ISLR_1st_place_Hoyeol_Sohn.ipynb

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        "outputId": "208b1865-b04b-4cd5-fbfb-9e9de4c25394"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive\n"
          ]
        }
      ],
      "source": [
        "cd ./drive/MyDrive"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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          "text": [
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/591.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m337.9/591.0 kB\u001b[0m \u001b[31m9.7 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m591.0/591.0 kB\u001b[0m \u001b[31m8.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m17.1/17.1 MB\u001b[0m \u001b[31m25.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for tf-utils (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "orbax-checkpoint 0.2.1 requires jax>=0.4.8, but you have jax 0.3.25 which is incompatible.\n",
            "flax 0.6.9 requires jax>=0.4.2, but you have jax 0.3.25 which is incompatible.\n",
            "chex 0.1.7 requires jax>=0.4.6, but you have jax 0.3.25 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "!pip install -q tensorflow-addons\n",
        "!pip install -q git+https://github.com/hoyso48/tf-utils@main"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wD7tqFC_qAFQ",
        "outputId": "84447eef-c825-42e0-aa1a-6e4f0bf9cb8b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/tensorflow_addons/utils/tfa_eol_msg.py:23: UserWarning: \n",
            "\n",
            "TensorFlow Addons (TFA) has ended development and introduction of new features.\n",
            "TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024.\n",
            "Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). \n",
            "\n",
            "For more information see: https://github.com/tensorflow/addons/issues/2807 \n",
            "\n",
            "  warnings.warn(\n",
            "<ipython-input-3-f8b6455d81d0>:9: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
            "  from tqdm.autonotebook import tqdm\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tensorflow Version: 2.12.0\n",
            "Python Version: 3.10.11 (main, Apr  5 2023, 14:15:10) [GCC 9.4.0]\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "import tensorflow_addons as tfa\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib as mpl\n",
        "import tensorflow.keras.mixed_precision as mixed_precision\n",
        "\n",
        "from tqdm.autonotebook import tqdm\n",
        "import sklearn\n",
        "\n",
        "from tf_utils.schedules import OneCycleLR, ListedLR\n",
        "from tf_utils.callbacks import Snapshot, SWA\n",
        "from tf_utils.learners import FGM, AWP\n",
        "\n",
        "import os\n",
        "import time\n",
        "import pickle\n",
        "import math\n",
        "import random\n",
        "import sys\n",
        "import cv2\n",
        "import gc\n",
        "import glob\n",
        "import datetime\n",
        "\n",
        "print(f'Tensorflow Version: {tf.__version__}')\n",
        "print(f'Python Version: {sys.version}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u74o98JxqAFQ",
        "outputId": "14c06c69-8f24-45df-f79e-5e62a58b36a3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "connecting to TPU...\n",
            "REPLICAS: 8\n"
          ]
        }
      ],
      "source": [
        "# Seed all random number generators\n",
        "def seed_everything(seed=42):\n",
        "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
        "    random.seed(seed)\n",
        "    np.random.seed(seed)\n",
        "    tf.random.set_seed(seed)\n",
        "    \n",
        "def get_strategy(device='TPU'):\n",
        "    if \"TPU\" in device:\n",
        "        tpu = 'local' if device=='TPU-VM' else None\n",
        "        print(\"connecting to TPU...\")\n",
        "        tpu = tf.distribute.cluster_resolver.TPUClusterResolver.connect(tpu=tpu)\n",
        "        strategy = tf.distribute.TPUStrategy(tpu)\n",
