【深度学习】【部署】Flask快速部署Pytorch模型【入门】

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文章目录

• 【深度学习】【部署】Flask快速部署Pytorch模型【入门】
• 前言
• 搭建简单的Web服务
• 搭建深度学习的Web服务
• win10下打包成exe(选看)
• 总结

  ---

  前言

  Django和Flask都是python的服务框架，Flask相较于Django的优势是更加轻量级，因此尝试用Flask构建API服务，Flask快速部署深度学习模型再打包exe与深度学习模型直接打包exe相比，前者模型只需要加载一次权重就可以一直使用，而后者每一次预测都需要重新加载一次模型权重，严重浪费了时间和资源。

  【打包exe参考】

  【环境安装参考】

  # 安装flask
  pip install flask 
  # 安装pytorch环境
  pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117
  

  ---

  搭建简单的Web服务

  将以下代码段保存在名为app.py的文件中，并运行代码。

  from flask import Flask
  app = Flask(__name__)
  @app.route('/')
  def hello():
      return 'Hello World!'
  if __name__ == "__main__":
  	app.run()
  

  在web浏览器中访问http://127.0.0.1:5000/时，查看到Hello World的文本！

  

  

  ---

  搭建深度学习的Web服务

  博主以人脸检测之Retinaface算法为例讲解代码：【retinaface-pytorch代码】。有兴趣的朋友可以看看完整的代码，这里博主只保留了与预测相关的代码，并做了部分修改。

  app.py中与Web服务相关的部分，博主设置了俩个路由路径：

  1. 一个是/http://127.0.0.1:5000/setmodel–用于修改模型的主干网络及其权重，因为一个检测模型可能有多个backbone，本例是有resnet50和alpha不同的多个mobilenetV1作为backbone。
  2. 另一个是/http://127.0.0.1:5000/predict–用于检测，这里博主保留了源码中图片单一和批量俩种预测方式。

  # 初始化模型 默认mobilenetX1.00
  Model = retinaface("mobilenetX1.00")
  from flask import Flask, request
  app = Flask(__name__)
  # /setmodel用于修改模型
  @app.route('/setmodel', methods=['POST'])
  def initialization():
      if request.method == 'POST':
          model_mold = request.form
          Model.setModel(model_mold["model_mold"])
      return "initialization " + model_mold["model_mold"] + " finish"
  # /predict用于预测
  @app.route('/predict', methods=['POST'])
  def predict():
      if request.method == 'POST':
          file = request.form
          keys_Points = Model.detect_image(file['mode'], file['path'])
          return keys_Points
  if __name__ == '__main__':
      app.run()
  

  app.py中与Retinaface算法相关的部分，用retinaface类进一步封装了Retinaface模型，设置了俩个方法，detect_image方法对于flask中的predict，setModel方法对应flask中的initialization。

  class retinaface(object):
      def __init__(self, model_mold):
          if model_mold == "mobilenetX1.00":
              model_path= r"model_save/mobilenetX1.00.pth"
          elif model_mold == "mobilenetX0.75":
              model_path = r"model_save/mobilenetX0.75.pth"
          elif model_mold == "mobilenetX0.50":
              model_path = r"model_save/mobilenetX0.50.pth"
          elif model_mold == "mobilenetX0.25":
              model_path = r"model_save/mobilenetX0.25.pth"
          else:
              model_path = r"model_save/resnet50.pth"
          self.retinaface = Retinaface(model_path=model_path, backbone=model_mold)
      def detect_image(self, mode, path):
          import os
          if mode == "predict" and (path.lower().endswith(('.bmp', '.dib', '.png', '.jpg', '.jpeg', '.pbm', '.pgm', '.ppm', '.tif', '.tiff'))):
              while True:
                  image = cv2.imread(path)
                  if image is None:
                      print('Open Error! Try again!')
                      continue
                  else:
                      image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
                      r_image_points = self.retinaface.detect_image(image).tolist()
          elif mode == "dir_predict" and os.path.isdir(path):
              import os
              import time
              r_image_points = []
              img_names = os.listdir(path)
              for img_name in img_names:
                  t = time.time()
                  if img_name.lower().endswith(('.bmp', '.dib', '.png', '.jpg', '.jpeg', '.pbm', '.pgm', '.ppm', '.tif', '.tiff')):
                      image_path = os.path.join(path, img_name)
                      image = cv2.imread(image_path)
                      image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
                      r_image_points.append(self.retinaface.detect_image(image).tolist())
          else:
              return []
          return r_image_points
      def setModel(self,model_mold):
          if model_mold == "mobilenetX1.00":
              model_path= r"model_save/mobilenetX1.00.pth"
          elif model_mold == "mobilenetX0.75":
              model_path = r"model_save/mobilenetX0.75.pth"
          elif model_mold == "mobilenetX0.50":
              model_path = r"model_save/mobilenetX0.50.pth"
          elif model_mold == "mobilenetX0.25":
              model_path = r"model_save/mobilenetX0.25.pth"
          else:
              model_path = r"model_save/resnet50.pth"
          self.retinaface = Retinaface(model_path=model_path, backbone=model_mold)
  

  test.py用于HTTP Post请求过程模拟。

  "http://localhost:5000/setmodel"加载指定backbone，假设不指定backbone，默认加载mobilenetX1.00。

  import requests
  resp = requests.post("http://localhost:5000/setmodel",
                       data={'model_mold': 'mobilenetX0.25'})
  print(resp.text)
  

