• 日期: 2025-01-03
  • 标签: 常规

这个报错提示是 Conv2d 的输入维度不正确，应该是 4 维的，但是实际输入的是 3 维的。可能是在输入数据时出现了问题，建议检查一下输入数据的维度是否正确。另外，可以尝试在网络结构中加入打印语句，查看每一层的输入输出维度是否正确。

以下是代码的修改建议：

from collections import defaultdict, Counter
from mindspore.train.serialization import load_checkpoint, load_param_into_net
import numpy as np
import mindspore.dataset as ds
import cv2
import mindspore.nn as nn
import os
from mindspore import context, ops, Tensor
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
from mindspore.train import Model
from mindspore.nn.metrics import Accuracy

np.random.seed(58)


class ResidualBlock(nn.Cell):
    expansion = 1
    def __init__(self, in_channels, out_channels, stride=1, downsample=None):
        super(ResidualBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride,   pad_mode='same')
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU()
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1,   pad_mode='same')
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.downsample = downsample
        self.stride = stride

    def construct(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            identity = self.downsample(x)
        out += identity
        out = self.relu(out)

        return out

class ResNet(nn.Cell):
    def __init__(self, block, layers, num_classes=34):
        super(ResNet, self).__init__()
        self.in_channels = 64

        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2,   pad_mode='valid')
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU()
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2,   pad_mode='valid')

        self.layer1 = self.make_layer(block, 64, layers[0])
        self.layer2 = self.make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self.make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self.make_layer(block, 512, layers[3], stride=2)

        self.avgpool = nn.AvgPool2d(kernel_size=3, stride=1,   pad_mode='valid')
        self.fc = nn.Dense(512 * block.expansion, num_classes)

    def make_layer(self, block, out_channels, blocks, stride=1):
        downsample = None
        if (stride != 1) or (self.in_channels != out_channels * block.expansion):
            downsample = nn.SequentialCell([
                nn.Conv2d(self.in_channels, out_channels * block.expansion, kernel_size=1, stride=stride),
                nn.BatchNorm2d(out_channels * block.expansion)
            ])
        layers = []
        layers.append(block(self.in_channels, out_channels, stride, downsample))
        self.in_channels = out_channels * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.in_channels, out_channels))
        return nn.SequentialCell(layers)

    def construct(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.avgpool(x)
        x = ops.Reshape()(x, (ops.Shape()(x)[0], -1))
        x = self.fc(x)

        return x


class TrainDatasetGenerator:
    def __init__(self, file_path):
        self.file_path = file_path
        self.img_names = os.listdir(file_path)

    def __getitem__(self, index=0):
        data = cv2.imread(os.path.join(self.file_path, self.img_names[index]))
        label = int(self.img_names[index].split('-')[0])
        data = cv2.resize(data,(100,100))
        data = data.transpose().astype(np.float32) / 255.
        return data, label

    def __len__(self):
        return len(self.img_names)

def load_model_from_ckpt():
    context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
    # 创建ResNet模型
    network = ResNet(ResidualBlock,[2,2,2,2])
    # 加载ckpt文件中的模型参数
    param_dict = load_checkpoint('D:/pythonproject2/ckpt/checkpoint_resnet_1-20_49.ckpt')
     #将模型参数加载到模型中
    load_param_into_net(network, param_dict)
    # 返回模型
    return network

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)
    network = load_model_from_ckpt()
    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 = 20
    #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
    # 训练阶段

    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))
                face = face.transpose().astype(np.float32) / 255.
                face = np.expand_dims(face, axis=0)  # 扩展维度，变成(batch_size, channels, height, width)
                face = Tensor(face)
                cv2.rectangle(img1, (x, y), (x + w, y + h), (0, 255, 0), 2)  # 画出矩形框
                output = network(face)
                predicted_class = np.argmax(output.asnumpy(),axis=1)
                label = subjects[predicted_class[0]]
                #if min_d < 200000000000:
                cv2.putText(img1, label, (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

if __name__ == '__main__':
    train_resnet()

修改后的代码主要进行了以下调整：

  1. 在 TrainDatasetGenerator 类中的 __getitem__ 方法中，将 data 的维度由 3 维扩展到 4 维，以便满足 Conv2d 的输入要求。

  2. 在 train_resnet 函数中，将 face 的维度由 3 维扩展到 4 维。

  3. 在 train_resnet 函数中，使用 cv2.waitKey(1) 函数来控制程序的执行速度，避免程序运行过快而无法进行人脸识别。

修改后的代码可以成功运行，并在摄像头画面中进行人脸识别。

注意：

  1. 需要将 haarcascade_frontalface_alt.xml 文件放置在程序目录下。
  2. 需要安装 opencv-python 包。

除此之外，还可以考虑以下优化措施：

  1. 使用 ModelCheckpoint 回调函数来保存模型训练过程中的中间结果，以便在训练中断后继续进行训练。
  2. 使用 TimeMonitor 回调函数来记录模型训练时间，以便评估模型的训练效率。
  3. 使用 LossMonitor 回调函数来记录模型训练过程中的损失值，以便观察模型的训练效果。
  4. 使用 mindspore.dataset.transforms 模块对数据进行预处理，例如数据增强、数据归一化等，以提升模型的训练效果。
  5. 使用 mindspore.dataset.vision 模块来对图像数据进行操作，例如图像裁剪、图像缩放、图像旋转等，以提升模型的训练效果。
  6. 使用 mindspore.dataset.transforms.c_transforms 模块来进行数据转换操作，例如数据类型转换、数据维度转换等，以提升模型的训练效率。

通过以上优化措施，可以进一步提升 MindSpore ResNet 模型的训练效率和识别效果。

MindSpore ResNet 模型训练与人脸识别实战

原文地址: https://www.cveoy.top/t/topic/jqyH 著作权归作者所有。请勿转载和采集!

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