版本：PaddlePaddle 2.1.0
错误信息： "result": "too many values to unpack (expected 3)"
自定义转换器代码：
输入函数：

import re
import base64
import numpy as np
import paddle.fluid as fluid
from PIL import Image
from io import BytesIO

## 将 Base64 转成 PIL Image

def base64_to_pil(image_base64):
    """read image from memory"""
    image_base64 = re.sub('^data:image/.+;base64,', '', image_base64) # 需要去除头部格式信息
    image_mem = BytesIO(base64.b64decode(image_base64)) # python3
    image_pil = Image.open(image_mem).convert('RGB')
    return image_pil

## 预处理代码

def preprocess(img):
    # 图像缩放
    if img.mode != 'RGB':
        img = img.convert('RGB')
    img = img.resize((224, 224), Image.BILINEAR)
    img = np.array(img).astype('float32')
    img = img.transpose((2, 0, 1)) / 255 # HWC to CHW 及归一化
    return img

def feed(data_args = None):

def reader_infer(data_args):
"""
用于输入待预测数据，返回值会直接复值给paddle.static.Executor的run方法的feed参数。
模型加载方式选择：
paddle2.x:paddle.static.load_inference_model
paddle1.x:paddle.fluid.io.load_inference_model

:param data_args: 客户端请求参数，以k-v形式
:return dict
"""
def reader():
# x <type 'base64'> default value:'' 
    image = data_args['imagebase']

# todo
    
    # 格式转换
    image = base64_to_pil(image)
    
    # 预处理
    image = preprocess(image)
    
    image = image[np.newaxis,:, : ,:]  #reshape(-1,3,224,224)
   
    
    # 根据输入 Tensor 的名称和 Shape 构建 DataFeeder
    c, h, w = image.shape
    print("image shape:", image.shape)
    
    img = fluid.layers.data(name='x', shape=[c, h, w], dtype='float32')
    feeder = fluid.DataFeeder([img], fluid.CPUPlace())

    return [[image]], feeder
# return image, feeder
# arg_x = None
return reader

输出函数：
def output(results, data_args):
"""
模型评测结果输出转换
:param results
:param data_args 请求参数
:return dict
"""

todo