checkpoint and config_file

[hibernationT]

Hello, thanks for your great work. I ran into a problem while running the ./tools/test.py using "/workspace/ADA-Track/plugin/configs/ada_track_petr.py /workspace/PF-Track/models/f1/f1_q5_fullres_e24.pth --seed 42 --eval map --out /workspace/ADA-Track/results". The problem is "The model and loaded state dict do not match exactly". I download the checkpoint from PF-Track. Is there any error in the usage of config file or any other errors present?Looking forward to your answer,thank you.

[dsx0511]

Hi, the reason of your problem is that you restored the weights of the base detector (PETR) for the tracker during inference. The tracker has more weights in the learnable data association module than the detector, which leads to the mismatch.

You can still restore that checkpoint during training, in this case, all weights that matches the checkpoint are initialized from it, other weights are randomly initialized. However, during inference, you need to restore a checkpoint that exactly matches your model architecture.

[CMSC740Student]

@dsx0511 A similar issue is observed during training. I download weights from PETR PETR-vov-p4-1600x640 & loaded it my config file. Is this expected?

2024-09-01 09:20:49,173 - mmdet - INFO - load checkpoint from local path: /home//ada-track/petr_vovnet_p4_1600_640_query500.pth
Tracking is True
Tracking is True
2024-09-01 09:20:50,359 - mmdet - WARNING - The model and loaded state dict do not match exactly

size mismatch for pts_bbox_head.cls_branches.0.6.weight: copying a param with shape torch.Size([10, 256]) from checkpoint, the shape in current model is torch.Size([7, 256]).
size mismatch for pts_bbox_head.cls_branches.0.6.bias: copying a param with shape torch.Size([10]) from checkpoint, the shape in current model is torch.Size([7]).
size mismatch for pts_bbox_head.cls_branches.1.6.weight: copying a param with shape torch.Size([10, 256]) from checkpoint, the shape in current model is torch.Size([7, 256]).
size mismatch for pts_bbox_head.cls_branches.1.6.bias: copying a param with shape torch.Size([10]) from checkpoint, the shape in current model is torch.Size([7]).
size mismatch for pts_bbox_head.cls_branches.2.6.weight: copying a param with shape torch.Size([10, 256]) from checkpoint, the shape in current model is torch.Size([7, 256]).
size mismatch for pts_bbox_head.cls_branches.2.6.bias: copying a param with shape torch.Size([10]) from checkpoint, the shape in current model is torch.Size([7]).
size mismatch for pts_bbox_head.cls_branches.3.6.weight: copying a param with shape torch.Size([10, 256]) from checkpoint, the shape in current model is torch.Size([7, 256]).
size mismatch for pts_bbox_head.cls_branches.3.6.bias: copying a param with shape torch.Size([10]) from checkpoint, the shape in current model is torch.Size([7]).
size mismatch for pts_bbox_head.cls_branches.4.6.weight: copying a param with shape torch.Size([10, 256]) from checkpoint, the shape in current model is torch.Size([7, 256]).
size mismatch for pts_bbox_head.cls_branches.4.6.bias: copying a param with shape torch.Size([10]) from checkpoint, the shape in current model is torch.Size([7]).
size mismatch for pts_bbox_head.cls_branches.5.6.weight: copying a param with shape torch.Size([10, 256]) from checkpoint, the shape in current model is torch.Size([7, 256]).
size mismatch for pts_bbox_head.cls_branches.5.6.bias: copying a param with shape torch.Size([10]) from checkpoint, the shape in current model is torch.Size([7]).
unexpected key in source state_dict: pts_bbox_head.code_weights, pts_bbox_head.reference_points.weight, pts_bbox_head.query_embedding.0.weight, pts_bbox_head.query_embedding.0.bias, pts_bbox_head.query_embedding.2.weight, pts_bbox_head.query_embedding.2.bias

missing keys in source state_dict: pts_bbox_head.asso_branches.0.0.weight, pts_bbox_head.asso_branches.0.0.bias, pts_bbox_head.asso_branches.0.1.weight, pts_bbox_head.asso_branches.0.1.bias, pts_bbox_head.asso_branches.0.3.weight, pts_bbox_head.asso_branches.0.3.bias, pts_bbox_head.asso_branches.0.4.weight, pts_bbox_head.asso_branches.0.4.bias, pts_bbox_head.asso_branches.0.6.weight, pts_bbox_head.asso_branches.0.6.bias, pts_bbox_head.asso_branches.1.0.weight, pts_bbox_head.asso_branches.1.0.bias, pts_bbox_head.asso_branches.1.1.weight, pts_bbox_head.asso_branches.1.1.bias, pts_bbox_head.asso_branches.1.3.weight, pts_bbox_head.asso_branches.1.3.bias, pts_bbox_head.asso_branches.1.4.weight, pts_bbox_head.asso_branches.1.4.bias, pts_bbox_head.asso_branches.1.6.weight, pts_bbox_head.asso_branches.1.6.bias, pts_bbox_head.asso_branches.2.0.weight, pts_bbox_head.asso_branches.2.0.bias, pts_bbox_head.asso_branches.2.1.weight, pts_bbox_head.asso_branches.2.1.bias, pts_bbox_head.asso_branches.2.3.weight, pts_bbox_head.asso_branches.2.3.bias, pts_bbox_head.asso_branches.2.4.weight, pts_bbox_head.asso_branches.2.4.bias, pts_bbox_head.asso_branches.2.6.weight, pts_bbox_head.asso_branches.2.6.bias, pts_bbox_head.asso_branches.3.0.weight, pts_bbox_head.asso_branches.3.0.bias, pts_bbox_head.asso_branches.3.1.weight, pts_bbox_head.asso_branches.3.1.bias, pts_bbox_head.asso_branches.3.3.weight, pts_bbox_head.asso_branches.3.3.bias, pts_bbox_head.asso_branches.3.4.weight, pts_bbox_head.asso_branches.3.4.bias, pts_bbox_head.asso_branches.3.6.weight, pts_bbox_head.asso_branches.3.6.bias, pts_bbox_head.asso_branches.4.0.weight, pts_bbox_head.asso_branches.4.0.bias, pts_bbox_head.asso_branches.4.1.weight, pts_bbox_head.asso_branches.4.1.bias, pts_bbox_head.asso_branches.4.3.weight, pts_bbox_head.asso_branches.4.3.bias, pts_bbox_head.asso_branches.4.4.weight, pts_bbox_head.asso_branches.4.4.bias, pts_bbox_head.asso_branches.4.6.weight, 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