From b9b59724de9c3dd1f5e8976941bff43dd4a02974 Mon Sep 17 00:00:00 2001 From: Alexey Panferov Date: Wed, 20 Sep 2023 16:53:02 +0300 Subject: [PATCH 01/10] 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 +``` From abdedb2738628d9dbf78ceb425e7f12f3f16bcaa Mon Sep 17 00:00:00 2001 From: Alexey Panferov Date: Wed, 20 Sep 2023 16:53:02 +0300 Subject: [PATCH 02/10] docs(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 +``` From a94021bad234f631a11054f63c7489fae2b2eff8 Mon Sep 17 00:00:00 2001 From: Alexey Panferov Date: Thu, 21 Sep 2023 13:57:10 +0300 Subject: [PATCH 03/10] docs(ansible): add file checksums to readme Signed-off-by: Alexey Panferov --- ansible/roles/artifacts/README.md | 100 +++++++++++++++++++++++++----- 1 file changed, 85 insertions(+), 15 deletions(-) diff --git a/ansible/roles/artifacts/README.md b/ansible/roles/artifacts/README.md index ce327951e3f..86415763f2c 100644 --- a/ansible/roles/artifacts/README.md +++ b/ansible/roles/artifacts/README.md @@ -1,4 +1,4 @@ -# Machine learning models +# Autoware artifacts The Autoware perception stack uses models for inference. These models are automatically downloaded if using `ansible`, but they can also be downloaded manually. @@ -6,71 +6,127 @@ The Autoware perception stack uses models for inference. These models are automa ### Download instructions -The ONNX model files are stored in a common location, hosted by Web.Auto +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: +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 +#### yabloc_pose_initializer + +| file | checksum | +|------------------|-------------------------------------------------------------------------| +| resources.tar.gz | sha256:1f660e15f95074bade32b1f80dbf618e9cee1f0b9f76d3f4671cb9be7f56eb3a | +```console $ 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 -# image_projection_based_fusion +| file | checksum | +|------------------|-------------------------------------------------------------------------| +| pts_voxel_encoder_pointpainting.onnx | md5:25c70f76a45a64944ccd19f604c99410 | +| pts_backbone_neck_head_pointpainting.onnx | md5:2c7108245240fbd7bf0104dd68225868 | +```console $ 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 -# lidar_apollo_instance_segmentation +| file | checksum | +|------------------|-------------------------------------------------------------------------| +| vlp-16.onnx checksum: | md5:63a5a1bb73f7dbb64cf70d04eca45fb4 | +| hdl-64.onnx checksum: | md5:009745e33b1df44b68296431cc384cd2 | +| vls-128.onnx checksum: | md5:b2d709f56f73ae2518c9bf4d0214468f | +```console $ 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 -# lidar_centerpoint +| file | checksum | +|------------------|-------------------------------------------------------------------------| +| pts_voxel_encoder_centerpoint.onnx | sha256:dc1a876580d86ee7a341d543f8ade2ede7f43bd032dc5b44155b1f0175405764 | +| pts_backbone_neck_head_centerpoint.onnx | sha256:3fe7e128955646740c41a25be0c8f141d5a94594fe79d7405fe2a859e391542e | +| pts_voxel_encoder_centerpoint_tiny.onnx | sha256:2c53465715c1fd2e9dc5727ef3fca74f4cdf0538f74286b0946e219d0ca5693b | +| pts_backbone_neck_head_centerpoint_tiny.onnx | md5:e4658325b70222f7c3637fe00e586b82 | +```console $ 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 -# tensorrt_yolo +| file | checksum | +|------------------|-------------------------------------------------------------------------| +| yolov3.onnx | sha256:61e922f76918dd3d8e0abdc5fb7406f390609e08bd8ab9e5d3b97afb00f30f8c | +| yolov4.onnx | sha256:7c7343156c1bd4b397fd1e44b27334691a6219db3ce2e29a03b72af65ddb8f39 | +| yolov4-tiny.onnx | sha256:0e877c716fbf8a2b431ee3e57f6c7411a6741319b52c32c6dafc53c7e1b17027 | +| yolov5s.onnx | sha256:be335ff7746957debf1a6903a61fa3f568b780b4afe4958edf2d4bc98e9e0825 | +| yolov5m.onnx | sha256:ee6f67f7c00a34cc4cef2fdd9db30dd714df1a4fb2d7e9fc1731cfe85b673133 | +| yolov5l.onnx | sha256:a627e5f70180a8746482b572194090466db62c8d1256602c1cd20374dd960e34 | +| yolov5x.onnx | sha256:d7cb4cd7078f87bda22a37828d72867accecedf9f74d0d87b5cc1f6f1180a019 | +| coco.names | sha256:634a1132eb33f8091d60f2c346ababe8b905ae08387037aed883953b7329af84 | +```console $ 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/c \ + https://awf.ml.dev.web.auto/perception/models/c \ 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 -# tensorrt_yolox +| file | checksum | +|------------------|-------------------------------------------------------------------------| +| yolox-tiny.onnx | sha256:471a665f4243e654dff62578394e508db22ee29fe65d9e389dfc3b0f2dee1255 | +| yolox-sPlus-opt.onnx | md5:bf3b0155351f90fcdca2626acbfd3bcf | +| yolox-sPlus-opt.EntropyV2-calibration.table | md5:c6e6f1999d5724a017516a956096701f | +| label.txt | sha256:3540a365bfd6d8afb1b5d8df4ec47f82cb984760d3270c9b41dbbb3422d09a0c | +```console $ 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 -# traffic_light_classifier +| file | checksum | +|------------------|--------------------------------------------------------------------------------------------------------------| +| traffic_light_classifier_mobilenetv2_batch_1.onnx | md5:caa51f2080aa2df943e4f884c41898eb | +| traffic_light_classifier_mobilenetv2_batch_4.onnx | md5:c2beaf60210f471debfe72b86d076ca0 | +| traffic_light_classifier_mobilenetv2_batch_6.onnx | md5:28b408710bcb24f4cdd4d746301d4e78 | +| traffic_light_classifier_efficientNet_b1_batch_1.onnx | md5:82baba4fcf1abe0c040cd55005e34510 | +| traffic_light_classifier_efficientNet_b1_batch_4.onnx | md5:21b549c2fe4fbb20d32cc019e6d70cd7 | +| traffic_light_classifier_efficientNet_b1_batch_6.onnx | md5:378526d9aa9fc6705cf399f7b35b3053 | +| lamp_labels.txt | sha256:1a5a49eeec5593963eab8d70f48b8a01bfb07e753e9688eb1510ad26e803579d | +```console $ 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 \ @@ -80,20 +136,34 @@ $ wget -P ~/autoware_data/traffic_light_classifier/ \ 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 -# traffic_light_fine_detector +| file | checksum | +|------------------|----------------------------------------------| +| tlr_yolox_s_batch_1.onnx | md5:2b72d085022b8ee6aacff06bd722cfda | +| tlr_yolox_s_batch_4.onnx | md5:4044daa86e7776ce241e94d98a09cc0e | +| tlr_yolox_s_batch_6.onnx | md5:47255a11bde479320d703f1f45db1242 | +| tlr_labels.txt | md5:e9f45efb02f2a9aa8ac27b3d5c164905 | +```console $ 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 -# traffic_light_ssd_fine_detector +| file | checksum | +|------------------|------------------------------------------------------------------------------| +| mb2-ssd-lite-tlr.onnx | sha256:e29e6ee68751a270fb285fd037713939ca7f61a897b4c3a7ab22b0d6a9a21ddf | +| voc_labels_tl.txt | sha256:a41e6e3324e32c30b3b2fe38908eaf3471e2bfdaeb9e14ca0c1c3bc0275119c6 | +```console $ 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 \ From 54743e7f7984f3188ac886683705295af3e39377 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 21 Sep 2023 10:58:49 +0000 Subject: [PATCH 04/10] style(pre-commit): autofix --- ansible/roles/artifacts/README.md | 36 +++++++++++++++---------------- 1 file changed, 18 insertions(+), 18 deletions(-) diff --git a/ansible/roles/artifacts/README.md b/ansible/roles/artifacts/README.md index 86415763f2c..cf54697c830 100644 --- a/ansible/roles/artifacts/README.md +++ b/ansible/roles/artifacts/README.md @@ -13,7 +13,7 @@ Any tool that can download files from the web (e.g. `wget` or `curl`) is the onl #### yabloc_pose_initializer | file | checksum | -|------------------|-------------------------------------------------------------------------| +| ---------------- | ----------------------------------------------------------------------- | | resources.tar.gz | sha256:1f660e15f95074bade32b1f80dbf618e9cee1f0b9f76d3f4671cb9be7f56eb3a | ```console @@ -24,9 +24,9 @@ $ wget -P ~/autoware_data/yabloc_pose_initializer/ \ #### image_projection_based_fusion -| file | checksum | -|------------------|-------------------------------------------------------------------------| -| pts_voxel_encoder_pointpainting.onnx | md5:25c70f76a45a64944ccd19f604c99410 | +| file | checksum | +| ----------------------------------------- | ------------------------------------ | +| pts_voxel_encoder_pointpainting.onnx | md5:25c70f76a45a64944ccd19f604c99410 | | pts_backbone_neck_head_pointpainting.onnx | md5:2c7108245240fbd7bf0104dd68225868 | ```console @@ -38,8 +38,8 @@ $ wget -P ~/autoware_data/image_projection_based_fusion/ \ #### lidar_apollo_instance_segmentation -| file | checksum | -|------------------|-------------------------------------------------------------------------| +| file | checksum | +| ---------------------- | ------------------------------------ | | vlp-16.onnx checksum: | md5:63a5a1bb73f7dbb64cf70d04eca45fb4 | | hdl-64.onnx checksum: | md5:009745e33b1df44b68296431cc384cd2 | | vls-128.onnx checksum: | md5:b2d709f56f73ae2518c9bf4d0214468f | @@ -54,8 +54,8 @@ $ wget -P ~/autoware_data/lidar_apollo_instance_segmentation/ \ #### lidar_centerpoint -| file | checksum | -|------------------|-------------------------------------------------------------------------| +| file | checksum | +| -------------------------------------------- | ----------------------------------------------------------------------- | | pts_voxel_encoder_centerpoint.onnx | sha256:dc1a876580d86ee7a341d543f8ade2ede7f43bd032dc5b44155b1f0175405764 | | pts_backbone_neck_head_centerpoint.onnx | sha256:3fe7e128955646740c41a25be0c8f141d5a94594fe79d7405fe2a859e391542e | | pts_voxel_encoder_centerpoint_tiny.onnx | sha256:2c53465715c1fd2e9dc5727ef3fca74f4cdf0538f74286b0946e219d0ca5693b | @@ -73,7 +73,7 @@ $ wget -P ~/autoware_data/lidar_centerpoint/ \ #### tensorrt_yolo | file | checksum | -|------------------|-------------------------------------------------------------------------| +| ---------------- | ----------------------------------------------------------------------- | | yolov3.onnx | sha256:61e922f76918dd3d8e0abdc5fb7406f390609e08bd8ab9e5d3b97afb00f30f8c | | yolov4.onnx | sha256:7c7343156c1bd4b397fd1e44b27334691a6219db3ce2e29a03b72af65ddb8f39 | | yolov4-tiny.onnx | sha256:0e877c716fbf8a2b431ee3e57f6c7411a6741319b52c32c6dafc53c7e1b17027 | @@ -98,8 +98,8 @@ $ wget -P ~/autoware_data/tensorrt_yolo/ \ #### tensorrt_yolox -| file | checksum | -|------------------|-------------------------------------------------------------------------| +| file | checksum | +| ------------------------------------------- | ----------------------------------------------------------------------- | | yolox-tiny.onnx | sha256:471a665f4243e654dff62578394e508db22ee29fe65d9e389dfc3b0f2dee1255 | | yolox-sPlus-opt.onnx | md5:bf3b0155351f90fcdca2626acbfd3bcf | | yolox-sPlus-opt.EntropyV2-calibration.table | md5:c6e6f1999d5724a017516a956096701f | @@ -114,10 +114,10 @@ $ wget -P ~/autoware_data/tensorrt_yolox/ \ https://awf.ml.dev.web.auto/perception/models/label.txt ``` -#### traffic_light_classifier +#### traffic_light_classifier -| file | checksum | -|------------------|--------------------------------------------------------------------------------------------------------------| +| file | checksum | +| ----------------------------------------------------- | ----------------------------------------------------------------------- | | traffic_light_classifier_mobilenetv2_batch_1.onnx | md5:caa51f2080aa2df943e4f884c41898eb | | traffic_light_classifier_mobilenetv2_batch_4.onnx | md5:c2beaf60210f471debfe72b86d076ca0 | | traffic_light_classifier_mobilenetv2_batch_6.onnx | md5:28b408710bcb24f4cdd4d746301d4e78 | @@ -140,8 +140,8 @@ $ wget -P ~/autoware_data/traffic_light_classifier/ \ #### traffic_light_fine_detector -| file | checksum | -|------------------|----------------------------------------------| +| file | checksum | +| ------------------------ | ------------------------------------ | | tlr_yolox_s_batch_1.onnx | md5:2b72d085022b8ee6aacff06bd722cfda | | tlr_yolox_s_batch_4.onnx | md5:4044daa86e7776ce241e94d98a09cc0e | | tlr_yolox_s_batch_6.onnx | md5:47255a11bde479320d703f1f45db1242 | @@ -158,8 +158,8 @@ $ wget -P ~/autoware_data/traffic_light_fine_detector/ \ #### traffic_light_ssd_fine_detector -| file | checksum | -|------------------|------------------------------------------------------------------------------| +| file | checksum | +| --------------------- | ----------------------------------------------------------------------- | | mb2-ssd-lite-tlr.onnx | sha256:e29e6ee68751a270fb285fd037713939ca7f61a897b4c3a7ab22b0d6a9a21ddf | | voc_labels_tl.txt | sha256:a41e6e3324e32c30b3b2fe38908eaf3471e2bfdaeb9e14ca0c1c3bc0275119c6 | From 73e60fb2bf74fddd05bb932c371023a3c37cba2d Mon Sep 17 00:00:00 2001 From: Alexey Panferov Date: Mon, 25 Sep 2023 15:14:52 +0300 Subject: [PATCH 05/10] docs(ansible): add sha256sum instruction for downloaded files Signed-off-by: Alexey Panferov --- ansible/roles/artifacts/README.md | 150 +++++++++++++----------------- 1 file changed, 66 insertions(+), 84 deletions(-) diff --git a/ansible/roles/artifacts/README.md b/ansible/roles/artifacts/README.md index cf54697c830..156749615b3 100644 --- a/ansible/roles/artifacts/README.md +++ b/ansible/roles/artifacts/README.md @@ -6,127 +6,73 @@ The Autoware perception stack uses models for inference. These models are automa ### Download instructions -The ONNX model files are stored in a common location, hosted by Web.Auto. +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. - -#### yabloc_pose_initializer - -| file | checksum | -| ---------------- | ----------------------------------------------------------------------- | -| resources.tar.gz | sha256:1f660e15f95074bade32b1f80dbf618e9cee1f0b9f76d3f4671cb9be7f56eb3a | +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 -| file | checksum | -| ----------------------------------------- | ------------------------------------ | -| pts_voxel_encoder_pointpainting.onnx | md5:25c70f76a45a64944ccd19f604c99410 | -| pts_backbone_neck_head_pointpainting.onnx | md5:2c7108245240fbd7bf0104dd68225868 | +# image_projection_based_fusion -```console $ 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 -| file | checksum | -| ---------------------- | ------------------------------------ | -| vlp-16.onnx checksum: | md5:63a5a1bb73f7dbb64cf70d04eca45fb4 | -| hdl-64.onnx checksum: | md5:009745e33b1df44b68296431cc384cd2 | -| vls-128.onnx checksum: | md5:b2d709f56f73ae2518c9bf4d0214468f | +# lidar_apollo_instance_segmentation -```console $ 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 -| file | checksum | -| -------------------------------------------- | ----------------------------------------------------------------------- | -| pts_voxel_encoder_centerpoint.onnx | sha256:dc1a876580d86ee7a341d543f8ade2ede7f43bd032dc5b44155b1f0175405764 | -| pts_backbone_neck_head_centerpoint.onnx | sha256:3fe7e128955646740c41a25be0c8f141d5a94594fe79d7405fe2a859e391542e | -| pts_voxel_encoder_centerpoint_tiny.onnx | sha256:2c53465715c1fd2e9dc5727ef3fca74f4cdf0538f74286b0946e219d0ca5693b | -| pts_backbone_neck_head_centerpoint_tiny.onnx | md5:e4658325b70222f7c3637fe00e586b82 | +# lidar_centerpoint -```console $ 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 -| file | checksum | -| ---------------- | ----------------------------------------------------------------------- | -| yolov3.onnx | sha256:61e922f76918dd3d8e0abdc5fb7406f390609e08bd8ab9e5d3b97afb00f30f8c | -| yolov4.onnx | sha256:7c7343156c1bd4b397fd1e44b27334691a6219db3ce2e29a03b72af65ddb8f39 | -| yolov4-tiny.onnx | sha256:0e877c716fbf8a2b431ee3e57f6c7411a6741319b52c32c6dafc53c7e1b17027 | -| yolov5s.onnx | sha256:be335ff7746957debf1a6903a61fa3f568b780b4afe4958edf2d4bc98e9e0825 | -| yolov5m.onnx | sha256:ee6f67f7c00a34cc4cef2fdd9db30dd714df1a4fb2d7e9fc1731cfe85b673133 | -| yolov5l.onnx | sha256:a627e5f70180a8746482b572194090466db62c8d1256602c1cd20374dd960e34 | -| yolov5x.onnx | sha256:d7cb4cd7078f87bda22a37828d72867accecedf9f74d0d87b5cc1f6f1180a019 | -| coco.names | sha256:634a1132eb33f8091d60f2c346ababe8b905ae08387037aed883953b7329af84 | +# tensorrt_yolo -```console $ mkdir -p ~/autoware_data/tensorrt_yolo/ $ wget -P ~/autoware_data/tensorrt_yolo/ \ - https://awf.ml.dev.web.auto/perception/models/c \ - https://awf.ml.dev.web.auto/perception/models/c \ + 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 -| file | checksum | -| ------------------------------------------- | ----------------------------------------------------------------------- | -| yolox-tiny.onnx | sha256:471a665f4243e654dff62578394e508db22ee29fe65d9e389dfc3b0f2dee1255 | -| yolox-sPlus-opt.onnx | md5:bf3b0155351f90fcdca2626acbfd3bcf | -| yolox-sPlus-opt.EntropyV2-calibration.table | md5:c6e6f1999d5724a017516a956096701f | -| label.txt | sha256:3540a365bfd6d8afb1b5d8df4ec47f82cb984760d3270c9b41dbbb3422d09a0c | +# tensorrt_yolox -```console $ 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/label.txt -``` -#### traffic_light_classifier -| file | checksum | -| ----------------------------------------------------- | ----------------------------------------------------------------------- | -| traffic_light_classifier_mobilenetv2_batch_1.onnx | md5:caa51f2080aa2df943e4f884c41898eb | -| traffic_light_classifier_mobilenetv2_batch_4.onnx | md5:c2beaf60210f471debfe72b86d076ca0 | -| traffic_light_classifier_mobilenetv2_batch_6.onnx | md5:28b408710bcb24f4cdd4d746301d4e78 | -| traffic_light_classifier_efficientNet_b1_batch_1.onnx | md5:82baba4fcf1abe0c040cd55005e34510 | -| traffic_light_classifier_efficientNet_b1_batch_4.onnx | md5:21b549c2fe4fbb20d32cc019e6d70cd7 | -| traffic_light_classifier_efficientNet_b1_batch_6.onnx | md5:378526d9aa9fc6705cf399f7b35b3053 | -| lamp_labels.txt | sha256:1a5a49eeec5593963eab8d70f48b8a01bfb07e753e9688eb1510ad26e803579d | +# traffic_light_classifier -```console $ 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 \ @@ -136,36 +82,72 @@ $ wget -P ~/autoware_data/traffic_light_classifier/ \ 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 -| file | checksum | -| ------------------------ | ------------------------------------ | -| tlr_yolox_s_batch_1.onnx | md5:2b72d085022b8ee6aacff06bd722cfda | -| tlr_yolox_s_batch_4.onnx | md5:4044daa86e7776ce241e94d98a09cc0e | -| tlr_yolox_s_batch_6.onnx | md5:47255a11bde479320d703f1f45db1242 | -| tlr_labels.txt | md5:e9f45efb02f2a9aa8ac27b3d5c164905 | +# traffic_light_fine_detector -```console $ 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 -| file | checksum | -| --------------------- | ----------------------------------------------------------------------- | -| mb2-ssd-lite-tlr.onnx | sha256:e29e6ee68751a270fb285fd037713939ca7f61a897b4c3a7ab22b0d6a9a21ddf | -| voc_labels_tl.txt | sha256:a41e6e3324e32c30b3b2fe38908eaf3471e2bfdaeb9e14ca0c1c3bc0275119c6 | +# traffic_light_ssd_fine_detector -```console $ 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 ``` + +After downloading you can check intergity of the files with ```sha256sum```. + +You need to put ```SHA256SUMS``` file listed below in to autoware_data directory: + +```console +7fe62fcebe0e0f62a000d06aa94d779feb444d933671a4a3189fe01be8c19a00 ./image_projection_based_fusion/pts_backbone_neck_head_pointpainting.onnx +3ca452ea5ca9467bf782955f75704ba8466841e275e8b8acd991b9911d53249e ./image_projection_based_fusion/pts_voxel_encoder_pointpainting.onnx +86348d8c4bced750f54288b01cc471c0d4f1ec9c693466169ef19413731e6ecc ./lidar_apollo_instance_segmentation/hdl-64.onnx +eec521ebad7553d0ea2c90472a293aecb7499ab592632f0e100481c8196eb421 ./lidar_apollo_instance_segmentation/vlp-16.onnx +95ef950bb694bd6de91b7e47f5d191d557e92a7f5e2a6bdf655a8b5eed4075cc ./lidar_apollo_instance_segmentation/vls-128.onnx +3fe7e128955646740c41a25be0c8f141d5a94594fe79d7405fe2a859e391542e ./lidar_centerpoint/pts_backbone_neck_head_centerpoint.onnx +9bb0b634f3664bd098ce7d6a3d8a9fb7cc8d9b8252b27f302c71e43316bab551 ./lidar_centerpoint/pts_backbone_neck_head_centerpoint_tiny.onnx +dc1a876580d86ee7a341d543f8ade2ede7f43bd032dc5b44155b1f0175405764 ./lidar_centerpoint/pts_voxel_encoder_centerpoint.onnx +2c53465715c1fd2e9dc5727ef3fca74f4cdf0538f74286b0946e219d0ca5693b ./lidar_centerpoint/pts_voxel_encoder_centerpoint_tiny.onnx +634a1132eb33f8091d60f2c346ababe8b905ae08387037aed883953b7329af84 ./tensorrt_yolo/coco.names +61e922f76918dd3d8e0abdc5fb7406f390609e08bd8ab9e5d3b97afb00f30f8c ./tensorrt_yolo/yolov3.onnx +0e877c716fbf8a2b431ee3e57f6c7411a6741319b52c32c6dafc53c7e1b17027 ./tensorrt_yolo/yolov4-tiny.onnx +7c7343156c1bd4b397fd1e44b27334691a6219db3ce2e29a03b72af65ddb8f39 ./tensorrt_yolo/yolov4.onnx +a627e5f70180a8746482b572194090466db62c8d1256602c1cd20374dd960e34 ./tensorrt_yolo/yolov5l.onnx +ee6f67f7c00a34cc4cef2fdd9db30dd714df1a4fb2d7e9fc1731cfe85b673133 ./tensorrt_yolo/yolov5m.onnx +be335ff7746957debf1a6903a61fa3f568b780b4afe4958edf2d4bc98e9e0825 ./tensorrt_yolo/yolov5s.onnx +d7cb4cd7078f87bda22a37828d72867accecedf9f74d0d87b5cc1f6f1180a019 ./tensorrt_yolo/yolov5x.onnx +3540a365bfd6d8afb1b5d8df4ec47f82cb984760d3270c9b41dbbb3422d09a0c ./tensorrt_yolox/label.txt +cc378d327db5616b0b3a4d077bf37100c25a50ecd22d2b542f54098da100f34c ./tensorrt_yolox/yolox-sPlus-T4-960x960-pseudo-finetune.EntropyV2-calibration.table +f5054e8a890c3be86dc1b4b89a5a36fb2279d4f6110b0159e793be062641bf65 ./tensorrt_yolox/yolox-sPlus-T4-960x960-pseudo-finetune.onnx +b9e9d7da33342262ccaea4469b4d02b8abb32b6d7bf737f9e0883fece1b8f580 ./tensorrt_yolox/yolox-sPlus-opt.EntropyV2-calibration.table +36b0832177b01e6b278e00c7369f1de71e616c36261cbae50f0753d41289da01 ./tensorrt_yolox/yolox-sPlus-opt.onnx +471a665f4243e654dff62578394e508db22ee29fe65d9e389dfc3b0f2dee1255 ./tensorrt_yolox/yolox-tiny.onnx +1a5a49eeec5593963eab8d70f48b8a01bfb07e753e9688eb1510ad26e803579d ./traffic_light_classifier/lamp_labels.txt +55ebb0d117a5e8943f8d1c6769f1d856b533079d4d871d8e923255cc992ad48a ./traffic_light_classifier/traffic_light_classifier_efficientNet_b1_batch_1.onnx +684e29843e3128eadb774018730644b3ab9b0a06dc4cdaeed579c2f3fa5d5265 ./traffic_light_classifier/traffic_light_classifier_efficientNet_b1_batch_4.onnx +44d94540fa8b89dfb39cd9a8523cf010ddfb10ea2f1f9b53bf3618ce7f4912ad ./traffic_light_classifier/traffic_light_classifier_efficientNet_b1_batch_6.onnx +455b71b3b20d3a96aa0e49f32714ba50421f668a2f9b9907c30b1346ac8a3703 ./traffic_light_classifier/traffic_light_classifier_mobilenetv2_batch_1.onnx +41bb79a23a4ac57956adb8e9cb3904420db1b0cd032e97b670cc4f8b174ae3fe ./traffic_light_classifier/traffic_light_classifier_mobilenetv2_batch_4.onnx +e4792eed6a46fdbd02be2f3a4f1ce91f36fa77698493caf3102e445178c0f058 ./traffic_light_classifier/traffic_light_classifier_mobilenetv2_batch_6.onnx +a41e6e3324e32c30b3b2fe38908eaf3471e2bfdaeb9e14ca0c1c3bc0275119c6 ./traffic_light_fine_detector/tlr_labels.txt +922839fcf22bd32ae5065146fcec193e9d6360ca03bd4c83faea835045daf8eb ./traffic_light_fine_detector/tlr_yolox_s_batch_1.onnx +b3c6e00acc6ff547d165469684ffb620a9a6330e9d591d445f50c4cf5cb4e292 ./traffic_light_fine_detector/tlr_yolox_s_batch_4.onnx +2824d4c5b7ab5f6bfd41e43e82747107c53e1c727b1cf1dd6746bc49e6749128 ./traffic_light_fine_detector/tlr_yolox_s_batch_6.onnx +e29e6ee68751a270fb285fd037713939ca7f61a897b4c3a7ab22b0d6a9a21ddf ./traffic_light_ssd_fine_detector/mb2-ssd-lite-tlr.onnx +a41e6e3324e32c30b3b2fe38908eaf3471e2bfdaeb9e14ca0c1c3bc0275119c6 ./traffic_light_ssd_fine_detector/voc_labels_tl.txt +1f660e15f95074bade32b1f80dbf618e9cee1f0b9f76d3f4671cb9be7f56eb3a ./yabloc_pose_initializer/resources.tar.gz +``` + +And run the command: + +```console +sha256sum -c SHA256SUMS 2>&1 +``` From 