def fill_pf_list(pf_list, n_pf, fill_with_fnc=(identity,())):
""" Fill the pre-process function list.
Args:
pf_list (list): input list of pre-process functions
n_pf (int): required number of pre-process functions
fill_with_fnc: function used to fill the list
Returns:
list of pre-process function
"""
if pf_list == None:
return [fill_with_fnc for i in range(n_pf)]
new_list = []
pf_list = utils.make_list(pf_list)
for pf in pf_list:
if not pf:
pf = fill_with_fnc
new_list.append(pf)
pf_list = new_list
if len(pf_list) > n_pf:
raise ValueError('Invalid number of preprocessing functions')
pf_list = pf_list + [fill_with_fnc for i in range(n_pf - len(pf_list))]
return pf_list
def __init__(self,
input_len,
im_size,
n_channels,
cat_n_class_list,
n_continuous=0,
n_discrete=0,
mutual_info_weight=1.0,
max_grad_norm=10.):
"""
Args:
input_len (int): length of input random vector
im_size (int or list with length 2): size of generate image
n_channels (int): number of image channels
n_latent (int): number of structured latent variables
mutual_info_weight (float): weight for mutual information regularization term
"""
im_size = L.get_shape2D(im_size)
self.in_len = input_len
self.im_h, self.im_w = im_size
self.n_channels = n_channels
self.n_code = input_len
assert n_discrete >= 0 and n_continuous >= 0
self.n_continuous = n_continuous
self.n_discrete = n_discrete
self.cat_n_class_list = utils.make_list(cat_n_class_list)
assert len(self.cat_n_class_list) >= self.n_discrete
self.n_latent = n_continuous + n_discrete
self._lambda = mutual_info_weight
self._max_grad_norm = max_grad_norm
self.layers = {}
def __init__(
self,
data_name_list,
data_dir='',
shuffle=True,
batch_dict_name=None,
load_fnc_list=None,
):
"""
Args:
data_name_list (list of str): list of filenames or part of filenames
of each data channel
data_dir (list of str): list of directories of each data channel
shuffle (bool): whether shuffle data or not
batch_dict_name (list of str): list of keys for each channel of batch data
load_fnc_list (list): list of pre-process functions for each channel of data
"""
data_name_list = utils.make_list(data_name_list)
load_fnc_list = utils.make_list(load_fnc_list)
data_dir = utils.make_list(data_dir)
# pad data_dir with the same path only when data_dir is a single input
if len(data_dir) == 1:
data_dir_list = [data_dir[0] for i in range(len(load_fnc_list))]
data_dir = data_dir_list
dataflow_list = []
self._load_fnc_list = []
for data_name, load_fnc in zip(data_name_list, load_fnc_list):
if data_name is not None and load_fnc is not None:
dataflow_list.append(data_name)
self._load_fnc_list.append(load_fnc)
else:
break
self._n_dataflow = len(dataflow_list)
self._shuffle = shuffle
self._batch_dict_name = batch_dict_name
self._data_id = 0
self.setup(epoch_val=0, batch_size=1)
self._load_file_list(dataflow_list, data_dir)
self._cur_file_name = [[] for i in range(len(self._file_name_list))]
def __init__(
self,
data_name_list,
data_dir='',
shuffle=True,
batch_dict_name=None,
load_fnc_list=None,
):
data_name_list = utils.make_list(data_name_list)
load_fnc_list = utils.make_list(load_fnc_list)
data_dir = utils.make_list(data_dir)
if len(data_dir) == 1:
data_dir_list = [data_dir[0] for i in range(len(load_fnc_list))]
data_dir = data_dir_list
dataflow_list = []
self._load_fnc_list = []
for data_name, load_fnc in zip(data_name_list, load_fnc_list):
if data_name is not None and load_fnc is not None:
dataflow_list.append(data_name)
self._load_fnc_list.append(load_fnc)
else:
break
self._n_dataflow = len(dataflow_list)
# pf_list = utils.make_list(pf_list)
# utils.assert_len([data_name_list, load_fnc_list])
# self._n_dataflow = len(data_name_list)
# self._load_fnc_list = load_fnc_list
# self._data_dir = data_dir
self._shuffle = shuffle
self._batch_dict_name = batch_dict_name
self._data_id = 0
self.setup(epoch_val=0, batch_size=1)
self._load_file_list(dataflow_list, data_dir)
self._cur_file_name = [[] for i in range(len(self._file_name_list))]
def fill_pf_list(pf_list, n_pf, fill_with_fnc=identity):
if pf_list == None:
return [identity for i in range(n_pf)]
# pf_list = [pf for pf in pf_list if pf is not None else identity]
pf_list = utils.make_list(pf_list)
new_list = []
for pf in pf_list:
if not pf:
pf = identity
new_list.append(pf)
pf_list = new_list
if len(pf_list) > n_pf:
raise ValueError('Invalid number of preprocessing functions')
pf_list = pf_list + [fill_with_fnc for i in range(n_pf - len(pf_list))]
return pf_list