open-mmlab
centerpoint_pillar pretrained model error
Hello. when i am using the following command: python demo/pcd_demo.py --pcd demo/data/nuscenes/1.bin --config configs/centerpoint/centerpoint_02pillar_second_secfpn_circlenms_4x8_cyclic_20e_nus.py --checkpoint checkpoints/centerpoint_02pillar_second_secfpn_circlenms_4x8_cyclic_20e_nus_20210816_064624-0f3299c0.pth --show
and the model is downloaded from the model link in configs/centerpoint
Run the above command and it will get the following results： load checkpoint from local path: checkpoints/centerpoint_02pillar_second_secfpn_circlenms_4x8_cyclic_20e_nus_20210816_064624-0f3299c0.pth The model and loaded state dict do not match exactly size mismatch for pts_voxel_encoder.pfn_layers.0.linear.weight: copying a param with shape torch.Size([64, 10]) from checkpoint, the shape in current model is torch.Size([64, 11])
and my env list:
mmcls 0.23.1
mmcv-full 1.5.3
mmdeploy 0.5.0
mmdet 2.25.0
mmdet3d 1.0.0rc3
mmsegmentation 0.25.0
Is this a bug, or the model of centerpoint pillar is not updated?
@ZCMax Please check this problem. Maybe it is another problem related to the previous KITTI performance upgrading of PointPillars.
also encountered this problem. What caused this problem? Has it been solved?
@ZCMax Hello! When can you solve this problem? I need the pretrained model.
@ZCMax Hello! When can you solve this problem? I need the pretrained model.
This checkpoint will be provided in one or two days, and other centerpoint checkpoints will be provided in this week.
@ZCMax Hello! When can you solve this problem? I need the pretrained model.
Sorry for the inconvenience caused by the model checkpoint problem
@ZCMax Hello! When can you solve this problem? I need the pretrained model.
Sorry for the inconvenience caused by the model checkpoint problem
Thank you for your reply. I will wait for the new checkpoint.
@ZCMax Hello! When can you solve this problem? I need the pretrained model.
This checkpoint will be provided in one or two days, and other centerpoint checkpoints will be provided in this week.
@ZCMax Hello! I wonder if the new checkpoints are available. It seems the checkpoint links are not updated in this page
I've prepared a pretrained model of centerpoint_02pillar_second_secfpn_4x8_cyclic_20e_nus for temporal link:
link: https://pan.baidu.com/s/1u1dS6XPbzhvrMNfquuoeuA?pwd=g5qi password: g5qi
I tried to use pretrained model from the documentation for centerpoint_pillar02_second_secfpn_8xb4-cyclic-20e_nus-3d.py to test Centerpoint with the following command.
python tools/test.py --task lidar_det configs/centerpoint/centerpoint_pillar02_second_secfpn_8xb4-cyclic-20e_nus-3d.py checkpoints/centerpoint/centerpoint_01voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus_20220810_030004-9061688e.pth --show --show-dir ./data/centerpoint/show_results
I'm facing weights mismatch issue with the following log....
