qing
823e91a30e
- hailo_infer: 帧归一化判定改为量化网格 dequant(0)（旧启发式把归一化 帧当原始像素喂入，图像路径饱和破坏；实机 max_diff 2.55→0.37， img 敏感性 1.44→22.26） - export_deploy: pool kernel 由卷积真实输出尺寸探测（H//16 在 1080 高度下静默丢弃末行）；mobilenet backbone 给出明确报错 - mt_parser: @Action 为文件末位块时解析成功，不再静默丢数据 - config: checkpoint train 段未知键/非 dict 校验；init-from 兼容性 警告助手（normalize/action_dim），train 与 pretrain 共用 - dataset: stats 文件形状校验；predict: 移除误导性 --action-dim - dfc_compile: --frame-count 参数化（原硬编码 3 帧） - gen_calibration: 旧模式校准图像改分块 memmap 生成（1080p 原一次性 分配 ~600MB） - _npu_test: torch 参考反归一化对齐后再与 HEF 比较 - 单测 81→90 全过；README 与测速报告更新为验证轮次数据
133 lines
5.4 KiB
Python
133 lines
5.4 KiB
Python
"""actpred HEF NPU 输入敏感性复验脚本（重编译后的验收门槛）
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用法: venv/bin/python _npu_test.py
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4 角测试（真实/全零帧 × 真实/全零历史）+ 与 PyTorch 参考对比。
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HEF 尺寸/布局从 vstream info 自动推导，checkpoint 用
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checkpoints/pretrain_best_inference.pt。
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"""
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import sys
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import time
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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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import torch.nn as nn
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from PIL import Image
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from hailo_platform import HEF
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from config import parse_checkpoint_config
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from model import ActionPredictor
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from hailo_infer import HailoPredictor
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HEF_PATH = "checkpoints/actpred_pretrain.hef"
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CKPT = "checkpoints/pretrain_best_inference.pt"
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IMG_DIR = "data/episode_0000/images"
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hef = HEF(HEF_PATH)
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print("=== HEF vstream infos ===")
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for i in hef.get_input_vstream_infos():
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q = i.quant_info
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print(f"IN {i.name:16s} {tuple(i.shape)} {i.format.type} "
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f"zp={q.qp_zp} scale={q.qp_scale:.6g}")
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for o in hef.get_output_vstream_infos():
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q = o.quant_info
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print(f"OUT {o.name:16s} {tuple(o.shape)} {o.format.type} "
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f"zp={q.qp_zp} scale={q.qp_scale:.6g}")
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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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# PyTorch 参考（checkpoint 自带 config）
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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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pred_learned = model.pred_token.data.unsqueeze(1).numpy() # (1,1,1,d)
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# 归一化模型：torch 参考在喂入前 z-score；HailoPredictor 传 stats_path
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# 由封装内部归一化（hist_real/hist_zero 保持原始值，避免双重归一化）
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import os
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stats_path = None
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if cfg.data.normalize_actions:
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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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if not os.path.exists(stats_path):
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raise FileNotFoundError(f"统计文件不存在: {stats_path}")
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print(f"HailoPredictor stats: {stats_path}")
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from torchvision import transforms
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tf = transforms.Compose([
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transforms.Resize((cfg.data.image_height, cfg.data.image_width)),
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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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def torch_pred(imgs, hist, use_learned_pred=True):
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x = torch.stack([tf(Image.fromarray(im)) for im in imgs]).unsqueeze(0)
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h = torch.tensor(hist).unsqueeze(0)
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if stats_path is not None:
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stats = np.load(stats_path)
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h = (h - torch.tensor(stats[0])) / torch.tensor(stats[1])
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if use_learned_pred:
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with torch.no_grad():
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return model(x, h).squeeze(0).numpy()
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saved = model.pred_token.data.clone()
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model.pred_token.data.zero_()
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with torch.no_grad():
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out = model(x, h).squeeze(0).numpy()
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model.pred_token.data.copy_(saved)
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return out
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with HailoPredictor(HEF_PATH, stats_path=stats_path) as p:
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print(f"\ndims: frame_len={p.frame_len} img={p.image_width}x{p.image_height} "
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f"history={p.history_len} action_dim={p.action_dim}")
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t0 = time.perf_counter()
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a = p.predict(imgs_real, hist_real, pred_token=pred_learned)
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b = p.predict(imgs_zero, hist_real, pred_token=pred_learned)
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c = p.predict(imgs_real, hist_zero, pred_token=pred_learned)
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d = p.predict(imgs_zero, hist_zero, pred_token=pred_learned)
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t1 = time.perf_counter()
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print(f"4x predict: {(t1 - t0) / 4 * 1000:.1f} ms/次")
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for name, r in [("real_img+real_hist", a), ("zero_img+real_hist", b),
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("real_img+zero_hist", c), ("zero_img+zero_hist", d)]:
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print(f"{name:20s} max_abs={np.abs(r).max():.4f} out={np.round(r[:6], 3)}...")
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img_sens = np.abs(a - b).max()
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hist_sens = np.abs(a - c).max()
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print(f"\nimg sensitivity = {img_sens:.4f}（>0.1 视为输入连通）")
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print(f"hist sensitivity = {hist_sens:.4f}（>0.1 视为输入连通）")
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ref = torch_pred(imgs_real, hist_real, True)
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ref_zero_pred = torch_pred(imgs_real, hist_real, False)
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# torch 参考是模型原始 z 空间输出；HailoPredictor 带 stats 时已反归一化
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# 到原始动作空间，比较前须同空间对齐，否则归一化模型下 max_diff 无意义
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if stats_path is not None:
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s = np.load(stats_path)
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ref = ref * s[1] + s[0]
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ref_zero_pred = ref_zero_pred * s[1] + s[0]
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print(f"torch learned_pred : {np.round(ref[:6], 3)}...")
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print(f"torch zero_pred : {np.round(ref_zero_pred[:6], 3)}...")
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print(f"HEF vs torch(learned) max_diff={np.abs(a - ref).max():.4f}"
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f"（原始动作空间）")
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ok = img_sens > 0.1 and hist_sens > 0.1
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print("\nPASS: HEF 输入连通，可继续与 torch 对比量化误差"
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if ok else "\nFAIL: HEF 输出对输入不敏感，编译仍有问题")
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