qing
14964b9574
- hailo_infer: 帧输入按 input_layerN 排序绑定（vstream 报告顺序实测 为 1,5,4,3,2，按报告顺序会 frame1/frame2 错位），新增 --pred-token - gen_calibration: pred_token 校准改为学习值 ±30% 抖动，修复常量 校准量化退化（scale=18571.7，任何输入都被量化成 0） - export_deploy: 新增 --pred-token-out 保存学习值供推理端喂入 - dfc_compile: parser/optimize 后自动检查输入层连通性，断开即报错 - 新增 _npu_test.py（NPU 4 角敏感性复验）与 _onnx_validate.py （编译前 ONNX 数值门槛），test_actpred 补乱序帧序用例
143 lines
6.1 KiB
Python
143 lines
6.1 KiB
Python
"""生成 DFC 量化校准数据集
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两种模式：
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默认模式（data/calibration.npz）：为旧版 dfc_compile.py 的 ClientModel
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流程准备，目前保留兼容。
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--deploy 模式（data/calibration_deploy.npz）：为 export_deploy.py 导出
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的部署版 ONNX 生成校准集，键名匹配 HN 层名 actpred_deploy/input_layerN，
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形状匹配 HEF 输入：frame0~2 (N,H,W,3) 已归一化 float32、
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action_history (N,1,H_len,D)、pred_token (N,1,1,d) 学习值 ± 抖动
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（常量校准会使量化范围退化，见 --pred-jitter）。
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图像分辨率与 --model checkpoint 的配置一致。
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数据源：
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- 图像：无真实 episode 时用 [0,255] 均匀采样像素 + ImageNet 归一化
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- 动作历史：从 bot4cj/scripts/*.mt 的真实 @Action 矩阵滑窗采样
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（列不足 18 补零，与 pretrain_mt.py 语义一致）
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真实数据到位后应替换为真实帧重新校准。
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用法:
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python gen_calibration.py --deploy --model checkpoints/pretrain_best_inference.pt
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"""
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import argparse
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import numpy as np
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from config import DataConfig, parse_checkpoint_config
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from mt_parser import parse_mt_directory
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IMAGENET_MEAN = np.array([0.485, 0.456, 0.406], dtype=np.float32)
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IMAGENET_STD = np.array([0.229, 0.224, 0.225], dtype=np.float32)
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def build_action_history_samples(mt_dir, n_samples, history_len, action_dim, rng):
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sequences = []
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for _path, matrix in parse_mt_directory(mt_dir):
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n_cols = matrix.shape[1]
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if n_cols < action_dim:
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matrix = np.pad(matrix, ((0, 0), (0, action_dim - n_cols)))
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elif n_cols > action_dim:
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matrix = matrix[:, :action_dim]
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if len(matrix) >= history_len:
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sequences.append(matrix)
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if not sequences:
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raise ValueError(f"{mt_dir} 中没有可用的 @Action 序列")
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windows = []
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for matrix in sequences:
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for i in range(len(matrix) - history_len + 1):
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windows.append(matrix[i:i + history_len])
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idx = rng.integers(0, len(windows), size=n_samples)
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return np.stack([windows[i] for i in idx]).astype(np.float32)
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def build_image_samples(n_samples, frame_len, height, width, rng):
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# 存 uint8 原始像素值（0~255），加载时再做 ToTensor+Normalize，节省磁盘
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return rng.integers(0, 256,
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size=(n_samples, frame_len, 3, height, width),
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dtype=np.uint8)
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def build_deploy_set(args):
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import torch
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from model import ActionPredictor
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ckpt = torch.load(args.model, map_location="cpu", weights_only=True)
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cfg = parse_checkpoint_config(ckpt["config"]).data
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rng = np.random.default_rng(args.seed)
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h, w = cfg.image_height, cfg.image_width
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raw = rng.integers(0, 256, size=(args.samples, h, w, 3),
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dtype=np.uint8).astype(np.float32) / 255.0
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norm = ((raw - IMAGENET_MEAN) / IMAGENET_STD).astype(np.float32)
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frames = [norm.copy() for _ in range(cfg.frame_len)]
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history = build_action_history_samples(
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args.mt_dir, args.samples, cfg.history_len, cfg.action_dim, rng)
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history = history[:, None, :, :] # (N, 1, H_len, D)
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model = ActionPredictor(parse_checkpoint_config(ckpt["config"]).model,
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cfg, pretrained=False)
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model.load_state_dict(ckpt["model_state_dict"])
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d = model.pred_token.shape[-1]
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v = model.pred_token.data.unsqueeze(1).numpy() # (1, 1, d) 学习到的常量
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# 常量校准会使量化范围退化（上次实测 scale=18571.7，任何输入都被
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# 量化成 0）；按学习值 ±相对抖动生成校准样本，运行时喂学习值即可
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# 无损还原（量化误差 ~1e-4）
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jitter = v * rng.uniform(-args.pred_jitter, args.pred_jitter,
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size=(args.samples, 1, 1, d)).astype(np.float32)
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jitter = jitter + rng.normal(0.0, args.pred_jitter_abs,
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size=jitter.shape).astype(np.float32)
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pred = np.broadcast_to(v, (args.samples, 1, 1, d)).astype(np.float32) + jitter
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np.savez_compressed(
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args.output,
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**{f"actpred_deploy/input_layer{i + 1}": arr
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for i, arr in enumerate([*frames, history, pred])})
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print(f"部署校准集已保存: {args.output}")
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print(f" frame0~2: {frames[0].shape}（分辨率 {w}x{h}，与 checkpoint 一致）")
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print(f" action_history: {history.shape} pred_token: {pred.shape}"
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f"（学习值 ±{args.pred_jitter:.0%} 抖动）")
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def main():
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parser = argparse.ArgumentParser(description="Generate DFC calibration dataset")
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parser.add_argument("--deploy", action="store_true",
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help="生成部署版（export_deploy.py）校准集")
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parser.add_argument("--model", default="checkpoints/pretrain_best_inference.pt",
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help="--deploy 模式的 checkpoint（决定分辨率与 pred_token）")
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parser.add_argument("--mt-dir", default="../bot4cj/scripts")
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parser.add_argument("--output", default=None)
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parser.add_argument("--samples", type=int, default=32)
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parser.add_argument("--seed", type=int, default=42)
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parser.add_argument("--pred-jitter", type=float, default=0.3,
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help="pred_token 校准相对抖动幅度（默认 ±30%）")
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parser.add_argument("--pred-jitter-abs", type=float, default=0.005,
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help="pred_token 校准绝对抖动标准差（近零元素保底）")
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args = parser.parse_args()
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if args.deploy:
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args.output = args.output or "data/calibration_deploy.npz"
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build_deploy_set(args)
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return
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args.output = args.output or "data/calibration.npz"
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cfg = DataConfig()
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rng = np.random.default_rng(args.seed)
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history = build_action_history_samples(
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args.mt_dir, args.samples, cfg.history_len, cfg.action_dim, rng)
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images = build_image_samples(
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args.samples, cfg.frame_len, cfg.image_height, cfg.image_width, rng)
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np.savez_compressed(args.output, images=images, action_history=history)
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print(f"校准集已保存: {args.output}")
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print(f" images: {images.shape} dtype={images.dtype}")
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print(f" action_history: {history.shape} dtype={history.dtype}")
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if __name__ == "__main__":
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main()
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