        "        IS_TPU = True\n",
        "\n",
        "    if device == \"GPU\"  or device==\"CPU\":\n",
        "        ngpu = len(tf.config.experimental.list_physical_devices('GPU'))\n",
        "        if ngpu>1:\n",
        "            print(\"Using multi GPU\")\n",
        "            strategy = tf.distribute.MirroredStrategy()\n",
        "        elif ngpu==1:\n",
        "            print(\"Using single GPU\")\n",
        "            strategy = tf.distribute.get_strategy()\n",
        "        else:\n",
        "            print(\"Using CPU\")\n",
        "            strategy = tf.distribute.get_strategy()\n",
        "            CFG.device = \"CPU\"\n",
        "\n",
        "    if device == \"GPU\":\n",
        "        print(\"Num GPUs Available: \", ngpu)\n",
        "\n",
        "    AUTO     = tf.data.experimental.AUTOTUNE\n",
        "    REPLICAS = strategy.num_replicas_in_sync\n",
        "    print(f'REPLICAS: {REPLICAS}')\n",
        "    \n",
        "    return strategy, REPLICAS, IS_TPU\n",
        "\n",
        "STRATEGY, N_REPLICAS, IS_TPU = get_strategy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QVDc3vkwqAFQ",
        "outputId": "f7a6ec97-4cac-41bb-9ca4-7204b2d5fd6d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "187\n",
            "Copying gs://kds-07ef6b204aa5081e269a277f7fe8c8c1361ce30f6900d5d439a44074/train.csv...\n",
            "/ [1 files][  6.1 MiB/  6.1 MiB]                                                \n",
            "Operation completed over 1 objects/6.1 MiB.                                      \n",
            "Copying gs://kds-07ef6b204aa5081e269a277f7fe8c8c1361ce30f6900d5d439a44074/sign_to_prediction_index_map.json...\n",
            "/ [1 files][  3.3 KiB/  3.3 KiB]                                                \n",
            "Operation completed over 1 objects/3.3 KiB.                                      \n"
          ]
        }
      ],
      "source": [
        "#NOTE: you should run KaggleDatasets.get_gcs_path(dataset_name) in the kaggle notebook to update gcs_path as they expires after several weeks..\n",
        "#notebook: https://www.kaggle.com/code/hoyso48/islr-get-gcs-path\n",
        "\n",
        "GCS_PATH = {\n",
        "            'ISLR':'gs://kds-07ef6b204aa5081e269a277f7fe8c8c1361ce30f6900d5d439a44074',\n",
        "            '5fold':'gs://kds-b525bf8be78012d70e4e11b3fb9907d0bb2f265d748a7321c2d58f33',\n",
        "            '5fold_randsplit':'gs://kds-06ac5cc1c0e92db27870f1acae71b8c815f2ce024d19bd0d354bd4a5',\n",
        "            }\n",
        "\n",
        "TRAIN_FILENAMES = tf.io.gfile.glob(GCS_PATH['5fold']+'/*.tfrecords')\n",
        "COMPETITION_PATH = GCS_PATH['ISLR']\n",
        "\n",
        "print(len(TRAIN_FILENAMES))\n",
        "!gsutil cp {COMPETITION_PATH}/train.csv .\n",
        "!gsutil cp {COMPETITION_PATH}/sign_to_prediction_index_map.json ."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 414
        },
        "id": "DtrBI-jwqAFQ",
        "outputId": "639ad461-edaf-499f-ac5b-21c6c885570c"
      },
      "outputs": [
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              "                                            path  participant_id  sequence_id  \\\n",
              "0  train_landmark_files/26734/1000035562.parquet           26734   1000035562   \n",
              "1  train_landmark_files/28656/1000106739.parquet           28656   1000106739   \n",
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              "3  train_landmark_files/25571/1000210073.parquet           25571   1000210073   \n",
              "4  train_landmark_files/62590/1000240708.parquet           62590   1000240708   \n",
              "\n",
              "    sign  \n",
              "0   blow  \n",
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              "      <th>path</th>\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 94477 entries, 0 to 94476\n",
            "Data columns (total 4 columns):\n",
            " #   Column          Non-Null Count  Dtype \n",
