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| # 与 Encodec_24k_240d main3_ddp.py 相比只有鉴别器不同 | |
| import argparse | |
| import itertools | |
| import os | |
| import time | |
| import torch | |
| import torch.distributed as dist | |
| from academicodec.models.encodec.distributed.launch import launch | |
| from academicodec.models.encodec.msstftd import MultiScaleSTFTDiscriminator | |
| from academicodec.models.encodec.net3 import SoundStream | |
| from academicodec.models.soundstream.dataset import NSynthDataset | |
| from academicodec.models.soundstream.loss import criterion_d | |
| from academicodec.models.soundstream.loss import criterion_g | |
| from academicodec.models.soundstream.loss import loss_dis | |
| from academicodec.models.soundstream.loss import loss_g | |
| from academicodec.models.soundstream.models import MultiPeriodDiscriminator | |
| from academicodec.models.soundstream.models import MultiScaleDiscriminator | |
| from academicodec.utils import Logger | |
| from academicodec.utils import seed_everything | |
| from torch.nn.parallel import DistributedDataParallel as DDP | |
| from tqdm import tqdm | |
| NODE_RANK = os.environ['INDEX'] if 'INDEX' in os.environ else 0 | |
| NODE_RANK = int(NODE_RANK) | |
| MASTER_ADDR, MASTER_PORT = (os.environ['CHIEF_IP'], | |
| 22275) if 'CHIEF_IP' in os.environ else ( | |
| "127.0.0.1", 29500) | |
| MASTER_PORT = int(MASTER_PORT) | |
| DIST_URL = 'tcp://%s:%s' % (MASTER_ADDR, MASTER_PORT) | |
| NUM_NODE = os.environ['HOST_NUM'] if 'HOST_NUM' in os.environ else 1 | |
| def getModelSize(model): | |
| param_size = 0 | |
| param_sum = 0 | |
| for param in model.parameters(): | |
| param_size += param.nelement() * param.element_size() | |
| param_sum += param.nelement() | |
| buffer_size = 0 | |
| buffer_sum = 0 | |
| for buffer in model.buffers(): | |
| buffer_size += buffer.nelement() * buffer.element_size() | |
| buffer_sum += buffer.nelement() | |
| all_size = (param_size + buffer_size) / 1024 / 1024 | |
| print('模型总大小为：{:.3f}MB'.format(all_size)) | |
| return (param_size, param_sum, buffer_size, buffer_sum, all_size) | |
| def get_args(): | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument( | |
| '--num_node', | |
| type=int, | |
| default=NUM_NODE, | |
| help='number of nodes for distributed training') | |
| parser.add_argument( | |
| '--ngpus_per_node', | |
| type=int, | |
| default=8, | |
| help='number of gpu on one node') | |
| parser.add_argument( | |
| '--node_rank', | |
| type=int, | |
| default=NODE_RANK, | |
| help='node rank for distributed training') | |
| parser.add_argument( | |
| '--dist_url', | |
| type=str, | |
| default=DIST_URL, | |
| help='url used to set up distributed training') | |
| parser.add_argument( | |
| '--gpu', | |
| type=int, | |
| default=None, | |
| help='GPU id to use. If given, only the specific gpu will be' | |
| ' used, and ddp will be disabled') | |
| parser.add_argument( | |
| '--local_rank', | |
| default=-1, | |
| type=int, | |
| help='node rank for distributed training') | |
| # args for random | |
| parser.add_argument( | |
| '--seed', | |
| type=int, | |
| default=None, | |
| help='seed for initializing training. ') | |
| parser.add_argument( | |
| '--cudnn_deterministic', | |
| action='store_true', | |
| help='set cudnn.deterministic True') | |
| parser.add_argument( | |
| '--tensorboard', | |
| action='store_true', | |
| help='use tensorboard for logging') | |
| # args for training | |
| parser.add_argument( | |
| '--LAMBDA_ADV', | |
| type=float, | |
