ResNet网络：解决'RuntimeError: mindspore\core\ops\conv2d.cc:185 Conv2dInferShape] x_shape[C_in] / group must equal to w_shape[C_in] = 1, but got 3'错误

ResNet网络：解决'RuntimeError: mindspore\core\ops\conv2d.cc:185 Conv2dInferShape] x_shape[C_in] / group must equal to w_shape[C_in] = 1, but got 3'错误

在使用ResNet网络处理单通道图像时，可能会遇到如下错误：

RuntimeError: mindspore\core\ops\conv2d.cc:185 Conv2dInferShape] x_shape[C_in] / group must equal to w_shape[C_in] = 1, but got 3

这个错误通常发生在输入图像的通道数与卷积层中权重矩阵的通道数不匹配时。

错误原因：

ResNet网络的第一个卷积层默认输入通道数为3，对应于三通道彩色图像。然而，在处理单通道图像时，输入通道数应该是1。

解决方法：

将ResNet网络第一个卷积层的输入通道数修改为1：

self.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, pad_mode='valid')

代码示例：

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

        # 修改输入通道数为1
        self.conv1 = nn.Conv2d(1, 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

完成上述修改后，ResNet网络即可正常处理单通道图像。