def main():
args = parse_args()
update_config(cfg, args)
if args.load_path is None:
raise AttributeError("Please specify load path.")
# cudnn related setting
cudnn.benchmark = cfg.CUDNN.BENCHMARK
torch.backends.cudnn.deterministic = cfg.CUDNN.DETERMINISTIC
torch.backends.cudnn.enabled = cfg.CUDNN.ENABLED
# Set the random seed manually for reproducibility.
np.random.seed(cfg.SEED)
torch.manual_seed(cfg.SEED)
torch.cuda.manual_seed_all(cfg.SEED)
# model and optimizer
if cfg.MODEL.NAME == 'model':
if args.load_path and os.path.exists(args.load_path):
checkpoint = torch.load(args.load_path)
genotype = checkpoint['genotype']
else:
raise AssertionError('Please specify the model to evaluate')
model = Network(cfg.MODEL.INIT_CHANNELS, cfg.MODEL.NUM_CLASSES, cfg.MODEL.LAYERS, genotype)
model.drop_path_prob = 0.0
else:
model = eval('resnet.{}(num_classes={})'.format(cfg.MODEL.NAME, cfg.MODEL.NUM_CLASSES))
model = model.cuda()
# resume && make log dir and logger
if args.load_path and os.path.exists(args.load_path):
checkpoint = torch.load(args.load_path)
# load checkpoint
model.load_state_dict(checkpoint['state_dict'])
args.path_helper = checkpoint['path_helper']
logger = create_logger(os.path.dirname(args.load_path))
logger.info("=> loaded checkpoint '{}'".format(args.load_path))
else:
raise AssertionError('Please specify the model to evaluate')
logger.info(args)
logger.info(cfg)
# dataloader
test_dataset_verification = VoxcelebTestset(
Path(cfg.DATASET.DATA_DIR), cfg.DATASET.PARTIAL_N_FRAMES
)
test_loader_verification = torch.utils.data.DataLoader(
dataset=test_dataset_verification,
batch_size=1,
num_workers=cfg.DATASET.NUM_WORKERS,
pin_memory=True,
shuffle=False,
drop_last=False,
)
validate_verification(cfg, model, test_loader_verification)
def main():
args = parse_args()
update_config(cfg, args)
# cudnn related setting
cudnn.benchmark = cfg.CUDNN.BENCHMARK
torch.backends.cudnn.deterministic = cfg.CUDNN.DETERMINISTIC
torch.backends.cudnn.enabled = cfg.CUDNN.ENABLED
# Set the random seed manually for reproducibility.
np.random.seed(cfg.SEED)
torch.manual_seed(cfg.SEED)
torch.cuda.manual_seed_all(cfg.SEED)
# Loss
criterion = CrossEntropyLoss(cfg.MODEL.NUM_CLASSES).cuda()
# load arch
genotype = eval(args.text_arch)
model = Network(cfg.MODEL.INIT_CHANNELS, cfg.MODEL.NUM_CLASSES,
cfg.MODEL.LAYERS, genotype)
model = model.cuda()
optimizer = optim.Adam(
model.parameters(),
lr=cfg.TRAIN.LR,
weight_decay=cfg.TRAIN.WD,
)
# resume && make log dir and logger
if args.load_path and os.path.exists(args.load_path):
checkpoint_file = os.path.join(args.load_path, 'Model',
'checkpoint_best.pth')
assert os.path.exists(checkpoint_file)
checkpoint = torch.load(checkpoint_file)
# load checkpoint
begin_epoch = checkpoint['epoch']
last_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
best_acc1 = checkpoint['best_acc1']
optimizer.load_state_dict(checkpoint['optimizer'])
args.path_helper = checkpoint['path_helper']
logger = create_logger(args.path_helper['log_path'])
logger.info("=> loaded checkloggpoint '{}'".format(checkpoint_file))
else:
exp_name = args.cfg.split('/')[-1].split('.')[0]
args.path_helper = set_path('logs_scratch', exp_name)
