{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.11.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceId":12948212,"sourceType":"datasetVersion","datasetId":8194097},{"sourceId":13287683,"sourceType":"datasetVersion","datasetId":8421324}],"dockerImageVersionId":31154,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# Cell 1 — cấu hình chung\nimport os\nfrom pathlib import Path\nimport random\nimport numpy as np\nimport torch\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom collections import Counter\n\n# Thay path nếu cần (theo cấu trúc bạn chia sẻ)\nDATASET_DIR = Path(\"/kaggle/input/fas-datasets/publics_data_train/publics_data_train\")\nOUT_DIR = Path(\"/kaggle/working/fas_6class\")\nOUT_DIR.mkdir(parents=True, exist_ok=True)\n\nSELECTED_CLASSES = [\n \"live\",\n \"cutout\",\n \"mask\",\n \"mask3d\",\n \"PC_Replay\",\n \"Smartphone_Replay\"\n]\n\nLABEL_MAP = {cls: idx for idx, cls in enumerate(SELECTED_CLASSES)}\nINV_LABEL_MAP = {v:k for k,v in LABEL_MAP.items()}\n\n# Hyperparams\nFRAMES_PER_CLIP = 16\nFRAME_SIZE = 112\nBATCH_SIZE = 4 # tăng giảm tuỳ GPU\nEPOCHS = 5\nLR = 1e-4\nSEED = 42\nMAX_PER_CLASS = 50 # giới hạn tối đa mỗi lớp\n\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\nprint(\"DEVICE:\", device, \"LABEL_MAP:\", LABEL_MAP)\n\nrandom.seed(SEED)\nnp.random.seed(SEED)\ntorch.manual_seed(SEED)\nif device == \"cuda\":\n torch.cuda.manual_seed_all(SEED)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-16T02:54:28.028655Z","iopub.execute_input":"2025-10-16T02:54:28.028834Z","iopub.status.idle":"2025-10-16T02:54:32.978518Z","shell.execute_reply.started":"2025-10-16T02:54:28.028818Z","shell.execute_reply":"2025-10-16T02:54:32.977696Z"}},"outputs":[{"name":"stdout","text":"DEVICE: cuda LABEL_MAP: {'live': 0, 'cutout': 1, 'mask': 2, 'mask3d': 3, 'PC_Replay': 4, 'Smartphone_Replay': 5}\n","output_type":"stream"}],"execution_count":1},{"cell_type":"code","source":"# Cell 2 — Thu thập danh sách video (lọc 6 nhãn)\n# ==============================\ndef collect_videos(base_path: Path, selected_classes):\n videos = []\n labels = []\n for cls in selected_classes:\n if cls == \"live\":\n cls_path = base_path / \"live\"\n else:\n cls_path = base_path / f\"spoof/level_1/{cls}\"\n if not cls_path.exists():\n print(\"⚠️ Warning: not found\", cls_path)\n continue\n # tìm các định dạng video\n for ext in (\"*.mp4\", \"*.avi\", \"*.MOV\", \"*.MP4\", \"*.AVI\"):\n for p in cls_path.glob(f\"**/{ext}\"):\n videos.append(str(p))\n labels.append(cls)\n return videos, labels\n\nvideos_all, labels_all = collect_videos(DATASET_DIR, SELECTED_CLASSES)\nprint(\"Total videos found:\", len(videos_all))\nprint(\"Counts per class:\", Counter(labels_all))\n\n# ==============================\n# Giới hạn số video tối đa mỗi lớp\n# ==============================\nbalanced_videos = []\nbalanced_labels = []\n\nfor cls in SELECTED_CLASSES:\n cls_videos = [v for v, l in zip(videos_all, labels_all) if l == cls]\n np.random.seed(SEED)\n np.random.shuffle(cls_videos)\n cls_videos = cls_videos[:MAX_PER_CLASS] # lấy tối đa 50\n balanced_videos.extend(cls_videos)\n balanced_labels.extend([cls] * len(cls_videos))\n\nprint(\"After balancing:\")\nprint(Counter(balanced_labels))\n\n# ==============================\n# Cell 3 — Chia train/val (80/20, stratified)\n# ==============================\npaths = np.array(balanced_videos)\nlabs = np.array([LABEL_MAP[l] for l in balanced_labels])\n\ntrain_idx, val_idx = train_test_split(\n np.arange(len(paths)),\n test_size=0.20,\n stratify=labs,\n random_state=SEED\n)\n\ntrain_videos = paths[train_idx].tolist()\ntrain_labels = labs[train_idx].tolist()\nval_videos = paths[val_idx].tolist()\nval_labels = labs[val_idx].tolist()\n\nprint(f\"Train videos: {len(train_videos)}, Val videos: {len(val_videos)}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-16T02:54:33.013599Z","iopub.execute_input":"2025-10-16T02:54:33.013867Z","iopub.status.idle":"2025-10-16T02:54:35.685251Z","shell.execute_reply.started":"2025-10-16T02:54:33.013833Z","shell.execute_reply":"2025-10-16T02:54:35.684425Z"}},"outputs":[{"name":"stdout","text":"Total