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!")