def create_init_fn_to_restore(self,
master_checkpoint,
inception_checkpoint=None):
"""Creates an init operations to restore weights from various checkpoints.
Args:
master_checkpoint: path to a checkpoint which contains all weights for the
whole model.
inception_checkpoint: path to a checkpoint which contains weights for the
inception part only.
Returns:
a function to run initialization ops.
"""
all_assign_ops = []
all_feed_dict = {}
def assign_from_checkpoint(variables, checkpoint):
logging.info('Request to re-store %d weights from %s',
len(variables), checkpoint)
if not variables:
logging.error('Can\'t find any variables to restore.')
sys.exit(1)
assign_op, feed_dict = slim.assign_from_checkpoint(
checkpoint, variables)
all_assign_ops.append(assign_op)
all_feed_dict.update(feed_dict)
# For the reduced charset restore all variables except AttentionOcr_v1/sequence_logit_fn/SQLR/softmax_w
logging.info('\n variables_to_restore:\n%s',
utils.variables_to_restore().keys())
logging.info(
'moving_average_variables:\n%s',
[v.op.name for v in tf.compat.v1.moving_average_variables()])
logging.info('trainable_variables:\n%s',
[v.op.name for v in tf.compat.v1.trainable_variables()])
if master_checkpoint:
assign_from_checkpoint(utils.variables_to_restore(),
master_checkpoint)
if inception_checkpoint:
variables = utils.variables_to_restore(
'AttentionOcr_v1/conv_tower_fn/INCE', strip_scope=True)
assign_from_checkpoint(variables, inception_checkpoint)
def init_assign_fn(sess):
logging.info('Restoring checkpoint(s)')
sess.run(all_assign_ops, all_feed_dict)
return init_assign_fn
def create_init_fn_to_restore(self, master_checkpoint,
caps_checkpoint=None,
trainable_base=True):
"""Creates an init operations to restore weights from various checkpoints.
Args:
master_checkpoint: path to a checkpointwhich contains all weights for
the whole model.
inception_checkpoint: path to a checkpoint which contains weights for the
inception part only.
Returns:
a function to run initialization ops.
"""
all_assign_ops = []
all_feed_dict = {}
def assign_from_checkpoint(variables, checkpoint):
logging.info('Request to re-store %d weights from %s',
len(variables), checkpoint)
if not variables:
logging.error('Can\'t find any variables to restore.')
sys.exit(1)
assign_op, feed_dict = slim.assign_from_checkpoint(checkpoint, variables)
all_assign_ops.append(assign_op)
all_feed_dict.update(feed_dict)
logging.info('variables_to_restore:\n%s' % utils.variables_to_restore().keys())
logging.info('moving_average_variables:\n%s' % [v.op.name for v in tf.moving_average_variables()])
logging.info('trainable_variables:\n%s' % [v.op.name for v in tf.trainable_variables()])
if master_checkpoint:
assign_from_checkpoint(utils.variables_to_restore(), master_checkpoint)
if caps_checkpoint:
variables = utils.variables_to_restore(
'AttentionOcr_v1/caps_fn/CAPS',
strip_scope=True)
if not trainable_base:
utils.variables_to_freeze(variables)
assign_from_checkpoint(variables, caps_checkpoint)
def init_assign_fn(sess):
logging.info('Restoring checkpoint(s)')
sess.run(all_assign_ops, all_feed_dict)
return init_assign_fn
def create_init_fn_to_restore(self, master_checkpoint,
inception_checkpoint=None):
"""Creates an init operations to restore weights from various checkpoints.
Args:
master_checkpoint: path to a checkpoint which contains all weights for
the whole model.
inception_checkpoint: path to a checkpoint which contains weights for the
inception part only.
Returns:
a function to run initialization ops.
"""
all_assign_ops = []
all_feed_dict = {}
def assign_from_checkpoint(variables, checkpoint):
logging.info('Request to re-store %d weights from %s',
len(variables), checkpoint)
if not variables:
logging.error('Can\'t find any variables to restore.')
sys.exit(1)
assign_op, feed_dict = slim.assign_from_checkpoint(checkpoint, variables)
all_assign_ops.append(assign_op)
all_feed_dict.update(feed_dict)
logging.info('variables_to_restore:\n%s' % utils.variables_to_restore().keys())
logging.info('moving_average_variables:\n%s' % [v.op.name for v in tf.moving_average_variables()])
logging.info('trainable_variables:\n%s' % [v.op.name for v in tf.trainable_variables()])
if master_checkpoint:
assign_from_checkpoint(utils.variables_to_restore(), master_checkpoint)
if inception_checkpoint:
variables = utils.variables_to_restore(
'AttentionOcr_v1/conv_tower_fn/INCE', strip_scope=True)
assign_from_checkpoint(variables, inception_checkpoint)
def init_assign_fn(sess):
logging.info('Restoring checkpoint(s)')
sess.run(all_assign_ops, all_feed_dict)
return init_assign_fn
def create_init_fn_to_restore(self,
master_checkpoint,
inception_checkpoint=None):
"""Creates an init operations to restore weights from various checkpoints.
Args:
master_checkpoint: path to a checkpoint which contains all weights for
the whole model.
inception_checkpoint: path to a checkpoint which contains weights for the
inception part only.
Returns:
a function to run initialization ops.
"""
all_assign_ops = []
all_feed_dict = {}
def assign_from_checkpoint(variables, checkpoint):
logging.info('Request to re-store %d weights from %s',
len(variables), checkpoint)
if not variables:
logging.error('Can\'t find any variables to restore.')
sys.exit(1)
assign_op, feed_dict = slim.assign_from_checkpoint(
checkpoint, variables)
all_assign_ops.append(assign_op)
all_feed_dict.update(feed_dict)
if master_checkpoint:
assign_from_checkpoint(utils.variables_to_restore(),
master_checkpoint)
if inception_checkpoint:
variables = utils.variables_to_restore(
'AttentionOcr_v1/conv_tower_fn/INCE', strip_scope=True)
assign_from_checkpoint(variables, inception_checkpoint)
def init_assign_fn(sess):
logging.info('Restoring checkpoint(s)')
sess.run(all_assign_ops, all_feed_dict)
return init_assign_fn
