Delete s2_train.py

s2_train.py

@@ -1,601 +0,0 @@
-import warnings
-warnings.filterwarnings("ignore")
-import utils, os
-hps = utils.get_hparams(stage=2)
-os.environ["CUDA_VISIBLE_DEVICES"] = hps.train.gpu_numbers.replace("-", ",")
-import torch
-from torch.nn import functional as F
-from torch.utils.data import DataLoader
-from torch.utils.tensorboard import SummaryWriter
-import torch.multiprocessing as mp
-import torch.distributed as dist, traceback
-from torch.nn.parallel import DistributedDataParallel as DDP
-from torch.cuda.amp import autocast, GradScaler
-from tqdm import tqdm
-import logging, traceback
-
-logging.getLogger("matplotlib").setLevel(logging.INFO)
-logging.getLogger("h5py").setLevel(logging.INFO)
-logging.getLogger("numba").setLevel(logging.INFO)
-from random import randint
-from module import commons
-
-from module.data_utils import (
-    TextAudioSpeakerLoader,
-    TextAudioSpeakerCollate,
-    DistributedBucketSampler,
-)
-from module.models import (
-    SynthesizerTrn,
-    MultiPeriodDiscriminator,
-)
-from module.losses import generator_loss, discriminator_loss, feature_loss, kl_loss
-from module.mel_processing import mel_spectrogram_torch, spec_to_mel_torch
-from process_ckpt import savee
-
-torch.backends.cudnn.benchmark = False
-torch.backends.cudnn.deterministic = False
-###反正A100fp32更快，那试试tf32吧
-torch.backends.cuda.matmul.allow_tf32 = True
-torch.backends.cudnn.allow_tf32 = True
-torch.set_float32_matmul_precision("medium")  # 最低精度但最快（也就快一丁点），对于结果造成不了影响
-# from config import pretrained_s2G,pretrained_s2D
-global_step = 0
-
-device = "cpu"  # cuda以外的设备，等mps优化后加入
-
-
-def main():
-
-    if torch.cuda.is_available():
-        n_gpus = torch.cuda.device_count()
-    else:
-        n_gpus = 1
-    os.environ["MASTER_ADDR"] = "localhost"
-    os.environ["MASTER_PORT"] = str(randint(20000, 55555))
-
-    mp.spawn(
-        run,
-        nprocs=n_gpus,
-        args=(
-            n_gpus,
-            hps,
-        ),
-    )
-
-
-def run(rank, n_gpus, hps):
-    global global_step
-    if rank == 0:
-        logger = utils.get_logger(hps.data.exp_dir)
-        logger.info(hps)
-        # utils.check_git_hash(hps.s2_ckpt_dir)
-        writer = SummaryWriter(log_dir=hps.s2_ckpt_dir)
-        writer_eval = SummaryWriter(log_dir=os.path.join(hps.s2_ckpt_dir, "eval"))
-
-    dist.init_process_group(
-        backend = "gloo" if os.name == "nt" or not torch.cuda.is_available() else "nccl",
-        init_method="env://",
-        world_size=n_gpus,
-        rank=rank,
-    )
-    torch.manual_seed(hps.train.seed)
-    if torch.cuda.is_available():
-        torch.cuda.set_device(rank)
-
-    train_dataset = TextAudioSpeakerLoader(hps.data)  ########
-    train_sampler = DistributedBucketSampler(
-        train_dataset,
-        hps.train.batch_size,
-        [
-            32,
-            300,
-            400,
-            500,
-            600,
-            700,
-            800,
-            900,
-            1000,
-            1100,
-            1200,
-            1300,
-            1400,
-            1500,
-            1600,
-            1700,
-            1800,
-            1900,
-        ],
-        num_replicas=n_gpus,
-        rank=rank,
-        shuffle=True,
-    )
-    collate_fn = TextAudioSpeakerCollate()
-    train_loader = DataLoader(
-        train_dataset,
-        num_workers=6,
-        shuffle=False,
-        pin_memory=True,
-        collate_fn=collate_fn,
-        batch_sampler=train_sampler,
-        persistent_workers=True,
-        prefetch_factor=4,
-    )
-    # if rank == 0:
-    #     eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps.data, val=True)
-    #     eval_loader = DataLoader(eval_dataset, num_workers=0, shuffle=False,
-    #                              batch_size=1, pin_memory=True,
-    #                              drop_last=False, collate_fn=collate_fn)
-
-    net_g = SynthesizerTrn(
-        hps.data.filter_length // 2 + 1,
-        hps.train.segment_size // hps.data.hop_length,
-        n_speakers=hps.data.n_speakers,
-        **hps.model,
-    ).cuda(rank) if torch.cuda.is_available() else SynthesizerTrn(
-        hps.data.filter_length // 2 + 1,
-        hps.train.segment_size // hps.data.hop_length,
-        n_speakers=hps.data.n_speakers,
-        **hps.model,
-    ).to(device)
-
-    net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank) if torch.cuda.is_available() else MultiPeriodDiscriminator(hps.model.use_spectral_norm).to(device)
-    for name, param in net_g.named_parameters():
-        if not param.requires_grad:
-            print(name, "not requires_grad")
-
