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kagglehub\n\n# Download latest version\npath = kagglehub.competition_download('dlmmdd-workshop-synthetic-source-attribution-challenge')\n\nprint(\"Path to competition files:\", path)","metadata":{"executionInfo":{"elapsed":4829,"status":"ok","timestamp":1779938219077,"user":{"displayName":"Максим Ситников","userId":"14131355620639785572"},"user_tz":-180},"id":"EOBW26_KRJ1H","outputId":"5c483087-7569-4753-d67b-8a6f5e6c3dc8"},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import os\nimport numpy as np\nimport pandas as pd\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.nn.functional as F\nimport cv2\nimport timm\nimport random\nimport io\n\nfrom PIL import Image, ImageFilter, ImageEnhance\nfrom torch.utils.data import Dataset, DataLoader\nfrom torch.amp import autocast, GradScaler\nfrom timm.utils import ModelEmaV2\nfrom PIL import Image\nfrom torchvision import transforms, models\nimport torchvision.transforms.functional as TF\nfrom tqdm.auto import tqdm\nfrom sklearn.model_selection import train_test_split, StratifiedKFold","metadata":{"id":"4iTkPPoaRFFH"},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def set_seed(seed=42):\n    random.seed(seed)\n    np.random.seed(seed)\n    torch.manual_seed(seed)\n    torch.cuda.manual_seed_all(seed)\n    torch.backends.cudnn.deterministic = True\n    torch.backends.cudnn.benchmark     = False","metadata":{"id":"vtCvw-5_RFFI"},"outputs":[],"execution_count":null},{"cell_type":"code","source":"class CFG:\n    root = \"/root/.cache/kagglehub/competitions/dlmmdd-workshop-synthetic-source-attribution-challenge/Data/Data\"\n    train_csv = os.path.join(root, \"training.csv\")\n    test_csv = os.path.join(root, \"test.csv\")\n    train_dir = os.path.join(root, \"Training\")\n    test_dir = os.path.join(root, \"Test\")\n    device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n    debug = False\n    batch_size = 16\n    accum_steps = 4\n    epochs = 1 if debug else 20\n    pseudo_epochs = 10\n    lr = 3e-4\n    pseudo_lr = 1e-5\n    num_classes = 10\n    img_size = 512\n    label_smoothing = 0.1\n    mixup_alpha = 0.2\n    cutmix_alpha = 1.0\n    prob = 0.5\n    pseudo_weight = 1.0\n\ndef resolve_path(p):\n    for c in [\n        os.path.join(CFG.root, p),\n        os.path.join(CFG.train_dir, os.path.basename(p)),\n        os.path.join(CFG.test_dir, os.path.basename(p)),\n        p\n    ]:\n        if os.path.exists(c):\n            return c\n    return None","metadata":{"id":"ny4hnjovRFFJ"},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def apply_random_test_ops(img: Image.Image, num_ops=None) -> Image.Image:\n\n    ops = [\n        op_jpeg,\n        op_webp,\n        op_random_central_crop,\n        op_resize,\n        op_small_rotation,\n        op_contrast_brightness,\n        op_gaussian_blur,\n        op_grayscale,\n    ]\n\n    if num_ops is None:\n        num_ops = random.randint(1, 3)\n\n    chosen = random.sample(ops, min(num_ops, len(ops)))\n    for op in chosen:\n        img = op(img)\n    return img\n\ndef op_jpeg(img):\n    buf = io.BytesIO()\n    img.save(buf, format=\"JPEG\", quality=random.randint(30, 95))\n    return Image.open(buf).copy()\n\ndef op_webp(img):\n    buf = io.BytesIO()\n    img.save(buf, format=\"WEBP\", quality=random.randint(30, 95))\n    return Image.open(buf).copy()\n\ndef op_random_central_crop(img):\n    