{"version":2,"relationshipURLBase":"https://codingai-lec03.pages.dev/course/notebook-blocks/1086347139a144a386633c49d3bc189a.html","relationshipResolution":"Resolve relative relationship URLs against relationshipURLBase; complete records and absolute URLs are in blocks.","title":"三类逐像素预测的可执行定义 · 实现代码","url":"https://codingai-lec03.pages.dev/course/notebook-blocks/1086347139a144a386633c49d3bc189a.html","markdown":"https://codingai-lec03.pages.dev/course/notebook-blocks/1086347139a144a386633c49d3bc189a.md","pages":[{"id":"reading-opening","title":"三类逐像素预测的可执行定义 · 实现代码","unit":"三类逐像素预测的可执行定义 · 实现代码","unitId":"notebook-blocks/1086347139a144a386633c49d3bc189a","chapter":"course","number":1}],"aliases":[{"oldId":"b-3d2c8a44e93db9bfb501adc6","targetId":"b-3d2c8a44e93db9bfb501adc6","page":"reading-opening","operation":"move","beforeSHA256":"f6dfd09a50caab375f02e0abcc3ff7a454ea579ed8eef0c35b95c95b0619c304","afterSHA256":"f6dfd09a50caab375f02e0abcc3ff7a454ea579ed8eef0c35b95c95b0619c304","reason":"Same source body in authored semantic page"},{"oldId":"b-0603815a820f2000c2ba53bf","targetId":"b-0603815a820f2000c2ba53bf","page":"reading-opening","operation":"move","beforeSHA256":"5156d8285183adcc249cfaf6fbf98b19074793f76d6af218bc817c8880dccc14","afterSHA256":"5156d8285183adcc249cfaf6fbf98b19074793f76d6af218bc817c8880dccc14","reason":"Same source body in authored semantic page"},{"oldId":"b-6fe4697297c44d12b068befd","targetId":"b-6fe4697297c44d12b068befd","page":"reading-opening","operation":"move","beforeSHA256":"e91f24e4e4ca40e2099a0d81d30db6568f4bf2074b499eeee6e3959617bd97ac","afterSHA256":"e91f24e4e4ca40e2099a0d81d30db6568f4bf2074b499eeee6e3959617bd97ac","reason":"Same source body in authored semantic page"},{"oldId":"b-2df99c7d1befb766916f062a","targetId":"b-2df99c7d1befb766916f062a","page":"reading-opening","operation":"move","beforeSHA256":"6f0b115c3c32032428a4c0d5ec4791e16d6b63aa3d643457bed758bf6af2b765","afterSHA256":"6f0b115c3c32032428a4c0d5ec4791e16d6b63aa3d643457bed758bf6af2b765","reason":"Same source body in authored semantic page"},{"oldId":"b-0701191fdddb7e033ad8676b","targetId":"b-0701191fdddb7e033ad8676b","page":"reading-opening","operation":"move","beforeSHA256":"e64d051480b4b1cf6eb274e004d842c611e788263325a81de2897d635d096447","afterSHA256":"e64d051480b4b1cf6eb274e004d842c611e788263325a81de2897d635d096447","reason":"Same source body in authored semantic page"}],"blocks":[{"id":"b-3d2c8a44e93db9bfb501adc6","type":"p","scope":"document","url":"https://codingai-lec03.pages.dev/course/notebook-blocks/1086347139a144a386633c49d3bc189a.html#b-3d2c8a44e93db9bfb501adc6","content":"[LAB 11 · 从 RGB 到像素：读出、FCN 与边界](/course/notebooks/9e268706471944e3a59e6f568f9ce9a3.html)","sha256":"f6dfd09a50caab375f02e0abcc3ff7a454ea579ed8eef0c35b95c95b0619c304","chapter":{"id":"course","title":"三类逐像素预测的可执行定义 · 实现代码"},"unit":{"id":"notebook-blocks/1086347139a144a386633c49d3bc189a","title":"三类逐像素预测的可执行定义 · 实现代码"},"page":{"id":"reading-opening","title":"三类逐像素预测的可执行定义 · 实现代码","number":1},"sectionTitle":"三类逐像素预测的可执行定义 · 实现代码","currentQuestion":"三类逐像素预测的可执行定义 · 实现代码","sourceSlides":[],"knowledgeBlock":null,"formulas":[],"previousBlock":null,"nextBlock":"b-0603815a820f2000c2ba53bf","prerequisites":[],"relatedBlocks":[]},{"id":"b-0603815a820f2000c2ba53bf","type":"h1","scope":"document","url":"https://codingai-lec03.pages.dev/course/notebook-blocks/1086347139a144a386633c49d3bc189a.html#b-0603815a820f2000c2ba53bf","content":"# 