-    te_p = list(map(id, net_g.enc_p.text_embedding.parameters()))
-    et_p = list(map(id, net_g.enc_p.encoder_text.parameters()))
-    mrte_p = list(map(id, net_g.enc_p.mrte.parameters()))
-    base_params = filter(
-        lambda p: id(p) not in te_p + et_p + mrte_p and p.requires_grad,
-        net_g.parameters(),
-    )
-
-    # te_p=net_g.enc_p.text_embedding.parameters()
-    # et_p=net_g.enc_p.encoder_text.parameters()
-    # mrte_p=net_g.enc_p.mrte.parameters()
-
-    optim_g = torch.optim.AdamW(
-        # filter(lambda p: p.requires_grad, net_g.parameters()),###默认所有层lr一致
-        [
-            {"params": base_params, "lr": hps.train.learning_rate},
-            {
-                "params": net_g.enc_p.text_embedding.parameters(),
-                "lr": hps.train.learning_rate * hps.train.text_low_lr_rate,
-            },
-            {
-                "params": net_g.enc_p.encoder_text.parameters(),
-                "lr": hps.train.learning_rate * hps.train.text_low_lr_rate,
-            },
-            {
-                "params": net_g.enc_p.mrte.parameters(),
-                "lr": hps.train.learning_rate * hps.train.text_low_lr_rate,
-            },
-        ],
-        hps.train.learning_rate,
-        betas=hps.train.betas,
-        eps=hps.train.eps,
-    )
-    optim_d = torch.optim.AdamW(
-        net_d.parameters(),
-        hps.train.learning_rate,
-        betas=hps.train.betas,
-        eps=hps.train.eps,
-    )
-    if torch.cuda.is_available():
-        net_g = DDP(net_g, device_ids=[rank], find_unused_parameters=True)
-        net_d = DDP(net_d, device_ids=[rank], find_unused_parameters=True)
-    else:
-        net_g = net_g.to(device)
-        net_d = net_d.to(device)
-
-    try:  # 如果能加载自动resume
-        _, _, _, epoch_str = utils.load_checkpoint(
-            utils.latest_checkpoint_path("%s/logs_s2" % hps.data.exp_dir, "D_*.pth"),
-            net_d,
-            optim_d,
-        )  # D多半加载没事
-        if rank == 0:
-            logger.info("loaded D")
-        # _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g, optim_g,load_opt=0)
-        _, _, _, epoch_str = utils.load_checkpoint(
-            utils.latest_checkpoint_path("%s/logs_s2" % hps.data.exp_dir, "G_*.pth"),
-            net_g,
-            optim_g,
-        )
-        global_step = (epoch_str - 1) * len(train_loader)
-        # epoch_str = 1
-        # global_step = 0
-    except:  # 如果首次不能加载，加载pretrain
-        # traceback.print_exc()
-        epoch_str = 1
-        global_step = 0
-        if hps.train.pretrained_s2G != ""and hps.train.pretrained_s2G != None and os.path.exists(hps.train.pretrained_s2G):
-            if rank == 0:
-                logger.info("loaded pretrained %s" % hps.train.pretrained_s2G)
-            print(
-                net_g.module.load_state_dict(
-                    torch.load(hps.train.pretrained_s2G, map_location="cpu")["weight"],
-                    strict=False,
-                ) if torch.cuda.is_available() else net_g.load_state_dict(
-                    torch.load(hps.train.pretrained_s2G, map_location="cpu")["weight"],
-                    strict=False,
-                )
-            )  ##测试不加载优化器
-        if hps.train.pretrained_s2D != ""and hps.train.pretrained_s2D != None and os.path.exists(hps.train.pretrained_s2D):
-            if rank == 0:
-                logger.info("loaded pretrained %s" % hps.train.pretrained_s2D)
-            print(
-                net_d.module.load_state_dict(
-                    torch.load(hps.train.pretrained_s2D, map_location="cpu")["weight"]
-                ) if torch.cuda.is_available() else net_d.load_state_dict(
-                    torch.load(hps.train.pretrained_s2D, map_location="cpu")["weight"]
-                )
-            )
-
-    # scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2)
-    # scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2)
-
-    scheduler_g = torch.optim.lr_scheduler.ExponentialLR(
-        optim_g, gamma=hps.train.lr_decay, last_epoch=-1
-    )
-    scheduler_d = torch.optim.lr_scheduler.ExponentialLR(
-        optim_d, gamma=hps.train.lr_decay, last_epoch=-1
-    )
-    for _ in range(epoch_str):
-        scheduler_g.step()
-        scheduler_d.step()
-
-    scaler = GradScaler(enabled=hps.train.fp16_run)
-
-    for epoch in range(epoch_str, hps.train.epochs + 1):
-        if rank == 0:
-            train_and_evaluate(
-                rank,
-                epoch,
-                hps,
-                [net_g, net_d],
-                [optim_g, optim_d],
-                [scheduler_g, scheduler_d],
-                scaler,