| default=1, | |
| help='hyper-parameter for adver loss') | |
| parser.add_argument( | |
| '--LAMBDA_FEAT', | |
| type=float, | |
| default=1, | |
| help='hyper-parameter for feat loss') | |
| parser.add_argument( | |
| '--LAMBDA_REC', | |
| type=float, | |
| default=1, | |
| help='hyper-parameter for rec loss') | |
| parser.add_argument( | |
| '--LAMBDA_COM', | |
| type=float, | |
| default=1000, | |
| help='hyper-parameter for commit loss') | |
| parser.add_argument( | |
| '--N_EPOCHS', type=int, default=100, help='Total training epoch') | |
| parser.add_argument( | |
| '--st_epoch', type=int, default=0, help='start training epoch') | |
| parser.add_argument( | |
| '--global_step', type=int, default=0, help='record the global step') | |
| parser.add_argument('--discriminator_iter_start', type=int, default=500) | |
| parser.add_argument('--BATCH_SIZE', type=int, default=2, help='batch size') | |
| parser.add_argument( | |
| '--PATH', | |
| type=str, | |
| default='model_path/', | |
| help='The path to save the model') | |
| parser.add_argument('--sr', type=int, default=24000, help='sample rate') | |
| parser.add_argument( | |
| '--print_freq', type=int, default=10, help='the print number') | |
| parser.add_argument( | |
| '--save_dir', type=str, default='log', help='log save path') | |
| parser.add_argument( | |
| '--train_data_path', | |
| type=str, | |
| default='path_to_wavs', | |
| help='training data') | |
| parser.add_argument( | |
| '--valid_data_path', | |
| type=str, | |
| default='path_to_val_wavs', | |
| help='training data') | |
| parser.add_argument( | |
| '--resume', action='store_true', help='whether re-train model') | |
| parser.add_argument( | |
| '--resume_path', type=str, default='path_to_resume', help='resume_path') | |
| parser.add_argument( | |
| '--ratios', | |
| type=int, | |
| nargs='+', | |
| # probs(ratios) = hop_size | |
| default=[8, 5, 4, 2], | |
| help='ratios of SoundStream, shoud be set for different hop_size (32d, 320, 240d, ...)' | |
| ) | |
| parser.add_argument( | |
| '--target_bandwidths', | |
| type=float, | |
| nargs='+', | |
| # default for 16k_320d | |
| default=[1, 1.5, 2, 4, 6, 12], | |
| help='target_bandwidths of net3.py') | |
| args = parser.parse_args() | |
| time_str = time.strftime('%Y-%m-%d-%H-%M') | |
| if args.resume: | |
| args.PATH = args.resume_path # direcly use the old model path | |
| else: | |
| args.PATH = os.path.join(args.PATH, time_str) | |
| args.save_dir = os.path.join(args.save_dir, time_str) | |
| os.makedirs(args.PATH, exist_ok=True) | |
| return args | |
| def get_input(x): | |
| x = x.to(memory_format=torch.contiguous_format) | |
| return x.float() | |
| def main(): | |
| args = get_args() | |
| if args.seed is not None or args.cudnn_deterministic: | |
| seed_everything(args.seed, args.cudnn_deterministic) | |
| if args.num_node == 1: | |
| args.dist_url == "auto" | |
| else: | |
| assert args.num_node > 1 | |
| args.ngpus_per_node = torch.cuda.device_count() | |
| args.world_size = args.ngpus_per_node * args.num_node # | |
| launch( | |
| main_worker, | |
| args.ngpus_per_node, | |
| args.num_node, | |
| args.node_rank, | |
| args.dist_url, | |
| args=(args, )) | |
| def main_worker(local_rank, args): | |
| args.local_rank = local_rank | |
| args.global_rank = args.local_rank + args.node_rank * args.ngpus_per_node | |
| args.distributed = args.world_size > 1 | |
| #CUDA_VISIBLE_DEVICES = int(args.local_rank) | |
| logger = Logger(args) | |
| # 240倍下采 | |
