qing
9179281420
- v3-P2-1: _npu_test/_onnx_validate 按 normalize_mode 分支（fixed_range 免 stats， HailoPredictor 透传模式，torch 参考同口径反归一化） - v3-P2-2: export_deploy/export_onnx 一致性 diff 超 1e-4 即 sys.exit（P2-f 闭环） - v3-P2-3: run_loop overrun 告警节流（第1次+每100次），降 hz 建议仅 >10% 输出 - P2-a1: default_column_weights 扩展列（action_dim>18）默认 1.0 - P2-c: 补 predictor 异常清理路径测试（异常传播 + finally 落盘） - P2-d/P2-e: Resize 口径与 resume scheduler T_max 语义文档化 - R2-4: _set.sh 安装后断言 CPU 版 torch，误装 CUDA 即拦截 测试: pybot 89 passed (+1)、actpred 148 passed/18 skipped
139 lines
5.3 KiB
Python
139 lines
5.3 KiB
Python
"""部署版 ONNX 数值验证：输入敏感性 + 与 PyTorch 一致性（重编译前门槛）
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用法: venv/bin/python _onnx_validate.py
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"""
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import sys
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sys.path.insert(0, "/opt/blackbird/actpred")
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import numpy as np
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import torch
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from PIL import Image
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from torchvision import transforms
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import onnxruntime as ort
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from config import parse_checkpoint_config
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from model import ActionPredictor
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CKPT = "checkpoints/pretrain_best_inference.pt"
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ONNX = "checkpoints/actpred_deploy.onnx"
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IMG_DIR = "data/episode_0000/images"
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ck = torch.load(CKPT, map_location="cpu", weights_only=True)
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cfg = parse_checkpoint_config(ck["config"])
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model = ActionPredictor(cfg.model, cfg.data, pretrained=False)
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model.load_state_dict(ck["model_state_dict"])
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model.eval()
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action_stats = None
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norm_mode = cfg.data.normalize_mode if cfg.data.normalize_actions else "none"
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if norm_mode == "zscore":
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import os
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stats_path = cfg.data.action_stats_path or os.path.join(
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os.path.dirname(os.path.abspath(CKPT)), "action_stats.npy")
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if not os.path.isabs(stats_path):
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stats_path = os.path.join(os.path.dirname(os.path.abspath(CKPT)),
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stats_path)
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action_stats = np.load(stats_path)
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if action_stats.shape != (2, cfg.data.action_dim):
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raise ValueError(f"action stats 形状应为 (2, {cfg.data.action_dim})，"
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f"实际为 {action_stats.shape}: {stats_path}")
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print(f"history 将 z-score 归一化（{stats_path}）")
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elif norm_mode == "fixed_range":
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print("history 将 fixed_range min-max 归一化（action_norm.py，免 stats）")
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sess = ort.InferenceSession(ONNX)
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in_names = [i.name for i in sess.get_inputs()]
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print("ONNX inputs:", in_names)
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h, w = cfg.data.image_height, cfg.data.image_width
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tf = transforms.Compose([
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transforms.Resize((h, w)),
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transforms.ToTensor(),
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transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
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])
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imgs_real = [np.array(Image.open(f"{IMG_DIR}/000{i}.jpg").convert("RGB"))
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for i in range(3)]
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imgs_zero = [np.zeros_like(imgs_real[0]) for _ in range(3)]
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hist_real = np.array([
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[45, 0, 45, 0, 45, 0, 45, 0, 90, 90, 10, 200, 0, 0, 0, 0, 0, 0],
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[45, 0, 45, 0, 45, 0, 45, 0, 90, 90, 0, 20, 0, 0, 0, 0, 0, 0],
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[45, 0, 45, 0, 45, 0, 45, 0, 90, 90, 0, 0, 0, 0, 0, 0, 0, 0],
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[45, 0, 45, 0, 45, 0, 45, 0, 90, 90, 0, 0, 0, 0, 0, 0, 0, 0],
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[45, 0, 45, 0, 45, 0, 45, 0, 90, 90, 0, 0, 0, 0, 0, 0, 0, 0],
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], dtype=np.float32)
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hist_zero = np.zeros((5, 18), dtype=np.float32)