            "---  ------          --------------  ----- \n",
            " 0   path            94477 non-null  object\n",
            " 1   participant_id  94477 non-null  int64 \n",
            " 2   sequence_id     94477 non-null  int64 \n",
            " 3   sign            94477 non-null  object\n",
            "dtypes: int64(2), object(2)\n",
            "memory usage: 2.9+ MB\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "None"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Train DataFrame\n",
        "train_df = pd.read_csv('train.csv')\n",
        "display(train_df.head())\n",
        "display(train_df.info())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iAc9FKztqAFQ",
        "outputId": "7719966c-42f6-4995-ae5c-9b9e84c77d74"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "94477 94477\n"
          ]
        }
      ],
      "source": [
        "import re\n",
        "def count_data_items(filenames):\n",
        "    n = [int(re.compile(r\"-([0-9]*)\\.\").search(filename.split('/')[-1]).group(1)) for filename in filenames]\n",
        "    return np.sum(n)\n",
        "print(count_data_items(TRAIN_FILENAMES), len(train_df))\n",
        "assert count_data_items(TRAIN_FILENAMES) == len(train_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6xyloTyiqAFQ",
        "outputId": "4b95d536-a5cd-47f9-fd8b-491e0f512ee1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "118\n",
            "708\n"
          ]
        }
      ],
      "source": [
        "ROWS_PER_FRAME = 543\n",
        "MAX_LEN = 384\n",
        "CROP_LEN = MAX_LEN\n",
        "NUM_CLASSES  = 250\n",
        "PAD = -100.\n",
        "NOSE=[\n",
        "    1,2,98,327\n",
        "]\n",
        "LNOSE = [98]\n",
        "RNOSE = [327]\n",
        "LIP = [ 0, \n",
        "    61, 185, 40, 39, 37, 267, 269, 270, 409,\n",
        "    291, 146, 91, 181, 84, 17, 314, 405, 321, 375,\n",
        "    78, 191, 80, 81, 82, 13, 312, 311, 310, 415,\n",
        "    95, 88, 178, 87, 14, 317, 402, 318, 324, 308,\n",
        "]\n",
        "LLIP = [84,181,91,146,61,185,40,39,37,87,178,88,95,78,191,80,81,82]\n",
        "RLIP = [314,405,321,375,291,409,270,269,267,317,402,318,324,308,415,310,311,312]\n",
        "\n",
        "POSE = [500, 502, 504, 501, 503, 505, 512, 513]\n",
        "LPOSE = [513,505,503,501]\n",
        "RPOSE = [512,504,502,500]\n",
        "\n",
        "REYE = [\n",
        "    33, 7, 163, 144, 145, 153, 154, 155, 133,\n",
        "    246, 161, 160, 159, 158, 157, 173,\n",
        "]\n",
        "LEYE = [\n",
        "    263, 249, 390, 373, 374, 380, 381, 382, 362,\n",
        "    466, 388, 387, 386, 385, 384, 398,\n",
        "]\n",
        "\n",
        "LHAND = np.arange(468, 489).tolist()\n",
        "RHAND = np.arange(522, 543).tolist()\n",
        "\n",
        "POINT_LANDMARKS = LIP + LHAND + RHAND + NOSE + REYE + LEYE #+POSE\n",
        "\n",
        "NUM_NODES = len(POINT_LANDMARKS)\n",
        "CHANNELS = 6*NUM_NODES\n",
        "\n",
        "print(NUM_NODES)\n",
        "print(CHANNELS)\n",
        "\n",
        "def interp1d_(x, target_len, method='random'):\n",
        "    length = tf.shape(x)[1]\n",
        "    target_len = tf.maximum(1,target_len)\n",
        "    if method == 'random':\n",
        "        if tf.random.uniform(()) < 0.33:\n",
        "            x = tf.image.resize(x, (target_len,tf.shape(x)[1]),'bilinear')\n",
        "        else:\n",
        "            if tf.random.uniform(()) < 0.5:\n",
        "                x = tf.image.resize(x, (target_len,tf.shape(x)[1]),'bicubic')\n",
        "            else:\n",
        "                x = tf.image.resize(x, (target_len,tf.shape(x)[1]),'nearest')\n",
        "    else:\n",
        "        x = tf.image.resize(x, (target_len,tf.shape(x)[1]),method)\n",
        "    return x\n",
        "\n",
        "def tf_nan_mean(x, axis=0, keepdims=False):\n",
        "    return tf.reduce_sum(tf.where(tf.math.is_nan(x), tf.zeros_like(x), x), axis=axis, keepdims=keepdims) / tf.reduce_sum(tf.where(tf.math.is_nan(x), tf.zeros_like(x), tf.ones_like(x)), axis=axis, keepdims=keepdims)\n",