Loads checkpoint by local backend from path: checkpoints/centerpoint/centerpoint_01voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus_20220810_030004-9061688e.pth
The model and loaded state dict do not match exactly
size mismatch for pts_backbone.blocks.0.0.weight: copying a param with shape torch.Size([128, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]). size mismatch for pts_backbone.blocks.0.1.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]). size mismatch for pts_backbone.blocks.0.1.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]). size mismatch for pts_backbone.blocks.0.1.running_mean: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]). size mismatch for pts_backbone.blocks.0.1.running_var: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]). size mismatch for pts_backbone.blocks.0.3.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]). size mismatch for pts_backbone.blocks.0.4.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]). size mismatch for pts_backbone.blocks.0.4.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]). size mismatch for pts_backbone.blocks.0.4.running_mean: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]). size mismatch for pts_backbone.blocks.0.4.running_var: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]). size mismatch for pts_backbone.blocks.0.6.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]). size mismatch for pts_backbone.blocks.0.7.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]). size mismatch for pts_backbone.blocks.0.7.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]). size mismatch for pts_backbone.blocks.0.7.running_mean: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]). size mismatch for pts_backbone.blocks.0.7.running_var: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]). size mismatch for pts_backbone.blocks.0.9.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]). size mismatch for pts_backbone.blocks.0.10.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]). size mismatch for pts_backbone.blocks.0.10.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]). size mismatch for pts_backbone.blocks.0.10.running_mean: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]). size mismatch for pts_backbone.blocks.0.10.running_var: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]). size mismatch for pts_backbone.blocks.1.0.weight: copying a param with shape torch.Size([256, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 64, 3, 3]). size mismatch for pts_backbone.blocks.1.1.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for pts_backbone.blocks.1.1.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for pts_backbone.blocks.1.1.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for pts_backbone.blocks.1.1.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for pts_backbone.blocks.1.3.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]). size mismatch for pts_backbone.blocks.1.4.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for pts_backbone.blocks.1.4.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for pts_backbone.blocks.1.4.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for pts_backbone.blocks.1.4.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for pts_backbone.blocks.1.6.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]). size mismatch for pts_backbone.blocks.1.7.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for pts_backbone.blocks.1.7.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for pts_backbone.blocks.1.7.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for pts_backbone.blocks.1.7.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for pts_backbone.blocks.1.9.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]). size mismatch for pts_backbone.blocks.1.10.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for pts_backbone.blocks.1.10.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for pts_backbone.blocks.1.10.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for pts_backbone.blocks.1.10.