videos found: 1011\nCounts per class: Counter({'mask': 525, 'live': 238, 'mask3d': 106, 'cutout': 68, 'Smartphone_Replay': 44, 'PC_Replay': 30})\nAfter balancing:\nCounter({'live': 50, 'cutout': 50, 'mask': 50, 'mask3d': 50, 'Smartphone_Replay': 44, 'PC_Replay': 30})\nTrain videos: 219, Val videos: 55\n","output_type":"stream"}],"execution_count":4},{"cell_type":"code","source":"# Cell 4 — Dataset\nimport cv2\nimport torch\nfrom torch.utils.data import Dataset\nimport torchvision.transforms as T\nimport numpy as np\nimport random\nfrom PIL import Image\n\n# Basic transforms on single frame (applied per-frame)\nframe_transform = T.Compose([\n T.ToTensor(), # from H,W,C in 0..255 to C,H,W in 0..1\n T.Normalize(mean=[0.45,0.45,0.45], std=[0.225,0.225,0.225])\n])\n\ndef sample_frames_from_video(path, num_frames=16, resize=(112,112)):\n cap = cv2.VideoCapture(path)\n total = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n if total <= 0:\n cap.release()\n blank = np.zeros((num_frames, resize[1], resize[0], 3), dtype=np.uint8)\n return blank\n # chọn các frame cách đều\n if total <= num_frames:\n idxs = list(range(total)) + [total-1]*(num_frames-total)\n else:\n start = random.randint(0, max(0, total - num_frames))\n idxs = list(range(start, start + num_frames))\n frames = []\n for fid in idxs:\n cap.set(cv2.CAP_PROP_POS_FRAMES, fid)\n ret, frame = cap.read()\n if not ret:\n if frames:\n frames.append(frames[-1])\n else:\n frames.append(np.zeros((resize[1],resize[0],3),dtype=np.uint8))\n continue\n frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n frame = cv2.resize(frame, resize)\n frames.append(frame)\n cap.release()\n return np.array(frames) # shape (T, H, W, C)\n\nclass VideoSegmentDataset(Dataset):\n def __init__(self, video_paths, labels, frames_per_clip=16, transform=frame_transform, augment=False):\n self.video_paths = video_paths\n self.labels = labels\n self.frames_per_clip = frames_per_clip\n self.transform = transform\n self.augment = augment\n self.color_jitter = T.ColorJitter(0.15,0.15,0.15,0.05)\n \n def __len__(self):\n return len(self.video_paths)\n \n def __getitem__(self, idx):\n path = self.video_paths[idx]\n label = self.labels[idx]\n frames = sample_frames_from_video(path, self.frames_per_clip)\n\n # augmentation: random horizontal flip\n if self.augment and random.random() < 0.5:\n frames = np.ascontiguousarray(frames[:, :, ::-1, :]) # ✅ copy tránh stride âm\n\n out_frames = []\n for f in frames:\n pil = Image.fromarray(f.copy()) # ✅ dùng .copy() đảm bảo stride dương\n img = self.transform(pil) # C H W\n if self.augment and random.random() < 0.3:\n img = self.color_jitter(img)\n out_frames.append(img)\n \n out = torch.stack(out_frames) # [T,C,H,W]\n out = out.permute(1,0,2,3) # [C,T,H,W]\n return out, torch.tensor(label, dtype=torch.long)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-16T02:54:35.686099Z","iopub.execute_input":"2025-10-16T02:54:35.686361Z","iopub.status.idle":"2025-10-16T02:54:40.117076Z","shell.execute_reply.started":"2025-10-16T02:54:35.686343Z","shell.execute_reply":"2025-10-16T02:54:40.116420Z"}},"outputs":[],"execution_count":5},{"cell_type":"code","source":"# Cell 5 — DataLoader\nfrom torch.utils.data import DataLoader\n\ntrain_ds = VideoSegmentDataset(train_videos, train_labels, augment=True)\nval_ds = VideoSegmentDataset(val_videos, val_labels, augment=False)\n\ntrain_loader = DataLoader(train_ds, batch_size=BATCH_SIZE, shuffle=True, num_workers=2, pin_memory=True)\nval_loader = DataLoader(val_ds, batch_size=BATCH_SIZE, shuffle=False, num_workers=2, pin_memory=True)\n\n# quick sanity batch\nx, y = next(iter(train_loader))\nprint(\"Batch shape (C,T,H,W):\", x.shape, \"Labels:\", y)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-16T02:54:40.117902Z","iopub.execute_input":"2025-10-16T02:54:40.118565Z","iopub.status.idle":"2025-10-16T02:55:31.780308Z","shell.execute_reply.started":"2025-10-16T02:54:40.118535Z","shell.execute_reply":"2025-10-16T02:55:31.776917Z"}},"outputs":[{"name":"stdout","text":"Batch