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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 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rdclef-2025-ensemble-of-solutions/custom.css\n/kaggle/input/birdclef-2025-ensemble-of-solutions/__results___files/__results___39_3.png\n/kaggle/input/birdclef-2025-ensemble-of-solutions/__results___files/__results___39_4.png\n/kaggle/input/birdclef-2025-ensemble-of-solutions/__results___files/__results___39_5.png\n/kaggle/input/birdclef-2025-ensemble-of-solutions/__results___files/__results___39_2.png\n/kaggle/input/birdclef-2025-ensemble-of-solutions/__results___files/__results___39_6.png\n/kaggle/input/birdclef-2025-ensemble-of-solutions/iframe_figures/figure_25.html\n/kaggle/input/dataga/sample_submission.csv\n/kaggle/input/dataga/taxonomy.csv\n/kaggle/input/dataga/train.csv\n/kaggle/input/dataga/recording_location.txt\n/kaggle/input/efficientnet-b0-test_4/pytorch/default/1/models/model_fold0.pth\n/kaggle/input/efficientnet-b0-test_4/pytorch/default/1/models/model_fold1.pth\n/kaggle/input/efficientnet-b0-test_4/pytorch/default/1/models/model_fold2.pth\n","output_type":"stream"}],"execution_count":1},{"cell_type":"code","source":"import os\nimport gc\nimport warnings\nimport logging\nimport time\nimport math\nimport cv2\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport librosa\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport timm\nfrom tqdm.auto import tqdm\n\nwarnings.filterwarnings(\"ignore\")\nlogging.basicConfig(level=logging.ERROR)","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"class CFG:\n \n    test_soundscapes = '/kaggle/input/birdclef-2025/train_soundscapes'\n    submission_csv = '/kaggle/input/sample-submission/sample_submission.csv'\n    taxonomy_csv = '/kaggle/input/birdclef-2025/taxonomy.csv'\n    model_path = '/kaggle/input/efficientnet-b0-test_4/pytorch/default/1/models'  \n    \n    # Audio parameters\n    FS = 32000  \n    WINDOW_SIZE = 5  \n    \n    # Mel spectrogram parameters\n    N_FFT = 1024\n    HOP_LENGTH = 512\n    N_MELS = 148\n    FMIN = 50\n    FMAX = 14000\n    TARGET_SHAPE = (256, 256)\n    \n    model_name = 'efficientnet_b0'\n    in_channels = 1\n    device = 'cpu'  \n    \n    # Inference parameters\n    batch_size = 16\n    use_tta = False  \n    tta_count = 3   \n    threshold = 0.5\n    \n    use_specific_folds = False  # If False, use all found models\n    folds = [0, 1]  # Used only if use_specific_folds is True\n    \n    debug = False\n    debug_count = 3\n\ncfg = CFG()","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"print(f\"Using device: {cfg.device}\")\nprint(f\"Loading taxonomy data...\")\ntaxonomy_df = pd.read_csv(cfg.taxonomy_csv)\nspecies_ids = taxonomy_df['primary_label'].tolist()\nnum_classes = len(species_ids)\nprint(f\"Number of classes: {num_classes}\")","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"class BirdCLEFModel(nn.Module):\n    def __init__(self, cfg, num_classes):\n        super().__init__()\n        self.cfg = cfg\n        \n        self.backbone = timm.create_model(\n            cfg.model_name,\n            pretrained=False,  \n            in_chans=cfg.in_channels,\n            drop_rate=0.0,    \n            drop_path_rate=0.0\n        )\n        \n        if 'efficientnet' in cfg.model_name:\n            backbone_out = self.backbone.classifier.in_features\n            self.backbone.classifier = nn.Identity()\n        elif 'resnet' in cfg.model_name:\n            backbone_out = self.backbone.fc.in_features\n            self.backbone.fc = nn.Identity()\n        else:\n            backbone_out = self.backbone.get_classifier().in_features\n            self.backbone.reset_classifier(0, '')\n        \n        self.pooling = nn.AdaptiveAvgPool2d(1)\n        self.feat_dim = backbone_out\n        self.classifier = nn.Linear(backbone_out, num_classes)\n        \n    def forward(self, x):\n        features = self.backbone(x)\n        \n        if isinstance(features, dict):\n            features = features['features']\n            \n        if len(features.shape) == 4:\n            features = self.pooling(features)\n            features = features.view(features.size(0), -1)\n        \n        logits = self.classifier(features)\n        return logits","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def audio2melspec(audio_data, cfg):\n    \"\"\"Convert audio data to mel spectrogram\"\"\"\n    if np.isnan(audio_data).any():\n        mean_signal = np.nanmean(audio_data)\n        audio_data = np.nan_to_num(audio_data, nan=mean_signal)\n\n    mel_spec = librosa.feature.melspectrogram(\n        y=audio_data,\n        sr=cfg.FS,\n        n_fft=cfg.N_FFT,\n        hop_length=cfg.HOP_LENGTH,\n        n_mels=cfg.N_MELS,\n        fmin=cfg.FMIN,\n        fmax=cfg.FMAX,\n        power=2.0\n    )\n\n    mel_spec_db = librosa.power_to_db(mel_spec, ref=np.max)\n    mel_spec_norm = (mel_spec_db - mel_spec_db.min()) / (mel_spec_db.max() - mel_spec_db.min() + 1e-8)\n    \n    return mel_spec_norm\n\ndef process_audio_segment(audio_data, cfg):\n    \"\"\"Process audio segment to get mel spectrogram\"\"\"\n    if len(audio_data) < cfg.FS * cfg.WINDOW_SIZE:\n        audio_data = np.pad(audio_data, \n                          (0, cfg.FS * cfg.WINDOW_SIZE - len(audio_data)), \n                          mode='constant')\n    \n    mel_spec = audio2melspec(audio_data, cfg)\n    \n    # Resize if needed\n    if mel_spec.shape != cfg.TARGET_SHAPE:\n        mel_spec = cv2.resize(mel_spec, cfg.TARGET_SHAPE, interpolation=cv2.INTER_LINEAR)\n        \n    return mel_spec.astype(np.float32)","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def find_model_files(cfg):\n    \"\"\"\n    Find all .pth model files in the specified model directory\n    \"\"\"\n    model_files = []\n    \n    model_dir = Path(cfg.model_path)\n    \n    for path in model_dir.glob('**/*.pth'):\n        model_files.append(str(path))\n    \n    return model_files\n\ndef load_models(cfg, num_classes):\n    \"\"\"\n    Load all found model files and prepare them for ensemble\n    \"\"\"\n    models = []\n    \n    model_files = find_model_files(cfg)\n    \n    if not model_files:\n        print(f\"Warning: No model files found under {cfg.model_path}!\")\n        return models\n    \n    print(f\"Found a total of {len(model_files)} model files.\")\n    \n    if cfg.use_specific_folds:\n        filtered_files = []\n        for fold in cfg.folds:\n            fold_files = [f for f in model_files if f\"fold{fold}\" in f]\n            filtered_files.extend(fold_files)\n        model_files = filtered_files\n        print(f\"Using {len(model_files)} model files for the specified folds ({cfg.folds}).\")\n    \n    for model_path in model_files:\n        try:\n            print(f\"Loading model: {model_path}\")\n            checkpoint = torch.load(model_path, map_location=torch.device(cfg.device))\n            \n            model = BirdCLEFModel(cfg, num_classes)\n            model.load_state_dict(checkpoint['model_state_dict'])\n            model = model.to(cfg.device)\n            model.eval()\n            \n            models.append(model)\n        except Exception as e:\n            print(f\"Error loading model {model_path}: {e}\")\n    \n    return models\n\ndef predict_on_spectrogram(audio_path, models, cfg, species_ids):\n    \"\"\"Process a single audio file and predict species presence for each 5-second segment\"\"\"\n    predictions = []\n    row_ids = []\n    soundscape_id = Path(audio_path).stem\n    \n    try:\n        print(f\"Processing {soundscape_id}\")\n        audio_data, _ = librosa.load(audio_path, sr=cfg.FS)\n        \n        total_segments = int(len(audio_data) / (cfg.FS * cfg.WINDOW_SIZE))\n        \n        for segment_idx in range(total_segments):\n            start_sample = segment_idx * cfg.FS * cfg.WINDOW_SIZE\n            end_sample = start_sample + cfg.FS * cfg.WINDOW_SIZE\n            segment_audio = audio_data[start_sample:end_sample]\n            \n            end_time_sec = (segment_idx + 1) * cfg.WINDOW_SIZE\n            row_id = f\"{soundscape_id}_{end_time_sec}\"\n            row_ids.append(row_id)\n\n            if cfg.use_tta:\n                all_preds = []\n                \n                for tta_idx in range(cfg.tta_count):\n                    mel_spec = process_audio_segment(segment_audio, cfg)\n                    mel_spec = apply_tta(mel_spec, tta_idx)\n\n                    mel_spec = torch.tensor(mel_spec, dtype=torch.float32).unsqueeze(0).unsqueeze(0)\n                    mel_spec = mel_spec.to(cfg.device)\n\n                    if len(models) == 1:\n                        with torch.no_grad():\n                            outputs = models[0](mel_spec)\n                            probs = torch.sigmoid(outputs).cpu().numpy().squeeze()\n                            all_preds.append(probs)\n                    else:\n                        segment_preds = []\n                        for model in models:\n                            with torch.no_grad():\n                                outputs = model(mel_spec)\n                                probs = torch.sigmoid(outputs).cpu().numpy().squeeze()\n                                segment_preds.append(probs)\n                        \n                        avg_preds = np.mean(segment_preds, axis=0)\n                        all_preds.append(avg_preds)\n\n                final_preds = np.mean(all_preds, axis=0)\n            else:\n                mel_spec = process_audio_segment(segment_audio, cfg)\n                \n                mel_spec = torch.tensor(mel_spec, dtype=torch.float32).unsqueeze(0).unsqueeze(0)\n                mel_spec = mel_spec.to(cfg.device)\n                \n                if len(models) == 1:\n                    with torch.no_grad():\n                        outputs = models[0](mel_spec)\n                        final_preds = torch.sigmoid(outputs).cpu().numpy().squeeze()\n                else:\n                    segment_preds = []\n                    for model in models:\n                        with torch.no_grad():\n                            outputs = model(mel_spec)\n                            probs = torch.sigmoid(outputs).cpu().numpy().squeeze()\n                            segment_preds.append(probs)\n\n                    final_preds = np.mean(segment_preds, axis=0)\n                    \n            predictions.append(final_preds)\n            \n    except Exception as e:\n        print(f\"Error processing {audio_path}: {e}\")\n    \n    return row_ids, predictions\n","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def apply_tta(spec, tta_idx):\n    \"\"\"Apply test-time augmentation\"\"\"\n    if tta_idx == 0:\n        # Original spectrogram\n        return spec\n    elif tta_idx == 1:\n        # Time shift (horizontal flip)\n        return np.flip(spec, axis=1)\n    elif tta_idx == 2:\n        # Frequency shift (vertical flip)\n        return np.flip(spec, axis=0)\n    else:\n        return spec\n\ndef run_inference(cfg, models, species_ids):\n    \"\"\"Run inference on all test soundscapes\"\"\"\n    test_files = list(Path(cfg.test_soundscapes).glob('*.ogg'))\n    \n    if cfg.debug:\n        print(f\"Debug mode enabled, using only {cfg.debug_count} files\")\n        test_files = test_files[:cfg.debug_count]\n    \n    print(f\"Found {len(test_files)} test soundscapes\")\n\n    all_row_ids = []\n    all_predictions = []\n\n    for audio_path in tqdm(test_files):\n        row_ids, predictions = predict_on_spectrogram(str(audio_path), models, cfg, species_ids)\n        all_row_ids.extend(row_ids)\n        all_predictions.extend(predictions)\n    \n    return all_row_ids, all_predictions\n\ndef create_submission(row_ids, predictions, species_ids, cfg):\n    \"\"\"Create submission dataframe\"\"\"\n    print(\"Creating submission dataframe...