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Sync: commaai/openpilot:master → sunnypilot/sunnypilot:master (#1800)

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This commit is contained in:
Jason Wen
2026-04-14 13:17:24 -04:00
committed by GitHub
parent 63107e6e6b 2406b32d55
commit d95bc0dcfe
18 changed files with 39 additions and 72 deletions
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common/model.h
+1 -1
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@@ -1 +1 @@
#define DEFAULT_MODEL "OP Model 7 (Default)"
#define DEFAULT_MODEL "POP model (Default)"
scripts/reporter.py
+2 -11
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@@ -33,16 +33,7 @@ if __name__ == "__main__":
print("|-| ----- | --------- |")
for f in glob.glob(BASEDIR + MODEL_PATH + "/*.onnx"):
# TODO: add checkpoint to DM
if "dmonitoring" in f:
continue
fn = os.path.basename(f)
master_path = MASTER_PATH + MODEL_PATH + fn
if os.path.exists(master_path):
master = get_checkpoint(master_path)
master_col = f"[{master}](https://reporter.comma.life/experiment/{master})"
else:
master_col = "N/A (new model)"
master = get_checkpoint(MASTER_PATH + MODEL_PATH + fn)
pr = get_checkpoint(BASEDIR + MODEL_PATH + fn)
print("|", fn, "|", master_col, "|", f"[{pr}](https://reporter.comma.life/experiment/{pr})", "|")
print("|", fn, "|", f"[{master}](https://reporterv2.comma.life/{master})", "|", f"[{pr}](https://reporterv2.comma.life/{pr})", "|")
selfdrive/modeld/SConscript
+3 -2
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@@ -21,7 +21,7 @@ tg_flags = {
}.get(arch, 'DEV=CPU CPU_LLVM=1 THREADS=0')
# Get model metadata
for model_name in ['driving_vision', 'driving_off_policy', 'driving_on_policy', 'dmonitoring_model']:
for model_name in ['driving_vision', 'driving_policy', 'dmonitoring_model']:
fn = File(f"models/{model_name}").abspath
script_files = [File(Dir("#selfdrive/modeld").File("get_model_metadata.py").abspath)]
cmd = f'{tg_flags} python3 {Dir("#selfdrive/modeld").abspath}/get_model_metadata.py {fn}.onnx'
@@ -59,5 +59,6 @@ def tg_compile(flags, model_name):
)
# Compile small models
for model_name in ['driving_vision', 'driving_off_policy', 'driving_on_policy', 'dmonitoring_model']:
for model_name in ['driving_vision', 'driving_policy', 'dmonitoring_model']:
tg_compile(tg_flags, model_name)
selfdrive/modeld/modeld.py
+6 -24
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@@ -40,10 +40,8 @@ SEND_RAW_PRED = os.getenv('SEND_RAW_PRED')
MODELS_DIR = Path(__file__).parent / 'models'
VISION_PKL_PATH = MODELS_DIR / 'driving_vision_tinygrad.pkl'
VISION_METADATA_PATH = MODELS_DIR / 'driving_vision_metadata.pkl'
ON_POLICY_PKL_PATH = MODELS_DIR / 'driving_on_policy_tinygrad.pkl'
ON_POLICY_METADATA_PATH = MODELS_DIR / 'driving_on_policy_metadata.pkl'
OFF_POLICY_PKL_PATH = MODELS_DIR / 'driving_off_policy_tinygrad.pkl'
OFF_POLICY_METADATA_PATH = MODELS_DIR / 'driving_off_policy_metadata.pkl'
POLICY_PKL_PATH = MODELS_DIR / 'driving_policy_tinygrad.pkl'
POLICY_METADATA_PATH = MODELS_DIR / 'driving_policy_metadata.pkl'
LAT_SMOOTH_SECONDS = 0.0
LONG_SMOOTH_SECONDS = 0.3
@@ -158,13 +156,7 @@ class ModelState(ModelStateBase):
self.vision_output_slices = vision_metadata['output_slices']
vision_output_size = vision_metadata['output_shapes']['outputs'][1]
with open(OFF_POLICY_METADATA_PATH, 'rb') as f:
off_policy_metadata = pickle.load(f)
self.off_policy_input_shapes = off_policy_metadata['input_shapes']
self.off_policy_output_slices = off_policy_metadata['output_slices']
off_policy_output_size = off_policy_metadata['output_shapes']['outputs'][1]
with open(ON_POLICY_METADATA_PATH, 'rb') as f:
with open(POLICY_METADATA_PATH, 'rb') as f:
policy_metadata = pickle.load(f)
self.policy_input_shapes = policy_metadata['input_shapes']
self.policy_output_slices = policy_metadata['output_slices']
