PaddleX/paddlex/modules/image_classification/trainer.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 json
import shutil
import paddle
from pathlib import Path

from ..base import BaseTrainer, BaseTrainDeamon
from .support_models import SUPPORT_MODELS
from ...utils.config import AttrDict


class ClsTrainer(BaseTrainer):
    """ Image Classification Model Trainer """
    support_models = SUPPORT_MODELS

    def dump_label_dict(self, src_label_dict_path: str):
        """dump label dict config

        Args:
            src_label_dict_path (str): path to label dict file to be saved.
        """
        dst_label_dict_path = Path(self.global_config.output).joinpath(
            "label_dict.txt")
        shutil.copyfile(src_label_dict_path, dst_label_dict_path)

    def build_deamon(self, config: AttrDict) -> "ClsTrainDeamon":
        """build deamon thread for saving training outputs timely

        Args:
            config (AttrDict): PaddleX pipeline config, which is loaded from pipeline yaml file.

        Returns:
            ClsTrainDeamon: the training deamon thread object for saving training outputs timely.
        """
        return ClsTrainDeamon(config)

    def update_config(self):
        """update training config
        """
        if self.train_config.log_interval:
            self.pdx_config.update_log_interval(self.train_config.log_interval)
        if self.train_config.eval_interval:
            self.pdx_config.update_eval_interval(
                self.train_config.eval_interval)
        if self.train_config.save_interval:
            self.pdx_config.update_save_interval(
                self.train_config.save_interval)

        self.pdx_config.update_dataset(self.global_config.dataset_dir,
                                       "ClsDataset")
        if self.train_config.num_classes is not None:
            self.pdx_config.update_num_classes(self.train_config.num_classes)
        if self.train_config.pretrain_weight_path and self.train_config.pretrain_weight_path != "":
            self.pdx_config.update_pretrained_weights(
                self.train_config.pretrain_weight_path)

        label_dict_path = Path(self.global_config.dataset_dir).joinpath(
            "label.txt")
        if label_dict_path.exists():
            self.dump_label_dict(label_dict_path)
        if self.train_config.batch_size is not None:
            self.pdx_config.update_batch_size(self.train_config.batch_size)
        if self.train_config.learning_rate is not None:
            self.pdx_config.update_learning_rate(
                self.train_config.learning_rate)
        if self.train_config.epochs_iters is not None:
            self.pdx_config._update_epochs(self.train_config.epochs_iters)
        if self.train_config.warmup_steps is not None:
            self.pdx_config.update_warmup_epochs(self.train_config.warmup_steps)
        if self.global_config.output is not None:
            self.pdx_config._update_output_dir(self.global_config.output)

    def get_train_kwargs(self) -> dict:
        """get key-value arguments of model training function

        Returns:
            dict: the arguments of training function.
        """
        train_args = {"device": self.get_device()}
        if self.train_config.resume_path is not None and self.train_config.resume_path != "":
            train_args["resume_path"] = self.train_config.resume_path
        return train_args


class ClsTrainDeamon(BaseTrainDeamon):
    """ ClsTrainResultDemon """

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

    def get_the_pdparams_suffix(self):
        """ get the suffix of pdparams file """
        return "pdparams"

    def get_the_pdema_suffix(self):
        """ get the suffix of pdema file """
        return "pdema"

    def get_the_pdopt_suffix(self):
        """ get the suffix of pdopt file """
        return "pdopt"

    def get_the_pdstates_suffix(self):
        """ get the suffix of pdstates file """
        return "pdstates"

    def get_ith_ckp_prefix(self, epoch_id):
        """ get the prefix of the epoch_id checkpoint file """
        return f"epoch_{epoch_id}"

    def get_best_ckp_prefix(self):
        """ get the prefix of the best checkpoint file """
        return "best_model"

    def get_score(self, pdstates_path):
        """ get the score by pdstates file """
        if not Path(pdstates_path).exists():
            return 0
        return paddle.load(pdstates_path)["metric"]

    def get_epoch_id_by_pdparams_prefix(self, pdparams_prefix):
        """ get the epoch_id by pdparams file """
        return int(pdparams_prefix.split("_")[-1])

    def update_label_dict(self, train_output):
        """ update label dict """
        dict_path = train_output.joinpath("label_dict.txt")
        if not dict_path.exists():
            return ""
        return dict_path