def main():
args = get_parser()
# create logger
if not os.path.exists(args.log_dir):
os.makedirs(args.log_dir)
#logger = Logger(str(args.log_dir)+'\'+name, args.manual_seed)
#logger.print ("args :\n{:}".format(args))
assert torch.cuda.is_available(), 'You must have at least one GPU'
# set random seed
torch.backends.cudnn.benchmark = True
np.random.seed(args.manual_seed)
torch.manual_seed(args.manual_seed)
torch.cuda.manual_seed(args.manual_seed)
name_list=[
'ECG200',
]
ratio_number_list = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]
#ratio_number_list = [0.1,0.2,0.3]
ind_number_list = [0,1,2,3,4,5,6,7,8,9]
for name in name_list:
for ratio_number_ind, ratio_number in enumerate(ratio_number_list):
for ind_number in ind_number_list:
result_matrix = load_proto_result(name,ratio_number_list, ind_number_list)
if result_matrix[ratio_number_ind][ind_number]!=0:
print(ratio_number,ind_number,'already done result = ', result_matrix[ratio_number_ind][ind_number])
continue
try:
proto_bag= [name,ratio_number,ind_number]
logger = Logger(str(args.log_dir)+'/'+name+'/'+str(ratio_number)+'/'+str(ind_number), args.manual_seed)
logger.print ("args :\n{:}".format(args))
# create dataloader
train_dataset = timeSeriesDataset(args.dataset_root, 'train', name,ratio_number, ind_number)
train_sampler = FewShotSampler(train_dataset.label, args.num_support_tr + args.num_query_tr, args.iterations)
train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_sampler=train_sampler, num_workers=args.workers)
test_dataset = timeSeriesDataset(args.dataset_root, 'test', name,ratio_number, ind_number)
test_sampler = FewShotSampler(test_dataset.label, args.num_support_tr + args.num_query_tr, 600)
test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_sampler=test_sampler, num_workers=args.workers)
# create model
model = models.__dict__[args.arch](train_dataset.fea_dim,256,64)
model = torch.nn.DataParallel(model).cuda()
logger.print ("model:::\n{:}".format(model))
criterion = nn.CrossEntropyLoss().cuda()
params = [p for p in model.parameters()]
optimizer = torch.optim.Adam(params, lr=args.lr, weight_decay=args.weight_decay)
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer=optimizer, gamma=args.lr_gamma, step_size=args.lr_step)
info_path ,test_acc1_avg= run(args, model, logger, criterion, optimizer, lr_scheduler, train_dataloader, test_dataloader,proto_bag)
logger.print ('save into {:}'.format(info_path))
sentence = 'dataset_name=\t'+name+'\t'+'ratio=\t'+str(ratio_number)+'\t'+'ind=\t'+str(ind_number)+'\t'+'test_acc=\t'+str(test_acc1_avg)
save_to_file(sentence, name)
except:
print(ratio_number)
def get_proto(args,model,proto_bag):
name = proto_bag[0]
ratio_number = proto_bag[1]
ind_number = proto_bag[2]
train_dataset = timeSeriesDataset(args.dataset_root, 'train', name,ratio_number, ind_number)
feas = train_dataset.feature
labels = train_dataset.label
embs = model(feas)
cpu_labels = labels.cpu().tolist()
idxs_dict = defaultdict(list)
for i, l in enumerate(cpu_labels):
idxs_dict[l].append(i)
idxs_dict = dict(sorted(idxs_dict.items()))
grouped_s_idxs = []
for lab, idxs in idxs_dict.items():
grouped_s_idxs.append(torch.LongTensor(idxs[:]))
proto_lst = [torch.mean(embs[s_idxs], dim=0) for s_idxs in grouped_s_idxs]
proto = torch.stack(proto_lst, dim=0)
return proto
def main():
args = get_parser()
args.dataset_root= os.getcwd()[:-18]+'/SFA_Python-master/test/BOSS_feature_Data_pyts/'
print(args.dataset_root)
#SFA_Python-master/test/BOSS_feature_Data_pyts/
# create logger
if not os.path.exists(args.log_dir):
os.makedirs(args.log_dir)
