模型存储的参数恢复
模型存取介绍
见该博客。
模型恢复
使用代码
def get_restorer():checkpoint_path = tf.train.latest_checkpoint(os.path.join(FLAGS.trained_checkpoint, cfgs.VERSION))if checkpoint_path != None:if RESTORE_FROM_RPN:print('___restore from rpn___')model_variables = slim.get_model_variables()restore_variables = [var for var in model_variables if not var.name.startswith('Fast_Rcnn')] + [slim.get_or_create_global_step()]for var in restore_variables:print(var.name)restorer = tf.train.Saver(restore_variables)else:restorer = tf.train.Saver()print("model restore from :", checkpoint_path)else:checkpoint_path = FLAGS.pretrained_model_pathprint("model restore from pretrained mode, path is :", checkpoint_path)model_variables = slim.get_model_variables()restore_variables = [var for var in model_variables if(var.name.startswith(cfgs.NET_NAME)and not var.name.startswith('{}/logits'.format(cfgs.NET_NAME)))]for var in restore_variables:print(var.name)restorer = tf.train.Saver(restore_variables)return restorer, checkpoint_path
需在FLAGS.pretrained_model_path给出预训练模型参数的路径。
.ckpt文件与{.ckpt.meta, .ckpt.index, .ckpt.data-00000-of-00001}的区别
两者没有区别，只是在设置路径时（FLAGS.pretrained_model_path），对于.ckpt文件，路径需写至文件夹路径/.ckpt；而对于{.ckpt.meta, .ckpt.index, .ckpt.data-00000-of-00001}，路径需写至文件夹路径/.ckpt-160186，其中.ckpt-160186是.meta的前缀。