Hi Thank for sharing your great task.

I'm trying to training with single GPU.

If I set batch size > 1, always cuda out of memory issues are occur, so I set the batch size = 1

2021-06-03 16:12:13,039 INFO Start training home/kimsuyeon/a/CenterPoint-KITTI/tools/cfgs/kitti_models/centerpoint_rcnn(default) epochs: 0%| | 0/80 [00:01<?, ?it/s] Traceback (most recent call last): | 0/3712 [00:00<?, ?it/s] File "train.py", line 198, in main() File "train.py", line 153, in main train_model( File "/home/kimsuyeon/a/CenterPoint-KITTI/tools/train_utils/train_utils.py", line 86, in train_model accumulated_iter = train_one_epoch( File "/home/kimsuyeon/a/CenterPoint-KITTI/tools/train_utils/train_utils.py", line 38, in train_one_epoch loss, tb_dict, disp_dict = model_func(model, batch) File "/home/kimsuyeon/a/CenterPoint-KITTI/pcdet/models/init.py", line 30, in model_func ret_dict, tb_dict, disp_dict = model(batch_dict) File "/home/kimsuyeon/a/lib/python3.8/site-packages/torch/nn/modules/module.py", line 889, in _call_impl result = self.forward(*input, **kwargs) File "/home/kimsuyeon/a/CenterPoint-KITTI/pcdet/models/detectors/centerpoint_rcnn.py", line 11, in forward batch_dict = cur_module(batch_dict) File "/home/kimsuyeon/a/lib/python3.8/site-packages/torch/nn/modules/module.py", line 889, in _call_impl result = self.forward(*input, **kwargs) File "/home/kimsuyeon/a/CenterPoint-KITTI/pcdet/models/dense_heads/centerpoint_head_single.py", line 77, in forward targets_dict = self.assign_targets( File "/home/kimsuyeon/a/CenterPoint-KITTI/pcdet/models/dense_heads/centerpoint_head_single.py", line 142, in assign_targets heatmaps = np.array(heatmaps).transpose(1, 0).tolist() File "/home/kimsuyeon/a/lib/python3.8/site-packages/torch/tensor.py", line 621, in array return self.numpy() TypeError: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.

And I got this problem. Do you have any idea?

Hi, I followed the installation and get_start files to complete necessary running environment, and this bug happened. How should I solve this, and please help me ! Thanks!

Hi, I am now trying to study how to training Centerpoint on KITTI. My setting is training on the original KITTI dataset of 20 epoches. And what I got is as follows:

Car [email protected], 0.70, 0.70: bbox AP:94.7996, 88.9117, 87.8464 bev AP:89.7246, 86.8356, 83.5177 3d AP:86.4783, 76.2949, 72.4491 aos AP:94.75, 88.76, 87.63 Car [email protected], 0.70, 0.70: bbox AP:96.9610, 91.3264, 88.8810 bev AP:93.9845, 87.7600, 85.2048 3d AP:88.2733, 75.9294, 73.1899 aos AP:96.92, 91.16, 88.66 Car [email protected], 0.50, 0.50: bbox AP:94.7996, 88.9117, 87.8464 bev AP:94.9225, 89.3233, 88.8077 3d AP:94.8194, 89.2578, 88.6416 aos AP:94.75, 88.76, 87.63 Car [email protected], 0.50, 0.50: bbox AP:96.9610, 91.3264, 88.8810 bev AP:97.2669, 93.8379, 93.0098 3d AP:97.1964, 93.4531, 91.2541 aos AP:96.92, 91.16, 88.66 Pedestrian [email protected], 0.50, 0.50: bbox AP:66.8845, 65.2675, 62.3663 bev AP:57.1777, 53.5311, 50.7458 3d AP:50.1384, 48.6857, 44.5479 aos AP:64.70, 62.75, 59.74 Pedestrian [email protected], 0.50, 0.50: bbox AP:68.0557, 65.3659, 62.4463 bev AP:55.0341, 52.0861, 48.6433 3d AP:48.6057, 45.7464, 41.7122 aos AP:65.56, 62.53, 59.48 Pedestrian [email protected], 0.25, 0.25: bbox AP:66.8845, 65.2675, 62.3663 bev AP:74.2538, 72.4498, 69.3171 3d AP:74.1821, 72.2494, 69.1611 aos AP:64.70, 62.75, 59.74 Pedestrian [email protected], 0.25, 0.25: bbox AP:68.0557, 65.3659, 62.4463 bev AP:74.8110, 73.2887, 70.1591 3d AP:74.7206, 73.0705, 69.9633 aos AP:65.56, 62.53, 59.48 Cyclist [email protected], 0.50, 0.50: bbox AP:84.2834, 73.6666, 70.5905 bev AP:80.0970, 66.7770, 63.7807 3d AP:74.6868, 62.2825, 57.8456 aos AP:84.12, 73.11, 70.07 Cyclist [email protected], 0.50, 0.50: bbox AP:88.0991, 74.7699, 71.1338 bev AP:81.6918, 67.8036, 63.8179 3d AP:75.0102, 61.3894, 57.8612 aos AP:87.91, 74.20, 70.57 Cyclist [email protected], 0.25, 0.25: bbox AP:84.2834, 73.6666, 70.5905 bev AP:81.9637, 69.6221, 67.1479 3d AP:81.9637, 69.6218, 67.1479 aos AP:84.12, 73.11, 70.07 Cyclist [email protected], 0.25, 0.25: bbox AP:88.0991, 74.7699, 71.1338 bev AP:85.6864, 70.9843, 67.4068 3d AP:85.6864, 70.9842, 67.4062 aos AP:87.91, 74.20, 70.57

