def channel(filename):
parser = argparse.ArgumentParser('UCLANESL ASVSpoof2019 model')
parser.add_argument('--eval',
action='store_true',
default=False,
help='eval mode') #终端参数的调用
parser.add_argument('--model_path',
type=str,
default=None,
help='Model checkpoint')
parser.add_argument('--eval_output',
type=str,
default=None,
help='Path to save the evaluation result')
parser.add_argument('--batch_size', type=int, default=32)
parser.add_argument('--num_epochs', type=int, default=20) #轮数
parser.add_argument('--lr', type=float, default=0.00005)
parser.add_argument('--comment',
type=str,
default=None,
help='Comment to describe the saved mdoel')
parser.add_argument('--track', type=str, default='logical')
parser.add_argument('--features', type=str, default='spect')
parser.add_argument('--is_eval', action='store_true', default=False)
parser.add_argument('--eval_part', type=int, default=0)
if not os.path.exists('models'):
os.mkdir('models')
args = parser.parse_args() #把所有add_argument项目返回到args中
track = args.track
assert args.features in ['mfcc', 'spect', 'cqcc'], 'Not supported feature'
model_tag = 'model_{}_{}_{}_{}_{}'.format(track, args.features,
args.num_epochs, args.batch_size,
args.lr)
if args.comment:
model_tag = model_tag + '_{}'.format(args.comment)
model_save_path = os.path.join('models', model_tag)
assert track in ['logical', 'physical'], 'Invalid track given'
is_logical = (track == 'logical')
if not os.path.exists(model_save_path):
os.mkdir(model_save_path)
if args.features == 'mfcc':
feature_fn = compute_mfcc_feats
model_cls = MFCCModel
elif args.features == 'spect':
feature_fn = get_log_spectrum
model_cls = SpectrogramModel
elif args.features == 'cqcc':
feature_fn = None # cqcc feature is extracted in Matlab script
model_cls = CQCCModel
transforms = transforms.Compose([ #transform为预处理函数，实现以下四种预处理
lambda x: pad(x), #lambda函数实现简单的输入输出功能
lambda x: librosa.util.normalize(x),
lambda x: feature_fn(x), #计算特征，譬如mfcc
lambda x: Tensor(x)
])
device = 'cuda' if torch.cuda.is_available() else 'cpu'
dev_set = data_utils.ASVDataset(is_train=False,
is_logical=is_logical,
transform=transforms,
feature_name=args.features,
is_eval=True,
eval_part=args.eval_part,
file=filename)
dev_loader = DataLoader(dev_set, batch_size=args.batch_size, shuffle=True)
model = model_cls().to(device)
model.load_state_dict(
torch.load('/backup1/datas/models/students/epoch_19.pth'))
pred = evaluate_accuracy(dev_loader, model, device)
return pred
feature_fn = get_log_spectrum
model_cls = SpectrogramModel
elif args.features == 'cqcc':
feature_fn = None # cqcc feature is extracted in Matlab script
model_cls = CQCCModel
transforms = transforms.Compose([ #transform为预处理函数，实现以下四种预处理
lambda x: pad(x), #lambda函数实现简单的输入输出功能
lambda x: librosa.util.normalize(x),
lambda x: feature_fn(x), #计算特征，譬如mfcc
lambda x: Tensor(x)
])
device = 'cuda' if torch.cuda.is_available() else 'cpu'
dev_set = data_utils.ASVDataset(is_train=False, is_logical=is_logical,
transform=transforms,
feature_name=args.features, is_eval=args.is_eval, eval_part=args.eval_part)
dev_loader = DataLoader(dev_set, batch_size=args.batch_size, shuffle=True)
model = model_cls().to(device)
print(args)
if args.model_path:
model.load_state_dict(torch.load(args.model_path))
print('Model loaded : {}'.format(args.model_path))
if args.eval:#是否进入评估阶段，默认为否
assert args.eval_output is not None, 'You must provide an output path'
assert args.model_path is not None, 'You must provide model checkpoint'
produce_evaluation_file(dev_set, model, device, args.eval_output)
sys.exit(0)
print(args)
# load data & run
if args.eval:
# if evaluation
# 加载模型参数
if args.model_path:
model.load_state_dict(torch.load(args.model_path))
print('Model loaded : {}'.format(args.model_path))
assert args.eval_output is not None, 'You must provide an output path'
assert args.model_path is not None, 'You must provide model checkpoint'
# 加载数据 - dev/eval
dev_set = data_utils.ASVDataset(is_train=False, is_logical=is_logical,
transform=transforms,
feature_name=args.features, is_eval=args.is_eval)
dev_loader = DataLoader(dev_set, batch_size=args.batch_size, shuffle=True)
# run
produce_evaluation_file(dev_set, model, device, args.eval_output)
else:
# if training
# 加载数据 - train
train_set = data_utils.ASVDataset(is_train=True, is_logical=is_logical,
transform=transforms,
feature_name=args.features)
train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True)
# 加载数据 - dev 观察训练过程中accuracy