python - 如何使用 flask 为keras模型提供推理服务？

Question

我有一个yolo v3 keras模型的推理对象检测代码

#! /usr/bin/env python

import os
import argparse
import json
import cv2
from utils.utils import get_yolo_boxes, makedirs
from utils.bbox import draw_boxes
from keras.models import load_model
from tqdm import tqdm
import numpy as np
import flask
import io
from PIL import Image
from keras.preprocessing.image import img_to_array

config_path  = "config.json"
input_path   = "test.jpg"
output_path  = "output"

with open(config_path) as config_buffer:    
        config = json.load(config_buffer)

makedirs(output_path)

net_h, net_w = 416, 416 
obj_thresh, nms_thresh = 0.5, 0.45

os.environ['CUDA_VISIBLE_DEVICES'] = config['train']['gpus']
infer_model = load_model(config['train']['saved_weights_name'])


image = cv2.imread(input_path)

# predict the bounding boxes
boxes = get_yolo_boxes(infer_model, [image], net_h, net_w, config['model']['anchors'], obj_thresh, nms_thresh)[0]

# draw bounding boxes on the image using labels
_,outputs =  draw_boxes(image, boxes, config['model']['labels'], obj_thresh) 

print(outputs)

# write the image with bounding boxes to file
cv2.imwrite(output_path + input_path.split('/')[-1], np.uint8(image))

这工作完全正常，在终端中给出了预期的输出类和坐标

{'classes': 'person 99.97%', 'X2': '389', 'X1': '174', 'Y1': '8', 'Y2': '8'}

但是当我通过使用官方 keras 转换将上面的代码转换为 flask 文档来将其转换为基于服务的 REST api 时，如下所示:

#! /usr/bin/env python

import os
import argparse
import json
import cv2
from utils.utils import get_yolo_boxes, makedirs
from utils.bbox import draw_boxes
from keras.models import load_model
from tqdm import tqdm
import numpy as np
import flask
import io
from PIL import Image
from keras.preprocessing.image import img_to_array


config_path  = "config.json"
input_path   = "test.jpg"
output_path  = "output"

with open(config_path) as config_buffer:    
        config = json.load(config_buffer)

makedirs(output_path)

net_h, net_w = 416, 416 
obj_thresh, nms_thresh = 0.5, 0.45

app = flask.Flask(__name__)

os.environ['CUDA_VISIBLE_DEVICES'] = config['train']['gpus']
infer_model = load_model(config['train']['saved_weights_name'])

def prepare_image(image_path):
    image = cv2.imread(image_path)
    return image

@app.route("/predict", methods=["POST"])
def predict():
    # initialize the data dictionary that will be returned from the
    # view
    data = {"success": False}

    # ensure an image was properly uploaded to our endpoint
    if flask.request.method == "POST":
        if flask.request.files.get("image"):
            # read the image in PIL format
            image = flask.request.files["image"].read()
            image = Image.open(io.BytesIO(image))

            # preprocess the image and prepare it for classification            
            image = img_to_array(image)


            boxes = get_yolo_boxes(infer_model, [image], net_h, net_w, config['model']['anchors'], obj_thresh, nms_thresh)[0]


            _,outputs =  draw_boxes(image, boxes, config['model']['labels'], obj_thresh)


            data.append(outputs)

            print(data)


            # indicate that the request was a success
            data["success"] = True

    # return the data dictionary as a JSON response
    return flask.jsonify(data)



if __name__ == "__main__":
    print(("* Loading Keras model and Flask starting server..."
        "please wait until server has fully started"))
    app.run()

在5000端口运行成功

但是当我尝试使用

通过 POST api 进行预测时

curl -X POST -F image=@test.jpg 'http://localhost:5000/predict'

出现这个错误

raise ValueError("Tensor %s is not an element of this graph." % obj) ValueError: Tensor Tensor("conv2d_59/BiasAdd:0", shape=(?, ?, ?,

255), dtype=float32) is not an element of this graph. 127.0.0.1 - - [15/Aug/2019 15:11:23] "POST /predict HTTP/1.1" 500 -

我不明白为什么同样的预测函数在没有 flask 的情况下也能正常工作，但在这方面却出现了错误。

Answer 1

我遇到了同样的问题，这是一个keras问题。似乎主要是在有异步事件处理程序时触发在加载经过训练的模型后立即添加 model._make_predict_function() 对我有用。例如，

from keras.models import load_model
   model=load_model('yolo.h5')
   model._make_predict_function()

另一种对其他人有用的方法是使用图形并在上下文中进行推断，例如:

global graph
graph = tf.get_default_graph()
with graph.as_default():
res = model.predict()

更多见解，请引用以下链接:

https://github.com/keras-team/keras/issues/6462

https://github.com/keras-team/keras/issues/2397