My questions are:

1. I am not sure if it's right or not? Did you get similar results?
2. And what does the "Cyclist [email protected], 0.25, 0.25" mean? Could we modify the setting? Or this setting is fixed because we use the KITTI dataset?

Thanks in advance!

Could you please explain why you did not extend the original repository for KITTI dataset? I'm not sure which repository to use as I would also like to evaluate on KITTI (besides NuScenes and Waymo).

Hi, Tianwei. Thanks for your great work. I have checked your implementation of CenterPoint model based on PCDet in this repo. The version is one stage centerpoint? When can we get the official two stage model? Or the latest CenterPoint++ model?

Thanks!

Hi I ran this code in colab after installing all the requirements mentioned in Install.md and followed Getting_started.md ! python /content/CenterPoint-KITTI-main/OpenPCDet/tools/train.py I am getting this error:

Traceback (most recent call last): File "/content/CenterPoint-KITTI-main/OpenPCDet/tools/train.py", line 198, in main() File "/content/CenterPoint-KITTI-main/OpenPCDet/tools/train.py", line 59, in main args, cfg = parse_config() File "/content/CenterPoint-KITTI-main/OpenPCDet/tools/train.py", line 48, in parse_config cfg_from_yaml_file(args.cfg_file, cfg) File "/content/CenterPoint-KITTI-main/OpenPCDet/pcdet/config.py", line 72, in cfg_from_yaml_file with open(cfg_file, 'r') as f: TypeError: expected str, bytes or os.PathLike object, not NoneType [ ]

I've inferred the Pandset(Waymo) dataset by your KITTI weight, it can't work well. but can inference the KITTI by cityscape models in 2D. What are the differences between different point cloud datasets?(except the number of laser lines)

This is a dataset/general query.

I have trained an object detector with "centerpoint_rnn.cfg" config and the model has trained and is working well. But I noticed just now that the KITTI dataset has gt bbox information just for objects which are in the FOV of the cameras. So the cars at the back of the vehicle are not detected. This has caused problems for me since I am working on Moving Object Segmentation and I want to detect vehicles that are at the back as well. Any suggestion on how that can be achieved. In the image, it can be seen that the cars in front of the vehicle bearing laser scanner are detected but there is a car just behind the vehicle which remains undetected

When I run the script pip install pcdet-0.3.0+95b7309-cp37-cp37m-linux_x86_64.whl. the error is below. My environment is below: pytorch1.7.1 cuda11.1 A600

Processing ./pcdet-0.3.0+95b7309-cp37-cp37m-linux_x86_64.whl Requirement already satisfied: pyyaml in /home/CN/zizhang.wu/anaconda3/envs/CaDDN/lib/python3.7/site-packages (from pcdet==0.3.0+95b7309) (5.4.1) Requirement already satisfied: numba in /home/CN/zizhang.wu/anaconda3/envs/CaDDN/lib/python3.7/site-packages (from pcdet==0.3.0+95b7309) (0.52.0) Requirement already satisfied: numpy in /home/CN/zizhang.wu/anaconda3/envs/CaDDN/lib/python3.7/site-packages (from pcdet==0.3.0+95b7309) (1.20.1) ERROR: Could not find a version that satisfies the requirement spconv (from pcdet) ERROR: No matching distribution found for spconv

Hi,

sorry to bother you. Regarding the model of CenterPoint, I have the following questions:

1. Which is the first network layer for CenterPoint? In my opinion, the first layer of CenterPoint network may be the MeanVFE, correct?

