基本的flask部署训练好的模型

服务端(flask_server)

import io
import json
import flask
import torch
import torch.nn.functional as F
from PIL import Image
from torch import nn
from torchvision import transforms,models,datasets
from torch.autograd import Variable

#初始化app
app = flask.Flask(__name__)
model = None
use_gpu = False

#加载模型
def load_model():
    """Load the pre-trained model, you can use your model just as easily.
    """
    global model
    # 这里我们直接加载官方工具包里提供的训练好的模型（代码会自动下载）括号内参数为是否下载模型对应的配置信息
    model = models.resnet18()
    num_ftrs = model.fc.in_features
    model.fc = nn.Sequential(nn.Linear(num_ftrs,102)) # 类别数自己根据自己任务来

    checkpoint = torch.load("best.pth")
    model.load_state_dict(checkpoint['state_dict'])
    #将模型改为测试的模式
    model.eval()
    #是否使用gpu
    if use_gpu:
        model.eval()

#数据预处理
def prepare_image(image,target_size):
    """Do image preprocessing before prediction on any data.

        :param image:       original image
        :param target_size: target image size
        :return:
                            preprocessed image
    """

    # 针对不同模型，image的格式不同，但需要统一至RGB格式
    if image.mode != 'RGB':
        image = image.convert("RGB")

    # Resize the input image and preprocess it.(按照所使用的模型将输入图片的尺寸修改，并转为tensor)
    image = transforms.Resize(target_size)(image)
    image = transforms.ToTensor()(image)

    # Convert to Torch.Tensor and normalize. mean与std   （RGB三通道）这里的参数和数据集中是对应的，训练过程中一致
    image = transforms.Normalize([0.485,0.456,0.406],[0.229,0.224,0.225])(image)

    # Add batch_size axis.增加一个维度，用于按batch测试   本次这里一次测试一张
    image = image[None]
    if use_gpu:
        image = image.cuda()
    return Variable(image,volatile=True)#不需要求导


# 开启服务   这里的predict只是一个名字，可自定义
@app.route("/predict",methods = ["POST"])
def predict():
    # Initialize the data dictionary that will be returned from the view.
    # 做一个标志，刚开始无图像传入时为false，传入图像时为true
    data = {
    
    "success":False}

    #如果收到请求
    if flask.request.method == "POST":
        #判断是否为图像
        if flask.request.files.get("image"):
            # Read the image in PIL format
            # 将收到的图像进行读取
            image = flask.request.files["image"].read()
            image = Image.open(io.BytesIO(image)) #二进制的数据

            #利用上面预处理的函数读入的图像进行预处理
            image = prepare_image(image, target_size=(64, 64))
            preds = F.softmax(model(image),dim = 1)
            results = torch.topk(preds.cpu().data,k = 3,dim = 1)
            results = (results[0].cpu().numpy(),results[1].cpu().numpy())
            # 将data字典增加一个key,value,其中value为list格式
            data["predictions"] = list()

            # Loop over the results and add them to the list of returned predictions

            for prob,label in zip(results[0][0],results[1][0]):
                # label_name = idx2label[str(label)]
                r = {
    
    'label':str(label),'probability':float(prob)}
                # 将预测结果添加至data字典
                data["predictions"].append(r)

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

    # 将最终结果以json格式文件传出
    return flask.jsonify(data)
"""
test_json = {
                "status_code": 200,
                "success": {
                            "message": "image uploaded",
                            "code": 200
                        },
                "video":{
                    "video_name":opt['source'].split('/')[-1],
                    "video_path":opt['source'],
                    "description":"1",
                    "length": str(hour)+','+str(minute)+','+str(round(second,4)),
                    "model_object_completed":model_flag
                    }
                    "status_txt": "OK"
                    }
                    response = requests.post(
                        'http://xxx.xxx.xxx.xxx:8090/api/ObjectToKafka/',,
                        data={'json': str(test_json)})
"""

if __name__ == "__main__":
    print("Loading PyTorch model and Flask starting server ...")
    print("Please wait until server has fully started")
    #先加载模型
    load_model()
    #再开启服务
    app.run(port="5012")

客户端(flask_predict)

import requests
import argparse

#url和端口写成自己的
flask_url = "http://127.0.0.1:5012/predict"

def predict_result(image_path):
    #用合适的方法就行
    image= open(image_path,'rb').read()
    payload = {
    
    "image":image}
    # request发给server.
    r = requests.post(flask_url, files=payload).json()

    #成功运行的话再进行返回
    if r['success']:
        #输出结果
        for(i,result) in enumerate(r['predictions']):
            print('{}. {}: {:.4f}'.format(i + 1, result['label'],
                                          result['probability']))
    #失败了就打印
    else:
        print('Request failed')

if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='Classification demo')
    parser.add_argument('--file', default='./flower_data/train_filelist/image_06998.jpg', type=str,
                        help='test image file')
    args = parser.parse_args()
    predict_result(args.file)