def load(self, data_path, session, ignore_missing=False):
'''
Load network weights.
data_path: The path to the numpy-serialized network weights
session: The current TensorFlow session
ignore_missing: If true, serialized weights for missing layers are ignored.
'''
data_dict = np.load(data_path, encoding='bytes').item()
for op_name, param_dict in data_dict.items():
if isinstance(data_dict[op_name], np.ndarray):
if 'RMSProp' in op_name:
continue
with tf.variable_scope('', reuse=True):
var = tf.get_variable(op_name.replace(':0', ''))
try:
session.run(var.assign(data_dict[op_name]))
except Exception as e:
print(op_name)
print(e)
sys.exit(-1)
else:
op_name = to_str(op_name)
# if op_name > 'conv4':
# print(op_name, 'skipped')
# continue
# print(op_name, 'restored')
with tf.variable_scope(op_name, reuse=True):
for param_name, data in param_dict.items():
try:
var = tf.get_variable(to_str(param_name))
session.run(var.assign(data))
except ValueError as e:
print(e)
if not ignore_missing:
raise
def load_from_numpy(sess, net, data_path, logger = None, ignore_missing=False):
'''
Load network weights from numpy.
data_path: The path to the numpy-serialized network weights
session: The current TensorFlow session
ignore_missing: If true, serialized weights for missing layers are ignored.
'''
data_dict = np.load(data_path, allow_pickle=True, encoding='bytes').item()
for op_name, param_dict in data_dict.items():
if isinstance(data_dict[op_name], np.ndarray):
if op_name not in net.restorable_variables():
continue
with tf.variable_scope('', reuse=True):
var = tf.get_variable(op_name.replace(':0', ''))
try:
sess.run(var.assign(data_dict[op_name]))
except Exception as ex:
if logger:
logger.error(op_name)
logger.error(ex)
else:
print(op_name)
print(ex)
sys.exit(-1)
else:
op_name = to_str(op_name)
with tf.variable_scope(op_name, reuse=True):
for param_name, data in param_dict.items():
try:
var = tf.get_variable(to_str(param_name))
sess.run(var.assign(data))
except ValueError as ex:
if logger:
logger.error(param_name)
logger.error(ex)
else:
print(param_name)
print(ex)
if not ignore_missing:
raise
