def load_weights(self):
"""
Creates operations to load weights from checkpoint for each of the
variables defined in the module. It is assumed that all variables
of the module are included in the checkpoint but with a different
prefix.
Returns:
load_op: Load weights operation or no_op.
"""
if self._config.get('weights') is None and \
not self._config.get('download'):
return tf.no_op(name='not_loading_base_network')
if self._config.get('weights') is None:
# Download the weights (or used cached) if not specified in the
# config file.
# Weights are downloaded by default to the $LUMI_HOME folder if
# running locally, or to the job bucket if running in Google Cloud.
# TODO: Shouldn't _config['weights'] be called weights_path or
# something similar?
self._config['weights'] = get_checkpoint_file(self._architecture)
if self.pretrained_weights_scope:
# We may have defined the base network in a particular scope
module_variables = tf.get_collection(
tf.GraphKeys.MODEL_VARIABLES,
scope=self.pretrained_weights_scope)
else:
module_variables = snt.get_variables_in_module(
self, tf.GraphKeys.MODEL_VARIABLES)
assert len(module_variables) > 0
load_variables = []
variables = [(v, v.op.name) for v in module_variables]
variable_scope_len = len(self.variable_scope.name) + 1
for var, var_name in variables:
checkpoint_var_name = var_name[variable_scope_len:]
var_value = tf.contrib.framework.load_variable(
self._config['weights'], checkpoint_var_name)
load_variables.append(tf.assign(var, var_value))
tf.logging.info('Constructing op to load {} variables from pretrained '
'checkpoint {}'.format(len(load_variables),
self._config['weights']))
load_op = tf.group(*load_variables)
return load_op
def load_weights(self):
"""
Creates operations to load weights from checkpoint for each of the
variables defined in the module. It is assumed that all variables
of the module are included in the checkpoint but with a different
prefix.
Returns:
load_op: Load weights operation or no_op.
"""
if self._config.get('weights') is None and \
not self._config.get('download'):
return tf.no_op(name='not_loading_base_network')
if self._config.get('weights') is None:
# Download the weights (or used cached) if not specified in the
# config file.
# Weights are downloaded by default to the ~/.luminoth folder if
# running locally, or to the job bucket if running in Google Cloud.
self._config['weights'] = get_checkpoint_file(self._architecture)
module_variables = snt.get_variables_in_module(
self, tf.GraphKeys.MODEL_VARIABLES
)
assert len(module_variables) > 0
load_variables = []
variables = [(v, v.op.name) for v in module_variables]
variable_scope_len = len(self.variable_scope.name) + 1
for var, var_name in variables:
checkpoint_var_name = var_name[variable_scope_len:]
var_value = tf.contrib.framework.load_variable(
self._config['weights'], checkpoint_var_name
)
load_variables.append(
tf.assign(var, var_value)
)
tf.logging.info(
'Constructing op to load {} variables from pretrained '
'checkpoint {}'.format(
len(load_variables), self._config['weights']
))
load_op = tf.group(*load_variables)
return load_op
def load_weights(self):
"""
Creates operations to load weigths from checkpoint for each of the
variables defined in the module. It is assumed that all variables
of the module are included in the checkpoint but with a different
prefix.
Returns:
load_op: Load weights operation or no_op.
"""
if self._config.get('weights') is None and \
not self._config.get('download'):
return tf.no_op(name='not_loading_base_network')
if self._config.get('weights') is None:
# Download the weights (or used cached) if is is not specified in
# config file.
# Weights are downloaded by default on the ~/.luminoth folder.
self._config['weights'] = get_checkpoint_file(self._architecture)
module_variables = snt.get_variables_in_module(
self, tf.GraphKeys.MODEL_VARIABLES)
assert len(module_variables) > 0
load_variables = []
variables = [(v, v.op.name) for v in module_variables]
variable_scope_len = len(self.variable_scope.name) + 1
for var, var_name in variables:
checkpoint_var_name = var_name[variable_scope_len:]
var_value = tf.contrib.framework.load_variable(
self._config['weights'], checkpoint_var_name)
load_variables.append(tf.assign(var, var_value))
tf.logging.info('Constructing op to load {} variables from pretrained '
'checkpoint {}'.format(len(load_variables),
self._config['weights']))
load_op = tf.group(*load_variables)
return load_op
def get_checkpoint_file(self):
return get_checkpoint_file(self._architecture)