\")\n\n    submission_dict = {'row_id': row_ids}\n    \n    for i, species in enumerate(species_ids):\n        submission_dict[species] = [pred[i] for pred in predictions]\n\n    submission_df = pd.DataFrame(submission_dict)\n\n    submission_df.set_index('row_id', inplace=True)\n\n    sample_sub = pd.read_csv(cfg.submission_csv, index_col='row_id')\n\n    missing_cols = set(sample_sub.columns) - set(submission_df.columns)\n    if missing_cols:\n        print(f\"Warning: Missing {len(missing_cols)} species columns in submission\")\n        for col in missing_cols:\n            submission_df[col] = 0.0\n\n    submission_df = submission_df[sample_sub.columns]\n\n    submission_df = submission_df.reset_index()\n    \n    return submission_df","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def main():\n    start_time = time.time()\n    print(\"Starting BirdCLEF-2025 inference...\")\n    print(f\"TTA enabled: {cfg.use_tta} (variations: {cfg.tta_count if cfg.use_tta else 0})\")\n\n    models = load_models(cfg, num_classes)\n    \n    if not models:\n        print(\"No models found! Please check model paths.\")\n        return\n    \n    print(f\"Model usage: {'Single model' if len(models) == 1 else f'Ensemble of {len(models)} models'}\")\n\n    row_ids, predictions = run_inference(cfg, models, species_ids)\n\n    submission_df = create_submission(row_ids, predictions, species_ids, cfg)\n\n    submission_path = 'submission.csv'\n    submission_df.to_csv(submission_path, index=False)\n    print(f\"Submission saved to {submission_path}\")\n    \n    end_time = time.time()\n    print(f\"Inference completed in {(end_time - start_time)/60:.2f} minutes\")","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"if __name__ == \"__main__\":\n    main()","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Load submission and (assumed) true labels\nsub = pd.read_csv('/kaggle/input/sample-submission/sample_submission.csv')\ntrue_df = pd.read_csv('true_labels.csv')  # <-- Replace with your true label file\nassert (sub['row_id'] == true_df['row_id']).all(), \"row_id mismatch between prediction and ground truth!\"\n\ncols = sub.columns[1:]\ngroups = sub['row_id'].str.rsplit('_', n=1).str[0].values\n\nfor group in np.unique(groups):\n    sub_group = sub[group == groups]\n    predictions = sub_group[cols].values\n    new_predictions = predictions.copy()\n    \n    for i in range(1, predictions.shape[0] - 1):\n        new_predictions[i] = (\n            predictions[i - 1] * 0.2 +\n            predictions[i] * 0.6 +\n            predictions[i + 1] * 0.2\n        )\n    new_predictions[0] = predictions[0] * 0.8 + predictions[1] * 0.2\n    new_predictions[-1] = predictions[-1] * 0.8 + predictions[-2] * 0.2\n\n    sub.loc[group == groups, cols] = new_predictions\n\n# Save submission\nsub.to_csv(\"submission.csv\", index=False)\n\n# 🧠 Compute accuracy\ny_true = true_df[cols].values\ny_pred = (sub[cols].values > 0.5).astype(int)\n\nacc = accuracy_score(y_true.flatten(), y_pred.flatten())  # Flattened for multi-label binary accuracy\nprint(f\"Smoothed Prediction Accuracy: {acc:.4f}\")","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import torch\nfrom sklearn.metrics import accuracy_score\nimport numpy as np\n\n# Put model in eval mode\nmodel.eval()\n\n# Move data and model to same device\ndevice = cfg.device  # from your CFG class, usually 'cpu' or 'cuda'\nmodel.to(device)\n\n# Create DataLoader for training data\nfrom torch.utils.data import TensorDataset, DataLoader\n\n# Convert numpy arrays to torch tensors (change dtype if needed)\nX_tensor = torch.tensor(X_train, dtype=torch.float32)\ny_tensor = torch.tensor(y_train, dtype=torch.long)  # for multi-class\n# y_tensor = torch.tensor(y_train, dtype=torch.float32)  # for multi-label\n\ntrain_dataset = TensorDataset(X_tensor, y_tensor)\ntrain_loader = DataLoader(train_dataset, batch_size=32, shuffle=False)\n\n# Run inference\nall_preds = []\nall_targets = []\n\nwith torch.no_grad():\n    for X_batch, y_batch in train_loader:\n        X_batch = X_batch.to(device)\n        y_batch = y_batch.to(device)\n        \n        outputs = model(X_batch)\n        \n        # For multi-class:\n        preds = torch.argmax(outputs, dim=1)\n\n        # For multi-label:\n        # preds = (torch.sigmoid(outputs) > 0.5).int()\n        \n        all_preds.append(preds.cpu().numpy())\n        all_targets.append(y_batch.cpu().numpy())\n\n# Combine all predictions\nall_preds = np.concatenate(all_preds)\nall_targets = np.concatenate(all_targets)\n\n# Accuracy\nacc = accuracy_score(all_targets, all_preds)\nprint(f\"Training Accuracy: {acc:.4f}\")","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import time\nSTART = time.time()\n\nfrom concurrent.futures import ThreadPoolExecutor\nimport glob\nimport librosa\nimport numpy as np\nimport os\nimport pandas as pd\nimport re\nimport sys\nimport cv2\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport timm\nimport torchaudio\nimport torchaudio.transforms as AT\nimport matplotlib.pyplot as plt\nfrom tqdm.auto import tqdm\nfrom scipy import signal\nimport gc\nimport warnings\nimport logging\nfrom pathlib import Path\nimport random\n\n# 抑制警告和限制日志输出\nwarnings.filterwarnings(\"ignore\")\nlogging.basicConfig(level=logging.ERROR)\n\n# 强制使用CPU\nos.environ['CUDA_VISIBLE_DEVICES'] = ''\ntorch.set_num_threads(4)  # 限制CPU线程数\n\nTERMINATE_TIME = START + 5300  # 时间限制约88分钟","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"class CFG:\n    # 基本配置\n    test_soundscapes = '/kaggle/input/dataga/train.csv'\n    train_soundscapes = '/kaggle/input/dataga/train.csv'\n    submission_csv = '/kaggle/input/birdclef-2025/sample_submission.csv'\n    taxonomy_csv = '/kaggle/input/birdclef-2025/taxonomy.csv'\n    model_path = '/kaggle/input/efficientnet-b0-test_4/pytorch/default/1/models'\n    \n    # 调试设置\n    debug = False\n    debug_start_num = 0\n    debug_num = 8\n    \n    # 音频参数\n    FS = 32000\n    WINDOW_SIZE = 5\n    \n    # 梅尔频谱图参数\n    N_FFT = 1034  \n    HOP_LENGTH = 64  \n    N_MELS = 136 \n    FMIN = 20\n    FMAX = 16000\n    TARGET_SHAPE = (256, 256)\n    \n    # 模型参数\n    model_name = 'efficientnet_b0'\n    in_channels = 1\n    device = 'cpu'  # 强制使用CPU\n    \n    # 推理参数\n    batch_size = 16  # 减小批量大小以适应CPU\n    use_tta = False  # 禁用TTA以加快处理速度\n    tta_count = 3\n    threshold = 0.7\n    \n    # 模型选择参数\n    use_specific_folds = False  # 不使用特定fold，使用所有找到的模型\n    folds = [0, 1]  # 仅当use_specific_folds为True时使用\n    \n    # 是否应用平滑\n    apply_smoothing = True\n    \n    # 音频预处理参数\n    apply_noise_reduction = True\n    apply_normalization = True\n    noise_reduction_strength = 0.1\n    \n    # 平滑参数\n    smoothing_window = 5  # 增加窗口大小，捕获更长时间的相关性\n    smoothing_weights = [0.15, 0.2, 0.3, 0.2, 0.15]  # 更精细的权重\n    \n    # 内存管理\n    clear_cache_frequency = 5  # 每处理多少个音频文件后清理一次内存\n    \n    # 新增数据增强参数\n    use_spec_augment = True\n\n    # 时间掩码参数\n    time_mask_param = 30\n    # 频率掩码参数\n    freq_mask_param = 20\n    # 时间掩码次数\n    time_mask_count = 1\n    # 频率掩码次数\n    freq_mask_count = 1\n    \n    # 新增频谱对比度增强\n    apply_spec_contrast = True\n    contrast_factor = 0.15\n    \n    # 阈值调整\n    class_thresholds = None  # 将在运行时动态设置为每个类别的最优阈值\n    \n    # 预测融合\n    prediction_blend = [0.7, 0.3]  # 70% 为原始预测，30% 为相邻时间点预测的加权平均\n\n    # 添加参考代码中使用的一个额外平滑选项\n    apply_secondary_smoothing = True  # 启用从参考代码中借鉴的二次平滑\n    secondary_smoothing_weights = [0.2, 0.6, 0.2]  # 参考代码中的权重：前帧、当前帧、后帧\n\ndef set_seed(seed=42):\n    \"\"\"设置随机种子以确保可重复性\"\"\"\n    random.seed(seed)\n    np.random.seed(seed)\n    torch.manual_seed(seed)\n    if torch.cuda.is_available():\n        torch.cuda.manual_seed(seed)\n    os.environ['PYTHONHASHSEED'] = str(seed)\n\n# 设置随机种子\nset_seed(42)\n\n# PyTorch鸟类分类模型\nclass BirdCLEFModel(nn.Module):\n    \"\"\"\n    使用timm作为骨干网络的自定义PyTorch神经网络模型。\n    \"\"\"\n    def __init__(self, cfg, num_classes):\n        \"\"\"\n        初始化BirdCLEFModel。\n        \n        :param cfg: 配置参数。\n        :param num_classes: 输出类别数量。\n        \"\"\"\n        super().__init__()\n        self.cfg = cfg\n        self.backbone = timm.create_model(\n            cfg.model_name,\n            pretrained=False,\n            in_chans=cfg.in_channels,\n            drop_rate=0.0,\n            drop_path_rate=0.0\n        )\n        \n        if 'efficientnet' in cfg.model_name:\n            backbone_out = self.backbone.classifier.in_features\n            self.backbone.classifier = nn.Identity()\n        elif 'resnet' in cfg.model_name:\n            backbone_out = self.backbone.fc.in_features\n            self.backbone.fc = nn.Identity()\n        else:\n            backbone_out = self.backbone.get_classifier().in_features\n            self.backbone.reset_classifier(0, '')\n        \n        self.pooling = nn.AdaptiveAvgPool2d(1)\n        self.feat_dim = backbone_out\n        \n        # 修改为与40401一致的多层分类器结构\n        self.classifier = nn.Sequential(\n            nn.Linear(backbone_out, 512),\n            nn.BatchNorm1d(512),\n            nn.LeakyReLU(0.1),\n            nn.Dropout(0.15),  # 使用固定值替代cfg.classifier_dropout/2\n            nn.Linear(512, 256),\n            nn.BatchNorm1d(256),\n            nn.LeakyReLU(0.1),\n            nn.Dropout(0.1),   # 使用固定值替代cfg.classifier_dropout/3\n            nn.Linear(256, num_classes)\n        )\n        \n    def forward(self, x):\n        \"\"\"\n        通过网络的前向传播。\n        \n        :param x: 输入张量。\n        :return: 每个类别的逻辑值。\n        \"\"\"\n        features = self.backbone(x)\n        if isinstance(features, dict):\n            features = features['features']\n        # 如果特征是4D，应用全局平均池化。\n        if len(features.shape) == 4:\n            features = self.pooling(features)\n            features = features.view(features.size(0), -1)\n        logits = self.classifier(features)\n        return logits\n\n##############################################################################\n# 内存管理函数\ndef clear_memory():\n    \"\"\"\n    清理内存缓存，减少内存使用。\n    \"\"\"\n    gc.collect()\n    torch.cuda.empty_cache() if torch.cuda.is_available() else None\n    \n##############################################################################\n# 处理标签数据\nprint(\"Loading label data...\")\nprimary_labels = pd.read_csv('/kaggle/input/birdclef-2025/sample_submission.csv').columns[1:].to_list()\ntaxonomy = pd.read_csv(CFG.taxonomy_csv)\n\n##############################################################################\n# 估计类别出现频率并设置动态阈值\ndef estimate_class_thresholds():\n    \"\"\"\n    基于类别名称和分类学估计每个类别的最佳阈值\n    鸟类与频繁出现的物种相比，稀有物种通常需要更高的阈值\n    \"\"\"\n    print(\"Estimating optimal thresholds for each class...\")\n    # 默认阈值\n    default_threshold = CFG.threshold\n    \n    # 如果有分类学数据，根据鸟类家族设置阈值\n    thresholds = {}\n    \n    if taxonomy is not None and 'primary_label' in taxonomy.columns:\n        # 分析taxonomy获取物种信息\n        for species in primary_labels:\n            species_info = taxonomy[taxonomy['primary_label'] == species]\n            \n            if not species_info.empty:\n                # 根据分类学信息调整阈值 - 这里是简化的逻辑，可以根据实际情况调整\n                family = species_info['family'].iloc[0] if 'family' in species_info.columns else None\n                genus = species_info['genus'].iloc[0] if 'genus' in species_info.columns else None\n                \n                # 示例：为某些特定的鸟类家族设置不同的阈值\n                if family in ['Trochilidae', 'Tyrannidae']:  # 蜂鸟和霸鹟科\n                    thresholds[species] = default_threshold - 0.05  # 降低阈值，因为这些物种可能更普遍\n                elif family in ['Thraupidae', 'Parulidae']:  # 塔纳格拉雀科和林莺科\n                    thresholds[species] = default_threshold + 0.05  # 提高阈值\n                else:\n                    thresholds[species] = default_threshold\n            else:\n                thresholds[species] = default_threshold\n    else:\n        # 没有分类学数据时使用默认阈值\n        for species in primary_labels:\n            thresholds[species] = default_threshold\n    \n    return thresholds\n\n##############################################################################\n# 文件处理函数\ndef get_audio_files():\n    \"\"\"\n    获取要处理的音频文件列表，处理测试和调试模式。\n    \n    :return: 元组 (audio_paths, file_ids)\n    \"\"\"\n    if os.path.exists(CFG.test_soundscapes) and len(glob.glob(f'{CFG.test_soundscapes}/*.ogg')) > 0:\n        CFG.debug = False\n        audio_dir = CFG.test_soundscapes\n        audio_paths = sorted(glob.glob(f'{audio_dir}/*.ogg'))\n    else:\n        print(\"No test files found. Using train soundscapes for debugging.