ResNet 模型训练和人脸识别：使用 MindSpore 和 OpenCV

def train_resnet():
    context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
    train_dataset_generator = TrainDatasetGenerator('D:/pythonproject2/digital_mindspore/dataset')
    ds_train = ds.GeneratorDataset(train_dataset_generator, ['data', 'label'], shuffle=True)
    ds_train = ds_train.shuffle(buffer_size=10)
    ds_train = ds_train.batch(batch_size=4, drop_remainder=True)
    #valid_dataset_generator = TrainDatasetGenerator('D:/pythonproject2/test1')
    #ds_valid = ds.GeneratorDataset(valid_dataset_generator, ['data', 'label'], shuffle=True)
    #ds_valid = ds_valid.batch(batch_size=4, drop_remainder=True)
    network = ResNet(ResidualBlock,[2,2,2,2])
    net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
    net_opt = nn.Momentum(network.trainable_params(), learning_rate=0.001, momentum=0.9)
    time_cb = TimeMonitor(data_size=ds_train.get_dataset_size())
    config_ck = CheckpointConfig(save_checkpoint_steps=10,
                                 keep_checkpoint_max=10)
    config_ckpt_path = 'D:/pythonproject2/ckpt/'
    ckpoint_cb = ModelCheckpoint(prefix='checkpoint_resnet', directory=config_ckpt_path, config=config_ck)


    model = Model(network, net_loss, net_opt, metrics={'Accuracy': Accuracy()})
    epoch_size = 10
    print('============== Starting Training =============')
    model.train(epoch_size, ds_train, callbacks=[time_cb, ckpoint_cb, LossMonitor()])
    face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_alt.xml')  # 加载检测器

    cap = cv2.VideoCapture(0)
    stop = False

    while not stop:
        success, img = cap.read()
        subjects = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17',
                    '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33']
        # 生成图像的副本，这样就能保留原始图像
        img1 = img.copy()
        # 检测人脸
        # 将测试图像转换为灰度图像，因为opencv人脸检测器需要灰度图像
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        # 检测多尺度图像，返回值是一张脸部区域信息的列表（x,y,宽,高）
        rect = face_cascade.detectMultiScale(img, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30),
                                             flags=cv2.CASCADE_SCALE_IMAGE)
        # 如果未检测到面部
        if len(rect) == 0:
            txt = 'no face!'
            cv2.putText(img1, txt, (10, 20), cv2.FONT_HERSHEY_COMPLEX, 1, (128, 128, 0), 2)
        if not rect is None:
            for (x, y, w, h) in rect:
                face = gray[y:y + w, x:x + h].astype(np.float32)  # 数值转换
                face = cv2.resize(face, (100, 100))
                cv2.rectangle(img1, (x, y), (x + w, y + h), (0, 255, 0), 2)  # 画出矩形框
                #k = cv2.waitKey(100)  # 每0.1秒读取一次键盘
                min_d = 1000000000000
                c = -1
                for f in ds_train.create_dict_iterator(output_numpy=True):  # i,f是训练数据的信息，遍历训练数据
                    d = ((face - f['data']) ** 2).sum()  # 计算test和训练图片的欧氏距离
                    if d < min_d:
                        min_d = d
                        label1 = f['label']
                        label_counter = Counter(label1)
                        most_common_label = label_counter.most_common(1)[0][0]
                        c = most_common_label
                if min_d < 200000000000:
                    cv2.putText(img1, subjects[c], (x, y), cv2.FONT_HERSHEY_COMPLEX, 1, (128, 128, 0), 2)
                else:
                    label = 'unknown'
                    cv2.putText(img1, label, (x, y), cv2.FONT_HERSHEY_COMPLEX, 1, (128, 128, 0), 2)
        cv2.imshow('img', img1)
        if (cv2.waitKey(1) & 0xFF == ord('q')):  # 按下q程序结束
                stop = True
                cv2.destroyAllWindows()  # 释放窗口

## 加载ckpt
加载ckpt会使运行变快吗，如果是，代码示例
其他不需要修改吗内容：加载ckpt可以使运行变快，因为它可以避免重新训练模型，直接加载已训练好的参数进行推理或继续训练。

以下是加载ckpt的代码示例：

```python
from mindspore.train.serialization import load_checkpoint, load_param_into_net

# 定义网络
network = ResNet(ResidualBlock,[2,2,2,2])
# 加载已训练好的参数
param_dict = load_checkpoint('checkpoint_resnet-10_1562.ckpt')
# 将参数加载到网络中
load_param_into_net(network, param_dict)