def restore(sess):
"""choose which param to restore"""
if FLAGS.pretrained_model:
if tf.gfile.IsDirectory(FLAGS.pretrained_model):
checkpoint_path = tf.train.latest_checkpoint(
FLAGS.pretrained_model)
else:
checkpoint_path = FLAGS.pretrained_model
if FLAGS.checkpoint_exclude_scopes is None:
FLAGS.checkpoint_exclude_scopes = 'pyramid'
if FLAGS.checkpoint_include_scopes is None:
FLAGS.checkpoint_include_scopes = 'resnet_v1_50'
vars_to_restore = get_var_list_to_restore()
for var in vars_to_restore:
print('restoring ', var.name)
try:
restorer = tf.train.Saver(vars_to_restore)
restorer.restore(sess, checkpoint_path)
print('Restored %d(%d) vars from %s' %
(len(vars_to_restore), len(
tf.global_variables()), checkpoint_path))
except:
print('Checking your params %s' % (checkpoint_path))
raise
def restore(sess):
"""choose which param to restore"""
if FLAGS.restore_previous_if_exists:
try:
print (FLAGS.train_dir)
checkpoint_path = tf.train.latest_checkpoint(FLAGS.train_dir)
reader = tf.train.NewCheckpointReader(checkpoint_path)
saved_shapes = reader.get_variable_to_shape_map()
var_names = sorted([(var.name, var.name.split(':')[0]) for var in tf.global_variables()if var.name.split(':')[0] in saved_shapes])
restore_vars = []
name2var = dict(zip(map(lambda x:x.name.split(':')[0], tf.global_variables()), tf.global_variables()))
with tf.variable_scope('', reuse=True):
for var_name, saved_var_name in var_names:
curr_var = name2var[saved_var_name]
var_shape = curr_var.get_shape().as_list()
if var_shape == saved_shapes[saved_var_name]:
restore_vars.append(curr_var)
restorer = tf.train.Saver(restore_vars)
restorer.restore(sess, checkpoint_path)
print ('restored previous model %s from %s'\
%(checkpoint_path, FLAGS.train_dir))
time.sleep(2)
return
except:
print ('--restore_previous_if_exists is set, but failed to restore in %s %s'\
% (FLAGS.train_dir, checkpoint_path))
time.sleep(2)
if FLAGS.pretrained_model:
if tf.gfile.IsDirectory(FLAGS.pretrained_model):
checkpoint_path = tf.train.latest_checkpoint(FLAGS.pretrained_model)
else:
checkpoint_path = FLAGS.pretrained_model
if FLAGS.checkpoint_exclude_scopes is None:
FLAGS.checkpoint_exclude_scopes='pyramid'
if FLAGS.checkpoint_include_scopes is None:
FLAGS.checkpoint_include_scopes='resnet_v1_50'
vars_to_restore = get_var_list_to_restore()
for var in vars_to_restore:
print ('restoring ', var.name)
try:
restorer = tf.train.Saver(vars_to_restore)
restorer.restore(sess, checkpoint_path)
print ('Restored %d(%d) vars from %s' %(
len(vars_to_restore), len(tf.global_variables()),
checkpoint_path ))
except:
print ('Checking your params %s' %(checkpoint_path))
raise
def restore(sess):
"""choose which param to restore"""
if FLAGS.restore_previous_if_exists:
try:
checkpoint_path = tf.train.latest_checkpoint(FLAGS.train_dir)
###########
restorer = tf.train.Saver()
restorer.restore(sess, checkpoint_path)
print ('restored previous model %s from %s'\
%(checkpoint_path, FLAGS.train_dir))
time.sleep(2)
return
except:
print ('--restore_previous_if_exists is set, but failed to restore in %s %s'\
% (FLAGS.train_dir, checkpoint_path))
time.sleep(2)
if FLAGS.pretrained_model:
if tf.gfile.IsDirectory(FLAGS.pretrained_model):
checkpoint_path = tf.train.latest_checkpoint(
FLAGS.pretrained_model)
else:
checkpoint_path = FLAGS.pretrained_model
if FLAGS.checkpoint_exclude_scopes is None:
FLAGS.checkpoint_exclude_scopes = 'pyramid'
if FLAGS.checkpoint_include_scopes is None:
FLAGS.checkpoint_include_scopes = 'resnet_v1_50'
vars_to_restore = get_var_list_to_restore()
for var in vars_to_restore:
print('restoring ', var.name)
try:
restorer = tf.train.Saver(vars_to_restore)
restorer.restore(sess, checkpoint_path)
print('Restored %d(%d) vars from %s' %
(len(vars_to_restore), len(
tf.global_variables()), checkpoint_path))
except:
print('Checking your params %s' % (checkpoint_path))
raise
def restore(sess):
"""choose which param to restore"""
if FLAGS.restore_previous_if_exists:
try:
checkpoint_path = tf.train.latest_checkpoint(FLAGS.train_dir)
restorer = tf.train.Saver()
restorer.restore(sess, checkpoint_path)
print ('restored previous model %s from %s'\
%(checkpoint_path, FLAGS.train_dir))
time.sleep(2)
return
except:
print ('--restore_previous_if_exists is set, but failed to restore in %s %s'\
% (FLAGS.train_dir, checkpoint_path))
time.sleep(2)
if FLAGS.pretrained_model:
if tf.gfile.IsDirectory(FLAGS.pretrained_model):
checkpoint_path = tf.train.latest_checkpoint(FLAGS.pretrained_model)
else:
checkpoint_path = FLAGS.pretrained_model
