fix: update model download instrution

Signed-off-by: badai-nguyen <[email protected]>

docs/models/index.md

@@ -1,43 +1,18 @@
 # 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.
+The Autoware perception stack uses models for inference. These models are automatically downloaded as part of the `setup-dev-env.sh` script.
 
-## ONNX model files
+The models are hosted by Web.Auto.
 
-### Download instructions
+Default models directory (`data_dir`) is `~/autoware_data`.
 
-The ONNX model files are stored in a common location, hosted by Web.Auto
+## Download instructions
 
-Any tool that can download files from the web (e.g. `wget` or `curl`) is the only requirement for downloading these files:
+Please follow the download instruction in [autoware download instructions](https://github.com/autowarefoundation/autoware/blob/main/ansible/roles/artifacts/README.md#L15) for updated models downloading.
 
-```console
-# yabloc_pose_initializer
+The models can be also downloaded manually using download tools such as `wget` or `curl`. The latest urls of weight files and param files for each model can be found at [autoware main.yaml file](https://github.com/autowarefoundation/autoware/blob/main/ansible/roles/artifacts/tasks/main.yaml)
 
-$ 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 \
-       https://awf.ml.dev.web.auto/perception/models/pointpainting/v4/detection_class_remapper.param.yaml \
-       https://awf.ml.dev.web.auto/perception/models/pointpainting/v4/pointpainting_ml_package.param.yaml
-
-
-# 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
+The example of downloading `lidar_centerpoint` model:
 
 $ mkdir -p ~/autoware_data/lidar_centerpoint/
 $ wget -P ~/autoware_data/lidar_centerpoint/ \
@@ -49,77 +24,4 @@ $ wget -P ~/autoware_data/lidar_centerpoint/ \
        https://awf.ml.dev.web.auto/perception/models/centerpoint/v2/centerpoint_tiny_ml_package.param.yaml \
        https://awf.ml.dev.web.auto/perception/models/centerpoint/v2/centerpoint_sigma_ml_package.param.yaml \
        https://awf.ml.dev.web.auto/perception/models/centerpoint/v2/detection_class_remapper.param.yaml \
-       https://awf.ml.dev.web.auto/perception/models/centerpoint/v2/deploy_metadata.yaml
-
-# lidar_transfusion
-
-$ mkdir -p ~/autoware_data/lidar_transfusion/
-$ wget -P ~/autoware_data/lidar_transfusion/ \
-       https://awf.ml.dev.web.auto/perception/models/transfusion/t4xx1_90m/v2/transfusion.onnx \
-       https://awf.ml.dev.web.auto/perception/models/transfusion/t4xx1_90m/v2/transfusion.param.yaml \
-       https://awf.ml.dev.web.auto/perception/models/transfusion/t4xx1_90m/v2/detection_class_remapper.param.yaml
-
-# 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/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/object_detection_semseg_yolox_s/v1/yolox-sPlus-opt-pseudoV2-T4-960x960-T4-seg16cls.onnx \
-       https://awf.ml.dev.web.auto/perception/models/object_detection_semseg_yolox_s/v1/yolox-sPlus-opt-pseudoV2-T4-960x960-T4-seg16cls.EntropyV2-calibration.table \
-       https://awf.ml.dev.web.auto/perception/models/object_detection_semseg_yolox_s/v1/semseg_color_map.csv \
-       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/v3/ped_traffic_light_classifier_mobilenetv2_batch_1.onnx \
-       https://awf.ml.dev.web.auto/perception/models/traffic_light_classifier/v3/ped_traffic_light_classifier_mobilenetv2_batch_4.onnx \
-       https://awf.ml.dev.web.auto/perception/models/traffic_light_classifier/v3/ped_traffic_light_classifier_mobilenetv2_batch_6.onnx \
-       https://awf.ml.dev.web.auto/perception/models/traffic_light_classifier/v3/lamp_labels_ped.txt \
-       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/v3/tlr_car_ped_yolox_s_batch_1.onnx \
-       https://awf.ml.dev.web.auto/perception/models/tlr_yolox_s/v3/tlr_car_ped_yolox_s_batch_4.onnx \
-       https://awf.ml.dev.web.auto/perception/models/tlr_yolox_s/v3/tlr_car_ped_yolox_s_batch_6.onnx \
-       https://awf.ml.dev.web.auto/perception/models/tlr_yolox_s/v3/tlr_labels.txt
-
-# tvm_utility
-
-$ mkdir -p ~/autoware_data/tvm_utility/models/
-$ wget -p ~/autoware_data/tvm_utility/ \
-       https://autoware-modelzoo.s3.us-east-2.amazonaws.com/models/3.0.0-20221221/yolo_v2_tiny-x86_64-llvm-3.0.0-20221221.tar.gz
-$ tar -xvzf ~/autoware_data/tvm_utility/yolo_v2_tiny-x86_64-llvm-3.0.0-20221221.tar.gz -C ~/autoware_data/tvm_utility/models/
-
-```
+       https://awf.ml.dev.web.auto/perception/models/centerpoint/v2/deploy_metadata.yaml