def main(args):
rePrint('-------------------------------------------------------------------------------------')
vadDict = pickle.load(open(args.vad_file, 'rb'))
validFile = os.path.join(args.valid_path, args.valid_file.format(args.split_num-1))
rePrint(' [LoadModelData {:s}]'.format(validFile))
#validData = LoadModelData(validFile, 'valid')
validData = LoadModelDataVAD(validFile, vadDict, args.alpha, 'valid')
validLoad = torch.utils.data.DataLoader(validData,
batch_size=args.batch_size, shuffle=False, num_workers=4)
rePrint(' [Create model: {:s} num_classes: {:d}]'.format(args.model, args.num_classes))
net = eval(args.model)(num_classes=args.num_classes)
net = net.cuda()
optimizer = torch.optim.SGD(net.parameters(), lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
scheduler = ReduceLROnPlateau(optimizer, mode='min',
factor=args.factor,
patience=args.patience,
verbose=False, threshold=1e-6,
threshold_mode='rel', cooldown=0, min_lr=0, eps=1e-8)
rePrint('')
rePrint('-------------------------------------------------------------------------------------')
rePrint(' [Train {:s}]'.format(args.model))
rePrint('-------------------------------------------------------------------------------------')
modelPath = os.path.join(args.model_path, args.model+args.fea_type)
make_path(modelPath)
for epoch in range(args.epochs):
#print type(epoch), epoch
save_file = os.path.join(modelPath, args.model_file).format(epoch)
for splitID in range(args.split_num-1):
train_file = os.path.join(args.train_path, args.train_file.format(splitID))
rePrint(' [LoadModelData {:s}]'.format(train_file))
#trainData = LoadModelData(train_file, 'train')
trainData = LoadModelDataVAD(train_file, vadDict, args.alpha, 'train')
trainLoad = torch.utils.data.DataLoader(trainData,
batch_size=args.batch_size, shuffle=True, num_workers=4)
net = train_model(net=net,
trainLoad=trainLoad,
optimizer=optimizer,
log_interval=args.log_interval,
epoch=epoch,
batch_size=args.batch_size,
lr=optimizer.param_groups[0]['lr'],
save_file=None)
torch.save(net.state_dict(), save_file)
rePrint(save_file)
eval_loss = valid_model(net=net, validLoad=validLoad)
scheduler.step(eval_loss)
rePrint('-------------------------------------------------------------------------------------')
rePrint(' [Done]')
rePrint('-------------------------------------------------------------------------------------')
def main(args):
rePrint(
'-------------------------------------------------------------------------------------'
)
rePrint(' [LoadModelData {:s}]'.format(args.train_file))
trainData = LoadModelData(args.train_file, 'train')
trainLoad = torch.utils.data.DataLoader(trainData,
batch_size=args.batch_size,
shuffle=True,
num_workers=4)
rePrint(' [LoadModelData {:s}]'.format(args.valid_file))
validData = LoadModelData(args.valid_file, 'valid')
validLoad = torch.utils.data.DataLoader(validData,
batch_size=args.batch_size,
shuffle=False,
num_workers=4)
rePrint(' [Create model: {:s} num_classes: {:d}]'.format(
args.model, args.num_classes))
net = eval(args.model)(num_classes=args.num_classes)
net = net.cuda()
optimizer = torch.optim.SGD(net.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
scheduler = ReduceLROnPlateau(optimizer,
mode='min',
factor=args.factor,
patience=args.patience,
verbose=False,
threshold=1e-6,
threshold_mode='rel',
cooldown=0,
min_lr=0,
eps=1e-8)
rePrint('')
rePrint(
'-------------------------------------------------------------------------------------'
)
rePrint(' [Train {:s}]'.format(args.model))
rePrint(
'-------------------------------------------------------------------------------------'
)
make_path(os.path.join(args.model_path, args.model))
for epoch in range(args.epochs):
save_file = os.path.join(args.model_path, args.model,
args.model_file).format(epoch)
train_model(net=net,
trainLoad=trainLoad,
optimizer=optimizer,
log_interval=args.log_interval,
epoch=epoch,
batch_size=args.batch_size,
lr=optimizer.param_groups[0]['lr'],
save_file=save_file)
eval_loss = valid_model(net=net, validLoad=validLoad)
scheduler.step(eval_loss)
rePrint(
'-------------------------------------------------------------------------------------'
)
rePrint(' [Done]')
rePrint(
'-------------------------------------------------------------------------------------'
)
