preprocess/tools/note_transcription/utils/commons/base_task.py

import logging
import os
import random
import subprocess
import sys
from datetime import datetime
import numpy as np
import torch.utils.data
from torch import nn
from torch.utils.tensorboard import SummaryWriter
from .dataset_utils import data_loader
from .hparams import hparams
from .meters import AvgrageMeter
from .tensor_utils import tensors_to_scalars
from .trainer import Trainer

torch.multiprocessing.set_sharing_strategy(os.getenv('TORCH_SHARE_STRATEGY', 'file_system'))

log_format = '%(asctime)s %(message)s'
logging.basicConfig(stream=sys.stdout, level=logging.INFO,
                    format=log_format, datefmt='%m/%d %I:%M:%S %p')


class BaseTask(nn.Module):
    def __init__(self, *args, **kwargs):
        super(BaseTask, self).__init__()
        self.current_epoch = 0
        self.global_step = 0
        self.trainer = None
        self.use_ddp = False
        self.gradient_clip_norm = hparams['clip_grad_norm']
        self.gradient_clip_val = hparams.get('clip_grad_value', 0)
        self.model = None
        self.training_losses_meter = None
        self.logger: SummaryWriter = None

    ######################
    # build model, dataloaders, optimizer, scheduler and tensorboard
    ######################
    def build_model(self):
        raise NotImplementedError

    @data_loader
    def train_dataloader(self):
        raise NotImplementedError

    @data_loader
    def test_dataloader(self):
        raise NotImplementedError

    @data_loader
    def val_dataloader(self):
        raise NotImplementedError

    def build_scheduler(self, optimizer):
        return None

    def build_optimizer(self, model):
        raise NotImplementedError

    def configure_optimizers(self):
        optm = self.build_optimizer(self.model)
        self.scheduler = self.build_scheduler(optm)
        if isinstance(optm, (list, tuple)):
            return optm
        return [optm]

    def build_tensorboard(self, save_dir, name, **kwargs):
        log_dir = os.path.join(save_dir, name)
        os.makedirs(log_dir, exist_ok=True)
        self.logger = SummaryWriter(log_dir=log_dir, **kwargs)

    ######################
    # training
    ######################
    def on_train_start(self):
        pass

    def on_train_end(self):
        pass

    def on_epoch_start(self):
        self.training_losses_meter = {'total_loss': AvgrageMeter()}

    def on_epoch_end(self):
        loss_outputs = {k: round(v.avg, 4) for k, v in self.training_losses_meter.items()}
        print(f"Epoch {self.current_epoch} ended. Steps: {self.global_step}. {loss_outputs}")

    def _training_step(self, sample, batch_idx, optimizer_idx):
        """

        :param sample:
        :param batch_idx:
        :return: total loss: torch.Tensor, loss_log: dict
        """
        raise NotImplementedError

    def training_step(self, sample, batch_idx, optimizer_idx=-1):
        """

        :param sample:
        :param batch_idx:
        :param optimizer_idx:
        :return: {'loss': torch.Tensor, 'progress_bar': dict, 'tb_log': dict}
        """
        loss_ret = self._training_step(sample, batch_idx, optimizer_idx)
        if loss_ret is None:
            return {'loss': None}
        total_loss, log_outputs = loss_ret
        log_outputs = tensors_to_scalars(log_outputs)
        for k, v in log_outputs.items():
            if k not in self.training_losses_meter:
                self.training_losses_meter[k] = AvgrageMeter()
            if not np.isnan(v):
                self.training_losses_meter[k].update(v)
        self.training_losses_meter['total_loss'].update(total_loss.item())

        if optimizer_idx >= 0:
            log_outputs[f'lr_{optimizer_idx}'] = self.trainer.optimizers[optimizer_idx].param_groups[0]['lr']

        progress_bar_log = log_outputs
        tb_log = {f'tr/{k}': v for k, v in log_outputs.items()}
        return {
            'loss': total_loss,
            'progress_bar': progress_bar_log,
            'tb_log': tb_log
        }

