Merge branch 'master' into spatial-feat

* Fix UI path color and thickness based on lateral steering state (Issue #1441)

* Fix UI path color and thickness based on lateral control engagement (Issue #1441)

* Fix UI path width and color based on MADS lateral engagement (Issue #1441)

* Fix UI path width and color based on MADS lateral engagement (Issue #1441)

* move to ModelRendererSP

* match torque bar

* same behavior across the board

* simplify

---------

Co-authored-by: Brennan Browne <brennanbrowne@google.com>
Co-authored-by: Jason Wen <haibin.wen3@gmail.com>

.github/workflows/build-default-big-model.yaml

@@ -0,0 +1,78 @@
+name: Build default big model
+
+on:
+  workflow_dispatch:
+
+env:
+  HF_REPO: sunnypilot/sunnypilot_models_v1
+  HF_DEFAULTS_PATH: models/defaults/big
+
+jobs:
+  build_model:
+    uses: ./.github/workflows/sunnypilot-build-model.yaml
+    with:
+      upstream_branch: ${{ github.sha }}
+      custom_name: default-big-model
+      target_hardware: usbgpu
+    secrets: inherit
+
+  upload_defaults:
+    needs: build_model
+    runs-on: ubuntu-24.04
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          submodules: recursive
+      - run: git lfs pull -I "openpilot/selfdrive/modeld/models/big_driving_supercombo.onnx"
+
+      - name: Install huggingface_hub
+        run: pip install --upgrade "huggingface_hub>=0.22.0"
+
+      - name: Download artifact name
+        uses: actions/download-artifact@v4
+        with:
+          name: artifact-name-default-big-model
+          path: artifact_name
+
+      - name: Read artifact name
+        id: artifact
+        run: |
+          ARTIFACT_NAME=$(cat artifact_name/artifact_name.txt)
+          echo "artifact_name=$ARTIFACT_NAME" >> $GITHUB_OUTPUT
+
+      - name: Download model artifact
+        uses: actions/download-artifact@v4
+        with:
+          name: ${{ steps.artifact.outputs.artifact_name }}
+          path: output
+
+      - name: Upload model to HF defaults
+        env:
+          HF_OIDC_RESOURCE: datasets/${{ env.HF_REPO }}
+          ARTIFACT_NAME: ${{ steps.artifact.outputs.artifact_name }}
+        run: |
+          rm -f output/artifact_name.txt
+          hf upload ${{ env.HF_REPO }} \
+            output/ \
+            "${HF_DEFAULTS_PATH}/${ARTIFACT_NAME}/" \
+            --repo-type=dataset
+
+      - name: Get tinygrad ref and ONNX hash
+        id: meta
+        run: |
+          export PYTHONPATH=$(pwd)
+          echo "tinygrad_ref=$(python3 openpilot/sunnypilot/models/tinygrad_ref.py)" >> $GITHUB_OUTPUT
+          echo "onnx_sha256=$(sha256sum openpilot/selfdrive/modeld/models/big_driving_supercombo.onnx | cut -d' ' -f1)" >> $GITHUB_OUTPUT
+
+      - name: Update default_models.json on HF
+        env:
+          HF_OIDC_RESOURCE: datasets/${{ env.HF_REPO }}
+          ARTIFACT_NAME: ${{ steps.artifact.outputs.artifact_name }}
+        run: |
+          python3 release/ci/upload_default_model.py \
+            --hf-repo "${{ env.HF_REPO }}" \
+            --hf-defaults-path "${{ env.HF_DEFAULTS_PATH }}" \
+            --artifact-name "$ARTIFACT_NAME" \
+            --metadata-path "output/metadata.json" \
+            --onnx-sha256 "${{ steps.meta.outputs.onnx_sha256 }}" \
+            --tinygrad-ref "${{ steps.meta.outputs.tinygrad_ref }}"

openpilot/selfdrive/ui/onroad/model_renderer.py

@@ -192,7 +192,7 @@ class ModelRenderer(Widget, ChevronMetrics, ModelRendererSP):
 
     max_idx = self._get_path_length_idx(path_x_array, max_distance)
     self._path.projected_points = self._map_line_to_polygon(
-      self._path.raw_points, 0.9, self._path_offset_z, max_idx, max_distance, allow_invert=False
+      self._path.raw_points, self._get_path_half_width(), self._path_offset_z, max_idx, max_distance, allow_invert=False
     )
 
     self._update_experimental_gradient()
@@ -292,7 +292,7 @@ class ModelRenderer(Widget, ChevronMetrics, ModelRendererSP):
     allow_throttle = sm['longitudinalPlan'].allowThrottle or not self._longitudinal_control
     self._blend_filter.update(int(allow_throttle))
 