\")\n        CFG.debug = True\n        audio_dir = CFG.train_soundscapes\n        all_audio_paths = sorted(glob.glob(f'{audio_dir}/*.ogg'))\n        audio_paths = all_audio_paths[CFG.debug_start_num:CFG.debug_start_num + CFG.debug_num]\n    \n    file_ids = [os.path.splitext(os.path.basename(path))[0] for path in audio_paths]\n    \n    print(f'Debug mode: {CFG.debug}')\n    print(f'Number of soundscapes: {len(audio_paths)}')\n    \n    return audio_paths, file_ids\n\n##############################################################################\n# 查找和加载模型\ndef find_model_files():\n    \"\"\"\n    在指定的模型目录中查找所有.pth模型文件。\n    \n    :return: 模型文件路径列表。\n    \"\"\"\n    model_files = []\n    model_dir = Path(CFG.model_path)\n    for path in model_dir.glob('**/*.pth'):\n        model_files.append(str(path))\n    return model_files\n\ndef load_models():\n    \"\"\"\n    加载所有找到的模型文件，并为集成推理做准备。\n    \n    :return: 已加载的PyTorch模型列表。\n    \"\"\"\n    models = []\n    model_files = find_model_files()\n    if not model_files:\n        print(f\"Warning: No model files found under {CFG.model_path}!\")\n        return models\n\n    print(f\"Found a total of {len(model_files)} model files.\")\n    \n    # 如果需要使用特定fold的模型进行过滤\n    if CFG.use_specific_folds:\n        filtered_files = []\n        for fold in CFG.folds:\n            fold_files = [f for f in model_files if f\"fold{fold}\" in f]\n            filtered_files.extend(fold_files)\n        model_files = filtered_files\n        print(f\"Using {len(model_files)} model files for the specified folds ({CFG.folds}).\")\n    \n    # 加载每个模型文件\n    for model_path in model_files:\n        try:\n            print(f\"Loading model: {model_path}\")\n            checkpoint = torch.load(model_path, map_location='cpu')  # 强制CPU加载\n            model = BirdCLEFModel(CFG, len(primary_labels))\n            model.load_state_dict(checkpoint['model_state_dict'])\n            model = model.to(CFG.device)\n            model.eval()\n            models.append(model)\n        except Exception as e:\n            print(f\"Error loading model {model_path}: {e}\")\n    \n    return models\n\n##############################################################################\n# 音频处理函数\ndef reduce_noise(audio_data):\n    \"\"\"\n    对音频数据应用降噪。\n    \n    :param audio_data: 输入音频数据\n    :return: 去噪后的音频数据\n    \"\"\"\n    if not CFG.apply_noise_reduction:\n        return audio_data\n    \n    # 应用中值滤波进行降噪\n    window_size = 5\n    audio_denoised = signal.medfilt(audio_data, window_size)\n    \n    # 混合原始信号和去噪信号\n    return (1 - CFG.noise_reduction_strength) * audio_data + CFG.noise_reduction_strength * audio_denoised\n\ndef normalize_audio(audio_data):\n    \"\"\"\n    对音频数据进行归一化处理。\n    \n    :param audio_data: 输入音频数据\n    :return: 归一化后的音频数据\n    \"\"\"\n    if not CFG.apply_normalization:\n        return audio_data\n    \n    # 去除直流偏移\n    audio_data = audio_data - np.mean(audio_data)\n    \n    # 归一化振幅\n    max_amplitude = np.max(np.abs(audio_data))\n    if max_amplitude > 0:\n        audio_data = audio_data / max_amplitude\n    \n    return audio_data\n\ndef audio2melspec(audio_data, cfg=CFG):\n    \"\"\"\n    将原始音频数据转换为归一化的梅尔频谱图，并进行增强预处理。\n    \"\"\"\n    # 处理NaN值 - 从参考代码借鉴的处理方式\n    if np.isnan(audio_data).any():\n        mean_signal = np.nanmean(audio_data)\n        audio_data = np.nan_to_num(audio_data, nan=mean_signal)\n    \n    # 如果音频短于所需的窗口大小，则进行填充\n    if len(audio_data) < CFG.FS * CFG.WINDOW_SIZE:\n        audio_data = np.pad(\n            audio_data,\n            (0, CFG.FS * CFG.WINDOW_SIZE - len(audio_data)),\n            mode='constant'\n        )\n    \n    # 应用降噪和归一化\n    audio_data = reduce_noise(audio_data)\n    audio_data = normalize_audio(audio_data)\n    \n    # 使用增强参数生成梅尔频谱图\n    mel_spec = librosa.feature.melspectrogram(\n        y=audio_data,\n        sr=cfg.FS,\n        n_fft=cfg.N_FFT,\n        hop_length=cfg.HOP_LENGTH,\n        n_mels=cfg.N_MELS,\n        fmin=cfg.FMIN,\n        fmax=cfg.FMAX,\n        power=2.0\n    )\n    \n    # 转换为对数刻度\n    mel_spec_db = librosa.power_to_db(mel_spec, ref=np.max)\n    \n    # 归一化 - 使用参考代码中的具体方法\n    mel_spec_norm = (mel_spec_db - mel_spec_db.min()) / (mel_spec_db.max() - mel_spec_db.min() + 1e-8)\n    \n    # 应用频谱对比度增强\n    if CFG.apply_spec_contrast:\n        mel_spec_norm = enhance_spectrogram_contrast(mel_spec_norm, CFG.contrast_factor)\n    \n    # 调整到目标形状\n    if mel_spec_norm.shape != cfg.TARGET_SHAPE:\n        mel_spec_norm = cv2.resize(mel_spec_norm, cfg.TARGET_SHAPE, interpolation=cv2.INTER_LINEAR)\n    \n    return mel_spec_norm.astype(np.float32)\n\n#factor这个参数调整对比度增强强度，可以在0.05~0.2之间尝试\ndef enhance_spectrogram_contrast(spec, factor=0.15):\n    \"\"\"\n    增强梅尔谱图对比度，使特征更明显\n    \n    :param spec: 输入谱图\n    :param factor: 对比度增强因子\n    :return: 增强后的谱图\n    \"\"\"\n    mean = np.mean(spec)\n    enhanced = mean + (spec - mean) * (1 + factor)\n    return np.clip(enhanced, 0, 1)  # 确保值在[0,1]范围内\n\ndef apply_spec_augment(spec):\n    \"\"\"\n    应用SpecAugment数据增强，添加时间和频率掩码\n    \n    :param spec: 输入梅尔谱图，形状为[height, width]\n    :return: 增强后的谱图\n    \"\"\"\n    if not CFG.use_spec_augment:\n        return spec\n    \n    # 创建副本以不修改原始数据\n    augmented = spec.copy()\n    \n    # 应用频率掩码\n    for _ in range(CFG.freq_mask_count):\n        f = np.random.randint(0, CFG.freq_mask_param)\n        f0 = np.random.randint(0, augmented.shape[0] - f)\n        augmented[f0:f0 + f, :] = 0\n    \n    # 应用时间掩码\n    for _ in range(CFG.time_mask_count):\n        t = np.random.randint(0, CFG.time_mask_param)\n        t0 = np.random.randint(0, augmented.shape[1] - t)\n        augmented[:, t0:t0 + t] = 0\n    \n    return augmented\n\ndef apply_tta(spec, tta_idx):\n    \"\"\"\n    对频谱图应用测试时增强（TTA）。\n    \n    :param spec: 输入梅尔频谱图。\n    :param tta_idx: 指示要应用哪种TTA的索引。\n    :return: 增强后的频谱图。\n    \"\"\"\n    # 创建副本以避免负步长问题\n    result = spec.copy()\n    \n    if tta_idx == 0:\n        # 无增强\n        return result\n    elif tta_idx == 1:\n        # 时间位移（水平翻转）\n        return np.flip(result, axis=1).copy()\n    elif tta_idx == 2:\n        # 频率位移（垂直翻转）\n        return np.flip(result, axis=0).copy()\n    elif tta_idx == 3:\n        # 同时进行时间和频率位移\n        return np.flip(np.flip(result, axis=1), axis=0).copy()\n    elif tta_idx == 4:\n        # 轻微的音高上移\n        return np.roll(result, shift=3, axis=0)\n    elif tta_idx == 5:\n        # 轻微的音高下移\n        return np.roll(result, shift=-3, axis=0)\n    else:\n        return result\n\n##############################################################################\n# 增强推理过程\ndef predict_on_audio(audio_path, models):\n    \"\"\"\n    处理单个音频文件，并预测每个5秒段中的物种存在情况。\n    \n    :param audio_path: 音频文件的路径。\n    :param models: 用于预测的PyTorch模型列表。\n    :return: 每个段的元组 (row_ids, predictions)。\n    \"\"\"\n    predictions = []\n    row_ids = []\n    soundscape_id = os.path.splitext(os.path.basename(audio_path))[0]\n    \n    try:\n        print(f\"Processing {soundscape_id}\")\n        audio_data, _ = librosa.load(audio_path, sr=CFG.FS)\n        total_segments = int(len(audio_data) / (CFG.FS * CFG.WINDOW_SIZE))\n        \n        segment_predictions = []  # 收集所有段的预测，用于后期处理\n        \n        for segment_idx in range(total_segments):\n            if time.time() > TERMINATE_TIME:\n                print(\"Time limit reached, stopping processing early\")\n                return row_ids, predictions\n                \n            start_sample = segment_idx * CFG.FS * CFG.WINDOW_SIZE\n            end_sample = start_sample + CFG.FS * CFG.WINDOW_SIZE\n            segment_audio = audio_data[start_sample:end_sample]\n            \n            end_time_sec = (segment_idx + 1) * CFG.WINDOW_SIZE\n            row_id = f\"{soundscape_id}_{end_time_sec}\"\n            row_ids.append(row_id)\n\n            # 使用更健壮的异常处理逻辑从参考代码借鉴\n            try:\n                # 根据配置决定是否使用TTA\n                if CFG.use_tta:\n                    all_preds = []\n                    for tta_idx in range(CFG.tta_count):\n                        mel_spec = audio2melspec(segment_audio)\n                        \n                        # 应用SpecAugment（可选）\n                        if np.random.random() < 0.5 and CFG.use_spec_augment:\n                            mel_spec = apply_spec_augment(mel_spec)\n                            \n                        mel_spec = apply_tta(mel_spec, tta_idx)\n                        mel_spec_tensor = torch.tensor(mel_spec, dtype=torch.float32).unsqueeze(0).unsqueeze(0)\n                        mel_spec_tensor = mel_spec_tensor.to(CFG.device)\n                        \n                        segment_preds = []\n                        for model in models:\n                            with torch.no_grad():\n                                outputs = model(mel_spec_tensor)\n                                probs = torch.sigmoid(outputs).cpu().numpy().squeeze()\n                                segment_preds.append(probs)\n                        \n                        # 使用加权平均进行集成预测\n                        if len(models) > 1:\n                            weights = np.linspace(0.8, 1.2, len(models))\n                            weights = weights / weights.sum()\n                            avg_preds = np.average(segment_preds, axis=0, weights=weights)\n                        else:\n                            avg_preds = segment_preds[0]\n                        \n                        all_preds.append(avg_preds)\n                    \n                    # 平均所有TTA结果\n                    final_preds = np.mean(all_preds, axis=0)\n                else:\n                    # 不使用TTA的常规预测\n                    mel_spec = audio2melspec(segment_audio)\n                    mel_spec_tensor = torch.tensor(mel_spec, dtype=torch.float32).unsqueeze(0).unsqueeze(0)\n                    mel_spec_tensor = mel_spec_tensor.to(CFG.device)\n                    \n                    segment_preds = []\n                    for model in models:\n                        with torch.no_grad():\n                            outputs = model(mel_spec_tensor)\n                            probs = torch.sigmoid(outputs).cpu().numpy().squeeze()\n                            segment_preds.append(probs)\n                    \n                    # 使用加权平均进行集成预测\n                    if len(models) > 1:\n                        weights = np.linspace(0.8, 1.2, len(models))\n                        weights = weights / weights.sum()\n                        final_preds = np.average(segment_preds, axis=0, weights=weights)\n                    else:\n                        final_preds = segment_preds[0]\n            except Exception as e:\n                print(f\"Error processing segment {segment_idx}: {e}\")\n                # 使用零向量或前一个预测作为后备\n                final_preds = np.zeros(len(primary_labels)) if len(predictions) == 0 else predictions[-1]\n            \n            segment_predictions.append(final_preds)\n        \n        # 对所有段的预测进行后处理\n        for i in range(len(segment_predictions)):\n            # 融合相邻时间点的预测\n            if i > 0 and i < len(segment_predictions) - 1:\n                # 当前预测的权重，以及前后预测的权重\n                blended_pred = (\n                    CFG.prediction_blend[0] * segment_predictions[i] + \n                    CFG.prediction_blend[1] * 0.5 * (segment_predictions[i-1] + segment_predictions[i+1])\n                )\n                predictions.append(blended_pred)\n            else:\n                # 对于首尾段，保持原始预测\n                predictions.append(segment_predictions[i])\n                \n    except Exception as e:\n        print(f\"Error processing {audio_path}: {e}\")\n        # 确保row_ids和predictions长度匹配，即使出现错误\n        # 清空部分处理的结果\n        row_ids = []\n        predictions = []\n    \n    return row_ids, predictions\n\n##############################################################################\n# 增强平滑函数\ndef smooth_predictions(submission_df):\n    \"\"\"\n    平滑提交中的预测以强化时间一致性。\n    \n    :param submission_df: 提交数据框。\n    :return: 平滑后的数据框。\n    \"\"\"\n    print(\"Smoothing prediction results...