PaddleX/paddlex/modules/formula_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

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


class FormulaRecTrainer(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 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"
            )
        elif self.global_config["model"] in (
            "UniMERNet",
            "PP-FormulaNet-L",
            "PP-FormulaNet-S",
        ):
            self.pdx_config.update_dataset(
                self.global_config.dataset_dir, "SimpleDataSet"
            )

        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 is not None
            ):
                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_train is not None
                and self.train_config.batch_size_val is not None
            ):
                self.pdx_config.update_batch_size(
                    self.train_config.batch_size_train, self.train_config.batch_size_val
                )

        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(),
            "dy2st": self.train_config.get("dy2st", False),
        }