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if norm_mode == "fixed_range":
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from action_norm import normalize_action
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hist_real = normalize_action(hist_real, cfg.data.action_dim)
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hist_zero = normalize_action(hist_zero, cfg.data.action_dim)
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elif action_stats is not None:
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hist_real = (hist_real - action_stats[0]) / action_stats[1]
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hist_zero = (hist_zero - action_stats[0]) / action_stats[1]
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pred_learned = model.pred_token.data.unsqueeze(1).numpy() # (1,1,1,128)
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pred_none = np.zeros((1, 1, 1, 128), dtype=np.float32)
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# R2 方案一：imu_dim>0 时模型/ONNX 多了归一化姿态输入
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imu_dim = cfg.data.imu_dim
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imu_real = None
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imu_zero = None
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if imu_dim > 0:
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from imu_norm import normalize_imu
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# 原始姿态（roll=5°, pitch=-3°, 其余 0）→ 部署版输入布局 (1,1,1,imu_dim)
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imu_real = normalize_imu([5.0, -3.0] + [0.0] * 6, imu_dim).reshape(
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1, 1, 1, imu_dim)
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imu_zero = np.zeros((1, 1, 1, imu_dim), dtype=np.float32)
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def frames_nhwc(imgs):
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"""列表帧 → (1,H,W,3) 归一化 float32 NHWC × frame_len"""
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chw = np.stack([tf(Image.fromarray(im)).numpy() for im in imgs])
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return [chw[i].transpose(1, 2, 0)[None] for i in range(len(imgs))]
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def run_onnx(imgs, hist, pred, imu=None):
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feeds = {f"frame{i}": f for i, f in enumerate(frames_nhwc(imgs))}
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feeds["action_history"] = hist[None, None]
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feeds["pred_token"] = pred
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if imu_dim > 0:
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feeds["imu"] = imu if imu is not None else imu_zero
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return sess.run(None, feeds)[0].reshape(-1)
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def run_torch(imgs, hist, imu=None):
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x = torch.stack([tf(Image.fromarray(im)) for im in imgs]).unsqueeze(0)
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hh = torch.tensor(hist).unsqueeze(0)
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ii = None
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if imu_dim > 0:
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ii = torch.tensor(
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imu if imu is not None else imu_zero).reshape(1, imu_dim).float()
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with torch.no_grad():
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return model(x, hh, ii).squeeze(0).numpy()
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a = run_onnx(imgs_real, hist_real, pred_learned, imu_real)
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b = run_onnx(imgs_zero, hist_real, pred_learned, imu_real)
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c = run_onnx(imgs_real, hist_zero, pred_learned, imu_real)
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d = run_onnx(imgs_zero, hist_zero, pred_learned)
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p0 = run_onnx(imgs_real, hist_real, pred_none, imu_real)
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print(f"onnx real_img+real_hist: {np.round(a[:6], 3)}...")
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print(f"onnx pred_token=0 diff : max={np.abs(a - p0).max():.4f}")
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print(f"img sensitivity (a-b): max={np.abs(a - b).max():.4f}")
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print(f"hist sensitivity (a-c): max={np.abs(a - c).max():.4f}")
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ok = np.abs(a - b).max() > 1e-4 and np.abs(a - c).max() > 1e-4
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if imu_dim > 0:
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e = run_onnx(imgs_real, hist_real, pred_learned, imu_zero)
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print(f"imu sensitivity (a-e): max={np.abs(a - e).max():.4f}")
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ref = run_torch(imgs_real, hist_real, imu_real)
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print(f"torch reference : {np.round(ref[:6], 3)}...")
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print(f"onnx vs torch max_diff : {np.abs(a - ref).max():.6e}")
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ok = ok and np.abs(a - ref).max() < 1e-4
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print("PASS" if ok else "FAIL")
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