{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.11.11","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":102335,"databundleVersionId":12518947,"sourceType":"competition"},{"sourceId":12089615,"sourceType":"datasetVersion","datasetId":7590629},{"sourceId":243788259,"sourceType":"kernelVersion"}],"dockerImageVersionId":31040,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# <b><span style='color:#F1A424'>|</span> CMI: <span style='color:#F1A424'>WaveNet</span><span style='color:#ABABAB'> [Inference]</span></b> \n\n***\n\nYour goal in this competition is to differentiate between body-focused repetitive behaviors (BFRBs), like hair pulling, from non-BFRB everyday gestures, like adjusting glasses, etc. Your task is to develop a predictive model that distinguishes BFRB-like and non-BFRB-like activity using data from a variety of sensors collected via a wrist-worn device\n\nIn this notebook you will learn how to infer with a `WaveNet` model for BFRBs using PyTorch. Hope you enjoy it and find it useful.\n\n### <b><span style='color:#F1A424'>Table of Contents</span></b> <a class='anchor' id='top'></a>\n<div style=\" background-color:#3b3745; padding: 13px 13px; border-radius: 8px; color: white\">\n    <li> <a href=\"#introduction\">Introduction</a></li>\n    <li> <a href=\"#install_libraries\">Install libraries</a></li>\n    <li><a href=\"#import_libraries\">Import Libraries</a></li>\n    <li><a href=\"#configuration\">Configuration</a></li>\n    <li><a href=\"#utils\">Utils</a></li>\n    <li><a href=\"#load_data\">Load Data</a></li>\n    <li><a href=\"#dataset\">Dataset</a></li>\n    <li><a href=\"#model\">Model</a></li>\n    <li><a href=\"#inference\">Inference</a></li>\n</div>\n\n\n# <b><span style='color:#F1A424'>|</span> Introduction</b><a class='anchor' id='introduction'></a> [↑](#top) \n\n***\n\n### <b><span style='color:#F1A424'>Problem description</span></b>\n\n\nIn this competition, you are asked to tackle two problems at the same time. The first one is a **multiclass classification problem** and the second one a **binary classification problem**. You will be asked to predict hand **gestures**. There are 2 **types** of gestures `Target` (this type has 8 gestures) and `Non-Target` (this type has 10 gestures). The multiclass classification task involves predicting the 8 `Target` gestures and `Non-Target` as an aditional class. This task can be tackled as predicting either 18 classes (8+10 gestures) and then collapsing the 10 `Non-Target` gestures into 1 class or directly 9 classes (8 + 1 gestures) if you consider all `Non-Target` as one. The second binary classification task involves predicting whether a gesture is `Target` or `Non-Target`. This task is much easier than the first one.\n\nThe metric you will be evaluated on is a simple average of the `F1-binary` (score of the binary classification task) and the `F1-macro` (score of the multiclass classification task).\n\n### <b><span style='color:#F1A424'>Data</span></b>\n\nOur data consists of recordings of sensor data for 81 subjects/persons and some demographics metadata, such as sex, age, height, etc. Each person has different sequences (signal readings from the sensors), each one indicating a gesture. So, each `subject` can have multiple `sequence_id` associated to him/her. The goal is to predict for each `sequence_id` the corresponding `gesture`.\n\nYou are presented with different readings from mainly 3 types of sensors:\n- the IMU sensor which registers acceleration and rotation readings from accelerometers, gyroscopes and magnetometers.