\")\n    sub = submission_df.copy()\n    cols = sub.columns[1:]\n    sub['group'] = sub['row_id'].str.rsplit('_', n=1).str[0]\n    unique_groups = sub['group'].unique()\n    \n    for group in unique_groups:\n        group_mask = sub['group'] == group\n        sub_group = sub[group_mask].copy()\n        predictions = sub_group[cols].values\n        new_predictions = predictions.copy()\n        \n        if predictions.shape[0] > 1:\n            # 使用可配置窗口和权重的增强平滑\n            window = CFG.smoothing_window\n            weights = CFG.smoothing_weights\n            half_window = window // 2\n            \n            # 处理边缘情况\n            for i in range(half_window):\n                # 起始几个元素使用可用的窗口\n                valid_window = i + half_window + 1\n                valid_weights = weights[-valid_window:]\n                valid_weights = valid_weights / np.sum(valid_weights)  # 重新归一化权重\n                new_predictions[i] = np.average(predictions[:valid_window], axis=0, weights=valid_weights)\n                \n                # 末尾几个元素\n                valid_window = i + half_window + 1\n                valid_weights = weights[:valid_window]\n                valid_weights = valid_weights / np.sum(valid_weights)  # 重新归一化权重\n                new_predictions[-(i+1)] = np.average(predictions[-valid_window:], axis=0, weights=valid_weights)\n            \n            # 处理中间元素 - 使用滑动窗口平均\n            for i in range(half_window, predictions.shape[0] - half_window):\n                window_start = i - half_window\n                window_end = i + half_window + 1\n                new_predictions[i] = np.average(predictions[window_start:window_end], axis=0, weights=weights)\n        \n        sub.loc[group_mask, cols] = new_predictions\n    \n    sub.drop('group', axis=1, inplace=True)\n    return sub\n\n##############################################################################\n# 应用阈值到预测结果\ndef apply_thresholds(submission_df, thresholds):\n    \"\"\"\n    使用每个类别的最佳阈值二进制化预测结果\n    \n    :param submission_df: 预测结果DataFrame\n    :param thresholds: 每个类别的阈值字典\n    :return: 处理后的DataFrame\n    \"\"\"\n    print(\"Applying class-specific thresholds...\")\n    result_df = submission_df.copy()\n    \n    for species in primary_labels:\n        threshold = thresholds.get(species, CFG.threshold)\n        result_df[species] = (result_df[species] >= threshold).astype(float)\n    \n    return result_df\n\n##############################################################################\n# 主处理流程\ndef run_pipeline():\n    \"\"\"\n    执行完整的推理流程。\n    \"\"\"\n    print(f\"Device: {CFG.device}\")\n    print(f\"TTA enabled: {CFG.use_tta} (variations: {CFG.tta_count if CFG.use_tta else 0})\")\n    \n    # 估计每个类别的最佳阈值\n    if CFG.class_thresholds is None:\n        CFG.class_thresholds = estimate_class_thresholds()\n    \n    # 加载模型\n    models = load_models()\n    if not models:\n        print(\"No models found! Please check model paths.\")\n        return None\n    \n    print(f\"Model usage: {'Single model' if len(models) == 1 else f'Ensemble of {len(models)} models'}\")\n    \n    # 获取音频文件\n    audio_paths, file_ids = get_audio_files()\n    print(f\"Found {len(audio_paths)} audio files\")\n    \n    all_row_ids = []\n    all_predictions = []\n    \n    # 处理每个音频文件\n    for i, audio_path in enumerate(tqdm(audio_paths)):\n        row_ids, predictions = predict_on_audio(audio_path, models)\n        \n        # 只有在两者都有数据时才添加结果\n        if len(row_ids) > 0 and len(predictions) > 0 and len(row_ids) == len(predictions):\n            all_row_ids.extend(row_ids)\n            all_predictions.extend(predictions)\n        else:\n            print(f\"Skipping results for {audio_path} due to length mismatch or empty results\")\n        \n        # 定期清理内存，提高效率\n        if (i + 1) % CFG.clear_cache_frequency == 0:\n            clear_memory()\n    \n    # 创建提交的DataFrame\n    print(\"Creating submission dataframe...\")\n    submission_dict = {'row_id': all_row_ids}\n    for i, species in enumerate(primary_labels):\n        submission_dict[species] = [pred[i] for pred in all_predictions]\n    \n    # 检查所有项目的长度是否相同\n    lengths = [len(v) for v in submission_dict.values()]\n    if len(set(lengths)) > 1:\n        print(f\"Warning: Inconsistent lengths in submission_dict: {lengths}\")\n        # 找出最短的长度\n        min_length = min(lengths)\n        # 截断所有项目到最短长度\n        for k in submission_dict:\n            submission_dict[k] = submission_dict[k][:min_length]\n    \n    submission_df = pd.DataFrame(submission_dict)\n    \n    # 确保所有列都包含在提交中\n    sample_sub = pd.read_csv(CFG.submission_csv)\n    missing_cols = set(sample_sub.columns) - set(submission_df.columns)\n    if missing_cols:\n        print(f\"Warning: Missing {len(missing_cols)} columns in submission\")\n        for col in missing_cols:\n            submission_df[col] = 0.0\n            \n    # 确保列顺序与样本提交一致\n    if 'row_id' in sample_sub.columns:\n        submission_df = submission_df[sample_sub.columns]\n    \n    # 应用平滑\n    if CFG.apply_smoothing:\n        submission_df = smooth_predictions(submission_df)\n    \n    # 添加参考代码中的二次平滑作为补充优化\n    if CFG.apply_secondary_smoothing:\n        cols = submission_df.columns[1:]\n        groups = submission_df['row_id'].str.rsplit('_', n=1).str[0].values\n            \n        for group in np.unique(groups):\n            group_mask = (groups == group)\n            sub_group = submission_df[group_mask]\n            predictions = sub_group[cols].values\n            new_predictions = predictions.copy()\n            \n            # 处理中间部分\n            for i in range(1, predictions.shape[0]-1):\n                new_predictions[i] = (predictions[i-1] * 0.2) + (predictions[i] * 0.6) + (predictions[i+1] * 0.2)\n            \n            # 处理首尾两端\n            if predictions.shape[0] > 1:\n                new_predictions[0] = (predictions[0] * 0.8) + (predictions[1] * 0.2)\n                new_predictions[-1] = (predictions[-1] * 0.8) + (predictions[-2] * 0.2)\n            \n            submission_df.loc[group_mask, cols] = new_predictions\n    \n    # 应用每个类别的阈值\n    # 注意：Kaggle评估使用概率，所以我们不应用二值化\n    # final_df = apply_thresholds(submission_df, CFG.class_thresholds)\n    final_df = submission_df\n    \n    # 写入CSV\n    submission_path = 'submission.csv'\n    final_df.to_csv(submission_path, index=False)\n    print(f\"Submission saved to {submission_path}\")\n    \n    # 显示提交数据框\n    print(\"\\nSubmission head:\")\n    print(final_df.head(5))\n    \n    print(\"\\nSubmission tail:\")\n    print(final_df.tail(5))\n    \n    return final_df\n\n##############################################################################\n# 可视化函数\ndef create_mel_transform():\n    \"\"\"\n    创建用于可视化的梅尔频谱图转换。\n    \n    :return: MelSpectrogram转换\n    \"\"\"\n    return AT.MelSpectrogram(\n        sample_rate=CFG.FS,\n        n_fft=CFG.N_FFT,\n        win_length=CFG.N_FFT,\n        hop_length=CFG.HOP_LENGTH,\n        center=True,\n        f_min=CFG.FMIN,\n        f_max=CFG.FMAX,\n        pad_mode=\"reflect\",\n        power=2.0,\n        norm='slaney',\n        n_mels=CFG.N_MELS,\n        mel_scale=\"htk\",\n    )\n\ndef audio_to_mel_debug(filepath):\n    \"\"\"\n    将音频文件转换为梅尔频谱图进行可视化。\n    \n    :param filepath: 音频文件路径\n    :return: 梅尔频谱图\n    \"\"\"\n    waveform, _ = torchaudio.load(filepath, backend=\"soundfile\")\n    waveform = waveform / torch.max(torch.abs(waveform))\n    mel_transform = create_mel_transform()\n    melspec = mel_transform(waveform)\n    melspec = 10 * torch.log10(melspec + 1e-10)  # 添加小常数以避免log(0)\n    return melspec\n\ndef plot_results(results, file_name, audio_dir):\n    \"\"\"\n    绘制音频频谱图和预测结果。\n    \n    :param results: 预测结果数据框\n    :param file_name: 不带扩展名的音频文件名\n    :param audio_dir: 包含音频文件的目录\n    \"\"\"\n    path = os.path.join(audio_dir, file_name + \".ogg\")\n    specgram = audio_to_mel_debug(path)\n    \n    fig, axes = plt.subplots(2, 1, figsize=(12, 8))\n    axes[0].set_title(file_name)\n    im = axes[0].imshow(specgram[0], origin=\"lower\", aspect=\"auto\")\n    axes[0].set_ylabel(\"mel bin\")\n    axes[0].set_xlabel(\"frame\")\n    fig.colorbar(im, ax=axes[0])\n    \n    # 筛选当前文件的结果\n    file_results = results[results[\"row_id\"].str.contains(file_name)]\n    \n    # 获取时间段数量（通常60秒的文件有12个段）\n    time_segments = file_results.shape[0]\n    \n    # 绘制预测热图\n    heatmap = axes[1].pcolor(file_results.iloc[:, 1:].values.T, edgecolors='k', \n                             linewidths=0.1, vmin=0, vmax=1, cmap='Blues')\n    fig.colorbar(heatmap, ax=axes[1])\n    \n    # 根据时间段数量设置x轴刻度\n    axes[1].set_xticks(np.arange(0, time_segments, 1))\n    axes[1].set_xticklabels(np.arange(0, time_segments * 5, 5))\n    \n    axes[1].set_ylabel(\"species\")\n    axes[1].set_xlabel(\"sec\")\n    \n    # 在y轴上添加物种标签\n    if len(primary_labels) <= 30:  # 只有在标签不太多时才显示\n        axes[1].set_yticks(np.arange(0.5, len(primary_labels), 1))\n        axes[1].set_yticklabels(primary_labels)\n    \n    fig.tight_layout()\n    plt.savefig(f'{file_name}_prediction.png')\n    plt.close()\n\n##############################################################################\n# 主执行\nif __name__ == \"__main__\":\n    try:\n        # 运行推理流程\n        results = run_pipeline()\n        \n        # 清理内存，确保后续可视化有足够内存\n        clear_memory()\n        \n        # 如果处于调试模式并且结果可用，则进行可视化\n        if CFG.debug and results is not None:\n            audio_paths, file_ids = get_audio_files()\n            audio_dir = CFG.train_soundscapes if CFG.debug else CFG.test_soundscapes\n            \n            print(\"\\nGenerating visualizations...\")\n            for file_id in file_ids:\n                plot_results(results, file_id, audio_dir)\n                print(f\"Visualization saved for {file_id}\")\n        \n        print(f\"\\nTotal execution time: {(time.time() - START) / 60:.2f} minutes\")\n    except Exception as e:\n        print(f\"Error during execution: {e}\")\n        import traceback\n        traceback.print_exc() ","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-13T16:50:51.317084Z","iopub.execute_input":"2025-04-13T16:50:51.317616Z","iopub.status.idle":"2025-04-13T16:51:09.366966Z","shell.execute_reply.started":"2025-04-13T16:50:51.317579Z","shell.execute_reply":"2025-04-13T16:51:09.36607Z"}},"outputs":[{"name":"stdout","text":"Loading label data...\nDevice: cpu\nTTA enabled: False (variations: 0)\nEstimating optimal thresholds for each class...\nFound a total of 3 model files.\nLoading model: /kaggle/input/efficientnet-b0-test_4/pytorch/default/1/models/model_fold0.pth\nLoading model: /kaggle/input/efficientnet-b0-test_4/pytorch/default/1/models/model_fold1.pth\nLoading model: /kaggle/input/efficientnet-b0-test_4/pytorch/default/1/models/model_fold2.pth\nModel usage: Ensemble of 3 models\nNo test files found. Using train soundscapes for debugging.\nDebug mode: True\nNumber of soundscapes: 8\nFound 8 audio files\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"  0%|          | 0/8 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"25eef27dba8e4931b1d24eaed95ca64c"}},"metadata":{}},{"name":"stdout","text":"Processing H02_20230420_074000\nError processing /kaggle/input/birdclef-2025/train_soundscapes/H02_20230420_074000.ogg: name 'TERMINATE_TIME' is not defined\nSkipping results for /kaggle/input/birdclef-2025/train_soundscapes/H02_20230420_074000.ogg due to length mismatch or empty results\nProcessing H02_20230420_112000\nError processing /kaggle/input/birdclef-2025/train_soundscapes/H02_20230420_112000.ogg: name 'TERMINATE_TIME' is not defined\nSkipping results for /kaggle/input/birdclef-2025/train_soundscapes/H02_20230420_112000.ogg due to length mismatch or empty results\nProcessing H02_20230420_154500\nError processing /kaggle/input/birdclef-2025/train_soundscapes/H02_20230420_154500.ogg: name 'TERMINATE_TIME' is not defined\nSkipping results for /kaggle/input/birdclef-2025/train_soundscapes/H02_20230420_154500.ogg due to length mismatch or empty results\nProcessing H02_20230420_164000\nError processing /kaggle/input/birdclef-2025/train_soundscapes/H02_20230420_164000.ogg: name 'TERMINATE_TIME' is not defined\nSkipping results for /kaggle/input/birdclef-2025/train_soundscapes/H02_20230420_164000.ogg due to length mismatch or empty results\nProcessing H02_20230420_223500\nError processing /kaggle/input/birdclef-2025/train_soundscapes/H02_20230420_223500.ogg: name 'TERMINATE_TIME' is not defined\nSkipping results for /kaggle/input/birdclef-2025/train_soundscapes/H02_20230420_223500.ogg due to length mismatch or empty results\nProcessing H02_20230421_093000\nError processing /kaggle/input/birdclef-2025/train_soundscapes/H02_20230421_093000.ogg: name 'TERMINATE_TIME' is not defined\nSkipping results for /kaggle/input/birdclef-2025/train_soundscapes/H02_20230421_093000.ogg due to length mismatch or empty results\nProcessing H02_20230421_113500\nError processing /kaggle/input/birdclef-2025/train_soundscapes/H02_20230421_113500.ogg: name 'TERMINATE_TIME' is not defined\nSkipping results for /kaggle/input/birdclef-2025/train_soundscapes/H02_20230421_113500.ogg due to length mismatch or empty results\nProcessing H02_20230421_170000\nError processing /kaggle/input/birdclef-2025/train_soundscapes/H02_20230421_170000.ogg: name 'TERMINATE_TIME' is not defined\nSkipping results for /kaggle/input/birdclef-2025/train_soundscapes/H02_20230421_170000.ogg due to length mismatch or empty results\nCreating submission dataframe...