  "http://localhost:5000/predict"指定模式mode(单张图片或批量图片)和图片路径path。

  import requests
  resp = requests.post("http://localhost:5000/predict",
                       data={'mode': 'dir_predict', 'path': 'img'})
  print(resp.text)
  

  启动服务后，post发送http请求，默认backbone加载mobilenetX1.00，path指定批量图片的文件夹地址，dir_predict(批量预测图片)mode方式预测所以图片的五个关键点(双眼、鼻子和俩边嘴角)xy像素坐标，源码其实还有框的坐标和高宽，博主不需要就去掉了。

  多张图片，每张图片数据保存在一个list里–5个关键点xy像素坐标共10个值。

  

  【代码上传】，只保留预测相关文件，功能如下图所示，不做过多讲解，主要讲解部署和使用。

  

  win10下打包成exe(选看)

  零基础先参考python程序打包成可执行文件【进阶篇】

  # 安装PyInstaller包,5.4.0版本
  pip install -i https://pypi.tuna.tsinghua.edu.cn/simple Pyinstaller==5.4.0
  # 生成spec文件
  pyi-makespec -w app.py
  

  安装了5.7.0及以后版本的Pyinstaller，打包部署启动的时候会报如下错误：

  

  解决方式：pip指定5.4.0版本的安装包安装。

  修改app.spec内容：由于打包的主文件是app.py，因此生成的spec文件名称为app.spec，俩者在同一个目录下。

  

      ['app.py',
      "E:/deep-learning-for-image-processing-master/face_detector/flask-retinaface/retinaface.py",
      "E:/deep-learning-for-image-processing-master/face_detector/flask-retinaface/nets/layers.py",
      "E:/deep-learning-for-image-processing-master/face_detector/flask-retinaface/nets/mobilenet.py",
      "E:/deep-learning-for-image-processing-master/face_detector/flask-retinaface/nets/resnet.py",
      "E:/deep-learning-for-image-processing-master/face_detector/flask-retinaface/nets/retinaface.py",
      "E:/deep-learning-for-image-processing-master/face_detector/flask-retinaface/nets/retinaface_training.py",
      "E:/deep-learning-for-image-processing-master/face_detector/flask-retinaface/utils/anchors.py",
      "E:/deep-learning-for-image-processing-master/face_detector/flask-retinaface/utils/callbacks.py",
      "E:/deep-learning-for-image-processing-master/face_detector/flask-retinaface/utils/config.py",
      "E:/deep-learning-for-image-processing-master/face_detector/flask-retinaface/utils/dataloader.py",
      "E:/deep-learning-for-image-processing-master/face_detector/flask-retinaface/utils/utils.py",
      "E:/deep-learning-for-image-processing-master/face_detector/flask-retinaface/utils/utils_bbox.py",
      "E:/deep-learning-for-image-processing-master/face_detector/flask-retinaface/utils/utils_fit.py",
      "E:/deep-learning-for-image-processing-master/face_detector/flask-retinaface/utils/utils_map.py",
      ],
      pathex=["E:/deep-learning-for-image-processing-master/face_detector/flask-retinaface"],
      binaries=[  ("E:/deep-learning-for-image-processing-master/face_detector/flask-retinaface/model_save/mobilenetX0.25.pth", "model_save"),
                  ("E:/deep-learning-for-image-processing-master/face_detector/flask-retinaface/model_save/mobilenetX0.50.pth", "model_save"),
                  ("E:/deep-learning-for-image-processing-master/face_detector/flask-retinaface/model_save/mobilenetX0.75.pth", "model_save"),
                  ("E:/deep-learning-for-image-processing-master/face_detector/flask-retinaface/model_save/mobilenetX1.00.pth", "model_save"),
                  ("E:/deep-learning-for-image-processing-master/face_detector/flask-retinaface/model_save/resnet50.pth", "model_save"),
      ],
  

  # 运行spec文件进行打包
  pyinstaller app.spec
  

  完成打包后在pathex指定文件夹下产生build(可以删除)和dist文件夹，所需的可执行文件在dist目录内

  

  双击运行exe，运行test.py进行Post请求，测试结果如下图：

  

  

  ---

  总结

  尽可能简单、详细的介绍Flask快速部署深度学习模型的过程。

   
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