logger = create_logger(args.path_helper['log_path'])
begin_epoch = cfg.TRAIN.BEGIN_EPOCH
best_acc1 = 0.0
last_epoch = -1
logger.info(args)
logger.info(cfg)
logger.info(f"selected architecture: {genotype}")
logger.info("Number of parameters: {}".format(count_parameters(model)))
# copy model file
this_dir = os.path.dirname(__file__)
shutil.copy2(os.path.join(this_dir, './models', cfg.MODEL.NAME + '.py'),
args.path_helper['ckpt_path'])
# dataloader
train_dataset = DeepSpeakerDataset(Path(cfg.DATASET.DATA_DIR),
cfg.DATASET.PARTIAL_N_FRAMES, 'train')
train_loader = torch.utils.data.DataLoader(
dataset=train_dataset,
batch_size=cfg.TRAIN.BATCH_SIZE,
num_workers=cfg.DATASET.NUM_WORKERS,
pin_memory=True,
shuffle=True,
drop_last=True,
)
test_dataset = DeepSpeakerDataset(Path(cfg.DATASET.DATA_DIR),
cfg.DATASET.PARTIAL_N_FRAMES,
'test',
is_test=True)
test_loader = torch.utils.data.DataLoader(
dataset=test_dataset,
batch_size=1,
num_workers=cfg.DATASET.NUM_WORKERS,
pin_memory=True,
shuffle=True,
drop_last=True,
)
# training setting
writer_dict = {
'writer': SummaryWriter(args.path_helper['log_path']),
'train_global_steps': begin_epoch * len(train_loader),
'valid_global_steps': begin_epoch // cfg.VAL_FREQ,
}
# training loop
lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer,
cfg.TRAIN.END_EPOCH,
cfg.TRAIN.LR_MIN,
last_epoch=last_epoch)
for epoch in tqdm(range(begin_epoch, cfg.TRAIN.END_EPOCH),
desc='train progress'):
model.train()
model.drop_path_prob = cfg.MODEL.DROP_PATH_PROB * epoch / cfg.TRAIN.END_EPOCH
train_from_scratch(cfg, model, optimizer, train_loader, criterion,
epoch, writer_dict)
if epoch % cfg.VAL_FREQ == 0 or epoch == cfg.TRAIN.END_EPOCH - 1:
acc = validate_identification(cfg, model, test_loader, criterion)
# remember best acc@1 and save checkpoint
is_best = acc > best_acc1
best_acc1 = max(acc, best_acc1)
# save
logger.info('=> saving checkpoint to {}'.format(
args.path_helper['ckpt_path']))
save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'best_acc1': best_acc1,
'optimizer': optimizer.state_dict(),
'path_helper': args.path_helper,
'genotype': genotype,
}, is_best, args.path_helper['ckpt_path'],
'checkpoint_{}.pth'.format(epoch))
lr_scheduler.step(epoch)
train_loaders = [None] * len(HEADS)
for i in range(len(HEADS)):
train_datasets[i] = loadedDataset(train_dir, HEADS[i], args.frame_num, args.time_freq, args.patch_size)
train_loaders[i] = torch.utils.data.DataLoader(
train_datasets[i], batch_size=args.batch_size, shuffle=True, pin_memory=True)
model = Network(in_channels=3, out_channels=3).cuda()
rmse_loss = RMSE_Loss()
criterion = nn.MSELoss()
if os.path.exists(os.path.join(save_dir, 'checkpoint.pth.tar')):
# load existing model
print('==> loading existing model')
model_info = torch.load(os.path.join(save_dir, 'checkpoint.pth.tar'))
model.load_state_dict(model_info['state_dict'])
optimizer = torch.optim.Adam(model.parameters())
optimizer.load_state_dict(model_info['optimizer'])
cur_epoch = model_info['epoch']
else:
if not os.path.isdir(save_dir):
os.makedirs(save_dir)
# create model
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
cur_epoch = 0
for epoch in range(cur_epoch, args.epochs + 1):
rmse = AverageMeter()
losses = AverageMeter()