Hi, I transfer trained my original dataset after training FCHarDnet 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