    def on_before_optimization(self, opt_idx):
        if self.gradient_clip_norm > 0:
            torch.nn.utils.clip_grad_norm_(self.parameters(), self.gradient_clip_norm)
        if self.gradient_clip_val > 0:
            torch.nn.utils.clip_grad_value_(self.parameters(), self.gradient_clip_val)

    def on_after_optimization(self, epoch, batch_idx, optimizer, optimizer_idx):
        if self.scheduler is not None:
            # self.scheduler.step(self.global_step // hparams['accumulate_grad_batches'])
            # the code above causes EPOCH_DEPRECATION_WARNING, changed it and changed the optimizer init with
            # step_size divided by accumulate_grad_batches
            self.scheduler.step()

    ######################
    # validation
    ######################
    def validation_start(self):
        pass

    def validation_step(self, sample, batch_idx):
        """

        :param sample:
        :param batch_idx:
        :return: output: {"losses": {...}, "total_loss": float, ...} or (total loss: torch.Tensor, loss_log: dict)
        """
        raise NotImplementedError

    def validation_end(self, outputs):
        """

        :param outputs:
        :return: loss_output: dict
        """
        all_losses_meter = {'total_loss': AvgrageMeter()}
        for output in outputs:
            if len(output) == 0 or output is None:
                continue
            if isinstance(output, dict):
                assert 'losses' in output, 'Key "losses" should exist in validation output.'
                n = output.pop('nsamples', 1)
                losses = tensors_to_scalars(output['losses'])
                total_loss = output.get('total_loss', sum(losses.values()))
            else:
                assert len(output) == 2, 'Validation output should only consist of two elements: (total_loss, losses)'
                n = 1
                total_loss, losses = output
                losses = tensors_to_scalars(losses)
            if isinstance(total_loss, torch.Tensor):
                total_loss = total_loss.item()
            for k, v in losses.items():
                if k not in all_losses_meter:
                    all_losses_meter[k] = AvgrageMeter()
                all_losses_meter[k].update(v, n)
            all_losses_meter['total_loss'].update(total_loss, n)
        loss_output = {k: round(v.avg, 4) for k, v in all_losses_meter.items()}
        print(f"| Validation results@{self.global_step}: {loss_output}")
        return {
            'tb_log': {f'val/{k}': v for k, v in loss_output.items()},
            'val_loss': loss_output['total_loss']
        }

    ######################
    # testing
    ######################
    def test_start(self):
        pass

    def test_step(self, sample, batch_idx):
        return self.validation_step(sample, batch_idx)

    def test_end(self, outputs):
        return self.validation_end(outputs)

    ######################
    # start training/testing
    ######################
    @classmethod
    def start(cls):
        os.environ['MASTER_PORT'] = str(random.randint(15000, 30000))
        random.seed(hparams['seed'])
        np.random.seed(hparams['seed'])
        work_dir = hparams['work_dir']
        trainer = Trainer(
            work_dir=work_dir,
            val_check_interval=hparams['val_check_interval'],
            tb_log_interval=hparams['tb_log_interval'],
            max_updates=hparams['max_updates'],
            num_sanity_val_steps=hparams['num_sanity_val_steps'] if not hparams['validate'] else 10000,
            accumulate_grad_batches=hparams['accumulate_grad_batches'],
            print_nan_grads=hparams['print_nan_grads'],
            resume_from_checkpoint=hparams.get('resume_from_checkpoint', 0),
            amp=hparams['amp'],
            monitor_key=hparams['valid_monitor_key'],
            monitor_mode=hparams['valid_monitor_mode'],
            num_ckpt_keep=hparams['num_ckpt_keep'],
            save_best=hparams['save_best'],
            seed=hparams['seed'],
            debug=hparams['debug']
        )
        if not hparams['infer']:  # train
            trainer.fit(cls)
        else:
            trainer.test(cls)

    def on_keyboard_interrupt(self):
        pass