Hi
I am trying to implement SE block with any pretrained resnet architecture.
self.encoder = torchvision.models.resnet34(pretrained=pretrained)

self.conv1 = nn.Sequential(
    self.encoder.conv1,
    self.encoder.bn1,
    self.encoder.relu
    SeBlock(64,2),
    nn.MaxPool2d(2, 2) )

self.conv2 = nn.Sequential(
    self.encoder.layer1,
    SeBlock(64,2),
    )

self.conv3 = nn.Sequential(
    self.encoder.layer2,
    SeBlock(128,2),
    )

self.conv4 = nn.Sequential(
    self.encoder.layer3,
    SeBlock(256,2),
    )

self.conv5 = nn.Sequential(
    self.encoder.layer4,
    SeBlock(512,2),
    )

Above is my code but It is not working out. Looking into githubs they build resnet from scratch and induce SE block and then load model.state_dict() weights for the layers of resnet and train the remaining model.
I just need to know what is the correct procedure to use SE block with pretrained resnet.?
Thanks.!!