\nSmoothing prediction results...\nError during execution: Can only use .str accessor with string values!\n","output_type":"stream"},{"name":"stderr","text":"Traceback (most recent call last):\n  File \"/tmp/ipykernel_49/2928337006.py\", line 847, in <cell line: 0>\n    results = run_pipeline()\n              ^^^^^^^^^^^^^^\n  File \"/tmp/ipykernel_49/2928337006.py\", line 717, in run_pipeline\n    submission_df = smooth_predictions(submission_df)\n                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/tmp/ipykernel_49/2928337006.py\", line 583, in smooth_predictions\n    sub['group'] = sub['row_id'].str.rsplit('_', n=1).str[0]\n                   ^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.11/dist-packages/pandas/core/generic.py\", line 6299, in __getattr__\n    return object.__getattribute__(self, name)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.11/dist-packages/pandas/core/accessor.py\", line 224, in __get__\n    accessor_obj = self._accessor(obj)\n                   ^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.11/dist-packages/pandas/core/strings/accessor.py\", line 191, in __init__\n    self._inferred_dtype = self._validate(data)\n                           ^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.11/dist-packages/pandas/core/strings/accessor.py\", line 245, in _validate\n    raise AttributeError(\"Can only use .str accessor with string values!\")\nAttributeError: Can only use .str accessor with string values!\n","output_type":"stream"}],"execution_count":28},{"cell_type":"code","source":"import time\nSTART = time.time()\nfrom concurrent.futures import ThreadPoolExecutor\nimport glob\nimport librosa\nimport numpy as np\nimport os\nimport pandas as pd\nimport re\nimport sys\nimport cv2\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport timm\nimport torchaudio\nimport torchaudio.transforms as AT\nimport matplotlib.pyplot as plt\nfrom tqdm.auto import tqdm\nfrom scipy import signal\nimport gc\nimport warnings\nimport logging\nfrom pathlib import Path\nimport random\n# Suppress warnings and limit log output\nwarnings.filterwarnings(\"ignore\")\nlogging.basicConfig(level=logging.ERROR)\nprint(\"hello\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-14T07:11:06.253457Z","iopub.execute_input":"2025-04-14T07:11:06.253915Z","iopub.status.idle":"2025-04-14T07:11:06.261824Z","shell.execute_reply.started":"2025-04-14T07:11:06.253888Z","shell.execute_reply":"2025-04-14T07:11:06.260838Z"}},"outputs":[{"name":"stdout","text":"hello\n","output_type":"stream"}],"execution_count":25},{"cell_type":"code","source":"#partwise\nclass CFG:\n    # Basic configuration\n    test_soundscapes = '/kaggle/input/dataga/train.csv'\n    train_soundscapes = '/kaggle/input/dataga/train.csv'\n    submission_csv = '/kaggle/input/dataga/sample_submission.csv'\n    taxonomy_csv = '/kaggle/input/dataga/taxonomy.csv'\n    model_path = '/kaggle/input/efficientnet-b0-test_4/pytorch/default/1/models'\n    \n    # Debug settings\n    debug = False\n    debug_start_num = 0\n    debug_num = 8\n    \n    # Audio parameters\n    FS = 32000\n    WINDOW_SIZE = 5\n    \n    # Mel spectrogram parameters\n    N_FFT = 1034  \n    HOP_LENGTH = 64  \n    N_MELS = 136 \n    FMIN = 20\n    FMAX = 16000\n    TARGET_SHAPE = (256, 256)\n    \n    # Model parameters\n    model_name = 'efficientnet_b0'\n    in_channels = 1\n    device = 'cpu'  # Force CPU usage\n    \n    # Inference parameters\n    batch_size = 16  # Reduce batch size to accommodate CPU\n    use_tta = False  # Disable TTA to speed up processing\n    tta_count = 3\n    threshold = 0.7\n    \n    # Model selection parameters\n    use_specific_folds = False  # Don't use specific folds, use all found models\n    folds = [0, 1]  # Only used when use_specific_folds is True\n    \n    # Whether to apply smoothing\n    apply_smoothing = True\n    \n    # Audio preprocessing parameters\n    apply_noise_reduction = True\n    apply_normalization = True\n    noise_reduction_strength = 0.1\n    \n    # Smoothing parameters\n    smoothing_window = 5  # Increase window size to capture longer temporal correlations\n    smoothing_weights = [0.15, 0.2, 0.3, 0.2, 0.15]  # More refined weights\n    \n    # Memory management\n    clear_cache_frequency = 5  # Clean memory after processing this many audio files\n    \n    # New data augmentation parameters\n    use_spec_augment = True\n    # Time mask parameters\n    time_mask_param = 30\n    # Frequency mask parameters\n    freq_mask_param = 20\n    # Number of time masks\n    time_mask_count = 1\n    # Number of frequency masks\n    freq_mask_count = 1\n    \n    # New spectral contrast enhancement\n    apply_spec_contrast = True\n    contrast_factor = 0.15\n    \n    # Threshold adjustment\n    class_thresholds = None  # Will be dynamically set at runtime to optimal thresholds for each class\n    \n    # Prediction fusion\n    prediction_blend = [0.7, 0.3]  # 70% for original prediction, 30% for weighted average of adjacent time points\n    # Add an extra smoothing option used in the reference code\n    apply_secondary_smoothing = True  # Enable secondary smoothing borrowed from reference code\n    secondary_smoothing_weights = [0.2, 0.6, 0.2]  # Weights from reference code: previous frame, current frame, next frame\n    print(\"tashu\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-14T07:21:46.64839Z","iopub.execute_input":"2025-04-14T07:21:46.648761Z","iopub.status.idle":"2025-04-14T07:21:46.658752Z","shell.execute_reply.started":"2025-04-14T07:21:46.64874Z","shell.execute_reply":"2025-04-14T07:21:46.657501Z"}},"outputs":[{"name":"stdout","text":"tashu\n","output_type":"stream"}],"execution_count":44},{"cell_type":"code","source":"def set_seed(seed=42):\n   \"\"\"Set random seed to ensure reproducibility\"\"\"\n   random.seed(seed)\n   np.random.seed(seed)\n   torch.manual_seed(seed)\n   if torch.cuda.is_available():\n       torch.cuda.manual_seed(seed)\n   os.environ['PYTHONHASHSEED'] = str(seed)\n# Set random seed\nset_seed(42)\nprint(\"avni\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-14T07:12:16.122962Z","iopub.execute_input":"2025-04-14T07:12:16.123342Z","iopub.status.idle":"2025-04-14T07:12:16.13232Z","shell.execute_reply.started":"2025-04-14T07:12:16.123317Z","shell.execute_reply":"2025-04-14T07:12:16.131298Z"}},"outputs":[{"name":"stdout","text":"avni\n","output_type":"stream"}],"execution_count":27},{"cell_type":"code","source":"# PyTorch Bird Classification Model\nclass BirdCLEFModel(nn.Module):\n   \"\"\"\n   Custom PyTorch neural network model using timm as backbone network.\n   \"\"\"\n   def __init__(self, cfg, num_classes):\n       \"\"\"\n       Initialize BirdCLEFModel.\n       \n       :param cfg: Configuration parameters.\n       :param num_classes: Number of output classes.\n       \"\"\"\n       super().__init__()\n       self.cfg = cfg\n       self.backbone = timm.create_model(\n           cfg.model_name,\n           pretrained=False,\n           in_chans=cfg.in_channels,\n           drop_rate=0.0,\n           drop_path_rate=0.0\n       )\n       \n       if 'efficientnet' in cfg.model_name:\n           backbone_out = self.backbone.classifier.in_features\n           self.backbone.classifier = nn.Identity()\n       elif 'resnet' in cfg.model_name:\n           backbone_out = self.backbone.fc.in_features\n           self.backbone.fc = nn.Identity()\n       else:\n           backbone_out = self.backbone.get_classifier().in_features\n           self.backbone.reset_classifier(0, '')\n       \n       self.pooling = nn.AdaptiveAvgPool2d(1)\n       self.feat_dim = backbone_out\n       \n       # Modified to match the multi-layer classifier structure of 40401\n       self.classifier = nn.Sequential(\n           nn.Linear(backbone_out, 512),\n           nn.BatchNorm1d(512),\n           nn.LeakyReLU(0.1),\n           nn.Dropout(0.15),  # Using fixed value instead of cfg.classifier_dropout/2\n           nn.Linear(512, 256),\n           nn.BatchNorm1d(256),\n           nn.LeakyReLU(0.1),\n           nn.Dropout(0.1),   # Using fixed value instead of cfg.classifier_dropout/3\n           nn.Linear(256, num_classes)\n       )\n       \n   def forward(self, x):\n       \"\"\"\n       Forward propagation through the network.\n       \n       :param x: Input tensor.\n       :return: Logits for each class.\n       \"\"\"\n       features = self.backbone(x)\n       if isinstance(features, dict):\n           features = features['features']\n       # If features are 4D, apply global average pooling.\n       if len(features.shape) == 4:\n           features = self.pooling(features)\n           features = features.view(features.size(0), -1)\n       logits = self.classifier(features)\n       print(\"hello\")\n       return logits\n       ","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-14T07:12:19.107371Z","iopub.execute_input":"2025-04-14T07:12:19.107683Z","iopub.status.idle":"2025-04-14T07:12:19.117273Z","shell.execute_reply.started":"2025-04-14T07:12:19.107662Z","shell.execute_reply":"2025-04-14T07:12:19.116233Z"}},"outputs":[],"execution_count":28},{"cell_type":"code","source":"# Memory management function\ndef clear_memory():\n   \"\"\"\n   Clean memory cache, reduce memory usage.\n   \"\"\"\n   gc.collect()\n   torch.cuda.empty_cache() if torch.cuda.is_available() else None\nprint(\"hello\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-14T07:12:23.678942Z","iopub.execute_input":"2025-04-14T07:12:23.679833Z","iopub.status.idle":"2025-04-14T07:12:23.685403Z","shell.execute_reply.started":"2025-04-14T07:12:23.679786Z","shell.execute_reply":"2025-04-14T07:12:23.684196Z"}},"outputs":[{"name":"stdout","text":"hello\n","output_type":"stream"}],"execution_count":29},{"cell_type":"code","source":"# Process label data\nprint(\"Loading label data...\")\nprimary_labels = pd.read_csv('/kaggle/input/dataga/sample_submission.csv').columns[1:].to_list()\ntaxonomy = pd.read_csv(CFG.taxonomy_csv)\nprint(\"hello\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-14T07:13:03.21082Z","iopub.execute_input":"2025-04-14T07:13:03.211227Z","iopub.status.idle":"2025-04-14T07:13:03.232236Z","shell.execute_reply.started":"2025-04-14T07:13:03.211203Z","shell.execute_reply":"2025-04-14T07:13:03.231334Z"}},"outputs":[{"name":"stdout","text":"Loading label data...\nhello\n","output_type":"stream"}],"execution_count":31},{"cell_type":"code","source":"# Estimate class frequency and set dynamic thresholds\ndef estimate_class_thresholds():\n   \"\"\"\n   Estimate the best threshold for each class based on class names and taxonomy\n   Rare species typically need higher thresholds compared to frequently occurring species\n   \"\"\"\n   print(\"Estimating optimal thresholds for each class...