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for pts_backbone.blocks.1.12.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]). size mismatch for pts_backbone.blocks.1.13.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for pts_backbone.blocks.1.13.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for pts_backbone.blocks.1.13.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for pts_backbone.blocks.1.13.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for pts_backbone.blocks.1.15.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]). size mismatch for pts_backbone.blocks.1.16.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for pts_backbone.blocks.1.16.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for pts_backbone.blocks.1.16.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for pts_backbone.blocks.1.16.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for pts_neck.deblocks.0.0.weight: copying a param with shape torch.Size([256, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 64, 2, 2]). size mismatch for pts_neck.deblocks.0.1.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for pts_neck.deblocks.0.1.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for pts_neck.deblocks.0.1.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for pts_neck.deblocks.0.1.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for pts_neck.deblocks.1.0.weight: copying a param with shape torch.Size([256, 256, 2, 2]) from checkpoint, the shape in current model is torch.Size([128, 128, 1, 1]). size mismatch for pts_neck.deblocks.1.1.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for pts_neck.deblocks.1.1.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for pts_neck.deblocks.1.1.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for pts_neck.deblocks.1.1.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for pts_bbox_head.shared_conv.conv.weight: copying a param with shape torch.Size([64, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 384, 3, 3]). unexpected key in source state_dict: pts_middle_encoder.conv_input.0.weight, pts_middle_encoder.conv_input.1.weight, pts_middle_encoder.conv_input.1.bias, pts_middle_encoder.conv_input.1.running_mean, pts_middle_encoder.conv_input.1.running_var, pts_middle_encoder.conv_input.1.num_batches_tracked, pts_middle_encoder.encoder_layers.encoder_layer1.0.conv1.weight, pts_middle_encoder.encoder_layers.encoder_layer1.0.bn1.weight, pts_middle_encoder.encoder_layers.encoder_layer1.0.bn1.bias, pts_middle_encoder.encoder_layers.encoder_layer1.0.bn1.running_mean, pts_middle_encoder.encoder_layers.encoder_layer1.0.bn1.running_var, pts_middle_encoder.encoder_layers.encoder_layer1.0.bn1.num_batches_tracked, pts_middle_encoder.encoder_layers.encoder_layer1.0.conv2.weight, pts_middle_encoder.encoder_layers.encoder_layer1.0.bn2.weight, pts_middle_encoder.encoder_layers.encoder_layer1.0.bn2.bias, pts_middle_encoder.encoder_layers.encoder_layer1.0.bn2.running_mean, pts_middle_encoder.encoder_layers.encoder_layer1.0.bn2.running_var, pts_middle_encoder.encoder_layers.encoder_layer1.0.bn2.num_batches_tracked, pts_middle_encoder.encoder_layers.encoder_layer1.1.conv1.weight, pts_middle_encoder.encoder_layers.encoder_layer1.1.bn1.weight, pts_middle_encoder.encoder_layers.encoder_layer1.1.bn1.bias, pts_middle_encoder.encoder_layers.encoder_layer1.1.bn1.running_mean, pts_middle_encoder.encoder_layers.encoder_layer1.1.bn1.running_var, pts_middle_encoder.encoder_layers.encoder_layer1.1.bn1.num_batches_tracked, pts_middle_encoder.encoder_layers.encoder_layer1.1.conv2.weight, pts_middle_encoder.encoder_layers.encoder_layer1.1.bn2.weight, pts_middle_encoder.encoder_layers.encoder_layer1.1.bn2.bias, pts_middle_encoder.encoder_layers.encoder_layer1.1.bn2.running_mean, pts_middle_encoder.encoder_layers.encoder_layer1.1.bn2.running_var, pts_middle_encoder.encoder_layers.encoder_layer1.1.bn2.num_batches_tracked, pts_middle_encoder.encoder_layers.encoder_layer1.2.0.weight, pts_middle_encoder.encoder_layers.encoder