def creat_inference_graph():
images = tf.placeholder(dtype=tf.float32,
shape=(None, 112, 112, 3),
name='input_images')
is_training_dropout = tf.constant(False,
dtype=bool,
shape=[],
name='train_phase_dropout')
is_training_bn = tf.constant(False,
dtype=bool,
shape=[],
name='train_phase_bn')
embds = get_embd(images, is_training_dropout, is_training_bn)
embds = tf.identity(embds, 'embeddings')
return embds
if __name__ == '__main__':
args = get_args()
if args.mode == 'build':
print('building...')
config = yaml.load(open(args.config_path))
images = tf.placeholder(
dtype=tf.float32,
shape=[None, config['image_size'], config['image_size'], 3],
name='input_image')
train_phase_dropout = tf.placeholder(dtype=tf.bool,
shape=None,
name='train_phase')
train_phase_bn = tf.placeholder(dtype=tf.bool,
shape=None,
name='train_phase_last')
embds, _ = get_embd(images, train_phase_dropout, train_phase_bn,
config)
print('done!')
tf_config = tf.ConfigProto(allow_soft_placement=True)
tf_config.gpu_options.allow_growth = True
with tf.Session(config=tf_config) as sess:
tf.global_variables_initializer().run()
print('loading...')
saver = tf.train.Saver(var_list=tf.trainable_variables())
saver.restore(sess, args.model_path)
print('done!')
batch_size = config['batch_size']
imgs, imgs_f, fns = load_image(args.read_path,
config['image_size'])
print('forward running...')
embds_arr = run_embds(sess, imgs, batch_size, config['image_size'],
def inference(images, labels, is_training_dropout, is_training_bn, config):
embds, end_points = get_embd(images, is_training_dropout, is_training_bn, config)
logits = get_logits(embds, labels, config)
end_points['logits'] = logits
return embds, logits, end_points
def inference(images, labels, is_training_dropout, is_training_bn, config):
embds = get_embd(images, is_training_dropout, is_training_bn)
logits = get_logits(embds, labels, config)
return embds, logits
