From b9b59724de9c3dd1f5e8976941bff43dd4a02974 Mon Sep 17 00:00:00 2001 From: Alexey Panferov Date: Wed, 20 Sep 2023 16:53:02 +0300 Subject: [PATCH] doc(ansible): add readme with manual artifacts downloading explanation Signed-off-by: Alexey Panferov --- ansible/roles/artifacts/README.md | 101 ++++++++++++++++++++++++++++++ 1 file changed, 101 insertions(+) create mode 100644 ansible/roles/artifacts/README.md diff --git a/ansible/roles/artifacts/README.md b/ansible/roles/artifacts/README.md new file mode 100644 index 00000000000..ce327951e3f --- /dev/null +++ b/ansible/roles/artifacts/README.md @@ -0,0 +1,101 @@ +# Machine learning models + +The Autoware perception stack uses models for inference. These models are automatically downloaded if using `ansible`, but they can also be downloaded manually. + +## ONNX model files + +### Download instructions + +The ONNX model files are stored in a common location, hosted by Web.Auto + +Any tool that can download files from the web (e.g. `wget` or `curl`) is the only requirement for downloading these files: + +```console +# yabloc_pose_initializer + +$ mkdir -p ~/autoware_data/yabloc_pose_initializer/ +$ wget -P ~/autoware_data/yabloc_pose_initializer/ \ + https://s3.ap-northeast-2.wasabisys.com/pinto-model-zoo/136_road-segmentation-adas-0001/resources.tar.gz + + +# image_projection_based_fusion + +$ mkdir -p ~/autoware_data/image_projection_based_fusion/ +$ wget -P ~/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 ~/autoware_data/lidar_apollo_instance_segmentation/ +$ wget -P ~/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 ~/autoware_data/lidar_centerpoint/ +$ wget -P ~/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 ~/autoware_data/tensorrt_yolo/ +$ wget -P ~/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 ~/autoware_data/tensorrt_yolox/ +$ wget -P ~/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/label.txt + + +# traffic_light_classifier + +$ mkdir -p ~/autoware_data/traffic_light_classifier/ +$ wget -P ~/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 ~/autoware_data/traffic_light_fine_detector/ +$ wget -P ~/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 ~/autoware_data/traffic_light_ssd_fine_detector/ +$ wget -P ~/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 +```