        "\n",
        "def tf_nan_std(x, center=None, axis=0, keepdims=False):\n",
        "    if center is None:\n",
        "        center = tf_nan_mean(x, axis=axis,  keepdims=True)\n",
        "    d = x - center\n",
        "    return tf.math.sqrt(tf_nan_mean(d * d, axis=axis, keepdims=keepdims))\n",
        "\n",
        "class Preprocess(tf.keras.layers.Layer):\n",
        "    def __init__(self, max_len=MAX_LEN, point_landmarks=POINT_LANDMARKS, **kwargs):\n",
        "        super().__init__(**kwargs)\n",
        "        self.max_len = max_len\n",
        "        self.point_landmarks = point_landmarks\n",
        "\n",
        "    def call(self, inputs):\n",
        "        if tf.rank(inputs) == 3:\n",
        "            x = inputs[None,...]\n",
        "        else:\n",
        "            x = inputs\n",
        "        \n",
        "        mean = tf_nan_mean(tf.gather(x, [17], axis=2), axis=[1,2], keepdims=True)\n",
        "        mean = tf.where(tf.math.is_nan(mean), tf.constant(0.5,x.dtype), mean)\n",
        "        x = tf.gather(x, self.point_landmarks, axis=2) #N,T,P,C\n",
        "        std = tf_nan_std(x, center=mean, axis=[1,2], keepdims=True)\n",
        "        \n",
        "        x = (x - mean)/std\n",
        "\n",
        "        if self.max_len is not None:\n",
        "            x = x[:,:self.max_len]\n",
        "        length = tf.shape(x)[1]\n",
        "        x = x[...,:2]\n",
        "\n",
        "        dx = tf.cond(tf.shape(x)[1]>1,lambda:tf.pad(x[:,1:] - x[:,:-1], [[0,0],[0,1],[0,0],[0,0]]),lambda:tf.zeros_like(x))\n",
        "\n",
        "        dx2 = tf.cond(tf.shape(x)[1]>2,lambda:tf.pad(x[:,2:] - x[:,:-2], [[0,0],[0,2],[0,0],[0,0]]),lambda:tf.zeros_like(x))\n",
        "\n",
        "        x = tf.concat([\n",
        "            tf.reshape(x, (-1,length,2*len(self.point_landmarks))),\n",
        "            tf.reshape(dx, (-1,length,2*len(self.point_landmarks))),\n",
        "            tf.reshape(dx2, (-1,length,2*len(self.point_landmarks))),\n",
        "        ], axis = -1)\n",
        "        \n",
        "        x = tf.where(tf.math.is_nan(x),tf.constant(0.,x.dtype),x)\n",
        "        \n",
        "        return x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "r17ZnZaGqAFQ"
      },
      "outputs": [],
      "source": [
        "def decode_tfrec(record_bytes):\n",
        "    features = tf.io.parse_single_example(record_bytes, {\n",
        "        'coordinates': tf.io.FixedLenFeature([], tf.string),\n",
        "        'sign': tf.io.FixedLenFeature([], tf.int64),\n",
        "    })\n",
        "    out = {}\n",
        "    out['coordinates']  = tf.reshape(tf.io.decode_raw(features['coordinates'], tf.float32), (-1,ROWS_PER_FRAME,3))\n",
        "    out['sign'] = features['sign']\n",
        "    return out\n",
        "\n",
        "def filter_nans_tf(x, ref_point=POINT_LANDMARKS):\n",
        "    mask = tf.math.logical_not(tf.reduce_all(tf.math.is_nan(tf.gather(x,ref_point,axis=1)), axis=[-2,-1]))\n",
        "    x = tf.boolean_mask(x, mask, axis=0)\n",
        "    return x\n",
        "\n",
        "def preprocess(x, augment=False, max_len=MAX_LEN):\n",
        "    coord = x['coordinates']\n",
        "    coord = filter_nans_tf(coord)\n",
        "    if augment:\n",
        "        coord = augment_fn(coord, max_len=max_len)\n",
        "    coord = tf.ensure_shape(coord, (None,ROWS_PER_FRAME,3))\n",
        "    \n",
        "    return tf.cast(Preprocess(max_len=max_len)(coord)[0],tf.float32), tf.one_hot(x['sign'], NUM_CLASSES)\n",
        "\n",
        "def flip_lr(x):\n",
        "    x,y,z = tf.unstack(x, axis=-1)\n",
        "    x = 1-x\n",
        "    new_x = tf.stack([x,y,z], -1)\n",
        "    new_x = tf.transpose(new_x, [1,0,2])\n",
        "    lhand = tf.gather(new_x, LHAND, axis=0)\n",
        "    rhand = tf.gather(new_x, RHAND, axis=0)\n",
