瀚博半导体载天VA1 模型转换过程
创始人
2024-06-03 15:23:46
背景：
使用训练出来的.pth模型 ----> .onnx 模型 ----> .json .params .so 三件套
- 模型由格式pth 转 onnx
转换过程中建议用显卡直接专用，避免出现有关cuda的问题，并且完成这个过程后再装瀚博半导体载天VA1加速卡：
转换环境参考：ubuntu 18.04 OpenPCDet pointpillar v0.5.2 环境搭建与问题解决
安装过程：
$ conda create -n OpenPCDetv0602 python==3.7
$ conda activate OpenPCDetv0602
$ python -m pip install spconv-cu102 -i https://mirror.baidu.com/pypi/simple
$ conda install pytorch==1.10.0 torchvision==0.11.0 torchaudio==0.10.0 cudatoolkit=10.2 -c pytorch
打开 requirements.txt 把有关 pytorch 的注释掉
$ gedit requirements.txt
numpy
llvmlite
numba
#torch>=1.1
tensorboardX
easydict
pyyaml
scikit-image
tqdm
torchvision
SharedArray
opencv-python
$ python -m pip install -r requirements.txt -i https://mirror.baidu.com/pypi/simple
然后：
python setup.py develop
记得每次 pytorch 版本变动，都得 python setup.py develop，重新生成.so 动态库
用的配置文件根据自己的需要修改，pointpillar_single_head.yaml：
CLASS_NAMES: ['car', 'truck', 'bus', 'non_motor_vehicles', 'pedestrians']DATA_CONFIG: _BASE_CONFIG_: cfgs/dataset_configs/kitti_dataset.yamlPOINT_CLOUD_RANGE: [0, -49.6, -1,80, 49.6, 4] #[0, -49.6, -3,80, 49.6, 1]DATA_PROCESSOR:- NAME: mask_points_and_boxes_outside_rangeREMOVE_OUTSIDE_BOXES: True- NAME: shuffle_pointsSHUFFLE_ENABLED: {'train': True,'test': False}- NAME: transform_points_to_voxelsVOXEL_SIZE: [0.2, 0.2, 5]MAX_POINTS_PER_VOXEL: 32MAX_NUMBER_OF_VOXELS: {'train': 16000,'test': 40000}DATA_AUGMENTOR:DISABLE_AUG_LIST: ['placeholder']AUG_CONFIG_LIST:- NAME: gt_samplingUSE_ROAD_PLANE: FalseDB_INFO_PATH:- kitti_dbinfos_train.pklPREPARE: {filter_by_min_points: ['car:5', 'truck:5', 'bus:5', 'non_motor_vehicles:5', 'pedestrians:5'],filter_by_difficulty: [-1],}SAMPLE_GROUPS: ['car:20', 'truck:20', 'bus:20', 'non_motor_vehicles:15', 'pedestrians:15']NUM_POINT_FEATURES: 4DATABASE_WITH_FAKELIDAR: FalseREMOVE_EXTRA_WIDTH: [0.0, 0.0, 0.0]LIMIT_WHOLE_SCENE: False- NAME: random_world_flipALONG_AXIS_LIST: ['x']- NAME: random_world_rotationWORLD_ROT_ANGLE: [-0.78539816, 0.78539816]- NAME: random_world_scalingWORLD_SCALE_RANGE: [0.95, 1.05]MODEL:NAME: PointPillarVFE:NAME: PillarVFEWITH_DISTANCE: FalseUSE_ABSLOTE_XYZ: TrueUSE_NORM: TrueNUM_FILTERS: [64]MAP_TO_BEV:NAME: PointPillarScatterNUM_BEV_FEATURES: 64BACKBONE_2D:NAME: BaseBEVBackboneLAYER_NUMS: [3, 5, 5]LAYER_STRIDES: [2, 2, 2]NUM_FILTERS: [64, 128, 256]UPSAMPLE_STRIDES: [1, 2, 4]NUM_UPSAMPLE_FILTERS: [128, 128, 128]DENSE_HEAD:NAME: AnchorHeadSingleCLASS_AGNOSTIC: FalseUSE_DIRECTION_CLASSIFIER: TrueDIR_OFFSET: 0.78539DIR_LIMIT_OFFSET: 0.0NUM_DIR_BINS: 2ANCHOR_GENERATOR_CONFIG: [{'class_name': 'car','anchor_sizes': [ [ 4.6, 1.9, 1.7 ] ], #l,w,h'anchor_rotations': [ 0, 1.57 ],'anchor_bottom_heights': [ -0.005 ],'align_center': False,'feature_map_stride': 2,'matched_threshold': 0.6,'unmatched_threshold': 0.45},{'class_name': 'truck','anchor_sizes': [ [ 7.2, 2.5, 2.7 ] ],'anchor_rotations': [ 0, 1.57 ],'anchor_bottom_heights': [ 0.016 ],'align_center': False,'feature_map_stride': 2,'matched_threshold': 0.6,'unmatched_threshold': 0.45},{'class_name': 'bus','anchor_sizes': [ [ 10.0, 2.8, 3.2 ] ],'anchor_rotations': [0, 1.57],'anchor_bottom_heights': [ -0.056 ],'align_center': False,'feature_map_stride': 2,'matched_threshold': 0.6,'unmatched_threshold': 0.45},{'class_name': 'non_motor_vehicles','anchor_sizes': [ [ 1.8, 0.81, 1.61 ] ],#1.98, 0.91, 1.52'anchor_rotations': [ 0, 1.57 ],'anchor_bottom_heights': [ 0.077 ],'align_center': False,'feature_map_stride': 2,'matched_threshold': 0.5,'unmatched_threshold': 0.35},{'class_name': 'pedestrians','anchor_sizes': [ [ 0.7, 0.68, 1.65 ] ],# 0.83, 0.75, 1.66'anchor_rotations': [ 0, 1.57 ],'anchor_bottom_heights': [ 0.13 ],'align_center': False,'feature_map_stride': 2,'matched_threshold': 0.5,'unmatched_threshold': 0.35}]TARGET_ASSIGNER_CONFIG:NAME: AxisAlignedTargetAssignerPOS_FRACTION: -1.0SAMPLE_SIZE: 512NORM_BY_NUM_EXAMPLES: FalseMATCH_HEIGHT: FalseBOX_CODER: ResidualCoderLOSS_CONFIG:LOSS_WEIGHTS: {'cls_weight': 1.0,'loc_weight': 2.0,'dir_weight': 0.2,'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}POST_PROCESSING:RECALL_THRESH_LIST: [0.3, 0.5, 0.7]SCORE_THRESH: 0.3OUTPUT_RAW_SCORE: FalseEVAL_METRIC: kittiNMS_CONFIG:MULTI_CLASSES_NMS: FalseNMS_TYPE: nms_gpuNMS_THRESH: 0.01NMS_PRE_MAXSIZE: 4096NMS_POST_MAXSIZE: 500OPTIMIZATION:BATCH_SIZE_PER_GPU: 8NUM_EPOCHS: 200OPTIMIZER: adam_onecycleLR: 0.003WEIGHT_DECAY: 0.01MOMENTUM: 0.9MOMS: [0.95, 0.85]PCT_START: 0.2DIV_FACTOR: 10DECAY_STEP_LIST: [35, 45]LR_DECAY: 0.1LR_CLIP: 0.0000001LR_WARMUP: FalseWARMUP_EPOCH: 1GRAD_NORM_CLIP: 10
修改 export_ls.py：
import argparse
import glob
from pathlib import Pathimport numpy as np
import torch
from pcdet.config import cfg, cfg_from_yaml_file
from pcdet.datasets import DatasetTemplate
from pcdet.models import build_network
from pcdet.utils import common_utils
from torch.onnx import register_custom_op_symbolic
from torch.onnx.symbolic_helper import parse_args@parse_args("v","v","v","i","i","i","i")
def symbolic(g, pillar_features,coords,mask,size_x,size_y,size_z,size_features):return g.op('custom::PointPillarScatterFunction',pillar_features,coords,mask,size_x,size_y,size_z,size_features)
register_custom_op_symbolic("vacc::PointPillarScatterFunction", symbolic,11)class DemoDataset(DatasetTemplate):def __init__(self, dataset_cfg, class_names, training=True, root_path=None, logger=None, ext='.bin'):"""Args:root_path:dataset_cfg:class_names:training:logger:"""super().__init__(dataset_cfg=dataset_cfg, class_names=class_names, training=training, root_path=root_path, logger=logger)self.root_path = root_pathself.ext = extdata_file_list = glob.glob(str(root_path / f'*{self.ext}')) if self.root_path.is_dir() else [self.root_path]data_file_list.sort()self.sample_file_list = data_file_listdef __len__(self):return len(self.sample_file_list)def __getitem__(self, index):if self.ext == '.bin':points = np.fromfile(self.sample_file_list[index], dtype=np.float32).reshape(-1, 4)elif self.ext == '.npy':points = np.load(self.sample_file_list[index])else:raise NotImplementedErrorinput_dict = {'points': points,'frame_id': index,}data_dict = self.prepare_data(data_dict=input_dict)return data_dictdef parse_config():parser = argparse.ArgumentParser(description='arg