三类逐像素预测的可执行定义 · 实现代码","sha256":"5156d8285183adcc249cfaf6fbf98b19074793f76d6af218bc817c8880dccc14","chapter":{"id":"course","title":"三类逐像素预测的可执行定义 · 实现代码"},"unit":{"id":"notebook-blocks/1086347139a144a386633c49d3bc189a","title":"三类逐像素预测的可执行定义 · 实现代码"},"page":{"id":"reading-opening","title":"三类逐像素预测的可执行定义 · 实现代码","number":1},"sectionTitle":"三类逐像素预测的可执行定义 · 实现代码","currentQuestion":"三类逐像素预测的可执行定义 · 实现代码","sourceSlides":[],"knowledgeBlock":null,"formulas":[],"previousBlock":"b-3d2c8a44e93db9bfb501adc6","nextBlock":"b-6fe4697297c44d12b068befd","prerequisites":[{"id":"b-3d2c8a44e93db9bfb501adc6","url":"#b-3d2c8a44e93db9bfb501adc6","role":"problem-context"}],"relatedBlocks":[]},{"id":"b-6fe4697297c44d12b068befd","type":"p","scope":"document","url":"https://codingai-lec03.pages.dev/course/notebook-blocks/1086347139a144a386633c49d3bc189a.html#b-6fe4697297c44d12b068befd","content":"按问题继续","sha256":"e91f24e4e4ca40e2099a0d81d30db6568f4bf2074b499eeee6e3959617bd97ac","chapter":{"id":"course","title":"三类逐像素预测的可执行定义 · 实现代码"},"unit":{"id":"notebook-blocks/1086347139a144a386633c49d3bc189a","title":"三类逐像素预测的可执行定义 · 实现代码"},"page":{"id":"reading-opening","title":"三类逐像素预测的可执行定义 · 实现代码","number":1},"sectionTitle":"三类逐像素预测的可执行定义 · 实现代码","currentQuestion":"三类逐像素预测的可执行定义 · 实现代码","sourceSlides":[],"knowledgeBlock":null,"formulas":[],"previousBlock":"b-0603815a820f2000c2ba53bf","nextBlock":"b-2df99c7d1befb766916f062a","prerequisites":[{"id":"b-0603815a820f2000c2ba53bf","url":"#b-0603815a820f2000c2ba53bf","role":"section-title"},{"id":"b-3d2c8a44e93db9bfb501adc6","url":"#b-3d2c8a44e93db9bfb501adc6","role":"problem-context"}],"relatedBlocks":[]},{"id":"b-2df99c7d1befb766916f062a","type":"p","scope":"document","url":"https://codingai-lec03.pages.dev/course/notebook-blocks/1086347139a144a386633c49d3bc189a.html#b-2df99c7d1befb766916f062a","content":"前景/背景/mIoU口径、RGB到mask训练、FCN与1×1读出及边界","sha256":"6f0b115c3c32032428a4c0d5ec4791e16d6b63aa3d643457bed758bf6af2b765","chapter":{"id":"course","title":"三类逐像素预测的可执行定义 · 实现代码"},"unit":{"id":"notebook-blocks/1086347139a144a386633c49d3bc189a","title":"三类逐像素预测的可执行定义 · 实现代码"},"page":{"id":"reading-opening","title":"三类逐像素预测的可执行定义 · 实现代码","number":1},"sectionTitle":"三类逐像素预测的可执行定义 · 实现代码","currentQuestion":"三类逐像素预测的可执行定义 · 实现代码","sourceSlides":[],"knowledgeBlock":null,"formulas":[],"previousBlock":"b-6fe4697297c44d12b068befd","nextBlock":"b-0701191fdddb7e033ad8676b","prerequisites":[{"id":"b-0603815a820f2000c2ba53bf","url":"#b-0603815a820f2000c2ba53bf","role":"section-title"},{"id":"b-3d2c8a44e93db9bfb501adc6","url":"#b-3d2c8a44e93db9bfb501adc6","role":"problem-context"}],"relatedBlocks":[]},{"id":"b-0701191fdddb7e033ad8676b","type":"pre","scope":"document","url":"https://codingai-lec03.pages.dev/course/notebook-blocks/1086347139a144a386633c49d3bc189a.html#b-0701191fdddb7e033ad8676b","content":"```\n%%writefile lab11_segmentation.py\n\"\"\"LAB11: sampling warm-up, learned pixel readout, and image-to-mask FCN.\nRun: python lab11_segmentation.py --out lab11-results --epochs 20\nDependencies: torch numpy matplotlib. Self-contained; CPU only.\n\"\"\"\n\"\"\"Shared transparent geometry and image construction for LAB10/11; no downloads.