examples/nx_mpnet/step_2_train_model.py

#!/usr/bin/python3
# -*- coding: utf-8 -*-
"""
https://github.com/yxlu-0102/MP-SENet/blob/main/train.py
"""
import argparse
import json
import logging
from logging.handlers import TimedRotatingFileHandler
import os
import platform
from pathlib import Path
import random
import sys
import shutil
from typing import List

pwd = os.path.abspath(os.path.dirname(__file__))
sys.path.append(os.path.join(pwd, "../../"))

import numpy as np
import torch
from torch.nn import functional as F
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm

from toolbox.torch.utils.data.dataset.denoise_excel_dataset import DenoiseExcelDataset
from toolbox.torchaudio.models.nx_mpnet.configuration_nx_mpnet import NXMPNetConfig
from toolbox.torchaudio.models.nx_mpnet.discriminator import MetricDiscriminatorPretrainedModel
from toolbox.torchaudio.models.nx_mpnet.modeling_nx_mpnet import NXMPNet, NXMPNetPretrainedModel
from toolbox.torchaudio.models.nx_mpnet.utils import mag_pha_stft, mag_pha_istft
from toolbox.torchaudio.models.nx_mpnet.metrics import run_batch_pesq, run_pesq_score
from toolbox.torchaudio.models.nx_mpnet.loss import phase_losses


def get_args():
    parser = argparse.ArgumentParser()
    parser.add_argument("--train_dataset", default="train.xlsx", type=str)
    parser.add_argument("--valid_dataset", default="valid.xlsx", type=str)

    parser.add_argument("--max_epochs", default=100, type=int)

    parser.add_argument("--num_serialized_models_to_keep", default=10, type=int)
    parser.add_argument("--patience", default=5, type=int)
    parser.add_argument("--serialization_dir", default="serialization_dir", type=str)

    parser.add_argument("--config_file", default="config.yaml", type=str)

    args = parser.parse_args()
    return args


def logging_config(file_dir: str):
    fmt = "%(asctime)s - %(name)s - %(levelname)s  %(filename)s:%(lineno)d >  %(message)s"

    logging.basicConfig(format=fmt,
                        datefmt="%m/%d/%Y %H:%M:%S",
                        level=logging.INFO)
    file_handler = TimedRotatingFileHandler(
        filename=os.path.join(file_dir, "main.log"),
        encoding="utf-8",
        when="D",
        interval=1,
        backupCount=7
    )
    file_handler.setLevel(logging.INFO)
    file_handler.setFormatter(logging.Formatter(fmt))
    logger = logging.getLogger(__name__)
    logger.addHandler(file_handler)

    return logger


class CollateFunction(object):
    def __init__(self):
        pass

    def __call__(self, batch: List[dict]):
        clean_audios = list()
        noisy_audios = list()

        for sample in batch:
            # noise_wave: torch.Tensor = sample["noise_wave"]
            clean_audio: torch.Tensor = sample["speech_wave"]
            noisy_audio: torch.Tensor = sample["mix_wave"]
            # snr_db: float = sample["snr_db"]

            clean_audios.append(clean_audio)
            noisy_audios.append(noisy_audio)

        clean_audios = torch.stack(clean_audios)
        noisy_audios = torch.stack(noisy_audios)

        # assert
        if torch.any(torch.isnan(clean_audios)) or torch.any(torch.isinf(clean_audios)):
            raise AssertionError("nan or inf in clean_audios")
        if torch.any(torch.isnan(noisy_audios)) or torch.any(torch.isinf(noisy_audios)):
            raise AssertionError("nan or inf in noisy_audios")
        return clean_audios, noisy_audios


collate_fn = CollateFunction()


def main():
    args = get_args()

    config = NXMPNetConfig.from_pretrained(
        pretrained_model_name_or_path=args.config_file,
    )

    serialization_dir = Path(args.serialization_dir)
    serialization_dir.mkdir(parents=True, exist_ok=True)

    logger = logging_config(serialization_dir)

    random.seed(config.seed)
    np.random.seed(config.seed)
    torch.manual_seed(config.seed)
    logger.info(f"set seed: {config.seed}")

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    n_gpu = torch.cuda.device_count()
    logger.info(f"GPU available count: {n_gpu}; device: {device}")

