KeyError: "BEVFusion: 'encoders.camera.backbone.stages.0.blocks.0.attn.w_msa.relative_position_bias_table'"

[fdy61]

I use my own lidar-only and camera-only pth to train the fusion model,and encountered this problem,How can I solve it?

[xmutyjs]

Pls did you solve it, I also had the same problem

[kongwah]

I faced the same problem, when trying to use the new saved checkpoints from training a cam-only centerhead detector. I am not sure if it has anything to do with the centerhead being different the transfusion.

Let me be specific.

1. I first train a new cam-only detector using the following:

torchpack dist-run -np 1 python tools/train.py
configs/nuscenes/det/centerhead/lssfpn/camera/256x704/swint/default.yaml
--model.encoders.camera.backbone.init_cfg.checkpoint pretained/swint-nuimages-pretrained.pth

2. the checkpoints of (1) are saved into runs/ folder

3. Then now I try to train a cam+lidar detector using one of the saved checkpoints in (2):

torchpack dist-run -np 1 python tools/train.py \ configs/nuscenes/det/transfusion/secfpn/camera+lidar/swint_v0p075/convfuser.yaml
--model.encoders.camera.backbone.init_cfg.checkpoint runs/run-531bf67d-d3138be2/epoch_20.pth
--load_from pretrained/lidar-only-det.pth

And the errors are as follow:

Traceback (most recent call last): File "tools/train.py", line 68, in main model = build_model(cfg.model) File "/home/bevfusion/mmdet3d/models/builder.py", line 41, in build_model return build_fusion_model(cfg, train_cfg=train_cfg, test_cfg=test_cfg) File "/home/bevfusion/mmdet3d/models/builder.py", line 35, in build_fusion_model return FUSIONMODELS.build( File "/opt/conda/lib/python3.8/site-packages/mmcv/utils/registry.py", line 212, in build return self.build_func(*args, **kwargs, registry=self) File "/opt/conda/lib/python3.8/site-packages/mmcv/utils/registry.py", line 55, in build_from_cfg raise type(e)(f'{obj_cls.name}: {e}') KeyError: "BEVFusion: 'encoders.camera.backbone.stages.0.blocks.0.attn.w_msa.relative_position_bias_table'"

[fdy61]

The model you trained in (1) means you obtain a single-modality model which just uses the camera images to do the object detection. That means you cannot use it as the camera backbone to train the fusion model.

[kongwah]

Hi fdy61,

I notice the same "pretained/swint-nuimages-pretrained.pth" was used as a checkpoint for training the cam-only detector, and also as a checkpoint to train the cam+lidar detector.

This is why I was of the impression that the saved cam-only checkpoints "runs/run-531bf67d-d3138be2/epoch_20.pth" will be usable to train the cam+lidar detector.

May I have your kind advise, how then can I train an appropriate camera backbone to train the cam+lidar fusion model?

Thanks

[fdy61]

pretained/swint-nuimages-pretrained.pth is just a image backbone model, if I remember correctly，which has the same structure compared with SwinTransformer. But runs/run-531bf67d-d3138be2/epoch_20.pth is an entire model, and its' weights parameter have been changed completely. you can print out the model weights name to see.

[kongwah]

Hi fdy61,

Thanks for your tips. Indeed when I "ls -l" the 2 files, they are very different:

ls -l pretrained/swint-nuimages-pretrained.pth run/run-531bf67d-d3138be2/epoch_20.pth -rw-r--r-- 1 root root 110370759 Sep 26 2022 pretrained/swint-nuimages-pretrained.pth -rw-r--r-- 1 root root 523728374 May 27 03:45 run/run-531bf67d-d3138be2_epoch_20.pth

I am not sure how else can I print examine their "model weight name". Can you kindly advise me?

So the question then becomes how do I re-train the cam+lidar fusion model, using my new image data? I believe you may have the same goal, when you mentioned in your first post "I use my own lidar-only and camera-only pth to train the fusion model....". You also have the camera-only pth checkpoint? Did you manage to solve it? If so, can you advise me please?

Thanks

[kongwah]

Can I do the following instead:

torchpack dist-run -np 8
python tools/train.py configs/nuscenes/det/transfusion/secfpn/camera+lidar/swint_v0p075/convfuser.yaml
--model.encoders.camera.backbone.init_cfg.checkpoint pretrained/swint-nuimages-pretrained.pth
--load_from runs/run-531bf67d-d3138be2/epoch_20.pth
--load_from pretrained/lidar-only-det.pth

That is, have 2 "--load_from", loading both the cam-only checkpoint and the lidar-only checkpoint.

Thanks

[fdy61]

torchpack dist-run -np 8 python tools/train.py configs/nuscenes/det/transfusion/secfpn/camera+lidar/swint_v0p075/convfuser.yaml --model.encoders.camera.backbone.init_cfg.checkpoint pretrained/swint-nuimages-pretrained.pth --load_from pretrained/lidar-only-det.pth

[kongwah]

After some debugging, it seems that the issue is that the state_dict keys for

  pretrained/swint-nuimages-pretrained.pth 

differ from that of

runs/run-531bf67d-d3138be2/epoch_20.pth 

by the prefix "encoders.camera.backbone".

I am hoping that if I change the key to skip this prefix, then this error will go away!

[fdy61]

You just use the pretrained/swint-nuimages-pretrained.pth and it's done. I have told you that runs/run-531bf67d-d3138be2/epoch_20.pth is the camera-only model, which has been trained only based on images and state_dict keys totally different from pretrained/swint-nuimages-pretrained.pth.

[kongwah]

Hi fdy61, Thanks. The background is that I have trained the camera-only model, and using the "runs/run-531bf67d-d3138be2/epoch_20.pth" checkpoint, I have obtained mAP improvements over the "pretrained/camera-only-det.pth"

Hence, my thinking is to use this "epoch_20.pth" checkpoint to train the cam+lidar fusion model.

OK, I will try to check/confirm if the state_dict keys of "epoch_20.pth" are totally different from the "camera-only-det.pth", or if they only differ by the prefix "encoders.camera.backbone". I will post the update here.

Thanks

[Ruvennsiow]

Hi fdy61,

Since we have to use pretrained/swint-nuimages-pretrained.pth to train the C+L fusion model, do you have an idea of how the pretrained model was trained? Alternatively, is there any way we can replicate the training process for the pretrained models?

Thanks~