python train.py --model danet --resume models/DANet101.pth.tar --dataset citys --backbone resnet101 --base-size 2080 --crop-size 1890 --batch-size 4 error

[yuki3304]

I used the command　python train.py --model danet --resume models/DANet101.pth.tar --dataset citys --backbone resnet101 --base-size 2080 --crop-size 1890 --batch-size 4

I tried running the command above to train the model myself. However, I encountered the following error.

Traceback (most recent call last):
File "train.py", line 280, in
trainer = Trainer(args)
File "train.py", line 161, in init
model = get_segmentation_model(args.model, dataset=args.dataset,
File "/opt/conda/lib/python3.8/site-packages/torch_encoding-1.2.2b20241007-py3.8.egg/encoding/models/sseg/init.py", line 24, in get_segmentation_model
return modelsname.lower()
File "/opt/conda/lib/python3.8/site-packages/torch_encoding-1.2.2b20241007-py3.8.egg/encoding/models/sseg/encnet.py", line 140, in get_encnet
model = EncNet(datasets[dataset.lower()].NUM_CLASS, backbone=backbone, root=root, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/torch_encoding-1.2.2b20241007-py3.8.egg/encoding/models/sseg/encnet.py", line 23, in init
super(EncNet, self).init(nclass, backbone, aux, se_loss,
File "/opt/conda/lib/python3.8/site-packages/torch_encoding-1.2.2b20241007-py3.8.egg/encoding/models/sseg/base.py", line 69, in init
self.pretrained = get_backbone(backbone, pretrained=True, dilated=dilated,
File "/opt/conda/lib/python3.8/site-packages/torch_encoding-1.2.2b20241007-py3.8.egg/encoding/models/sseg/base.py", line 51, in get_backbone
net = modelsname
File "/opt/conda/lib/python3.8/site-packages/torch_encoding-1.2.2b20241007-py3.8.egg/encoding/models/backbone/resnet.py", line 349, in resnet50
model.load_state_dict(torch.load(
File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1406, in load_state_dict
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for ResNet:
size mismatch for bn1.weight: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for bn1.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for bn1.running_mean: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for bn1.running_var: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for layer1.0.conv1.weight: copying a param with shape torch.Size([64, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 128, 1, 1]).
size mismatch for layer1.0.downsample.0.weight: copying a param with shape torch.Size([256, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 128, 1, 1]).

Do you know how to fix the error? I used the Cityscapes dataset.