    # datasets
    train_dataset = DenoiseExcelDataset(
        excel_file=args.train_dataset,
        expected_sample_rate=8000,
        max_wave_value=32768.0,
    )
    valid_dataset = DenoiseExcelDataset(
        excel_file=args.valid_dataset,
        expected_sample_rate=8000,
        max_wave_value=32768.0,
    )
    train_data_loader = DataLoader(
        dataset=train_dataset,
        batch_size=config.batch_size,
        shuffle=True,
        sampler=None,
        # Linux 系统中可以使用多个子进程加载数据, 而在 Windows 系统中不能.
        num_workers=0 if platform.system() == "Windows" else os.cpu_count() // 2,
        collate_fn=collate_fn,
        pin_memory=False,
        # prefetch_factor=64,
    )
    valid_data_loader = DataLoader(
        dataset=valid_dataset,
        batch_size=config.batch_size,
        shuffle=True,
        sampler=None,
        # Linux 系统中可以使用多个子进程加载数据, 而在 Windows 系统中不能.
        num_workers=0 if platform.system() == "Windows" else os.cpu_count() // 2,
        collate_fn=collate_fn,
        pin_memory=False,
        # prefetch_factor=64,
    )

    # models
    logger.info(f"prepare models. config_file: {args.config_file}")
    generator = NXMPNetPretrainedModel(config).to(device)
    discriminator = MetricDiscriminatorPretrainedModel(config).to(device)

    # optimizer
    logger.info("prepare optimizer, lr_scheduler")
    num_params = 0
    for p in generator.parameters():
        num_params += p.numel()
    logger.info("total parameters (generator): {:.3f}M".format(num_params/1e6))

    optim_g = torch.optim.AdamW(generator.parameters(), config.learning_rate, betas=[config.adam_b1, config.adam_b2])
    optim_d = torch.optim.AdamW(discriminator.parameters(), config.learning_rate, betas=[config.adam_b1, config.adam_b2])

    # resume training
    last_epoch = -1
    for epoch_i in serialization_dir.glob("epoch-*"):
        epoch_i = Path(epoch_i)
        epoch_idx = epoch_i.stem.split("-")[1]
        epoch_idx = int(epoch_idx)
        if epoch_idx > last_epoch:
            last_epoch = epoch_idx

    if last_epoch != -1:
        logger.info(f"resume from epoch-{last_epoch}.")
        generator_pt = serialization_dir / f"epoch-{last_epoch}/generator.pt"
        discriminator_pt = serialization_dir / f"epoch-{last_epoch}/discriminator.pt"
        optim_g_pth = serialization_dir / f"epoch-{last_epoch}/optim_g.pth"
        optim_d_pth = serialization_dir / f"epoch-{last_epoch}/optim_d.pth"

        logger.info(f"load state dict for generator.")
        with open(generator_pt.as_posix(), "rb") as f:
            state_dict = torch.load(f, map_location="cpu", weights_only=True)
        generator.load_state_dict(state_dict, strict=True)
        logger.info(f"load state dict for discriminator.")
        with open(discriminator_pt.as_posix(), "rb") as f:
            state_dict = torch.load(f, map_location="cpu", weights_only=True)
        discriminator.load_state_dict(state_dict, strict=True)

        logger.info(f"load state dict for optim_g.")
        with open(optim_g_pth.as_posix(), "rb") as f:
            state_dict = torch.load(f, map_location="cpu", weights_only=True)
        optim_g.load_state_dict(state_dict)
        logger.info(f"load state dict for optim_d.")
        with open(optim_d_pth.as_posix(), "rb") as f:
            state_dict = torch.load(f, map_location="cpu", weights_only=True)
        optim_d.load_state_dict(state_dict)

    scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=config.lr_decay, last_epoch=last_epoch)
    scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=config.lr_decay, last_epoch=last_epoch)