\n- the the thermopile sensor, anon-contact temperature sensor that measures infrared radiation.\n- the TOF (Time-Of-Flight) sensor that measures distance by detecting how long it takes for emitted infrared light to bounce back from objects.\n\nWe will be using the **IMU sensor data only**, this is because half of the future test data will have IMU data only, so it's crucial to maximize the performance of our models with this data:\n> Half of the hidden-test sequences are recorded with IMU only; the thermopile (thm_) and time-of-flight (tof__v*) columns are still present but contain null values for those sequences.\n\nLater, you can add the rest of the sensor to maximize the overall performance but it is essential to get good performance on IMU-only data.\n\n### <b><span style='color:#F1A424'>Model</span></b>\n\nOur WaveNet model will handle the sequential data, but you can also tackle this problem with gradient boosted trees models such as LGBM, Catboost or XGBoost by taking aggregate statistics for each sequence. Here is a diagram of our model:\n\n![Screenshot 2025-06-05 at 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"}}},{"cell_type":"markdown","source":"# <b><span style='color:#F1A424'>|</span> Install libraries</b><a class='anchor' id='install_libraries'></a> [↑](#top) \n\n***","metadata":{}},{"cell_type":"code","source":"import gc\nimport kaggle_evaluation.cmi_inference_server\nimport math\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport os\nimport pandas as pd\nimport polars as pl\nimport plotly.express as px\nimport plotly.io as pio\nimport random\nimport time\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nfrom glob import glob\nfrom torch.utils.data import DataLoader, Dataset, Subset\nfrom tqdm import tqdm\n\n!mkdir output\n# os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0,1\"\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\npio.renderers.default = 'iframe'","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2025-06-07T14:21:30.518055Z","iopub.execute_input":"2025-06-07T14:21:30.518420Z","iopub.status.idle":"2025-06-07T14:21:39.044526Z","shell.execute_reply.started":"2025-06-07T14:21:30.518388Z","shell.execute_reply":"2025-06-07T14:21:39.043131Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <b><span style='color:#F1A424'>|</span> Configuration</b><a class='anchor' id='configuration'></a> [↑](#top) \n\n***","metadata":{}},{"cell_type":"code","source":"class config:\n    BATCH_SIZE_TEST = 1\n    NUM_WORKERS = 0 # multiprocessing.cpu_count()\n    PRINT_FREQ = 20\n    SEED = 20\n\nclass paths:\n    OUTPUT_DIR = \"/kaggle/working/output\"\n    TEST_CSV = \"/kaggle/input/cmi-detect-behavior-with-sensor-data/test.csv\"\n    TEST_DEMOGRAPHICS = \"/kaggle/input/cmi-detect-behavior-with-sensor-data/test_demographics.csv\"\n    TRAIN_CSV = \"/kaggle/input/cmi-detect-behavior-with-sensor-data/train.csv\"\n    TRAIN_DEMOGRAPHICS = \"/kaggle/input/cmi-detect-behavior-with-sensor-data/train_demographics.csv\"","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-07T14:21:39.046188Z","iopub.execute_input":"2025-06-07T14:21:39.046562Z","iopub.status.idle":"2025-06-07T14:21:39.052469Z","shell.execute_reply.started":"2025-06-07T14:21:39.046520Z","shell.execute_reply":"2025-06-07T14:21:39.051429Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <b><span style='color:#F1A424'>|</span> Utils</b><a class='anchor' id='utils'></a> [↑](#top) \n\n***","metadata":{}},{"cell_type":"code","source":"def format_for_scoring(df_preds: pd.DataFrame) ->tuple[pd.DataFrame, pd.DataFrame]: \n    solution = df_preds[[\"sequence_id\", \"y_true\"]].copy()\n    