fcjian / aedet Goto Github PK

My computer has CUDA11.3 installed. But when I ran the "python setup.py develop", I met a error that showed in the terminal:
No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda-11.3:/usr/local/cuda'
Compiling voxel_pooling_ext without CUDA
running develop
/home/ssz/anaconda3/envs/aedet/lib/python3.8/site-packages/setuptools/command/easy_install.py:156: EasyInstallDeprecationWarning: easy_install command is deprecated. Use build and pip and other standards-based tools.

But I do have the CUDA in the direction.

文件：AeDet-main\mmdetection3d\mmdet3d\ops\aeconv\aeconv.py 中
计算方位角偏差
ae_offset = torch.bmm(rot_matrix, conv_offset.transpose(1, 2)).transpose(1, 2) - conv_offset
想问下，最后的 - conv_offset 的含义是什么

Missing logger folder: outputs/aedet_lss_r50_256x704_128x128_24e_2key/lightning_logs
Restoring states from the checkpoint path at /home/ww/Coding/AeDet/data/nuscenes/nuscenes_12hz_infos_train.pkl
Traceback (most recent call last):
File "/home/ww/Coding/AeDet/exps/aedet/aedet_lss_r50_256x704_128x128_24e_2key.py", line 109, in
run_cli()
File "/home/ww/Coding/AeDet/exps/aedet/aedet_lss_r50_256x704_128x128_24e_2key.py", line 105, in run_cli
main(args)
File "/home/ww/Coding/AeDet/exps/aedet/aedet_lss_r50_256x704_128x128_24e_2key.py", line 75, in main
trainer.fit(model, ckpt_path=args.ckpt_path)
File "/home/ww/.conda/envs/aedet2/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 771, in fit
self._call_and_handle_interrupt(
File "/home/ww/.conda/envs/aedet2/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 722, in _call_and_handle_interrupt
return self.strategy.launcher.launch(trainer_fn, *args, trainer=self, **kwargs)
File "/home/ww/.conda/envs/aedet2/lib/python3.8/site-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 93, in launch
return function(*args, **kwargs)
File "/home/ww/.conda/envs/aedet2/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 812, in _fit_impl
results = self._run(model, ckpt_path=self.ckpt_path)
File "/home/ww/.conda/envs/aedet2/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1180, in _run
self._restore_modules_and_callbacks(ckpt_path)
File "/home/ww/.conda/envs/aedet2/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1140, in _restore_modules_and_callbacks
self._checkpoint_connector.resume_start(checkpoint_path)
File "/home/ww/.conda/envs/aedet2/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/checkpoint_connector.py", line 84, in resume_start
self._loaded_checkpoint = self._load_and_validate_checkpoint(checkpoint_path)
File "/home/ww/.conda/envs/aedet2/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/checkpoint_connector.py", line 88, in _load_and_validate_checkpoint
loaded_checkpoint = self.trainer.strategy.load_checkpoint(checkpoint_path)
File "/home/ww/.conda/envs/aedet2/lib/python3.8/site-packages/pytorch_lightning/strategies/strategy.py", line 316, in load_checkpoint
return self.checkpoint_io.load_checkpoint(checkpoint_path)
File "/home/ww/.conda/envs/aedet2/lib/python3.8/site-packages/pytorch_lightning/plugins/io/torch_plugin.py", line 85, in load_checkpoint
return pl_load(path, map_location=map_location)
File "/home/ww/.conda/envs/aedet2/lib/python3.8/site-packages/pytorch_lightning/utilities/cloud_io.py", line 47, in load
return torch.load(f, map_location=map_location)
File "/home/ww/.conda/envs/aedet2/lib/python3.8/site-packages/torch/serialization.py", line 608, in load
return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args)
File "/home/ww/.conda/envs/aedet2/lib/python3.8/site-packages/torch/serialization.py", line 780, in _legacy_load
raise RuntimeError("Invalid magic number; corrupt file?")
RuntimeError: Invalid magic number; corrupt file?

