def update():
opt.data = ops.join(opt.dataset_root, 'features')
opt.gt = ops.join(opt.dataset_root, 'groundTruth')
opt.output_dir = ops.join(opt.dataset_root, 'output')
opt.mapping_dir = ops.join(opt.dataset_root, 'mapping')
dir_check(opt.output_dir)
opt.f_norm = False
if torch.cuda.is_available():
opt.device = 'cuda'
opt.embed_dim = 30
if not opt.load_model:
opt.lr = 1e-3
opt.epochs = 30
if opt.model_name == 'nothing':
opt.load_embed_feat = True
update_opt_str()
logger = path_logger()
vars_iter = list(vars(opt))
for arg in sorted(vars_iter):
logger.debug('%s: %s' % (arg, getattr(opt, arg)))
def update():
opt.data = ops.join(opt.dataset_root, 'features')
opt.gt = ops.join(opt.dataset_root, 'groundTruth')
opt.output_dir = ops.join(opt.dataset_root, 'output')
opt.mapping_dir = ops.join(opt.dataset_root, 'mapping')
dir_check(opt.output_dir)
opt.f_norm = True
if torch.cuda.is_available():
opt.device = 'cuda'
if opt.global_pipe:
opt.embed_dim = 30
else:
opt.embed_dim = 20
if not opt.load_model:
if opt.global_pipe:
opt.lr = 1e-4
else:
opt.lr = 1e-4
opt.epochs = 60
opt.bg = False # YTI argument
opt.gr_lev = '' # 50Salads argument
if opt.model_name == 'nothing':
opt.load_embed_feat = True
update_opt_str()
logger = path_logger()
vars_iter = list(vars(opt))
for arg in sorted(vars_iter):
logger.debug('%s: %s' % (arg, getattr(opt, arg)))
def all_actions(actions):
return_stat_all = None
lr_init = opt.lr
for action in actions:
opt.subaction = action
if not opt.resume:
opt.lr = lr_init
update_opt_str()
return_stat_single = temp_embed()
return_stat_all = join_return_stat(return_stat_all, return_stat_single)
logger.debug(return_stat_all)
parse_return_stat(return_stat_all)