feat(models): 纯卷积缺陷分类定位模型

面向实时设备部署，全程只用 convolution，不含 attention / transformer。

- backbones: 自研 tiny_conv（深度可分离，默认）与 torchvision 封装（resnet/mobilenet/shufflenet）
- necks: nearest 上采样 + 3x3 卷积，把特征恢复到 stride 4，兼顾小缺陷定位与速度
- heads: anchor-free 中心点定位头（heatmap + wh + offset）与图像级分类头；
  分类头输出维度随 label_mode 切换（多类多热 / 单类互斥含背景位）
- postprocess: 3x3 max-pool 峰值抑制替代 NMS 循环，无循环、可导出 TensorRT / OpenVINO

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

models/layers.py

@@ -0,0 +1,51 @@
+"""基础卷积算子。全部为纯卷积结构，便于导出 ONNX / TensorRT。"""
+
+from __future__ import annotations
+
+import torch
+from torch import nn
+
+
+class ConvBNAct(nn.Sequential):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: int = 3,
+        stride: int = 1,
+        groups: int = 1,
+        act: bool = True,
+    ) -> None:
+        padding = kernel_size // 2
+        layers: list[nn.Module] = [
+            nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, groups=groups, bias=False),
+            nn.BatchNorm2d(out_channels),
+        ]
+        if act:
+            layers.append(nn.ReLU(inplace=True))
+        super().__init__(*layers)
+
+
+class DepthwiseSeparableConv(nn.Sequential):
+    """深度可分离卷积，实时设备上的默认选择。"""
+
+    def __init__(self, in_channels: int, out_channels: int, stride: int = 1) -> None:
+        super().__init__(
+            ConvBNAct(in_channels, in_channels, 3, stride, groups=in_channels),
+            ConvBNAct(in_channels, out_channels, 1),
+        )
+
+
+class ResidualBlock(nn.Module):
+    def __init__(self, in_channels: int, out_channels: int, stride: int = 1) -> None:
+        super().__init__()
+        self.conv1 = ConvBNAct(in_channels, out_channels, 3, stride)
+        self.conv2 = ConvBNAct(out_channels, out_channels, 3, 1, act=False)
+        self.act = nn.ReLU(inplace=True)
+        if stride != 1 or in_channels != out_channels:
+            self.shortcut: nn.Module = ConvBNAct(in_channels, out_channels, 1, stride, act=False)
+        else:
+            self.shortcut = nn.Identity()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.act(self.conv2(self.conv1(x)) + self.shortcut(x))