    # training loop

    # state
    loss_d = 10000000000
    loss_g = 10000000000
    pesq_metric = 10000000000
    mag_err = 10000000000
    pha_err = 10000000000
    com_err = 10000000000
    stft_err = 10000000000

    model_list = list()
    best_idx_epoch = None
    best_metric = None
    patience_count = 0

    logger.info("training")
    for idx_epoch in range(max(0, last_epoch+1), args.max_epochs):
        # train
        generator.train()
        discriminator.train()

        total_loss_d = 0.
        total_loss_g = 0.
        total_batches = 0.
        progress_bar = tqdm(
            total=len(train_data_loader),
            desc="Training; epoch: {}".format(idx_epoch),
        )
        for batch in train_data_loader:
            clean_audio, noisy_audio = batch
            clean_audio = clean_audio.to(device)
            noisy_audio = noisy_audio.to(device)
            one_labels = torch.ones(clean_audio.shape[0]).to(device)

            clean_mag, clean_pha, clean_com = mag_pha_stft(clean_audio, config.n_fft, config.hop_size, config.win_size, config.compress_factor)
            noisy_mag, noisy_pha, noisy_com = mag_pha_stft(noisy_audio, config.n_fft, config.hop_size, config.win_size, config.compress_factor)

            mag_g, pha_g, com_g = generator.forward(noisy_mag, noisy_pha)

            audio_g = mag_pha_istft(mag_g, pha_g, config.n_fft, config.hop_size, config.win_size, config.compress_factor)
            mag_g_hat, pha_g_hat, com_g_hat = mag_pha_stft(audio_g, config.n_fft, config.hop_size, config.win_size, config.compress_factor)

            audio_list_r, audio_list_g = list(clean_audio.cpu().numpy()), list(audio_g.detach().cpu().numpy())
            pesq_score_list: List[float] = run_batch_pesq(audio_list_r, audio_list_g, sample_rate=config.sample_rate, mode="nb")

            # Discriminator
            optim_d.zero_grad()
            metric_r = discriminator.forward(clean_mag, clean_mag)
            metric_g = discriminator.forward(clean_mag, mag_g_hat.detach())
            loss_disc_r = F.mse_loss(one_labels, metric_r.flatten())

            if -1 in pesq_score_list:
                # print("-1 in batch_pesq_score!")
                loss_disc_g = 0
            else:
                pesq_score_list: torch.FloatTensor = torch.tensor([(score - 1) / 3.5 for score in pesq_score_list], dtype=torch.float32)
                loss_disc_g = F.mse_loss(pesq_score_list.to(device), metric_g.flatten())

            loss_disc_all = loss_disc_r + loss_disc_g
            loss_disc_all.backward()
            optim_d.step()

            # Generator
            optim_g.zero_grad()
            # L2 Magnitude Loss
            loss_mag = F.mse_loss(clean_mag, mag_g)
            # Anti-wrapping Phase Loss
            loss_ip, loss_gd, loss_iaf = phase_losses(clean_pha, pha_g)
            loss_pha = loss_ip + loss_gd + loss_iaf
            # L2 Complex Loss
            loss_com = F.mse_loss(clean_com, com_g) * 2
            # L2 Consistency Loss
            loss_stft = F.mse_loss(com_g, com_g_hat) * 2
            # Time Loss
            loss_time = F.l1_loss(clean_audio, audio_g)
            # Metric Loss
            metric_g = discriminator.forward(clean_mag, mag_g_hat)
            loss_metric = F.mse_loss(metric_g.flatten(), one_labels)

            loss_gen_all = loss_mag * 0.9 + loss_pha * 0.3  + loss_com * 0.1 + loss_stft * 0.1 + loss_metric * 0.05 + loss_time * 0.2

            loss_gen_all.backward()
            optim_g.step()

            total_loss_d += loss_disc_all.item()
            total_loss_g += loss_gen_all.item()
            total_batches += 1

            loss_d = round(total_loss_d / total_batches, 4)
            loss_g = round(total_loss_g / total_batches, 4)

            progress_bar.update(1)
            progress_bar.set_postfix({
                "loss_d": loss_d,
                "loss_g": loss_g,
            })

        # evaluation
        generator.eval()
        discriminator.eval()

        torch.cuda.empty_cache()
        total_pesq_score = 0.
        total_mag_err = 0.
        total_pha_err = 0.
        total_com_err = 0.
        total_stft_err = 0.
        total_batches = 0.