3982c02acbf5f1d58b82c0a90e539884554db55a Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 25 Sep 2023 12:16:34 +0000 Subject: [PATCH 06/10] style(pre-commit): autofix --- ansible/roles/artifacts/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ansible/roles/artifacts/README.md b/ansible/roles/artifacts/README.md index 156749615b3..4daa48ba7be 100644 --- a/ansible/roles/artifacts/README.md +++ b/ansible/roles/artifacts/README.md @@ -102,9 +102,9 @@ $ wget -P ~/autoware_data/traffic_light_ssd_fine_detector/ \ https://awf.ml.dev.web.auto/perception/models/voc_labels_tl.txt ``` -After downloading you can check intergity of the files with ```sha256sum```. +After downloading you can check intergity of the files with `sha256sum`. -You need to put ```SHA256SUMS``` file listed below in to autoware_data directory: +You need to put `SHA256SUMS` file listed below in to autoware_data directory: ```console 7fe62fcebe0e0f62a000d06aa94d779feb444d933671a4a3189fe01be8c19a00 ./image_projection_based_fusion/pts_backbone_neck_head_pointpainting.onnx From 7b76d9fce41dc46e2723035776e4d224190f4e57 Mon Sep 17 00:00:00 2001 From: Alexey Panferov Date: Mon, 25 Sep 2023 15:17:36 +0300 Subject: [PATCH 07/10] docs(ansible): typo fix Signed-off-by: Alexey Panferov --- ansible/roles/artifacts/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ansible/roles/artifacts/README.md b/ansible/roles/artifacts/README.md index 156749615b3..c092c2d2292 100644 --- a/ansible/roles/artifacts/README.md +++ b/ansible/roles/artifacts/README.md @@ -102,7 +102,7 @@ $ wget -P ~/autoware_data/traffic_light_ssd_fine_detector/ \ https://awf.ml.dev.web.auto/perception/models/voc_labels_tl.txt ``` -After downloading you can check intergity of the files with ```sha256sum```. +After downloading you can check integrity of the files with ```sha256sum```. You need to put ```SHA256SUMS``` file listed below in to autoware_data directory: From fdadf8525ab8a9eb3469c92370de1564f7e3b581 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 25 Sep 2023 12:19:59 +0000 Subject: [PATCH 08/10] style(pre-commit): autofix --- ansible/roles/artifacts/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ansible/roles/artifacts/README.md b/ansible/roles/artifacts/README.md index c3a3e4b742b..629343ca6da 100644 --- a/ansible/roles/artifacts/README.md +++ b/ansible/roles/artifacts/README.md @@ -102,7 +102,7 @@ $ wget -P ~/autoware_data/traffic_light_ssd_fine_detector/ \ https://awf.ml.dev.web.auto/perception/models/voc_labels_tl.txt ``` -After downloading you can check integrity of the files with ```sha256sum```. +After downloading you can check integrity of the files with `sha256sum`. You need to put `SHA256SUMS` file listed below in to autoware_data directory: From 54524bd911d97240f7614e8b434a7f34598e084e Mon Sep 17 00:00:00 2001 From: Alexey Panferov Date: Tue, 26 Sep 2023 14:01:15 +0300 Subject: [PATCH 09/10] docs(ansible): update README, add checksum file Signed-off-by: Alexey Panferov --- ansible/roles/artifacts/README.md | 49 ++---------------------------- ansible/roles/artifacts/SHA256SUMS | 37 ++++++++++++++++++++++ 2 files changed, 40 insertions(+), 46 deletions(-) create mode 100644 ansible/roles/artifacts/SHA256SUMS diff --git a/ansible/roles/artifacts/README.md b/ansible/roles/artifacts/README.md index c3a3e4b742b..ca8a4d67271 100644 --- a/ansible/roles/artifacts/README.md +++ b/ansible/roles/artifacts/README.md @@ -102,52 +102,9 @@ $ wget -P ~/autoware_data/traffic_light_ssd_fine_detector/ \ https://awf.ml.dev.web.auto/perception/models/voc_labels_tl.txt ``` -After downloading you can check integrity of the files with ```sha256sum```. - -You need to put `SHA256SUMS` file listed below in to autoware_data directory: - -```console -7fe62fcebe0e0f62a000d06aa94d779feb444d933671a4a3189fe01be8c19a00 ./image_projection_based_fusion/pts_backbone_neck_head_pointpainting.onnx -3ca452ea5ca9467bf782955f75704ba8466841e275e8b8acd991b9911d53249e ./image_projection_based_fusion/pts_voxel_encoder_pointpainting.onnx -86348d8c4bced750f54288b01cc471c0d4f1ec9c693466169ef19413731e6ecc ./lidar_apollo_instance_segmentation/hdl-64.onnx -eec521ebad7553d0ea2c90472a293aecb7499ab592632f0e100481c8196eb421 ./lidar_apollo_instance_segmentation/vlp-16.onnx -95ef950bb694bd6de91b7e47f5d191d557e92a7f5e2a6bdf655a8b5eed4075cc ./lidar_apollo_instance_segmentation/vls-128.onnx -3fe7e128955646740c41a25be0c8f141d5a94594fe79d7405fe2a859e391542e ./lidar_centerpoint/pts_backbone_neck_head_centerpoint.onnx -9bb0b634f3664bd098ce7d6a3d8a9fb7cc8d9b8252b27f302c71e43316bab551 ./lidar_centerpoint/pts_backbone_neck_head_centerpoint_tiny.onnx -dc1a876580d86ee7a341d543f8ade2ede7f43bd032dc5b44155b1f0175405764 ./lidar_centerpoint/pts_voxel_encoder_centerpoint.onnx -2c53465715c1fd2e9dc5727ef3fca74f4cdf0538f74286b0946e219d0ca5693b ./lidar_centerpoint/pts_voxel_encoder_centerpoint_tiny.onnx -634a1132eb33f8091d60f2c346ababe8b905ae08387037aed883953b7329af84 ./tensorrt_yolo/coco.names -61e922f76918dd3d8e0abdc5fb7406f390609e08bd8ab9e5d3b97afb00f30f8c ./tensorrt_yolo/yolov3.onnx -0e877c716fbf8a2b431ee3e57f6c7411a6741319b52c32c6dafc53c7e1b17027 ./tensorrt_yolo/yolov4-tiny.onnx -7c7343156c1bd4b397fd1e44b27334691a6219db3ce2e29a03b72af65ddb8f39 ./tensorrt_yolo/yolov4.onnx -a627e5f70180a8746482b572194090466db62c8d1256602c1cd20374dd960e34 ./tensorrt_yolo/yolov5l.onnx -ee6f67f7c00a34cc4cef2fdd9db30dd714df1a4fb2d7e9fc1731cfe85b673133 ./tensorrt_yolo/yolov5m.onnx -be335ff7746957debf1a6903a61fa3f568b780b4afe4958edf2d4bc98e9e0825 ./tensorrt_yolo/yolov5s.onnx -d7cb4cd7078f87bda22a37828d72867accecedf9f74d0d87b5cc1f6f1180a019 ./tensorrt_yolo/yolov5x.onnx -3540a365bfd6d8afb1b5d8df4ec47f82cb984760d3270c9b41dbbb3422d09a0c ./tensorrt_yolox/label.txt -cc378d327db5616b0b3a4d077bf37100c25a50ecd22d2b542f54098da100f34c ./tensorrt_yolox/yolox-sPlus-T4-960x960-pseudo-finetune.EntropyV2-calibration.table -f5054e8a890c3be86dc1b4b89a5a36fb2279d4f6110b0159e793be062641bf65 ./tensorrt_yolox/yolox-sPlus-T4-960x960-pseudo-finetune.onnx -b9e9d7da33342262ccaea4469b4d02b8abb32b6d7bf737f9e0883fece1b8f580 ./tensorrt_yolox/yolox-sPlus-opt.EntropyV2-calibration.table -36b0832177b01e6b278e00c7369f1de71e616c36261cbae50f0753d41289da01 ./tensorrt_yolox/yolox-sPlus-opt.onnx -471a665f4243e654dff62578394e508db22ee29fe65d9e389dfc3b0f2dee1255 ./tensorrt_yolox/yolox-tiny.onnx -1a5a49eeec5593963eab8d70f48b8a01bfb07e753e9688eb1510ad26e803579d ./traffic_light_classifier/lamp_labels.txt -55ebb0d117a5e8943f8d1c6769f1d856b533079d4d871d8e923255cc992ad48a ./traffic_light_classifier/traffic_light_classifier_efficientNet_b1_batch_1.onnx -684e29843e3128eadb774018730644b3ab9b0a06dc4cdaeed579c2f3fa5d5265 ./traffic_light_classifier/traffic_light_classifier_efficientNet_b1_batch_4.onnx -44d94540fa8b89dfb39cd9a8523cf010ddfb10ea2f1f9b53bf3618ce7f4912ad ./traffic_light_classifier/traffic_light_classifier_efficientNet_b1_batch_6.onnx -455b71b3b20d3a96aa0e49f32714ba50421f668a2f9b9907c30b1346ac8a3703 ./traffic_light_classifier/traffic_light_classifier_mobilenetv2_batch_1.onnx -41bb79a23a4ac57956adb8e9cb3904420db1b0cd032e97b670cc4f8b174ae3fe ./traffic_light_classifier/traffic_light_classifier_mobilenetv2_batch_4.onnx -e4792eed6a46fdbd02be2f3a4f1ce91f36fa77698493caf3102e445178c0f058 ./traffic_light_classifier/traffic_light_classifier_mobilenetv2_batch_6.onnx -a41e6e3324e32c30b3b2fe38908eaf3471e2bfdaeb9e14ca0c1c3bc0275119c6 ./traffic_light_fine_detector/tlr_labels.txt -922839fcf22bd32ae5065146fcec193e9d6360ca03bd4c83faea835045daf8eb ./traffic_light_fine_detector/tlr_yolox_s_batch_1.onnx -b3c6e00acc6ff547d165469684ffb620a9a6330e9d591d445f50c4cf5cb4e292 ./traffic_light_fine_detector/tlr_yolox_s_batch_4.onnx -2824d4c5b7ab5f6bfd41e43e82747107c53e1c727b1cf1dd6746bc49e6749128 ./traffic_light_fine_detector/tlr_yolox_s_batch_6.onnx -e29e6ee68751a270fb285fd037713939ca7f61a897b4c3a7ab22b0d6a9a21ddf ./traffic_light_ssd_fine_detector/mb2-ssd-lite-tlr.onnx -a41e6e3324e32c30b3b2fe38908eaf3471e2bfdaeb9e14ca0c1c3bc0275119c6 ./traffic_light_ssd_fine_detector/voc_labels_tl.txt -1f660e15f95074bade32b1f80dbf618e9cee1f0b9f76d3f4671cb9be7f56eb3a ./yabloc_pose_initializer/resources.tar.gz -``` - -And run the command: +After downloading you can check integrity of the files with ```sha256sum```: ```console -sha256sum -c SHA256SUMS 2>&1 +$ cd ~/autoware_data/ +$ wget -q -O - https://raw.githubusercontent.com/autowarefoundation/autoware/main/ansible/roles/artifacts/SHA256SUMS | sha256sum -c ``` diff --git a/ansible/roles/artifacts/SHA256SUMS b/ansible/roles/artifacts/SHA256SUMS new file mode 100644 index 00000000000..49416eecc64 --- /dev/null +++ b/ansible/roles/artifacts/SHA256SUMS @@ -0,0 +1,37 @@ +7fe62fcebe0e0f62a000d06aa94d779feb444d933671a4a3189fe01be8c19a00 ./image_projection_based_fusion/pts_backbone_neck_head_pointpainting.onnx +3ca452ea5ca9467bf782955f75704ba8466841e275e8b8acd991b9911d53249e ./image_projection_based_fusion/pts_voxel_encoder_pointpainting.onnx +86348d8c4bced750f54288b01cc471c0d4f1ec9c693466169ef19413731e6ecc ./lidar_apollo_instance_segmentation/hdl-64.onnx +eec521ebad7553d0ea2c90472a293aecb7499ab592632f0e100481c8196eb421 ./lidar_apollo_instance_segmentation/vlp-16.onnx +95ef950bb694bd6de91b7e47f5d191d557e92a7f5e2a6bdf655a8b5eed4075cc ./lidar_apollo_instance_segmentation/vls-128.onnx +3fe7e128955646740c41a25be0c8f141d5a94594fe79d7405fe2a859e391542e ./lidar_centerpoint/pts_backbone_neck_head_centerpoint.onnx +9bb0b634f3664bd098ce7d6a3d8a9fb7cc8d9b8252b27f302c71e43316bab551 ./lidar_centerpoint/pts_backbone_neck_head_centerpoint_tiny.onnx +dc1a876580d86ee7a341d543f8ade2ede7f43bd032dc5b44155b1f0175405764 ./lidar_centerpoint/pts_voxel_encoder_centerpoint.onnx +2c53465715c1fd2e9dc5727ef3fca74f4cdf0538f74286b0946e219d0ca5693b ./lidar_centerpoint/pts_voxel_encoder_centerpoint_tiny.onnx +634a1132eb33f8091d60f2c346ababe8b905ae08387037aed883953b7329af84 ./tensorrt_yolo/coco.names +61e922f76918dd3d8e0abdc5fb7406f390609e08bd8ab9e5d3b97afb00f30f8c ./tensorrt_yolo/yolov3.onnx +0e877c716fbf8a2b431ee3e57f6c7411a6741319b52c32c6dafc53c7e1b17027 ./tensorrt_yolo/yolov4-tiny.onnx +7c7343156c1bd4b397fd1e44b27334691a6219db3ce2e29a03b72af65ddb8f39 ./tensorrt_yolo/yolov4.onnx +a627e5f70180a8746482b572194090466db62c8d1256602c1cd20374dd960e34 ./tensorrt_yolo/yolov5l.onnx +ee6f67f7c00a34cc4cef2fdd9db30dd714df1a4fb2d7e9fc1731cfe85b673133 ./tensorrt_yolo/yolov5m.onnx +be335ff7746957debf1a6903a61fa3f568b780b4afe4958edf2d4bc98e9e0825 ./tensorrt_yolo/yolov5s.onnx +d7cb4cd7078f87bda22a37828d72867accecedf9f74d0d87b5cc1f6f1180a019 ./tensorrt_yolo/yolov5x.onnx +3540a365bfd6d8afb1b5d8df4ec47f82cb984760d3270c9b41dbbb3422d09a0c ./tensorrt_yolox/label.txt +cc378d327db5616b0b3a4d077bf37100c25a50ecd22d2b542f54098da100f34c ./tensorrt_yolox/yolox-sPlus-T4-960x960-pseudo-finetune.EntropyV2-calibration.table +f5054e8a890c3be86dc1b4b89a5a36fb2279d4f6110b0159e793be062641bf65 ./tensorrt_yolox/yolox-sPlus-T4-960x960-pseudo-finetune.onnx +b9e9d7da33342262ccaea4469b4d02b8abb32b6d7bf737f9e0883fece1b8f580 ./tensorrt_yolox/yolox-sPlus-opt.EntropyV2-calibration.table +36b0832177b01e6b278e00c7369f1de71e616c36261cbae50f0753d41289da01 ./tensorrt_yolox/yolox-sPlus-opt.onnx +471a665f4243e654dff62578394e508db22ee29fe65d9e389dfc3b0f2dee1255 ./tensorrt_yolox/yolox-tiny.onnx +1a5a49eeec5593963eab8d70f48b8a01bfb07e753e9688eb1510ad26e803579d ./traffic_light_classifier/lamp_labels.txt +55ebb0d117a5e8943f8d1c6769f1d856b533079d4d871d8e923255cc992ad48a ./traffic_light_classifier/traffic_light_classifier_efficientNet_b1_batch_1.onnx +684e29843e3128eadb774018730644b3ab9b0a06dc4cdaeed579c2f3fa5d5265 ./traffic_light_classifier/traffic_light_classifier_efficientNet_b1_batch_4.onnx +44d94540fa8b89dfb39cd9a8523cf010ddfb10ea2f1f9b53bf3618ce7f4912ad ./traffic_light_classifier/traffic_light_classifier_efficientNet_b1_batch_6.onnx +455b71b3b20d3a96aa0e49f32714ba50421f668a2f9b9907c30b1346ac8a3703 ./traffic_light_classifier/traffic_light_classifier_mobilenetv2_batch_1.onnx +41bb79a23a4ac57956adb8e9cb3904420db1b0cd032e97b670cc4f8b174ae3fe ./traffic_light_classifier/traffic_light_classifier_mobilenetv2_batch_4.onnx +e4792eed6a46fdbd02be2f3a4f1ce91f36fa77698493caf3102e445178c0f058 ./traffic_light_classifier/traffic_light_classifier_mobilenetv2_batch_6.onnx +a41e6e3324e32c30b3b2fe38908eaf3471e2bfdaeb9e14ca0c1c3bc0275119c6 ./traffic_light_fine_detector/tlr_labels.txt +922839fcf22bd32ae5065146fcec193e9d6360ca03bd4c83faea835045daf8eb ./traffic_light_fine_detector/tlr_yolox_s_batch_1.onnx +b3c6e00acc6ff547d165469684ffb620a9a6330e9d591d445f50c4cf5cb4e292 ./traffic_light_fine_detector/tlr_yolox_s_batch_4.onnx +2824d4c5b7ab5f6bfd41e43e82747107c53e1c727b1cf1dd6746bc49e6749128 ./traffic_light_fine_detector/tlr_yolox_s_batch_6.onnx +e29e6ee68751a270fb285fd037713939ca7f61a897b4c3a7ab22b0d6a9a21ddf ./traffic_light_ssd_fine_detector/mb2-ssd-lite-tlr.onnx +a41e6e3324e32c30b3b2fe38908eaf3471e2bfdaeb9e14ca0c1c3bc0275119c6 ./traffic_light_ssd_fine_detector/voc_labels_tl.txt +1f660e15f95074bade32b1f80dbf618e9cee1f0b9f76d3f4671cb9be7f56eb3a ./yabloc_pose_initializer/resources.tar.gz From cb5cdc636f7ec1774cc58c0d9318c9e31660ff25 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 26 Sep 2023 11:07:22 +0000 Subject: [PATCH 10/10] style(pre-commit): autofix --- ansible/roles/artifacts/README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/ansible/roles/artifacts/README.md b/ansible/roles/artifacts/README.md index ca8a4d67271..d34f3e168c1 100644 --- a/ansible/roles/artifacts/README.md +++ b/ansible/roles/artifacts/README.md @@ -102,9 +102,9 @@ $ wget -P ~/autoware_data/traffic_light_ssd_fine_detector/ \ https://awf.ml.dev.web.auto/perception/models/voc_labels_tl.txt ``` -After downloading you can check integrity of the files with ```sha256sum```: +After downloading you can check integrity of the files with `sha256sum`: ```console -$ cd ~/autoware_data/ -$ wget -q -O - https://raw.githubusercontent.com/autowarefoundation/autoware/main/ansible/roles/artifacts/SHA256SUMS | sha256sum -c +cd ~/autoware_data/ +wget -q -O - https://raw.githubusercontent.com/autowarefoundation/autoware/main/ansible/roles/artifacts/SHA256SUMS | sha256sum -c ```