def get_model_init_fn(train_logdir,
tf_initial_checkpoint,
initialize_last_layer,
last_layers,
ignore_missing_vars=True):
"""Gets the function initializing model variables from a checkpoint.
Args:
train_logdir: Log directory for training.
tf_initial_checkpoint: TensorFlow checkpoint for initialization.
initialize_last_layer: Initialize last layer or not.
last_layers: Last layers of the model.
ignore_missing_vars: Ignore missing variables in the checkpoint.
Returns:
Initialization function.
"""
if tf_initial_checkpoint is None:
tf.logging.info('Not initializing the model from a checkpoint.')
return None
if tf.train.latest_checkpoint(train_logdir):
tf.logging.info('Ignoring initialization; other checkpoint exists')
return None
tf.logging.info('Initializing model from path: %s', tf_initial_checkpoint)
# Variables that will not be restored.
# MobilenetV3_Large
#exclude_list = ['global_step']
# MobilenetV3_Small
#exclude_list = ['global_step', 'image_pooling/', 'aspp0/', 'decoder/feature_projection0', 'MobilenetV3/expanded_conv/squeeze_excite/']
exclude_list = ['global_step', 'decoder/feature_projection0', 'decoder/decoder_conv0_depthwise', 'decoder/decoder_conv0_pointwise', 'decoder/decoder_conv0_pointwise/']
#exclude_list = ['global_step']
if not initialize_last_layer:
exclude_list.extend(last_layers)
variables_to_restore = contrib_framework.get_variables_to_restore(
exclude=exclude_list)
if variables_to_restore:
init_op, init_feed_dict = contrib_framework.assign_from_checkpoint(
tf_initial_checkpoint,
variables_to_restore,
ignore_missing_vars=ignore_missing_vars)
global_step = tf.train.get_or_create_global_step()
def restore_fn(sess):
sess.run(init_op, init_feed_dict)
sess.run([global_step])
return restore_fn
return None
def get_model_init_fn(train_logdir,
tf_initial_checkpoint,
initialize_last_layer,
last_layers,
ignore_missing_vars=False):
"""Gets the function initializing model variables from a checkpoint.
Args:
train_logdir: Log directory for training.
tf_initial_checkpoint: TensorFlow checkpoint for initialization.
initialize_last_layer: Initialize last layer or not.
last_layers: Last layers of the model.
ignore_missing_vars: Ignore missing variables in the checkpoint.
Returns:
Initialization function.
"""
if tf_initial_checkpoint is None:
tf.compat.v1.logging.info(
'Not initializing the model from a checkpoint.')
return None
if tf.train.latest_checkpoint(train_logdir):
tf.compat.v1.logging.info(
'Ignoring initialization; other checkpoint exists')
return None
tf.compat.v1.logging.info('Initializing model from path: %s',
tf_initial_checkpoint)
# Variables that will not be restored.
exclude_list = ['global_step', 'logits']
if not initialize_last_layer:
exclude_list.extend(last_layers)
variables_to_restore = contrib_framework.get_variables_to_restore(
exclude=exclude_list)
if variables_to_restore:
init_op, init_feed_dict = contrib_framework.assign_from_checkpoint(
tf_initial_checkpoint,
variables_to_restore,
ignore_missing_vars=ignore_missing_vars)
global_step = tf.compat.v1.train.get_or_create_global_step()
def restore_fn(sess):
sess.run(init_op, init_feed_dict)
sess.run([global_step])
return restore_fn
return None
def train(train_dir,
config,
dataset_fn,
checkpoints_to_keep=5,
keep_checkpoint_every_n_hours=1,
num_steps=None,
master='',
num_sync_workers=0,
num_ps_tasks=0,
task=0):
"""Train loop."""
