feat: 导入 IRSTD 研究代码与文档（小目标检测）

- 新增 irstd 核心包、scripts、tests、configs
- 新增研究文档：literature_review / mathematical_analysis / novelty_matrix / experiment_plan / research_decision / research_log / manuscript_outline
- 新增 evidence、results 数据与审计产物
- 新增 pyproject.toml、requirements、.gitignore

irstd/models.py

@@ -0,0 +1,51 @@
+"""Original compact baseline, NOT a reproduction of MSHNet or DNANet."""
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+class TinyUNet(nn.Module):
+    def __init__(self, width=16):
+        super().__init__()
+        def block(a,b):
+            return nn.Sequential(nn.Conv2d(a,b,3,padding=1),nn.ReLU(),
+                                 nn.Conv2d(b,b,3,padding=1),nn.ReLU())
+        self.enc = block(1,width)
+        self.mid = block(width,width*2)
+        self.dec = block(width*3,width)
+        self.head = nn.Conv2d(width,1,1)
+    def forward(self,x):
+        a = self.enc(x)
+        b = self.mid(F.max_pool2d(a,2,ceil_mode=True))
+        b = F.interpolate(b,size=a.shape[-2:],mode='bilinear',align_corners=False)
+        return self.head(self.dec(torch.cat((a,b),1)))
+
+class CandidateGate(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.net=nn.Sequential(nn.Linear(5,16),nn.ReLU(),nn.Linear(16,1))
+    def forward(self,x):
+        return self.net(x).squeeze(-1)
+
+def segmentation_loss(logits, target, name='bce_dice'):
+    p = logits.sigmoid()
+    bce = F.binary_cross_entropy_with_logits(logits,target)
+    dims=tuple(range(1,p.ndim)); eps=1e-6
+    inter=(p*target).sum(dims)
+    if name=='bce': return bce
+    if name=='focal':
+        pt=p*target+(1-p)*(1-target)
+        weight=.25*target+.75*(1-target)
+        return (weight*(1-pt).pow(2)*F.binary_cross_entropy_with_logits(logits,target,reduction='none')).mean()
+    dice=(1-(2*inter+eps)/(p.sum(dims)+target.sum(dims)+eps)).mean()
+    if name=='dice': return dice
+    if name=='bce_dice': return bce+dice
+    if name=='iou': return (1-(inter+eps)/(p.sum(dims)+target.sum(dims)-inter+eps)).mean()
+    raise ValueError(name)
+
+def gate_loss(logits, labels, areas, dual, alpha, size_weight=True, tau=1.):
+    import math
+    pos=labels>0.5; neg=~pos
+    weight=areas.clamp_min(1).rsqrt().clamp(.1,1) if size_weight else torch.ones_like(areas)
+    positive=(F.softplus(-logits[pos])*weight[pos]).sum()/max(1,int(pos.sum()))
+    upper=F.softplus(logits[neg]/tau).sum()/math.log(2)
+    return positive+dual*(upper-alpha), upper