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| import os | |
| from typing import Optional, Union | |
| from transformers.trainer import Trainer | |
| from transformers.modeling_outputs import SequenceClassifierOutput | |
| from logger_config import logger | |
| from metrics import accuracy | |
| from utils import AverageMeter | |
| class RerankerTrainer(Trainer): | |
| def __init__(self, *pargs, **kwargs): | |
| super(RerankerTrainer, self).__init__(*pargs, **kwargs) | |
| self.acc_meter = AverageMeter('acc', round_digits=2) | |
| self.last_epoch = 0 | |
| def _save(self, output_dir: Optional[str] = None, state_dict=None): | |
| output_dir = output_dir if output_dir is not None else self.args.output_dir | |
| os.makedirs(output_dir, exist_ok=True) | |
| logger.info("Saving model checkpoint to {}".format(output_dir)) | |
| self.model.save_pretrained(output_dir) | |
| if self.tokenizer is not None and self.is_world_process_zero(): | |
| self.tokenizer.save_pretrained(output_dir) | |
| def compute_loss(self, model, inputs, return_outputs=False): | |
| outputs: SequenceClassifierOutput = model(inputs) | |
| loss = outputs.loss | |
| if self.model.training: | |
| labels = inputs['labels'] | |
| step_acc = accuracy(output=outputs.logits.detach(), target=labels)[0] | |
| self.acc_meter.update(step_acc) | |
| if self.state.global_step > 0 and self.state.global_step % self.args.logging_steps == 0: | |
| logger.info('step: {}, {}'.format(self.state.global_step, self.acc_meter)) | |
| self._reset_meters_if_needed() | |
| return (loss, outputs) if return_outputs else loss | |
| def _reset_meters_if_needed(self): | |
| if int(self.state.epoch) != self.last_epoch: | |
| self.last_epoch = int(self.state.epoch) | |
| self.acc_meter.reset() | |