resnet_kan.py

import torch.nn as nn
from torchvision.models import resnet50, ResNet50_Weights
from kan1 import KANLinear

class ResNetKAN(nn.Module):
    def __init__(self, num_classes=10, freeze_backbone=True):
        super().__init__()
        weights = ResNet50_Weights.DEFAULT
        self.resnet = resnet50(weights=weights)
        if freeze_backbone:
            for p in self.resnet.parameters():
                p.requires_grad = False
        for p in self.resnet.layer3.parameters():
            p.requires_grad = True
        for p in self.resnet.layer4.parameters():
            p.requires_grad = True
        num_features = self.resnet.fc.in_features
        self.resnet.fc = nn.Identity()
        self.kan1 = KANLinear(num_features, 512)
        self.bn1 = nn.BatchNorm1d(512)
        self.act1 = nn.ReLU()
        self.kan2 = KANLinear(512, 512)
        self.bn2 = nn.BatchNorm1d(512)
        self.act2 = nn.ReLU()
        self.kan3 = KANLinear(512, num_classes)

    def forward(self, x):
        x = self.resnet(x)
        x = x.view(x.size(0), -1)
        x = self.kan1(x)
        x = self.bn1(x)
        x = self.act1(x)
        x = self.kan2(x)
        x = self.bn2(x)
        x = self.act2(x)
        x = self.kan3(x)
        return x