        progress_bar = tqdm(
            total=len(valid_data_loader),
            desc="Evaluation; epoch: {}".format(idx_epoch),
        )
        with torch.no_grad():
            for batch in valid_data_loader:
                clean_audio, noisy_audio = batch
                clean_audio = clean_audio.to(device)
                noisy_audio = noisy_audio.to(device)

                clean_mag, clean_pha, clean_com = mag_pha_stft(clean_audio, config.n_fft, config.hop_size, config.win_size, config.compress_factor)
                noisy_mag, noisy_pha, noisy_com = mag_pha_stft(noisy_audio, config.n_fft, config.hop_size, config.win_size, config.compress_factor)

                mag_g, pha_g, com_g = generator.forward(noisy_mag, noisy_pha)

                audio_g = mag_pha_istft(mag_g, pha_g, config.n_fft, config.hop_size, config.win_size, config.compress_factor)
                mag_g_hat, pha_g_hat, com_g_hat = mag_pha_stft(audio_g, config.n_fft, config.hop_size, config.win_size, config.compress_factor)

                clean_audio_list = torch.split(clean_audio, 1, dim=0)
                enhanced_audio_list = torch.split(audio_g, 1, dim=0)
                clean_audio_list = [t.squeeze().cpu().numpy() for t in clean_audio_list]
                enhanced_audio_list = [t.squeeze().cpu().numpy() for t in enhanced_audio_list]
                pesq_score = run_pesq_score(
                    clean_audio_list,
                    enhanced_audio_list,
                    sample_rate = config.sample_rate,
                    mode = "nb",
                )
                total_pesq_score += pesq_score
                total_mag_err += F.mse_loss(clean_mag, mag_g).item()
                val_ip_err, val_gd_err, val_iaf_err = phase_losses(clean_pha, pha_g)
                total_pha_err += (val_ip_err + val_gd_err + val_iaf_err).item()
                total_com_err += F.mse_loss(clean_com, com_g).item()
                total_stft_err += F.mse_loss(com_g, com_g_hat).item()

                total_batches += 1

                pesq_metric = round(total_pesq_score / total_batches, 4)
                mag_err = round(total_mag_err / total_batches, 4)
                pha_err = round(total_pha_err / total_batches, 4)
                com_err = round(total_com_err / total_batches, 4)
                stft_err = round(total_stft_err / total_batches, 4)

                progress_bar.update(1)
                progress_bar.set_postfix({
                    "pesq_metric": pesq_metric,
                    "mag_err": mag_err,
                    "pha_err": pha_err,
                    "com_err": com_err,
                    "stft_err": stft_err,
                })

        # scheduler
        scheduler_g.step()
        scheduler_d.step()

        # save path
        epoch_dir = serialization_dir / "epoch-{}".format(idx_epoch)
        epoch_dir.mkdir(parents=True, exist_ok=False)

        # save models
        generator.save_pretrained(epoch_dir.as_posix())
        discriminator.save_pretrained(epoch_dir.as_posix())

        # save optim
        torch.save(optim_d.state_dict(), (epoch_dir / "optim_d.pth").as_posix())
        torch.save(optim_g.state_dict(), (epoch_dir / "optim_g.pth").as_posix())

        model_list.append(epoch_dir)
        if len(model_list) >= args.num_serialized_models_to_keep:
            model_to_delete: Path = model_list.pop(0)
            shutil.rmtree(model_to_delete.as_posix())

        # save metric
        if best_metric is None:
            best_idx_epoch = idx_epoch
            best_metric = pesq_metric
        elif pesq_metric > best_metric:
            # great is better.
            best_idx_epoch = idx_epoch
            best_metric = pesq_metric
        else:
            pass

        metrics = {
            "idx_epoch": idx_epoch,
            "best_idx_epoch": best_idx_epoch,
            "loss_d": loss_d,
            "loss_g": loss_g,

            "pesq_metric": pesq_metric,
            "mag_err": mag_err,
            "pha_err": pha_err,
            "com_err": com_err,
            "stft_err": stft_err,

        }
        metrics_filename = epoch_dir / "metrics_epoch.json"
        with open(metrics_filename, "w", encoding="utf-8") as f:
            json.dump(metrics, f, indent=4, ensure_ascii=False)

        # save best
        best_dir = serialization_dir / "best"
        if best_idx_epoch == idx_epoch:
            if best_dir.exists():
                shutil.rmtree(best_dir)
            shutil.copytree(epoch_dir, best_dir)

        # early stop
        early_stop_flag = False
        if best_idx_epoch == idx_epoch:
            patience_count = 0
        else:
            patience_count += 1
        if patience_count >= args.patience:
            early_stop_flag = True

        # early stop
        if early_stop_flag:
            break

    return


if __name__ == "__main__":
    main()