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from meldataset import build_dataloader
from optimizers import build_optimizer
from utils import *
from models import build_model
from trainer import Trainer
import os
import os.path as osp
import re
import sys
import yaml
import shutil
import numpy as np
import torch
from torch.utils.tensorboard import SummaryWriter
import click
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
@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', 10)
device = config.get('device', 'cpu')
epochs = config.get('epochs', 1000)
save_freq = config.get('save_freq', 20)
train_path = config.get('train_data', None)
val_path = config.get('val_data', None)
train_list, val_list = get_data_path_list(train_path, val_path)
train_dataloader = build_dataloader(train_list,
batch_size=batch_size,
num_workers=8,
dataset_config=config.get('dataset_params', {}),
device=device)
val_dataloader = build_dataloader(val_list,
batch_size=batch_size,
validation=True,
num_workers=2,
device=device,
dataset_config=config.get('dataset_params', {}))
model = build_model(model_params=config['model_params'] or {})
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})
blank_index = train_dataloader.dataset.text_cleaner.word_index_dictionary[" "] # get blank index
criterion = build_criterion(critic_params={
'ctc': {'blank': blank_index},
})
trainer = Trainer(model=model,
criterion=criterion,
optimizer=optimizer,
scheduler=scheduler,
device=device,
train_dataloader=train_dataloader,
val_dataloader=val_dataloader,
logger=logger)
if config.get('pretrained_model', '') != '':
trainer.load_checkpoint(config['pretrained_model'],
load_only_params=config.get('load_only_params', True))
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:
for v in value:
writer.add_figure('eval_attn', plot_image(v), epoch)
if (epoch % save_freq) == 0:
trainer.save_checkpoint(osp.join(log_dir, 'epoch_%05d.pth' % epoch))
return 0
if __name__=="__main__":
main() |