| soundstream = SoundStream(n_filters=32, D=512, ratios=args.ratios) | |
| msd = MultiScaleDiscriminator() | |
| mpd = MultiPeriodDiscriminator() | |
| #print('soundstream ', soundstream) | |
| # assert 1==2 | |
| stft_disc = MultiScaleSTFTDiscriminator(filters=32) | |
| getModelSize(soundstream) | |
| getModelSize(msd) | |
| getModelSize(mpd) | |
| getModelSize(stft_disc) | |
| if args.distributed: | |
| soundstream = torch.nn.SyncBatchNorm.convert_sync_batchnorm(soundstream) | |
| stft_disc = torch.nn.SyncBatchNorm.convert_sync_batchnorm(stft_disc) | |
| msd = torch.nn.SyncBatchNorm.convert_sync_batchnorm(msd) | |
| mpd = torch.nn.SyncBatchNorm.convert_sync_batchnorm(mpd) | |
| # torch.distributed.barrier() | |
| args.device = torch.device('cuda', args.local_rank) | |
| soundstream.to(args.device) | |
| stft_disc.to(args.device) | |
| msd.to(args.device) | |
| mpd.to(args.device) | |
| if args.distributed: | |
| soundstream = DDP( | |
| soundstream, | |
| device_ids=[args.local_rank], | |
| find_unused_parameters=True | |
| ) # device_ids=[args.local_rank], output_device=args.local_rank | |
| stft_disc = DDP(stft_disc, | |
| device_ids=[args.local_rank], | |
| find_unused_parameters=True) | |
| msd = DDP(msd, | |
| device_ids=[args.local_rank], | |
| find_unused_parameters=True) | |
| mpd = DDP(mpd, | |
| device_ids=[args.local_rank], | |
| find_unused_parameters=True) | |
| train_dataset = NSynthDataset(audio_dir=args.train_data_path) | |
| valid_dataset = NSynthDataset(audio_dir=args.valid_data_path) | |
| args.sr = train_dataset.sr | |
| if args.distributed: | |
| train_sampler = torch.utils.data.distributed.DistributedSampler( | |
| train_dataset, drop_last=True, shuffle=True) | |
| valid_sampler = torch.utils.data.distributed.DistributedSampler( | |
| valid_dataset) | |
| else: | |
| train_sampler = None | |
| valid_sampler = None | |
| train_loader = torch.utils.data.DataLoader( | |
| train_dataset, | |
| batch_size=args.BATCH_SIZE, | |
| num_workers=8, | |
| sampler=train_sampler) | |
| valid_loader = torch.utils.data.DataLoader( | |
| valid_dataset, | |
| batch_size=args.BATCH_SIZE, | |
| num_workers=8, | |
| sampler=valid_sampler) | |
| optimizer_g = torch.optim.AdamW( | |
| soundstream.parameters(), lr=3e-4, betas=(0.5, 0.9)) | |
| lr_scheduler_g = torch.optim.lr_scheduler.ExponentialLR( | |
| optimizer_g, gamma=0.999) | |
| optimizer_d = torch.optim.AdamW( | |
| itertools.chain(stft_disc.parameters(), | |
| msd.parameters(), mpd.parameters()), | |
| lr=3e-4, | |
| betas=(0.5, 0.9)) | |
| lr_scheduler_d = torch.optim.lr_scheduler.ExponentialLR( | |
| optimizer_d, gamma=0.999) | |
| if args.resume: | |
| latest_info = torch.load(args.resume_path + '/latest.pth') | |
| args.st_epoch = latest_info['epoch'] | |
| soundstream.load_state_dict(latest_info['soundstream']) | |
| stft_disc.load_state_dict(latest_info['stft_disc']) | |
| mpd.load_state_dict(latest_info['mpd']) | |
| msd.load_state_dict(latest_info['msd']) | |
| optimizer_g.load_state_dict(latest_info['optimizer_g']) | |
| lr_scheduler_g.load_state_dict(latest_info['lr_scheduler_g']) | |
| optimizer_d.load_state_dict(latest_info['optimizer_d']) | |
| lr_scheduler_d.load_state_dict(latest_info['lr_scheduler_d']) | |
| train(args, soundstream, stft_disc, msd, mpd, train_loader, valid_loader, | |
| optimizer_g, optimizer_d, lr_scheduler_g, lr_scheduler_d, logger) | |
| def train(args, soundstream, stft_disc, msd, mpd, train_loader, valid_loader, | |
| optimizer_g, optimizer_d, lr_scheduler_g, lr_scheduler_d, logger): | |