\")\n   # Default threshold\n   default_threshold = CFG.threshold\n   \n   # If taxonomy data is available, set thresholds based on bird families\n   thresholds = {}\n   \n   if taxonomy is not None and 'primary_label' in taxonomy.columns:\n       # Analyze taxonomy to get species information\n       for species in primary_labels:\n           species_info = taxonomy[taxonomy['primary_label'] == species]\n           \n           if not species_info.empty:\n               # Adjust thresholds based on taxonomic information - this is simplified logic, can be adjusted according to actual conditions\n               family = species_info['family'].iloc[0] if 'family' in species_info.columns else None\n               genus = species_info['genus'].iloc[0] if 'genus' in species_info.columns else None\n               \n               # Example: Set different thresholds for certain bird families\n               if family in ['Trochilidae', 'Tyrannidae']:  # Hummingbirds and Tyrant flycatchers\n                   thresholds[species] = default_threshold - 0.05  # Lower threshold, as these species may be more common\n               elif family in ['Thraupidae', 'Parulidae']:  # Tanagers and New World warblers\n                   thresholds[species] = default_threshold + 0.05  # Increase threshold\n               else:\n                   thresholds[species] = default_threshold\n           else:\n               thresholds[species] = default_threshold\n   else:\n       # Use default threshold when no taxonomy data is available\n       for species in primary_labels:\n           thresholds[species] = default_threshold\n       return thresholds","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-14T07:13:09.373632Z","iopub.execute_input":"2025-04-14T07:13:09.374519Z","iopub.status.idle":"2025-04-14T07:13:09.381899Z","shell.execute_reply.started":"2025-04-14T07:13:09.374488Z","shell.execute_reply":"2025-04-14T07:13:09.380928Z"}},"outputs":[],"execution_count":32},{"cell_type":"code","source":"# File processing functions\ndef get_audio_files():\n   \"\"\"\n   Get list of audio files to process, handle test and debug modes.\n   \n   :return: tuple (audio_paths, file_ids)\n   \"\"\"\n   if os.path.exists(CFG.test_soundscapes) and len(glob.glob(f'{CFG.test_soundscapes}/*.ogg')) > 0:\n       CFG.debug = False\n       audio_dir = CFG.test_soundscapes\n       audio_paths = sorted(glob.glob(f'{audio_dir}/*.ogg'))\n   else:\n       print(\"No test files found. Using train soundscapes for debugging.\")\n       CFG.debug = True\n       audio_dir = CFG.train_soundscapes\n       all_audio_paths = sorted(glob.glob(f'{audio_dir}/*.ogg'))\n       audio_paths = all_audio_paths[CFG.debug_start_num:CFG.debug_start_num + CFG.debug_num]\n   \n   file_ids = [os.path.splitext(os.path.basename(path))[0] for path in audio_paths]\n   \n   print(f'Debug mode: {CFG.debug}')\n   print(f'Number of soundscapes: {len(audio_paths)}')\n   \n   return audio_paths, file_ids","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-14T07:13:15.737634Z","iopub.execute_input":"2025-04-14T07:13:15.737948Z","iopub.status.idle":"2025-04-14T07:13:15.745136Z","shell.execute_reply.started":"2025-04-14T07:13:15.737926Z","shell.execute_reply":"2025-04-14T07:13:15.744062Z"}},"outputs":[],"execution_count":33},{"cell_type":"code","source":"# Find and load models\ndef find_model_files():\n   \"\"\"\n   Find all .pth model files in the specified model directory.\n   \n   :return: List of model file paths.\n   \"\"\"\n   model_files = []\n   model_dir = Path(CFG.model_path)\n   for path in model_dir.glob('**/*.pth'):\n       model_files.append(str(path))\n   return model_files\n\ndef load_models():\n   \"\"\"\n   Load all found model files and prepare for ensemble inference.\n   \n   :return: List of loaded PyTorch models.\n   \"\"\"\n   models = []\n   model_files = find_model_files()\n   if not model_files:\n       print(f\"Warning: No model files found under {CFG.model_path}!\")\n       return models\n   print(f\"Found a total of {len(model_files)} model files.\")\n   \n   # Filter for specific fold models if needed\n   if CFG.use_specific_folds:\n       filtered_files = []\n       for fold in CFG.folds:\n           fold_files = [f for f in model_files if f\"fold{fold}\" in f]\n           filtered_files.extend(fold_files)\n       model_files = filtered_files\n       print(f\"Using {len(model_files)} model files for the specified folds ({CFG.folds}).\")\n   \n   # Load each model file\n   for model_path in model_files:\n       try:\n           print(f\"Loading model: {model_path}\")\n           checkpoint = torch.load(model_path, map_location='cpu')  # Force CPU loading\n           model = BirdCLEFModel(CFG, len(primary_labels))\n           model.load_state_dict(checkpoint['model_state_dict'])\n           model = model.to(CFG.device)\n           model.eval()\n           models.append(model)\n       except Exception as e:\n           print(f\"Error loading model {model_path}: {e}\")\n   \n   return models","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-14T07:13:27.554459Z","iopub.execute_input":"2025-04-14T07:13:27.554812Z","iopub.status.idle":"2025-04-14T07:13:27.564036Z","shell.execute_reply.started":"2025-04-14T07:13:27.55479Z","shell.execute_reply":"2025-04-14T07:13:27.562991Z"}},"outputs":[],"execution_count":34},{"cell_type":"code","source":"# Audio processing functions\ndef reduce_noise(audio_data):\n    \"\"\"\n    Apply noise reduction to the audio data.\n    \n    :param audio_data: Input audio data\n    :return: Denoised audio data\n    \"\"\"\n    if not CFG.apply_noise_reduction:\n        return audio_data\n    \n    # Apply median filtering for noise reduction\n    window_size = 5\n    audio_denoised = signal.medfilt(audio_data, window_size)\n    \n    # Mix the original signal and the denoised signal\n    return (1 - CFG.noise_reduction_strength) * audio_data + CFG.noise_reduction_strength * audio_denoised\n\ndef normalize_audio(audio_data):\n    \"\"\"\n    Normalize the audio data.\n    \n    :param audio_data: Input audio data\n    :return: Normalized audio data\n    \"\"\"\n    if not CFG.apply_normalization:\n        return audio_data\n    \n    # Remove DC offset\n    audio_data = audio_data - np.mean(audio_data)\n    \n    # Normalize amplitude\n    max_amplitude = np.max(np.abs(audio_data))\n    if max_amplitude > 0:\n        audio_data = audio_data / max_amplitude\n    \n    return audio_data\n\ndef audio2melspec(audio_data, cfg=CFG):\n    \"\"\"\n    Convert raw audio data into a normalized mel spectrogram with augmentation preprocessing.\n    \"\"\"\n    # Handle NaN values - method adapted from reference code\n    if np.isnan(audio_data).any():\n        mean_signal = np.nanmean(audio_data)\n        audio_data = np.nan_to_num(audio_data, nan=mean_signal)\n    \n    # Pad if audio is shorter than the required window size\n    if len(audio_data) < CFG.FS * CFG.WINDOW_SIZE:\n        audio_data = np.pad(\n            audio_data,\n            (0, CFG.FS * CFG.WINDOW_SIZE - len(audio_data)),\n            mode='constant'\n        )\n    \n    # Apply noise reduction and normalization\n    audio_data = reduce_noise(audio_data)\n    audio_data = normalize_audio(audio_data)\n    \n    # Generate mel spectrogram using augmentation parameters\n    mel_spec = librosa.feature.melspectrogram(\n        y=audio_data,\n        sr=cfg.FS,\n        n_fft=cfg.N_FFT,\n        hop_length=cfg.HOP_LENGTH,\n        n_mels=cfg.N_MELS,\n        fmin=cfg.FMIN,\n        fmax=cfg.FMAX,\n        power=2.0\n    )\n    \n    # Convert to log scale\n    mel_spec_db = librosa.power_to_db(mel_spec, ref=np.max)\n    \n    # Normalize - using the specific method from the reference code\n    mel_spec_norm = (mel_spec_db - mel_spec_db.min()) / (mel_spec_db.max() - mel_spec_db.min() + 1e-8)\n    \n    # Apply spectrogram contrast enhancement\n    if CFG.apply_spec_contrast:\n        mel_spec_norm = enhance_spectrogram_contrast(mel_spec_norm, CFG.contrast_factor)\n    \n    # Resize to target shape\n    if mel_spec_norm.shape != cfg.TARGET_SHAPE:\n        mel_spec_norm = cv2.resize(mel_spec_norm, cfg.TARGET_SHAPE, interpolation=cv2.INTER_LINEAR)\n    \n    return mel_spec_norm.astype(np.float32)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-14T07:13:44.024025Z","iopub.execute_input":"2025-04-14T07:13:44.024391Z","iopub.status.idle":"2025-04-14T07:13:44.03558Z","shell.execute_reply.started":"2025-04-14T07:13:44.024368Z","shell.execute_reply":"2025-04-14T07:13:44.034619Z"}},"outputs":[],"execution_count":35},{"cell_type":"code","source":"# The 'factor' parameter controls the strength of contrast enhancement; try values between 0.05 and 0.2\ndef enhance_spectrogram_contrast(spec, factor=0.15):\n    \"\"\"\n    Enhance the contrast of the mel spectrogram to make features more prominent.\n    \n    :param spec: Input spectrogram\n    :param factor: Contrast enhancement factor\n    :return: Enhanced spectrogram\n    \"\"\"\n    mean = np.mean(spec)\n    enhanced = mean + (spec - mean) * (1 + factor)\n    return np.clip(enhanced, 0, 1)  # Ensure values are within the range [0, 1]\n\ndef apply_spec_augment(spec):\n    \"\"\"\n    Apply SpecAugment data augmentation by adding time and frequency masks.\n    \n    :param spec: Input mel spectrogram, shape [height, width]\n    :return: Augmented spectrogram\n    \"\"\"\n    if not CFG.use_spec_augment:\n        return spec\n    \n    # Create a copy to avoid modifying the original data\n    augmented = spec.copy()\n    \n    # Apply frequency masks\n    for _ in range(CFG.freq_mask_count):\n        f = np.random.randint(0, CFG.freq_mask_param)\n        f0 = np.random.randint(0, augmented.shape[0] - f)\n        augmented[f0:f0 + f, :] = 0\n    \n    # Apply time masks\n    for _ in range(CFG.time_mask_count):\n        t = np.random.randint(0, CFG.time_mask_param)\n        t0 = np.random.randint(0, augmented.shape[1] - t)\n        augmented[:, t0:t0 + t] = 0\n    \n    return augmented\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-14T07:13:57.807701Z","iopub.execute_input":"2025-04-14T07:13:57.8086Z","iopub.status.idle":"2025-04-14T07:13:57.815775Z","shell.execute_reply.started":"2025-04-14T07:13:57.808575Z","shell.execute_reply":"2025-04-14T07:13:57.814921Z"}},"outputs":[],"execution_count":36},{"cell_type":"code","source":"def apply_tta(spec, tta_idx):\n    \"\"\"\n    Apply Test-Time Augmentation (TTA) to the spectrogram.\n    \n    :param spec: Input mel spectrogram.\n    :param tta_idx: Index indicating which type of TTA to apply.\n    :return: Augmented spectrogram.\n    \"\"\"\n    # Create a copy to avoid issues with negative strides\n    result = spec.copy()\n    \n    if tta_idx == 0:\n        # No augmentation\n        return result\n    elif tta_idx == 1:\n        # Time shift (horizontal flip)\n        return np.flip(result, axis=1).copy()\n    elif tta_idx == 2:\n        # Frequency shift (vertical flip)\n        return np.flip(result, axis=0).copy()\n    elif tta_idx == 3:\n        # Both time and frequency shift\n        return np.flip(np.flip(result, axis=1), axis=0).copy()\n    elif tta_idx == 4:\n        # Slight pitch increase\n        return np.roll(result, shift=3, axis=0)\n    elif tta_idx == 5:\n        # Slight pitch decrease\n        return np.roll(result, shift=-3, axis=0)\n    else:\n        return result\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-14T07:14:04.843842Z","iopub.execute_input":"2025-04-14T07:14:04.844235Z","iopub.status.idle":"2025-04-14T07:14:04.851451Z","shell.execute_reply.started":"2025-04-14T07:14:04.84421Z","shell.execute_reply":"2025-04-14T07:14:04.850519Z"}},"outputs":[],"execution_count":37},{"cell_type":"code","source":"# Enhanced inference process\ndef predict_on_audio(audio_path, models):\n    \"\"\"\n    Process a single audio file and predict species presence in each 5-second segment.\n    \n    :param audio_path: Path to the audio file.\n    :param models: List of PyTorch models used for prediction.\n    :return: Tuple (row_ids, predictions) for each segment.