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open-mmlab
my CenterPoint config for KITTI dataset
This config file is modified from both official code released for KITTI and OpenPCDet version for WAYMO. And if anyone wants to get a pre-training model, please let me know. Update:
- 2022-06-30: I've uploaded the pre-trained model
centerpoint@KITTIto the Google Drive. It is available atPerformance and Modelssection in the repo
CLASS_NAMES: ['Car', 'Pedestrian', 'Cyclist']
DATA_CONFIG:
_BASE_CONFIG_: cfgs/dataset_configs/kitti_dataset.yaml
MODEL:
NAME: CenterPoint
VFE:
NAME: MeanVFE
BACKBONE_3D:
NAME: VoxelResBackBone8x
MAP_TO_BEV:
NAME: HeightCompression
NUM_BEV_FEATURES: 256
BACKBONE_2D:
NAME: BaseBEVBackbone
LAYER_NUMS: [5]
LAYER_STRIDES: [1]
NUM_FILTERS: [128]
UPSAMPLE_STRIDES: [2]
NUM_UPSAMPLE_FILTERS: [256]
DENSE_HEAD:
NAME: CenterHead
CLASS_AGNOSTIC: False
CLASS_NAMES_EACH_HEAD: [
[ 'Car', 'Pedestrian', 'Cyclist' ]
]
SHARED_CONV_CHANNEL: 64
USE_BIAS_BEFORE_NORM: True
NUM_HM_CONV: 2 # heatmap
SEPARATE_HEAD_CFG:
HEAD_ORDER: ['center', 'center_z', 'dim', 'rot']
HEAD_DICT: {
'center': {'out_channels': 2, 'num_conv': 2}, # offset
'center_z': {'out_channels': 1, 'num_conv': 2},
'dim': {'out_channels': 3, 'num_conv': 2},
'rot': {'out_channels': 2, 'num_conv': 2},
}
TARGET_ASSIGNER_CONFIG:
FEATURE_MAP_STRIDE: 4
NUM_MAX_OBJS: 500
GAUSSIAN_OVERLAP: 0.1
MIN_RADIUS: 2
LOSS_CONFIG:
LOSS_WEIGHTS: {
'cls_weight': 1.0,
'loc_weight': 2.0,
'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
}
POST_PROCESSING:
SCORE_THRESH: 0.1
POST_CENTER_LIMIT_RANGE: [-75.2, -75.2, -2, 75.2, 75.2, 4]
MAX_OBJ_PER_SAMPLE: 500
NMS_CONFIG:
NMS_TYPE: nms_gpu
NMS_THRESH: 0.7
NMS_PRE_MAXSIZE: 4096
NMS_POST_MAXSIZE: 500
POST_PROCESSING:
RECALL_THRESH_LIST: [0.3, 0.5, 0.7]
SCORE_THRESH: 0.1
OUTPUT_RAW_SCORE: False
EVAL_METRIC: kitti
NMS_CONFIG:
MULTI_CLASSES_NMS: False
NMS_TYPE: nms_gpu
NMS_THRESH: 0.01
NMS_PRE_MAXSIZE: 4096
NMS_POST_MAXSIZE: 500
OPTIMIZATION:
BATCH_SIZE_PER_GPU: 4
NUM_EPOCHS: 80
OPTIMIZER: adam_onecycle
LR: 0.003
WEIGHT_DECAY: 0.01
MOMENTUM: 0.9
MOMS: [0.95, 0.85]
PCT_START: 0.4
DIV_FACTOR: 10
DECAY_STEP_LIST: [35, 45]
LR_DECAY: 0.1
LR_CLIP: 0.0000001
LR_WARMUP: False
WARMUP_EPOCH: 1
GRAD_NORM_CLIP: 10
the result get from tools/test.py are as follwing:
Car [email protected], 0.70, 0.70:
bbox AP:92.4371, 89.7997, 89.1875
bev AP:89.0399, 87.5611, 86.4736
3d AP:87.3393, 79.4999, 77.3796
aos AP:92.42, 89.72, 89.07
Car [email protected], 0.70, 0.70:
bbox AP:97.1744, 93.6886, 92.1958
bev AP:92.7800, 89.0816, 88.0308
3d AP:88.9477, 81.6019, 79.1999
aos AP:97.15, 93.59, 92.05
Car [email protected], 0.50, 0.50:
bbox AP:92.4371, 89.7997, 89.1875
bev AP:92.3280, 91.4396, 89.4304
3d AP:92.3222, 89.8208, 89.3696
aos AP:92.42, 89.72, 89.07
Car [email protected], 0.50, 0.50:
bbox AP:97.1744, 93.6886, 92.1958
bev AP:97.1386, 94.7012, 93.3835
3d AP:97.1264, 93.9522, 93.1182
aos AP:97.15, 93.59, 92.05
Pedestrian [email protected], 0.50, 0.50:
bbox AP:74.4678, 71.9525, 69.1918
bev AP:60.7042, 59.1109, 55.1364
3d AP:57.3194, 54.2235, 50.6287
aos AP:72.65, 69.44, 66.21
Pedestrian [email protected], 0.50, 0.50:
bbox AP:75.1197, 72.9895, 69.8245
bev AP:60.5372, 58.2984, 54.0787
3d AP:55.7938, 53.0594, 48.9036
aos AP:73.24, 70.18, 66.46
Pedestrian [email protected], 0.25, 0.25:
bbox AP:74.4678, 71.9525, 69.1918
bev AP:78.9672, 78.2260, 75.2896
3d AP:78.8915, 77.9085, 75.0464
aos AP:72.65, 69.44, 66.21
Pedestrian [email protected], 0.25, 0.25:
bbox AP:75.1197, 72.9895, 69.8245
bev AP:81.4859, 80.1064, 76.6350
3d AP:81.3868, 79.6834, 76.2700
aos AP:73.24, 70.18, 66.46
Cyclist [email protected], 0.50, 0.50:
bbox AP:87.5590, 75.4489, 71.9498
bev AP:88.6395, 68.9562, 65.1039
3d AP:85.7151, 65.6614, 62.1453
aos AP:87.46, 74.95, 71.55
Cyclist [email protected], 0.50, 0.50:
bbox AP:91.6160, 76.7370, 72.7161
bev AP:89.0516, 69.3149, 65.6969
3d AP:85.7833, 65.5833, 62.2746
aos AP:91.49, 76.25, 72.24
Cyclist [email protected], 0.25, 0.25:
bbox AP:87.5590, 75.4489, 71.9498
bev AP:87.8537, 72.0860, 68.6397
3d AP:87.8537, 72.0850, 68.6397
aos AP:87.46, 74.95, 71.55
Cyclist [email protected], 0.25, 0.25:
bbox AP:91.6160, 76.7370, 72.7161
bev AP:91.3929, 73.1575, 69.0485
3d AP:91.3929, 73.1573, 69.0482
aos AP:91.49, 76.25, 72.24
Would you please provide centerpoint-pointpillars config?
