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
137 lines
6.1 KiB
Python
137 lines
6.1 KiB
Python
import argparse
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import json
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import os
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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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from config import parse_checkpoint_config
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from model import ActionPredictor
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class ActionPredictorInference:
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def __init__(self, checkpoint_path: str, device: str = None):
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checkpoint = torch.load(checkpoint_path, map_location="cpu", weights_only=True)
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cfg = parse_checkpoint_config(checkpoint["config"])
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self._data_cfg = cfg.data
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self._model_cfg = cfg.model
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if device is None:
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device = "cuda" if torch.cuda.is_available() else "cpu"
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self._device = torch.device(device)
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self._model = ActionPredictor(
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self._model_cfg, self._data_cfg, pretrained=False).to(self._device)
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self._model.load_state_dict(checkpoint["model_state_dict"])
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self._model.eval()
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self.action_dim = self._data_cfg.action_dim
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self.history_len = self._data_cfg.history_len
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self.frame_len = self._data_cfg.frame_len
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self._transform = transforms.Compose([
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transforms.Resize((self._data_cfg.image_height,
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self._data_cfg.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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self._action_mean = None
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self._action_std = None
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if self._data_cfg.normalize_actions:
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# 优先使用 checkpoint config 记录的 stats 路径（相对路径按 checkpoint 目录解析），
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# 旧 checkpoint 无该字段时回退到 checkpoint 同目录的 action_stats.npy
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stats_path = self._data_cfg.action_stats_path
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if stats_path:
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if not os.path.isabs(stats_path):
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stats_path = os.path.join(
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os.path.dirname(os.path.abspath(checkpoint_path)), stats_path)
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else:
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stats_path = os.path.join(
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os.path.dirname(os.path.abspath(checkpoint_path)),
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"action_stats.npy")
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if not os.path.exists(stats_path):
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raise FileNotFoundError(
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f"模型训练时启用了动作归一化，但未找到统计文件: {stats_path}")
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stats = np.load(stats_path)
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if stats.shape != (2, self._data_cfg.action_dim):
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raise ValueError(
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f"action_stats.npy 形状应为 (2, {self._data_cfg.action_dim})，"
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f"实际为 {stats.shape}，请检查文件: {stats_path}")
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self._action_mean = torch.from_numpy(stats[0].astype(np.float32)).to(self._device)
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self._action_std = torch.from_numpy(stats[1].astype(np.float32)).to(self._device)
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@torch.no_grad()
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def predict(self, images, action_history: np.ndarray) -> np.ndarray:
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"""images: 长度为 frame_len 的 RGB 图像序列（旧→新，末帧为当前帧）"""
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if not isinstance(images, (list, tuple)):
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images = [images]
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if len(images) != self._data_cfg.frame_len:
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raise ValueError(
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f"images 应为 {self._data_cfg.frame_len} 帧，实际为 {len(images)} 帧。"
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f"冷启动时可重复传入同一帧。")
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expected_shape = (self._data_cfg.history_len, self._data_cfg.action_dim)
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if action_history.shape != expected_shape:
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raise ValueError(
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f"action_history 形状应为 {expected_shape}，实际为 {action_history.shape}")
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frames = []
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for image in images:
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img = Image.fromarray(image) if isinstance(image, np.ndarray) else image
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frames.append(self._transform(img))
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img_tensor = torch.stack(frames).unsqueeze(0).to(self._device) # (1, F, 3, S, S)
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history = torch.tensor(action_history, dtype=torch.float32)
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if self._action_mean is not None:
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history = (history - self._action_mean) / self._action_std
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history = history.unsqueeze(0).to(self._device)
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pred = self._model(img_tensor, history).squeeze(0)
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if self._action_mean is not None:
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pred = pred * self._action_std + self._action_mean
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return pred.cpu().numpy()
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def parse_action_history(s: str, action_dim: int) -> np.ndarray:
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steps = s.split(":")
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history = []
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for step in steps:
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values = [float(v) for v in step.split(",")]
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if len(values) != action_dim:
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raise ValueError(
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f"Expected {action_dim} values per step, got {len(values)}")
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history.append(values)
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return np.array(history, dtype=np.float32)
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def main():
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parser = argparse.ArgumentParser(description="actpred inference")
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parser.add_argument("--model", required=True, help="checkpoint path")
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parser.add_argument("--images", required=True, nargs="+",
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help="按时间顺序（旧→新）传入 frame_len 个图像路径；"
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"冷启动时可重复传同一帧路径")
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parser.add_argument("--action-history", required=True,
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help="冒号分隔各步，每步 action_dim 个值，共 history_len 步。"
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"e.g. '0,0,0,0:1,0,0,0' (action_dim=4, history_len=2)")
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parser.add_argument("--output-format", choices=["json", "raw"], default="json")
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parser.add_argument("--device", type=str, default=None)
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args = parser.parse_args()
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predictor = ActionPredictorInference(args.model, args.device)
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action_dim = predictor.action_dim
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if len(args.images) != predictor.frame_len:
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raise SystemExit(
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f"--images 需传入 {predictor.frame_len} 个路径（旧→新），"
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f"实际为 {len(args.images)} 个。冷启动时可重复传同一帧路径。")
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images = [np.array(Image.open(p).convert("RGB")) for p in args.images]
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history = parse_action_history(args.action_history, action_dim)
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predicted = predictor.predict(images, history)
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if args.output_format == "json":
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print(json.dumps({"predicted_action": predicted.tolist()}, indent=2))
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else:
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print(" ".join(f"{v:.4f}" for v in predicted))
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if __name__ == "__main__":
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main()
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