I get this error when i try to run inference:

Traceback (most recent call last):
File "../opt/project/main.py", line 392, in
inference(args)
File "/opt/project/inference_tools/inference_engine.py", line 37, in inference
model.load_state_dict(checkpoint['model'], strict=False)
File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1490, in load_state_dict
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for PoET:
size mismatch for transformer.encoder.layers.0.self_attn.sampling_offsets.weight: copying a param with shape torch.Size([512, 256]) from checkpoint, the shape in current model is torch.Size([256, 256]).
size mismatch for transformer.encoder.layers.0.self_attn.sampling_offsets.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for transformer.encoder.layers.0.self_attn.attention_weights.weight: copying a param with shape torch.Size([256, 256]) from checkpoint, the shape in current model is torch.Size([128, 256]).
size mismatch for transformer.encoder.layers.0.self_attn.attention_weights.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for transformer.encoder.layers.1.self_attn.sampling_offsets.weight: copying a param with shape torch.Size([512, 256]) from checkpoint, the shape in current model is torch.Size([256, 256]).
size mismatch for transformer.encoder.layers.1.self_attn.sampling_offsets.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for transformer.encoder.layers.1.self_attn.attention_weights.weight: copying a param with shape torch.Size([256, 256]) from checkpoint, the shape in current model is torch.Size([128, 256]).
size mismatch for transformer.encoder.layers.1.self_attn.attention_weights.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for transformer.encoder.layers.2.self_attn.sampling_offsets.weight: copying a param with shape torch.Size([512, 256]) from checkpoint, the shape in current model is torch.Size([256, 256]).
size mismatch for transformer.encoder.layers.2.self_attn.sampling_offsets.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for transformer.encoder.layers.2.self_attn.attention_weights.weight: copying a param with shape torch.Size([256, 256]) from checkpoint, the shape in current model is torch.Size([128, 256]).
size mismatch for transformer.encoder.layers.2.self_attn.attention_weights.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for transformer.encoder.layers.3.self_attn.sampling_offsets.weight: copying a param with shape torch.Size([512, 256]) from checkpoint, the shape in current model is torch.Size([256, 256]).
size mismatch for transformer.encoder.layers.3.self_attn.sampling_offsets.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for transformer.encoder.layers.3.self_attn.attention_weights.weight: copying a param with shape torch.Size([256, 256]) from checkpoint, the shape in current model is torch.Size([128, 256]).
size mismatch for transformer.encoder.layers.3.self_attn.attention_weights.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for transformer.encoder.layers.4.self_attn.sampling_offsets.weight: copying a param with shape torch.Size([512, 256]) from checkpoint, the shape in current model is torch.Size([256, 256]).
size mismatch for transformer.encoder.layers.4.self_attn.sampling_offsets.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for transformer.encoder.layers.4.self_attn.attention_weights.weight: copying a param with shape torch.Size([256, 256]) from checkpoint, the shape in current model is torch.Size([128, 256]).
size mismatch for transformer.encoder.layers.4.self_attn.attention_weights.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for transformer.decoder.layers.0.cross_attn.sampling_offsets.weight: copying a param with shape torch.Size([512, 256]) from checkpoint, the shape in current model is torch.Size([256, 256]).
size mismatch for transformer.decoder.layers.0.cross_attn.sampling_offsets.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for transformer.decoder.layers.0.cross_attn.attention_weights.weight: copying a param with shape torch.Size([256, 256]) from checkpoint, the shape in current model is torch.Size([128, 256]).
size mismatch for transformer.decoder.layers.0.cross_attn.attention_weights.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for transformer.decoder.layers.1.cross_attn.sampling_offsets.weight: copying a param with shape torch.Size([512, 256]) from checkpoint, the shape in current model is torch.Size([256, 256]).
size mismatch for transformer.decoder.layers.1.cross_attn.sampling_offsets.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for transformer.decoder.layers.1.cross_attn.attention_weights.weight: copying a param with shape torch.Size([256, 256]) from checkpoint, the shape in current model is torch.Size([128, 256]).
size mismatch for transformer.decoder.layers.1.cross_attn.attention_weights.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for transformer.decoder.layers.2.cross_attn.sampling_offsets.weight: copying a param with shape torch.Size([512, 256]) from checkpoint, the shape in current model is torch.Size([256, 256]).
size mismatch for transformer.decoder.layers.2.cross_attn.sampling_offsets.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for transformer.decoder.layers.2.cross_attn.attention_weights.weight: copying a param with shape torch.Size([256, 256]) from checkpoint, the shape in current model is torch.Size([128, 256]).