-                # [train_loader, eval_loader], logger, [writer, writer_eval])
-                [train_loader, None],
-                logger,
-                [writer, writer_eval],
-            )
-        else:
-            train_and_evaluate(
-                rank,
-                epoch,
-                hps,
-                [net_g, net_d],
-                [optim_g, optim_d],
-                [scheduler_g, scheduler_d],
-                scaler,
-                [train_loader, None],
-                None,
-                None,
-            )
-        scheduler_g.step()
-        scheduler_d.step()
-
-
-def train_and_evaluate(
-    rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers
-):
-    net_g, net_d = nets
-    optim_g, optim_d = optims
-    # scheduler_g, scheduler_d = schedulers
-    train_loader, eval_loader = loaders
-    if writers is not None:
-        writer, writer_eval = writers
-
-    train_loader.batch_sampler.set_epoch(epoch)
-    global global_step
-
-    net_g.train()
-    net_d.train()
-    for batch_idx, (
-        ssl,
-        ssl_lengths,
-        spec,
-        spec_lengths,
-        y,
-        y_lengths,
-        text,
-        text_lengths,
-    ) in enumerate(tqdm(train_loader)):
-        if torch.cuda.is_available():
-            spec, spec_lengths = spec.cuda(rank, non_blocking=True), spec_lengths.cuda(
-                rank, non_blocking=True
-            )
-            y, y_lengths = y.cuda(rank, non_blocking=True), y_lengths.cuda(
-                rank, non_blocking=True
-            )
-            ssl = ssl.cuda(rank, non_blocking=True)
-            ssl.requires_grad = False
-            # ssl_lengths = ssl_lengths.cuda(rank, non_blocking=True)
-            text, text_lengths = text.cuda(rank, non_blocking=True), text_lengths.cuda(
-                rank, non_blocking=True
-            )
-        else:
-            spec, spec_lengths = spec.to(device), spec_lengths.to(device)
-            y, y_lengths = y.to(device), y_lengths.to(device)
-            ssl = ssl.to(device)
-            ssl.requires_grad = False
-            # ssl_lengths = ssl_lengths.cuda(rank, non_blocking=True)
-            text, text_lengths = text.to(device), text_lengths.to(device)
-
-        with autocast(enabled=hps.train.fp16_run):
-            (
-                y_hat,
-                kl_ssl,
-                ids_slice,
-                x_mask,
-                z_mask,
-                (z, z_p, m_p, logs_p, m_q, logs_q),
-                stats_ssl,
-            ) = net_g(ssl, spec, spec_lengths, text, text_lengths)
-
-            mel = spec_to_mel_torch(
-                spec,
-                hps.data.filter_length,
-                hps.data.n_mel_channels,
-                hps.data.sampling_rate,
-                hps.data.mel_fmin,
-                hps.data.mel_fmax,
-            )
-            y_mel = commons.slice_segments(
-                mel, ids_slice, hps.train.segment_size // hps.data.hop_length
-            )
-            y_hat_mel = mel_spectrogram_torch(
-                y_hat.squeeze(1),
-                hps.data.filter_length,
-                hps.data.n_mel_channels,
-                hps.data.sampling_rate,
-                hps.data.hop_length,
-                hps.data.win_length,
-                hps.data.mel_fmin,
-                hps.data.mel_fmax,
-            )
-
-            y = commons.slice_segments(
-                y, ids_slice * hps.data.hop_length, hps.train.segment_size
-            )  # slice
-
-            # Discriminator
-            y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach())
-            with autocast(enabled=False):
-                loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(
-                    y_d_hat_r, y_d_hat_g
-                )
-                loss_disc_all = loss_disc
-        optim_d.zero_grad()
-        scaler.scale(loss_disc_all).backward()
-        scaler.unscale_(optim_d)
-        grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None)
-        scaler.step(optim_d)
-
-        with autocast(enabled=hps.train.fp16_run):
-            # Generator
-            y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat)
-            with autocast(enabled=False):
-                loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel
-                loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl
-
-                loss_fm = feature_loss(fmap_r, fmap_g)
-                loss_gen, losses_gen = generator_loss(y_d_hat_g)
-                loss_gen_all = loss_gen + loss_fm + loss_mel + kl_ssl * 1 + loss_kl
-
-        optim_g.zero_grad()
-        scaler.scale(loss_gen_all).backward()
