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
273 lines
13 KiB
Python
273 lines
13 KiB
Python
"""导出 Hailo 部署版 ONNX：DFC 友好图结构
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训练态 ONNX 对 DFC 不友好：5D 图像输入、AdaptiveAvgPool2d（被
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torch.export 展开成 Loop 节点）、MultiheadAttention 的 head 拆分
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Reshape 序列。此脚本逐项改造（权重数值完全不变）：
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1. 图像输入拆为 F 个独立 (1,H,W,3) NHWC 输入，按帧展开 CNN 分支，
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各分支持有独立权重副本（共享 initializer 会被 DFC 误融合）
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2. AdaptiveAvgPool2d 换成等价的固定 kernel AvgPool2d
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3. 自注意力手工展开为每头独立 Linear（128→32），无 head reshape；
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pre-LN 残差结构与训练态 nn.TransformerEncoderLayer(norm_first=True)
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逐位等价
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"""
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import argparse
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import os
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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 config import parse_checkpoint_config
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from model import ActionPredictor
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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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MAX_TOKEN_ABS = 100.0
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MERGE_RATIO_WARN = 32.0
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class ManualAttnBlock(nn.Module):
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"""等价替换 nn.TransformerEncoderLayer(d=128, nhead=4, ff=256, norm_first=True)"""
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def __init__(self, layer: nn.TransformerEncoderLayer):
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super().__init__()
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d = layer.self_attn.embed_dim
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nh = layer.self_attn.num_heads
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self.head_dim = d // nh
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self.nh = nh
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self.scale = self.head_dim ** -0.5
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ipw = layer.self_attn.in_proj_weight # (3d, d) = [Wq; Wk; Wv]
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ipb = layer.self_attn.in_proj_bias
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self.q_proj = nn.ModuleList()
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self.k_proj = nn.ModuleList()
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self.v_proj = nn.ModuleList()
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for h in range(nh):
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sl = slice(h * self.head_dim, (h + 1) * self.head_dim)
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slices = (sl, slice(d + sl.start, d + sl.stop),
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slice(2 * d + sl.start, 2 * d + sl.stop))
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for proj, ws in ((self.q_proj, slices[0]), (self.k_proj, slices[1]),
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(self.v_proj, slices[2])):
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lin = nn.Linear(d, self.head_dim)
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lin.weight.data.copy_(ipw[ws])
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proj.append(lin)
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for h in range(nh):
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self.q_proj[h].bias.data.copy_(ipb[h * self.head_dim:(h + 1) * self.head_dim])
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self.k_proj[h].bias.data.copy_(ipb[d + h * self.head_dim:d + (h + 1) * self.head_dim])
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self.v_proj[h].bias.data.copy_(ipb[2 * d + h * self.head_dim:2 * d + (h + 1) * self.head_dim])
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self.out_proj = layer.self_attn.out_proj
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self.norm1 = layer.norm1
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self.norm2 = layer.norm2
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self.fc1 = layer.linear1
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self.fc2 = layer.linear2
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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# x: (1, 1, S, d)，seq 占 W 维，pre-LN 自注意力 + 残差
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n = self.norm1(x)
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heads = []
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for h in range(self.nh):
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q = self.q_proj[h](n)
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k = self.k_proj[h](n)
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v = self.v_proj[h](n)
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s = torch.matmul(q, k.transpose(-1, -2)) * self.scale
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a = torch.softmax(s, dim=-1)
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heads.append(torch.matmul(a, v))
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attn = self.out_proj(torch.cat(heads, dim=3)) # feature 维 concat
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x = x + attn
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x = x + self.fc2(torch.relu(self.fc1(self.norm2(x))))
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return x
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class DeployWrapper(nn.Module):
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def __init__(self, model: ActionPredictor):
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super().__init__()
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import copy
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m = model
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if not hasattr(m.image_encoder, "net"):
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raise ValueError(
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"部署版导出仅支持 backbone=simple（MobileNet 的 DFC 改造未实现）")
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ref_cnn = nn.Sequential(*list(m.image_encoder.net.children())[:-1])
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self.cnns = nn.ModuleList(
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[copy.deepcopy(ref_cnn) for _ in range(m._frame_len)])
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# img_proj ∘ AvgPool == AvgPool ∘ Conv1x1（均为线性算子，可交换）。
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# 把 img_proj 的权重折进每分支末尾的 1×1 卷积，偏置单独用加法，
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# 避免 DFC 对 3D Linear 的错误卷积融合
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self.proj_convs = nn.ModuleList()
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for _ in range(m._frame_len):
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c = nn.Conv2d(m.img_proj.in_features, m.img_proj.out_features, 1, bias=False)