| print('args ', args.global_rank) | |
| best_val_loss = float("inf") | |
| best_val_epoch = -1 | |
| global_step = 0 | |
| for epoch in range(args.st_epoch, args.N_EPOCHS + 1): | |
| soundstream.train() | |
| stft_disc.train() | |
| msd.train() | |
| mpd.train() | |
| train_loss_d = 0.0 | |
| train_adv_g_loss = 0.0 | |
| train_feat_loss = 0.0 | |
| train_rec_loss = 0.0 | |
| train_loss_g = 0.0 | |
| train_commit_loss = 0.0 | |
| k_iter = 0 | |
| if args.distributed: | |
| train_loader.sampler.set_epoch(epoch) | |
| for x in tqdm(train_loader): | |
| x = x.to(args.device) | |
| k_iter += 1 | |
| global_step += 1 # record the global step | |
| for optimizer_idx in [0, 1]: # we have two optimizer | |
| x_wav = get_input(x) | |
| G_x, commit_loss, last_layer = soundstream(x_wav) | |
| if optimizer_idx == 0: | |
| # update generator | |
| y_disc_r, fmap_r = stft_disc(x_wav.contiguous()) | |
| y_disc_gen, fmap_gen = stft_disc(G_x.contiguous()) | |
| y_df_hat_r, y_df_hat_g, fmap_f_r, fmap_f_g = mpd( | |
| x_wav.contiguous(), G_x.contiguous()) | |
| y_ds_hat_r, y_ds_hat_g, fmap_s_r, fmap_s_g = msd( | |
| x_wav.contiguous(), G_x.contiguous()) | |
| total_loss_g, rec_loss, adv_g_loss, feat_loss, d_weight = loss_g( | |
| commit_loss, | |
| x_wav, | |
| G_x, | |
| fmap_r, | |
| fmap_gen, | |
| y_disc_r, | |
| y_disc_gen, | |
| global_step, | |
| y_df_hat_r, | |
| y_df_hat_g, | |
| y_ds_hat_r, | |
| y_ds_hat_g, | |
| fmap_f_r, | |
| fmap_f_g, | |
| fmap_s_r, | |
| fmap_s_g, | |
| last_layer=last_layer, | |
| is_training=True, | |
| args=args) | |
| train_commit_loss += commit_loss | |
| train_loss_g += total_loss_g.item() | |
| train_adv_g_loss += adv_g_loss.item() | |
| train_feat_loss += feat_loss.item() | |
| train_rec_loss += rec_loss.item() | |
| optimizer_g.zero_grad() | |
| total_loss_g.backward() | |
| optimizer_g.step() | |
| else: | |
| # update discriminator | |
| y_disc_r_det, fmap_r_det = stft_disc(x.detach()) | |
| y_disc_gen_det, fmap_gen_det = stft_disc(G_x.detach()) | |
| # MPD | |
| y_df_hat_r, y_df_hat_g, fmap_f_r, fmap_f_g = mpd( | |
| x.detach(), G_x.detach()) | |
| #MSD | |
| y_ds_hat_r, y_ds_hat_g, fmap_s_r, fmap_s_g = msd( | |
| x.detach(), G_x.detach()) | |
| loss_d = loss_dis( | |
| y_disc_r_det, y_disc_gen_det, fmap_r_det, fmap_gen_det, | |
| y_df_hat_r, y_df_hat_g, fmap_f_r, fmap_f_g, y_ds_hat_r, | |
| y_ds_hat_g, fmap_s_r, fmap_s_g, global_step, args) | |
| train_loss_d += loss_d.item() | |
| optimizer_d.zero_grad() | |
| loss_d.backward() | |
| optimizer_d.step() | |
| message = '<epoch:{:d}, iter:{:d}, total_loss_g:{:.4f}, adv_g_loss:{:.4f}, feat_loss:{:.4f}, rec_loss:{:.4f}, commit_loss:{:.4f}, loss_d:{:.4f}, d_weight: {:.4f}>'.format( | |
| epoch, k_iter, | |
| total_loss_g.item(), | |
| adv_g_loss.item(), | |
| feat_loss.item(), | |
| rec_loss.item(), | |
| commit_loss.item(), loss_d.item(), d_weight.item()) | |
| if k_iter % args.print_freq == 0: | |
| logger.log_info(message) | |
| lr_scheduler_g.step() | |
| lr_scheduler_d.step() | |
| message = '<epoch:{:d}, <total_loss_g_train:{:.4f}, recon_loss_train:{:.4f}, adversarial_loss_train:{:.4f}, feature_loss_train:{:.4f}, commit_loss_train:{:.4f}>'.format( | |
| epoch, train_loss_g / len(train_loader), train_rec_loss / | |
| len(train_loader), train_adv_g_loss / len(train_loader), | |
| train_feat_loss / len(train_loader), | |
| train_commit_loss / len(train_loader)) | |
| logger.log_info(message) | |
| with torch.no_grad(): | |
| soundstream.eval() | |
| stft_disc.eval() | |
| mpd.eval() | |
| msd.eval() | |