w, h = img.size\n    scale = random.uniform(0.6, 0.95)\n    new_w, new_h = int(w * scale), int(h * scale)\n    left = (w - new_w) // 2\n    top  = (h - new_h) // 2\n    img  = img.crop((left, top, left + new_w, top + new_h))\n    return img.resize((w, h), Image.BILINEAR)\n\ndef op_resize(img):\n\n    w, h = img.size\n\n    scale = random.uniform(0.4, 1.5)\n\n    interp1 = random.choice([\n        Image.BILINEAR,\n        Image.BICUBIC,\n        Image.LANCZOS,\n        Image.NEAREST\n    ])\n\n    interp2 = random.choice([\n        Image.BILINEAR,\n        Image.BICUBIC,\n        Image.LANCZOS,\n        Image.NEAREST\n    ])\n\n    img = img.resize(\n        (int(w * scale), int(h * scale)),\n        interp1\n    )\n\n    return img.resize((w, h), interp2)\n\ndef op_small_rotation(img):\n    import math\n    angle = random.uniform(-10, 10)\n    w, h  = img.size\n\n    rotated = img.rotate(angle, expand=False)\n\n    angle_rad = math.radians(abs(angle))\n    cos_a = math.cos(angle_rad)\n    sin_a = math.sin(angle_rad)\n\n    if w <= h:\n        new_w = int(w * cos_a - h * sin_a) if w * cos_a > h * sin_a else 0\n        new_h = int(h * cos_a - w * sin_a) if h * cos_a > w * sin_a else 0\n    else:\n        new_w = int(h * cos_a - w * sin_a) if h * cos_a > w * sin_a else 0\n        new_h = int(w * cos_a - h * sin_a) if w * cos_a > h * sin_a else 0\n\n    new_w = max(new_w, w // 2)\n    new_h = max(new_h, h // 2)\n\n    left = (w - new_w) // 2\n    top  = (h - new_h) // 2\n    img  = rotated.crop((left, top, left + new_w, top + new_h))\n\n    return img.resize((w, h), Image.BILINEAR)\n\ndef op_contrast_brightness(img):\n    img = ImageEnhance.Contrast(img).enhance(random.uniform(0.6, 1.6))\n    img = ImageEnhance.Brightness(img).enhance(random.uniform(0.6, 1.6))\n    return img\n\ndef op_gaussian_blur(img):\n    radius = random.uniform(0.5, 2.5)\n    return img.filter(ImageFilter.GaussianBlur(radius=radius))\n\ndef op_grayscale(img):\n    return img.convert(\"L\").convert(\"RGB\")","metadata":{"id":"VytbJsP4RFFJ"},"outputs":[],"execution_count":null},{"cell_type":"code","source":"class TrainDataset(Dataset):\n    def __init__(self, df, tfm):\n        self.df = df\n        self.tfm = tfm\n\n    def __len__(self):\n        return len(self.df)\n\n    def __getitem__(self, i):\n        r = self.df.iloc[i]\n        path = resolve_path(r[\"path\"])\n        img = Image.open(path).convert(\"RGB\")\n\n        if random.random() < 0.7:\n            img = apply_random_test_ops(img)\n\n        return self.tfm(img), torch.tensor(r[\"y\"]).long()\n\nclass ValDataset(Dataset):\n    def __init__(self, df, tfm):\n        self.df  = df\n        self.tfm = tfm\n\n    def __len__(self):\n        return len(self.df)\n\n    def __getitem__(self, i):\n        r    = self.df.iloc[i]\n        path = resolve_path(r[\"path\"])\n        img  = Image.open(path).convert(\"RGB\")\n        return self.tfm(img), torch.tensor(r[\"y\"]).long()\n\nclass TestDataset(Dataset):\n    def __init__(self, df, tfm):\n        self.df = df\n        self.tfm = tfm\n\n    def __len__(self):\n        return len(self.df)\n\n    def __getitem__(self, i):\n        r = self.df.iloc[i]\n        path = resolve_path(r[\"path\"])\n        img = Image.open(path).convert(\"RGB\")\n        return self.tfm(img), r[\"ID\"]\n\nclass PseudoDataset(Dataset):\n    def __init__(self, df, tfm):\n        