solution.columns = [\"id\", \"gesture\"]\n    solution[\"gesture\"] = solution[\"gesture\"].map(num_to_label)\n\n    submission = df_preds[[\"sequence_id\", \"y_pred\"]].copy()\n    submission.columns = [\"id\", \"gesture\"]\n    submission[\"gesture\"] = submission[\"gesture\"].map(num_to_label)\n    \n    return solution, submission\n\n\ndef seed_everything(seed: int):\n    random.seed(seed)\n    np.random.seed(seed)\n    torch.manual_seed(seed)\n    os.environ['PYTHONHASHSEED'] = str(seed) \n    \n\ndef sep():\n    print(\"—\"*100)\n\n\nlabel_to_num = {\n    'Above ear - pull hair': 0,  # < ------- TARGETS START\n    'Cheek - pinch skin': 1,\n    'Eyebrow - pull hair': 2,\n    'Eyelash - pull hair': 3,\n    'Forehead - pull hairline': 4,\n    'Forehead - scratch': 5,\n    'Neck - pinch skin': 6,\n    'Neck - scratch': 7,  # < ------- TARGETS END\n    'Drink from bottle/cup': 8,  # < ------- NON-TARGETS START\n    'Feel around in tray and pull out an object': 8,\n    'Glasses on/off': 8,\n    'Pinch knee/leg skin': 8,\n    'Pull air toward your face': 8,\n    'Scratch knee/leg skin': 8,\n    'Text on phone': 8,\n    'Wave hello': 8,\n    'Write name in air': 8,\n    'Write name on leg': 8  # < ------- NON-TARGETS END\n}\ntype_to_num = {\"Target\": 1, \"Non-Target\":0}\nnum_to_label = {v: k for k, v in label_to_num.items()}\nnum_to_type = {v: k for k, v in type_to_num.items()}\nseed_everything(config.SEED)","metadata":{"execution":{"iopub.status.busy":"2025-06-07T14:21:39.054069Z","iopub.execute_input":"2025-06-07T14:21:39.054416Z","iopub.status.idle":"2025-06-07T14:21:39.080073Z","shell.execute_reply.started":"2025-06-07T14:21:39.054385Z","shell.execute_reply":"2025-06-07T14:21:39.079232Z"},"trusted":true,"_kg_hide-input":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <b><span style='color:#F1A424'>|</span> Load Data</b><a class='anchor' id='load_data'></a> [↑](#top) \n\n***","metadata":{}},{"cell_type":"code","source":"df_test = pd.read_csv(paths.TEST_CSV)\ndf_test_demographics = pd.read_csv(paths.TEST_DEMOGRAPHICS)\n\nprint(f\"Test dataframe shape: {df_test.shape}\"), sep()\nprint(f\"Test demographics dataframe shape: {df_test_demographics.shape}\")\n\ndisplay(df_test.head())\ndisplay(df_test_demographics.head())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-07T14:21:39.081029Z","iopub.execute_input":"2025-06-07T14:21:39.081727Z","iopub.status.idle":"2025-06-07T14:21:39.170215Z","shell.execute_reply.started":"2025-06-07T14:21:39.081695Z","shell.execute_reply":"2025-06-07T14:21:39.169297Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def min_max_scale(arr: np.ndarray) -> np.ndarray:\n    min_vals = np.nanmin(arr, axis=0)\n    max_vals = np.nanmax(arr, axis=0)\n    ranges = np.where(max_vals - min_vals == 0, 1, max_vals - min_vals)\n    scaled = (arr - min_vals) / ranges\n    return scaled\n\n\ndef standard_scale(arr: np.ndarray) -> np.ndarray:\n    means = np.nanmean(arr, axis=0)\n    stds = np.nanstd(arr, axis=0)\n    stds = np.where(stds == 0, 1, stds)  # Prevent division by zero for constant columns\n    scaled = (arr - means) / stds\n    return scaled\n\n\ndef pad_or_truncate(\n    arr: np.ndarray,\n    max_length: int = 200,\n    pad_value: int = 0,\n    mode: str = \"random\"  # \"regular\" or \"random\"\n) -> np.ndarray:\n    L, D = arr.shape\n\n    if L > max_length:\n        return arr[:max_length, :]\n\n    elif L < max_length:\n        if mode == \"regular\":\n            padding = np.full((max_length - L, D), pad_value)\n            return np.vstack((arr, padding))\n        \n        elif mode == \"random\":\n            total_padding = max_length - L\n            pad_start = np.random.randint(0, total_padding + 1)\n            pad_end = total_padding - pad_start\n\n            start_padding = np.full((pad_start, D), pad_value)\n            end_padding = np.full((pad_end, D), pad_value)\n\n            return np.vstack((start_padding, arr, end_padding))\n        \n        else:\n            raise ValueError(f\"Unknown mode: {mode}. Use 'regular' or 'random'.