KAN conv instead of ResNet conv

[woodszp]

Great work!
I want to use KAN conv instead of ResNet conv, how can i do it?

First, from kan_convolutional.KANConv import KAN_Convolutional_Layer from https://github.com/AntonioTepsich/Convolutional-KANs.
Second, change the ResNet conv
But there is a problem with the code, can you help me solve it?

import torch.nn as nn
import torch
from kan_convolutional.KANConv import KAN_Convolutional_Layer


def conv3x3(in_planes, out_planes, stride=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=1, bias=False)

def conv1x1(in_planes, out_planes, stride= 1):
    """1x1 convolution"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)

class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_channel, out_channel, stride=(1,1), downsample=None, device: str = 'cuda:0'):
        super(BasicBlock, self).__init__()

        # use the kan convolutional
        self.conv1 = KAN_Convolutional_Layer(
            n_convs = 5,
            kernel_size= (3,3),
            stride=stride,
            device = device
        )

        self.conv2 = KAN_Convolutional_Layer(
            n_convs = 5,
            kernel_size= (3,3),
            device = device
        )

        # self.conv1 = conv3x3(in_channel, out_channel, stride)  
        self.bn1 = nn.BatchNorm2d(15)
        self.relu = nn.ReLU(inplace=True)
        # self.conv2 = conv3x3(out_channel, out_channel)  
        self.bn2 = nn.BatchNorm2d(15)
        self.downsample = downsample
        self.stride = stride  
 

    def forward(self, x):
        residual = x
        print(f'Input shape: {x.shape}')  
        out = self.conv1(x)  # 3x3conv，s=1

        print(f'After conv1 shape: {out.shape}')  
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)  
        out = self.bn2(out)
        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual  
        out = self.relu(out)
 
        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, in_channel, out_channel, stride=1, downsample=None):
        super(Bottleneck, self).__init__()

        self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=out_channel,
                               kernel_size=1, stride=1, bias=False)  # squeeze channels
        self.bn1 = nn.BatchNorm2d(out_channel)
        # -----------------------------------------
        self.conv2 = nn.Conv2d(in_channels=out_channel, out_channels=out_channel,
                               kernel_size=3, stride=stride, bias=False, padding=1)
        self.bn2 = nn.BatchNorm2d(out_channel)
        # -----------------------------------------
        self.conv3 = nn.Conv2d(in_channels=out_channel, out_channels=out_channel*self.expansion,
                               kernel_size=1, stride=1, bias=False)  # unsqueeze channels
        self.bn3 = nn.BatchNorm2d(out_channel*self.expansion)
        self.relu = nn.ReLU(inplace=True)

        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = 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:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)
 
        return out


class ResNet(nn.Module):

    def __init__(self, block, blocks_num, num_classes=1000, include_top=True, device: str = 'cuda:0'):
        super(ResNet, self).__init__()
        self.include_top = include_top
        self.in_channel = 64

        # self.conv1 = nn.Conv2d(3, self.in_channel, kernel_size=7, stride=2,
        #                        padding=3, bias=False)
        
        self.conv1 = KAN_Convolutional_Layer(
            n_convs = 5,
            kernel_size= (7,7),
            stride=(2,2),
            padding=(3,3),
            device = device
        )       

    
        self.bn1 = nn.BatchNorm2d(15)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, blocks_num[0])
        self.layer2 = self._make_layer(block, 128, blocks_num[1], stride=2)
        self.layer3 = self._make_layer(block, 256, blocks_num[2], stride=2)
        self.layer4 = self._make_layer(block, 512, blocks_num[3], stride=2)
        if self.include_top:
            self.avgpool = nn.AdaptiveAvgPool2d((1, 1))  # output size = (1, 1)
            self.fc = nn.Linear(512 * block.expansion, num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')

    def _make_layer(self, block, channel, block_num, stride=1, device: str = 'cuda:0'):
        downsample = None
        if stride != 1 or self.in_channel != channel * block.expansion:
            downsample = nn.Sequential(
                KAN_Convolutional_Layer(n_convs = 5, kernel_size= (1,1), stride=(stride,stride), device = device),       
                # nn.Conv2d(self.in_channel, channel * block.expansion, kernel_size=1, stride=stride, bias=False),
                # nn.BatchNorm2d(KAN_Convolutional_Layer(n_convs = 5, kernel_size= (1,1), stride=(stride,stride), device = device).convs[0].conv.in_features)
                nn.BatchNorm2d(15)
                
                )

        layers = []
        layers.append(block(self.in_channel, channel, downsample=downsample, stride=stride))
        self.in_channel = channel * block.expansion

        for _ in range(1, block_num):
            layers.append(block(self.in_channel, channel))

        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)

        if self.include_top:
            x = self.avgpool(x)
            x = torch.flatten(x, 1)
            x = self.fc(x)

        return x


def resnet18(num_classes=1000, include_top=True):
    return ResNet(BasicBlock, [2, 2, 2, 2], num_classes=num_classes, include_top=include_top)