self.df  = df\n        self.tfm = tfm\n\n    def __len__(self):\n        return len(self.df)\n\n    def __getitem__(self, i):\n        r = self.df.iloc[i]\n        path = resolve_path(r[\"path\"])\n        img = Image.open(path).convert(\"RGB\")\n\n        is_pseudo = bool(r.get(\"is_pseudo\", 0))\n\n        if not is_pseudo and random.random() < 0.7:\n            img = apply_random_test_ops(img)\n\n        return (\n            self.tfm(img),\n            torch.tensor(r[\"y\"]).long(),\n            torch.tensor(is_pseudo).float()\n        )\n\ndef get_train_tfm(mean, std):\n    return transforms.Compose([\n        transforms.Resize((CFG.img_size, CFG.img_size)),\n        transforms.RandomHorizontalFlip(),\n        transforms.ToTensor(),\n        transforms.Normalize([0.5]*3, [0.5]*3)\n    ])\n\ndef get_val_tfm(mean, std):\n    return transforms.Compose([\n        transforms.Resize((CFG.img_size, CFG.img_size)),\n        transforms.ToTensor(),\n        transforms.Normalize([0.5]*3, [0.5]*3)\n    ])","metadata":{"id":"a709wGThRFFJ"},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def soft_cross_entropy(pred, soft_targets):\n    logprobs = torch.nn.functional.log_softmax(pred, dim=1)\n    return -(soft_targets * logprobs).sum(dim=1).mean()\n\ndef to_one_hot(y, num_classes):\n    return torch.nn.functional.one_hot(y, num_classes).float()\n\ndef mixup_data(x, y, alpha=CFG.mixup_alpha):\n    lam = np.random.beta(alpha, alpha)\n    batch_size = x.size(0)\n    index = torch.randperm(batch_size).to(x.device)\n\n    x_mix = lam * x + (1 - lam) * x[index]\n    y1, y2 = y, y[index]\n\n    return x_mix, y1, y2, lam\n\ndef cutmix_data(x, y, alpha=CFG.cutmix_alpha):\n    lam = np.random.beta(alpha, alpha)\n    batch_size, _, H, W = x.size()\n    index = torch.randperm(batch_size).to(x.device)\n\n    cx = np.random.randint(W)\n    cy = np.random.randint(H)\n\n    w = int(W * np.sqrt(1 - lam))\n    h = int(H * np.sqrt(1 - lam))\n\n    x1 = np.clip(cx - w // 2, 0, W)\n    x2 = np.clip(cx + w // 2, 0, W)\n    y1_ = np.clip(cy - h // 2, 0, H)\n    y2_ = np.clip(cy + h // 2, 0, H)\n\n    x[:, :, y1_:y2_, x1:x2] = x[index, :, y1_:y2_, x1:x2]\n\n    lam = 1 - ((x2 - x1) * (y2_ - y1_) / (W * H))\n\n    return x, y, y[index], lam","metadata":{"id":"sHdpKhzoRFFJ"},"outputs":[],"execution_count":null},{"cell_type":"code","source":"class Model(nn.Module):\n\n    def __init__(self):\n        super().__init__()\n        self.net = timm.create_model(\n            \"tf_efficientnetv2_xl\",\n            pretrained=True,\n            num_classes=CFG.num_classes\n        )\n\n    def forward(self, x):\n        return self.net(x)\n\ndef get_norm(model):\n    return [0.5] * 3, [0.5] * 3\n\ndef train_one_epoch(model, loader, loss_fn, opt, scaler, scheduler, epoch, ema=None, use_pseudo=False):\n\n    def soft_cross_entropy(logits, target):\n        log_probs = torch.log_softmax(logits, dim=1)\n        return -(target * log_probs).sum(dim=1)\n\n    model.train()\n    total_loss, correct, total = 0, 0, 0\n    opt.zero_grad(set_to_none=True)\n    pbar = tqdm(loader, desc=\"Train\")\n\n    for batch_idx, batch in enumerate(pbar):\n        if use_pseudo:\n            x, y, is_pseudo = batch\n            is_pseudo = is_pseudo.to(CFG.device)\n        else:\n            x, y = batch\n\n        x = x.to(CFG.device, memory_format=torch.channels_last)\n        y = y.to(CFG.device)\n\n        