size mismatch for transformer.decoder.layers.2.cross_attn.attention_weights.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for transformer.decoder.layers.3.cross_attn.sampling_offsets.weight: copying a param with shape torch.Size([512, 256]) from checkpoint, the shape in current model is torch.Size([256, 256]).
size mismatch for transformer.decoder.layers.3.cross_attn.sampling_offsets.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for transformer.decoder.layers.3.cross_attn.attention_weights.weight: copying a param with shape torch.Size([256, 256]) from checkpoint, the shape in current model is torch.Size([128, 256]).
size mismatch for transformer.decoder.layers.3.cross_attn.attention_weights.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for transformer.decoder.layers.4.cross_attn.sampling_offsets.weight: copying a param with shape torch.Size([512, 256]) from checkpoint, the shape in current model is torch.Size([256, 256]).
size mismatch for transformer.decoder.layers.4.cross_attn.sampling_offsets.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for transformer.decoder.layers.4.cross_attn.attention_weights.weight: copying a param with shape torch.Size([256, 256]) from checkpoint, the shape in current model is torch.Size([128, 256]).
size mismatch for transformer.decoder.layers.4.cross_attn.attention_weights.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for translation_head.0.layers.2.weight: copying a param with shape torch.Size([27, 256]) from checkpoint, the shape in current model is torch.Size([66, 256]).
size mismatch for translation_head.0.layers.2.bias: copying a param with shape torch.Size([27]) from checkpoint, the shape in current model is torch.Size([66]).
size mismatch for translation_head.1.layers.2.weight: copying a param with shape torch.Size([27, 256]) from checkpoint, the shape in current model is torch.Size([66, 256]).
size mismatch for translation_head.1.layers.2.bias: copying a param with shape torch.Size([27]) from checkpoint, the shape in current model is torch.Size([66]).
size mismatch for translation_head.2.layers.2.weight: copying a param with shape torch.Size([27, 256]) from checkpoint, the shape in current model is torch.Size([66, 256]).
size mismatch for translation_head.2.layers.2.bias: copying a param with shape torch.Size([27]) from checkpoint, the shape in current model is torch.Size([66]).
size mismatch for translation_head.3.layers.2.weight: copying a param with shape torch.Size([27, 256]) from checkpoint, the shape in current model is torch.Size([66, 256]).
size mismatch for translation_head.3.layers.2.bias: copying a param with shape torch.Size([27]) from checkpoint, the shape in current model is torch.Size([66]).
size mismatch for translation_head.4.layers.2.weight: copying a param with shape torch.Size([27, 256]) from checkpoint, the shape in current model is torch.Size([66, 256]).
size mismatch for translation_head.4.layers.2.bias: copying a param with shape torch.Size([27]) from checkpoint, the shape in current model is torch.Size([66]).
size mismatch for rotation_head.0.layers.2.weight: copying a param with shape torch.Size([54, 256]) from checkpoint, the shape in current model is torch.Size([132, 256]).
size mismatch for rotation_head.0.layers.2.bias: copying a param with shape torch.Size([54]) from checkpoint, the shape in current model is torch.Size([132]).
size mismatch for rotation_head.1.layers.2.weight: copying a param with shape torch.Size([54, 256]) from checkpoint, the shape in current model is torch.Size([132, 256]).
size mismatch for rotation_head.1.layers.2.bias: copying a param with shape torch.Size([54]) from checkpoint, the shape in current model is torch.Size([132]).
size mismatch for rotation_head.2.layers.2.weight: copying a param with shape torch.Size([54, 256]) from checkpoint, the shape in current model is torch.Size([132, 256]).
size mismatch for rotation_head.2.layers.2.bias: copying a param with shape torch.Size([54]) from checkpoint, the shape in current model is torch.Size([132]).
size mismatch for rotation_head.3.layers.2.weight: copying a param with shape torch.Size([54, 256]) from checkpoint, the shape in current model is torch.Size([132, 256]).
size mismatch for rotation_head.3.layers.2.bias: copying a param with shape torch.Size([54]) from checkpoint, the shape in current model is torch.Size([132]).
size mismatch for rotation_head.4.layers.2.weight: copying a param with shape torch.Size([54, 256]) from checkpoint, the shape in current model is torch.Size([132, 256]).
size mismatch for rotation_head.4.layers.2.bias: copying a param with shape torch.Size([54]) from checkpoint, the shape in current model is torch.Size([132]).

I run this comand:

docker run --rm --gpus all
-v poet:/opt/project
-v poet/my_data:/data
-v poet/inference_output:/output
aaucns/poet:latest
python -u ../opt/project/main.py
--enc_layers 5
--dec_layers 5
--resume /opt/project/checkpoints/poet_lmo_maskrcnn.pth
--inference
--backbone maskrcnn
--inference_path /data/images
--inference_output /output
--backbone_cfg /opt/project/configs/lmo_rcnn.yaml
--backbone_weights /opt/project/models/lmo_maskrcnn_checkpoint.pth.tar

Do you have any advice on how to make it work?

Thanks