\n    \"\"\"\n    predictions = []\n    row_ids = []\n    soundscape_id = os.path.splitext(os.path.basename(audio_path))[0]\n    \n    try:\n        print(f\"Processing {soundscape_id}\")\n        audio_data, _ = librosa.load(audio_path, sr=CFG.FS)\n        total_segments = int(len(audio_data) / (CFG.FS * CFG.WINDOW_SIZE))\n        \n        segment_predictions = []  # Collect predictions for all segments for post-processing\n        \n        for segment_idx in range(total_segments):\n            if time.time() > TERMINATE_TIME:\n                print(\"Time limit reached, stopping processing early\")\n                return row_ids, predictions\n                \n            start_sample = segment_idx * CFG.FS * CFG.WINDOW_SIZE\n            end_sample = start_sample + CFG.FS * CFG.WINDOW_SIZE\n            segment_audio = audio_data[start_sample:end_sample]\n            \n            end_time_sec = (segment_idx + 1) * CFG.WINDOW_SIZE\n            row_id = f\"{soundscape_id}_{end_time_sec}\"\n            row_ids.append(row_id)\n\n            # Use more robust exception handling logic inspired by reference code\n            try:\n                # Use TTA based on configuration\n                if CFG.use_tta:\n                    all_preds = []\n                    for tta_idx in range(CFG.tta_count):\n                        mel_spec = audio2melspec(segment_audio)\n                        \n                        # Optionally apply SpecAugment\n                        if np.random.random() < 0.5 and CFG.use_spec_augment:\n                            mel_spec = apply_spec_augment(mel_spec)\n                            \n                        mel_spec = apply_tta(mel_spec, tta_idx)\n                        mel_spec_tensor = torch.tensor(mel_spec, dtype=torch.float32).unsqueeze(0).unsqueeze(0)\n                        mel_spec_tensor = mel_spec_tensor.to(CFG.device)\n                        \n                        segment_preds = []\n                        for model in models:\n                            with torch.no_grad():\n                                outputs = model(mel_spec_tensor)\n                                probs = torch.sigmoid(outputs).cpu().numpy().squeeze()\n                                segment_preds.append(probs)\n                        \n                        # Ensemble prediction using weighted average\n                        if len(models) > 1:\n                            weights = np.linspace(0.8, 1.2, len(models))\n                            weights = weights / weights.sum()\n                            avg_preds = np.average(segment_preds, axis=0, weights=weights)\n                        else:\n                            avg_preds = segment_preds[0]\n                        \n                        all_preds.append(avg_preds)\n                    \n                    # Average all TTA results\n                    final_preds = np.mean(all_preds, axis=0)\n                else:\n                    # Standard prediction without TTA\n                    mel_spec = audio2melspec(segment_audio)\n                    mel_spec_tensor = torch.tensor(mel_spec, dtype=torch.float32).unsqueeze(0).unsqueeze(0)\n                    mel_spec_tensor = mel_spec_tensor.to(CFG.device)\n                    \n                    segment_preds = []\n                    for model in models:\n                        with torch.no_grad():\n                            outputs = model(mel_spec_tensor)\n                            probs = torch.sigmoid(outputs).cpu().numpy().squeeze()\n                            segment_preds.append(probs)\n                    \n                    # Ensemble prediction using weighted average\n                    if len(models) > 1:\n                        weights = np.linspace(0.8, 1.2, len(models))\n                        weights = weights / weights.sum()\n                        final_preds = np.average(segment_preds, axis=0, weights=weights)\n                    else:\n                        final_preds = segment_preds[0]\n            except Exception as e:\n                print(f\"Error processing segment {segment_idx}: {e}\")\n                # Use zero vector or previous prediction as fallback\n                final_preds = np.zeros(len(primary_labels)) if len(predictions) == 0 else predictions[-1]\n            \n            segment_predictions.append(final_preds)\n        \n        # Post-process predictions for all segments\n        for i in range(len(segment_predictions)):\n            # Blend predictions of adjacent time segments\n            if i > 0 and i < len(segment_predictions) - 1:\n                # Weights for current and neighboring predictions\n                blended_pred = (\n                    CFG.prediction_blend[0] * segment_predictions[i] + \n                    CFG.prediction_blend[1] * 0.5 * (segment_predictions[i-1] + segment_predictions[i+1])\n                )\n                predictions.append(blended_pred)\n            else:\n                # Keep original prediction for first and last segments\n                predictions.append(segment_predictions[i])\n                \n    except Exception as e:\n        print(f\"Error processing {audio_path}: {e}\")\n        # Ensure row_ids and predictions match in length, even in case of error\n        # Clear partially processed results\n        row_ids = []\n        predictions = []\n    \n    return row_ids, predictions\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-14T07:14:15.092824Z","iopub.execute_input":"2025-04-14T07:14:15.093196Z","iopub.status.idle":"2025-04-14T07:14:15.110924Z","shell.execute_reply.started":"2025-04-14T07:14:15.093172Z","shell.execute_reply":"2025-04-14T07:14:15.109795Z"}},"outputs":[],"execution_count":38},{"cell_type":"code","source":"# Enhanced smoothing function\ndef smooth_predictions(submission_df):\n    \"\"\"\n    Smooth predictions in the submission to reinforce temporal consistency.\n    \n    :param submission_df: Submission DataFrame.\n    :return: Smoothed DataFrame.\n    \"\"\"\n    print(\"Smoothing prediction results...\")\n    sub = submission_df.copy()\n    cols = sub.columns[1:]\n    sub['group'] = sub['row_id'].str.rsplit('_', n=1).str[0]\n    unique_groups = sub['group'].unique()\n    \n    for group in unique_groups:\n        group_mask = sub['group'] == group\n        sub_group = sub[group_mask].copy()\n        predictions = sub_group[cols].values\n        new_predictions = predictions.copy()\n        \n        if predictions.shape[0] > 1:\n            # Enhanced smoothing using configurable window and weights\n            window = CFG.smoothing_window\n            weights = CFG.smoothing_weights\n            half_window = window // 2\n            \n            # Handle edge cases\n            for i in range(half_window):\n                # First few elements use available window\n                valid_window = i + half_window + 1\n                valid_weights = weights[-valid_window:]\n                valid_weights = valid_weights / np.sum(valid_weights)  # Re-normalize weights\n                new_predictions[i] = np.average(predictions[:valid_window], axis=0, weights=valid_weights)\n                \n                # Last few elements\n                valid_window = i + half_window + 1\n                valid_weights = weights[:valid_window]\n                valid_weights = valid_weights / np.sum(valid_weights)  # Re-normalize weights\n                new_predictions[-(i+1)] = np.average(predictions[-valid_window:], axis=0, weights=valid_weights)\n            \n            # Process middle elements - use sliding window average\n            for i in range(half_window, predictions.shape[0] - half_window):\n                window_start = i - half_window\n                window_end = i + half_window + 1\n                new_predictions[i] = np.average(predictions[window_start:window_end], axis=0, weights=weights)\n        \n        sub.loc[group_mask, cols] = new_predictions\n    \n    sub.drop('group', axis=1, inplace=True)\n    return sub\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-14T07:14:31.045225Z","iopub.execute_input":"2025-04-14T07:14:31.046068Z","iopub.status.idle":"2025-04-14T07:14:31.055711Z","shell.execute_reply.started":"2025-04-14T07:14:31.046042Z","shell.execute_reply":"2025-04-14T07:14:31.054704Z"}},"outputs":[],"execution_count":39},{"cell_type":"code","source":"# Apply thresholds to prediction results\ndef apply_thresholds(submission_df, thresholds):\n    \"\"\"\n    Binarize prediction results using optimal thresholds for each class.\n    \n    :param submission_df: Prediction results DataFrame\n    :param thresholds: Dictionary of thresholds for each class\n    :return: Processed DataFrame\n    \"\"\"\n    print(\"Applying class-specific thresholds...\")\n    result_df = submission_df.copy()\n    \n    for species in primary_labels:\n        threshold = thresholds.get(species, CFG.threshold)\n        result_df[species] = (result_df[species] >= threshold).astype(float)\n    \n    return result_df\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-14T07:14:39.27923Z","iopub.execute_input":"2025-04-14T07:14:39.279555Z","iopub.status.idle":"2025-04-14T07:14:39.286002Z","shell.execute_reply.started":"2025-04-14T07:14:39.279535Z","shell.execute_reply":"2025-04-14T07:14:39.284873Z"}},"outputs":[],"execution_count":40},{"cell_type":"code","source":"# Main Inference Pipeline\ndef run_pipeline():\n    \"\"\"\n    Execute the complete inference pipeline.\n    \"\"\"\n    print(f\"Device: {CFG.device}\")\n    print(f\"TTA enabled: {CFG.use_tta} (variations: {CFG.tta_count if CFG.use_tta else 0})\")\n    \n    # Estimate optimal threshold for each class\n    if CFG.class_thresholds is None:\n        CFG.class_thresholds = estimate_class_thresholds()\n    \n    # Load models\n    models = load_models()\n    if not models:\n        print(\"No models found! Please check model paths.\")\n        return None\n    \n    print(f\"Model usage: {'Single model' if len(models) == 1 else f'Ensemble of {len(models)} models'}\")\n    \n    # Retrieve audio files\n    audio_paths, file_ids = get_audio_files()\n    print(f\"Found {len(audio_paths)} audio files\")\n    \n    all_row_ids = []\n    all_predictions = []\n    \n    # Process each audio file\n    for i, audio_path in enumerate(tqdm(audio_paths)):\n        row_ids, predictions = predict_on_audio(audio_path, models)\n        \n        # Add results only if both row_ids and predictions are valid and match in length\n        if len(row_ids) > 0 and len(predictions) > 0 and len(row_ids) == len(predictions):\n            all_row_ids.extend(row_ids)\n            all_predictions.extend(predictions)\n        else:\n            print(f\"Skipping results for {audio_path} due to length mismatch or empty results\")\n        \n        # Periodically clear memory to improve efficiency\n        if (i + 1) % CFG.clear_cache_frequency == 0:\n            clear_memory()\n    \n    # Create the submission DataFrame\n    print(\"Creating submission dataframe...\")\n    submission_dict = {'row_id': all_row_ids}\n    for i, species in enumerate(primary_labels):\n        submission_dict[species] = [pred[i] for pred in all_predictions]\n    \n    # Check if all items have the same length\n    lengths = [len(v) for v in submission_dict.values()]\n    if len(set(lengths)) > 1:\n        print(f\"Warning: Inconsistent lengths in submission_dict: {lengths}\")\n        # Find the shortest length\n        min_length = min(lengths)\n        # Truncate all items to the shortest length\n        for k in submission_dict:\n            submission_dict[k] = submission_dict[k][:min_length]\n    \n    submission_df = pd.DataFrame(submission_dict)\n    \n    # Ensure all columns are present in the submission\n    sample_sub = pd.read_csv(CFG.submission_csv)\n    missing_cols = set(sample_sub.columns) - set(submission_df.columns)\n    if missing_cols:\n        print(f\"Warning: Missing {len(missing_cols)} columns in submission\")\n        for col in missing_cols:\n            submission_df[col] = 0.0\n            \n    # Ensure column order matches the sample submission\n    if 'row_id' in sample_sub.columns:\n        submission_df = submission_df[sample_sub.columns]\n    \n    # Apply smoothing\n    if CFG.apply_smoothing:\n        submission_df = smooth_predictions(submission_df)\n    \n    # Apply secondary smoothing as an additional optimization (from reference code)\n    if CFG.apply_secondary_smoothing:\n        cols = submission_df.columns[1:]\n        groups = submission_df['row_id'].str.rsplit('_', n=1).str[0].values\n            \n        for group in np.unique(groups):\n            group_mask = (groups == group)\n            sub_group = submission_df[group_mask]\n            predictions = sub_group[cols].values\n            new_predictions = predictions.copy()\n            \n            # Process middle section\n            for i in range(1, predictions.shape[0]-1):\n                new_predictions[i] = (predictions[i-1] * 0.2) + (predictions[i] * 0.6) + (predictions[i+1] * 0.2)\n            \n            # Handle head and tail\n            if predictions.shape[0] > 1:\n                new_predictions[0] = (predictions[0] * 0.8) + (predictions[1] * 0.2)\n                new_predictions[-1] = (predictions[-1] * 0.8) + (predictions[-2] * 0.2)\n            \n            submission_df.loc[group_mask, cols] = new_predictions\n    \n    # Apply class-specific thresholds\n    # Note: Kaggle evaluation uses probabilities, so we don't binarize the predictions\n    # final_df = apply_thresholds(submission_df, CFG.class_thresholds)\n    final_df = submission_df\n    \n    # Save to CSV\n    submission_path = 'submission.csv'\n    final_df.to_csv(submission_path, index=False)\n    print(f\"Submission saved to {submission_path}\")\n    \n    # Display submission DataFrame\n    print(\"\\nSubmission head:\")\n    print(final_df.head(5))\n    \n    print(\"\\nSubmission tail:\")\n    print(final_df.tail(5))\n    \n    return final_df\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-14T07:14:59.877069Z","iopub.execute_input":"2025-04-14T07:14:59.877499Z","iopub.status.idle":"2025-04-14T07:14:59.894021Z","shell.execute_reply.started":"2025-04-14T07:14:59.877472Z","shell.execute_reply":"2025-04-14T07:14:59.892696Z"}},"outputs":[],"execution_count":41},{"cell_type":"code","source":"# Visualization Functions\ndef create_mel_transform():\n    \"\"\"\n    Create a Mel Spectrogram transform for visualization.