[Bug] Cannot load the parameters of the advanced model (SwinL-based) on HICO-DET

[QihanZhao]

run main.py with args as

            "args": [
                "--backbone", "swin_large",
                "--drop-path-rate", "0.5", 
                "--num-queries-one2one", "900",
                "--num-queries-one2many", "1500",
                "--world-size", "1",
                "--batch-size", "1",
                "--eval", 
                "--resume", "./checkpoints/h-defm-detr-swinL-dp0-mqs-lft-iter-2stg-hicodet.pth"
            ],

when it goes to

model.load_state_dict(checkpoint['model_state_dict'])

error appears as follows [ in two parts: Missing keys and Unexpected keys]

It seems that the model has been updated since the parameters were released. Could you please provide the latest version that aligns with the current code? Thank you very much!

RuntimeError('Error(s) in loading state_dict for PViC:

Missing key(s) in state_dict: "detector.transformer.level_embed", "detector.transformer.encoder.layers.0.self_attn.sampling_offsets.weight", "detector.transformer.encoder.layers.0.self_attn.sampling_offsets.bias", "detector.transformer.encoder.layers.0.self_attn.attention_weights.weight", "detector.transformer.encoder.layers.0.self_attn.attention_weights.bias", "detector.transformer.encoder.layers.0.self_attn.value_proj.weight", "detector.transformer.encoder.layers.0.self_attn.value_proj.bias", "detector.transformer.encoder.layers.0.self_attn.output_proj.weight", "detector.transformer.encoder.layers.0.self_attn.output_proj.bias", "detector.transformer.encoder.layers.0.norm1.weight", "detector.transformer.encoder.layers.0.norm1.bias", "detector.transformer.encoder.layers.0.linear1.weight", "detector.transformer.encoder.layers.0.linear1.bias", "detector.transformer.encoder.layers.0.linear2.weight", "detector.transformer.encoder.layers.0.linear2.bias", 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(...)

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[fredzzhang]

Hi @QihanZhao,

The weights you loaded are only for the object detector, not the HOI detection model. The --resume flag takes a trained PViC model, which includes both the detector weights and the downstream interaction head weights. The object detector weights should be loaded with the --pretrained flag.

You could train the model with the following command

DETR=advanced python main.py --backbone swin_large \
                             --drop-path-rate 0.5 \
                             --num-queries-one2one 900 \
                             --num-queries-one2many 1500 \
                             --pretrained checkpoints/h-defm-detr-swinL-dp0-mqs-lft-iter-2stg-hicodet.pth \
                             --use-checkpoint \
                             --output-dir outputs/pvic-h-defm-detr-swinL-hicodet

Fred.

[fredzzhang]

Hi @QihanZhao,

Were you able to reproduce the results? I forgot to add the flag --use-checkpoint in the original documentation, which I have fixed now. The flag uses memory checkpointing to significantly reduce memory consumption. You should be able to fit the model with ease.

Let me know if you have issues with the reproduction.

Fred.

[QihanZhao]

Dear Dr. Zhang, @fredzzhang

Thank you for your prompt and informative response. I appreciate the detailed instructions on how to train the PViC model using the object detector weights with the --pretrained flag.

However, my current objective is to evaluate the performance of the model through testing, not training. I would like to confirm my understanding based on your response: the fully trained PViC model parameters, which include both the detector weights and the downstream interaction head weights, have not been made available. Is it correct to assume that only the backbone's pretrained weights are provided, and not the complete set of trained parameters for the entire PViC model?

If my understanding is correct, would it be possible for you to provide the full weights for the PViC model so that we can proceed with the testing phase? Having access to the pretrained model parameters would enable us to evaluate the model's performance accurately and contribute further to the community's understanding of its capabilities.

I apologize for any confusion and thank you once again for your assistance.

Looking forward to your clarification.

Best regards, Qihan

[fredzzhang]

As we tested the model with four different object detectors, storage of checkpoints was a slight issue. For that reason, only the PViC-DETR-R50 checkpoint was kept. You can download the weights from a link in the inference section of the docs.

Fred.

[QihanZhao]

ok, I am training it now. Thanks for help. Feedback later

[fredzzhang]

Since there haven't been any reported issues. I'm assuming it is working as intended.