-    if ui_state.rainbow_path:
+    if ui_state.rainbow_path and self._lateral_active:
       self.rainbow_path.draw_rainbow_path(self._rect, self._path)
       return
 

openpilot/selfdrive/ui/sunnypilot/onroad/model_renderer.py

@@ -4,11 +4,23 @@ Copyright (c) 2021-, Haibin Wen, sunnypilot, and a number of other contributors.
 This file is part of sunnypilot and is licensed under the MIT License.
 See the LICENSE.md file in the root directory for more details.
 """
+from openpilot.common.filter_simple import FirstOrderFilter
+from openpilot.selfdrive.ui.ui_state import ui_state, UIStatus
 from openpilot.selfdrive.ui.sunnypilot.onroad.chevron_metrics import ChevronMetrics
 from openpilot.selfdrive.ui.sunnypilot.onroad.rainbow_path import RainbowPath
+from openpilot.system.ui.lib.application import gui_app
 
 
 class ModelRendererSP:
   def __init__(self):
     self.rainbow_path = RainbowPath()
     self.chevron_metrics = ChevronMetrics()
+    self._width_filter = FirstOrderFilter(0.9, 0.1, 1 / gui_app.target_fps)
+
+  @property
+  def _lateral_active(self) -> bool:
+    return ui_state.status in (UIStatus.ENGAGED, UIStatus.LAT_ONLY)
+
+  def _get_path_half_width(self) -> float:
+    target = 0.9 if self._lateral_active else 0.40
+    return self._width_filter.update(target)

openpilot/sunnypilot/modeld_v2/compile_modeld.py

@@ -186,14 +186,14 @@ def make_run_policy(vision_runner, policy_runners: list, features_slice: slice,
     warped_dev = warped.to(Device.DEFAULT)
     Tensor.realize(packed_npy_inputs_dev, warped_dev)
 
-    img = shift_and_sample(img_q, warped_dev[0:1], sample_skip_fn).realize()
-    big_img = shift_and_sample(big_img_q, warped_dev[1:2], sample_skip_fn).realize()
+    img = shift_and_sample(img_q, warped_dev[0:1], sample_skip_fn)
+    big_img = shift_and_sample(big_img_q, warped_dev[1:2], sample_skip_fn)
 
     unpacked_tensors = [tensor.reshape(shape) for tensor, shape in zip(packed_npy_inputs_dev.split(npy_sizes), npy_shapes.values(), strict=True)]
     unpacked_dict = dict(zip(npy_shapes.keys(), unpacked_tensors, strict=True))
 
     desire_dev = unpacked_dict['desire']
-    desire_buf = shift_and_sample(desire_q, desire_dev.reshape(1, 1, -1), sample_desire_fn).realize()
+    desire_buf = shift_and_sample(desire_q, desire_dev.reshape(1, 1, -1), sample_desire_fn)
 
     inputs = {desire_key: desire_buf}
     for key, tensor_val in unpacked_dict.items():
@@ -202,22 +202,22 @@ def make_run_policy(vision_runner, policy_runners: list, features_slice: slice,
 
     if 'prev_feat' in unpacked_dict:
       prev_feat_dev = unpacked_dict['prev_feat']
-      feat_buf = shift_and_sample(feat_q, prev_feat_dev.reshape(1, 1, -1), sample_skip_fn).realize()
-      inputs['features_buffer'] = feat_buf.reshape(input_shapes['features_buffer'])
+      feat_buf = shift_and_sample(feat_q, prev_feat_dev.reshape(1, 1, -1), sample_skip_fn)
+      inputs['features_buffer'] = feat_buf if len(fb := input_shapes['features_buffer']) <= 3 else feat_buf.reshape(fb)
 
