PaddleX/paddlex/repo_apis/PaddleOCR_api/table_rec/model.py

# copyright (c) 2024 PaddlePaddle Authors. All Rights Reserve.
# 
# 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 os

from ....utils import logging
from ...base.utils.arg import CLIArgument
from ...base.utils.subprocess import CompletedProcess
from ....utils.misc import abspath
from ..text_rec.model import TextRecModel


class TableRecModel(TextRecModel):
    """ Table Recognition Model """

    METRICS = ['acc']

    def predict(self,
                weight_path: str,
                input_path: str,
                device: str='gpu',
                save_dir: str=None,
                **kwargs) -> CompletedProcess:
        """predict using specified weight

        Args:
            weight_path (str): the path of model weight file used to predict.
            input_path (str): the path of image file to be predicted.
            device (str, optional): the running device. Defaults to 'gpu'.
            save_dir (str, optional): the directory path to save predict output. Defaults to None.

        Returns:
            CompletedProcess: the result of predicting subprocess execution.
        """
        config = self.config.copy()
        weight_path = abspath(weight_path)
        config.update_pretrained_weights(weight_path)

        input_path = abspath(input_path)
        config._update_infer_img(input_path)

        # TODO: Handle `device`
        logging.warning("`device` will not be used.")

        if save_dir is not None:
            save_dir = abspath(save_dir)
        else:
            save_dir = abspath(config.get_predict_save_dir())
        config._update_save_res_path(save_dir)

        self._assert_empty_kwargs(kwargs)

        with self._create_new_config_file() as config_path:
            config.dump(config_path)
            return self.runner.predict(config_path, [], device)

    def infer(self,
              model_dir: str,
              input_path: str,
              device: str='gpu',
              save_dir: str=None,
              **kwargs) -> CompletedProcess:
        """predict image using infernece model

        Args:
            model_dir (str): the directory path of inference model files that would use to predict.
            input_path (str): the path of image that would be predict.
            device (str, optional): the running device. Defaults to 'gpu'.
            save_dir (str, optional): the directory path to save output. Defaults to None.

        Returns:
            CompletedProcess: the result of infering subprocess execution.
        """
        config = self.config.copy()
        cli_args = []

        model_dir = abspath(model_dir)
        cli_args.append(CLIArgument('--table_model_dir', model_dir))

        input_path = abspath(input_path)
        cli_args.append(CLIArgument('--image_dir', input_path))

        device_type, _ = self.runner.parse_device(device)
        cli_args.append(CLIArgument('--use_gpu', str(device_type == 'gpu')))

        if save_dir is not None:
            save_dir = abspath(save_dir)
        else:
            # `save_dir` is None
            save_dir = abspath(os.path.join('output', 'infer'))
        cli_args.append(CLIArgument('--output', save_dir))

        dict_path = kwargs.pop('dict_path', None)
        if dict_path is not None:
            dict_path = abspath(dict_path)
        else:
            dict_path = config.get_label_dict_path()
        cli_args.append(CLIArgument('--table_char_dict_path', dict_path))

        model_type = config._get_model_type()
        cli_args.append(CLIArgument('--table_algorithm', model_type))
        infer_shape = config._get_infer_shape()
        if infer_shape is not None:
            cli_args.append(CLIArgument('--table_max_len', infer_shape))

        self._assert_empty_kwargs(kwargs)

        with self._create_new_config_file() as config_path:
            config.dump(config_path)
            return self.runner.infer(config_path, cli_args, device)