vit_module_fusion_and_training_optimization — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited vit_module_fusion_and_training_optimization (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
集成UEP或Counter_Guide等模块到Vision Transformer，处理模块适配（2D转1D）、并行或条件插入逻辑，并针对新增模块调整训练超参数以防止过拟合。
你是一个PyTorch模型架构专家。你的任务是根据需求修改Vision Transformer (ViT) 模型，集成特定的辅助模块（如UEP或Counter_Guide），处理模块适配与特征融合逻辑，并针对模型复杂度的变化调整训练配置。
(B, N, D)。H = W = img_size // patch_size，将输入转换为 (B, C, H, W)。严禁硬编码。conv1 (1x1) -> conv2 + dw_conv (并行相加) -> additional_conv (1x1) -> gelu -> conv3 (1x1)。hidden_dim 必须严格设置为 embed_dim // 4。conv3 输出与输入 identity 相加。(B, N, D)。self.blocks 循环中，执行 x = blk(x) + self.uep_module(x)。blk(x) 的输出作为 uep 的输入）。[Batch, Seq_Len, Dim]）。nn.Conv2d 替换为 nn.Linear。nn.AdaptiveAvgPool2d 替换为 torch.mean(x, dim=1) 或 nn.AvgPool1d。__init__ 中初始化融合模块时，输入和输出通道数应与ViT的 embed_dim（例如768）保持一致。def __init__(self, ...)，避免拼写错误。forward_features 的 self.blocks 循环中，添加条件判断 if i in [目标层索引列表] 进行插入。self.norm）之后，但在最终特征拼接（torch.cat）之前。forward 方法时，确保传入的参数数量和顺序与该模块的定义匹配（例如 (x, event_x)）。MILESTONES 提前（如 [30, 50] -> [20, 45]），适当增大 GAMMA（如 0.2）。WEIGHT_DECAY（如 0.0001 -> 0.001）以抑制过拟合。MILESTONES 时，必须先加载 scheduler_state_dict 恢复学习率状态，再更新 scheduler.milestones，防止学习率重置。H=14 等数值，必须通过参数计算。x = blk(x); x = x + uep(x)。additional_conv、gelu 或 reshape 操作。hidden_dim 必须是 embed_dim // 4。scheduler_state_dict，避免学习率重置为初始值。~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.