@@ -188,13 +180,11 @@ class ModelState(ModelStateBase):
self.vision_output = np.zeros(vision_output_size, dtype=np.float32)
self.policy_inputs = {k: Tensor(v, device='NPY').realize() for k,v in self.numpy_inputs.items()}
self.policy_output = np.zeros(policy_output_size, dtype=np.float32)
self.off_policy_output = np.zeros(off_policy_output_size, dtype=np.float32)
self.parser = Parser()
self.frame_buf_params : dict[str, tuple[int, int, int, int]] = {}
self.update_imgs = None
self.vision_run = pickle.loads(read_file_chunked(str(VISION_PKL_PATH)))
self.policy_run = pickle.loads(read_file_chunked(str(ON_POLICY_PKL_PATH)))
self.off_policy_run = pickle.loads(read_file_chunked(str(OFF_POLICY_PKL_PATH)))
self.policy_run = pickle.loads(read_file_chunked(str(POLICY_PKL_PATH)))
def slice_outputs(self, model_outputs: np.ndarray, output_slices: dict[str, slice]) -> dict[str, np.ndarray]:
parsed_model_outputs = {k: model_outputs[np.newaxis, v] for k,v in output_slices.items()}
@@ -243,17 +233,9 @@ class ModelState(ModelStateBase):
self.policy_output = self.policy_run(**self.policy_inputs).contiguous().realize().uop.base.buffer.numpy().flatten()
policy_outputs_dict = self.parser.parse_policy_outputs(self.slice_outputs(self.policy_output, self.policy_output_slices))
self.off_policy_output = self.off_policy_run(**self.policy_inputs).contiguous().realize().uop.base.buffer.numpy()
off_policy_outputs_dict = self.parser.parse_off_policy_outputs(self.slice_outputs(self.off_policy_output, self.off_policy_output_slices))
off_policy_outputs_dict.pop('plan')
combined_outputs_dict = {**vision_outputs_dict, **off_policy_outputs_dict, **policy_outputs_dict}
if 'planplus' in combined_outputs_dict and 'plan' in combined_outputs_dict:
combined_outputs_dict['plan'] = combined_outputs_dict['plan'] + combined_outputs_dict['planplus']
combined_outputs_dict = {**vision_outputs_dict, **policy_outputs_dict}
if SEND_RAW_PRED:
combined_outputs_dict['raw_pred'] = np.concatenate([self.vision_output.copy(), self.policy_output.copy(), self.off_policy_output.copy()])
combined_outputs_dict['raw_pred'] = np.concatenate([self.vision_output.copy(), self.policy_output.copy()])
return combined_outputs_dict
selfdrive/modeld/models/big_driving_policy.onnx
Symlink
+1
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@@ -0,0 +1 @@
driving_policy.onnx
selfdrive/modeld/models/big_driving_vision.onnx
Symlink
+1
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@@ -0,0 +1 @@
driving_vision.onnx
selfdrive/modeld/models/driving_off_policy.onnx
-3
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@@ -1,3 +0,0 @@
version https://git-lfs.github.com/spec/v1
oid sha256:e53f4e0527766082ba7bde38e275def0fe3c14f6c59ae2854439e239884d3ecc
size 13393365
selfdrive/modeld/models/driving_on_policy.onnx
-3
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@@ -1,3 +0,0 @@
version https://git-lfs.github.com/spec/v1
oid sha256:ea89c50da3a16e710da292f97c81b083a982cfdee5c28eca0d37ed2fb99af6c5
size 13022642
selfdrive/modeld/models/driving_policy.onnx
+3
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@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:853c6634746ff439a848349d00e4d5581cd941f13f7c1862c31b72a31cc24858
size 14061595
selfdrive/modeld/models/driving_vision.onnx
+2 -2
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@@ -1,3 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:6263aa3fbb44cde6c68a34cdb7cd8c389789dbc02b15c1911afdac4e018281ae
size 23267151
oid sha256:940e9006a25f27f0b6e85da798e6a8fd1f6dd492dd7d0b9ff1a9436460f46129
size 46887794
selfdrive/modeld/parse_model_outputs.py
+3 -10
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@@ -96,17 +96,11 @@ class Parser:
self.parse_mdn('pose', outs, in_N=0, out_N=0, out_shape=(ModelConstants.POSE_WIDTH,))
self.parse_mdn('wide_from_device_euler', outs, in_N=0, out_N=0, out_shape=(ModelConstants.WIDE_FROM_DEVICE_WIDTH,))
