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Revert OP (#37812)

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* Revert "OP model 7 (#37760)"

This reverts commit 052692b25d.

* Revert "OP model (#37740)"

This reverts commit cb32793300.

* dead

* parse_model_outputs: drop extra space
This commit is contained in:
Harald Schäfer
2026-04-12 23:47:43 -04:00
committed by GitHub
parent 0584a5f5eb
commit c91a0a83f6
10 changed files with 21 additions and 51 deletions
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scripts/reporter.py
+2 -7
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@@ -38,11 +38,6 @@ if __name__ == "__main__":
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://reporter.comma.life/experiment/{master})", "|", f"[{pr}](https://reporter.comma.life/experiment/{pr})", "|")
selfdrive/modeld/SConscript
+3 -2
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@@ -44,7 +44,7 @@ compiled_flags_node = lenv.Command(
mac_brew_string = f'HOME={os.path.expanduser("~")}' if arch == 'Darwin' else ''
# 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} {mac_brew_string} python3 {Dir("#selfdrive/modeld").abspath}/get_model_metadata.py {fn}.onnx'
@@ -82,5 +82,6 @@ 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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@@ -36,10 +36,8 @@ SEND_RAW_PRED = os.getenv('SEND_RAW_PRED')
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
@@ -152,13 +150,7 @@ class ModelState:
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']
@@ -182,13 +174,11 @@ class ModelState:
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()}
@@ -236,17 +226,9 @@ class ModelState:
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