parser')parser.add_argument('--cfg_file', type=str, default='cfgs_ls/config/pointpillar_single_head.yaml',help='specify the config for demo')parser.add_argument('--data_path', type=str, default='data/000006.bin',help='specify the point cloud data file or directory')parser.add_argument('--ckpt', type=str, default="../pointpillar_7728.pth", help='specify the pretrained model')parser.add_argument('--ext', type=str, default='.bin', help='specify the extension of your point cloud data file')parser.add_argument('--model_path', type=str, default='./', help='specify model file or directory')args = parser.parse_args()cfg_from_yaml_file(args.cfg_file, cfg)return args, cfgdef main():args, cfg = parse_config()logger = common_utils.create_logger()logger.info('-----------------Quick Demo of OpenPCDet-------------------------')demo_dataset = DemoDataset(dataset_cfg=cfg.DATA_CONFIG, class_names=cfg.CLASS_NAMES, training=False,root_path=Path(args.data_path), ext=args.ext, logger=logger)logger.info(f'Total number of samples: \t{len(demo_dataset)}')model = build_network(model_cfg=cfg.MODEL, num_class=len(cfg.CLASS_NAMES), dataset=demo_dataset)print("model is:",model)model.load_params_from_file(filename=args.ckpt, logger=logger, to_cpu=True)############## onnx 导出################################model_path = args.model_path + args.ckpt.split("/")[-1].split(".")[-2]+ ".onnx" print(model_path)np.set_printoptions(threshold=np.inf)with torch.no_grad():MAX_VOXELS = 12000dummy_voxels = torch.zeros((MAX_VOXELS, 32, 10),dtype=torch.float32,device='cpu')dummy_voxel_idxs = torch.zeros((MAX_VOXELS,4),dtype=torch.int16,device='cpu')dummy_mask = torch.ones((1,MAX_VOXELS),dtype=torch.int8,device='cpu')dummy_input = dict()dummy_input['voxels'] = dummy_voxelsdummy_input['voxel_coords'] = dummy_voxel_idxsdummy_input['mask'] = dummy_mask#dummy_input['spatial_features'] = dummy_spatial_featuresdummy_input['batch_size'] = 1torch.onnx.export(model, # model being rundummy_input, # model input (or a tuple for multiple inputs)model_path, # where to save the model (can be a file or file-like object)export_params=True, # store the trained parameter weights inside the model fileopset_version=9, # the ONNX version to export the model todo_constant_folding=True, # whether to execute constant folding for optimizationkeep_initializers_as_inputs=True,#custom_opsets = 10input_names = ['voxels', 'voxel_coords', 'mask'], # the model's input names#input_names = ['spatial_features',],output_names = ['cls_preds', 'box_preds', 'dir_cls_preds'], # the model's output names)print("export ok")exit(0)if __name__ == '__main__':main()
转换模型：
$ python export_ls.py --ckpt /home/wxf/vacc/OpenPCDet/tools/checkpoint_epoch_191.pth
转换模型中出现一些问题时，不妨提高 pytorch 的版本，之前 pytorch 1.8.1 时转换有问题，提高到 1.10.0 后，成功转换
此时：
$ conda list
# packages in environment at /home/xxx/3rdparty/anaconda3/envs/OpenPCDetv0602:
#
# Name Version Build Channel
_libgcc_mutex 0.1 main https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
_openmp_mutex 5.1 1_gnu https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
blas 1.0 mkl https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
