def main(argv=None):
# pylint: disable=unused-argument
# pylint: disable=unused-variable
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.per_process_gpu_memory_fraction = 1.0
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
default_params = get_arguments()
log_dir = default_params.log_dir
ad = pathlib.Path(log_dir)
if not ad.exists():
ad.mkdir(parents=True)
# This makes sure that we can store a json and recove a namespace back
flags = Namespace(utils.load_and_save_params(vars(default_params),
log_dir))
def main(argv=None):
# pylint: disable=unused-argument
# pylint: disable=unused-variable
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.per_process_gpu_memory_fraction = 1.0
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
# Gets parameters.
default_params = get_arguments()
# Creates the experiment directory.
log_dir = default_params.log_dir
ad = pathlib.Path(log_dir)
if not ad.exists():
ad.mkdir(parents=True)
# Main function for training and evaluation.
flags = Namespace(utils.load_and_save_params(vars(default_params),
log_dir, ignore_existing=True))
train(flags=flags)
def __init__(self, model_path, batch_size, train_dataset, test_dataset):
self.train_batch_size = batch_size
self.test_batch_size = batch_size
self.test_dataset = test_dataset
self.train_dataset = train_dataset
latest_checkpoint = tf.train.latest_checkpoint(
checkpoint_dir=os.path.join(model_path, 'train'))
print(latest_checkpoint)
step = int(os.path.basename(latest_checkpoint).split('-')[1])
flags = Namespace(
utils.load_and_save_params(default_params=dict(), exp_dir=model_path))
image_size = data_loader.get_image_size(flags.dataset)
self.flags = flags
with tf.Graph().as_default():
self.tensor_images, self.tensor_labels = placeholder_inputs(
batch_size=self.train_batch_size,
image_size=image_size,
scope='inputs')
if flags.dataset == 'cifar10' or flags.dataset == 'cifar100':
tensor_images_aug = data_loader.augment_cifar(
self.tensor_images, is_training=False)
else:
tensor_images_aug = data_loader.augment_tinyimagenet(
self.tensor_images, is_training=False)
model = build_model(flags)
with tf.variable_scope('Proto_training'):
self.representation, self.variance = build_feature_extractor_graph(
inputs=tensor_images_aug,
flags=flags,
is_variance=True,
is_training=False,
model=model)
self.tensor_train_rep, self.tensor_test_rep, \
self.tensor_train_rep_label, self.tensor_test_rep_label,\
self.center = get_class_center_for_evaluation(
self.train_batch_size, self.test_batch_size, flags.num_classes_total)
self.prediction, self.acc \
= make_predictions_for_evaluation(self.center,
self.tensor_test_rep,
self.tensor_test_rep_label,
self.flags)
self.tensor_test_variance = tf.placeholder(
shape=[self.test_batch_size, feature_dim], dtype=tf.float32)
self.nll, self.confidence = confidence_estimation_and_evaluation(
self.center, self.tensor_test_rep, self.tensor_test_variance,
self.tensor_test_rep_label, flags)
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.allow_growth = True
self.sess = tf.Session(config=config)
# Runs init before loading the weights
self.sess.run(tf.global_variables_initializer())
# Loads weights
saver = tf.train.Saver()
saver.restore(self.sess, latest_checkpoint)
self.flags = flags
self.step = step
log_dir = flags.log_dir
graphpb_txt = str(tf.get_default_graph().as_graph_def())
with open(os.path.join(log_dir, 'eval', 'graph.pbtxt'), 'w') as f:
f.write(graphpb_txt)
