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   shape=right_shape if j==len(slots)-1 else str(rng.choice(['square','circle']))\n        box=(cx-r,cy-r,cx+r,cy+r)\n        if shape=='square':d.rectangle(box,fill=COLORS[color])\n        else:d.ellipse(box,fill=COLORS[color])\n    arr=np.array(img,dtype=np.float32)/255\n    return np.clip(arr+rng.normal(0,.014,arr.shape),0,1).astype(np.float32)\n\ndef make_vqa(n,seed,biased):\n    rng=np.random.default_rng(seed);images=[];answers=[];meta=[]\n    # Training has a 75% red prior. Test covers all 12 condition combinations equally.\n    combos=[(c,col,sh) for c in [1,2] for col in COLORS for sh in ['square','circle']]\n    for i in range(n):\n        if biased:\n            count=int(rng.choice([1,2]));color=str(rng.choice(list(COLORS),p=[.75,.125,.125]));shape=str(rng.choice(['square','circle']))\n        else:count,color,shape=combos[i%len(combos)]\n        images.append(draw_scene(count,color,shape,rng))\n        answers.append([A['one' if count==1 else 'two'],A[color],A[shape]])\n        meta.append({'count':count,'left_color':color,'right_shape':shape})\n    return torch.tensor(np.stack(images)).permute(0,3,1,2),torch.tensor(answers),meta\nXtr,Ytr,train_meta=make_vqa(720,14031,True)\nXte,Yte,test_meta=make_vqa(180,14032,False)\ntrain_hashes={hashlib.sha256(x.numpy().tobytes()).hexdigest() for x in Xtr}\nassert not train_hashes.intersection(hashlib.sha256(x.numpy().tobytes()).hexdigest() for x in Xte)\n# Metadata remains only in this data/label construction and plot captions.\n# model.forward receives tensors (pixels, question_id), never these dictionaries.\nnp.savez_compressed(OUT/'lab14-data.npz',train_images=Xtr.numpy(),train_answers=Ytr.numpy(),test_images=Xte.numpy(),test_answers=Yte.numpy())\nprint('train/test image counts:',len(Xtr),len(Xte),'pairs:',Ytr.numel(),Yte.numel())\nprint('train left-color prior:',{c:sum(m['left_color']==c for m in train_meta)/len(train_meta) for c in COLORS})\nprint('test left-color prior:',{c:sum(m['left_color']==c for m in test_meta)/len(test_meta) for c in COLORS})\n```","sha256":"36c815acc00c365f774968586f380e1fed0fcacf2b8ed3f268abf3b2845d3299","chapter":{"id":"course","title":"2. 先按图像切分，避免同一图的不同问题泄漏 · 实现代码"},"unit":{"id":"notebook-blocks/ef7e95916f594784b1269ebf22f89911","title":"2. 先按图像切分，避免同一图的不同问题泄漏 · 实现代码"},"page":{"id":"reading-opening","title":"2. 先按图像切分，避免同一图的不同问题泄漏 · 实现代码","number":1},"sectionTitle":"2. 先按图像切分，避免同一图的不同问题泄漏 · 实现代码","currentQuestion":"2. 先按图像切分，避免同一图的不同问题泄漏 · 实现代码","sourceSlides":[],"knowledgeBlock":null,"formulas":[],"previousBlock":"b-d3062bee3b0d69afb2821d70","nextBlock":null,"prerequisites":[{"id":"b-ac46e9a97c7f3d6e8d271ca1","url":"#b-ac46e9a97c7f3d6e8d271ca1","role":"section-title"},{"id":"b-300b342bd0a5ee230770ba6c","url":"#b-300b342bd0a5ee230770ba6c","role":"problem-context"}],"relatedBlocks":[]}]}