        "    new_x = tf.tensor_scatter_nd_update(new_x, tf.constant(LHAND)[...,None], rhand)\n",
        "    new_x = tf.tensor_scatter_nd_update(new_x, tf.constant(RHAND)[...,None], lhand)\n",
        "    llip = tf.gather(new_x, LLIP, axis=0)\n",
        "    rlip = tf.gather(new_x, RLIP, axis=0)\n",
        "    new_x = tf.tensor_scatter_nd_update(new_x, tf.constant(LLIP)[...,None], rlip)\n",
        "    new_x = tf.tensor_scatter_nd_update(new_x, tf.constant(RLIP)[...,None], llip)\n",
        "    lpose = tf.gather(new_x, LPOSE, axis=0)\n",
        "    rpose = tf.gather(new_x, RPOSE, axis=0)\n",
        "    new_x = tf.tensor_scatter_nd_update(new_x, tf.constant(LPOSE)[...,None], rpose)\n",
        "    new_x = tf.tensor_scatter_nd_update(new_x, tf.constant(RPOSE)[...,None], lpose)\n",
        "    leye = tf.gather(new_x, LEYE, axis=0)\n",
        "    reye = tf.gather(new_x, REYE, axis=0)\n",
        "    new_x = tf.tensor_scatter_nd_update(new_x, tf.constant(LEYE)[...,None], reye)\n",
        "    new_x = tf.tensor_scatter_nd_update(new_x, tf.constant(REYE)[...,None], leye)\n",
        "    lnose = tf.gather(new_x, LNOSE, axis=0)\n",
        "    rnose = tf.gather(new_x, RNOSE, axis=0)\n",
        "    new_x = tf.tensor_scatter_nd_update(new_x, tf.constant(LNOSE)[...,None], rnose)\n",
        "    new_x = tf.tensor_scatter_nd_update(new_x, tf.constant(RNOSE)[...,None], lnose)\n",
        "    new_x = tf.transpose(new_x, [1,0,2])\n",
        "    return new_x\n",
        "\n",
        "def resample(x, rate=(0.8,1.2)):\n",
        "    rate = tf.random.uniform((), rate[0], rate[1])\n",
        "    length = tf.shape(x)[0]\n",
        "    new_size = tf.cast(rate*tf.cast(length,tf.float32), tf.int32) #(tf.cast(rate*tf.cast(length,tf.float32), tf.int32),tf.shape(x)[1])\n",
        "    new_x = interp1d_(x, new_size) #tf.image.resize(x, new_size, method='bilinear')\n",
        "    return new_x\n",
        "\n",
        "def spatial_random_affine(xyz,\n",
        "    scale  = (0.8,1.2),\n",
        "    shear = (-0.15,0.15),\n",
        "    shift  = (-0.1,0.1),\n",
        "    degree = (-30,30),\n",
        "):\n",
        "    center = tf.constant([0.5,0.5])\n",
        "    if scale is not None:\n",
        "        scale = tf.random.uniform((),*scale)\n",
        "        xyz = scale*xyz\n",
        "\n",
        "    if shear is not None:\n",
        "        xy = xyz[...,:2]\n",
        "        z = xyz[...,2:]\n",
        "        shear_x = shear_y = tf.random.uniform((),*shear)\n",
        "        if tf.random.uniform(()) < 0.5:\n",
        "            shear_x = 0.\n",
        "        else:\n",
        "            shear_y = 0.\n",
        "        shear_mat = tf.identity([\n",
        "            [1.,shear_x],\n",
        "            [shear_y,1.]\n",
        "        ])\n",
        "        xy = xy @ shear_mat\n",
        "        center = center + [shear_y, shear_x]\n",
        "        xyz = tf.concat([xy,z], axis=-1)\n",
        "\n",
        "    if degree is not None:\n",
        "        xy = xyz[...,:2]\n",
        "        z = xyz[...,2:]\n",
        "        xy -= center\n",
        "        degree = tf.random.uniform((),*degree)\n",
        "        radian = degree/180*np.pi\n",
        "        c = tf.math.cos(radian)\n",
        "        s = tf.math.sin(radian)\n",
        "        rotate_mat = tf.identity([\n",
        "            [c,s],\n",
        "            [-s, c],\n",
        "        ])\n",
        "        xy = xy @ rotate_mat\n",
        "        xy = xy + center\n",
        "        xyz = tf.concat([xy,z], axis=-1)\n",
        "\n",
        "    if shift is not None:\n",
        "        shift = tf.random.uniform((),*shift)\n",
        "        xyz = xyz + shift\n",
        "\n",
        "    return xyz\n",
        "\n",
        "def temporal_crop(x, length=MAX_LEN):\n",
        "    l = tf.shape(x)[0]\n",
        "    offset = tf.random.uniform((), 0, tf.clip_by_value(l-length,1,length), dtype=tf.int32)\n",
        "    x = x[offset:offset+length]\n",
        "    return x\n",
