Hi,
I would like to build a 1DConvNet (2 channels) with residual connections but I don’t know how to add residual blocks to the model. All examples I found online describe residual blocks and their implementation using image data (2D convolutions). Here is my simple Conv1D model so far.
import torch
import torch.nn as nn
import torch.nn.functional as F

class SimpleNet(nn.Module):

    def __init__(self):
        super(SimpleNet, self).__init__()
        
        self.conv1 = nn.Conv1d(2, 32, kernel_size=1)
        self.conv2 = nn.Conv1d(32, 32, 3)
        self.conv3 = nn.Conv1d(32, 64, 3)
        self.conv4 = nn.Conv1d(64, 64, 3)
        self.avg_pool = nn.AdaptiveAvgPool1d(1)
        self.linear = nn.Linear(64, 3)

    def forward(self, x):
        x = F.max_pool1d(F.relu(self.conv1(x)), kernel_size=1, stride=1)
        x = F.max_pool1d(F.relu(self.conv2(x)), kernel_size=2, stride=2)
        x = F.max_pool1d(F.relu(self.conv3(x)), kernel_size=2, stride=2)
        x = F.max_pool1d(F.relu(self.conv4(x)), kernel_size=2, stride=2)
        x = self.avg_pool(x)
        x = x.view(x.size(0), -1)
        x = self.linear(x)

        return x

Thanks in advance for your help!