Autoware 代码下载

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Autoware 代码下载#

安装下载代码需要的工具#

#安装vcstool工具
sudo apt install python3-pip
sudo pip3 install vcstool
#安装gdown工具
sudo pip3 install gdown
#安装git-lfs
sudo apt install git-lfs

下载Autoware代码#

#下载autoware源码
cd /mnt
git clone https://github.com/autowarefoundation/autoware/ 

#下载miivii提供的仓库修改
git clone https://github.com/MiiViiDynamics/miivii_autoware_patch
cp miivii_autoware_patch/miivii-autowarev1.0.1.repos ./autoware/

#构建工作区
cd autoware
mkdir src
vcs --debug import src < miivii-autowarev1.0.1.repos

下载所需地图和模型#

#下载地图数据
gdown -O /mnt/autoware_map/ 'https://docs.google.com/uc?export=download&id=1499_nsbUbIeturZaDj7jhUownh5fvXHd'
unzip -d /mnt/autoware_map/ /mnt/autoware_map/sample-map-planning.zip

#下载模型
# yabloc_pose_initializer
mkdir -p /mnt/autoware_data/yabloc_pose_initializer/
wget -P /mnt/autoware_data/yabloc_pose_initializer/ \
       https://s3.ap-northeast-2.wasabisys.com/pinto-model-zoo/136_road-segmentation-adas-0001/resources.tar.gz
tar -xvf /mnt/autoware_data/yabloc_pose_initializer/resources.tar.gz -C /mnt/autoware_data/yabloc_pose_initializer/

# image_projection_based_fusion
mkdir -p /mnt/autoware_data/image_projection_based_fusion/
wget -P /mnt/autoware_data/image_projection_based_fusion/ \
       https://awf.ml.dev.web.auto/perception/models/pointpainting/v4/pts_voxel_encoder_pointpainting.onnx \
       https://awf.ml.dev.web.auto/perception/models/pointpainting/v4/pts_backbone_neck_head_pointpainting.onnx

# lidar_apollo_instance_segmentation
mkdir -p /mnt/autoware_data/lidar_apollo_instance_segmentation/
wget -P /mnt/autoware_data/lidar_apollo_instance_segmentation/ \
       https://awf.ml.dev.web.auto/perception/models/lidar_apollo_instance_segmentation/vlp-16.onnx \
       https://awf.ml.dev.web.auto/perception/models/lidar_apollo_instance_segmentation/hdl-64.onnx \
       https://awf.ml.dev.web.auto/perception/models/lidar_apollo_instance_segmentation/vls-128.onnx

# lidar_centerpoint
mkdir -p /mnt/autoware_data/lidar_centerpoint/
wget -P /mnt/autoware_data/lidar_centerpoint/ \
       https://awf.ml.dev.web.auto/perception/models/centerpoint/v2/pts_voxel_encoder_centerpoint.onnx \
       https://awf.ml.dev.web.auto/perception/models/centerpoint/v2/pts_backbone_neck_head_centerpoint.onnx \
       https://awf.ml.dev.web.auto/perception/models/centerpoint/v2/pts_voxel_encoder_centerpoint_tiny.onnx \
       https://awf.ml.dev.web.auto/perception/models/centerpoint/v2/pts_backbone_neck_head_centerpoint_tiny.onnx

# tensorrt_yolo
mkdir -p /mnt/autoware_data/tensorrt_yolo/
wget -P /mnt/autoware_data/tensorrt_yolo/ \
       https://awf.ml.dev.web.auto/perception/models/yolov3.onnx \
       https://awf.ml.dev.web.auto/perception/models/yolov4.onnx \
       https://awf.ml.dev.web.auto/perception/models/yolov4-tiny.onnx \
       https://awf.ml.dev.web.auto/perception/models/yolov5s.onnx \
       https://awf.ml.dev.web.auto/perception/models/yolov5m.onnx \
       https://awf.ml.dev.web.auto/perception/models/yolov5l.onnx \
       https://awf.ml.dev.web.auto/perception/models/yolov5x.onnx \
       https://awf.ml.dev.web.auto/perception/models/coco.names

# tensorrt_yolox
mkdir -p /mnt/autoware_data/tensorrt_yolox/
wget -P /mnt/autoware_data/tensorrt_yolox/ \
       https://awf.ml.dev.web.auto/perception/models/yolox-tiny.onnx \
       https://awf.ml.dev.web.auto/perception/models/yolox-sPlus-opt.onnx \
       https://awf.ml.dev.web.auto/perception/models/yolox-sPlus-opt.EntropyV2-calibration.table \
       https://awf.ml.dev.web.auto/perception/models/object_detection_yolox_s/v1/yolox-sPlus-T4-960x960-pseudo-finetune.onnx \
       https://awf.ml.dev.web.auto/perception/models/object_detection_yolox_s/v1/yolox-sPlus-T4-960x960-pseudo-finetune.EntropyV2-calibration.table \
       https://awf.ml.dev.web.auto/perception/models/label.txt

# traffic_light_classifier
mkdir -p /mnt/autoware_data/traffic_light_classifier/
wget -P /mnt/autoware_data/traffic_light_classifier/ \
       https://awf.ml.dev.web.auto/perception/models/traffic_light_classifier/v2/traffic_light_classifier_mobilenetv2_batch_1.onnx \
       https://awf.ml.dev.web.auto/perception/models/traffic_light_classifier/v2/traffic_light_classifier_mobilenetv2_batch_4.onnx \
       https://awf.ml.dev.web.auto/perception/models/traffic_light_classifier/v2/traffic_light_classifier_mobilenetv2_batch_6.onnx \
       https://awf.ml.dev.web.auto/perception/models/traffic_light_classifier/v2/traffic_light_classifier_efficientNet_b1_batch_1.onnx \
       https://awf.ml.dev.web.auto/perception/models/traffic_light_classifier/v2/traffic_light_classifier_efficientNet_b1_batch_4.onnx \
       https://awf.ml.dev.web.auto/perception/models/traffic_light_classifier/v2/traffic_light_classifier_efficientNet_b1_batch_6.onnx \
       https://awf.ml.dev.web.auto/perception/models/traffic_light_classifier/v2/lamp_labels.txt

# traffic_light_fine_detector
mkdir -p /mnt/autoware_data/traffic_light_fine_detector/
wget -P /mnt/autoware_data/traffic_light_fine_detector/ \
       https://awf.ml.dev.web.auto/perception/models/tlr_yolox_s/v2/tlr_yolox_s_batch_1.onnx \
       https://awf.ml.dev.web.auto/perception/models/tlr_yolox_s/v2/tlr_yolox_s_batch_4.onnx \
       https://awf.ml.dev.web.auto/perception/models/tlr_yolox_s/v2/tlr_yolox_s_batch_6.onnx \
       https://awf.ml.dev.web.auto/perception/models/tlr_yolox_s/v2/tlr_labels.txt

# traffic_light_ssd_fine_detector
mkdir -p /mnt/autoware_data/traffic_light_ssd_fine_detector/
wget -P /mnt/autoware_data/traffic_light_ssd_fine_detector/ \
       https://awf.ml.dev.web.auto/perception/models/mb2-ssd-lite-tlr.onnx \
       https://awf.ml.dev.web.auto/perception/models/voc_labels_tl.txt