bzip2 1.0.8 h7b6447c_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
ca-certificates 2023.01.10 h06a4308_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
ccimport 0.4.2 pypi_0 pypi
certifi 2022.12.7 py37h06a4308_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
charset-normalizer 3.1.0 pypi_0 pypi
cudatoolkit 10.2.89 hfd86e86_1 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
cumm-cu102 0.4.7 pypi_0 pypi
easydict 1.10 pypi_0 pypi
ffmpeg 4.3 hf484d3e_0 pytorch
fire 0.5.0 pypi_0 pypi
flit-core 3.6.0 pyhd3eb1b0_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
freetype 2.12.1 h4a9f257_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
giflib 5.2.1 h5eee18b_3 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
gmp 6.2.1 h295c915_3 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
gnutls 3.6.15 he1e5248_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
idna 3.4 pypi_0 pypi
imageio 2.26.0 pypi_0 pypi
importlib-metadata 6.0.0 pypi_0 pypi
intel-openmp 2021.4.0 h06a4308_3561 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
jpeg 9e h5eee18b_1 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
lame 3.100 h7b6447c_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
lark 1.1.5 pypi_0 pypi
lcms2 2.12 h3be6417_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
lerc 3.0 h295c915_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
libdeflate 1.17 h5eee18b_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
libedit 3.1.20221030 h5eee18b_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
libffi 3.2.1 hf484d3e_1007 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
libgcc-ng 11.2.0 h1234567_1 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
libgomp 11.2.0 h1234567_1 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
libiconv 1.16 h7f8727e_2 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
libidn2 2.3.2 h7f8727e_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
libpng 1.6.39 h5eee18b_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
libstdcxx-ng 11.2.0 h1234567_1 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
libtasn1 4.16.0 h27cfd23_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
libtiff 4.5.0 h6a678d5_2 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
libunistring 0.9.10 h27cfd23_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
libuv 1.44.2 h5eee18b_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
libwebp 1.2.4 h11a3e52_1 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
libwebp-base 1.2.4 h5eee18b_1 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
llvmlite 0.39.1 pypi_0 pypi
lz4-c 1.9.4 h6a678d5_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
mkl 2021.4.0 h06a4308_640 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
mkl-service 2.4.0 py37h7f8727e_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
mkl_fft 1.3.1 py37hd3c417c_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
mkl_random 1.2.2 py37h51133e4_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
ncurses 6.4 h6a678d5_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
