PaddleX/paddlex/modules/multilabel_classification/trainer.py

# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
#
# 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 shutil
from pathlib import Path

from ..base import BaseTrainer
from .model_list import MODELS


class MLClsTrainer(BaseTrainer):
    """Image Classification 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(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, "MLClsDataset")
        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
        train_args["dy2st"] = self.train_config.get("dy2st", False)
        # amp support 'O1', 'O2', 'OFF'
        train_args["amp"] = self.train_config.get("amp", "OFF")
        return train_args