def __init__(self, base_model_path, scaffold_path, **kwargs):
self.base_model_path = base_model_path
self.model_name = base_model_path.split('/')[
-1] # ex inception_resnet_v2
self.scaffold_path = scaffold_path
self.settings = DEFAULT_SETTINGS
for key in kwargs:
self.settings[key] = kwargs[key]
if 'base_checkpoint_path' not in self.settings:
checkpoint_dir = os.path.abspath(
os.path.join(self.base_model_path, 'state'))
if tf.train.latest_checkpoint(checkpoint_dir) is None:
base_path = os.path.join(self.base_model_path, 'state',
self.model_name + '.ckpt')
else:
base_path = tf.train.latest_checkpoint(checkpoint_dir)
self.settings['base_checkpoint_path'] = base_path
self.labels = load_labels(scaffold_path)
self.num_classes = len(self.labels)
self.preprocess = preprocessing_factory.get_preprocessing(
self.model_name, is_training=True)
self.model_definition = nets_factory.get_network_fn(self.model_name,
self.num_classes,
is_training=True)
def __init__(self, model_dir, model_name):
tf.reset_default_graph()
self.model_dir = model_dir
self.model_name = model_name # ex inception_resnet_v2
self.preprocess = preprocessing_factory.get_preprocessing(
self.model_name, is_training=False)
labels = load_labels(model_dir)
self.labels_by_node_id = {}
for label_id in labels:
label = labels[label_id]
node_id = label.get('node_id', None)
if node_id is None:
raise Exception(
'No Softmax node_id is known for label {}, aborting'.
format(label_id))
self.labels_by_node_id[node_id] = label
self.num_classes = len(self.labels_by_node_id)
self.model_definition = nets_factory.get_network_fn(self.model_name,
self.num_classes,
is_training=False)
with tf.Graph().as_default() as g:
with g.device(EVAL_DEVICE):
dataset = dataset_factory.get_dataset('cifar10', 'test', DATA_DIR)
tf_global_step = slim.get_or_create_global_step()
provider = slim.dataset_data_provider.DatasetDataProvider(
dataset,
shuffle=False,
common_queue_capacity=2 * BATCH_SIZE,
common_queue_min=BATCH_SIZE)
[image, label] = provider.get(['image', 'label'])
image_preprocessing_fn = preprocessing_factory.get_preprocessing(
'cifarnet', is_training=False)
image = image_preprocessing_fn(image, 32, 32)
images, labels = tf.train.batch([image, label],
batch_size=BATCH_SIZE,
num_threads=2,
capacity=5 * BATCH_SIZE)
logits, end_points = vgg_cifar10.inference(images)
predictions = tf.argmax(logits, 1)
accuracy, update_op = slim.metrics.streaming_accuracy(
predictions, labels)
tf.scalar_summary('eval/accuracy', accuracy)