shape (C,T,H,W): torch.Size([4, 3, 16, 112, 112]) Labels: tensor([2, 4, 0, 1])\n","output_type":"stream"}],"execution_count":6},{"cell_type":"code","source":"# Cell 6 — Model + opt\nimport torch.nn as nn\nfrom torchvision.models.video import r3d_18\n\nmodel = r3d_18(pretrained=True)\n# replace fc\nin_features = model.fc.in_features\nmodel.fc = nn.Linear(in_features, len(SELECTED_CLASSES))\nmodel = model.to(device)\n\noptimizer = torch.optim.AdamW(model.parameters(), lr=LR, weight_decay=1e-4)\nscheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=EPOCHS)\ncriterion = nn.CrossEntropyLoss()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-16T02:55:31.782341Z","iopub.execute_input":"2025-10-16T02:55:31.782716Z","iopub.status.idle":"2025-10-16T02:55:33.324894Z","shell.execute_reply.started":"2025-10-16T02:55:31.782672Z","shell.execute_reply":"2025-10-16T02:55:33.324062Z"}},"outputs":[{"name":"stderr","text":"/usr/local/lib/python3.11/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n warnings.warn(\n/usr/local/lib/python3.11/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=R3D_18_Weights.KINETICS400_V1`. You can also use `weights=R3D_18_Weights.DEFAULT` to get the most up-to-date weights.\n warnings.warn(msg)\nDownloading: \"https://download.pytorch.org/models/r3d_18-b3b3357e.pth\" to /root/.cache/torch/hub/checkpoints/r3d_18-b3b3357e.pth\n100%|██████████| 127M/127M [00:00<00:00, 194MB/s] \n","output_type":"stream"}],"execution_count":7},{"cell_type":"code","source":"# Cell 7 — Training + validation\nfrom tqdm.auto import tqdm\nfrom sklearn.metrics import classification_report, confusion_matrix\n\nscaler = torch.cuda.amp.GradScaler(enabled=(device==\"cuda\"))\nbest_val_acc = 0.0\nbest_path = OUT_DIR / \"best_r3d18.pth\"\n\nfor epoch in range(1, EPOCHS+1):\n # Train\n model.train()\n running_loss = 0.0\n running_correct = 0\n total = 0\n pbar = tqdm(train_loader, desc=f\"Epoch {epoch}/{EPOCHS} Train\")\n for x, y in pbar:\n x = x.to(device, non_blocking=True)\n y = y.to(device, non_blocking=True)\n optimizer.zero_grad()\n with torch.cuda.amp.autocast(enabled=(device==\"cuda\")):\n logits = model(x)\n loss = criterion(logits, y)\n scaler.scale(loss).backward()\n scaler.step(optimizer)\n scaler.update()\n running_loss += loss.item() * x.size(0)\n preds = logits.argmax(dim=1)\n running_correct += (preds == y).sum().item()\n total += x.size(0)\n pbar.set_postfix(loss=running_loss/total, acc=running_correct/total)\n scheduler.step()\n train_loss = running_loss / total\n train_acc = running_correct / total\n\n # Validation\n model.eval()\n val_loss = 0.0\n val_correct = 0\n val_total = 0\n all_preds = []\n all_labels = []\n with torch.no_grad():\n for x, y in tqdm(val_loader, desc=f\"Epoch {epoch}/{EPOCHS} Val\"):\n x = x.to(device, non_blocking=True)\n y = y.to(device, non_blocking=True)\n with torch.cuda.amp.autocast(enabled=(device==\"cuda\")):\n logits = model(x)\n loss = criterion(logits, y)\n val_loss += loss.item() * x.size(0)\n preds = logits.argmax(dim=1)\n val_correct += (preds == y).sum().item()\n val_total += x.size(0)\n all_preds.extend(preds.cpu().numpy().tolist())\n all_labels.extend(y.cpu().numpy().tolist())\n\n val_loss /= max(1, val_total)\n val_acc = val_correct / max(1, val_total)\n\n print(f\"Epoch {epoch}: Train loss {train_loss:.4f}, acc {train_acc:.4f} | Val loss {val_loss:.4f}, acc {val_acc:.4f}\")\n\n # Save best\n if val_acc > best_val_acc:\n best_val_acc = val_acc\n torch.save({\n \"epoch\": epoch,\n \"model_state\": model.state_dict(),\n \"optimizer_state\": optimizer.state_dict(),\n \"val_acc\": val_acc\n }, best_path)\n print(\"Saved best model:\", best_path)\n\n# Final report on validation\nprint(\"Best val acc:\", best_val_acc)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-16T02:55:33.327657Z","iopub.execute_input":"2025-10-16T02:55:33.328140Z","iopub.status.idle":"2025-10-16T04:22:15.327345Z","shell.execute_reply.started":"2025-10-16T02:55:33.328120Z","shell.execute_reply":"2025-10-16T04:22:15.322971Z"}},"outputs":[{"name":"stderr","text":"/tmp/ipykernel_37/2800665062.py:5: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.