     if vision_runner:
       vision_out_cast = next(iter(vision_runner({road_key: img, wide_key: big_img}).values())).cast('float32').realize()
       if 'features_buffer' not in inputs:
         new_feat = vision_out_cast[:, features_slice].reshape(1, -1).unsqueeze(0)
         feat_buf = shift_and_sample(feat_q, new_feat, sample_skip_fn).realize()
-        inputs['features_buffer'] = feat_buf.reshape(input_shapes['features_buffer'])
+        inputs['features_buffer'] = feat_buf if len(fb := input_shapes['features_buffer']) <= 3 else feat_buf.reshape(fb)
       policy_outs = [next(iter(pol_runner(inputs).values())).cast('float32').realize() for pol_runner in policy_runners]
       return (vision_out_cast, *policy_outs) if len(policy_outs) > 1 else (vision_out_cast, policy_outs[0])
 
     inputs.update({road_key: img, wide_key: big_img})
     if 'features_buffer' not in inputs:
       feat_buf = sample_skip_fn(feat_q)
-      inputs['features_buffer'] = feat_buf.reshape(input_shapes['features_buffer'])
+      inputs['features_buffer'] = feat_buf if len(fb := input_shapes['features_buffer']) <= 3 else feat_buf.reshape(fb)
 
     policy_out = next(iter(policy_runners[0](inputs).values())).cast('float32').realize()
     if 'features_buffer' not in inputs and features_slice is not None:

release/ci/upload_default_model.py

@@ -0,0 +1,74 @@
+#!/usr/bin/env python3
+"""
+Copyright (c) 2021-, Haibin Wen, sunnypilot, and a number of other contributors.
+
+This file is part of sunnypilot and is licensed under the MIT License.
+See the LICENSE.md file in the root directory for more details.
+"""
+
+import argparse
+import json
+import sys
+import tempfile
+
+from huggingface_hub import HfApi, hf_hub_download
+
+
+def main():
+  parser = argparse.ArgumentParser()
+  parser.add_argument("--hf-repo", required=True)
+  parser.add_argument("--hf-defaults-path", required=True)
+  parser.add_argument("--artifact-name", required=True)
+  parser.add_argument("--metadata-path", required=True)
+  parser.add_argument("--onnx-sha256", required=True)
+  parser.add_argument("--tinygrad-ref", required=True)
+  args = parser.parse_args()
+
+  with open(args.metadata_path) as f:
+    metadata = json.load(f)
+
+  bundle = metadata['bundles'][0]
+  bundle['onnx_sha256'] = args.onnx_sha256
+
+  artifact = bundle['models'][0]['artifact']
+  hf_base = f"https://huggingface.co/datasets/{args.hf_repo}/resolve/main/{args.hf_defaults_path}/{args.artifact_name}"
+  artifact['download_uri']['url'] = f"{hf_base}/{artifact['file_name']}"
+  for chunk in artifact.get('chunks', []):
+    chunk['url'] = f"{hf_base}/{chunk['file_name']}"
+
+  json_filename = f"{args.hf_defaults_path}/default_models.json"
+  try:
+    local_path = hf_hub_download(repo_id=args.hf_repo, repo_type='dataset', filename=json_filename)
+    with open(local_path) as f:
+      defaults_json = json.load(f)
+  except Exception:
+    defaults_json = {"tinygrad_ref": args.tinygrad_ref, "bundles": []}
+
+  defaults_json['tinygrad_ref'] = args.tinygrad_ref
+
+  existing_idx = next((i for i, b in enumerate(defaults_json['bundles'])
+                       if b.get('display_name') == bundle.get('display_name')), None)
+  if existing_idx is not None:
+    defaults_json['bundles'][existing_idx] = bundle
+  else:
+    defaults_json['bundles'].append(bundle)
+
+  print(json.dumps(defaults_json, indent=2))
+
+  api = HfApi()
+  with tempfile.NamedTemporaryFile(mode='w', suffix='.json', delete=False) as f:
+    json.dump(defaults_json, f, indent=2)
+    tmp_path = f.name
+
+  api.upload_file(
+    path_or_fileobj=tmp_path,
+    path_in_repo=json_filename,
+    repo_id=args.hf_repo,
+    repo_type="dataset",
+  )
+
+  print(f"Updated {json_filename}")
+
+
+if __name__ == "__main__":
+  main()