if FLAGS.checkpoint_exclude_scopes is None:
FLAGS.checkpoint_exclude_scopes='pyramid'
if FLAGS.checkpoint_include_scopes is None:
FLAGS.checkpoint_include_scopes='resnet_v1_50'
vars_to_restore = get_var_list_to_restore()
for var in vars_to_restore:
print ('restoring ', var.name)
try:
restorer = tf.train.Saver(vars_to_restore)
restorer.restore(sess, checkpoint_path)
print ('Restored %d(%d) vars from %s' %(
len(vars_to_restore), len(tf.global_variables()),
checkpoint_path ))
except:
print ('Checking your params %s' %(checkpoint_path))
raise
sess.run(init_op)
coord = tf.train.Coordinator()
tf.train.start_queue_runners(sess=sess, coord=coord)
## restore pretrained model
# FLAGS.pretrained_model = None
if FLAGS.pretrained_model:
if tf.gfile.IsDirectory(FLAGS.pretrained_model):
checkpoint_path = tf.train.latest_checkpoint(
FLAGS.pretrained_model)
else:
checkpoint_path = FLAGS.pretrained_model
FLAGS.checkpoint_exclude_scopes = 'pyramid'
FLAGS.checkpoint_include_scopes = 'resnet_v1_50'
vars_to_restore = get_var_list_to_restore()
for var in vars_to_restore:
print('restoring ', var.name)
try:
restorer = tf.train.Saver(vars_to_restore)
restorer.restore(sess, checkpoint_path)
print('Restored %d(%d) vars from %s' %
(len(vars_to_restore), len(
tf.global_variables()), checkpoint_path))
except:
print('Checking your params %s' % (checkpoint_path))
raise
# import libs.memory_util as memory_util
# memory_util.vlog(1)
def restore(sess):
"""choose which param to restore"""
if FLAGS.restore_previous_if_exists:
try:
checkpoint_path = tf.train.latest_checkpoint(FLAGS.train_dir)
###########
restorer = tf.train.Saver()
###########
###########
# not_restore = [ 'pyramid/fully_connected/weights:0',
# 'pyramid/fully_connected/biases:0',
# 'pyramid/fully_connected/weights:0',
# 'pyramid/fully_connected_1/biases:0',
# 'pyramid/fully_connected_1/weights:0',
# 'pyramid/fully_connected_2/weights:0',
# 'pyramid/fully_connected_2/biases:0',
# 'pyramid/fully_connected_3/weights:0',
# 'pyramid/fully_connected_3/biases:0',
# 'pyramid/Conv/weights:0',
# 'pyramid/Conv/biases:0',
# 'pyramid/Conv_1/weights:0',
# 'pyramid/Conv_1/biases:0',
# 'pyramid/Conv_2/weights:0',
# 'pyramid/Conv_2/biases:0',
# 'pyramid/Conv_3/weights:0',
# 'pyramid/Conv_3/biases:0',
# 'pyramid/Conv2d_transpose/weights:0',
# 'pyramid/Conv2d_transpose/biases:0',
# 'pyramid/Conv_4/weights:0',
# 'pyramid/Conv_4/biases:0',
# 'pyramid/fully_connected/weights/Momentum:0',
# 'pyramid/fully_connected/biases/Momentum:0',
# 'pyramid/fully_connected/weights/Momentum:0',
# 'pyramid/fully_connected_1/biases/Momentum:0',
# 'pyramid/fully_connected_1/weights/Momentum:0',
# 'pyramid/fully_connected_2/weights/Momentum:0',
# 'pyramid/fully_connected_2/biases/Momentum:0',
# 'pyramid/fully_connected_3/weights/Momentum:0',
# 'pyramid/fully_connected_3/biases/Momentum:0',
# 'pyramid/Conv/weights/Momentum:0',
# 'pyramid/Conv/biases/Momentum:0',
# 'pyramid/Conv_1/weights/Momentum:0',
# 'pyramid/Conv_1/biases/Momentum:0',
# 'pyramid/Conv_2/weights/Momentum:0',
# 'pyramid/Conv_2/biases/Momentum:0',
# 'pyramid/Conv_3/weights/Momentum:0',
# 'pyramid/Conv_3/biases/Momentum:0',
# 'pyramid/Conv2d_transpose/weights/Momentum:0',
# 'pyramid/Conv2d_transpose/biases/Momentum:0',
# 'pyramid/Conv_4/weights/Momentum:0',
# 'pyramid/Conv_4/biases/Momentum:0',]
# vars_to_restore = [v for v in tf.all_variables()if v.name not in not_restore]
# restorer = tf.train.Saver(vars_to_restore)
# for var in vars_to_restore:
# print ('restoring ', var.name)
############
restorer.restore(sess, checkpoint_path)
print ('restored previous model %s from %s'\
%(checkpoint_path, FLAGS.train_dir))
time.sleep(2)
return
except:
print ('--restore_previous_if_exists is set, but failed to restore in %s %s'\
% (FLAGS.train_dir, checkpoint_path))
time.sleep(2)
if FLAGS.pretrained_model:
if tf.gfile.IsDirectory(FLAGS.pretrained_model):
checkpoint_path = tf.train.latest_checkpoint(
FLAGS.pretrained_model)
else:
checkpoint_path = FLAGS.pretrained_model
if FLAGS.checkpoint_exclude_scopes is None:
FLAGS.checkpoint_exclude_scopes = 'pyramid'
if FLAGS.checkpoint_include_scopes is None:
FLAGS.checkpoint_include_scopes = 'resnet_v1_50'
vars_to_restore = get_var_list_to_restore()
for var in vars_to_restore:
print('restoring ', var.name)
try:
restorer = tf.train.Saver(vars_to_restore)
restorer.restore(sess, checkpoint_path)
print('Restored %d(%d) vars from %s' %
(len(vars_to_restore), len(
tf.global_variables()), checkpoint_path))
except:
print('Checking your params %s' % (checkpoint_path))
raise