self.parse_mdn('road_transform', outs, in_N=0, out_N=0, out_shape=(ModelConstants.POSE_WIDTH,))
self.parse_categorical_crossentropy('desire_pred', outs, out_shape=(ModelConstants.DESIRE_PRED_LEN,ModelConstants.DESIRE_PRED_WIDTH))
self.parse_binary_crossentropy('meta', outs)
return outs
def parse_off_policy_outputs(self, outs: dict[str, np.ndarray]) -> dict[str, np.ndarray]:
plan_mhp = self.is_mhp(outs, 'plan', ModelConstants.IDX_N * ModelConstants.PLAN_WIDTH)
plan_in_N, plan_out_N = (ModelConstants.PLAN_MHP_N, ModelConstants.PLAN_MHP_SELECTION) if plan_mhp else (0, 0)
self.parse_mdn('plan', outs, in_N=plan_in_N, out_N=plan_out_N, out_shape=(ModelConstants.IDX_N, ModelConstants.PLAN_WIDTH))
self.parse_mdn('lane_lines', outs, in_N=0, out_N=0, out_shape=(ModelConstants.NUM_LANE_LINES,ModelConstants.IDX_N,ModelConstants.LANE_LINES_WIDTH))
self.parse_mdn('road_edges', outs, in_N=0, out_N=0, out_shape=(ModelConstants.NUM_ROAD_EDGES,ModelConstants.IDX_N,ModelConstants.LANE_LINES_WIDTH))
self.parse_binary_crossentropy('lane_lines_prob', outs)
self.parse_categorical_crossentropy('desire_pred', outs, out_shape=(ModelConstants.DESIRE_PRED_LEN,ModelConstants.DESIRE_PRED_WIDTH))
self.parse_binary_crossentropy('meta', outs)
self.parse_binary_crossentropy('lead_prob', outs)
lead_mhp = self.is_mhp(outs, 'lead', ModelConstants.LEAD_MHP_SELECTION * ModelConstants.LEAD_TRAJ_LEN * ModelConstants.LEAD_WIDTH)
lead_in_N, lead_out_N = (ModelConstants.LEAD_MHP_N, ModelConstants.LEAD_MHP_SELECTION) if lead_mhp else (0, 0)
@@ -116,7 +110,7 @@ class Parser:
return outs
def parse_policy_outputs(self, outs: dict[str, np.ndarray]) -> dict[str, np.ndarray]:
plan_mhp = self.is_mhp(outs, 'plan', ModelConstants.IDX_N * ModelConstants.PLAN_WIDTH)
plan_mhp = self.is_mhp(outs, 'plan', ModelConstants.IDX_N * ModelConstants.PLAN_WIDTH)
plan_in_N, plan_out_N = (ModelConstants.PLAN_MHP_N, ModelConstants.PLAN_MHP_SELECTION) if plan_mhp else (0, 0)
self.parse_mdn('plan', outs, in_N=plan_in_N, out_N=plan_out_N, out_shape=(ModelConstants.IDX_N, ModelConstants.PLAN_WIDTH))
if 'planplus' in outs:
@@ -126,6 +120,5 @@ class Parser:
def parse_outputs(self, outs: dict[str, np.ndarray]) -> dict[str, np.ndarray]:
outs = self.parse_vision_outputs(outs)
outs = self.parse_off_policy_outputs(outs)
outs = self.parse_policy_outputs(outs)
return outs
sunnypilot/modeld_v2/SConscript
+2 -2
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@@ -13,7 +13,7 @@ if PC:
model_dir = Dir("models").abspath
cmd = f'python3 {Dir("#sunnypilot/modeld_v2").abspath}/install_models_pc.py {model_dir}'
for model_name in ['supercombo', 'driving_vision', 'driving_off_policy', 'driving_policy']:
for model_name in ['supercombo', 'driving_vision', 'driving_off_policy', 'driving_on_policy', 'driving_policy']:
if File(f"models/{model_name}.onnx").exists():
inputs.append(File(f"models/{model_name}.onnx"))
inputs.append(File(f"models/{model_name}_tinygrad.pkl"))
@@ -42,7 +42,7 @@ def tg_compile(flags, model_name):
)
# Compile models
for model_name in ['supercombo', 'driving_vision', 'driving_off_policy', 'driving_policy']:
for model_name in ['supercombo', 'driving_vision', 'driving_off_policy', 'driving_on_policy', 'driving_policy']:
if File(f"models/{model_name}.onnx").exists():
tg_compile(tg_flags, model_name)
sunnypilot/models/default_model.py
+3 -5
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@@ -8,16 +8,14 @@ from openpilot.sunnypilot import get_file_hash
DEFAULT_MODEL_NAME_PATH = os.path.join(BASEDIR, "common", "model.h")
MODEL_HASH_PATH = os.path.join(BASEDIR, "sunnypilot", "models", "tests", "model_hash")
VISION_ONNX_PATH = os.path.join(BASEDIR, "selfdrive", "modeld", "models", "driving_vision.onnx")
OFF_POLICY_ONNX_PATH = os.path.join(BASEDIR, "selfdrive", "modeld", "models", "driving_off_policy.onnx")
