The model and loaded state dict do not match exactly

[ulisb]

When I run with one GPU, it shows that the model does not match。 here is the log unexpected key in source state_dict: img_rpn_head.rpn_conv.weight, img_rpn_head.rpn_conv.bias, img_rpn_head.rpn_cls.weight, img_rpn_head.rpn_cls.bias, img_rpn_head.rpn_reg.weight, img_rpn_head.rpn_reg.bias, img_roi_head.bbox_head.fc_cls.weight, img_roi_head.bbox_head.fc_cls.bias, img_roi_head.bbox_head.fc_reg.weight, img_roi_head.bbox_head.fc_reg.bias, img_roi_head.bbox_head.shared_fcs.0.weight, img_roi_head.bbox_head.shared_fcs.0.bias, img_roi_head.bbox_head.shared_fcs.1.weight, img_roi_head.bbox_head.shared_fcs.1.bias

missing keys in source state_dict: backbone.conv1.kernel, backbone.norm1.bn.weight, backbone.norm1.bn.bias, backbone.norm1.bn.running_mean, backbone.norm1.bn.running_var, backbone.layer1.0.conv1.kernel, backbone.layer1.0.norm1.bn.weight, backbone.layer1.0.norm1.bn.bias, backbone.layer1.0.norm1.bn.running_mean, backbone.layer1.0.norm1.bn.running_var, backbone.layer1.0.conv2.kernel, backbone.layer1.0.norm2.bn.weight, backbone.layer1.0.norm2.bn.bias, backbone.layer1.0.norm2.bn.running_mean, backbone.layer1.0.norm2.bn.running_var, backbone.layer1.0.downsample.0.kernel, backbone.layer1.0.downsample.1.bn.weight, backbone.layer1.0.downsample.1.bn.bias, backbone.layer1.0.downsample.1.bn.running_mean, backbone.layer1.0.downsample.1.bn.running_var, backbone.layer1.1.conv1.kernel, backbone.layer1.1.norm1.bn.weight, backbone.layer1.1.norm1.bn.bias, backbone.layer1.1.norm1.bn.running_mean, backbone.layer1.1.norm1.bn.running_var, backbone.layer1.1.conv2.kernel, backbone.layer1.1.norm2.bn.weight, backbone.layer1.1.norm2.bn.bias, backbone.layer1.1.norm2.bn.running_mean, backbone.layer1.1.norm2.bn.running_var, backbone.layer1.2.conv1.kernel, backbone.layer1.2.norm1.bn.weight, backbone.layer1.2.norm1.bn.bias, backbone.layer1.2.norm1.bn.running_mean, backbone.layer1.2.norm1.bn.running_var, backbone.layer1.2.conv2.kernel, backbone.layer1.2.norm2.bn.weight, backbone.layer1.2.norm2.bn.bias, backbone.layer1.2.norm2.bn.running_mean, backbone.layer1.2.norm2.bn.running_var, backbone.layer2.0.conv1.kernel, backbone.layer2.0.norm1.bn.weight, backbone.layer2.0.norm1.bn.bias, backbone.layer2.0.norm1.bn.running_mean, backbone.layer2.0.norm1.bn.running_var, backbone.layer2.0.conv2.kernel, backbone.layer2.0.norm2.bn.weight, backbone.layer2.0.norm2.bn.bias, backbone.layer2.0.norm2.bn.running_mean, backbone.layer2.0.norm2.bn.running_var, backbone.layer2.0.downsample.0.kernel, backbone.layer2.0.downsample.1.bn.weight, backbone.layer2.0.downsample.1.bn.bias, backbone.layer2.0.downsample.1.bn.running_mean, backbone.layer2.0.downsample.1.bn.running_var, backbone.layer2.1.conv1.kernel, backbone.layer2.1.norm1.bn.weight, backbone.layer2.1.norm1.bn.bias, backbone.layer2.1.norm1.bn.running_mean, backbone.layer2.1.norm1.bn.running_var, backbone.layer2.1.conv2.kernel, backbone.layer2.1.norm2.bn.weight, backbone.layer2.1.norm2.bn.bias, backbone.layer2.1.norm2.bn.running_mean, backbone.layer2.1.norm2.bn.running_var, backbone.layer2.2.conv1.kernel, backbone.layer2.2.norm1.bn.weight, backbone.layer2.2.norm1.bn.bias, backbone.layer2.2.norm1.bn.running_mean, backbone.layer2.2.norm1.bn.running_var, backbone.layer2.2.conv2.kernel, backbone.layer2.2.norm2.bn.weight, backbone.layer2.2.norm2.bn.bias, backbone.layer2.2.norm2.bn.running_mean, backbone.layer2.2.norm2.bn.running_var, backbone.layer2.3.conv1.kernel, backbone.layer2.3.norm1.bn.weight, backbone.layer2.3.norm1.bn.bias, backbone.layer2.3.norm1.bn.running_mean, backbone.layer2.3.norm1.bn.running_var, backbone.layer2.3.conv2.kernel, backbone.layer2.3.norm2.bn.weight, backbone.layer2.3.norm2.bn.bias, backbone.layer2.3.norm2.bn.running_mean, backbone.layer2.3.norm2.bn.running_var, backbone.layer3.0.conv1.kernel, backbone.layer3.0.norm1.bn.weight, backbone.layer3.0.norm1.bn.bias, backbone.layer3.0.norm1.bn.running_mean, backbone.layer3.0.norm1.bn.running_var, backbone.layer3.0.conv2.kernel, backbone.layer3.0.norm2.bn.weight, backbone.layer3.0.norm2.bn.bias, backbone.layer3.0.norm2.bn.running_mean, backbone.layer3.0.norm2.bn.running_var, backbone.layer3.0.downsample.0.kernel, backbone.layer3.0.downsample.1.bn.weight, backbone.layer3.0.downsample.1.bn.bias, backbone.layer3.0.downsample.1.bn.running_mean, backbone.layer3.0.downsample.1.bn.running_var, backbone.layer3.1.conv1.kernel, backbone.layer3.1.norm1.bn.weight, backbone.layer3.1.norm1.bn.bias, backbone.layer3.1.norm1.bn.running_mean, backbone.layer3.1.norm1.bn.running_var, backbone.layer3.1.conv2.kernel, backbone.layer3.1.norm2.bn.weight, backbone.layer3.1.norm2.bn.bias, backbone.layer3.1.norm2.bn.running_mean, backbone.layer3.1.norm2.bn.running_var, backbone.layer3.2.conv1.kernel, backbone.layer3.2.norm1.bn.weight, backbone.layer3.2.norm1.bn.bias, backbone.layer3.2.norm1.bn.running_mean, backbone.layer3.2.norm1.bn.running_var, backbone.layer3.2.conv2.kernel, backbone.layer3.2.norm2.bn.weight, backbone.layer3.2.norm2.bn.bias, backbone.layer3.2.norm2.bn.running_mean, backbone.layer3.2.norm2.bn.running_var, backbone.layer3.3.conv1.kernel, backbone.layer3.3.norm1.bn.weight, backbone.layer3.3.norm1.bn.bias, backbone.layer3.3.norm1.bn.running_mean, backbone.layer3.3.norm1.bn.running_var, backbone.layer3.3.conv2.kernel, backbone.layer3.3.norm2.bn.weight, backbone.layer3.3.norm2.bn.bias, backbone.layer3.3.norm2.bn.running_mean, backbone.layer3.3.norm2.bn.running_var, backbone.layer3.4.conv1.kernel, backbone.layer3.4.norm1.bn.weight, backbone.layer3.4.norm1.bn.bias, backbone.layer3.4.norm1.bn.running_mean, backbone.layer3.4.norm1.bn.running_var, backbone.layer3.4.conv2.kernel, backbone.layer3.4.norm2.bn.weight, backbone.layer3.4.norm2.bn.bias, backbone.layer3.4.norm2.bn.running_mean, backbone.layer3.4.norm2.bn.running_var, backbone.layer3.5.conv1.kernel, backbone.layer3.5.norm1.bn.weight, backbone.layer3.5.norm1.bn.bias, backbone.layer3.5.norm1.bn.running_mean, backbone.layer3.5.norm1.bn.running_var, backbone.layer3.5.conv2.kernel, backbone.layer3.5.norm2.bn.weight, backbone.layer3.5.norm2.bn.bias, backbone.layer3.5.norm2.bn.running_mean, backbone.layer3.5.norm2.bn.running_var, backbone.layer4.0.conv1.kernel, backbone.layer4.0.norm1.bn.weight, backbone.layer4.0.norm1.bn.bias, backbone.layer4.0.norm1.bn.running_mean, backbone.layer4.0.norm1.bn.running_var, backbone.layer4.0.conv2.kernel, backbone.layer4.0.norm2.bn.weight, backbone.layer4.0.norm2.bn.bias, backbone.layer4.0.norm2.bn.running_mean, backbone.layer4.0.norm2.bn.running_var, backbone.layer4.0.downsample.0.kernel, backbone.layer4.0.downsample.1.bn.weight, backbone.layer4.0.downsample.1.bn.bias, backbone.layer4.0.downsample.1.bn.running_mean, backbone.layer4.0.downsample.1.bn.running_var, backbone.layer4.1.conv1.kernel, backbone.layer4.1.norm1.bn.weight, backbone.layer4.1.norm1.bn.bias, backbone.layer4.1.norm1.bn.running_mean, backbone.layer4.1.norm1.bn.running_var, backbone.layer4.1.conv2.kernel, backbone.layer4.1.norm2.bn.weight, backbone.layer4.1.norm2.bn.bias, backbone.layer4.1.norm2.bn.running_mean, backbone.layer4.1.norm2.bn.running_var, backbone.layer4.2.conv1.kernel, backbone.layer4.2.norm1.bn.weight, backbone.layer4.2.norm1.bn.bias, backbone.layer4.2.norm1.bn.running_mean, backbone.layer4.2.norm1.bn.running_var, backbone.layer4.2.conv2.kernel, backbone.layer4.2.norm2.bn.weight, backbone.layer4.2.norm2.bn.bias, backbone.layer4.2.norm2.bn.running_mean, backbone.layer4.2.norm2.bn.running_var, neck.lateral_block_0.0.kernel, neck.lateral_block_0.1.bn.weight, neck.lateral_block_0.1.bn.bias, neck.lateral_block_0.1.bn.running_mean, neck.lateral_block_0.1.bn.running_var, neck.out_block_0.0.kernel, neck.out_block_0.1.bn.weight, neck.out_block_0.1.bn.bias, neck.out_block_0.1.bn.running_mean, neck.out_block_0.1.bn.running_var, neck.up_block_1.0.kernel, neck.up_block_1.1.bn.weight, neck.up_block_1.1.bn.bias, neck.up_block_1.1.bn.running_mean, neck.up_block_1.1.bn.running_var, neck.lateral_block_1.0.kernel, neck.lateral_block_1.1.bn.weight, neck.lateral_block_1.1.bn.bias, neck.lateral_block_1.1.bn.running_mean, neck.lateral_block_1.1.bn.running_var, neck.out_block_1.0.kernel, neck.out_block_1.1.bn.weight, neck.out_block_1.1.bn.bias, neck.out_block_1.1.bn.running_mean, neck.out_block_1.1.bn.running_var, neck.up_block_2.0.kernel, neck.up_block_2.1.bn.weight, neck.up_block_2.1.bn.bias, neck.up_block_2.1.bn.running_mean, neck.up_block_2.1.bn.running_var, head.bbox_conv.kernel, head.bbox_conv.bias, head.cls_conv.kernel, head.cls_conv.bias, conv.0.kernel, conv.1.bn.weight, conv.1.bn.bias, conv.1.bn.running_mean, conv.1.bn.running_var