2. What is the input and output of the first layer network？What is the size of its input? Could you please point out where is it defined?

Thank you so much.

Hello! Thanks for your projects! I would like to ask if this project have the test of tracking on KITTI dataset. I can't find the code named tracking in tools. How can I run this project and get the effect of tracking. Looking forward to your reply!

Hi, thanks for your excellent work! I noticed that unlike the models on nuScenes and Waymo, the KITTI model has no 'Shared Conv' and 'Separate Heads'. The size of the feature maps for the detector head is also different.

Could you tell me the reason for such a model design? I would like to use CenterPoint on a new dataset that has a similar size to KITTI and uses the same LiDAR as used by KITTI. However, this dataset has 8 categories and there is a severe category imbalance like in nuScenes.

Can you give me some advice on the structure of CenterPoint, such as whether to use ‘Share Conv’ and ‘Separate Head’?

Also, I would like to ask how to set the ‘SAMPLE_GROUPS’ for different categories in GT Sampling.

Thanks in advance!

my trainning result with single gpu is like this but multi gpu training result is like this

can you help me see what's wrong when multi gpu mode ?

btw, in order to run the repo code ,i modify some code , like

1. modify pcdet/datasets/processor/data_process.py function transform_points_to_voxels some spconv api to adapt spconv2.x version

def transform_points_to_voxels(self, data_dict=None, config=None, voxel_generator=None):
        if data_dict is None:
#            try:
#                from spconv.utils import VoxelGeneratorV2 as VoxelGenerator
#            except:
#                from spconv.utils import VoxelGenerator
#
            from spconv.pytorch.utils import PointToVoxel
            voxel_generator = PointToVoxel(
                vsize_xyz=config.VOXEL_SIZE,
                coors_range_xyz=self.point_cloud_range,
                num_point_features=self.num_point_features,
                max_num_voxels=config.MAX_NUMBER_OF_VOXELS[self.mode],
                max_num_points_per_voxel=config.MAX_POINTS_PER_VOXEL
            )
#            voxel_generator = VoxelGenerator(
#                voxel_size=config.VOXEL_SIZE,
#                point_cloud_range=self.point_cloud_range,
#                max_num_points=config.MAX_POINTS_PER_VOXEL,
#                max_voxels=config.MAX_NUMBER_OF_VOXELS[self.mode]
#            )
            grid_size = (self.point_cloud_range[3:6] - self.point_cloud_range[0:3]) / np.array(config.VOXEL_SIZE)
            self.grid_size = np.round(grid_size).astype(np.int64)
            self.voxel_size = config.VOXEL_SIZE
            return partial(self.transform_points_to_voxels, voxel_generator=voxel_generator)

        points = data_dict['points']
        # voxel_output = voxel_generator.generate(points)
        voxel_output = voxel_generator(torch.from_numpy(points))
        if isinstance(voxel_output, dict):
            voxels, coordinates, num_points = \
                voxel_output['voxels'], voxel_output['coordinates'], voxel_output['num_points_per_voxel']
        else:
            voxels, coordinates, num_points = voxel_output

        if not data_dict['use_lead_xyz']:
            voxels = voxels[..., 3:]  # remove xyz in voxels(N, 3)

        data_dict['voxels'] = voxels
        data_dict['voxel_coords'] = coordinates
        data_dict['voxel_num_points'] = num_points
        return data_dict

2. modify pcdet/models/dense_heads/centerpoint_head_single.py function assign_targets to solve tensor convert problem because numpy version is too high.

def assign_targets(self, gt_boxes):
        """Generate targets.

        Args:
            gt_boxes: (B, M, 8) box + cls 

        Returns:
            Returns:
                tuple[list[torch.Tensor]]: Tuple of target including \
                    the following results in order.