| valid_loss_d = 0.0 | |
| valid_loss_g = 0.0 | |
| valid_commit_loss = 0.0 | |
| valid_adv_g_loss = 0.0 | |
| valid_feat_loss = 0.0 | |
| valid_rec_loss = 0.0 | |
| if args.distributed: | |
| valid_loader.sampler.set_epoch(epoch) | |
| for x in tqdm(valid_loader): | |
| x = x.to(args.device) | |
| for optimizer_idx in [0, 1]: | |
| x_wav = get_input(x) | |
| G_x, commit_loss, _ = soundstream(x_wav) | |
| if optimizer_idx == 0: | |
| valid_commit_loss += commit_loss | |
| y_disc_r, fmap_r = stft_disc(x_wav.contiguous()) | |
| y_disc_gen, fmap_gen = stft_disc(G_x.contiguous()) | |
| y_df_hat_r, y_df_hat_g, fmap_f_r, fmap_f_g = mpd( | |
| x_wav.contiguous(), G_x.contiguous()) | |
| y_ds_hat_r, y_ds_hat_g, fmap_s_r, fmap_s_g = msd( | |
| x_wav.contiguous(), G_x.contiguous()) | |
| total_loss_g, adv_g_loss, feat_loss, rec_loss = criterion_g( | |
| commit_loss, | |
| x_wav, | |
| G_x, | |
| fmap_r, | |
| fmap_gen, | |
| y_disc_r, | |
| y_disc_gen, | |
| y_df_hat_r, | |
| y_df_hat_g, | |
| fmap_f_r, | |
| fmap_f_g, | |
| y_ds_hat_r, | |
| y_ds_hat_g, | |
| fmap_s_r, | |
| fmap_s_g, | |
| args=args) | |
| valid_loss_g += total_loss_g.item() | |
| valid_adv_g_loss += adv_g_loss.item() | |
| valid_feat_loss += feat_loss.item() | |
| valid_rec_loss += rec_loss.item() | |
| else: | |
| y_disc_r_det, fmap_r_det = stft_disc( | |
| x_wav.contiguous().detach()) | |
| y_disc_gen_det, fmap_gen_det = stft_disc( | |
| G_x.contiguous().detach()) | |
| y_df_hat_r, y_df_hat_g, fmap_f_r, fmap_f_g = mpd( | |
| x_wav.contiguous().detach(), | |
| G_x.contiguous().detach()) | |
| y_ds_hat_r, y_ds_hat_g, fmap_s_r, fmap_s_g = msd( | |
| x_wav.contiguous().detach(), | |
| G_x.contiguous().detach()) | |
| loss_d = criterion_d(y_disc_r_det, y_disc_gen_det, | |
| fmap_r_det, fmap_gen_det, | |
| y_df_hat_r, y_df_hat_g, fmap_f_r, | |
| fmap_f_g, y_ds_hat_r, y_ds_hat_g, | |
| fmap_s_r, fmap_s_g) | |
| valid_loss_d += loss_d.item() | |
| if dist.get_rank() == 0: | |
| best_model = soundstream.state_dict().copy() | |
| latest_model_soundstream = soundstream.state_dict().copy() | |
| latest_model_dis = stft_disc.state_dict().copy() | |
| latest_mpd = mpd.state_dict().copy() | |
| latest_msd = msd.state_dict().copy() | |
| if valid_rec_loss < best_val_loss: | |
| best_val_loss = valid_rec_loss | |
| best_val_epoch = epoch | |
| torch.save(best_model, | |
| args.PATH + '/best_' + str(epoch) + '.pth') | |
| latest_save = {} | |
| latest_save['soundstream'] = latest_model_soundstream | |
| latest_save['stft_disc'] = latest_model_dis | |
| latest_save['mpd'] = latest_mpd | |
| latest_save['msd'] = latest_msd | |
| latest_save['epoch'] = epoch | |
| latest_save['optimizer_g'] = optimizer_g.state_dict() | |
| latest_save['optimizer_d'] = optimizer_d.state_dict() | |
| latest_save['lr_scheduler_g'] = lr_scheduler_g.state_dict() | |
| latest_save['lr_scheduler_d'] = lr_scheduler_d.state_dict() | |
| torch.save(latest_save, args.PATH + '/latest.pth') | |
| message = '<epoch:{:d}, total_loss_g_valid:{:.4f}, recon_loss_valid:{:.4f}, adversarial_loss_valid:{:.4f}, feature_loss_valid:{:.4f}, commit_loss_valid:{:.4f}, valid_loss_d:{:.4f}, best_epoch:{:d}>'.format( | |
| epoch, valid_loss_g / len(valid_loader), valid_rec_loss / | |
| len(valid_loader), valid_adv_g_loss / len(valid_loader), | |
| valid_feat_loss / len(valid_loader), | |
| valid_commit_loss / len(valid_loader), | |
| valid_loss_d / len(valid_loader), best_val_epoch) | |
| logger.log_info(message) | |
| if __name__ == '__main__': | |
| main() | |