Does point pillar model have a great relationship with lidar model？

qwetqwe commented 3 years ago

I use one 32-lidar and two 16-lidar fused point cloud. and I use velodyne64 configuration. But It can not detect enough obstacles. Not as many as apollo5.5. Does the point pillar only support velodyne128 lidar model？ p.s. Point clouds are disordered.

It also has some warnings，not sure whether it is important： Input filename: /apollo/modules/perception/production/data/perception/lidar/models/detection/point_pillars/rpn.onnx ONNX IR version: 0.0.6 Opset version: 9 Producer name: pytorch Producer version: 1.5 Domain:
Model version: 0 Doc string:
WARNING: onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32. WARNING: onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32. WARNING: onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32. WARNING: onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32. WARNING: onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32. WARNING: onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32. WARNING: onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32. WARNING: onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32. WARNING: onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32. WARNING: onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32. WARNING: onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32. WARNING: onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32. WARNING: onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32. WARNING: onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32. WARNING: onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32. WARNING: onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32. WARNING: onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32. WARNING: onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32. WARNING: onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32. WARNING: onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32. WARNING: onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32. WARNING: onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32. WARNING: onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32. WARNING: onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32. WARNING: onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32. WARNING: onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32. WARNING: onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32. WARNING: onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32. WARNING: onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32. WARNING: onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32. WARNING: onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32. WARNING: onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32. WARNING: onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32. WARNING: onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32. WARNING: onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32. WARNING: onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32. WARNING: Can't fuse pad and convolution with same pad mode WARNING: Can't fuse pad and convolution with caffe pad mode WARNING: Can't fuse pad and convolution with same pad mode WARNING: Can't fuse pad and convolution with caffe pad mode WARNING: Can't fuse pad and convolution with same pad mode WARNING: Can't fuse pad and convolution with caffe pad mode WARNING: Can't fuse pad and convolution with same pad mode WARNING: Can't fuse pad and convolution with caffe pad mode WARNING: Can't fuse pad and convolution with same pad mode WARNING: Can't fuse pad and convolution with caffe pad mode WARNING: Can't fuse pad and convolution with same pad mode WARNING: Can't fuse pad and convolution with caffe pad mode WARNING: Current optimization profile is: 0. Please ensure there are no enqueued operations pending in this context prior to switching profiles

System information

OS Platform and Distribution (e.g., Linux Ubuntu 18.04): 18.04
Apollo installed from (source or binary): source
Apollo version (3.5, 5.0, 5.5, 6.0): 6.0
Output of apollo.sh config if on master branch:

Steps to reproduce the issue:

Please use bullet points and include as much details as possible:

Supporting materials (screenshots, command lines, code/script snippets):

jeroldchen commented 3 years ago

Hi @qwetqwe , we have updated PointPillars model recently. Now the model has a better performance in detection, with less mistakes. You can try the new model by pulling the latest code of master branch. Thank you for your interest in Apollo!

qwetqwe commented 3 years ago

@jeroldchen Thanks for your reply. I tried new model,and it seems better than before.But it still can't detect bus or truck in lgsvl. Does dataset not have bus or truck point cloud?

panfengsu commented 3 years ago

The current pointpillars model only supports car cyclist pedestrian categories.If you want to detect all categories,you can use lidar segmentation model.

qwetqwe commented 3 years ago

Thanks a lot

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