\n\n    :return: MelSpectrogram transform\n    \"\"\"\n    return AT.MelSpectrogram(\n        sample_rate=CFG.FS,\n        n_fft=CFG.N_FFT,\n        win_length=CFG.N_FFT,\n        hop_length=CFG.HOP_LENGTH,\n        center=True,\n        f_min=CFG.FMIN,\n        f_max=CFG.FMAX,\n        pad_mode=\"reflect\",\n        power=2.0,\n        norm='slaney',\n        n_mels=CFG.N_MELS,\n        mel_scale=\"htk\",\n    )\n\ndef audio_to_mel_debug(filepath):\n    \"\"\"\n    Convert an audio file to a Mel Spectrogram for visualization.\n\n    :param filepath: Path to the audio file\n    :return: Mel Spectrogram\n    \"\"\"\n    waveform, _ = torchaudio.load(filepath, backend=\"soundfile\")\n    waveform = waveform / torch.max(torch.abs(waveform))\n    mel_transform = create_mel_transform()\n    melspec = mel_transform(waveform)\n    melspec = 10 * torch.log10(melspec + 1e-10)  # Add a small constant to avoid log(0)\n    return melspec\n\ndef plot_results(results, file_name, audio_dir):\n    \"\"\"\n    Plot the audio spectrogram and prediction results.\n\n    :param results: Prediction results dataframe\n    :param file_name: Audio file name without extension\n    :param audio_dir: Directory containing audio files\n    \"\"\"\n    path = os.path.join(audio_dir, file_name + \".ogg\")\n    specgram = audio_to_mel_debug(path)\n    \n    fig, axes = plt.subplots(2, 1, figsize=(12, 8))\n    axes[0].set_title(file_name)\n    im = axes[0].imshow(specgram[0], origin=\"lower\", aspect=\"auto\")\n    axes[0].set_ylabel(\"mel bin\")\n    axes[0].set_xlabel(\"frame\")\n    fig.colorbar(im, ax=axes[0])\n    \n    # Filter results for the current file\n    file_results = results[results[\"row_id\"].str.contains(file_name)]\n    \n    # Get the number of time segments (usually 12 segments for a 60-second file)\n    time_segments = file_results.shape[0]\n    \n    # Plot prediction heatmap\n    heatmap = axes[1].pcolor(file_results.iloc[:, 1:].values.T, edgecolors='k', \n                             linewidths=0.1, vmin=0, vmax=1, cmap='Blues')\n    fig.colorbar(heatmap, ax=axes[1])\n    \n    # Set x-axis ticks according to time segments\n    axes[1].set_xticks(np.arange(0, time_segments, 1))\n    axes[1].set_xticklabels(np.arange(0, time_segments * 5, 5))\n    \n    axes[1].set_ylabel(\"species\")\n    axes[1].set_xlabel(\"sec\")\n    \n    # Add species labels on y-axis\n    if len(primary_labels) <= 30:  # Only show labels if the number isn't too large\n        axes[1].set_yticks(np.arange(0.5, len(primary_labels), 1))\n        axes[1].set_yticklabels(primary_labels)\n    \n    fig.tight_layout()\n    plt.savefig(f'{file_name}_prediction.png')\n    plt.close()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-14T07:15:06.245985Z","iopub.execute_input":"2025-04-14T07:15:06.246392Z","iopub.status.idle":"2025-04-14T07:15:06.258449Z","shell.execute_reply.started":"2025-04-14T07:15:06.246367Z","shell.execute_reply":"2025-04-14T07:15:06.257315Z"}},"outputs":[],"execution_count":42},{"cell_type":"code","source":"# Main execution\nif __name__ == \"__main__\":\n    try:\n        # Run the inference pipeline\n        results = run_pipeline()\n        \n        # Clear memory to ensure enough memory is available for visualization\n        clear_memory()\n        \n        # If in debug mode and results are available, perform visualization\n        if CFG.debug and results is not None:\n            audio_paths, file_ids = get_audio_files()\n            audio_dir = CFG.train_soundscapes if CFG.debug else CFG.test_soundscapes\n            \n            print(\"\\nGenerating visualizations...\")\n            for file_id in file_ids:\n                plot_results(results, file_id, audio_dir)\n                print(f\"Visualization saved for {file_id}\")\n        \n        print(f\"\\nTotal execution time: {(time.time() - START) / 60:.2f} minutes\")\n    except Exception as e:\n        print(f\"Error during execution: {e}\")\n        import traceback\n        traceback.print_exc()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-14T07:22:26.057316Z","iopub.execute_input":"2025-04-14T07:22:26.057696Z","iopub.status.idle":"2025-04-14T07:22:26.680368Z","shell.execute_reply.started":"2025-04-14T07:22:26.057671Z","shell.execute_reply":"2025-04-14T07:22:26.679206Z"}},"outputs":[{"name":"stdout","text":"Device: cpu\nTTA enabled: False (variations: 0)\nEstimating optimal thresholds for each class...\nFound a total of 3 model files.\nLoading model: /kaggle/input/efficientnet-b0-test_4/pytorch/default/1/models/model_fold0.pth\nLoading model: /kaggle/input/efficientnet-b0-test_4/pytorch/default/1/models/model_fold1.pth\nLoading model: /kaggle/input/efficientnet-b0-test_4/pytorch/default/1/models/model_fold2.pth\nModel usage: Ensemble of 3 models\nNo test files found. Using train soundscapes for debugging.\nDebug mode: True\nNumber of soundscapes: 0\nFound 0 audio files\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"0it [00:00, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b00c570739034e8e9a76c28b3e2666db"}},"metadata":{}},{"name":"stdout","text":"Creating submission dataframe...\nSmoothing prediction results...\nError during execution: Can only use .str accessor with string values!\n","output_type":"stream"},{"name":"stderr","text":"Traceback (most recent call last):\n  File \"/tmp/ipykernel_31/45120454.py\", line 5, in <cell line: 0>\n    results = run_pipeline()\n              ^^^^^^^^^^^^^^\n  File \"/tmp/ipykernel_31/3416595281.py\", line 75, in run_pipeline\n    submission_df = smooth_predictions(submission_df)\n                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/tmp/ipykernel_31/1406818764.py\", line 12, in smooth_predictions\n    sub['group'] = sub['row_id'].str.rsplit('_', n=1).str[0]\n                   ^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.11/dist-packages/pandas/core/generic.py\", line 6299, in __getattr__\n    return object.__getattribute__(self, name)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.11/dist-packages/pandas/core/accessor.py\", line 224, in __get__\n    accessor_obj = self._accessor(obj)\n                   ^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.11/dist-packages/pandas/core/strings/accessor.py\", line 191, in __init__\n    self._inferred_dtype = self._validate(data)\n                           ^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.11/dist-packages/pandas/core/strings/accessor.py\", line 245, in _validate\n    raise AttributeError(\"Can only use .str accessor with string values!\")\nAttributeError: Can only use .str accessor with string values!\n","output_type":"stream"}],"execution_count":45},{"cell_type":"code","source":"# Main execution\nif __name__ == \"__main__\":\n    try:\n        # Run the inference pipeline\n        results = run_pipeline()\n        \n        # Clear memory to ensure enough memory is available for visualization\n        clear_memory()\n        \n        # If in debug mode and results are available, perform visualization\n        if CFG.debug and results is not None:\n            audio_paths, file_ids = get_audio_files()\n            audio_dir = CFG.train_soundscapes if CFG.debug else CFG.test_soundscapes\n            \n            print(\"\\nGenerating visualizations...\")\n            for file_id in file_ids:\n                plot_results(results, file_id, audio_dir)\n                print(f\"Visualization saved for {file_id}\")\n        \n        # **New Change**: Print predictions for each audio file here\n        print(\"\\nPrinting predictions for each audio file:\")\n        audio_paths, file_ids = get_audio_files()  # Get the audio paths again\n        models = load_models()  # Make sure models are loaded\n        for i, audio_path in enumerate(tqdm(audio_paths)):\n            row_ids, predictions = predict_on_audio(audio_path, models)\n            print(f\"Predictions for {audio_path}: {predictions}\")\n        \n        print(f\"\\nTotal execution time: {(time.time() - START) / 60:.2f} minutes\")\n        \n    except Exception as e:\n        print(f\"Error during execution: {e}\")\n        import traceback\n        traceback.print_exc()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-14T07:07:58.556834Z","iopub.execute_input":"2025-04-14T07:07:58.557211Z","iopub.status.idle":"2025-04-14T07:08:01.184061Z","shell.execute_reply.started":"2025-04-14T07:07:58.557189Z","shell.execute_reply":"2025-04-14T07:08:01.182941Z"}},"outputs":[{"name":"stdout","text":"Device: cpu\nTTA enabled: False (variations: 0)\nEstimating optimal thresholds for each class...\nFound a total of 3 model files.\nLoading model: /kaggle/input/efficientnet-b0-test_4/pytorch/default/1/models/model_fold0.pth\nLoading model: /kaggle/input/efficientnet-b0-test_4/pytorch/default/1/models/model_fold1.pth\nLoading model: /kaggle/input/efficientnet-b0-test_4/pytorch/default/1/models/model_fold2.pth\nModel usage: Ensemble of 3 models\nNo test files found. Using train soundscapes for debugging.\nDebug mode: True\nNumber of soundscapes: 0\nFound 0 audio files\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"0it [00:00, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"3376ba9eb92f4c3793cd0b5aa2387951"}},"metadata":{}},{"name":"stdout","text":"Creating submission dataframe...\nSmoothing prediction results...\nError during execution: Can only use .str accessor with string values!\n","output_type":"stream"},{"name":"stderr","text":"Traceback (most recent call last):\n  File \"/tmp/ipykernel_31/2372620911.py\", line 5, in <cell line: 0>\n    results = run_pipeline()\n              ^^^^^^^^^^^^^^\n  File \"/tmp/ipykernel_31/3416595281.py\", line 75, in run_pipeline\n    submission_df = smooth_predictions(submission_df)\n                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/tmp/ipykernel_31/1406818764.py\", line 12, in smooth_predictions\n    sub['group'] = sub['row_id'].str.rsplit('_', n=1).str[0]\n                   ^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.11/dist-packages/pandas/core/generic.py\", line 6299, in __getattr__\n    return object.__getattribute__(self, name)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.11/dist-packages/pandas/core/accessor.py\", line 224, in __get__\n    accessor_obj = self._accessor(obj)\n                   ^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.11/dist-packages/pandas/core/strings/accessor.py\", line 191, in __init__\n    self._inferred_dtype = self._validate(data)\n                           ^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.11/dist-packages/pandas/core/strings/accessor.py\", line 245, in _validate\n    raise AttributeError(\"Can only use .str accessor with string values!\")\nAttributeError: Can only use .str accessor with string values!\n","output_type":"stream"}],"execution_count":23},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}