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
159 lines
6.7 KiB
Python
159 lines
6.7 KiB
Python
import os
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import re
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import numpy as np
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from pathlib import Path
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from PIL import Image
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import torch
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from torch.utils.data import Dataset
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from torchvision import transforms
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def _natural_key(p):
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"""自然排序键：'1.jpg' < '2.jpg' < ... < '10.jpg'"""
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return [int(t) if t.isdigit() else t for t in re.split(r'(\d+)', p.name)]
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class ActionDataset(Dataset):
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def __init__(self, data_dir: str, history_len: int, action_dim: int,
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image_width: int = 1920, image_height: int = 1080,
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normalize_actions: bool = False,
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action_stats_path: str = None, episode_indices: list = None,
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frame_len: int = 3):
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self.history_len = history_len
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self.action_dim = action_dim
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self.image_width = image_width
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self.image_height = image_height
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self.frame_len = frame_len
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self.transform = transforms.Compose([
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transforms.Resize((image_height, 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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episodes = ActionDataset._valid_episode_dirs(data_dir)
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if episode_indices is not None:
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episodes = [episodes[i] for i in episode_indices]
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self.samples = []
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for ep_dir in episodes:
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actions_path = ep_dir / "actions.npy"
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images_dir = ep_dir / "images"
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if not actions_path.exists() or not images_dir.is_dir():
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continue
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actions = np.load(actions_path).astype(np.float32)
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if actions.ndim != 2 or actions.shape[1] != action_dim:
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raise ValueError(
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f"{actions_path} 形状应为 (T, {action_dim})，实际为 {actions.shape}。"
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f"请检查该 episode 的动作维度与 --action-dim 是否一致。")
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n_frames = len(actions)
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image_files = sorted(list(images_dir.glob("*.jpg")) + list(images_dir.glob("*.png")),
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key=_natural_key)
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n_images = len(image_files)
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if n_frames != n_images:
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print(f"WARNING: {ep_dir} 动作帧数 {n_frames} 与图像数 {n_images} 不一致，"
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f"按较少者（{min(n_frames, n_images)}）使用，请检查录制完整性")
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n = min(n_frames, n_images)
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# 每个样本：帧 [t-frame_len+1, t]（旧→新）+ 动作历史 [t-history_len, t) → 动作 t
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start = max(history_len, frame_len - 1)
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for t in range(start, n):
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self.samples.append((image_files[t - frame_len + 1: t + 1],
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actions[t - history_len:t], actions[t]))
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self._action_mean = None
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self._action_std = None
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if normalize_actions:
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if action_stats_path:
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if not os.path.exists(action_stats_path):
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raise FileNotFoundError(
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f"指定的 action_stats_path 不存在: {action_stats_path}")
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stats = np.load(action_stats_path)
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if stats.shape != (2, self.action_dim):
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raise ValueError(
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f"action stats 形状应为 (2, {self.action_dim})，"
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f"实际为 {stats.shape}: {action_stats_path}")
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self._action_mean = stats[0]
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self._action_std = stats[1]
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else:
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if not self.samples:
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raise ValueError(
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f"数据集为空（0 样本），无法计算归一化统计量。"
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f"请检查 data_dir={data_dir} 中的 episode 数据。")
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all_actions = np.stack(
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[np.concatenate([s[1], s[2][None]]) for s in self.samples]
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).reshape(-1, self.action_dim)
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self._action_mean = all_actions.mean(axis=0)
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self._action_std = all_actions.std(axis=0) + 1e-8
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def __len__(self):
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return len(self.samples)
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def __getitem__(self, idx):
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image_paths, action_history, target = self.samples[idx]
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frames = []
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for p in image_paths:
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image = Image.open(p).convert("RGB")
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frames.append(self.transform(image))
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images = torch.stack(frames) # (F, 3, S, S)
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action_history = torch.tensor(action_history, dtype=torch.float32)
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target = torch.tensor(target, dtype=torch.float32)
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if self._action_mean is not None:
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action_history = (action_history - self._action_mean) / self._action_std
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target = (target - self._action_mean) / self._action_std
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return {"images": images, "action_history": action_history, "target": target}
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def get_action_stats(self):
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if self._action_mean is not None:
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return np.stack([self._action_mean, self._action_std])
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return None
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@staticmethod
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def _valid_episode_dirs(data_dir: str) -> list:
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data_path = Path(data_dir)
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return sorted([d for d in data_path.iterdir()
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if d.is_dir() and (d / "actions.npy").exists() and (d / "images").is_dir()])
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@staticmethod
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def get_episode_count(data_dir: str) -> int:
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# 与 __init__ 的过滤条件保持一致，避免 split_episodes 索引错位
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return len(ActionDataset._valid_episode_dirs(data_dir))
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class DataRecorder:
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def __init__(self, output_dir: str, action_dim: int):
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self.output_dir = Path(output_dir)
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self.action_dim = action_dim
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self._frame_idx = 0
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self._actions = []
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ep_idx = 0
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for d in self.output_dir.glob("episode_*"):
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try:
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ep_idx = max(ep_idx, int(d.name.split("_")[1]) + 1)
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except (ValueError, IndexError):
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pass
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# 原子创建目录，避免并发 Recorder 分配到同一 episode
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while True:
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self.episode_dir = self.output_dir / f"episode_{ep_idx:04d}"
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self.images_dir = self.episode_dir / "images"
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try:
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self.images_dir.mkdir(parents=True, exist_ok=False)
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break
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except FileExistsError:
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ep_idx += 1
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def record(self, image: np.ndarray, action: np.ndarray):
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if action.shape != (self.action_dim,):
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raise ValueError(
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f"action 维度应为 ({self.action_dim},)，实际为 {action.shape}")
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img = Image.fromarray(image)
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img.save(self.images_dir / f"{self._frame_idx:04d}.jpg", quality=90)
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self._actions.append(action.astype(np.float32))
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self._frame_idx += 1
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def flush(self):
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if self._actions:
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actions = np.array(self._actions, dtype=np.float32)
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np.save(self.episode_dir / "actions.npy", actions)
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return str(self.episode_dir)
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