PaddleX/paddlex/modules/text_detection/trainer.py

# !/usr/bin/env python3
# -*- coding: UTF-8 -*-
################################################################################
#
# Copyright (c) 2024 Baidu.com, Inc. All Rights Reserved
#
################################################################################
"""
Author: PaddlePaddle Authors
"""
import os
from pathlib import Path
import paddle

from ..base.trainer import BaseTrainer
from ..base.train_deamon import BaseTrainDeamon
from ...utils.config import AttrDict
from .support_models import SUPPORT_MODELS


class TextDetTrainer(BaseTrainer):
    """ Text Detection Model Trainer """
    support_models = SUPPORT_MODELS

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

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

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

        self.pdx_config.update_dataset(self.global_config.dataset_dir,
                                       "TextDetDataset")
        if self.train_config.pretrain_weight_path:
            self.pdx_config.update_pretrained_weights(
                self.train_config.pretrain_weight_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.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 TextDetTrainDeamon(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
        return paddle.load(pdstates_path)['best_model_dict']['hmean']

    def get_epoch_id_by_pdparams_prefix(self, pdparams_prefix):
        """ get the epoch_id by pdparams file """
        return int(pdparams_prefix.split(".")[0].split("_")[-1])