-        scaler.unscale_(optim_g)
-        grad_norm_g = commons.clip_grad_value_(net_g.parameters(), None)
-        scaler.step(optim_g)
-        scaler.update()
-
-        if rank == 0:
-            if global_step % hps.train.log_interval == 0:
-                lr = optim_g.param_groups[0]["lr"]
-                losses = [loss_disc, loss_gen, loss_fm, loss_mel, kl_ssl, loss_kl]
-                logger.info(
-                    "Train Epoch: {} [{:.0f}%]".format(
-                        epoch, 100.0 * batch_idx / len(train_loader)
-                    )
-                )
-                logger.info([x.item() for x in losses] + [global_step, lr])
-
-                scalar_dict = {
-                    "loss/g/total": loss_gen_all,
-                    "loss/d/total": loss_disc_all,
-                    "learning_rate": lr,
-                    "grad_norm_d": grad_norm_d,
-                    "grad_norm_g": grad_norm_g,
-                }
-                scalar_dict.update(
-                    {
-                        "loss/g/fm": loss_fm,
-                        "loss/g/mel": loss_mel,
-                        "loss/g/kl_ssl": kl_ssl,
-                        "loss/g/kl": loss_kl,
-                    }
-                )
-
-                # scalar_dict.update({"loss/g/{}".format(i): v for i, v in enumerate(losses_gen)})
-                # scalar_dict.update({"loss/d_r/{}".format(i): v for i, v in enumerate(losses_disc_r)})
-                # scalar_dict.update({"loss/d_g/{}".format(i): v for i, v in enumerate(losses_disc_g)})
-                image_dict = {
-                    "slice/mel_org": utils.plot_spectrogram_to_numpy(
-                        y_mel[0].data.cpu().numpy()
-                    ),
-                    "slice/mel_gen": utils.plot_spectrogram_to_numpy(
-                        y_hat_mel[0].data.cpu().numpy()
-                    ),
-                    "all/mel": utils.plot_spectrogram_to_numpy(
-                        mel[0].data.cpu().numpy()
-                    ),
-                    "all/stats_ssl": utils.plot_spectrogram_to_numpy(
-                        stats_ssl[0].data.cpu().numpy()
-                    ),
-                }
-                utils.summarize(
-                    writer=writer,
-                    global_step=global_step,
-                    images=image_dict,
-                    scalars=scalar_dict,
-                )
-        global_step += 1
-    if epoch % hps.train.save_every_epoch == 0 and rank == 0:
-        if hps.train.if_save_latest == 0:
-            utils.save_checkpoint(
-                net_g,
-                optim_g,
-                hps.train.learning_rate,
-                epoch,
-                os.path.join(
-                    "%s/logs_s2" % hps.data.exp_dir, "G_{}.pth".format(global_step)
-                ),
-            )
-            utils.save_checkpoint(
-                net_d,
-                optim_d,
-                hps.train.learning_rate,
-                epoch,
-                os.path.join(
-                    "%s/logs_s2" % hps.data.exp_dir, "D_{}.pth".format(global_step)
-                ),
-            )
-        else:
-            utils.save_checkpoint(
-                net_g,
-                optim_g,
-                hps.train.learning_rate,
-                epoch,
-                os.path.join(
-                    "%s/logs_s2" % hps.data.exp_dir, "G_{}.pth".format(233333333333)
-                ),
-            )
-            utils.save_checkpoint(
-                net_d,
-                optim_d,
-                hps.train.learning_rate,
-                epoch,
-                os.path.join(
-                    "%s/logs_s2" % hps.data.exp_dir, "D_{}.pth".format(233333333333)
-                ),
-            )
-        if rank == 0 and hps.train.if_save_every_weights == True:
-            if hasattr(net_g, "module"):
-                ckpt = net_g.module.state_dict()
-            else:
-                ckpt = net_g.state_dict()
-            logger.info(
-                "saving ckpt %s_e%s:%s"
-                % (
-                    hps.name,
-                    epoch,
-                    savee(
-                        ckpt,
-                        hps.name + "_e%s_s%s" % (epoch, global_step),
-                        epoch,
-                        global_step,
-                        hps,
-                    ),
-                )
-            )
-
-    if rank == 0:
-        logger.info("====> Epoch: {}".format(epoch))
-
-
-def evaluate(hps, generator, eval_loader, writer_eval):
-    generator.eval()
-    image_dict = {}
-    audio_dict = {}
-    print("Evaluating ...")
