import tensorflow as tf
from nets.resnet_v1 import resnet_v1_50, resnet_arg_scope
_RGB_MEAN = [123.68, 116.78, 103.94]
def endpoints(image, is_training):
if image.get_shape().ndims != 4:
raise ValueError('Input must be of size [batch, height, width, 3]')
image = image - tf.constant(_RGB_MEAN, dtype=tf.float32, shape=(1,1,1,3))
with tf.contrib.slim.arg_scope(resnet_arg_scope(batch_norm_decay=0.9, weight_decay=0.0)):
_, endpoints = resnet_v1_50(image, num_classes=None, is_training=is_training, global_pool=True)
endpoints['model_output'] = endpoints['global_pool'] = tf.reduce_mean(
endpoints['resnet_v1_50/block4'], [1, 2], name='pool5')
return endpoints, 'resnet_v1_50'
