# whether use cuda
use_cuda = torch.cuda.is_available()
if use_cuda:
m_cnnblstm_with_adabn = cnnblstm_with_adabn(
use_cuda=1, params_dir=PARAMS_PATH_SYS,
enable_CORAL=enable_CORAL).cuda()
else:
m_cnnblstm_with_adabn = cnnblstm_with_adabn(use_cuda=0,
params_dir=PARAMS_PATH_SYS,
enable_CORAL=enable_CORAL)
print(m_cnnblstm_with_adabn)
# get train_x, train_y
train_x, train_y = tools_6dmg.preprocess(TRAIN_PATH_SYS)
# enable Kalman
if enable_Kalman:
train_x = tools.Kalman_Xs(train_x)
print(train_x.shape)
# enable PCA
if enable_PCA:
train_x = tools.PCA_Xs(train_x).astype(np.float32)
print(train_x.shape)
"""
Y, segments, maxlen_seg, n_files, seq_length = tools.getAllData(TRAIN_PATH)
train_x, train_y, _ = tools.transferData(Y, segments, n_files, seq_length)
"""
# get test_x, test_y
test_x, test_y = tools_6dmg.preprocess(TEST_PATH_SYS)
# enable Kalman
if enable_Kalman:
test_x = tools.Kalman_Xs(test_x)
print(test_x.shape)
PATH_SYS = sys.argv[2] enable_Kalman = (sys.argv[3] == "true") enable_PCA = (sys.argv[4] == "true") # whether use cuda use_cuda = torch.cuda.is_available() if use_cuda: cnnblstm = cnnblstm(params_file = "./params_6dmg.pkl", use_cuda = use_cuda).cuda() else: cnnblstm = cnnblstm(params_file = "./params_6dmg.pkl", use_cuda = use_cuda) print(cnnblstm) if (CMD == "train"): # get train_x, train_y train_x, train_y = tools_6dmg.preprocess(PATH_SYS) # enable Kalman if enable_Kalman: train_x = tools.Kalman_Xs(train_x).astype(np.float32) print(train_x.shape) # enable PCA if enable_PCA: train_x = tools.PCA_Xs(train_x).astype(np.float32) print(train_x.shape) train_x = torch.from_numpy(train_x) train_y = torch.from_numpy(train_y) # trainAllLayers cnnblstm.trainAllLayers(train_x, train_y, n_epoches = 30) elif (CMD == "test"): # get test_x, test_y test_x, test_y = tools_6dmg.preprocess(PATH_SYS) # enable Kalman if enable_Kalman: test_x = tools.Kalman_Xs(test_x).astype(np.float32)
m_transfer_cnnblstm_with_adabn = transfer_cnnblstm_with_adabn(use_cuda = use_cuda, params_dir = PARAMS_PATH, transfer_params_dir = TRANSFER_PARAMS_PATH) print(m_transfer_cnnblstm_with_adabn) # get transfer_x & transfer_y if IS_6DMG: transfer_x, transfer_y = tools_6dmg.preprocess(TRANSFER_PATH) else: Y, segments, maxlen_seg, n_files, seq_length = tools.getAllData(TRANSFER_PATH) transfer_x, transfer_y, _ = tools.transferData(Y, segments, n_files, seq_length) # get permutation index per = np.random.permutation(transfer_x.shape[0]) # premute transfer_x & transfer_y transfer_x = transfer_x[per, :, :] transfer_y = transfer_y[per] # enable Kalman if enable_Kalman: transfer_x = tools.Kalman_Xs(transfer_x).astype(np.float32) # print(transfer_x.shape) # enable PCA if enable_PCA: transfer_x = tools.PCA_Xs(transfer_x).astype(np.float32) # print(transfer_x.shape) # get train_x & train_y if N_TRAINSET != 0: train_x = torch.from_numpy(transfer_x[:N_TRAINSET, :, :]) train_y = torch.from_numpy(transfer_y[:N_TRAINSET]) # print(train_x.shape, train_y.shape) else: train_x, train_y = None, None # get test_x & test_y if (N_TRAINSET != 0): test_x = torch.from_numpy(transfer_x[N_TRAINSET:, :, :])