def cross_subject(data, label, session_id, category_number, batch_size, iteration, lr, momentum, log_interval):
# cross-subject, for 3 sessions, 1-14 as sources, 15 as target
one_session_data, one_session_label = copy.deepcopy(data[session_id]), copy.deepcopy(label[session_id])
target_data, target_label = one_session_data.pop(), one_session_label.pop()
source_data, source_label = copy.deepcopy(one_session_data), copy.deepcopy(one_session_label.copy())
# print(len(source_data))
source_data_comb = source_data[0]
source_label_comb = source_label[0]
for j in range(1, len(source_data)):
source_data_comb = np.vstack((source_data_comb, source_data[j]))
source_label_comb = np.vstack((source_label_comb, source_label[j]))
if bn == 'ele':
source_data_comb = utils.norminy(source_data_comb)
target_data = utils.norminy(target_data)
elif bn == 'sample':
source_data_comb = utils.norminx(source_data_comb)
target_data = utils.norminx(target_data)
elif bn == 'global':
source_data_comb = utils.normalization(source_data_comb)
target_data = utils.normalization(target_data)
elif bn == 'none':
pass
else:
pass
# source_data_comb = utils.norminy(source_data_comb)
# target_data = utils.norminy(target_data)
source_loader = torch.utils.data.DataLoader(dataset=utils.CustomDataset(source_data_comb, source_label_comb),
batch_size=batch_size,
shuffle=True,
drop_last=True)
# source_loaders = []
# for j in range(len(source_data)):
# source_loaders.append(torch.utils.data.DataLoader(dataset=utils.CustomDataset(source_data[j], source_label[j]),
# batch_size=batch_size,
# shuffle=True,
# drop_last=True))
target_loader = torch.utils.data.DataLoader(dataset=utils.CustomDataset(target_data, target_label),
batch_size=batch_size,
shuffle=True,
drop_last=True)
model = DANNet(model=models.DAN(pretrained=False, number_of_category=category_number),
source_loader=source_loader,
target_loader=target_loader,
batch_size=batch_size,
iteration=iteration,
lr=lr,
momentum=momentum,
log_interval=log_interval)
# print(model.__getModel__())
acc = model.train()
return acc
def cross_session(data, label, subject_id, category_number, batch_size, iteration, lr, momentum, log_interval):
target_data, target_label = copy.deepcopy(data[2][subject_id]), copy.deepcopy(label[2][subject_id])
source_data, source_label = [copy.deepcopy(data[0][subject_id]), copy.deepcopy(data[1][subject_id])], [copy.deepcopy(label[0][subject_id]), copy.deepcopy(label[1][subject_id])]
# one_sub_data, one_sub_label = data[i], label[i]
# target_data, target_label = one_session_data.pop(), one_session_label.pop()
# source_data, source_label = one_session_data.copy(), one_session_label.copy()
# print(len(source_data))
source_data_comb = np.vstack((source_data[0], source_data[1]))
source_label_comb = np.vstack((source_label[0], source_label[1]))
for j in range(1, len(source_data)):
source_data_comb = np.vstack((source_data_comb, source_data[j]))
source_label_comb = np.vstack((source_label_comb, source_label[j]))
if bn == 'ele':
source_data_comb = utils.norminy(source_data_comb)
target_data = utils.norminy(target_data)
elif bn == 'sample':
source_data_comb = utils.norminx(source_data_comb)
target_data = utils.norminx(target_data)
elif bn == 'global':
source_data_comb = utils.normalization(source_data_comb)
target_data = utils.normalization(target_data)
elif bn == 'none':
pass
else:
pass
# source_data_comb = utils.norminy(source_data_comb)
# target_data = utils.norminy(target_data)
source_loader = torch.utils.data.DataLoader(dataset=utils.CustomDataset(source_data_comb, source_label_comb),
batch_size=batch_size,
shuffle=True,
drop_last=True)
target_loader = torch.utils.data.DataLoader(dataset=utils.CustomDataset(target_data, target_label),
batch_size=batch_size,
shuffle=True,
drop_last=True)
model = DANNet(model=models.DAN(pretrained=False, number_of_category=category_number),
source_loader=source_loader,
target_loader=target_loader,
batch_size=batch_size,
iteration=iteration,
lr=lr,
momentum=momentum,
log_interval=log_interval)
# print(model.__getModel__())
acc = model.train()
return acc
for dataset_name in dataset_name_all:
print('Dataset name: ', dataset_name)
data, label = utils.load_data(dataset_name)
for bn in bn_all:
print('Normalization type: ', bn)
if bn == 'ele':
data_tmp = copy.deepcopy(data)
for i in range(len(data_tmp)):
for j in range(len(data_tmp[0])):
data_tmp[i][j] = utils.norminy(data_tmp[i][j])
elif bn == 'sample':
data_tmp = copy.deepcopy(data)
for i in range(len(data_tmp)):
for j in range(len(data_tmp[0])):
data_tmp[i][j] = utils.norminx(data_tmp[i][j])
elif bn == 'global':
data_tmp = copy.deepcopy(data)
for i in range(len(data_tmp)):
for j in range(len(data_tmp[0])):
data_tmp[i][j] = utils.normalization(data_tmp[i][j])
elif bn == 'none':
data_tmp = copy.deepcopy(data)
else:
pass
trial_total, category_number, _ = utils.get_number_of_label_n_trial(
dataset_name)
# training settings
batch_size = 32