                    - list[torch.Tensor]: Heatmap scores.
                    - list[torch.Tensor]: Ground truth boxes.
                    - list[torch.Tensor]: Indexes indicating the \
                        position of the valid boxes.
                    - list[torch.Tensor]: Masks indicating which \
                        boxes are valid.
        """
        gt_bboxes_3d, gt_labels_3d = gt_boxes[..., :-1], gt_boxes[..., -1]

        heatmaps, anno_boxes, inds, masks = multi_apply(
            self.get_targets_single, gt_bboxes_3d, gt_labels_3d)
        # transpose heatmaps, because the dimension of tensors in each task is
        # different, we have to use numpy instead of torch to do the transpose.
        # heatmaps = np.array(heatmaps).transpose(1, 0).tolist()
        heatmaps = list(map(list, zip(*heatmaps)))
        heatmaps = [torch.stack(hms_) for hms_ in heatmaps]
        # transpose anno_boxes
        # anno_boxes = np.array(anno_boxes).transpose(1, 0).tolist()
        anno_boxes = list(map(list, zip(*anno_boxes)))
        anno_boxes = [torch.stack(anno_boxes_) for anno_boxes_ in anno_boxes]
        # transpose inds
        # inds = np.array(inds).transpose(1, 0).tolist()
        inds = list(map(list, zip(*inds)))
        inds = [torch.stack(inds_) for inds_ in inds]
        # transpose inds
        # masks = np.array(masks).transpose(1, 0).tolist()
        masks = list(map(list, zip(*masks)))
        masks = [torch.stack(masks_) for masks_ in masks]
        
        all_targets_dict = {
            'heatmaps': heatmaps,
            'anno_boxes': anno_boxes,
            'inds': inds,
            'masks': masks
        }
        
        return all_targets_dict

my cmd is single gpu training : python train.py --cfg_file cfgs/kitti_models/centerpoint.yaml multi gpu training : bash scripts/dist_train.sh 8 --cfg_file cfgs/kitti_models/centerpoint.yaml

when I run the seyup.py there are some error and here is my conda environment. can someone help me to solve these problem

pcdet 0.3.0+95b7309 dev_0 pcre 8.45 h295c915_0
pillow 8.3.2 pypi_0 pypi pip 21.2.2 py36h06a4308_0
protobuf 3.17.3 pypi_0 pypi pycparser 2.21 pyhd3eb1b0_0
pyface 7.3.0 py36h06a4308_1
pygments 2.11.2 pyhd3eb1b0_0
pyparsing 2.4.7 pypi_0 pypi pyqt 5.9.2 py36h05f1152_2
python 3.6.13 h12debd9_1
python-dateutil 2.8.2 pypi_0 pypi pytorch 1.7.0 py3.6_cuda11.0.221_cudnn8.0.3_0 pyt

===================================================================== log msg:

running develop running egg_info writing pcdet.egg-info/PKG-INFO writing dependency_links to pcdet.egg-info/dependency_links.txt writing requirements to pcdet.egg-info/requires.txt writing top-level names to pcdet.egg-info/top_level.txt reading manifest file 'pcdet.egg-info/SOURCES.txt' adding license file 'LICENSE' writing manifest file 'pcdet.egg-info/SOURCES.txt' running build_ext building 'pcdet.ops.iou3d_nms.iou3d_nms_cuda' extension Emitting ninja build file /home/lidar/centerpoint_workspace/CenterPoint-KITTI/build/temp.linux-x86_64-3.6/build.ninja... Compiling objects... Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N) [1/1] /usr/local/cuda/bin/nvcc -I/home/lidar/anaconda3/envs/centerpoint/lib/python3.6/site-packages/torch/include -I/home/lidar/anaconda3/envs/centerpoint/lib/python3.6/site-packages/torch/include/torch/csrc/api/include -I/home/lidar/anaconda3/envs/centerpoint/lib/python3.6/site-packages/torch/include/TH -I/home/lidar/anaconda3/envs/centerpoint/lib/python3.6/site-packages/torch/include/THC -I/usr/local/cuda/include -I/home/lidar/anaconda3/envs/centerpoint/include/python3.6m -c -c /home/lidar/centerpoint_workspace/CenterPoint-KITTI/pcdet/ops/iou3d_nms/src/iou3d_nms_kernel.cu -o /home/lidar/centerpoint_workspace/CenterPoint-KITTI/build/temp.linux-x86_64-3.6/pcdet/ops/iou3d_nms/src/iou3d_nms_kernel.o -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options ''"'"'-fPIC'"'"'' -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=iou3d_nms_cuda -D_GLIBCXX_USE_CXX11_ABI=0 -gencode=arch=compute_86,code=sm_86 -std=c++14 FAILED: /home/lidar/centerpoint_workspace/CenterPoint-KITTI/build/temp.linux-x86_64-3.6/pcdet/ops/iou3d_nms/src/iou3d_nms_kernel.o /usr/local/cuda/bin/nvcc -I/home/lidar/anaconda3/envs/centerpoint/lib/python3.6/site-packages/torch/include -I/home/lidar/anaconda3/envs/centerpoint/lib/python3.6/site-packages/torch/include/torch/csrc/api/include -I/home/lidar/anaconda3/envs/centerpoint/lib/python3.6/site-packages/torch/include/TH -I/home/lidar/anaconda3/envs/centerpoint/lib/python3.6/site-packages/torch/include/THC -I/usr/local/cuda/include -I/home/lidar/anaconda3/envs/centerpoint/include/python3.6m -c -c /home/lidar/centerpoint_workspace/CenterPoint-KITTI/pcdet/ops/iou3d_nms/src/iou3d_nms_kernel.cu -o /home/lidar/centerpoint_workspace/CenterPoint-KITTI/build/temp.linux-x86_64-3.6/pcdet/ops/iou3d_nms/src/iou3d_nms_kernel.o -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options ''"'"'-fPIC'"'"'' -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=iou3d_nms_cuda -D_GLIBCXX_USE_CXX11_ABI=0 -gencode=arch=compute_86,code=sm_86 -std=c++14 nvcc fatal : Unsupported gpu architecture 'compute_86' ninja: build stopped: subcommand failed. Traceback (most recent call last): File "/home/lidar/anaconda3/envs/centerpoint/lib/python3.6/site-packages/torch/utils/cpp_extension.py", line 1522, in _run_ninja_build env=env) File "/home/lidar/anaconda3/envs/centerpoint/lib/python3.6/subprocess.py", line 438, in run output=stdout, stderr=stderr) subprocess.CalledProcessError: Command '['ninja', '-v']' returned non-zero exit status 1.