it's sry that i didn't try. But it's easy to implement, just using pillar as VFE and checking that BACKBONE_2D feature map size equal to Voxel Grid Size_XY / CenterHead stride
It's seems that I can't reproduce your result in my enviroment( RTX 3090 + cuda11.0 + spconv1.2.1 + OpenPCDet v0.5.0). I'm puzzed that the POST_CENTER_LIMIT_RANGE is not suit for KITTI in your configure file.
I need a pre-training mode, can you give me?
Hi, can you share your pretrained model? Thanks [email protected]
has been sent~
I need a pre-training mode, can you give me?
any email will be provided？
I need a pre-training mode, can you give me?
Hi, can you share your pretrained model? Thanks [email protected]
has been sent~
Hi, I used your pretrain-model to test kitti testing velodyne bin, but got error result.
python demo.py --cfg_file cfgs/kitti_models/centerpoints.yaml --ckpt ../checkpoints/centerpoint_kitti_80.pth --data_path ../testing/velodyne/000003.bin
@OuyangJunyuan , Thanks for your comment! I have implemented it.
Hi, can you share your yaml file? Thanks [email protected]
Aha, the CenterPoint's yaml file I used in kitti just like aforemention. You can try it, and train&test in normal way.
I need a pre-training mode, can you give me?
Hi, can you share your pretrained model? Thanks [email protected]
has been sent~
Hi, I used your pretrain-model to test kitti testing velodyne bin, but got error result.
python demo.py --cfg_file cfgs/kitti_models/centerpoints.yaml --ckpt ../checkpoints/centerpoint_kitti_80.pth --data_path ../testing/velodyne/000003.bin
you can set NMS_THRESH to 0.01 in MODEL.DENSE_HEAD.POST_PROCESSING config. Centerpoint model do nms in centerhead, so the config of MODEL.POST_PROCESSING is useless.
This issue is stale because it has been open for 30 days with no activity.
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@OuyangJunyuan 你好，可以分享一下预训练模型吗 [email protected]
Hi, can you share your pretrained model? Thanks a lot! [email protected]
sry for replying late. the pretrain model can obttain is available here
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Re: [open-mmlab/OpenPCDet] my CenterPoint config for KITTI dataset (Issue #750)
@OuyangJunyuan 你好，可以分享一下预训练模型吗 @.***
Hi, can you share your pretrained model? Thanks a lot! @.***
sry for replying late. the pretrain model can obttain is available here
—Reply to this email directly, view it on GitHub, or unsubscribe.You are receiving this because you commented.Message ID: @.***>
Hi, I have a question about 'FEATURE_MAP_STRIDE: 4'. It seems to be 8, since the backbone downsamples the feature map to 1/8. I have read the code of centerhead and config file of that on Waymo, I think 8 should be the right value. However, stride=4 gets better results than 8. And I have also conducted experiments on stride=1 and stride=2, they get nearly 0 mAP. Hope to get your reply.
Hi, I have a question about 'FEATURE_MAP_STRIDE: 4'. It seems to be 8, since the backbone downsamples the feature map to 1/8. I have read the code of centerhead and config file of that on Waymo, I think 8 should be the right value. However, stride=4 gets better results than 8. And I have also conducted experiments on stride=1 and stride=2, they get nearly 0 mAP. Hope to get your reply.
Indeed, what you thought are approximately right about the Backbone3D. But there is one thing you has ignored, i.e., BEV_Backbone. Please kindly refer to configuration file that you can find UPSAMPLE_STRIDES: [2] in it, resulting in 1/8 * 2 = 1/4 here.
This issue is stale because it has been open for 30 days with no activity.
This issue was closed because it has been inactive for 14 days since being marked as stale.
@OuyangJunyuan 你好，可以分享一下预训练模型吗 [email protected]
Hi, can you share your pretrained model? Thanks a lot! [email protected]
sry for replying late. the pretrain model can obttain is available here
hello, can you share your pretrained model ? I dont't find it at the issue.
@OuyangJunyuan 你好，可以分享一下预训练模型吗 [email protected]
Hi, can you share your pretrained model? Thanks a lot! [email protected]
sry for replying late. the pretrain model can obttain is available here
hello, can you share your pretrained model ? I dont't find it at the issue.
[email protected], thank u.
嗨，你能分享一下你的预训练模型吗？谢谢 [email protected]
Hello, can you share the pretrained model you obtained? Thanks [email protected]
@OuyangJunyuan , May I ask how do you achieve it? I tried to achieve it, but the loss has been kept around 8, and all AP are 0.
