def cross_subject(data, label, session_id, category_number, batch_size,
iteration, lr, momentum, log_interval):
one_session_data, one_session_label = copy.deepcopy(
data_tmp[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)
# print(len(source_data))
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 = MSMDAER(model=models.MSMDAERNet(
pretrained=False,
number_of_source=len(source_loaders),
number_of_category=category_number),
source_loaders=source_loaders,
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 __init__(self, model=models.MSMDAERNet(), source_loaders=0, target_loader=0, batch_size=64, iteration=10000, lr=0.001, momentum=0.9, log_interval=10):
self.model = model
self.model.to(device)
self.source_loaders = source_loaders
self.target_loader = target_loader
self.batch_size = batch_size
self.iteration = iteration
self.lr = lr
self.momentum = momentum
self.log_interval = log_interval
>>>>>>> Stashed changes
del one_session_label
del one_session_data
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 = MSMDAER(model=models.MSMDAERNet(pretrained=False, number_of_source=len(source_loaders), number_of_category=category_number),
source_loaders=source_loaders,
target_loader=target_loader,
batch_size=batch_size,
iteration=iteration,
lr=lr,
momentum=momentum,
log_interval=log_interval)
# print(model.__getModel__())
acc = model.train()
print('Target_subject_id: {}, current_session_id: {}, acc: {}'.format(test_idx, session_id, acc))
return acc
<<<<<<< Updated upstream
def cross_session(data, label, subject_id, category_number, batch_size, iteration, lr, momentum, log_interval):
