9c7833faf9
* Vegetarian Filet o Fish model * fix.. atc.. * test cluster_speed_limit * fix.. cluster_speed_limit.. 2 * fix.. clusterspeedlimit3 * cruise speed to roadlimit speed * fix.. * fix.. eng * deltaUp/Down for lanechange * fix.. atc desire... * fix.. * ff * ff * fix.. * fix.. eng * fix engsound * Update desire_helper.py * fix.. connect... * fix curve_min speed * Revert "fix curve_min speed" This reverts commit fcc9c2eb14eb3504abef3e420db93e8882e56f37. * Reapply "fix curve_min speed" This reverts commit 2d2bba476c58a7b4e13bac3c3ad0e4694c95515d. * fix.. auto speed up.. roadlimit * fix.. atc auto lanechange... * Update desire_helper.py * Update cruise.py * debug atc... * fix.. waze alert offset.. * fix.. * test atc.. * fix.. * fix.. atc * atc test.. * fix.. atc * fix.. atc2 * fix.. atc3 * KerryGold Model. latsmooth_sec = 0.0 * lat smooth seconds 0.13 * fix comment * fix.. auto cruise, and speed unit * change lanemode switching. * erase mazda lkas button.
50 lines
1.7 KiB
Python
50 lines
1.7 KiB
Python
import sys, onnx
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from tinygrad import Tensor, fetch, GlobalCounters
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from tinygrad.uop.ops import UOp
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from tinygrad.frontend.onnx import OnnxRunner
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from tinygrad.engine.grouper import get_kernelize_map
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from tinygrad.engine.schedule import create_schedule_with_vars
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from tinygrad.engine.realize import run_schedule
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# NOLOCALS=1 GPU=1 IMAGE=2 FLOAT16=1 VIZ=1 DEBUG=2 python3 examples/openpilot/compile4.py
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OPENPILOT_MODEL = sys.argv[1] if len(sys.argv) > 1 else "https://github.com/commaai/openpilot/raw/v0.9.7/selfdrive/modeld/models/supercombo.onnx"
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OUTPUT = sys.argv[2] if len(sys.argv) > 2 else "/tmp/openpilot.pkl"
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if __name__ == "__main__":
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fn = fetch(OPENPILOT_MODEL)
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onnx_file = fetch(OPENPILOT_MODEL)
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onnx_model = onnx.load(onnx_file)
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run_onnx = OnnxRunner(onnx_model)
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inputs = run_onnx.get_empty_input_data("npy")
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out: Tensor = next(iter(run_onnx({k:v.to(None) for k,v in inputs.items()}).values())).to('cpu')
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root = out.uop
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targets = [x.uop for x in inputs.values()]
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print(targets)
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# TODO: abstract this from gradient?
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# compute the target path (top down)
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in_target_path: dict[UOp, bool] = {}
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for u in root.toposort(): in_target_path[u] = any(x in targets or in_target_path[x] for x in u.src)
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independent_set = {}
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for u in root.toposort():
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if in_target_path[u]:
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for s in u.src:
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if not in_target_path[s]:
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independent_set[s] = None
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independent = UOp.sink(*independent_set.keys())
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kernelized = get_kernelize_map(independent)
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independent = independent.substitute(kernelized)
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schedule, var_vals = create_schedule_with_vars(independent)
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run_schedule(schedule)
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print("**** real ****")
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GlobalCounters.reset()
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out.uop = root.substitute(kernelized)
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out.kernelize()
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# realize
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out.realize()
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