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c.weight.data.copy_(m.img_proj.weight.data.view(
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m.img_proj.out_features, m.img_proj.in_features, 1, 1))
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self.proj_convs.append(c)
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self.proj_bias = nn.Parameter(m.img_proj.bias.data.clone())
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# 固定 kernel AvgPool 须与训练态 AdaptiveAvgPool2d(1) 逐值等价：
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# kernel 取 ref_cnn 的真实输出尺寸。H//16 只在分辨率能被 16 整除
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# 时成立（1080 高度下卷积输出 68 行，按 67 取 kernel 会静默丢弃
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# 末行，破坏与训练图的数值一致）
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with torch.no_grad():
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probe = ref_cnn(torch.zeros(
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1, 3, m._image_height, m._image_width))
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self.pool = nn.AvgPool2d((probe.shape[2], probe.shape[3]))
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# act_proj 权重注册为常量，用显式 MatMul+Add 代替 Linear，
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# 避免 DFC 对 3D Linear 的错误卷积融合
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self.act_w = nn.Parameter(m.act_proj.weight.data.t().clone()) # (D, d)
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self.act_b = nn.Parameter(m.act_proj.bias.data.clone())
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# pred_token 改为图输入（常量 initializer 会被 DFC 误转置），
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# 位置编码经 concat 后的 ew_add 广播可被正确推断
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self.pos_embed4 = nn.Parameter(m.pos_embed.data.unsqueeze(1).clone()) # (1, 1, S, d)
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self.blocks = nn.ModuleList(
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[ManualAttnBlock(l) for l in model.encoder.layers])
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self.head = m.head
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self._frame_len = m._frame_len
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def _frame_feat(self, frame_nhwc: torch.Tensor, i: int) -> torch.Tensor:
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x = self.cnns[i](frame_nhwc.permute(0, 3, 1, 2))
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x = self.proj_convs[i](x) # 1x1 conv ≡ 池化后的 img_proj
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return self.pool(x) # (1, d, 1, 1) NCHW
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def forward(self, frame0: torch.Tensor, frame1: torch.Tensor, frame2: torch.Tensor,
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action_history: torch.Tensor, pred_token: torch.Tensor) -> torch.Tensor:
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# 全程 (1, 1, S, d) 布局：seq 占 W 维，避免任何 transpose
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frames = [frame0, frame1, frame2]
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feats = [self._frame_feat(frames[i], i).permute(0, 2, 3, 1)
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for i in range(self._frame_len)] # (1, 1, 1, d) NHWC
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img_tokens = torch.cat(feats, dim=2) + self.proj_bias # (1, 1, F, d)
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act_tokens = torch.matmul(action_history, self.act_w) + self.act_b # (1, 1, H, d)
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tokens = torch.cat([img_tokens, act_tokens, pred_token], dim=2) # (1, 1, S, d)
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tokens = tokens + self.pos_embed4
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for blk in self.blocks:
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tokens = blk(tokens)
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return self.head(tokens[:, :, -1:, :]).squeeze(1) # (1, 1, action_dim)
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def check_token_ranges(wrapper: "DeployWrapper", frame_len: int, height: int,
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width: int, history_len: int, action_dim: int,
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pred_token: torch.Tensor, max_abs: float = MAX_TOKEN_ABS,
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action_stats_path: str = None):
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"""导出前检查 token 量级：幅度过大时 uint8 量化会把小信号压成 0
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预训练用全零图像占位 → BN running_var 停在初始小值 → 推理时每级
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BN 放大 ~50 倍，图像 token 可达 ±2e5。DFC 在 token merge 点按最大
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幅度分配共享量化 scale（实测 scale=1965.92，pred_token/act_tokens
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全部量化成 0），HEF 输出对任何输入恒定。必须在编译前拦截。
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归一化模型（normalize_actions）的 HEF 输入是 z-score 后的历史，
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检查输入须与推理端一致。
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"""
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rng = np.random.default_rng(42)
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raw = rng.integers(0, 256, size=(height, width, 3), dtype=np.uint8)
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norm = (raw.astype(np.float32) / 255.0 - IMAGENET_MEAN) / IMAGENET_STD
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frames = [torch.tensor(norm[None]) for _ in range(frame_len)]
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history = rng.uniform(0.0, 500.0, size=(1, 1, history_len, action_dim))
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if action_stats_path is not None:
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stats = np.load(action_stats_path)
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if stats.shape != (2, action_dim):
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raise ValueError(
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f"action stats 形状应为 (2, {action_dim})，"
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f"实际为 {stats.shape}: {action_stats_path}")
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# 归一化模型直接在 z 空间采样：raw 均匀采样会在零方差列上
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# 产生天文数字 z 值（实际推理中这些列恒为 0，z 恒为 0）
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history = rng.normal(0.0, 1.5, size=(1, 1, history_len, action_dim))
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history = torch.tensor(history, dtype=torch.float32)
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with torch.no_grad():
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feats = [wrapper._frame_feat(f, i).permute(0, 2, 3, 1)
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for i, f in enumerate(frames)]
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img_tokens = torch.cat(feats, dim=2) + wrapper.proj_bias
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act_tokens = torch.matmul(history, wrapper.act_w) + wrapper.act_b
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img_max = img_tokens.abs().max().item()
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act_max = act_tokens.abs().max().item()
