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nn.Conv2d(32,48,3,1,1),nn.ReLU(),nn.Conv2d(48,48,3,1,1),nn.ReLU())\n        self.class_head=nn.Conv2d(48,2*3,3,padding=1);self.box_head=nn.Conv2d(48,2*4,3,padding=1)\n    def forward(self,x):\n        z=self.backbone(x);b=x.shape[0]\n        return self.class_head(z).permute(0,2,3,1).reshape(b,-1,3),self.box_head(z).permute(0,2,3,1).reshape(b,-1,4)\n\ndef detection_loss(logits,offsets,classes,targets):\n    ce=F.cross_entropy(logits.transpose(1,2),classes,ignore_index=-1,reduction='none')\n    positive=classes>0;chosen=positive.clone()\n    for i in range(len(classes)):\n        negatives=torch.where(classes[i]==0)[0]; k=min(len(negatives),max(12,3*int(positive[i].sum())))\n        chosen[i,negatives[torch.argsort(ce[i,negatives],descending=True)[:k]]]=True\n    cls=ce[chosen].mean()\n    loc=F.smooth_l1_loss(offsets[positive],targets[positive],reduction='mean') if positive.any() else offsets.sum()*0\n    return cls+3*loc,cls,loc\n\n@torch.no_grad()\ndef predict(model,images,anchors,score=.01,nms_iou=.35):\n    model.eval();result=[]\n    for batch in images.split(32):\n        logits,offsets=model(batch); probs=logits.softmax(-1)\n        for p,o in zip(probs,offsets):\n            boxes=decode(anchors,o); all_b=[];all_s=[];all_l=[]\n            for cls in [1,2]:\n                indices=torch.where(p[:,cls]>=score)[0]\n                keep=nms(boxes[indices],p[indices,cls],nms_iou);indices=indices[keep]\n                all_b.append(boxes[indices]);all_s.append(p[indices,cls]);all_l.append(torch.full((len(indices),),cls))\n            result.append({'boxes':torch.cat(all_b),'scores':torch.cat(all_s),'labels':torch.cat(all_l)})\n    return result\n\n\n```","sha256":"d248fdb1d8ed367c289941e0f3eb2ce08c9c0e843c28541e882f8d24050567f2","chapter":{"id":"course","title":"从共享特征到两个输出头 · 实现代码"},"unit":{"id":"notebook-blocks/30839f038a25415ea1d0eb6e8fc1d456","title":"从共享特征到两个输出头 · 实现代码"},"page":{"id":"reading-opening","title":"从共享特征到两个输出头 · 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