You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.

99 lines
3.0 KiB

# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# 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.
import abc
from paddlex import create_predictor
from .._abstract import CLISubcommandExecutor
from .._common_args import (
add_common_cli_opts,
parse_common_args,
prepare_common_init_args,
)
_DEFAULT_ENABLE_HPI = False
class PaddleXPredictorWrapper(metaclass=abc.ABCMeta):
def __init__(
self,
*,
model_name=None,
model_dir=None,
**common_args,
):
super().__init__()
self._model_name = (
model_name if model_name is not None else self.default_model_name
)
self._model_dir = model_dir
self._common_args = parse_common_args(
common_args, default_enable_hpi=_DEFAULT_ENABLE_HPI
)
self.paddlex_predictor = self._create_paddlex_predictor()
@property
@abc.abstractmethod
def default_model_name(self):
raise NotImplementedError
def predict_iter(self, *args, **kwargs):
return self.paddlex_predictor.predict(*args, **kwargs)
def predict(self, *args, **kwargs):
result = list(self.predict_iter(*args, **kwargs))
return result
@classmethod
@abc.abstractmethod
def get_cli_subcommand_executor(cls):
raise NotImplementedError
def _get_extra_paddlex_predictor_init_args(self):
return {}
def _create_paddlex_predictor(self):
kwargs = prepare_common_init_args(self._model_name, self._common_args)
kwargs = {**self._get_extra_paddlex_predictor_init_args(), **kwargs}
# Should we check model names?
return create_predictor(
model_name=self._model_name, model_dir=self._model_dir, **kwargs
)
class PredictorCLISubcommandExecutor(CLISubcommandExecutor):
@property
@abc.abstractmethod
def subparser_name(self):
raise NotImplementedError
def add_subparser(self, subparsers):
subparser = subparsers.add_parser(name=self.subparser_name)
self._update_subparser(subparser)
subparser.add_argument("--model_name", type=str, help="Name of the model.")
subparser.add_argument(
"--model_dir", type=str, help="Directory where the model is stored."
)
add_common_cli_opts(
subparser,
default_enable_hpi=_DEFAULT_ENABLE_HPI,
allow_multiple_devices=False,
)
return subparser
@abc.abstractmethod
def _update_subparser(self, subparser):
raise NotImplementedError