PaddleX/paddlex/repo_apis/PaddleDetection_api/object_det/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 ...base import BaseModel
from ...base.utils.arg import CLIArgument
from ...base.utils.subprocess import CompletedProcess
from ....utils.misc import abspath

from .config import DetConfig


class DetModel(BaseModel):
    """ Object Detection Model """

    def train(self,
              batch_size: int=None,
              learning_rate: float=None,
              epochs_iters: int=None,
              ips: str=None,
              device: str='gpu',
              resume_path: str=None,
              dy2st: bool=False,
              amp: str='OFF',
              num_workers: int=None,
              use_vdl: bool=True,
              save_dir: str=None,
              **kwargs) -> CompletedProcess:
        """train self

        Args:
            batch_size (int, optional): the train batch size value. Defaults to None.
            learning_rate (float, optional): the train learning rate value. Defaults to None.
            epochs_iters (int, optional): the train epochs value. Defaults to None.
            ips (str, optional): the ip addresses of nodes when using distribution. Defaults to None.
            device (str, optional): the running device. Defaults to 'gpu'.
            resume_path (str, optional): the checkpoint file path to resume training. Train from scratch if it is set
                to None. Defaults to None.
            dy2st (bool, optional): Enable dynamic to static. Defaults to False.
            amp (str, optional): the amp settings. Defaults to 'OFF'.
            num_workers (int, optional): the workers number. Defaults to None.
            use_vdl (bool, optional): enable VisualDL. Defaults to True.
            save_dir (str, optional): the directory path to save train output. Defaults to None.

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

        if batch_size is not None:
            config.update_batch_size(batch_size, 'train')
        if learning_rate is not None:
            config.update_learning_rate(learning_rate)
        if epochs_iters is not None:
            config.update_epochs(epochs_iters)
            config.update_cossch_epoch(epochs_iters)
        device_type, _ = self.runner.parse_device(device)
        config.update_device(device_type)
        if resume_path is not None:
            assert resume_path.endswith('.pdparams'), \
                'resume_path should be endswith .pdparam'
            resume_dir = resume_path[0:-9]
            cli_args.append(CLIArgument('--resume', resume_dir))
        if dy2st:
            cli_args.append(CLIArgument('--to_static'))
        if amp != 'OFF' and amp is not None:
            # TODO: consider amp is O1 or O2 in ppdet
            cli_args.append(CLIArgument('--amp'))
        if num_workers is not None:
            config.update_num_workers(num_workers)
        if save_dir is None:
            save_dir = abspath(config.get_train_save_dir())
        else:
            save_dir = abspath(save_dir)
        config.update_save_dir(save_dir)
        if use_vdl:
            cli_args.append(CLIArgument('--use_vdl', use_vdl))
            cli_args.append(CLIArgument('--vdl_log_dir', save_dir))

        do_eval = kwargs.pop('do_eval', True)

        profile = kwargs.pop('profile', None)
        if profile is not None:
            cli_args.append(CLIArgument('--profiler_options', profile))

        enable_ce = kwargs.pop('enable_ce', None)
        if enable_ce is not None:
            cli_args.append(CLIArgument('--enable_ce', enable_ce))

        self._assert_empty_kwargs(kwargs)

        with self._create_new_config_file() as config_path:
            config.dump(config_path)
            return self.runner.train(
                config_path, cli_args, device, ips, save_dir, do_eval=do_eval)

    def evaluate(self,
                 weight_path: str,
                 batch_size: int=None,
                 ips: bool=None,
                 device: bool='gpu',
                 amp: bool='OFF',
                 num_workers: int=None,
                 **kwargs) -> CompletedProcess:
        """evaluate self using specified weight

        Args:
            weight_path (str): the path of model weight file to be evaluated.
            batch_size (int, optional): the batch size value in evaluating. Defaults to None.
            ips (str, optional): the ip addresses of nodes when using distribution. Defaults to None.
            device (str, optional): the running device. Defaults to 'gpu'.
            amp (str, optional): the AMP setting. Defaults to 'OFF'.
            num_workers (int, optional): the workers number in evaluating. Defaults to None.

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

        weight_path = abspath(weight_path)
        config.update_weights(weight_path)
        if batch_size is not None:
            config.update_batch_size(batch_size, 'eval')
        device_type, device_ids = self.runner.parse_device(device)
        if len(device_ids) > 1:
            raise ValueError(
                f"multi-{device_type} evaluation is not supported. Please use a single {device_type}."
            )
        config.update_device(device_type)
        if amp != 'OFF':
            # TODO: consider amp is O1 or O2 in ppdet
            cli_args.append(CLIArgument('--amp'))
        if num_workers is not None:
            config.update_num_workers(num_workers)

        self._assert_empty_kwargs(kwargs)

        with self._create_new_config_file() as config_path:
            config.dump(config_path)
            cp = self.runner.evaluate(config_path, cli_args, device, ips)
            return cp

    def predict(self,
                input_path: str,
                weight_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()
        cli_args = []