\")\n\n    else:\n        return arr\n\nimu_cols = [\"acc_x\", \"acc_y\", \"acc_z\", \"rot_w\", \"rot_x\", \"rot_y\", \"rot_z\"]\nX_test  = []\n\nfor sequence_id in tqdm(df_test.sequence_id.unique()):\n    ds = df_test[df_test[\"sequence_id\"] == sequence_id]\n    X = ds[imu_cols].values\n    X = pad_or_truncate(X)\n    X = np.concatenate((standard_scale(X[:, 0:3]), X[:, 3:]), axis=1)\n    X = np.where(np.isnan(X), 0.0, X)  # fill NaNs\n    X_test.append(X)\n\nX_test = np.array(X_test)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-07T14:21:39.172596Z","iopub.execute_input":"2025-06-07T14:21:39.172832Z","iopub.status.idle":"2025-06-07T14:21:39.200841Z","shell.execute_reply.started":"2025-06-07T14:21:39.172814Z","shell.execute_reply":"2025-06-07T14:21:39.199919Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <b><span style='color:#F1A424'>|</span> Dataset</b><a class='anchor' id='dataset'></a> [↑](#top) \n\n***","metadata":{}},{"cell_type":"code","source":"class CustomDataset(Dataset):\n    def __init__(\n        self, config, df: pd.DataFrame, X: np.ndarray\n    ): \n        \n        self.config = config\n        self.df = df\n        self.X = X\n        self.indexes = self.df.sequence_id.unique()\n        \n    def __len__(self):\n        \"\"\"\n        Length of dataset.\n        \"\"\"\n        return len(self.indexes)\n        \n    def __getitem__(self, index):\n        \"\"\"\n        Get one item.\n        \"\"\"\n        sequence_id = self.indexes[index]\n        X = self.X[index]\n        output = {\n            \"X\": torch.tensor(X, dtype=torch.float32),\n            \"sequence_id\": sequence_id\n        }\n        return output ","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-07T14:21:39.201924Z","iopub.execute_input":"2025-06-07T14:21:39.202287Z","iopub.status.idle":"2025-06-07T14:21:39.209587Z","shell.execute_reply.started":"2025-06-07T14:21:39.202257Z","shell.execute_reply":"2025-06-07T14:21:39.208882Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### <b><span style='color:#F1A424'>Visualize one example</span></b>\n","metadata":{}},{"cell_type":"code","source":"test_dataset = CustomDataset(config, df_test, X_test)\ntest_loader = DataLoader(\n    test_dataset,\n    batch_size=config.BATCH_SIZE_TEST,\n    shuffle=False,\n    num_workers=config.NUM_WORKERS, pin_memory=True, drop_last=True\n)\nidx = np.random.choice(range(0, len(test_dataset)))\ncols = imu_cols\nX = test_dataset[idx][\"X\"]\nsequence_id = test_dataset[idx][\"sequence_id\"]\nN = X.shape[0]\ndf = pd.DataFrame(X, columns=cols)\ndf['step'] = range(N)\ndf_melted = df.melt(id_vars='step', var_name='sequence', value_name='value')\n\nfig = px.line(\n    df_melted,\n    x='step',\n    y='value',\n    color='sequence',\n    title=f\"Sequences for {sequence_id}\",\n    template='plotly_dark',\n    width=800,\n    height=600\n)\n\nfig.