qing
a2ba797410
- pybot：recorder 逐帧落盘 imu.npy（T×8，roll,pitch,ax,ay,az,gx,gy,gz，与帧/动作同调用对齐）；run_loop 对声明 imu_dim 的 predictor 以 predict(..., imu=) 调用（鸭子类型，旧两参调用向后兼容） - actpred：新增 --imu-dim；model 序列 [img×F, act×H, (IMU), PRED]（imu_dim>0 时 F+H+2，IMU token 位于 PRED 前）；dataset/train 读 imu.npy（缺失按零向量兜底）；predict/ONNX/Hailo 三后端与 export/gen_calibration/dfc_compile/_npu_test 部署链适配 imu 输入 - 姿态按物理量程固定缩放归一化（imu_norm.py，训练/推理共用，零状态） - 测试：actpred 128 passed / pybot loop+recorder 28 passed
66 lines
2.5 KiB
Python
66 lines
2.5 KiB
Python
import argparse
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import torch
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from torch.export import Dim
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from config import parse_checkpoint_config
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from model import ActionPredictor
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def main():
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parser = argparse.ArgumentParser(description="Export actpred model to ONNX")
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parser.add_argument("--model", required=True, help="checkpoint path (best_inference.pt)")
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parser.add_argument("--output", default="model.onnx", help="output ONNX file path")
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args = parser.parse_args()
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checkpoint = torch.load(args.model, map_location="cpu", weights_only=True)
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cfg = parse_checkpoint_config(checkpoint["config"])
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data_cfg = cfg.data
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model_cfg = cfg.model
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model = ActionPredictor(model_cfg, data_cfg, pretrained=False)
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model.load_state_dict(checkpoint["model_state_dict"])
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model.eval()
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dummy_images = torch.randn(
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1, data_cfg.frame_len, 3, data_cfg.image_height, data_cfg.image_width)
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dummy_history = torch.randn(1, data_cfg.history_len, data_cfg.action_dim)
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# R2 方案一：imu_dim>0 时追加归一化姿态输入 (1, imu_dim)
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imu_dim = data_cfg.imu_dim
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extra_inputs = (torch.randn(1, imu_dim),) if imu_dim > 0 else ()
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extra_names = ["imu"] if imu_dim > 0 else []
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batch = Dim("batch")
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dynamic = {"images": {0: batch}, "action_history": {0: batch}}
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if imu_dim > 0:
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dynamic["imu"] = {0: batch}
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torch.onnx.export(
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model, (dummy_images, dummy_history, *extra_inputs),
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args.output,
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input_names=["images", "action_history"] + extra_names,
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output_names=["predicted_action"],
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dynamic_shapes=dynamic,
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opset_version=18,
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)
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print(f"ONNX model exported to: {args.output}"
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f"{'（含 imu 输入）' if imu_dim > 0 else ''}")
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# 可选：验证导出的 ONNX 模型一致性
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try:
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import onnxruntime as ort
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import numpy as np
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sess = ort.InferenceSession(args.output)
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feeds = {"images": dummy_images.numpy(),
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"action_history": dummy_history.numpy()}
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for name, t in zip(extra_names, extra_inputs):
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feeds[name] = t.numpy()
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ort_out = sess.run(None, feeds)[0]
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with torch.no_grad():
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torch_out = model(dummy_images, dummy_history, *extra_inputs).numpy()
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max_diff = np.abs(ort_out - torch_out).max()
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print(f"ONNX 一致性验证通过（最大差异: {max_diff:.6e}）")
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except ImportError:
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print("提示: 安装 onnxruntime 可验证导出模型一致性 (pip install onnxruntime)")
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if __name__ == "__main__":
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main()
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