\"\"\"\nimport json, hashlib, platform, sys, time\nfrom pathlib import Path\nfrom datetime import datetime, timezone\nimport numpy as np\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\nfrom matplotlib.patches import Rectangle\n\ndef seed_all(seed=7):\n    np.random.seed(seed); torch.manual_seed(seed); torch.set_num_threads(2)\n\ndef iou_matrix(a,b):\n    if len(a)==0 or len(b)==0: return torch.zeros((len(a),len(b)))\n    lo=torch.maximum(a[:,None,:2],b[None,:,:2]); hi=torch.minimum(a[:,None,2:],b[None,:,2:])\n    inter=(hi-lo).clamp(min=0).prod(-1)\n    area_a=(a[:,2:]-a[:,:2]).clamp(min=0).prod(-1)\n    area_b=(b[:,2:]-b[:,:2]).clamp(min=0).prod(-1)\n    return inter/(area_a[:,None]+area_b[None,:]-inter).clamp(min=1e-8)\n\ndef make_scenes(n,seed,size=48,scale=1.0):\n    \"\"\"Independent seed defines original sample, scale changes only object size.\n    Boxes xyxy normalized; red rectangle=1, blue ellipse=2; zero to three objects.\n    Images are rasterized from latent scene, labels are never a network input.\n    \"\"\"\n    rng=np.random.default_rng(seed); xs=[]; ys=[]; masks=[]\n    yy,xx=np.mgrid[:size,:size]\n    for index in range(n):\n        image=rng.uniform(.035,.095,(3,size,size)).astype('float32')\n        num=int(rng.choice([0,1,2,3],p=[.18,.30,.36,.16]))\n        boxes=[]; labels=[]; mask=np.zeros((size,size),np.int64)\n        original=[]\n        for obj in range(num):\n            for attempt in range(60):\n                w,h=rng.integers(9,19,size=2); x=rng.integers(2,size-w-2); y=rng.integers(2,size-h-2)\n                box=np.array([x,y,x+w,y+h],np.float32)/size\n                if not original or iou_matrix(torch.tensor(np.array(original)),torch.tensor(box[None])).max()<.08: break\n            original.append(box); label=int(rng.integers(1,3)); color=rng.uniform(.7,.98)\n            cx,cy=(box[0]+box[2])/2,(box[1]+box[3])/2\n            sw,sh=(box[2]-box[0])*scale,(box[3]-box[1])*scale\n            x1,y1,x2,y2=np.round(np.array([cx-sw/2,cy-sh/2,cx+sw/2,cy+sh/2])*size).astype(int)\n            x2=max(x2,x1+2); y2=max(y2,y1+2)\n            region=(xx>=x1)&(xx<x2)&(yy>=y1)&(yy<y2)\n            if label==2: region &= ((xx+.5-(x1+x2)/2)/((x2-x1)/2))**2+((yy+.5-(y1+y2)/2)/((y2-y1)/2))**2<=1\n            image[:,region]*=.2\n            image[0 if label==1 else 2,region]=color\n            image[1,region]=.15+color*.12\n            mask[region]=label\n            boxes.append([x1/size,y1/size,x2/size,y2/size]); labels.append(label)\n        xs.append(image); masks.append(mask)\n        ys.append({'boxes':torch.tensor(boxes,dtype=torch.float32).reshape(-1,4),'labels':torch.tensor(labels,dtype=torch.long),'sample_id':f'{seed}:{index}'})\n    return torch.from_numpy(np.stack(xs)),ys,torch.from_numpy(np.stack(masks))\n\ndef nms(boxes,scores,threshold=.35):\n    order=torch.argsort(scores,descending=True,stable=True); keep=[]\n    while len(order):\n        i=order[0]; keep.append(i.item()); order=order[1:]\n        if len(order): order=order[iou_matrix(boxes[i:i+1],boxes[order])[0]<=threshold]\n    return torch.tensor(keep,dtype=torch.long)\n\ndef average_precision(predictions,targets,iou_threshold=.5,score_threshold=0):\n    \"\"\"Per-class global score order, max-IoU GT then unused check; all-points AP.