使用paddle写一个50层的一维resnet网络模型，该模型的输入数据通道数为12，使用paddle.summary可视化该模型

好的，以下是使用Paddle实现的50层一维ResNet网络：

import paddle
import paddle.nn as nn

class BasicBlock(nn.Layer):
    expansion = 1

    def __init__(self, in_channels, out_channels, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        
        self.conv1 = nn.Conv1D(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias_attr=False)
        self.bn1 = nn.BatchNorm1D(out_channels)
        self.relu = nn.ReLU()
        self.conv2 = nn.Conv1D(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias_attr=False)
        self.bn2 = nn.BatchNorm1D(out_channels)
        self.downsample = downsample

    def forward(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 Bottleneck(nn.Layer):
    expansion = 4

    def __init__(self, in_channels, out_channels, stride=1, downsample=None):
        super(Bottleneck, self).__init__()

        self.conv1 = nn.Conv1D(in_channels, out_channels, kernel_size=1, bias_attr=False)
        self.bn1 = nn.BatchNorm1D(out_channels)
        self.conv2 = nn.Conv1D(out_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias_attr=False)
        self.bn2 = nn.BatchNorm1D(out_channels)
        self.conv3 = nn.Conv1D(out_channels, out_channels * self.expansion, kernel_size=1, bias_attr=False)
        self.bn3 = nn.BatchNorm1D(out_channels * self.expansion)
        self.relu = nn.ReLU()
        self.downsample = downsample

    def forward(self, x):
        identity = x

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

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

        out = self.conv3(out)
        out = self.bn3(out)

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

        out += identity
        out = self.relu(out)

        return out


class ResNet(nn.Layer):
    def __init__(self, block, layers, input_channels=12, num_classes=10):
        super(ResNet, self).__init__()

        self.in_channels = 64

        self.conv1 = nn.Conv1D(input_channels, 64, kernel_size=7, stride=2, padding=3, bias_attr=False)
        self.bn1 = nn.BatchNorm1D(64)
        self.relu = nn.ReLU()
        self.maxpool = nn.MaxPool1D(kernel_size=3, stride=2, padding=1)

        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.AdaptiveAvgPool1D(1)
        self.fc = nn.Linear(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.Sequential(
                nn.Conv1D(self.in_channels, out_channels * block.expansion, kernel_size=1, stride=stride, bias_attr=False),
                nn.BatchNorm1D(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.Sequential(*layers)

    def forward(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 = paddle.flatten(x, 1)
        x = self.fc(x)

        return x


def ResNet50(input_channels=12, num_classes=10):
    return ResNet(Bottleneck, [3, 4, 6, 3], input_channels, num_classes)

可以使用以下代码使用paddle.summary可视化该模型：

import paddle.summary as summary

model = ResNet50(input_channels=12, num_classes=10)
summary(model, (12, 1024))

其中，(12, 1024) 表示输入数据的维度为 (batch_size, input_channels, input_length)，在这里我们假设输入数据长度为 1024。