PaddleX/paddlex/repo_apis/PaddleClas_api/cls/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 ....utils import logging


class ClsModel(BaseModel):
    """ Image Classification 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.
        """
        if resume_path is not None:
            resume_path = abspath(resume_path)

        with self._create_new_config_file() as config_path:
            # Update YAML config file
            config = self.config.copy()
            config._update_amp(amp)
            config.update_device(device)
            config._update_to_static(dy2st)
            config._update_use_vdl(use_vdl)
            config.update_log_ranks(device)
            config.enable_print_mem_info()

            if batch_size is not None:
                config.update_batch_size(batch_size)
            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_checkpoints(resume_path)
            if save_dir is not None:
                save_dir = abspath(save_dir)
            else:
                # `save_dir` is None
                save_dir = abspath(config.get_train_save_dir())
            config._update_output_dir(save_dir)
            if num_workers is not None:
                config.update_num_workers(num_workers)
            config.dump(config_path)

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

            self._assert_empty_kwargs(kwargs)

            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: str=None,
                 device: str='gpu',
                 amp: str='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.
        """
        weight_path = abspath(weight_path)

        with self._create_new_config_file() as config_path:
            # Update YAML config file
            config = self.config.copy()
            config._update_amp(amp)
            config.update_device(device)
            config.update_pretrained_weights(weight_path)
            if batch_size is not None:
                config.update_batch_size(batch_size)
            if num_workers is not None:
                config.update_num_workers(num_workers)

            config.dump(config_path)

            self._assert_empty_kwargs(kwargs)

            cp = self.runner.evaluate(config_path, [], device, ips)
            return cp

    def predict(self,
                weight_path: str,
                input_path: str,
                input_list_path: str=None,
                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.
            input_list_path (str, optional): the paths of images to be predicted if is not None. Defaults to None.
            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.
        """
        weight_path = abspath(weight_path)
        input_path = abspath(input_path)
        if input_list_path:
            input_list_path = abspath(input_list_path)

        with self._create_new_config_file() as config_path:
            # Update YAML config file
            config = self.config.copy()
            config.update_pretrained_weights(weight_path)
            config._update_predict_img(input_path, input_list_path)
            config.update_device(device)
            config._update_save_predict_result(save_dir)

            config.dump(config_path)

            self._assert_empty_kwargs(kwargs)

            return self.runner.predict(config_path, [], 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.
        """
        weight_path = abspath(weight_path)
        save_dir = abspath(save_dir)

        with self._create_new_config_file() as config_path:
            # Update YAML config file
            config = self.config.copy()
            config.update_pretrained_weights(weight_path)
            config._update_save_inference_dir(save_dir)
            config.dump(config_path)

            self._assert_empty_kwargs(kwargs)

            return self.runner.export(config_path, [], None, save_dir)

    def infer(self,
              model_dir: str,
              input_path: str,
              device: str='gpu',
              save_dir: str=None,
              dict_path: 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.
            dict_path (str, optional): the label dict file path. 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:
            logging.warning("`save_dir` will not be used.")
        config_path = os.path.join(model_dir, 'inference.yml')
        config = self.config.copy()
        config.load(config_path)
        config._update_inference_model_dir(model_dir)
        config._update_infer_img(input_path)
        config._update_infer_device(device)
        if dict_path is not None:
            dict_path = abspath(dict_path)
            config.update_label_dict_path(dict_path)
        if 'enable_mkldnn' in kwargs:
            config._update_enable_mkldnn(kwargs.pop('enable_mkldnn'))

        with self._create_new_config_file() as config_path:
            config.dump(config_path)

            self._assert_empty_kwargs(kwargs)

            return self.runner.infer(config_path, [], device)

    def compression(self,
                    weight_path: str,
                    batch_size: int=None,
                    learning_rate: float=None,
                    epochs_iters: int=None,
                    device: str='gpu',
                    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)

        with self._create_new_config_file() as config_path:
            # Update YAML config file
            config = self.config.copy()
            config._update_amp(None)
            config.update_device(device)
            config._update_use_vdl(use_vdl)
            config._update_slim_config(self.model_info[
                'auto_compression_config_path'])
            config.update_pretrained_weights(weight_path)

            if batch_size is not None:
                config.update_batch_size(batch_size)
            if learning_rate is not None:
                config.update_learning_rate(learning_rate)
            if epochs_iters is not None:
                config._update_epochs(epochs_iters)
            if save_dir is not None:
                save_dir = abspath(save_dir)
            else:
                # `save_dir` is None
                save_dir = abspath(config.get_train_save_dir())
            config._update_output_dir(save_dir)
            config.dump(config_path)

            export_cli_args = []
            export_cli_args.append(
                CLIArgument(
                    '-o',
                    f"Global.save_inference_dir={os.path.join(save_dir, 'export')}"
                ))

            self._assert_empty_kwargs(kwargs)

            return self.runner.compression(config_path, [], export_cli_args,
                                           device, save_dir)