[filaPro]

Can you please provide the command you are running and the full log output?

[ulisb]

20240814_092946.log here is the full log output

[ulisb]

And my command is 'python tools/train.py configs/tr3d/tr3d-ff_sunrgbd-3d-10class.py'

[filaPro]

But looks like it it not an error just warning and the metrics are fine?

We load 2d backbone from imvotenet checkpoint, and this warning is about the extra head layers and missing 3d layers.

[ulisb]

But my ap@0.25 is only 0.6859 and ap@0.5 is only 0.5251. It's lower than you metrics .Your best metrics is that ap@0.25 is 69.4 and ap@0.5 is 53.4. how can I achieve your best metrics?

[filaPro]

In the paper we say that average mAP50 is 52.4, so to achieve 53.4 just run the same training for 5 times.

[ulisb]

Why do you need to use imvotenet's pre trained model？ when I don't use the imvotenet's pre trained model.It's metrics is very low . ap@0.25 is only 0.6558 and ap@0.5 is only 0.4871. here is the complete log without pre trained models. 20240815_132824.log If I modify the 2D image backbone of tr3d-ff, what kind of pre trained model should I use to improve my metrics?

[filaPro]

I think we do it, because resnet50 from imvotenet is already pre-trained on sunrgbd. Starting training with image backbone initialized with random values is generally not a good idea. Also we for some reason freeze some resnet50 layers in these 3 lines. If you start with your own image backbone you probably should unfreeze these layers, and also don't forget to update image normalization here.

[linQian99]

Thank you for your reply. These results are the inference outcomes from the TR3D+FF model you provided after training. They are also lower than the best performance. Is that normal?

[filaPro]

i think so. just +- 1% randomness between training runs

[linQian99]

Thank you for your rapid response. But inference should be without randomness, right?

Btw, may I ask why my training result of AP0.50 of ff config could be much worse than that which is shown below.

epoch 10 could be a little better, but still could not reach 52.5

[filaPro]

But inference should be without randomness, right?

Should be with minimal randomness because of sampling 100k points.

[linQian99]

Thank you for your detailed reply. So is my trainning result normal? 51.5 is a little bit too random