if np.random.rand() < CFG.prob:\n            x, y1, y2, lam = mixup_data(x, y)\n        else:\n            x, y1, y2, lam = cutmix_data(x, y)\n\n        with autocast(\"cuda\", enabled=True):\n\n            y1 = to_one_hot(y1, CFG.num_classes)\n            y2 = to_one_hot(y2, CFG.num_classes)\n\n            logits = model(x)\n            loss1 = soft_cross_entropy(logits, y1)\n            loss2 = soft_cross_entropy(logits, y2)\n            loss_per_sample = lam * loss1 + (1 - lam) * loss2\n\n            if use_pseudo:\n                weights = torch.ones_like(loss_per_sample)\n                weights[is_pseudo == 1] = CFG.pseudo_weight\n                loss = (weights * loss_per_sample).mean()\n            else:\n                loss = loss_per_sample.mean()\n\n        loss = loss / CFG.accum_steps\n        scaler.scale(loss).backward()\n\n        if (batch_idx + 1) % CFG.accum_steps == 0:\n            scaler.step(opt)\n            scaler.update()\n            opt.zero_grad(set_to_none=True)\n            if ema is not None:\n                ema.update(model)\n\n        total_loss += loss.item() * CFG.accum_steps * x.size(0)\n        preds = logits.argmax(1)\n        correct += (preds == y).sum().item()\n        total += y.size(0)\n        pbar.set_postfix({\"loss\": total_loss / total, \"acc\": correct / total})\n\n    return total_loss / total, correct / total\n\n\ndef valid(model, loader, loss_fn):\n    model.eval()\n\n    total_loss = 0\n    correct = 0\n    total = 0\n\n    pbar = tqdm(loader, desc=\"Valid\")\n\n    with torch.no_grad():\n        for x, y in pbar:\n            x, y = x.to(CFG.device, memory_format=torch.channels_last), y.to(CFG.device)\n\n            logits = model(x)\n            loss = loss_fn(logits, y)\n\n            total_loss += loss.item() * x.size(0)\n\n            preds = logits.argmax(1)\n            correct += (preds == y).sum().item()\n            total += y.size(0)\n\n            pbar.set_postfix({\n                \"val_loss\": total_loss / total,\n                \"val_acc\": correct / total\n            })\n\n    return total_loss / total, correct / total\n\ndef tta_hflip(model, x):\n    logits1 = model(x)\n    logits2 = model(torch.flip(x, dims=[3]))\n\n    probs = (torch.softmax(logits1, dim=1) + torch.softmax(logits2, dim=1)) / 2\n    return probs\n\ndef predict_proba(model, loader):\n    model.eval()\n    ids, probs = [], []\n\n    with torch.no_grad():\n        for x, i in tqdm(loader):\n            x = x.to(CFG.device)\n\n            p = tta_hflip(model, x).cpu().numpy()\n\n            ids.extend(i.numpy())\n            probs.append(p)\n\n    return ids, np.vstack(probs)","metadata":{"id":"VJTBlueiRFFK"},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def train_model(train_df, val_df, use_pseudo=False, ckpt_name=\"model.pth\", pretrained_ckpt=None):\n\n    model = Model().to(CFG.device)\n    model = model.to(memory_format=torch.channels_last)\n    model.net.set_grad_checkpointing(True)\n\n    if pretrained_ckpt is not None:\n        model.load_state_dict(torch.load(pretrained_ckpt, map_location=CFG.device))\n        print(f\"Loaded pretrained: {pretrained_ckpt}\")\n\n    ema_decay = 0.999 if pretrained_ckpt is not None else 0.99\n    ema = ModelEmaV2(model, decay=ema_decay, device=None)\n    ema.set(model)\n    mean, std = get_norm(model)\n\n    if use_pseudo:\n        train_dataset = PseudoDataset(train_df, get_train_tfm(mean, std))\n    else:\n        train_dataset = TrainDataset(train_df, get_train_tfm(mean, std))\n\n    train_loader = DataLoader(\n        train_dataset, batch_size=CFG.batch_size, shuffle=True,\n        num_workers=8, pin_memory=True, persistent_workers=True, prefetch_factor=4\n    )\n\n    val_loader = DataLoader(\n        ValDataset(val_df, get_val_tfm(mean, std)),\n        batch_size=CFG.batch_size, shuffle=False,\n        num_workers=8, pin_memory=True, persistent_workers=True, prefetch_factor=4\n    )\n\n    loss_fn = nn.CrossEntropyLoss(label_smoothing=CFG.label_smoothing)\n\n    lr = CFG.pseudo_lr if pretrained_ckpt is not None else CFG.lr\n    epochs = CFG.pseudo_epochs if pretrained_ckpt is not None else CFG.epochs\n\n    opt = optim.AdamW(model.parameters(), lr=lr)\n    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(opt, T_max=epochs, eta_min=1e-6)\n    scaler = GradScaler('cuda', enabled=True)\n\n    best_acc, no_improve = 0, 0\n    best_aug_acc = 0\n\n    for epoch in range(epochs):\n        print(f\"\\nEpoch {epoch+1:2d}/{epochs}\")\n\n        train_loss, train_acc = train_one_epoch(\n            model, train_loader, loss_fn, opt, scaler, scheduler, epoch, ema,\n            use_pseudo=use_pseudo\n        )\n\n        print(f\"Train | Loss: {train_loss:.4f} | Acc: {train_acc:.4f}\")\n\n        val_model = model if epoch < 2 else ema.module\n        val_loss, val_acc = valid(val_model, val_loader, loss_fn)\n        print(f\"Valid | Loss: {val_loss:.4f} | Acc: {val_acc:.4f}\")\n\n        if val_acc > best_acc:\n            best_acc = val_acc\n            no_improve = 0\n            torch.save(ema.module.state_dict(), ckpt_name)\n        else:\n            no_improve += 1\n\n        scheduler.step()\n\n        # if no_improve >= 10:\n        #     print(\"Early stopping\")\n        #     break\n\n    return ckpt_name","metadata":{"id":"E0hUG1L2RFFK"},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def predict_kfold(models, test_df, submission_name='submission.csv'):\n    all_probs = []\n    ids = None\n\n    for ckpt in models:\n        model = Model().to(CFG.device)\n        model.load_state_dict(torch.load(ckpt))\n        model.eval()\n\n        mean, std = get_norm(model)\n\n        test_loader = DataLoader(\n            TestDataset(test_df, get_val_tfm(mean, std)),\n            batch_size=CFG.batch_size,\n            shuffle=False\n        )\n\n        ids_fold, probs = predict_proba(model, test_loader)\n\n        if ids is None:\n            ids = ids_fold\n\n        all_probs.append(probs)\n\n    final_probs = np.mean(all_probs, axis=0)\n\n    preds = final_probs.argmax(1)\n\n    pd.DataFrame({\n        \"ID\": ids,\n        \"TARGET\": preds\n    }).to_csv(submission_name, index=False)\n\n    return ids, all_probs, preds","metadata":{"id":"6xetGBd5RFFL"},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_df = pd.read_csv(CFG.train_csv)\ntest_df = pd.read_csv(CFG.test_csv)","metadata":{"id":"6tF6PU_d5kAk"},"outputs":[],"execution_count":null},{"cell_type":"code","source":"final_probs = np.load(\"final_probs.npy\")\nconf        = final_probs.max(axis=1)\nids         = test_df[\"ID\"].tolist()\npreds       = final_probs.argmax(axis=1)\n\nfor t in [0.85, 0.90, 0.92, 0.95, 0.99]:\n    print(f\"Threshold {t}: {(conf > t).sum()} / {len(conf)} samples\")","metadata":{"id":"7qB3Yfglitir","executionInfo":{"status":"ok","timestamp":1779938251527,"user_tz":-180,"elapsed":3,"user":{"displayName":"Максим Ситников","userId":"14131355620639785572"}},"outputId":"be50d2c3-5149-4e75-b75a-cbb07eb89068"},"outputs":[],"execution_count":null},{"cell_type":"code","source":"threshold  = 0.8\nmask       = conf > threshold\npseudo_df  = test_df[mask].copy().reset_index(drop=True)\npseudo_df[\"y\"]          = final_probs.argmax(axis=1)[mask]\npseudo_df[\"is_pseudo\"]  = 1\ntrain_df[\"is_pseudo\"]   = 0\n\nprint(f\"Pseudo labels: {len(pseudo_df)}\")\nprint(pseudo_df[\"y\"].value_counts().sort_index())","metadata":{"id":"salo42kRiGFN","executionInfo":{"status":"ok","timestamp":1779938379105,"user_tz":-180,"elapsed":6,"user":{"displayName":"Максим Ситников","userId":"14131355620639785572"}},"outputId":"e30440f8-c1a3-4041-f589-79d19c998016"},"outputs":[],"execution_count":null},{"cell_type":"code","source":"kf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\npseudo_models = []\n\nfor fold, (train_idx, val_idx) in enumerate(kf.split(train_df, train_df[\"y\"])):\n\n    print(f\"\\n========== PSEUDO FOLD {fold} ==========\")\n\n    real_train = train_df.iloc[train_idx].reset_index(drop=True)\n    val_fold   = train_df.iloc[val_idx].reset_index(drop=True)\n    train_fold = pd.concat([real_train, pseudo_df], ignore_index=True)\n\n    ckpt_pseudo_name = f\"pseudo_fold{fold}_xl512.pth\"\n\n    train_model(\n        train_fold, val_fold,\n        use_pseudo=True,\n        ckpt_name=ckpt_pseudo_name,\n    )\n\n    pseudo_models.append(ckpt_pseudo_name)","metadata":{"id":"PegugUMikA7U","executionInfo":{"status":"error","timestamp":1779959044487,"user_tz":-180,"elapsed":5481462,"user":{"displayName":"Максим Ситников","userId":"14131355620639785572"}},"outputId":"dbc7e9e4-0eb0-43ce-ab2a-f317b944f2a6"},"outputs":[],"execution_count":null},{"cell_type":"code","source":"pseudo_models = [\n    'pseudo_fold0_xl512.pth',\n    'pseudo_fold1_xl512.pth',\n    'pseudo_fold2_xl512.pth',\n    'pseudo_fold3_xl512.pth',\n    'pseudo_fold4_xl512.pth',\n]\n\nids, all_probs, preds = predict_kfold(pseudo_models, test_df)","metadata":{"id":"KXDiqXkAwtXh","executionInfo":{"status":"ok","timestamp":1779959912383,"user_tz":-180,"elapsed":863675,"user":{"displayName":"Максим Ситников","userId":"14131355620639785572"}},"outputId":"27b3406e-560e-4037-c602-be125dd7299a"},"outputs":[],"execution_count":null},{"cell_type":"code","source":"final_probs = np.mean(all_probs, axis=0)\nconf        = final_probs.max(axis=1)\nids         = test_df[\"ID\"].tolist()\npreds       = final_probs.argmax(axis=1)\n\nfor t in [0.7, 0.75, 0.8, 0.85, 0.90, 0.92, 0.95, 0.99]:\n    print(f\"Threshold {t}: {(conf > t).sum()} / {len(conf)} samples\")\n\nthreshold  = 0.8\nmask       = conf > threshold\npseudo_df  = test_df[mask].copy().reset_index(drop=True)\npseudo_df[\"y\"]          = final_probs.argmax(axis=1)[mask]\npseudo_df[\"is_pseudo\"]  = 1\ntrain_df[\"is_pseudo\"]   = 0\n\nprint(f\"Pseudo labels: {len(pseudo_df)}\")\nprint(pseudo_df[\"y\"].value_counts().sort_index())\n\nnp.save(\"pseudo_final_probs_xl512.npy\", final_probs)","metadata":{"id":"0mzLro6hNOgF","executionInfo":{"status":"ok","timestamp":1779960849756,"user_tz":-180,"elapsed":9,"user":{"displayName":"Максим Ситников","userId":"14131355620639785572"}},"outputId":"905bf3c5-ff58-45f3-a4fd-51dcc52d9db8"},"outputs":[],"execution_count":null}]}