-    with torch.no_grad():
-        for batch_idx, (
-            ssl,
-            ssl_lengths,
-            spec,
-            spec_lengths,
-            y,
-            y_lengths,
-            text,
-            text_lengths,
-        ) in enumerate(eval_loader):
-            print(111)
-            if torch.cuda.is_available():
-                spec, spec_lengths = spec.cuda(), spec_lengths.cuda()
-                y, y_lengths = y.cuda(), y_lengths.cuda()
-                ssl = ssl.cuda()
-                text, text_lengths = text.cuda(), text_lengths.cuda()
-            else:
-                spec, spec_lengths = spec.to(device), spec_lengths.to(device)
-                y, y_lengths = y.to(device), y_lengths.to(device)
-                ssl = ssl.to(device)
-                text, text_lengths = text.to(device), text_lengths.to(device)
-            for test in [0, 1]:
-                y_hat, mask, *_ = generator.module.infer(
-                    ssl, spec, spec_lengths, text, text_lengths, test=test
-                ) if torch.cuda.is_available() else generator.infer(
-                    ssl, spec, spec_lengths, text, text_lengths, test=test
-                )
-                y_hat_lengths = mask.sum([1, 2]).long() * hps.data.hop_length
-
-                mel = spec_to_mel_torch(
-                    spec,
-                    hps.data.filter_length,
-                    hps.data.n_mel_channels,
-                    hps.data.sampling_rate,
-                    hps.data.mel_fmin,
-                    hps.data.mel_fmax,
-                )
-                y_hat_mel = mel_spectrogram_torch(
-                    y_hat.squeeze(1).float(),
-                    hps.data.filter_length,
-                    hps.data.n_mel_channels,
-                    hps.data.sampling_rate,
-                    hps.data.hop_length,
-                    hps.data.win_length,
-                    hps.data.mel_fmin,
-                    hps.data.mel_fmax,
-                )
-                image_dict.update(
-                    {
-                        f"gen/mel_{batch_idx}_{test}": utils.plot_spectrogram_to_numpy(
-                            y_hat_mel[0].cpu().numpy()
-                        )
-                    }
-                )
-                audio_dict.update(
-                    {f"gen/audio_{batch_idx}_{test}": y_hat[0, :, : y_hat_lengths[0]]}
-                )
-                image_dict.update(
-                    {
-                        f"gt/mel_{batch_idx}": utils.plot_spectrogram_to_numpy(
-                            mel[0].cpu().numpy()
-                        )
-                    }
-                )
-                audio_dict.update({f"gt/audio_{batch_idx}": y[0, :, : y_lengths[0]]})
-
-        # y_hat, mask, *_ = generator.module.infer(ssl, spec_lengths, speakers, y=None)
-        # audio_dict.update({
-        #     f"gen/audio_{batch_idx}_style_pred": y_hat[0, :, :]
-        # })
-
-    utils.summarize(
-        writer=writer_eval,
-        global_step=global_step,
-        images=image_dict,
-        audios=audio_dict,
-        audio_sampling_rate=hps.data.sampling_rate,
-    )
-    generator.train()
-
-
-if __name__ == "__main__":
-    main()