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-07T14:21:39.210518Z","iopub.execute_input":"2025-06-07T14:21:39.210831Z","iopub.status.idle":"2025-06-07T14:21:41.388305Z","shell.execute_reply.started":"2025-06-07T14:21:39.210811Z","shell.execute_reply":"2025-06-07T14:21:41.387352Z"},"_kg_hide-input":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <b><span style='color:#F1A424'>|</span> Model</b><a class='anchor' id='model'></a> [↑](#top) \n\n***","metadata":{}},{"cell_type":"code","source":"class Wave_Block(nn.Module):\n    def __init__(self, in_channels: int, out_channels: int, dilation_rates: int, kernel_size: int = 3):\n        \"\"\"\n        WaveNet building block.\n        :param in_channels: number of input channels.\n        :param out_channels: number of output channels.\n        :param dilation_rates: how many levels of dilations are used.\n        :param kernel_size: size of the convolving kernel.\n        \"\"\"\n        super(Wave_Block, self).__init__()\n        self.num_rates = dilation_rates\n        self.convs = nn.ModuleList()\n        self.filter_convs = nn.ModuleList()\n        self.gate_convs = nn.ModuleList()\n        # First conv: (B, in_channels, L) -> (B, out_channels, L)\n        self.convs.append(\n            nn.Conv1d(in_channels, out_channels, kernel_size=1, bias=True)\n        )\n        \n        dilation_rates = [2 ** i for i in range(dilation_rates)]\n        for dilation_rate in dilation_rates:\n            # Filter conv: (B, out_channels, L) -> (B, out_channels, L)\n            self.filter_convs.append(\n                nn.Conv1d(\n                    out_channels, out_channels, kernel_size=kernel_size,\n                    padding=int((dilation_rate*(kernel_size-1))/2), dilation=dilation_rate)\n            )\n            # Gate conv: (B, out_channels, L) -> (B, out_channels, L)\n            self.gate_convs.append(\n                nn.Conv1d(\n                    out_channels, out_channels, kernel_size=kernel_size,\n                    padding=int((dilation_rate*(kernel_size-1))/2), dilation=dilation_rate)\n            )\n            # Residual conv: (B, out_channels, L) -> (B, out_channels, L)\n            self.convs.append(nn.Conv1d(out_channels, out_channels, kernel_size=1, bias=True))\n        \n        for i in range(len(self.convs)):\n            nn.init.xavier_uniform_(self.convs[i].weight, gain=nn.init.calculate_gain('relu'))\n            nn.init.zeros_(self.convs[i].bias)\n\n        for i in range(len(self.filter_convs)):\n            nn.init.xavier_uniform_(self.filter_convs[i].weight, gain=nn.init.calculate_gain('relu'))\n            nn.init.zeros_(self.filter_convs[i].bias)\n\n        for i in range(len(self.gate_convs)):\n            nn.init.xavier_uniform_(self.gate_convs[i].weight, gain=nn.init.calculate_gain('relu'))\n            nn.init.zeros_(self.gate_convs[i].bias)\n\n    def forward(self, x: torch.Tensor):\n        # x: (B, in_channels, L)\n        x = self.convs[0](x)  # (B, in_channels, L) -> (B, out_channels, L)\n        res = x  # res: (B, out_channels, L)\n        for i in range(self.num_rates):\n            tanh_out = torch.tanh(self.filter_convs[i](x))  # (B, out_channels, L) -> (B, out_channels, L)\n            sigmoid_out = torch.sigmoid(self.gate_convs[i](x)) # (B, out_channels, L) -> (B, out_channels, L)\n            x = tanh_out * sigmoid_out  # (B, out_channels, L) * (B, out_channels, L) -> (B, out_channels, L)\n            x = self.convs[i + 1](x) # (B, out_channels, L) -> (B, out_channels, L)\n            res = res + x  # (B, out_channels, L) + (B, out_channels, L) -> (B, out_channels, L)\n        return res  # (B, out_channels, L)\n    \nclass WaveNet(nn.Module):\n    def __init__(self, input_channels: int = 1, kernel_size: int = 3):\n        super(WaveNet, self).