        "\n",
        "def temporal_mask(x, size=(0.2,0.4), mask_value=float('nan')):\n",
        "    l = tf.shape(x)[0]\n",
        "    mask_size = tf.random.uniform((), *size)\n",
        "    mask_size = tf.cast(tf.cast(l, tf.float32) * mask_size, tf.int32)\n",
        "    mask_offset = tf.random.uniform((), 0, tf.clip_by_value(l-mask_size,1,l), dtype=tf.int32)\n",
        "    x = tf.tensor_scatter_nd_update(x,tf.range(mask_offset, mask_offset+mask_size)[...,None],tf.fill([mask_size,543,3],mask_value))\n",
        "    return x\n",
        "\n",
        "def spatial_mask(x, size=(0.2,0.4), mask_value=float('nan')):\n",
        "    mask_offset_y = tf.random.uniform(())\n",
        "    mask_offset_x = tf.random.uniform(())\n",
        "    mask_size = tf.random.uniform((), *size)\n",
        "    mask_x = (mask_offset_x<x[...,0]) & (x[...,0] < mask_offset_x + mask_size)\n",
        "    mask_y = (mask_offset_y<x[...,1]) & (x[...,1] < mask_offset_y + mask_size)\n",
        "    mask = mask_x & mask_y\n",
        "    x = tf.where(mask[...,None], mask_value, x)\n",
        "    return x\n",
        "\n",
        "def augment_fn(x, always=False, max_len=None):\n",
        "    if tf.random.uniform(())<0.8 or always:\n",
        "        x = resample(x, (0.5,1.5))\n",
        "    if tf.random.uniform(())<0.5 or always:\n",
        "        x = flip_lr(x)\n",
        "    if max_len is not None:\n",
        "        x = temporal_crop(x, max_len)\n",
        "    if tf.random.uniform(())<0.75 or always:\n",
        "        x = spatial_random_affine(x)\n",
        "    if tf.random.uniform(())<0.5 or always:\n",
        "        x = temporal_mask(x)\n",
        "    if tf.random.uniform(())<0.5 or always:\n",
        "        x = spatial_mask(x)\n",
        "    return x\n",
        "\n",
        "def get_tfrec_dataset(tfrecords, batch_size=64, max_len=64, drop_remainder=False, augment=False, shuffle=False, repeat=False):\n",
        "    # Initialize dataset with TFRecords\n",
        "    ds = tf.data.TFRecordDataset(tfrecords, num_parallel_reads=tf.data.AUTOTUNE, compression_type='GZIP')\n",
        "    ds = ds.map(decode_tfrec, tf.data.AUTOTUNE)\n",
        "    ds = ds.map(lambda x: preprocess(x, augment=augment, max_len=max_len), tf.data.AUTOTUNE)\n",
        "\n",
        "    if repeat: \n",
        "        ds = ds.repeat()\n",
        "        \n",
        "    if shuffle:\n",
        "        ds = ds.shuffle(shuffle)\n",
        "        options = tf.data.Options()\n",
        "        options.experimental_deterministic = (False)\n",
        "        ds = ds.with_options(options)\n",
        "    \n",
        "    if batch_size:\n",
        "        ds = ds.padded_batch(batch_size, padding_values=PAD, padded_shapes=([max_len,CHANNELS],[NUM_CLASSES]), drop_remainder=drop_remainder)\n",
        "\n",
        "    ds = ds.prefetch(tf.data.AUTOTUNE)\n",
        "        \n",
        "    return ds\n",
        "\n",
        "ds = get_tfrec_dataset(TRAIN_FILENAMES, augment=True, batch_size=1024)\n",
        "for x in ds:\n",
        "    temp_train = x\n",
        "    break"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "gbVSTH9xqAFQ",
        "outputId": "c903b34b-902b-4cde-b912-5508b5278c47"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<_ParallelMapDataset element_spec={'coordinates': TensorSpec(shape=(None, 543, 3), dtype=tf.float32, name=None), 'sign': TensorSpec(shape=(), dtype=tf.int64, name=None)}>\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "from IPython.display import HTML\n",
        "import matplotlib.animation as animation\n",
        "from matplotlib.animation import FuncAnimation\n",
        "\n",
        "def filter_nans(frames):\n",
        "    return frames[~np.isnan(frames).all(axis=(-2,-1))]\n",
        "\n",
        "ds = tf.data.TFRecordDataset(TRAIN_FILENAMES, num_parallel_reads=tf.data.AUTOTUNE, compression_type='GZIP')\n",
        "ds = ds.map(decode_tfrec, tf.data.AUTOTUNE)\n",
        "print(ds)\n",
        "for x in ds:\n",
