PaddleX/paddlex/modules/text_recognition/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 os
import shutil
from pathlib import Path
import paddle

from ..base import BaseTrainer, BaseTrainDeamon
from ...utils.config import AttrDict
from .model_list import MODELS


class TextRecTrainer(BaseTrainer):
    """ Text Recognition Model Trainer """
    entities = 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) -> "TextRecTrainDeamon":
        """build deamon thread for saving training outputs timely

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

        Returns:
            TextRecTrainDeamon: the training deamon thread object for saving training outputs timely.
        """
        return TextRecTrainDeamon(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_by_epoch(
                self.train_config.eval_interval)
        if self.train_config.save_interval:
            self.pdx_config.update_save_interval(
                self.train_config.save_interval)
        
        if self.global_config['model']=='LaTeX_OCR_rec':        
            self.pdx_config.update_dataset(self.global_config.dataset_dir,
                                        "LaTeXOCRDataSet")
        else:
            self.pdx_config.update_dataset(self.global_config.dataset_dir,
                                        "MSTextRecDataset")
        
        label_dict_path = Path(self.global_config.dataset_dir).joinpath(
            "dict.txt")
        if label_dict_path.exists():
            self.pdx_config.update_label_dict_path(label_dict_path)
            self.dump_label_dict(label_dict_path)

        if self.train_config.pretrain_weight_path:
            self.pdx_config.update_pretrained_weights(
                self.train_config.pretrain_weight_path)
        
        if self.global_config['model']=='LaTeX_OCR_rec':
            if self.train_config.batch_size_train is not None and self.train_config.batch_size_val:
                self.pdx_config.update_batch_size_pair(self.train_config.batch_size_train, self.train_config.batch_size_val)
        else:
            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.resume_path is not None and self.train_config.resume_path != "":
            self.pdx_config._update_checkpoints(self.train_config.resume_path)
        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.
        """
        return {"device": self.get_device()}


class TextRecTrainDeamon(BaseTrainDeamon):
    """ TableRecTrainDeamon """

    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 "states"

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

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

    def get_score(self, pdstates_path):
        """ get the score by pdstates file """
        if not Path(pdstates_path).exists():
            return 0
        if self.global_config['model'] == 'LaTeX_OCR_rec': 
            return paddle.load(pdstates_path)['best_model_dict']['exp_rate']       
        else:
            return paddle.load(pdstates_path)['best_model_dict']['acc']

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