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from torch import optim
import argparse
from datetime import datetime
import wandb
import torch.backends.cudnn as cudnn
from torch import optim
from torch.utils.data import DataLoader
from torchinfo import summary
from timm.scheduler.cosine_lr import CosineLRScheduler
import lossfunction
import net
from DatasetLoader import *
from dataloader import TrainDataset
from SpeakerNet import *
from config import set_cfg, cfg
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument("--config_name", type=str, default="", help="the configs name that will as a base configs")
parser.add_argument("--project", default=None, type=str, help="project name")
parser.add_argument("--name", default=None, type=str, help="run name")
parser.add_argument("--save_dir", default=None, type=str, help="save path")
parser.add_argument("--resume", default=None, type=str, help="resume path")
parser.add_argument("--dataset", default=None, type=str, help="dataset path")
parser.add_argument("--epoch", default=None, type=int, help="max epoch")
parser.add_argument("--test_freq", default=None, type=int, help="frequency test epoch")
parser.add_argument("--batch_size", default=None, type=int, help="batch size")
parser.add_argument("--lr", default=None, type=float, help="learning rate")
parser.add_argument("--seed", default=None, type=int)
parser.add_argument("--wandb", action='store_true', default=False, help='use wandb to log ')
parser.add_argument("--note", type=str, default="", help='wandb note')
parser.add_argument('--eval', dest='eval', action='store_true', default=False, help='Eval only')
parser.add_argument('--score', dest='score', action='store_true', default=False, help='Eval only')
args = parser.parse_args()
return args
def main():
global cfg
args = get_args()
assert args.config_name is not None
if args.config_name:
set_cfg(args.config_name)
cfg.replace(vars(args))
del args
cfg.save_dir = os.path.join(cfg.save_dir, cfg.project + '_' + cfg.name, datetime.now().strftime('%Y%m%d'))
if not os.path.exists(cfg.save_dir):
os.makedirs(cfg.save_dir)
if cfg.wandb:
wandb.login(host="http://49.233.11.7:8080", key="local-7dc64cc63778f0723dc202d2624a97cef7043120")
wandb.init(project=cfg.project, name=cfg.name, config=cfg, save_code=True, notes=cfg.note)
# cudnn related setting
cudnn.benchmark = True
torch.backends.cudnn.deterministic = False
torch.backends.cudnn.enabled = True
start_epoch = 1
# ---------------模型---------------
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# device = torch.device("cpu")
# model = getattr(net, cfg.model)(cfg.nOut, cfg.encoder_type, cfg.log_input).to(device)
# ------ECAPA_TDNN.yaml------ResNet_TDNN----
model = getattr(net, cfg.model)().to(device)
# loss = getattr(lossfunction, cfg.loss)(cfg.nOut, cfg.nClasses, cfg.margin, cfg.scale).to(device)
# ----aamsoftmax----
loss = getattr(lossfunction, cfg.loss)(cfg.nOut, cfg.nClasses).to(device)
# model = SpeakerUnet(model=model, trainfunc=loss, nPerSpeaker=cfg.nPerSpeaker, segment=cfg.segment)
model = SpeakerNet(model=model, trainfunc=loss, nPerSpeaker=cfg.nPerSpeaker)
# swin
optimizer = optim.AdamW(model.parameters(), eps=1e-8, betas=(0.9, 0.999),
lr=cfg.lr, weight_decay=0.05)
# optimizer = optim.Adam(model.parameters(), lr=cfg.lr, weight_decay=0.000002)
# scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[30, 50, 70], gamma=0.1, last_epoch=-1)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.1, patience=5,
threshold=0.001, threshold_mode='rel',
cooldown=0, min_lr=1e-5, eps=1e-08, verbose=True)
# scheduler = optim.lr_scheduler.CyclicLR(optimizer, base_lr=cfg.lr, max_lr=0.003, mode='triangular2',
# step_size_up=12000, cycle_momentum=False)
if cfg.resume:
# ckpt = torch.load(cfg.resume, map_location="cpu")
ckpt = torch.load(cfg.resume)
model.load_state_dict(ckpt['model_state_dict'], strict=False)
# optimizer.load_state_dict(ckpt['optimizer_state_dict'])
# scheduler.load_state_dict(ckpt['scheduler_state_dict'])
# start_epoch = ckpt['epoch'] + 1
print("checkpoint加载完毕!")
# print(model)
# test, eval, train
trainer = Trainer(cfg, model, optimizer, scheduler, device)
# ---------------score--------------
if cfg.score:
score_dir = os.path.join('score', cfg.name+"_"+datetime.now().strftime('%Y%m%d'))
if not os.path.exists(score_dir):
os.makedirs(score_dir)
score_file = os.path.join(score_dir, 'scores.txt')
trainer.scoretxt(score_file, 'data/voxsrc2021_blind.txt', 'data/voxsrc2021', cfg.eval_frames)
# trainer.scoretxt(score_file, cfg.dataset.test_list, cfg.dataset.test_path, cfg.eval_frames)
# ---------------eval--------------
elif cfg.eval:
trainer.test(0, cfg.dataset.test_list, cfg.dataset.test_path, cfg.nDataLoaderThread, cfg.eval_frames)
else:
# ---------------训练--------------
train_dataset = train_dataset_loader(train_list=cfg.dataset.train_list,
augment=cfg.augment, musan_path=cfg.dataset.musan_path,
rir_path=cfg.dataset.rir_path, max_frames=cfg.max_frames,
segment=cfg.segment, train_path=cfg.dataset.train_path)
train_sampler = train_dataset_sampler(train_dataset, nPerSpeaker=cfg.nPerSpeaker,
max_seg_per_spk=cfg.max_seg_per_spk, batch_size=cfg.batch_size,
seed=cfg.seed)
# train_dataset = TrainDataset(train_list=cfg.dataset.train_list,
# augment=cfg.augment, musan_path=cfg.dataset.musan_path,
# rir_path=cfg.dataset.rir_path, max_frames=cfg.max_frames,
# train_path=cfg.dataset.train_path)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=cfg.batch_size,
num_workers=cfg.nDataLoaderThread,
sampler=train_sampler,
pin_memory=False,
drop_last=True,
)
x, y = iter(train_loader).next()
print('x.shape:', x.shape, 'y.shape:', y.shape)
print('x.dtype:', x.dtype, 'y.dtype:', y.dtype)
summary(model, input_size=(tuple(x.shape)))
it = 0
min_eer = float("inf")
for epoch in range(start_epoch, cfg.max_epoch):
trainer.train(epoch, train_loader)
if epoch % cfg.test_interval == 0:
eer = trainer.test(epoch, cfg.dataset.test_list, cfg.dataset.test_path, cfg.nDataLoaderThread,
cfg.eval_frames)
scheduler.step(eer)
# # -----Clr------
# if eer < min_eer:
# min_eer = eer
# it = 0
#
# else:
# it += 1
#
# if it >= 8:
# lr = cfg.lr * 0.1
# trainer.scheduler = optim.lr_scheduler.CyclicLR(optimizer, base_lr=lr, max_lr=cfg.lr,
# mode='triangular2',
# step_size_up=6000, cycle_momentum=False)
# it = 0
# # -----Clr------
trainer.save_model(epoch)
print("finishing")
if __name__ == "__main__":
main()
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