_layer1.2.1.weight, pts_middle_encoder.encoder_layers.encoder_layer1.2.1.bias, pts_middle_encoder.encoder_layers.encoder_layer1.2.1.running_mean, pts_middle_encoder.encoder_layers.encoder_layer1.2.1.running_var, pts_middle_encoder.encoder_layers.encoder_layer1.2.1.num_batches_tracked, pts_middle_encoder.encoder_layers.encoder_layer2.0.conv1.weight, pts_middle_encoder.encoder_layers.encoder_layer2.0.bn1.weight, pts_middle_encoder.encoder_layers.encoder_layer2.0.bn1.bias, pts_middle_encoder.encoder_layers.encoder_layer2.0.bn1.running_mean, pts_middle_encoder.encoder_layers.encoder_layer2.0.bn1.running_var, pts_middle_encoder.encoder_layers.encoder_layer2.0.bn1.num_batches_tracked, pts_middle_encoder.encoder_layers.encoder_layer2.0.conv2.weight, pts_middle_encoder.encoder_layers.encoder_layer2.0.bn2.weight, pts_middle_encoder.encoder_layers.encoder_layer2.0.bn2.bias, pts_middle_encoder.encoder_layers.encoder_layer2.0.bn2.running_mean, pts_middle_encoder.encoder_layers.encoder_layer2.0.bn2.running_var, pts_middle_encoder.encoder_layers.encoder_layer2.0.bn2.num_batches_tracked, pts_middle_encoder.encoder_layers.encoder_layer2.1.conv1.weight, pts_middle_encoder.encoder_layers.encoder_layer2.1.bn1.weight, pts_middle_encoder.encoder_layers.encoder_layer2.1.bn1.bias, pts_middle_encoder.encoder_layers.encoder_layer2.1.bn1.running_mean, pts_middle_encoder.encoder_layers.encoder_layer2.1.bn1.running_var, pts_middle_encoder.encoder_layers.encoder_layer2.1.bn1.num_batches_tracked, pts_middle_encoder.encoder_layers.encoder_layer2.1.conv2.weight, pts_middle_encoder.encoder_layers.encoder_layer2.1.bn2.weight, pts_middle_encoder.encoder_layers.encoder_layer2.1.bn2.bias, pts_middle_encoder.encoder_layers.encoder_layer2.1.bn2.running_mean, pts_middle_encoder.encoder_layers.encoder_layer2.1.bn2.running_var, pts_middle_encoder.encoder_layers.encoder_layer2.1.bn2.num_batches_tracked, pts_middle_encoder.encoder_layers.encoder_layer2.2.0.weight, pts_middle_encoder.encoder_layers.encoder_layer2.2.1.weight, pts_middle_encoder.encoder_layers.encoder_layer2.2.1.bias, pts_middle_encoder.encoder_layers.encoder_layer2.2.1.running_mean, pts_middle_encoder.encoder_layers.encoder_layer2.2.1.running_var, pts_middle_encoder.encoder_layers.encoder_layer2.2.1.num_batches_tracked, pts_middle_encoder.encoder_layers.encoder_layer3.0.conv1.weight, pts_middle_encoder.encoder_layers.encoder_layer3.0.bn1.weight, pts_middle_encoder.encoder_layers.encoder_layer3.0.bn1.bias, pts_middle_encoder.encoder_layers.encoder_layer3.0.bn1.running_mean, pts_middle_encoder.encoder_layers.encoder_layer3.0.bn1.running_var, pts_middle_encoder.encoder_layers.encoder_layer3.0.bn1.num_batches_tracked, pts_middle_encoder.encoder_layers.encoder_layer3.0.conv2.weight, pts_middle_encoder.encoder_layers.encoder_layer3.0.bn2.weight, pts_middle_encoder.encoder_layers.encoder_layer3.0.bn2.bias, pts_middle_encoder.encoder_layers.encoder_layer3.0.bn2.running_mean, pts_middle_encoder.encoder_layers.encoder_layer3.0.bn2.running_var, pts_middle_encoder.encoder_layers.encoder_layer3.0.bn2.num_batches_tracked, pts_middle_encoder.encoder_layers.encoder_layer3.1.conv1.weight, pts_middle_encoder.encoder_layers.encoder_layer3.1.bn1.weight, pts_middle_encoder.encoder_layers.encoder_layer3.1.bn1.bias, pts_middle_encoder.encoder_layers.encoder_layer3.1.bn1.running_mean, pts_middle_encoder.encoder_layers.encoder_layer3.1.bn1.running_var, pts_middle_encoder.encoder_layers.encoder_layer3.1.bn1.num_batches_tracked, pts_middle_encoder.encoder_layers.encoder_layer3.1.conv2.weight, pts_middle_encoder.encoder_layers.encoder_layer3.1.bn2.weight, pts_middle_encoder.encoder_layers.encoder_layer3.1.bn2.bias, pts_middle_encoder.encoder_layers.encoder_layer3.1.bn2.running_mean, pts_middle_encoder.encoder_layers.encoder_layer3.1.bn2.running_var, pts_middle_encoder.encoder_layers.encoder_layer3.1.bn2.num_batches_tracked, pts_middle_encoder.encoder_layers.encoder_layer3.2.0.weight, pts_middle_encoder.encoder_layers.encoder_layer3.2.1.weight, pts_middle_encoder.encoder_layers.encoder_layer3.2.1.bias, pts_middle_encoder.encoder_layers.encoder_layer3.2.1.running_mean, pts_middle_encoder.encoder_layers.encoder_layer3.2.1.running_var, pts_middle_encoder.encoder_layers.encoder_layer3.2.1.num_batches_tracked, pts_middle_encoder.encoder_layers.encoder_layer4.0.conv1.weight, pts_middle_encoder.encoder_layers.encoder_layer4.0.bn1.weight, pts_middle_encoder.encoder_layers.encoder_layer4.0.bn1.bias, pts_middle_encoder.encoder_layers.encoder_layer4.0.bn1.running_mean, pts_middle_encoder.encoder_layers.encoder_layer4.0.bn1.running_var, pts_middle_encoder.encoder_layers.encoder_layer4.0.bn1.num_batches_tracked, pts_middle_encoder.encoder_layers.encoder_layer4.0.conv2.weight, pts_middle_encoder.encoder_layers.encoder_layer4.0.bn2.weight, pts_middle_encoder.encoder_layers.encoder_layer4.0.bn2.bias, pts_middle_encoder.encoder_layers.encoder_layer4.0.bn2.running_mean, pts_middle_encoder.encoder_layers.encoder_layer4.0.bn2.running_var, pts_middle_encoder.encoder_layers.encoder_layer4.0.bn2.num_batches_tracked, pts_middle_encoder.encoder_layers.encoder_layer4.1.conv1.weight, pts_middle_encoder.encoder_layers.encoder_layer4.1.bn1.weight, pts_middle_encoder.encoder_layers.encoder_layer4.1.bn1.bias, pts_middle_encoder.encoder_layers.encoder_layer4.1.bn1.running_mean, pts_middle_encoder.encoder_layers.encoder_layer4.1.bn1.running_var, pts_middle_encoder.encoder_layers.encoder_layer4.1.bn1.num_batches_tracked, pts_middle_encoder.encoder_layers.encoder_layer4.1.conv2.weight, pts_middle_encoder.encoder_layers.encoder_layer4.1.bn2.weight, pts_middle_encoder.encoder_layers.encoder_layer4.1.bn2.bias, pts_middle_encoder.encoder_layers.encoder_layer4.1.bn2.running_mean, pts_middle_encoder.encoder_layers.encoder_layer4.1.bn2.running_var, pts_middle_encoder.encoder_layers.encoder_layer4.1.bn2.num_batches_tracked, pts_middle_encoder.conv_out.0.weight, pts_middle_encoder.conv_out.1.weight, pts_middle_encoder.conv_out.1.bias, pts_middle_encoder.conv_out.1.running_mean, pts_middle_encoder.conv_out.1.running_var, pts_middle_encoder.conv_out.1.num_batches_tracked, pts_backbone.blocks.0.12.weight, pts_backbone.blocks.0.13.weight, pts_backbone.blocks.0.13.bias, pts_backbone.blocks.0.13.running_mean, pts_backbone.blocks.0.13.running_var, pts_backbone.blocks.0.13.num_batches_tracked, pts_backbone.blocks.0.15.weight, pts_backbone.blocks.0.16.weight, pts_backbone.blocks.0.16.bias, pts_backbone.blocks.0.16.running_mean, pts_backbone.blocks.0.16.running_var, pts_backbone.blocks.0.16.num_batches_tracked
missing keys in source state_dict: pts_voxel_encoder.pfn_layers.0.norm.weight, pts_voxel_encoder.pfn_layers.0.norm.bias, pts_voxel_encoder.pfn_layers.0.norm.running_mean, pts_voxel_encoder.pfn_layers.0.norm.running_var, pts_voxel_encoder.pfn_layers.0.linear.weight, pts_backbone.blocks.2.0.weight, pts_backbone.blocks.2.1.weight, pts_backbone.blocks.2.1.bias, pts_backbone.blocks.2.1.running_mean, pts_backbone.blocks.2.1.running_var, pts_backbone.blocks.2.3.weight, pts_backbone.blocks.2.4.weight, pts_backbone.blocks.2.4.bias, pts_backbone.blocks.2.4.running_mean, pts_backbone.blocks.2.4.running_var, pts_backbone.blocks.2.6.weight, pts_backbone.blocks.2.7.weight, pts_backbone.blocks.2.7.bias, pts_backbone.blocks.2.7.running_mean, pts_backbone.blocks.2.7.running_var, pts_backbone.blocks.2.9.weight, pts_backbone.blocks.2.10.weight, pts_backbone.blocks.2.10.bias, pts_backbone.blocks.2.10.running_mean, pts_backbone.blocks.2.10.running_var, pts_backbone.blocks.2.12.weight, pts_backbone.blocks.2.13.weight, pts_backbone.blocks.2.13.bias, pts_backbone.blocks.2.13.running_mean, pts_backbone.blocks.2.13.running_var, pts_backbone.blocks.2.15.weight, pts_backbone.blocks.2.16.weight, pts_backbone.blocks.2.16.bias, pts_backbone.blocks.2.16.running_mean, pts_backbone.blocks.2.16.running_var, pts_neck.deblocks.2.0.weight, pts_neck.deblocks.2.1.weight, pts_neck.deblocks.2.1.bias, pts_neck.deblocks.2.1.running_mean, pts_neck.deblocks.2.1.running_var
@ZCMax , is there any reason for not making the model available in Baidu as official?
