Hi, I transfer trained my original dataset after training FCHarDnet 1 with CItyscapes data.
During loading the trained weight for inference, I met problem.
While transfer train FCHarDnet,
I inserted the layer to match the class number.
Like below
# While Training, I wrap HarDnet & layer with nn.Sequential
model = FCHarDnet.to(device) 
model = nn.Sequential(model,
                      nn.Conv2d(19, out_channels=11, kernel_size=1, stride=1,padding=0, bias=True)).to(device)

Then, I loaded trained weight like below,
# While inference
model = FCHarDnet.to(device) 
model = nn.Sequential(model,
                      nn.Conv2d(19, out_channels=11, kernel_size=1, stride=1,padding=0, bias=True)).to(device)
state = convert_state_dict(torch.load(args.model_path)["model_state"])
model.load_state_dict(state)

While
# convert_state_dict function

def convert_state_dict(state_dict):
    """Converts a state dict saved from a dataParallel module to normal
       module state_dict inplace
       :param state_dict is the loaded DataParallel model_state
    """
    new_state_dict = OrderedDict()
    for k, v in state_dict.items():
        name = k[7:]  # remove `module.`
        new_state_dict[name] = v
    return new_state_dict


And then met error like this…
Missing key(s) in state_dict: "0.base.0.conv.weight", "0.base.0.norm.weight", "0.base.0.norm.bias", "0.base.0.norm.running_mean",
     :
     :
     :
Unexpected key(s) in state_dict: "base.0.conv.weight", "base.0.norm.weight", "base.0.norm.bias", "base.0.norm.running_mean",
     :
     :
     :

I assume weight couldn’t be loaded because of key name didn’t match?
If that’s correct, will someone please share the code of how to fix this problem.
Thank you