nettle 3.7.3 hbbd107a_1 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
networkx 2.6.3 pypi_0 pypi
ninja 1.11.1 pypi_0 pypi
numba 0.56.4 pypi_0 pypi
numpy 1.21.6 pypi_0 pypi
numpy-base 1.21.5 py37ha15fc14_3 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
opencv-python 4.7.0.72 pypi_0 pypi
openh264 2.1.1 h4ff587b_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
openssl 1.0.2u h7b6447c_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
packaging 23.0 pypi_0 pypi
pccm 0.4.6 pypi_0 pypi
pcdet 0.6.0+31f6758 dev_0
pillow 9.4.0 py37h6a678d5_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
pip 22.3.1 py37h06a4308_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
portalocker 2.7.0 pypi_0 pypi
protobuf 3.20.3 pypi_0 pypi
pybind11 2.10.3 pypi_0 pypi
python 3.7.0 h6e4f718_3 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
pytorch 1.10.0 py3.7_cuda10.2_cudnn7.6.5_0 pytorch
pytorch-mutex 1.0 cuda pytorch
pywavelets 1.4.0 pypi_0 pypi
pyyaml 6.0 pypi_0 pypi
readline 7.0 h7b6447c_5 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
requests 2.28.2 pypi_0 pypi
scikit-image 0.19.3 pypi_0 pypi
scipy 1.7.3 pypi_0 pypi
setuptools 65.6.3 py37h06a4308_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
sharedarray 3.2.2 pypi_0 pypi
six 1.16.0 pyhd3eb1b0_1 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
spconv-cu102 2.3.3 pypi_0 pypi
sqlite 3.33.0 h62c20be_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
tensorboardx 2.6 pypi_0 pypi
termcolor 2.2.0 pypi_0 pypi
tifffile 2021.11.2 pypi_0 pypi
tk 8.6.12 h1ccaba5_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
torchaudio 0.10.0 py37_cu102 pytorch
torchvision 0.11.0 py37_cu102 pytorch
tqdm 4.65.0 pypi_0 pypi
typing-extensions 4.5.0 pypi_0 pypi
typing_extensions 4.4.0 py37h06a4308_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
urllib3 1.26.15 pypi_0 pypi
wheel 0.38.4 py37h06a4308_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
xz 5.2.10 h5eee18b_1 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
zipp 3.15.0 pypi_0 pypi
zlib 1.2.13 h5eee18b_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
zstd 1.5.2 ha4553b6_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main - 模型由格式onnx 转 模型三件套
卸掉显卡，安装瀚博半导体载天VA1 加速卡
查看驱动环境：
$ sudo dpkg --get-selections | grep vastaivastai-pci-d2-4-v2-1-a1-3-hv-hwtype-2-dkms install
这个时候也需要python环境，为了避免干扰重新建立一个Python 虚拟环境
因为 vamc 是配Python 3.7 的，所以虚拟环境 Python 的版本 为3.7
$ conda create --name VACC python=3.7
$ conda activate VACC
$ /xxx/1_模型转换工具/vamc工具使用/2.1.0/vamc_2.1.0
$ python -m pip install --no-cache-dir \
-r requirements.txt -i https://mirror.baidu.com/pypi/simple
$ cd ..
$ pip install vamc-2.1.0-cp37-cp37m-linux_x86_64.whl
然后;
$ vamc
Usage: vamc [OPTIONS] COMMAND [ARGS]...vamc Command Line Interface.vamc provides a unified API for deploy and run vastai model zoo.Options:--modelType show vamc support modelType and exit.--version Show the version and exit.-h, --help Show this message and exit.
此时不能使用 vamc build 命令
$ vamc build
Usage: vamc [OPTIONS] COMMAND [ARGS]...
Try 'vamc -h' for help.Error: No such command 'build'.