unexpected key in source state_dict: fc.weight, fc.bias

Traceback (most recent call last):
File "/home/ww/Coding/AeDet/exps/aedet/aedet_lss_r101_512x1408_256x256_24e_2key.py", line 86, in
run_cli()
File "/home/ww/Coding/AeDet/exps/aedet/aedet_lss_r101_512x1408_256x256_24e_2key.py", line 82, in run_cli
main(args)
File "/home/ww/Coding/AeDet/exps/aedet/aedet_lss_r101_512x1408_256x256_24e_2key.py", line 38, in main
model = AeDetLightningModel(**vars(args))
File "/home/ww/Coding/AeDet/exps/aedet/aedet_lss_r101_512x1408_256x256_24e_2key.py", line 14, in init
super().init(**kwargs)
File "/home/ww/Coding/AeDet/exps/aedet/aedet_lss_r101_512x1408_256x256_24e.py", line 242, in init
self.model = AeDet(self.backbone_conf,
File "/home/ww/Coding/AeDet/models/aedet.py", line 24, in init
self.head = AeDetHead(**head_conf)
File "/home/ww/Coding/AeDet/layers/heads/aedet_head.py", line 67, in init
super(AeDetHead, self).init(
File "/home/ww/.conda/envs/aedet8/lib/python3.8/site-packages/mmdet3d/models/dense_heads/centerpoint_head.py", line 307, in init
self.bbox_coder = build_bbox_coder(bbox_coder)
File "/home/ww/.conda/envs/aedet8/lib/python3.8/site-packages/mmdet/core/bbox/builder.py", line 21, in build_bbox_coder
return build_from_cfg(cfg, BBOX_CODERS, default_args)
File "/home/ww/.conda/envs/aedet8/lib/python3.8/site-packages/mmcv/utils/registry.py", line 58, in build_from_cfg
raise KeyError(
KeyError: 'AeDetBBoxCoder is not in the bbox_coder registry'

any apply is appreciated!!

Hi authors!
Thank you for sharing your great work preview!

I wonder when can I check the full code in your repository ?

Thanks,

Such as bevformer

..\AeDet\layers\backbones\lss_fpn.py中有这样一段代码：

def get_geometry(self, sensor2ego_mat, intrin_mat, ida_mat, bda_mat):
    """Transfer points from camera coord to ego coord.

    Args:
        rots(Tensor): Rotation matrix from camera to ego.
        trans(Tensor): Translation matrix from camera to ego.
        intrins(Tensor): Intrinsic matrix.
        post_rots_ida(Tensor): Rotation matrix for ida.
        post_trans_ida(Tensor): Translation matrix for ida
        post_rot_bda(Tensor): Rotation matrix for bda.

    Returns:
        Tensors: points ego coord.
    """
    batch_size, num_cams, _, _ = sensor2ego_mat.shape

    # undo post-transformation
    # B x N x D x H x W x 3
    points = self.frustum
    ida_mat = ida_mat.view(batch_size, num_cams, 1, 1, 1, 4, 4)
    points = ida_mat.inverse().matmul(points.unsqueeze(-1))
    # cam_to_ego
    points = torch.cat(
        (points[:, :, :, :, :, :2] * points[:, :, :, :, :, 2:3],
         points[:, :, :, :, :, 2:]), 5)

    combine = sensor2ego_mat.matmul(torch.inverse(intrin_mat))
    points = combine.view(batch_size, num_cams, 1, 1, 1, 4,
                          4).matmul(points)
    if bda_mat is not None:
        bda_mat = bda_mat.unsqueeze(1).repeat(1, num_cams, 1, 1).view(
            batch_size, num_cams, 1, 1, 1, 4, 4)
        points = (bda_mat @ points).squeeze(-1)
    else:
        points = points.squeeze(-1)
    return points[..., :3]

any apply is appreciated!!

11

Thanks for sharing the great work!

May i ask how you visualize the high-dimentional bev feature map?

没找到AeConv的代码实现位置，希望可以指出一下哈

@fcjian Hi, thanks for your great work.

I would like to know when using CDN, how to determine these hyperparameters for virtual depth. Could you please explain more?

训练的时候出现以下问题：
(aedet) ww@server3090-X570-AORUS-PRO-WIFI:~/Coding/AeDet$ python /home/ww/Coding/AeDet/mmdetection3d/tools/train.py --amp_backend native -b 8 --gpus 1
usage: train.py [-h] [--work-dir WORK_DIR] [--resume-from RESUME_FROM]
[--auto-resume] [--no-validate]
[--gpus GPUS | --gpu-ids GPU_IDS [GPU_IDS ...] | --gpu-id
GPU_ID] [--seed SEED] [--diff-seed] [--deterministic]
[--options OPTIONS [OPTIONS ...]]
[--cfg-options CFG_OPTIONS [CFG_OPTIONS ...]]
[--launcher {none,pytorch,slurm,mpi}]
[--local_rank LOCAL_RANK] [--autoscale-lr]
config
train.py: error: unrecognized arguments: --amp_backend -b 8