__init__()\n        self.model = nn.Sequential(\n                Wave_Block(input_channels, 32, 12, kernel_size),   # (B, input_channels, L) -> (B, 8, L)\n                Wave_Block(32, 64, 8, kernel_size),                # (B, 8, L) -> (B, 16, L)\n                Wave_Block(64, 128, 4, kernel_size),               # (B, 16, L) -> (B, 32, L)\n                Wave_Block(128, 256, 1, kernel_size),                # (B, 32, L) -> (B, 64, L)\n        )\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        # x: (B, L, input_channels) - typical input format\n        x = x.permute(0, 2, 1)  # (B, L, input_channels) -> (B, input_channels, L)\n        output = self.model(x)  # (B, input_channels, L) -> (B, 64, L)\n        return output  # (B, 64, L)\n\n\nclass TemporalAttentionPooling(nn.Module):\n    def __init__(self, input_channels, hidden_dim=64):\n        super(TemporalAttentionPooling, self).__init__()\n        self.attn = nn.Sequential(\n            nn.Conv1d(input_channels, hidden_dim, kernel_size=1),\n            nn.Tanh(),\n            nn.Conv1d(hidden_dim, 1, kernel_size=1)\n        )\n\n    def forward(self, x):\n        \"\"\"\n        x: (B, C, L)\n        returns: (B, C)\n        \"\"\"\n        # Compute attention scores\n        attn_scores = self.attn(x)  # (B, 1, L)\n        attn_weights = F.softmax(attn_scores, dim=-1)  # (B, 1, L)\n\n        # Weighted sum over time\n        weighted = x * attn_weights  # (B, C, L)\n        pooled = weighted.sum(dim=-1)  # (B, C)\n\n        return pooled\n\n\n\nclass CustomModel(nn.Module):\n    def __init__(self, num_classes):\n        super(CustomModel, self).__init__()\n        self.wavenet1 = WaveNet(input_channels=3)  # WaveNet: (B, input_channels, L) -> (B, 64, L)\n        self.wavenet2 = WaveNet(input_channels=4)  # WaveNet: (B, input_channels, L) -> (B, 64, L)\n        self.config = config\n        self.global_avg_pooling = nn.AdaptiveAvgPool1d(1)  # (B, 64, L) -> (B, 64, 1)\n        self.dropout = 0.2\n        self.head_1 = nn.Sequential(\n            nn.Flatten(), \n            nn.Linear(512, 256), # (B, 64) -> (B, 64)\n            nn.BatchNorm1d(256),  # (B, 64) -> (B, 64)\n            nn.ReLU(),  # (B, 64) -> (B, 64)\n            nn.Dropout(self.dropout),  # (B, 64) -> (B, 64)\n            nn.Linear(256, num_classes)  # (B, 64) -> (B, num_classes)\n        )\n        self.head_2 = nn.Sequential(\n            nn.Flatten(), \n            nn.Linear(512, 256), # (B, 64) -> (B, 64)\n            nn.BatchNorm1d(256),  # (B, 64) -> (B, 64)\n            nn.ReLU(),  # (B, 64) -> (B, 64)\n            nn.Dropout(self.dropout),  # (B, 64) -> (B, 64)\n            nn.Linear(256, 1)  # (B, 64) -> (B, num_classes)\n        )\n        \n    def forward(self, x: torch.Tensor):\n        \"\"\"\n        Forward pass.\n        \"\"\"\n        # x: (B, L, input_channels) - typical input format\n        x1 = self.wavenet1(x[:, :, 0:3])  # (B, L, input_channels) -> (B, 64, L)\n        x1 = self.global_avg_pooling(x1)  # (B, 64, L) -> (B, 64, 1)\n        x2 = self.wavenet2(x[:, :, 3:])  # (B, L, input_channels) -> (B, 64, L)\n        x2 = self.global_avg_pooling(x2)  # (B, 64, L) -> (B, 64, 1)\n        y = torch.concatenate([x1, x2], axis=1) # (B, 128)\n        z1 = self.head_1(y)  # (B, 64) -> (B, num_classes)\n        z2 = self.head_2(y)  # (B, 64) -> (B, num_classes)\n        return z1, z2  # (B, num_classes)\n\nmodel = CustomModel(num_classes=9)\ntotal_params = sum(p.numel() for p in model.parameters())\nprint(f\"Total number of parameters: {total_params}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-07T14:21:41.389215Z","iopub.execute_input":"2025-06-07T14:21:41.389498Z","iopub.status.idle":"2025-06-07T14:21:41.518290Z","shell.execute_reply.started":"2025-06-07T14:21:41.389474Z","shell.execute_reply":"2025-06-07T14:21:41.517235Z"},"_kg_hide-input":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <b><span style='color:#F1A424'>|</span> Inference</b><a class='anchor' id='inference'></a> [↑](#top) \n\n***\n\nThis competition requires using Kaggle API and not predicting the usual `submission.csv` file. In consequence, you need to design a `predict` function that takes a `polars` datafrmae named `sequence` as input containing the sensor data for the sequence and a `demographics` dataframe which contains the subject's demographic data. \n\n<div class=\"alert alert-block alert-warning\">  \n<b>Note:</b> It is important to note that sequence will be passes 1 at a time. So you must take this into account when designing the function.  \n</div>","metadata":{}},{"cell_type":"code","source":"def predict(sequence: pl.DataFrame, demographics: pl.DataFrame) -> str:\n    df_test = sequence.to_pandas()\n    imu_cols = [\"acc_x\", \"acc_y\", \"acc_z\", \"rot_w\", \"rot_x\", \"rot_y\", \"rot_z\"]\n    X_test, y_test, y_hard_test = [], [], []\n    \n    for sequence_id in tqdm(df_test.sequence_id.unique()):\n        ds = df_test[df_test[\"sequence_id\"] == sequence_id]\n        X = ds[imu_cols].values\n        X = pad_or_truncate(X)\n        X = np.concatenate((standard_scale(X[:, 0:3]), X[:, 3:]), axis=1)\n        X = np.where(np.isnan(X), 0.0, X)  # fill NaNs\n        X_test.append(X)\n\n    X_test = np.array(X_test)\n    model_paths = glob(\"/kaggle/input/cmi-wavenet/*.pth\")\n    all_preds = []\n    for model_path in model_paths:\n        test_dataset = valid_dataset = CustomDataset(config, df_test, X_test)\n        test_loader = DataLoader(\n            test_dataset,\n            batch_size=config.BATCH_SIZE_TEST,\n            shuffle=False,\n            num_workers=config.NUM_WORKERS, \n            pin_memory=True, drop_last=False\n        )\n        model = CustomModel(num_classes=9)\n        checkpoint = torch.load(model_path, map_location=device)\n        model.load_state_dict(checkpoint)\n        model.to(device)\n        model.eval()\n        softmax = nn.Softmax(dim=1)\n        \n        with tqdm(test_loader, unit=\"test_batch\", desc='Test') as tqdm_test_loader:\n            for step, batch in enumerate(tqdm_test_loader):\n                X = batch.pop(\"X\").to(device)\n                batch_size = X.size(0)\n                with torch.no_grad():\n                    y_preds, y_preds_hard = model(X)\n                y_preds = softmax(y_preds).to('cpu').numpy()\n                all_preds.append(y_preds)\n    all_preds = np.concatenate(all_preds)\n    all_preds = np.argmax(all_preds.mean(axis=0)).item()\n    prediction = num_to_label[all_preds]\n    return prediction\n\n# Launch inference server\ninference_server = kaggle_evaluation.cmi_inference_server.CMIInferenceServer(predict)\n\nif os.getenv('KAGGLE_IS_COMPETITION_RERUN'):\n    inference_server.serve()\nelse:\n    inference_server.run_local_gateway(\n        data_paths=(\n            '/kaggle/input/cmi-detect-behavior-with-sensor-data/test.csv',\n            '/kaggle/input/cmi-detect-behavior-with-sensor-data/test_demographics.csv',\n        )\n    )","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-07T14:21:41.519347Z","iopub.execute_input":"2025-06-07T14:21:41.519691Z","iopub.status.idle":"2025-06-07T14:21:45.722533Z","shell.execute_reply.started":"2025-06-07T14:21:41.519661Z","shell.execute_reply":"2025-06-07T14:21:45.721712Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}