The above exception was the direct cause of the following exception:

Traceback (most recent call last): File "setup.py", line 106, in 'src/sampling_gpu.cu', File "/home/lidar/anaconda3/envs/centerpoint/lib/python3.6/site-packages/setuptools/init.py", line 153, in setup return distutils.core.setup(**attrs) File "/home/lidar/anaconda3/envs/centerpoint/lib/python3.6/distutils/core.py", line 148, in setup dist.run_commands() File "/home/lidar/anaconda3/envs/centerpoint/lib/python3.6/distutils/dist.py", line 955, in run_commands self.run_command(cmd) File "/home/lidar/anaconda3/envs/centerpoint/lib/python3.6/distutils/dist.py", line 974, in run_command cmd_obj.run() File "/home/lidar/anaconda3/envs/centerpoint/lib/python3.6/site-packages/setuptools/command/develop.py", line 34, in run self.install_for_development() File "/home/lidar/anaconda3/envs/centerpoint/lib/python3.6/site-packages/setuptools/command/develop.py", line 114, in install_for_development self.run_command('build_ext') File "/home/lidar/anaconda3/envs/centerpoint/lib/python3.6/distutils/cmd.py", line 313, in run_command self.distribution.run_command(command) File "/home/lidar/anaconda3/envs/centerpoint/lib/python3.6/distutils/dist.py", line 974, in run_command cmd_obj.run() File "/home/lidar/anaconda3/envs/centerpoint/lib/python3.6/site-packages/setuptools/command/build_ext.py", line 79, in run _build_ext.run(self) File "/home/lidar/anaconda3/envs/centerpoint/lib/python3.6/distutils/command/build_ext.py", line 339, in run self.build_extensions() File "/home/lidar/anaconda3/envs/centerpoint/lib/python3.6/site-packages/torch/utils/cpp_extension.py", line 653, in build_extensions build_ext.build_extensions(self) File "/home/lidar/anaconda3/envs/centerpoint/lib/python3.6/distutils/command/build_ext.py", line 448, in build_extensions self._build_extensions_serial() File "/home/lidar/anaconda3/envs/centerpoint/lib/python3.6/distutils/command/build_ext.py", line 473, in _build_extensions_serial self.build_extension(ext) File "/home/lidar/anaconda3/envs/centerpoint/lib/python3.6/site-packages/setuptools/command/build_ext.py", line 202, in build_extension _build_ext.build_extension(self, ext) File "/home/lidar/anaconda3/envs/centerpoint/lib/python3.6/distutils/command/build_ext.py", line 533, in build_extension depends=ext.depends) File "/home/lidar/anaconda3/envs/centerpoint/lib/python3.6/site-packages/torch/utils/cpp_extension.py", line 482, in unix_wrap_ninja_compile with_cuda=with_cuda) File "/home/lidar/anaconda3/envs/centerpoint/lib/python3.6/site-packages/torch/utils/cpp_extension.py", line 1238, in _write_ninja_file_and_compile_objects error_prefix='Error compiling objects for extension') File "/home/lidar/anaconda3/envs/centerpoint/lib/python3.6/site-packages/torch/utils/cpp_extension.py", line 1538, in _run_ninja_build raise RuntimeError(message) from e RuntimeError: Error compiling objects for extension

