{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.12.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceType":"competition","sourceId":71885,"databundleVersionId":8143495}],"dockerImageVersionId":31329,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2026-04-09T07:22:12.237495Z","iopub.execute_input":"2026-04-09T07:22:12.237868Z","iopub.status.idle":"2026-04-09T07:22:16.515586Z","shell.execute_reply.started":"2026-04-09T07:22:12.237825Z","shell.execute_reply":"2026-04-09T07:22:16.514330Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# ==============================\n# INSTALL\n# ==============================\n!pip install albumentations timm scipy -q\n\n# ==============================\n# IMPORTS\n# ==============================\nimport os, cv2, torch, timm\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom tqdm import tqdm\nfrom torch import nn\nfrom torch.utils.data import Dataset, DataLoader\nimport albumentations as A\nfrom albumentations.pytorch import ToTensorV2\nfrom scipy.spatial.transform import Rotation as R\n\nDEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\nBASE = \"/kaggle/input/competitions/image-matching-challenge-2024\"\nROOT = f\"{BASE}/train\"\n\n# ==============================\n# PARSER\n# ==============================\ndef parse_scene(scene):\n    img_dir = os.path.join(ROOT, scene, \"images\")\n    sfm_file = os.path.join(ROOT, scene, \"sfm/images.txt\")\n\n    if not os.path.exists(img_dir) or not os.path.exists(sfm_file):\n        return []\n\n    pose_dict = {}\n\n    with open(sfm_file) as f:\n        lines = f.readlines()\n\n    for line in lines:\n        if line.startswith(\"#\"):\n            continue\n\n        parts = line.strip().split()\n        if len(parts) < 10:\n            continue\n\n        name = os.path.basename(parts[-1])\n        base = os.path.splitext(name)[0]\n\n        qw, qx, qy, qz = map(float, parts[1:5])\n        tx, ty, tz = map(float, parts[5:8])\n\n        quat = np.array([qx, qy, qz, qw])\n        quat = quat / np.linalg.norm(quat)\n\n        pose = np.concatenate([quat, [tx, ty, tz]])\n        pose_dict[base] = pose\n\n    data = []\n    for img_name in os.listdir(img_dir):\n        base = os.path.splitext(img_name)[0]\n        if base in pose_dict:\n            img_path = f\"{scene}/images/{img_name}\"\n            data.append((img_path, pose_dict[base]))\n\n    print(f\"{scene} → {len(data)} images\")\n    return data\n\n\n# ==============================\n# BUILD DATASET\n# ==============================\ndata = []\nfor s in os.listdir(ROOT):\n    data.extend(parse_scene(s))\n\nprint(\"TOTAL IMAGES:\", len(data))\n\n# Use more data\ndata = data[:1000]\n\nnp.random.shuffle(data)\n\n# ==============================\n# AUGMENTATIONS\n# ==============================\ntrain_transform = A.Compose([\n    A.Resize(224,224),\n    A.HorizontalFlip(p=0.5),\n    A.RandomBrightnessContrast(p=0.3),\n    A.Rotate(limit=20, p=0.3),\n    A.Normalize(),\n    ToTensorV2()\n])\n\nval_transform = A.Compose([\n    A.Resize(224,224),\n    A.Normalize(),\n    ToTensorV2()\n])\n\n# ==============================\n# DATASET CLASS\n# ==============================\nclass PoseDS(Dataset):\n    def __init__(self, data, transform):\n        self.data = data\n        self.transform = transform\n\n    def __len__(self):\n        return len(self.data)\n\n    def __getitem__(self, i):\n        path, target = self.data[i]\n\n        img = cv2.imread(os.path.join(ROOT, path))\n        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n\n        img = self.transform(image=img)['image']\n\n        return img, torch.tensor(target, dtype=torch.float32)\n\n# ==============================\n# SPLIT\n# ==============================\nsplit = int(0.8 * len(data))\n\ntrain_loader = DataLoader(\n    PoseDS(data[:split], train_transform),\n    batch_size=32,\n    shuffle=True\n)\n\nval_loader = DataLoader(\n    PoseDS(data[split:], val_transform),\n    batch_size=32\n)\n\n# ==============================\n# MODEL\n# ==============================\nclass PoseNet(nn.Module):\n    def __init__(self):\n        super().__init__()\n        self.backbone = timm.create_model(\n            \"efficientnet_b0\",\n            pretrained=True,\n            num_classes=0\n        )\n        self.head = nn.Sequential(\n            nn.Linear(1280, 512),\n            nn.ReLU(),\n            nn.Dropout(0.3),\n            nn.Linear(512, 7)\n        )\n\n    def forward(self, x):\n        x = self.backbone(x)\n        x = self.head(x)\n        return x\n\nmodel = PoseNet().to(DEVICE)\n\n# ==============================\n# LOSS FUNCTION\n# ==============================\ndef pose_loss(pred, target):\n    pq, pt = pred[:, :4], pred[:, 4:]\n    tq, tt = target[:, :4], target[:, 4:]\n\n    pq = pq / torch.norm(pq, dim=1, keepdim=True)\n    tq = tq / torch.norm(tq, dim=1, keepdim=True)\n\n    rot_loss = torch.mean(1 - torch.sum(pq * tq, dim=1) ** 2)\n    trans_loss = nn.functional.mse_loss(pt, tt)\n\n    return rot_loss + 10.0 * trans_loss\n\n\n# ==============================\n# OPTIMIZER\n# ==============================\noptimizer = torch.optim.AdamW(model.parameters(), lr=1e-4)\nscheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(\n    optimizer, mode='min', patience=3, factor=0.5\n)\n\n# ==============================\n# TRAINING\n# ==============================\nEPOCHS = 25\ntrain_loss, val_loss, accs = [], [], []\n\nfor epoch in range(EPOCHS):\n    model.train()\n    tl = 0\n\n    for x, y in tqdm(train_loader):\n        x, y = x.to(DEVICE), y.to(DEVICE)\n\n        optimizer.zero_grad()\n        out = model(x)\n        loss = pose_loss(out, y)\n        loss.backward()\n        optimizer.step()\n\n        tl += loss.item()\n\n    tl /= len(train_loader)\n\n    # Validation\n    model.eval()\n    vl = 0\n    err = []\n\n    with torch.no_grad():\n        for x, y in val_loader:\n            x, y = x.to(DEVICE), y.to(DEVICE)\n            out = model(x)\n\n            loss = pose_loss(out, y)\n            vl += loss.item()\n\n            mae = torch.mean(torch.abs(out - y)).item()\n            err.append(mae)\n\n    vl /= len(val_loader)\n    scheduler.step(vl)\n\n    acc = 1 / (1 + np.mean(err))\n\n    train_loss.append(tl)\n    val_loss.append(vl)\n    accs.append(acc)\n\n    print(f\"Epoch {epoch+1}: Train={tl:.4f} Val={vl:.4f} Acc={acc:.4f}\")\n\n# ==============================\n# PLOT LOSS\n# ==============================\nplt.plot(train_loss, label=\"Train\")\nplt.plot(val_loss, label=\"Val\")\nplt.legend()\nplt.title(\"Loss Curve\")\nplt.show()\n\n# ==============================\n# PLOT ACCURACY\n# ==============================\nplt.plot(accs)\nplt.title(\"Validation Accuracy\")\nplt.show()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-09T07:36:22.451116Z","iopub.execute_input":"2026-04-09T07:36:22.451414Z","iopub.status.idle":"2026-04-09T07:49:35.416887Z","shell.execute_reply.started":"2026-04-09T07:36:22.451387Z","shell.execute_reply":"2026-04-09T07:49:35.416058Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}