About detection training gpu num about largekernel3d

[yukang2017]

Hi,

For training, to reproduce, please disable the gt sampling augmentation in the last 5 epochs, this is a detailed trick, listed in the implementation details.

For testing, sorry for this misalignment, I double check the config file. There are some typos. I fixed it to be aligned with the checkpoint, please try it again.

[yukang2017]

Hi @fjzpcmj ，

Thanks for your interests in our work. Sorry for the late reply. I have some deadline this week. I will check the nusc_centerpoint_voxelnet_0075voxel_fix_bn_z_largekernel3d_large.py.

I used 4 GPUs for training.
Would you please have a try on nusc_centerpoint_voxelnet_0075voxel_fix_bn_z_largekernel3d_tiny.py? The performance of it is more stable.

Regards,
Yukang Chen

[fjzpcmj]

Thanks for your reply, I will try on try on nusc_centerpoint_voxelnet_0075voxel_fix_bn_z_largekernel3d_tiny.py. Would you like to tell me that which performance is better, "large" v.s. “tiny” ？

[yukang2017]

Thanks for your message. Generally, "large" performs a bit better than "tiny" (less than 0.5 mAP). But "tiny" is more stable and faster.

[fjzpcmj]

Thanks very much.

[fjzpcmj]

Dear @yukang2017 ，I train “tiny” model with 4GPUs，the mAP result is still 59, less than reported 63.3. Do you know what's the matter?
In addition，I download the pretrained model (63.3mAP) and testing. It seems the downloaded model structure is different from the structure in the "tiny" config.

---

The model and loaded state dict do not match exactly

unexpected key in source state_dict: backbone.conv1.0.conv1.conv3x3_1.weight, backbone.conv1.0.conv1.conv3x3_1.bias, backbone.conv1.0.conv2.conv3x3_1.weight, backbone.conv1.0.conv2.conv3x3_1.bias, backbone.conv1.1.conv1.conv3x3_1.weight, backbone.conv1.1.conv1.conv3x3_1.bias, backbone.conv1.1.conv2.conv3x3_1.weight, backbone.conv1.1.conv2.conv3x3_1.bias, backbone.conv2.3.conv1.weight, backbone.conv2.3.conv1.bias, backbone.conv2.3.conv2.weight, backbone.conv2.3.conv2.bias, backbone.conv2.4.conv1.weight, backbone.conv2.4.conv1.bias, backbone.conv2.4.conv2.weight, backbone.conv2.4.conv2.bias, backbone.conv3.3.conv1.weight, backbone.conv3.3.conv1.bias, backbone.conv3.3.conv2.weight, backbone.conv3.3.conv2.bias, backbone.conv3.4.conv1.weight, backbone.conv3.4.conv1.bias, backbone.conv3.4.conv2.weight, backbone.conv3.4.conv2.bias

missing keys in source state_dict: backbone.conv2.4.conv2.block.weight, backbone.conv1.1.conv2.block.position_embedding, backbone.conv3.4.conv2.block.bias, backbone.conv2.4.conv1.block.weight, backbone.conv3.4.conv1.conv3x3_1.weight, backbone.conv2.4.conv2.conv3x3_1.weight, backbone.conv3.3.conv1.conv3x3_1.weight, backbone.conv2.3.conv1.conv3x3_1.bias, backbone.conv1.1.conv1.block.position_embedding, backbone.conv3.3.conv1.block.weight, backbone.conv3.3.conv2.conv3x3_1.weight, backbone.conv3.4.conv2.conv3x3_1.bias, backbone.conv2.3.conv1.conv3x3_1.weight, backbone.conv3.4.conv1.block.bias, backbone.conv3.4.conv1.block.weight, backbone.conv2.3.conv2.conv3x3_1.bias, backbone.conv1.0.conv1.block.position_embedding, backbone.conv3.3.conv1.conv3x3_1.bias, backbone.conv2.4.conv2.block.bias, backbone.conv3.3.conv2.block.bias, backbone.conv3.4.conv1.conv3x3_1.bias, backbone.conv2.4.conv1.conv3x3_1.bias, backbone.conv3.3.conv2.conv3x3_1.bias, backbone.conv2.3.conv2.conv3x3_1.weight, backbone.conv2.3.conv2.block.weight, backbone.conv2.4.conv1.block.bias, backbone.conv1.0.conv2.block.position_embedding, backbone.conv3.4.conv2.block.weight, backbone.conv2.3.conv1.block.bias, backbone.conv2.3.conv2.block.bias, backbone.conv3.3.conv1.block.bias, backbone.conv2.4.conv1.conv3x3_1.weight, backbone.conv3.3.conv2.block.weight, backbone.conv2.4.conv2.conv3x3_1.bias, backbone.conv2.3.conv1.block.weight, backbone.conv3.4.conv2.conv3x3_1.weight

these keys have mismatched shape:
+-------------------------------------+---------------------------------+---------------------------------+
| key | expected shape | loaded shape |
+-------------------------------------+---------------------------------+---------------------------------+
| backbone.conv1.0.conv1.block.weight | torch.Size([3, 3, 3, 16, 16]) | torch.Size([7, 7, 7, 16, 16]) |
| backbone.conv1.0.conv2.block.weight | torch.Size([3, 3, 3, 16, 16]) | torch.Size([7, 7, 7, 16, 16]) |
| backbone.conv1.1.conv1.block.weight | torch.Size([3, 3, 3, 16, 16]) | torch.Size([7, 7, 7, 16, 16]) |
| backbone.conv1.1.conv2.block.weight | torch.Size([3, 3, 3, 16, 16]) | torch.Size([7, 7, 7, 16, 16]) |
| backbone.conv4.3.conv1.weight | torch.Size([128, 3, 3, 3, 128]) | torch.Size([5, 5, 5, 128, 128]) |
| backbone.conv4.3.conv2.weight | torch.Size([128, 3, 3, 3, 128]) | torch.Size([5, 5, 5, 128, 128]) |
| backbone.conv4.4.conv1.weight | torch.Size([128, 3, 3, 3, 128]) | torch.Size([5, 5, 5, 128, 128]) |
| backbone.conv4.4.conv2.weight | torch.Size([128, 3, 3, 3, 128]) | torch.Size([5, 5, 5, 128, 128]) |
+-------------------------------------+---------------------------------+---------------------------------+

[fjzpcmj]

thanks very much. I have reproduced the result