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pred_max = pred_token.abs().max().item()
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print(f"token 量级检查: |img|={img_max:.4g} |act|={act_max:.4g} "
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f"|pred|={pred_max:.4g}（图像上限 {max_abs}）")
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if img_max > max_abs:
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raise SystemExit(
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f"错误: 图像 token 幅度 {img_max:.3g} 超过可量化上限 {max_abs}。"
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f"checkpoint 的 BN 统计退化（预训练用全零图像占位，running_var "
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f"停在初始小值），uint8 量化会把小信号全部压成 0、HEF 输出恒定。"
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f"请先用真实/随机图像数据微调后再导出。")
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if act_max > MERGE_RATIO_WARN * max(img_max, 1.0):
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print(f"WARNING: act_tokens 幅度 {act_max:.3g} 远大于 img_tokens "
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f"{img_max:.3g}，merge 点共享 scale 会粗量化图像路径；"
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f"建议用 --normalize-actions 训练（动作归一化）压小 act_tokens")
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def main():
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parser = argparse.ArgumentParser(description="Export Hailo-friendly ONNX")
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parser.add_argument("--model", required=True, help="checkpoint path")
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parser.add_argument("--output", default="checkpoints/actpred_deploy.onnx")
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parser.add_argument("--pred-token-out", default=None,
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help="保存学习到的 pred_token 到 .npy（HEF 推理端喂入用，"
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"配合抖动校准避免常量量化退化）")
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args = parser.parse_args()
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checkpoint = torch.load(args.model, map_location="cpu", weights_only=True)
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cfg = parse_checkpoint_config(checkpoint["config"])
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model = ActionPredictor(cfg.model, cfg.data, pretrained=False)
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model.load_state_dict(checkpoint["model_state_dict"])
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model.eval()
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wrapper = DeployWrapper(model).eval()
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h, w = cfg.data.image_height, cfg.data.image_width
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f = cfg.data.frame_len
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stats_path = None
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if cfg.data.normalize_actions:
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# 与 predict.py 语义一致：相对路径按 checkpoint 目录解析
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stats_path = cfg.data.action_stats_path
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if not stats_path:
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stats_path = os.path.join(
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os.path.dirname(os.path.abspath(args.model)),
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"action_stats.npy")
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elif 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(args.model)), stats_path)
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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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check_token_ranges(wrapper, f, h, w, cfg.data.history_len,
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cfg.data.action_dim, model.pred_token.data.unsqueeze(1),
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action_stats_path=stats_path)
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dummy_frames = [torch.randn(1, h, w, 3) for _ in range(f)]
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dummy_history = torch.randn(1, 1, cfg.data.history_len, cfg.data.action_dim)
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dummy_pred = model.pred_token.data.unsqueeze(1).clone() # (1, 1, 1, d)
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with torch.no_grad():
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ref = wrapper(*dummy_frames, dummy_history, dummy_pred)
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torch.onnx.export(
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wrapper, (*dummy_frames, dummy_history, dummy_pred),
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args.output,
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input_names=[f"frame{i}" for i in range(f)] + ["action_history", "pred_token"],
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output_names=["predicted_action"],
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opset_version=18,
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)
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print(f"部署版 ONNX 已导出: {args.output}（{f} 个帧输入 (1,{h},{w},3) NHWC + action_history + pred_token）")
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if args.pred_token_out:
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import numpy as np
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np.save(args.pred_token_out, model.pred_token.data.unsqueeze(1).numpy())
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print(f"pred_token 已保存: {args.pred_token_out} (1,1,1,{model.pred_token.shape[-1]})")
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stacked = torch.stack([fr.permute(0, 3, 1, 2) for fr in dummy_frames], dim=1)
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with torch.no_grad():
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orig_out = model(stacked, dummy_history.squeeze(1))
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print(f"wrapper 与原模型最大差异: {(ref.squeeze(1) - orig_out).abs().max():.6e}")
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try:
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import onnxruntime as ort
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import numpy as np
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sess = ort.InferenceSession(args.output)
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feeds = {f"frame{i}": dummy_frames[i].numpy() for i in range(f)}
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feeds["action_history"] = dummy_history.numpy()
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feeds["pred_token"] = dummy_pred.numpy()
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ort_out = sess.run(None, feeds)[0]
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print(f"ONNX 一致性验证通过（最大差异: {np.abs(ort_out - ref.numpy()).max():.6e}）")
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except ImportError:
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pass
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if __name__ == "__main__":
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main()
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