resnet.py 文件源码

def __init__(self, checkpoint_path):
        layers = 50
        num_blocks = [3, 4, 6, 3]
        self.inference = lambda images, is_train : inference(images, 
                                                   is_training=is_train, 
                                                   num_classes=NUM_AGES*2,
                                                   num_blocks=num_blocks, 
                                                   bottleneck=True)

        self.x = tf.placeholder(tf.uint8, shape=(256,256,3), name='input_image')
        self.crops = fixed_crops(self.x)
        self.logits = self.inference(self.crops, is_train=False)
        self.pred = tf.nn.softmax(self.logits, name='prediction')

        # Restore saved weights
        restore_variables = tf.trainable_variables() \
                + tf.moving_average_variables()
        self.saver = tf.train.Saver(restore_variables)
        self.sess = tf.Session()
        self.saver.restore(self.sess, checkpoint_path)

        #self.sess.run(tf.initialize_variables([var for var \
        #        in tf.all_variables() if var not in restore_variables]))