        input_path = abspath(input_path)
        if os.path.isfile(input_path):
            cli_args.append(CLIArgument('--infer_img', input_path))
        else:
            cli_args.append(CLIArgument('--infer_dir', input_path))
        if 'infer_list' in kwargs:
            infer_list = abspath(kwargs.get('infer_list'))
            cli_args.append(CLIArgument('--infer_list', infer_list))
        if 'visualize' in kwargs:
            cli_args.append(CLIArgument('--visualize', kwargs['visualize']))
        if 'save_results' in kwargs:
            cli_args.append(
                CLIArgument('--save_results', kwargs['save_results']))
        if 'save_threshold' in kwargs:
            cli_args.append(
                CLIArgument('--save_threshold', kwargs['save_threshold']))
        if 'rtn_im_file' in kwargs:
            cli_args.append(CLIArgument('--rtn_im_file', kwargs['rtn_im_file']))
        weight_path = abspath(weight_path)
        config.update_weights(weight_path)
        device_type, _ = self.runner.parse_device(device)
        config.update_device(device_type)
        if save_dir is not None:
            save_dir = abspath(save_dir)
            cli_args.append(CLIArgument('--output_dir', 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, cli_args, device)

    def export(self, weight_path: str, save_dir: str,
               **kwargs) -> CompletedProcess:
        """export the dynamic model to static model

        Args:
            weight_path (str): the model weight file path that used to export.
            save_dir (str): the directory path to save export output.

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

        weight_path = abspath(weight_path)
        config.update_weights(weight_path)
        save_dir = abspath(save_dir)
        cli_args.append(CLIArgument('--output_dir', save_dir))
        input_shape = kwargs.pop('input_shape', None)
        if input_shape is not None:
            cli_args.append(
                CLIArgument('-o',
                            f"TestReader.inputs_def.image_shape={input_shape}"))

        use_trt = kwargs.pop('use_trt', None)
        if use_trt is not None:
            cli_args.append(CLIArgument('-o', f"trt={bool(use_trt)}"))

        exclude_nms = kwargs.pop('exclude_nms', None)
        if exclude_nms is not None:
            cli_args.append(
                CLIArgument('-o', f"exclude_nms={bool(exclude_nms)}"))

        self._assert_empty_kwargs(kwargs)

        with self._create_new_config_file() as config_path:
            config.dump(config_path)
            return self.runner.export(config_path, cli_args, None)

    def infer(self,
              model_dir: str,
              input_path: str,
              device: str='gpu',
              save_dir: str=None,
              **kwargs):
        """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.
        """
        model_dir = abspath(model_dir)
        input_path = abspath(input_path)
        if save_dir is not None:
            save_dir = abspath(save_dir)

        cli_args = []
        cli_args.append(CLIArgument('--model_dir', model_dir))
        cli_args.append(CLIArgument('--image_file', input_path))
        if save_dir is not None:
            cli_args.append(CLIArgument('--output_dir', save_dir))
        device_type, _ = self.runner.parse_device(device)
        cli_args.append(CLIArgument('--device', device_type))

        self._assert_empty_kwargs(kwargs)

        return self.runner.infer(cli_args, device)

    def compression(self,
                    weight_path: str,
                    batch_size: int=None,
                    learning_rate: float=None,
                    epochs_iters: int=None,
                    device: str=None,
                    use_vdl: bool=True,
                    save_dir: str=None,
                    **kwargs) -> CompletedProcess:
        """compression model

        Args:
            weight_path (str): the path to weight file of model.
            batch_size (int, optional): the batch size value of compression training. Defaults to None.
            learning_rate (float, optional): the learning rate value of compression training. Defaults to None.
            epochs_iters (int, optional): the epochs or iters of compression training. Defaults to None.
            device (str, optional): the device to run compression training. Defaults to 'gpu'.
            use_vdl (bool, optional): whether or not to use VisualDL. Defaults to True.
            save_dir (str, optional): the directory to save output. Defaults to None.

        Returns:
            CompletedProcess: the result of compression subprocess execution.
        """
        weight_path = abspath(weight_path)
        if save_dir is None:
            save_dir = self.config['save_dir']
        save_dir = abspath(save_dir)

        config = self.config.copy()
        cps_config = DetConfig(
            self.name,
            config_path=self.model_info['auto_compression_config_path'])
        train_cli_args = []
        export_cli_args = []

        cps_config.update_pretrained_weights(weight_path)
        if batch_size is not None:
            cps_config.update_batch_size(batch_size, 'train')
        if learning_rate is not None:
            cps_config.update_learning_rate(learning_rate)
        if epochs_iters is not None:
            cps_config.update_epochs(epochs_iters)
        if device is not None:
            device_type, _ = self.runner.parse_device(device)
            config.update_device(device_type)
        if save_dir is not None:
            save_dir = abspath(config.get_train_save_dir())
        else:
            save_dir = abspath(save_dir)
        cps_config.update_save_dir(save_dir)
        if use_vdl:
            train_cli_args.append(CLIArgument('--use_vdl', use_vdl))
            train_cli_args.append(CLIArgument('--vdl_log_dir', save_dir))

        export_cli_args.append(
            CLIArgument('--output_dir', os.path.join(save_dir, 'export')))

        with self._create_new_config_file() as config_path:
            config.dump(config_path)
            # TODO: refactor me
            cps_config_path = config_path[0:-4] + '_compression' + config_path[
                -4:]
            cps_config.dump(cps_config_path)
            train_cli_args.append(CLIArgument('--slim_config', cps_config_path))
            export_cli_args.append(
                CLIArgument('--slim_config', cps_config_path))

            self._assert_empty_kwargs(kwargs)

            self.runner.compression(config_path, train_cli_args,
                                    export_cli_args, device, save_dir)