__main__': paddle.enable_static() args = parse_args() feed_names, fetch_names = serving_io.inference_model_to_serving( dirname=args.inference_model_dir, serving_server="serving_server", serving_client="serving_client", model_filename=args.model_file, params_filename=args.params_file) print("model feed_names : %s" % feed_names) print("model fetch_names : %s" % fetch_names)
help= 'set alias names for feed vars, split by comma \',\', you should run --show_proto to check the number of feed vars' ) parser.add_argument( "--fetch_alias_names", type=str, default=None, help= 'set alias names for feed vars, split by comma \',\', you should run --show_proto to check the number of fetch vars' ) parser.add_argument( "--show_proto", type=bool, default=False, help= 'If yes, you can preview the proto and then determine your feed var alias name and fetch var alias name.' ) if __name__ == "__main__": paddle.enable_static() args = parser.parse_args() feed_names, fetch_names = serving_io.inference_model_to_serving( dirname=args.dirname, serving_server=args.server_path, serving_client=args.client_path, model_filename=args.model_filename, params_filename=args.params_filename, show_proto=args.show_proto, feed_alias_names=args.feed_alias_names, fetch_alias_names=args.fetch_alias_names)
# 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. import os import argparse from paddle_serving_client.io import inference_model_to_serving def parse_args(): parser = argparse.ArgumentParser() parser.add_argument("--model_dir", type=str) parser.add_argument("--server_dir", type=str, default="serving_server_dir") parser.add_argument("--client_dir", type=str, default="serving_client_dir") return parser.parse_args() args = parse_args() inference_model_dir = args.model_dir serving_server_dir = os.path.join(args.model_dir, args.server_dir) serving_client_dir = os.path.join(args.model_dir, args.client_dir) feed_var_names, fetch_var_names = inference_model_to_serving( inference_model_dir, serving_server_dir, serving_client_dir, model_filename="model", params_filename="params") print("success!")