使用http方式GPU环境推理模型调用报错

[yihuiluo235]

环境docker 基于 hub.baidubce.com/paddlepaddle/serving latest-cuda10.0-cudnn7
同样代码在cpu环境下没有问题
其他版本信息
paddle-serving-app (0.2.0) paddle-serving-client (0.4.0) paddle-serving-server-gpu (0.4.0.post10) paddlepaddle-gpu (1.8.5.post107)
报错信息如下：

相关运行代码
python3 uwsgi.py
curl -H "Content-Type:application/json" -X POST -d '{"feed":[{"x": [0.0137, -0.1136, 0.2553, -0.0692, 0.0582, -0.0727, -0.1583, -0.0584, 0.6283, 0.4919, 0.1856, 0.0795, -0.0332]}], "fetch":["multiclass_nms_0.tmp_0"]}' http://127.0.0.1:9393/uci/prediction

`#coding=utf-8
from paddle_serving_server_gpu.web_service import WebService
from paddle_serving_app.reader import *
from PIL import Image
from io import BytesIO

import cv2
import numpy as np
import sys
import base64

preprocess = Sequential([
File2Image(), BGR2RGB(), Resize(
(608, 608), interpolation=cv2.INTER_LINEAR), Div(255.0), Transpose(
(2, 0, 1))
])

class UciService(WebService):
def preprocess(self, feed=[], fetch=[]):
print(feed)
#data = base64.b64decode(feed[0]["image"].encode('utf8'))
#data = np.fromstring(data, np.uint8)
#im = cv2.imdecode(data, cv2.IMREAD_COLOR)
#调用本地图片进行测试
car_im = preprocess("./22.jpg")
is_batch = True
print(car_im.shape)
print(np.array(list(car_im.shape[1:])).shape)
feed = [{
"image": car_im,
"im_size": np.array(list(car_im.shape[1:]))
}]
return feed, fetch, is_batch
def postprocess(self, feed={}, fetch=[], fetch_map=None):
print(fetch)
print(fetch_map)
return fetch

#配置预测服务
uci_service = UciService(name = "uci")
uci_service.load_model_config("./car_server")
uci_service.prepare_server(workdir="./workdir", port=9393, device="gpu", gpuid=0)
uci_service.run_rpc_service()
uci_service.run_web_service()
#获取flask服务
app_instance = uci_service.get_app_instance()
`

[TeslaZhao]

请检查一下feed的shape