ON_POLICY_ONNX_PATH = os.path.join(BASEDIR, "selfdrive", "modeld", "models", "driving_on_policy.onnx")
POLICY_ONNX_PATH = os.path.join(BASEDIR, "selfdrive", "modeld", "models", "driving_policy.onnx")
def update_model_hash():
vision_hash = get_file_hash(VISION_ONNX_PATH)
off_policy_hash = get_file_hash(OFF_POLICY_ONNX_PATH)
on_policy_hash = get_file_hash(ON_POLICY_ONNX_PATH)
policy_hash = get_file_hash(POLICY_ONNX_PATH)
combined_hash = hashlib.sha256((vision_hash + off_policy_hash + on_policy_hash).encode()).hexdigest()
combined_hash = hashlib.sha256((vision_hash + policy_hash).encode()).hexdigest()
with open(MODEL_HASH_PATH, "w") as f:
f.write(combined_hash)
sunnypilot/models/tests/model_hash
+1 -1
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@@ -1 +1 @@
793b5d480edb5a30eed3d0d3bdb43259522978670f6bc3dea7a4d661261d3c48
5d4d21f1899de21137f69d74a4602c44cc5a6b04cf4e4aa9d0ec9206f8c30350
sunnypilot/models/tests/test_default_model.py
+3 -4
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@@ -6,17 +6,16 @@ See the LICENSE.md file in the root directory for more details.
"""
from openpilot.sunnypilot import get_file_hash
from openpilot.sunnypilot.models.default_model import MODEL_HASH_PATH, VISION_ONNX_PATH, OFF_POLICY_ONNX_PATH, ON_POLICY_ONNX_PATH
from openpilot.sunnypilot.models.default_model import MODEL_HASH_PATH, VISION_ONNX_PATH, POLICY_ONNX_PATH
import hashlib
class TestDefaultModel:
def test_compare_onnx_hashes(self):
vision_hash = get_file_hash(VISION_ONNX_PATH)
off_policy_hash = get_file_hash(OFF_POLICY_ONNX_PATH)
on_policy_hash = get_file_hash(ON_POLICY_ONNX_PATH)
policy_hash = get_file_hash(POLICY_ONNX_PATH)
combined_hash = hashlib.sha256((vision_hash + off_policy_hash + on_policy_hash).encode()).hexdigest()
combined_hash = hashlib.sha256((vision_hash + policy_hash).encode()).hexdigest()
with open(MODEL_HASH_PATH) as f:
current_hash = f.read().strip()
system/hardware/hardwared.py
+6 -2
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@@ -338,6 +338,9 @@ def hardware_thread(end_event, hw_queue) -> None:
show_alert = (not onroad_conditions["device_temp_good"] or not startup_conditions["device_temp_engageable"]) and onroad_conditions["ignition"]
set_offroad_alert_if_changed("Offroad_TemperatureTooHigh", show_alert, extra_text=extra_text)
if show_alert:
msg.deviceState.fanSpeedPercentDesired = 100
# Handle offroad/onroad transition
should_start = all(onroad_conditions.values())
if started_ts is None:
@@ -435,9 +438,10 @@ def hardware_thread(end_event, hw_queue) -> None:
statlog.gauge("fan_speed_percent_desired", msg.deviceState.fanSpeedPercentDesired)
statlog.gauge("screen_brightness_percent", msg.deviceState.screenBrightnessPercent)
# report to server once every 10 minutes
# report to server once every 10 minutes, or every 1s when thermally blocked
rising_edge_started = should_start and not should_start_prev
if rising_edge_started or (count % int(600. / DT_HW)) == 0:
status_packet_interval = 1. if show_alert else 600.
if rising_edge_started or (count % int(status_packet_interval / DT_HW)) == 0:
dat = {
'count': count,
'pandaStates': [strip_deprecated_keys(p.to_dict()) for p in pandaStates],
tools/cabana/cabana
+1 -1
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@@ -33,6 +33,6 @@ fi
# Build _cabana
cd "$ROOT"
scons -j4 tools/cabana/_cabana
scons -j4 tools/cabana/_cabana cereal/messaging/bridge
exec "$DIR/_cabana" "$@"
tools/lib/github_utils.py
+1 -1
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@@ -62,7 +62,7 @@ class GithubUtils:
self.api_call(github_path, data=data, method=HTTPMethod.POST, data_call=True)
def get_bucket_sha(self, bucket):
github_path = f"git/refs/heads/{bucket}"
github_path = f"git/ref/heads/{bucket}"
r = self.api_call(github_path, data_call=True, raise_on_failure=False)
return r.json()['object']['sha'] if r.ok else None