        "    temp = x['coordinates'].numpy()\n",
        "    if not len(filter_nans(temp[:,LHAND])) == 0:\n",
        "        break\n",
        "    \n",
        "edges = [(0,1),(1,2),(2,3),(3,4),(0,5),(0,17),(5,6),(6,7),(7,8),(5,9),(9,10),(10,11),(11,12),\n",
        "         (9,13),(13,14),(14,15),(15,16),(13,17),(17,18),(18,19),(19,20)]\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "def plot_frame(frame, edges=[], idxs=[]):\n",
        "        \n",
        "    frame[np.isnan(frame)] = 0\n",
        "    x = list(frame[...,0])\n",
        "    y = list(frame[...,1])\n",
        "    if len(idxs) == 0:\n",
        "        idxs = list(range(len(x)))\n",
        "    ax.clear()\n",
        "    ax.scatter(x, y, color='dodgerblue')\n",
        "    for i in range(len(x)):\n",
        "        ax.text(x[i], y[i], idxs[i])\n",
        "        \n",
        "    for edge in edges:\n",
        "        ax.plot([x[edge[0]], x[edge[1]]], [y[edge[0]], y[edge[1]]], color='salmon')\n",
        "    ax.set_xticks([])\n",
        "    ax.set_yticks([])\n",
        "    ax.set_xticklabels([])\n",
        "    ax.set_yticklabels([])\n",
        "\n",
        "def animate_frames(frames, edges=[], idxs=[]):\n",
        "    anim = FuncAnimation(fig, lambda frame: plot_frame(frame, edges, idxs), frames=frames, interval=100)\n",
        "    return HTML(anim.to_jshtml())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 579
        },
        "id": "ElHcJ18fqAFQ",
        "outputId": "d46dea45-f8f0-44ac-b228-3fc4f4b5a745"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<link rel=\"stylesheet\"\n",
              "href=\"https://maxcdn.bootstrapcdn.com/font-awesome/4.4.0/css/font-awesome.min.css\">\n",
              "<script language=\"javascript\">\n",
              "  function isInternetExplorer() {\n",
              "    ua = navigator.userAgent;\n",
              "    /* MSIE used to detect old browsers and Trident used to newer ones*/\n",
              "    return ua.indexOf(\"MSIE \") > -1 || ua.indexOf(\"Trident/\") > -1;\n",
              "  }\n",
              "\n",
              "  /* Define the Animation class */\n",
              "  function Animation(frames, img_id, slider_id, interval, loop_select_id){\n",
              "    this.img_id = img_id;\n",
              "    this.slider_id = slider_id;\n",
              "    this.loop_select_id = loop_select_id;\n",
              "    this.interval = interval;\n",
              "    this.current_frame = 0;\n",
              "    this.direction = 0;\n",
              "    this.timer = null;\n",
              "    this.frames = new Array(frames.length);\n",
              "\n",
              "    for (var i=0; i<frames.length; i++)\n",
              "    {\n",
              "     this.frames[i] = new Image();\n",
              "     this.frames[i].src = frames[i];\n",
              "    }\n",
              "    var slider = document.getElementById(this.slider_id);\n",
              "    slider.max = this.frames.length - 1;\n",
              "    if (isInternetExplorer()) {\n",
              "        // switch from oninput to onchange because IE <= 11 does not conform\n",
              "        // with W3C specification. It ignores oninput and onchange behaves\n",
              "        // like oninput. In contrast, Microsoft Edge behaves correctly.\n",
              "        slider.setAttribute('onchange', slider.getAttribute('oninput'));\n",
              "        slider.setAttribute('oninput', null);\n",
              "    }\n",
              "    this.set_frame(this.current_frame);\n",
              "  }\n",
              "\n",
              "  Animation.prototype.get_loop_state = function(){\n",
              "    var button_group = document[this.loop_select_id].state;\n",
              "    for (var i = 0; i < button_group.length; i++) {\n",
              "        var button = button_group[i];\n",
              "        if (button.checked) {\n",
              "            return button.value;\n",
              "        }\n",
              "    }\n",
