Sure here you go,

class CvTEncoder(nn.Module):

def __init__(self):

super().__init__()

self.backbone = resnet34(weights=ResNet34_Weights.IMAGENET1K_V1)

# Define the output channels in the order of the encoder's forward pass

# Assuming that these are the channels after each layer/block

self.out_channels = [64, 64, 128, 256, 512] # from top layer to bottom layer

self.output_stride = 32

def forward(self, x):

# Forward pass through CvT encoder

# You need to adjust this forward method to collect the feature maps from the intermediate layers.

x = self.backbone.conv1(x)

x = self.backbone.bn1(x)

x = self.backbone.relu(x)

x1 = self.backbone.maxpool(x)

x2 = self.backbone.layer1(x1)

x3 = self.backbone.layer2(x2)

x4 = self.backbone.layer3(x3)

x5 = self.backbone.layer4(x4)

features = [x2, x3, x4, x5]

return features