train.py

from model import JDCNet
from meldataset import build_dataloader
from optimizers import build_optimizer
from trainer import Trainer

import time
import os
import os.path as osp
import re
import sys
import yaml
import shutil
import numpy as np
import torch
import torch.nn as nn
from torch.utils.tensorboard import SummaryWriter
import click
from tqdm import tqdm

import logging
from logging import StreamHandler
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
handler = StreamHandler()
handler.setLevel(logging.DEBUG)
logger.addHandler(handler)

torch.backends.cudnn.benchmark = True

def get_data_path_list(train_path=None, val_path=None):
    if train_path is None:
        train_path = "Data/train_list.txt"
    if val_path is None:
        val_path = "Data/val_list.txt"

    with open(train_path, 'r') as f:
        train_list = f.readlines()
    with open(val_path, 'r') as f:
        val_list = f.readlines()

    # train_list = train_list[-500:]
    # val_list = train_list[:500]
    return train_list, val_list

@click.command()
@click.option('-p', '--config_path', default='./Configs/config.yml', type=str)
def main(config_path):
    config = yaml.safe_load(open(config_path))
    log_dir = config['log_dir']
    if not osp.exists(log_dir): os.mkdir(log_dir)
    shutil.copy(config_path, osp.join(log_dir, osp.basename(config_path)))

    writer = SummaryWriter(log_dir + "/tensorboard")

    # write logs
    file_handler = logging.FileHandler(osp.join(log_dir, 'train.log'))
    file_handler.setLevel(logging.DEBUG)
    file_handler.setFormatter(logging.Formatter('%(levelname)s:%(asctime)s: %(message)s'))
    logger.addHandler(file_handler)

    batch_size = config.get('batch_size', 32)
    device = config.get('device', 'cpu')
    epochs = config.get('epochs', 100)
    save_freq = config.get('save_freq', 10)
    train_path = config.get('train_data', None)
    val_path = config.get('val_data', None)
    num_workers = config.get('num_workers', 8)

    train_list, val_list = get_data_path_list(train_path, val_path)

    train_dataloader = build_dataloader(train_list,
                                        batch_size=batch_size,
                                        num_workers=num_workers,
                                        dataset_config=config.get('dataset_params', {}),
                                        device=device)

    val_dataloader = build_dataloader(val_list,
                                      batch_size=batch_size,
                                      validation=True,
                                      num_workers=num_workers // 2,
                                      device=device,
                                      dataset_config=config.get('dataset_params', {}))

    # define model
    model = JDCNet(num_class=1) # num_class = 1 means regression

    scheduler_params = {
            "max_lr": float(config['optimizer_params'].get('lr', 5e-4)),
            "pct_start": float(config['optimizer_params'].get('pct_start', 0.0)),
            "epochs": epochs,
            "steps_per_epoch": len(train_dataloader),
        }

    model.to(device)
    optimizer, scheduler = build_optimizer(
        {"params": model.parameters(), "optimizer_params":{}, "scheduler_params": scheduler_params})

    criterion = {'l1': nn.SmoothL1Loss(), # F0 loss (regression)
                 'ce':  nn.BCEWithLogitsLoss() # silence loss (binary classification)
                }

    loss_config = config['loss_params']

    trainer = Trainer(model=model,
                        criterion=criterion,
                        optimizer=optimizer,
                        scheduler=scheduler,
                        device=device,
                        train_dataloader=train_dataloader,
                        val_dataloader=val_dataloader,
                        loss_config=loss_config,
                        logger=logger)

    if config.get('pretrained_model', '') != '':
        trainer.load_checkpoint(config['pretrained_model'],
                                load_only_params=config.get('load_only_params', True))

    # compute all F0 for training and validation data
    print('Checking if all F0 data is computed...')
    for _ in enumerate(train_dataloader):
        continue
    for _ in enumerate(val_dataloader):
        continue
    print('All F0 data is computed.')

    for epoch in range(1, epochs+1):
            train_results = trainer._train_epoch()
            eval_results = trainer._eval_epoch()
            results = train_results.copy()
            results.update(eval_results)
            logger.info('--- epoch %d ---' % epoch)
            for key, value in results.items():
                if isinstance(value, float):
                    logger.info('%-15s: %.4f' % (key, value))
                    writer.add_scalar(key, value, epoch)
                else:
                    writer.add_figure(key, (v), epoch)
            if (epoch % save_freq) == 0:
                trainer.save_checkpoint(osp.join(log_dir, 'epoch_%05d.pth' % epoch))

    return 0

if __name__=="__main__":
    main()