\n scaler = torch.cuda.amp.GradScaler(enabled=(device==\"cuda\"))\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"Epoch 1/5 Train: 0%| | 0/55 [00:00\u001b[0;34m()\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0mtotal\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0mpbar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdesc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34mf\"Epoch {epoch}/{EPOCHS} Train\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;32min\u001b[0m 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\u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1179\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1180\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1181\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1182\u001b[0m \u001b[0;32myield\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1183\u001b[0m \u001b[0;31m# Update and possibly print the progressbar.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.11/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 706\u001b[0m \u001b[0;31m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 707\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# type: ignore[call-arg]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 708\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 709\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_yielded\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 710\u001b[0m if (\n","\u001b[0;32m/usr/local/lib/python3.11/dist-packages/torch/utils/data/dataloader.py\u001b[0m in 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timeout)\u001b[0m\n\u001b[1;32m 178\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mremaining\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 179\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mEmpty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnot_empty\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mremaining\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 181\u001b[0m \u001b[0mitem\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 182\u001b[0m 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332\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 333\u001b[0m \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "],"ename":"KeyboardInterrupt","evalue":"","output_type":"error"}],"execution_count":8},{"cell_type":"code","source":"# Cell 8 — load best & evaluate\nimport torch\nfrom sklearn.metrics import classification_report, confusion_matrix\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\nckpt = torch.load(best_path, map_location=device)\nmodel.load_state_dict(ckpt[\"model_state\"])\nmodel.eval()\n\nall_preds = []\nall_labels = []\nwith torch.no_grad():\n for x, y in tqdm(val_loader, desc=\"Final eval\"):\n x = x.to(device)\n logits = model(x)\n preds = logits.argmax(dim=1)\n all_preds.extend(preds.cpu().numpy().tolist())\n all_labels.extend(y.numpy().tolist())\n\nprint(classification_report(all_labels, all_preds, target_names=SELECTED_CLASSES))\n\ncm = confusion_matrix(all_labels, all_preds)\nplt.figure(figsize=(8,6))\nsns.heatmap(cm, annot=True, fmt=\"d\", xticklabels=SELECTED_CLASSES, yticklabels=SELECTED_CLASSES)\nplt.xlabel(\"Pred\")\nplt.ylabel(\"True\")\nplt.title(\"Confusion Matrix on Val\")\nplt.show()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-16T04:22:25.234500Z","iopub.execute_input":"2025-10-16T04:22:25.234754Z","iopub.status.idle":"2025-10-16T04:29:03.967176Z","shell.execute_reply.started":"2025-10-16T04:22:25.234730Z","shell.execute_reply":"2025-10-16T04:29:03.966150Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"Final eval: 0%| | 0/14 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\n"},"metadata":{}}],"execution_count":9}]}