#logger = Logger(str(args.log_dir)+'\'+name, args.manual_seed)
#logger.print ("args :\n{:}".format(args))
assert torch.cuda.is_available(), 'You must have at least one GPU'
# set random seed
torch.backends.cudnn.benchmark = True
np.random.seed(args.manual_seed)
torch.manual_seed(args.manual_seed)
torch.cuda.manual_seed(args.manual_seed)
name_list=[
'ArrowHead',
'BME',
'CBF',
'Chinatown',
'ECG200',
'GunPoint',
'GunPointAgeSpan',
'GunPointOldVersusYoung',
'ItalyPowerDemand',
'MoteStrain',
'Plane',
'SonyAIBORobotSurface1',
'SonyAIBORobotSurface2',
'SyntheticControl',
'ToeSegmentation1',
'TwoLeadECG',
'UMD',
'Wine',
]
ratio_number_list = [1]
ind_number_list = [10]
for name in name_list:
for ratio_number_ind, ratio_number in enumerate(ratio_number_list):
for ind_number in ind_number_list:
#try:
proto_bag= [name,ratio_number,ind_number]
logger = Logger(str(args.log_dir)+'/'+name+'/'+str(ratio_number)+'/'+str(ind_number), args.manual_seed)
logger.print ("args :\n{:}".format(args))
# create dataloader
train_dataset = timeSeriesDataset(args.dataset_root, 'train', name,ratio_number, ind_number)
train_sampler = FewShotSampler(train_dataset.label, args.num_support_tr + args.num_query_tr, args.iterations)
train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_sampler=train_sampler, num_workers=args.workers)
test_dataset = timeSeriesDataset(args.dataset_root, 'test', name,ratio_number, ind_number)
test_sampler = FewShotSampler(test_dataset.label, args.num_support_tr + args.num_query_tr, 600)
test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_sampler=test_sampler, num_workers=args.workers)
# create model
model = models.__dict__[args.arch](train_dataset.fea_dim,256,64)
model = torch.nn.DataParallel(model).cuda()
logger.print ("model:::\n{:}".format(model))
criterion = nn.CrossEntropyLoss().cuda()
params = [p for p in model.parameters()]
optimizer = torch.optim.Adam(params, lr=args.lr, weight_decay=args.weight_decay)
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer=optimizer, gamma=args.lr_gamma, step_size=args.lr_step)
info_path ,test_acc1_avg= run(args, model, logger, criterion, optimizer, lr_scheduler, train_dataloader, test_dataloader,proto_bag)
logger.print ('save into {:}'.format(info_path))
sentence = 'dataset_name=\t'+name+'\t'+'ratio=\t'+str(ratio_number)+'\t'+'ind=\t'+str(ind_number)+'\t'+'test_acc=\t'+str(test_acc1_avg)
save_to_file(sentence, name)
def load_SFA_train_data(name):
args_dataset_root = '/home/tangw/Desktop/TSC/BOSS_NN/SFA_Python-master/test/BOSS_feature_Data_pyts/'
train_dataset = timeSeriesDataset(args_dataset_root, 'train', name, 1, 10)
return train_dataset
name_list = [
'ECG200',
]
ratio_number_list = [1]
ind_number_list = [10]
args_arch = 'linear_transform'
args_dataset_root = '/home/tangw/Desktop/TSC/BOSS_NN/SFA_Python-master/test/BOSS_feature_Data_pyts/'
using_proto = True
pick_all = True
for name in name_list:
for ind_radio, ratio_number in enumerate(ratio_number_list):
for ind, ind_number in enumerate(ind_number_list):
model_lst_path = get_log_path(name, ratio_number, ind_number)
train_dataset = timeSeriesDataset(args_dataset_root, 'train', name,
ratio_number, ind_number)
model = models.__dict__[args_arch](train_dataset.fea_dim, 256, 64)
model = torch.nn.DataParallel(model).cuda()
checkpoint = torch.load(model_lst_path)
start_epoch = checkpoint['epoch'] + 1
best_acc = checkpoint['best_acc']
model.load_state_dict(checkpoint['model_state_dict'])
model.eval()
feas = train_dataset.feature
labels = train_dataset.label
embs = model(feas)
cpu_labels = labels.cpu().tolist()
idxs_dict = defaultdict(list)