0009-flask调用pytorch模型

以你之姓@ 2022-11-19 11:28 186阅读 0赞

# -*- encoding: utf-8 -*-
    """
    @File    : flask_torch.py
    @Time    : 2020/07/12 11:59
    @Author  : Johnson
    @Email   : 593956670@qq.com
    """
    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 as T
    from torchvision.models import resnet50
    
    #初始化flask应用
    app = flask.Flask(__name__)
    model = None
    use_gpu = True
    
    with open("class.txt",'r') as f:
        idx2label = eval(f.read())
    
    
    def load_model():
        """load the pre-trained model,you can used your model just as easy"""
        global model
        model = resnet50(pretrained=True)
        model.eval()
        if use_gpu:
            model.cuda()
    
    
    def prepare_image(image,target_size):
        '''
        对图片进行预处理
        '''
        if image.mode!="RGB":
            image = image.convert("RGB")
    
        #resize the image
        image = T.resize(target_size)(image)
        image = T.toTensor()(image)
    
        #转化为Tensor格式和归一化处理
        image = T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])(image)
    
        #add batch_size axis
        image = image[None]
        if use_gpu:
            image = image.cuda()
    
        return torch.autograd.Variable(image,volatile=True)
    
    @app.route("/predict",methods=["POST"])
    def predict():
        #initialize the data dic. that will be retured from the view
        data = {"success",False}
    
        #ensure the image was properly uploaded to out endpoint
        if flask.request.method=="POST":
            #read the image in PIL Image
            image = flask.request.files["image"].read()
            image = Image.open(io.BytesIO(image))
    
            #process the image and prepare it for classification
            image = prepare_image(image,target_size=(224,224))
    
            #预测
            preds = F.softmax(model(image),dim=1)
            results = torch.topk(preds.cpu().data,k=1,dim=1)
    
            #
            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]):
                prob = float(prob.item())
                label = int(label.item())
                label_name = idx2label[label]
                r = {"label": label_name, "probability": float(prob)}
                data['predictions'].append(r)
    
            # 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 PyTorch model and Flask starting server ...")
        print("Please wait until server has fully started")
        load_model()
        app.run(debug=True)