需要改变环境变量：
$ cd /xxx/vaststream
$ gedit env.sh
修改 env.sh 中的 export VASTSTREAM_HOME
#!/bin/bashexport VASTSTREAM_HOME=/xxx/vaststream
export LIBVA_DRIVER_NAME=hantro
export VACC_IRTEXT_ENABLE=1
export VASTSTREAM_PIPELINE=true
export TVM_HOME=$VASTSTREAM_HOME/tvm
export PYTHONPATH=$TVM_HOME/python:$TVM_HOME/topi/python:$TVM_HOME/vacc/python
export OS_NAME=ubuntu
export OS_VERSION=18.04
export LD_LIBRARY_PATH=$TVM_HOME/lib:$VASTSTREAM_HOME/lib:$LD_LIBRARY_PATH
export PATH=$VASTSTREAM_HOME/tool/:$PATH$ source env.sh
$ vamc
[03/14/23-11:10:47][I][global]: *****************************************************
[03/14/23-11:10:47][I][global]: Hardware name is vacc
[03/14/23-11:10:47][I][global]: DLC core number is 8
[03/14/23-11:10:47][I][global]: DLC big core number is 4
[03/14/23-11:10:47][I][global]: DLC tiny core number is 4
[03/14/23-11:10:47][I][global]: CE batch channel max is 192
[03/14/23-11:10:47][I][global]: PEP batch channel max is 128
[03/14/23-11:10:47][I][global]: TIEQ FIFO SIZE is 256
[03/14/23-11:10:47][I][global]: ODMA has 2 workers
[03/14/23-11:10:47][I][global]: Bank size of SRAM is 5120K
[03/14/23-11:10:47][I][global]: Sram alignment is 128 bytes
[03/14/23-11:10:47][I][global]: Capacity of big core's wdbuf is 4096K
[03/14/23-11:10:47][I][global]: Capacity of tiny core's wdbuf is 2048K
[03/14/23-11:10:47][I][global]: Bank size of WDBUF is 128K
[03/14/23-11:10:47][I][global]: Wdbuf alignment is 256 bytes
[03/14/23-11:10:47][I][global]: Margin for wdbuf weight area is 4096 bytes
[03/14/23-11:10:47][I][global]: *****************************************************
[03/14/23-11:10:47][I][global]: Stage cost time: 0.0s
Usage: vamc [OPTIONS] COMMAND [ARGS]...vamc Command Line Interface.vamc provides a unified API for deploy and run vastai model zoo.Options:--modelType show vamc support modelType and exit.--version Show the version and exit.-h, --help Show this message and exit.Commands:build convert front frame model to static vacc modelbuild-dynamic convert front frame model to dynamic vacc modelrun debug engine model.run-dynamic debug dynamic engine model.
修改 cfg_ls.yaml;
model:name: pointpillarinputs:voxels: [16000, 32, 10]voxel_coords: [3, 16000]mask: [1, 16000]checkpoint: /xxx/OpenPCDet1/tools/checkpoint_epoch_191.onnxengine:type: vacccommon:do_quantization: trueadd_extra_ops_to_graph:type: pointpillar_nmsparams:point_cloud_range: [0.0, -49.6, -1, 80, 49.6, 4]voxel_size: [0.2, 0.2, 5]grid_stride: [2, 2, 2]num_classes: 5nms_topk: 4096post_nms: 500confidence_thresh: 0.1nms_thresh: 0.02dir_offset: 0.78539dir_limit_offset: 0.0anchor_size: [4.6, 1.9, 1.7, 7.2, 2.5, 2.7,10.2, 2.8,3.2,1.8,0.81,1.61,0.7,0.68,1.68]anchor_rotations: [0.0, 1.57,0.0, 1.57,0.0, 1.57,0.0, 1.57,0.0, 1.57]anchor_heights: [0, 0, 0, 0, 0]calibration:quant_mode: percentileper_channel: trueskip_conv_layers: [0]dataset:path: /media/xxx/Elements/data/POINTPILLAR/bin_addZ_npysampler:get_data_num: 253workspace:work_dir: /media/xxx/Elements/data/POINTPILLAR/modelenable_ir: falsesave_log: false模型转换：
vamc build /xxx/vaststream/sample/config/cfg_ls.yaml
在/media/xxx/Elements/data/POINTPILLAR/model 目录下， 成功生成：
$ tree
.
└── pointpillar-int8-percentile-mutil_input-vacc├── pointpillar.json├── pointpillar.params└── pointpillar.so1 directory, 3 files
如果量化时数据有问题，看看 /media/xxx/Elements/data/POINTPILLAR/bin_addZ_npy目录下是否有空数据。
未完待续…
后续有问题继续整理。
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