qing
91b30040c7
config/model/dataset/train/pretrain/predict/export 全链路适配非方形输入， hailo_infer/onnx_infer 从模型元数据动态推导宽高；旧 224 checkpoint 配置键 不兼容（重训后替换）。Pi 5 CPU 冒烟验证 1080p 训练可行（~4.75s/步 batch=2）。
59 lines
2.0 KiB
Python
59 lines
2.0 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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batch = Dim("batch")
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torch.onnx.export(
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model, (dummy_images, dummy_history),
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args.output,
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input_names=["images", "action_history"],
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output_names=["predicted_action"],
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dynamic_shapes={
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"images": {0: batch},
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"action_history": {0: batch},
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},
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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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# 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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img_np = dummy_images.numpy()
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hist_np = dummy_history.numpy()
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ort_out = sess.run(None, {"images": img_np, "action_history": hist_np})[0]
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with torch.no_grad():
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torch_out = model(dummy_images, dummy_history).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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