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9a964a6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 | 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() |