def _main(name):
import tensorflow as tf
starter = get_starter(name)
continue_on_error = FLAGS.continue_on_error
if starter.has_checkpoint:
try:
graph = tf.Graph()
with graph.as_default():
image = tf.placeholder(shape=(None, 311, 311, 3),
dtype=tf.float32)
fn = starter.get_network_fn(is_training=True)
fn(image)
var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
saver = tf.train.Saver(var_list=var_list)
with tf.Session(graph=graph) as sess:
ckpt = starter.get_checkpoint()
saver.restore(sess, ckpt)
return True
except Exception:
if continue_on_error:
return False
else:
raise
else:
return None
def _main(name):
starter = get_starter(name)
print(name)
if starter.url is not None:
print(starter.get_checkpoint())
else:
print('No url')
print('---------------------------------------')
def main(_):
from tensorflow.python.tools.inspect_checkpoint import \
print_tensors_in_checkpoint_file
from slim_start import get_starter
name = FLAGS.name
starter = get_starter(name)
latest_ckp = starter.get_checkpoint()
print_tensors_in_checkpoint_file(latest_ckp,
tensor_name='',
all_tensors=False,
all_tensor_names=True)
def _main(name):
starter = get_starter(name)
print(name)
if starter.url is not None:
if starter.clean_archive():
print('Cleaned')
else:
print('No archive present')
else:
print('No url')
print('---------------------------------------')
def main(_):
import tensorflow as tf
from slim_start import get_starter
name = FLAGS.name
starter = get_starter(name)
image = tf.zeros((2, 224, 224, 3), dtype=tf.float32)
with tf.contrib.slim.arg_scope(starter.get_scope()):
out, endpoints = starter.get_unscoped_network_fn(
num_classes=None)(image)
if FLAGS.endpoints:
for k, v in endpoints.items():
print('%s: %s' % (k, str(v.shape)))
else:
vars = tf.get_collection(FLAGS.collection, scope=FLAGS.scope)
for var in vars:
print(var.name)
loss = slim.losses.get_total_loss()
optimizer = tf.train.AdamOptimizer()
train_op = slim.learning.create_train_op(loss, optimizer)
kwargs['loss'] = loss
kwargs['eval_metric_ops'] = dict(accuracy=accuracy)
kwargs['train_op'] = train_op
return tf.estimator.EstimatorSpec(**kwargs)
name = 'mobilenet_v2'
weight_decay = 0.0
num_classes = 10
bn_decay = 0.9
starter = get_starter(name)
vars_to_warm_start = 'MobilenetV2/*'
warm_start_settings = tf.estimator.WarmStartSettings(starter.get_checkpoint(),
vars_to_warm_start)
def logits_fn(features, mode):
is_training = mode == ModeKeys.TRAIN
f = starter.get_scoped_network_fn(is_training=is_training,
weight_decay=weight_decay,
bn_decay=bn_decay)
x, _ = f(features, base_only=True)
x = tf.reduce_mean(x, axis=(1, 2))
x = tf.layers.dense(x, num_classes)
return x