def test_operate(way, ns, nq, path, eval_stats, ob_domain=False, num_domain=30):
set_seed(0)
if ob_domain:
ns = nq = num_domain
running_params['train_split'] = num_domain * 4
model = DASMNLearner(n_way=way, n_support=ns, n_query=nq)
# CW: NC, IF, OF, RoF
# src_tasks, _ = generator.CW_10way(way=way, order=Load[0], examples=200, split=running_params['train_split'],
# normalize=True, data_len=DIM, label=False)
# _, test_tasks = generator.CW_10way(way=way, order=Load[1], examples=200, split=0, normalize=True,
# data_len=DIM, label=False)
# CW2SQ: NC, IF, OF
# src_tasks, _ = generator.CW_cross(way=way, examples=100, split=running_params['train_split'],
# normalize=True, data_len=DIM, label=False, tgt_set='sq')
# _, test_tasks = generator.SQ_37way(examples=100, split=0, way=way, normalize=True,
# label=False, data_len=DIM)
# _, test_tasks = generator.EB_3_13way(examples=running_params['test_samples'],
# split=running_params['test_split'],
# way=way, order=3, normalize=True, label=False, data_len=DIM)
# CW2SA: NC, OF, RoF(Ball)
# src_tasks, _ = generator.CW_cross(way=way, examples=100, split=running_params['train_split'], data_len=DIM,
# normalize=True, label=False, tgt_set='sa')
# _, test_tasks = generator.SA_37way(examples=200, split=0, way=way, normalize=True,
# label=False, data_len=DIM)
src_tasks, test_tasks = generator.SA_37way(examples=200, split=30, way=way, normalize=True,
label=False, data_len=DIM)
# src_tasks = src_tasks if ob_domain else None
src_tasks = src_tasks if ob_domain else None
running_params['test_episodes'] = 10 if ob_domain else 100
# if you do not want to observe the src and tgt results together
print('test_task shape: ', test_tasks.shape)
if ob_domain:
print('src_task shape: ', src_tasks.shape)
if eval_stats == 'yes':
model.load(path)
model.test(test_tasks, src_tasks, model_eval=True)
elif eval_stats == 'both':
model.load(path)
model.test(test_tasks, src_tasks, model_eval=True)
print('\n================Reloading the file====================')
model.load(path)
# Attention! Model.train() would change the trained weights(it's an invalid operation),
# so we have to reload the trained file again.
model.test(test_tasks, src_tasks, model_eval=False)
elif eval_stats == 'no':
model.load(path)
model.test(test_tasks, src_tasks, model_eval=False)
def train_operate(way, ns, nq, save_path, final_test=True, load_path=None):
set_seed(0)
nets = DASMNLearner(n_way=way, n_support=ns, n_query=nq)
if load_path is not None: # 若加载路径不为空，则默认：模型微调
nets.load(load_path)
# CW: NC, IF, OF, RoF
# src, _ = generator.CW_10way(way=way, order=Load[0], examples=200, split=running_params['train_split'],
# normalize=True, data_len=DIM, label=False)
# _, tar = generator.CW_10way(way=way, order=Load[1], examples=200, split=0, normalize=True,
# data_len=DIM, label=False)
# CW2SQ: NC, IF, OF
src, _ = generator.CW_cross(way=way,
examples=100,
split=running_params['train_split'],
data_len=DIM,
normalize=True,
label=False,
tgt_set='sq')
_, tar = generator.SQ_37way(examples=100,
split=0,
way=way,
label=False,
data_len=DIM,
normalize=True)
# src, tar = generator.EB_3_13way(examples=running_params['train_samples'],
# split=running_params['train_split'],
# way=way, order=3, normalize=True, label=False)
# CW2SA: NC, OF, RoF(Ball)
# src, _ = generator.CW_cross(way=way, examples=100, split=running_params['train_split'],
# normalize=True, data_len=DIM, label=False, tgt_set='sa')
# _, tar = generator.SA_37way(examples=200, split=0, way=way, data_len=DIM,
# normalize=True, label=False)
# ---------------self testing on SA----------------------------------------------------
# src, tar = generator.SA_37way(examples=200, split=running_params['train_split'],
# way=way, normalize=True, label=False, overlap=True)
# print('Train proto_1optimizer\n') # 推荐
# nets.train_proto_1op(src, tar) # turn on the GRL
# training 1:
print('Train joint_training') # 推荐 train with 2 optimizers
nets.joint_training_2op(src, tar, save_path) # turn on the GRL
# training 2: Turn off the GRL !!!!!!!
# print('Train proto_2loss')
# nets.train_proto_2loss(src, tar)
if final_test:
print('We test the model!')
nets.test(tar_tasks=tar, src_tasks=None, model_eval=True)
nets.test(tar_tasks=tar, src_tasks=None, model_eval=False)