type=int,
help="Use CUDA on the listed devices.")
parser.add_argument('-restore',
default='',
type=str,
help="restore checkpoint")
parser.add_argument('-seed', type=int, default=1234, help="Random seed")
parser.add_argument('-notrain',
default=False,
action='store_true',
help="train or not")
opt = parser.parse_args()
config = utils.read_config(opt.config)
torch.manual_seed(opt.seed)
print('#Start:', utils.format_time(time.localtime()))
# checkpoint
if opt.restore:
print('loading checkpoint...\n')
checkpoints = torch.load(opt.restore)
config = checkpoints['config']
threshold = checkpoints['threshold']
else:
threshold = 0.5
if 'train_batch_size' not in config:
config.train_batch_size = config.batch_size
config.test_batch_size = config.batch_size
config.load_emb = False
# cuda
# optim.set_parameters(list(model.parameters()))
if config.schedule:
# scheduler = L.CosineAnnealingLR(optim.optimizer, T_max=config.epoch)
scheduler = L.StepLR(optim.optimizer, step_size=15, gamma=0.2)
# total number of parameters
param_count = 0
for param in model.parameters():
param_count += param.view(-1).size()[0]
# logging modeule
if not os.path.exists(config.log):
os.mkdir(config.log)
if opt.log == '':
log_path = config.log + utils.format_time(time.localtime()) + '/'
else:
log_path = config.log + opt.log + '/'
if not os.path.exists(log_path):
os.mkdir(log_path)
print(('log_path:', log_path))
writer = SummaryWriter(log_path)
logging = utils.logging(log_path +
'log.txt') # 单独写一个logging的函数，直接调用，既print，又记录到Log文件里。
logging_csv = utils.logging_csv(log_path + 'record.csv')
for k, v in list(config.items()):
logging("%s:\t%s\n" % (str(k), str(v)))
logging("\n")
logging(repr(model) + "\n\n")
default='./data/data/target_label_dict.json',
type=str,
help="label_dict")
parser.add_argument('-label_dict_test_file',
default='./data/data/test_label_dict.json',
type=str,
help="label_dict")
opt = parser.parse_args()
config = utils.read_config(opt.config)
torch.manual_seed(opt.seed)
if not os.path.exists(config.log):
os.mkdir(config.log)
if opt.log == '':
log_path = config.log + str(utils.format_time(
time.localtime())).replace(':', '_') + '/'
else:
log_path = config.log + opt.log + '/'
if not os.path.exists(log_path):
os.mkdir(log_path)
logging = utils.logging(log_path + 'log.txt')
# checkpoint
if opt.restore:
print('loading checkpoint...\n')
checkpoints = torch.load(opt.restore, map_location='cpu')
# cuda
use_cuda = torch.cuda.is_available() and len(opt.gpus) > 0
