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| Author | SHA1 | Date | |
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| f240000e70 |
@@ -43,6 +43,24 @@ def get_gravity_orientation(quaternion: list[float] | np.ndarray) -> np.ndarray:
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return gravity_orientation
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def make_ort_session_options(intra_op_num_threads: int | None = None, inter_op_num_threads: int | None = None):
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"""Build quiet ONNX Runtime SessionOptions, optionally capping the CPU thread pool.
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These tiny MLP policies are latency-bound, not throughput-bound, so letting ORT grab
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every core starves the real-time control loop / torch policy and causes stutter. Pass
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1 intra + 1 inter thread for lowest-latency per-step inference.
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"""
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import onnxruntime as ort
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so = ort.SessionOptions()
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so.log_severity_level = 3
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if intra_op_num_threads is not None:
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so.intra_op_num_threads = intra_op_num_threads
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if inter_op_num_threads is not None:
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so.inter_op_num_threads = inter_op_num_threads
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return so
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class G1_29_JointArmIndex(IntEnum):
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# Left arm
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kLeftShoulderPitch = 15
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@@ -68,9 +68,15 @@ def load_groot_policies(
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filename="GR00T-WholeBodyControl-Walk.onnx",
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)
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# Load ONNX policies
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policy_balance = ort.InferenceSession(balance_path)
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policy_walk = ort.InferenceSession(walk_path)
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# Load ONNX policies with a capped thread pool. GR00T runs at 50 Hz in a
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# background thread alongside the (torch) upper-body policy and IK; letting ORT
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# grab every core starves those and makes the whole rollout stutter. These are
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# small MLPs, so 1 thread is both enough and lowest-latency.
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from .g1_utils import make_ort_session_options
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so = make_ort_session_options(intra_op_num_threads=1, inter_op_num_threads=1)
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policy_balance = ort.InferenceSession(balance_path, sess_options=so)
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policy_walk = ort.InferenceSession(walk_path, sess_options=so)
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logger.info("GR00T policies loaded successfully")
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