Align task API and add FunCaptcha support

models/fun_captcha_siamese.py

@@ -0,0 +1,72 @@
+"""
+FunCaptcha 专项 Siamese 模型。
+
+用于 `4_3d_rollball_animals` 这类 challenge：
+- 输入 1: 候选块 candidate (RGB)
+- 输入 2: 参考块 reference (RGB)
+- 输出: 单个匹配 logit，值越大表示越可能为正确候选
+"""
+
+from __future__ import annotations
+
+import torch
+import torch.nn as nn
+
+
+class _SharedEncoder(nn.Module):
+    def __init__(self, in_channels: int = 3, embedding_dim: int = 128):
+        super().__init__()
+        self.features = nn.Sequential(
+            nn.Conv2d(in_channels, 32, kernel_size=3, padding=1),
+            nn.BatchNorm2d(32),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(2, 2),
+
+            nn.Conv2d(32, 64, kernel_size=3, padding=1),
+            nn.BatchNorm2d(64),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(2, 2),
+
+            nn.Conv2d(64, 96, kernel_size=3, padding=1),
+            nn.BatchNorm2d(96),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(2, 2),
+
+            nn.Conv2d(96, 128, kernel_size=3, padding=1),
+            nn.BatchNorm2d(128),
+            nn.ReLU(inplace=True),
+            nn.AdaptiveAvgPool2d(1),
+        )
+        self.proj = nn.Linear(128, embedding_dim)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.features(x)
+        x = x.view(x.size(0), -1)
+        return self.proj(x)
+
+
+class FunCaptchaSiamese(nn.Module):
+    """
+    共享编码器 + 特征对比头。
+
+    输出 raw logits，训练时配合 `BCEWithLogitsLoss` 使用。
+    """
+
+    def __init__(self, in_channels: int = 3, embedding_dim: int = 128):
+        super().__init__()
+        self.encoder = _SharedEncoder(in_channels=in_channels, embedding_dim=embedding_dim)
+        self.head = nn.Sequential(
+            nn.Linear(embedding_dim * 4, 128),
+            nn.ReLU(inplace=True),
+            nn.Dropout(p=0.1),
+            nn.Linear(128, 1),
+        )
+
+    def forward(self, candidate: torch.Tensor, reference: torch.Tensor) -> torch.Tensor:
+        candidate_feat = self.encoder(candidate)
+        reference_feat = self.encoder(reference)
+
+        diff = torch.abs(candidate_feat - reference_feat)
+        prod = candidate_feat * reference_feat
+        features = torch.cat([candidate_feat, reference_feat, diff, prod], dim=1)
+        return self.head(features)