hello, this is my error when i try to train the centerpoint.yaml

cfg.OPTIMIZATION = edict() 2022-04-07 11:02:52,498 INFO cfg.OPTIMIZATION.BATCH_SIZE_PER_GPU: 4 2022-04-07 11:02:52,498 INFO cfg.OPTIMIZATION.NUM_EPOCHS: 80 2022-04-07 11:02:52,498 INFO cfg.OPTIMIZATION.OPTIMIZER: adam_onecycle 2022-04-07 11:02:52,498 INFO cfg.OPTIMIZATION.LR: 0.003 2022-04-07 11:02:52,498 INFO cfg.OPTIMIZATION.WEIGHT_DECAY: 0.01 2022-04-07 11:02:52,498 INFO cfg.OPTIMIZATION.MOMENTUM: 0.9 2022-04-07 11:02:52,498 INFO cfg.OPTIMIZATION.MOMS: [0.95, 0.85] 2022-04-07 11:02:52,498 INFO cfg.OPTIMIZATION.PCT_START: 0.4 2022-04-07 11:02:52,498 INFO cfg.OPTIMIZATION.DIV_FACTOR: 10 2022-04-07 11:02:52,498 INFO cfg.OPTIMIZATION.DECAY_STEP_LIST: [35, 45] 2022-04-07 11:02:52,498 INFO cfg.OPTIMIZATION.LR_DECAY: 0.1 2022-04-07 11:02:52,498 INFO cfg.OPTIMIZATION.LR_CLIP: 1e-07 2022-04-07 11:02:52,498 INFO cfg.OPTIMIZATION.LR_WARMUP: False 2022-04-07 11:02:52,498 INFO cfg.OPTIMIZATION.WARMUP_EPOCH: 1 2022-04-07 11:02:52,498 INFO cfg.OPTIMIZATION.GRAD_NORM_CLIP: 10 2022-04-07 11:02:52,498 INFO cfg.TAG: centerpoint 2022-04-07 11:02:52,498 INFO cfg.EXP_GROUP_PATH: kitti_models 2022-04-07 11:02:52,570 INFO Database filter by min points Car: 14357 => 13532 2022-04-07 11:02:52,571 INFO Database filter by min points Pedestrian: 2207 => 2168 2022-04-07 11:02:52,571 INFO Database filter by min points Cyclist: 734 => 705 2022-04-07 11:02:52,582 INFO Database filter by difficulty Car: 13532 => 10759 2022-04-07 11:02:52,584 INFO Database filter by difficulty Pedestrian: 2168 => 2075 2022-04-07 11:02:52,585 INFO Database filter by difficulty Cyclist: 705 => 581 2022-04-07 11:02:52,588 INFO Loading KITTI dataset 2022-04-07 11:02:52,646 INFO Total samples for KITTI dataset: 3712 Traceback (most recent call last): File "train.py", line 202, in main() File "train.py", line 116, in main model = build_network(model_cfg=cfg.MODEL, num_class=len(cfg.CLASS_NAMES), dataset=train_set) File "../pcdet/models/init.py", line 18, in build_network model_cfg=model_cfg, num_class=num_class, dataset=dataset File "../pcdet/models/detectors/init.py", line 30, in build_detector model_cfg=model_cfg, num_class=num_class, dataset=dataset File "../pcdet/models/detectors/centerpoint.py", line 7, in init self.module_list = self.build_networks() File "../pcdet/models/detectors/detector3d_template.py", line 47, in build_networks model_info_dict=model_info_dict File "../pcdet/models/detectors/detector3d_template.py", line 136, in build_dense_head voxel_size=model_info_dict.get('voxel_size', False) File "../pcdet/models/dense_heads/center_head.py", line 66, in init [self.class_names.index(x) for x in cur_class_names if x in class_names] RuntimeError: CUDA error: out of memory

base on GTX3070 cuda11.1 pytorch1.8.2 , can u give me some suggestions

Since this is a Lidar-only object detection model, could we preprocess the data without the images? I think if we don't use the evaluation metric of 'bbox', we can ignore the infos from images, right?

Another question is, why do we only count the points inside the gt in the FOV of the image?