flask部署pytorch模型

项目代码：
https://pan.baidu.com/s/1-FdTk7XjryvUsZR9CW9T3g
提取码：6uo5
该项目上传至阿里云仓库：docker——构建自己的项目（阿里云仓库）| docker构建仓库镜像教程

开启模型服务

run_pytorch_server.py

import io
import json
import flask
import torch
import torch
import torch.nn.functional as F
from PIL import Image
from torch import nn
from torchvision import transforms as T
from torchvision.models import resnet50
from torch.autograd import Variable 

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

# 返回结果用的
with open('imagenet_class.txt', 'r') as f:
    idx2label = eval(f.read())

# 加载模型进来
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 = resnet50(pretrained=True)
    #将模型指定为测试格式
    model.eval()
    #是否使用gpu
    if use_gpu:
        model.cuda()

# 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 = T.Resize(target_size)(image)
    image = T.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 = T.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}

    # Ensure an image was properly uploaded to our endpoint.
    # 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)) #二进制数据

            # Preprocess the image and prepare it for classification.
            # 利用上面的预处理函数将读入的图像进行预处理
            image = prepare_image(image, target_size=(224, 224))

            # Classify the input image and then initialize the list of predictions to return to the client.
            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[label]
                r = {
    
    "label": label_name, "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

    # Return the data dictionary as a JSON response.
    # 将最终结果以json格式文件传出
    return flask.jsonify(data)

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()

调用模型（响应）

simple_request.py

import requests
import argparse

# Initialize the PyTorch REST API endpoint URL.
PyTorch_REST_API_URL = 'http://127.0.0.1:5000/predict'


def predict_result(image_path):
    # Initialize image path
    image = open(image_path, 'rb').read()
    payload = {
    
    'image': image}

    # Submit the request.
    r = requests.post(PyTorch_REST_API_URL, files=payload).json()

    # Ensure the request was successful.
    if r['success']:
        # Loop over the predictions and display them.
        for (i, result) in enumerate(r['predictions']):
            print('{}. {}: {:.4f}'.format(i + 1, result['label'],
                                          result['probability']))
    # Otherwise, the request failed.
    else:
        print('Request failed')


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='Classification demo')
    parser.add_argument('--file',default='./dog.jpg', type=str, help='test image file')

    args = parser.parse_args()
    predict_result(args.file)