tf.gfile.MakeDirs(train_dir)
is_chief = (task == 0)
if is_chief:
_trial_summary(config.hparams, config.train_examples_path
or config.tfds_name, train_dir)
with tf.Graph().as_default():
with tf.device(
tf.train.replica_device_setter(num_ps_tasks,
merge_devices=True)):
model = config.model
model.build(config.hparams,
config.data_converter.output_depth,
encoder_train=config.encoder_train,
decoder_train=config.decoder_train)
optimizer = model.train(**_get_input_tensors(dataset_fn(), config))
restored_vars = _get_restore_vars(config.var_train_pattern)
_set_trainable_vars(config.var_train_pattern)
hooks = []
if num_sync_workers:
optimizer = tf.train.SyncReplicasOptimizer(
optimizer, num_sync_workers)
hooks.append(optimizer.make_session_run_hook(is_chief))
grads, var_list = zip(*optimizer.compute_gradients(model.loss))
global_norm = tf.global_norm(grads)
tf.summary.scalar('global_norm', global_norm)
if config.hparams.clip_mode == 'value':
g = config.hparams.grad_clip
clipped_grads = [
tf.clip_by_value(grad, -g, g) for grad in grads
]
elif config.hparams.clip_mode == 'global_norm':
clipped_grads = tf.cond(
global_norm < config.hparams.grad_norm_clip_to_zero,
lambda: tf.clip_by_global_norm(grads,
config.hparams.grad_clip,
use_norm=global_norm)[0],
lambda: [tf.zeros(tf.shape(g)) for g in grads])
else:
raise ValueError('Unknown clip_mode: {}'.format(
config.hparams.clip_mode))
train_op = optimizer.apply_gradients(zip(clipped_grads, var_list),
global_step=model.global_step,
name='train_step')
logging_dict = {
'global_step': model.global_step,
'loss': model.loss
}
hooks.append(
tf.train.LoggingTensorHook(logging_dict, every_n_iter=5))
if num_steps:
hooks.append(tf.train.StopAtStepHook(last_step=num_steps))
variables_to_restore = contrib_framework.get_variables_to_restore(
include=[v.name for v in restored_vars])
init_assign_op, init_feed_dict = contrib_framework.assign_from_checkpoint(
config.pretrained_path, variables_to_restore)
def InitAssignFn(scaffold, sess):
sess.run(init_assign_op, init_feed_dict)
scaffold = tf.train.Scaffold(
init_fn=InitAssignFn,
saver=tf.train.Saver(
max_to_keep=checkpoints_to_keep,
keep_checkpoint_every_n_hours=keep_checkpoint_every_n_hours,
))
contrib_training.train(train_op=train_op,
logdir=train_dir,
scaffold=scaffold,
hooks=hooks,
save_checkpoint_secs=60,
master=master,
is_chief=is_chief)
def get_model_init_fn(train_logdir,
tf_initial_checkpoint,
initialize_first_layer,
initialize_last_layer,
last_layers=None,
restore_adam=False,
ignore_missing_vars=False):
"""Gets the function initializing model variables from a checkpoint.
Args:
train_logdir: Log directory for training.
tf_initial_checkpoint: TensorFlow checkpoint for initialization.
initialize_last_layer: Initialize first layer or not.
initialize_last_layer: Initialize last layer or not.
last_layers: Last layers of the model.
restore_adam: Restore Adam optimization parameters or not.
ignore_missing_vars: Ignore missing variables in the checkpoint.
Returns:
Initialization function.
"""
if tf_initial_checkpoint is None:
tf.logging.info('Not initializing the model from a checkpoint.')
return None
if tf.train.latest_checkpoint(train_logdir):
tf.logging.info('Ignoring initialization; other checkpoint exists')
return None
tf.logging.info('Initializing model from path: %s', tf_initial_checkpoint)
# Variables that will not be restored.
exclude_list = ['global_step']
if not initialize_last_layer:
exclude_list.extend(last_layers)
if not initialize_first_layer:
exclude_list.append('resnet_v1_50/conv1_1/weights:0')
variables_to_restore = contrib_framework.get_variables_to_restore(
exclude=exclude_list)
# Restore without Adam parameters
if not restore_adam:
new_v = []
for v in variables_to_restore:
if "Adam" not in v.name:
new_v.append(v)
variables_to_restore = new_v
if variables_to_restore:
init_op, init_feed_dict = contrib_framework.assign_from_checkpoint(
tf_initial_checkpoint,
variables_to_restore,
ignore_missing_vars=ignore_missing_vars)
global_step = tf.train.get_or_create_global_step()
def restore_fn(scaffold, sess):
sess.run(init_op, init_feed_dict)
sess.run([global_step])
return restore_fn
return None