\"\"\"\n    result={}; all_tp=all_fp=all_gt=0\n    for cls in [1,2]:\n        gt={i:t['boxes'][t['labels']==cls] for i,t in enumerate(targets)}\n        used={i:set() for i in gt}; total=sum(len(v) for v in gt.values())\n        rows=[]\n        for i,p in enumerate(predictions):\n            for j in range(len(p['scores'])):\n                if int(p['labels'][j])==cls and float(p['scores'][j])>=score_threshold:\n                    rows.append((float(p['scores'][j]),i,j,p['boxes'][j]))\n        rows.sort(key=lambda r:(-r[0],r[1],r[2])); tp=[]; fp=[]\n        for score,i,j,b in rows:\n            ok=False\n            if len(gt[i]):\n                overlaps=iou_matrix(b[None],gt[i])[0]; best=int(overlaps.argmax())\n                ok=float(overlaps[best])>=iou_threshold and best not in used[i]\n                if ok:used[i].add(best)\n            tp.append(int(ok));fp.append(int(not ok))\n        tp=np.cumsum(tp);fp=np.cumsum(fp)\n        recall=tp/max(total,1); precision=tp/np.maximum(tp+fp,1)\n        mr=np.r_[0,recall,1];mp=np.r_[0,precision,0]\n        mp=np.maximum.accumulate(mp[::-1])[::-1];loc=np.where(mr[1:]!=mr[:-1])[0]\n        ap=float(((mr[loc+1]-mr[loc])*mp[loc+1]).sum()) if total else None\n        result[str(cls)]={'AP':ap,'GT':total,'TP':int(tp[-1]) if len(tp) else 0,'FP':int(fp[-1]) if len(fp) else 0}\n        all_tp+=result[str(cls)]['TP'];all_fp+=result[str(cls)]['FP'];all_gt+=total\n    return {'per_class':result,'mAP':float(np.mean([r['AP'] for r in result.values() if r['AP'] is not None])),\n            'precision':all_tp/max(all_tp+all_fp,1),'recall':all_tp/max(all_gt,1),'iou_threshold':iou_threshold,'score_threshold':score_threshold}\n\ndef provenance(path,started):\n    p=Path(path)\n    return {'utc_started':started,'utc_completed':datetime.now(timezone.utc).isoformat(),'execution':'CPU, complete script',\n            'python':sys.version,'torch':torch.__version__,'numpy':np.__version__,'platform':platform.platform(),'threads':torch.get_num_threads(),\n            'code_sha256':hashlib.sha256(p.read_bytes()).hexdigest(),\n            'device':'CPU', 'source_file':p.name}\n\ndef save_json(path,data):Path(path).write_text(json.dumps(data,indent=2,ensure_ascii=False))\n\ndef segmentation_metrics(pred,target):\n    valid=target!=255; t=target[valid];p=pred[valid]\n    cm=torch.bincount(t*3+p,minlength=9).reshape(3,3).double()\n    intersection=cm.diag();union=cm.sum(0)+cm.sum(1)-intersection\n    iou=intersection/union.clamp(min=1)\n    return {'IoU_background':float(iou[0]),'IoU_red_rectangle':float(iou[1]),'IoU_blue_ellipse':float(iou[2]),\n            'mIoU_3_classes':float(iou[union>0].mean()),'pixel_accuracy':float(intersection.sum()/cm.sum()),\n            'valid_pixels':int(valid.sum()),'ignored_pixels':int((~valid).sum()),'confusion_matrix_true_rows':cm.int().tolist()}\n\nimport argparse\n\n\n```","sha256":"e64d051480b4b1cf6eb274e004d842c611e788263325a81de2897d635d096447","chapter":{"id":"course","title":"三类逐像素预测的可执行定义 · 实现代码"},"unit":{"id":"notebook-blocks/1086347139a144a386633c49d3bc189a","title":"三类逐像素预测的可执行定义 · 实现代码"},"page":{"id":"reading-opening","title":"三类逐像素预测的可执行定义 · 实现代码","number":1},"sectionTitle":"三类逐像素预测的可执行定义 · 实现代码","currentQuestion":"三类逐像素预测的可执行定义 · 实现代码","sourceSlides":[],"knowledgeBlock":null,"formulas":[],"previousBlock":"b-2df99c7d1befb766916f062a","nextBlock":null,"prerequisites":[{"id":"b-0603815a820f2000c2ba53bf","url":"#b-0603815a820f2000c2ba53bf","role":"section-title"},{"id":"b-3d2c8a44e93db9bfb501adc6","url":"#b-3d2c8a44e93db9bfb501adc6","role":"problem-context"}],"relatedBlocks":[]}]}