文件夹结构

mobilenet{
		mainwin.ui
		mainwin.py
		img.jpg		预测的图片
		class.json
		mobilenet_v2.pth		预训练权重
		MobileNetV2.pth		自己的数据集训练好的权重
		mobilenet_v2.py
		myMainWin.py		编写调用窗口程序
		predict.py		预测
		train.py		训练
}

其中myMainWin.py代码如下

import sys
from PyQt5.QtWidgets import QMainWindow, QApplication, QFileDialog, QLabel
from PyQt5.QtGui import QIcon, QImage, QPixmap
from mainwin import Ui_MainWindow
import cv2
import numpy
from predict_new_v2 import predict_new
from PIL import Image


class myMainWin(QMainWindow, Ui_MainWindow):
    def __init__(self):
        super(myMainWin, self).__init__()
        self.setupUi(self)  

        # 设置主窗口的标题
        self.setWindowTitle('基于xxx分类系统')

        # 连接动作对应的函数

        self.pushButton_2.clicked.connect(self.openimg)  # 构造函数（label可以不用写）
        self.pushButton_3.clicked.connect(self.detect)

    def openimg(self):
        global fname
        # 选择且获取图片文件的地址
        fileName, filetype = QFileDialog.getOpenFileName(
            self,
            "选取文件",
            "F:/python/mobilenet",
            "Image Files (*.bmp *.jpg *.jpeg *.png)")
        self.showFile(fileName)
        fname = fileName

    # 将图片显示在label
    def showFile(self, fileName):
        srcImage = cv2.imdecode(numpy.fromfile(fileName, dtype=numpy.uint8), -1)
        image_height, image_width, image_depth = srcImage.shape  # 获取图像的高，宽以及深度。
        # opencv读图片是BGR，qt显示要RGB，所以需要转换一下
        QImg = cv2.cvtColor(srcImage, cv2.COLOR_BGR2RGB)
        QShowImage = QImage(QImg.data, image_width, image_height,  # 创建QImage格式的图像，并读入图像信息
                            image_width * image_depth,
                            QImage.Format_RGB888)
        self.label_img.clear()
        QShowImage = QShowImage.scaled(
            self.label_img.width(),
            self.label_img.height())  # 图片适应label大小
        self.label_img.setPixmap(QPixmap.fromImage(QShowImage))

    def detect(self):
        img = Image.open(fname)

        predict = predict_new(img)
        self.label_result.setText(predict)


# 只有单独执行调用条件语句
# 加个程序入口
if __name__ == '__main__':
    app = QApplication(sys.argv)  # 传入参数

    app.setWindowIcon(QIcon('./Knight.ico'))
    main = myMainWin()
    main.show()

    sys.exit(app.exec_())

predict.py的代码如下

import os
import json

import torch
from PIL import Image
from torchvision import transforms
import matplotlib.pyplot as plt

from model_v2 import MobileNetV2


def predict_new(img):
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

    data_transform = transforms.Compose(
        [transforms.Resize(256),
         transforms.CenterCrop(224),
         transforms.ToTensor(),
         transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])

    # load image
    # img = Image.open("./00006.jpg")

    # [N, C, H, W]
    # img = Image.open(img_path)
    img = data_transform(img)
    # expand batch dimension
    img = torch.unsqueeze(img, dim=0)

    # read class_indict
    json_path = './class_indices.json'
    assert os.path.exists(json_path), "file: '{}' dose not exist.".format(json_path)

    json_file = open(json_path, "r")
    class_indict = json.load(json_file)

    # create model
    model = MobileNetV2(num_classes=2).to(device)
    # load model weights
    model_weight_path = "./MobileNetV2.pth"
    model.load_state_dict(torch.load(model_weight_path, map_location=device))
    model.eval()
    with torch.no_grad():
        # predict class
        output = torch.squeeze(model(img.to(device))).cpu()
        predict = torch.softmax(output, dim=0)
        predict_cla = torch.argmax(predict).numpy()

    print_res = "class: {} \n  prob: {:.3}".format(class_indict[str(predict_cla)],
                                                 predict[predict_cla].numpy())

    return print_res

1. 犯了个小错误，在myMainWin.py中调用predict = predict_new(img)，传入的参数img没有和predict.py中的def predict_new(img):保持一致。
2. 传入的是图片路径，需要读取成图片再处理img = Image.open(fname)
3. 本文中初始化的时候label可以不用初始化，需要连接动作的函数进行初始化

至此，初学pyqt5，希望越学越顺利，开头难解决了还有中间难结尾难，慢慢来吧！

主要参考视频及文章：
https://www.bilibili.com/video/BV15u41197EQ?p=3&share_source=copy_web
https://zhuanlan.zhihu.com/p/274436031