              "    return undefined;\n",
              "  }\n",
              "\n",
              "  Animation.prototype.set_frame = function(frame){\n",
              "    this.current_frame = frame;\n",
              "    document.getElementById(this.img_id).src =\n",
              "            this.frames[this.current_frame].src;\n",
              "    document.getElementById(this.slider_id).value = this.current_frame;\n",
              "  }\n",
              "\n",
              "  Animation.prototype.next_frame = function()\n",
              "  {\n",
              "    this.set_frame(Math.min(this.frames.length - 1, this.current_frame + 1));\n",
              "  }\n",
              "\n",
              "  Animation.prototype.previous_frame = function()\n",
              "  {\n",
              "    this.set_frame(Math.max(0, this.current_frame - 1));\n",
              "  }\n",
              "\n",
              "  Animation.prototype.first_frame = function()\n",
              "  {\n",
              "    this.set_frame(0);\n",
              "  }\n",
              "\n",
              "  Animation.prototype.last_frame = function()\n",
              "  {\n",
              "    this.set_frame(this.frames.length - 1);\n",
              "  }\n",
              "\n",
              "  Animation.prototype.slower = function()\n",
              "  {\n",
              "    this.interval /= 0.7;\n",
              "    if(this.direction > 0){this.play_animation();}\n",
              "    else if(this.direction < 0){this.reverse_animation();}\n",
              "  }\n",
              "\n",
              "  Animation.prototype.faster = function()\n",
              "  {\n",
              "    this.interval *= 0.7;\n",
              "    if(this.direction > 0){this.play_animation();}\n",
              "    else if(this.direction < 0){this.reverse_animation();}\n",
              "  }\n",
              "\n",
              "  Animation.prototype.anim_step_forward = function()\n",
              "  {\n",
              "    this.current_frame += 1;\n",
              "    if(this.current_frame < this.frames.length){\n",
              "      this.set_frame(this.current_frame);\n",
              "    }else{\n",
              "      var loop_state = this.get_loop_state();\n",
              "      if(loop_state == \"loop\"){\n",
              "        this.first_frame();\n",
              "      }else if(loop_state == \"reflect\"){\n",
              "        this.last_frame();\n",
              "        this.reverse_animation();\n",
              "      }else{\n",
              "        this.pause_animation();\n",
              "        this.last_frame();\n",
              "      }\n",
              "    }\n",
              "  }\n",
              "\n",
              "  Animation.prototype.anim_step_reverse = function()\n",
              "  {\n",
              "    this.current_frame -= 1;\n",
              "    if(this.current_frame >= 0){\n",
              "      this.set_frame(this.current_frame);\n",
              "    }else{\n",
              "      var loop_state = this.get_loop_state();\n",
              "      if(loop_state == \"loop\"){\n",
              "        this.last_frame();\n",
              "      }else if(loop_state == \"reflect\"){\n",
              "        this.first_frame();\n",
              "        this.play_animation();\n",
              "      }else{\n",
              "        this.pause_animation();\n",
              "        this.first_frame();\n",
              "      }\n",
              "    }\n",
              "  }\n",
              "\n",
              "  Animation.prototype.pause_animation = function()\n",
              "  {\n",
              "    this.direction = 0;\n",
              "    if (this.timer){\n",
              "      clearInterval(this.timer);\n",
              "      this.timer = null;\n",
              "    }\n",
              "  }\n",
              "\n",
              "  Animation.prototype.play_animation = function()\n",
              "  {\n",
              "    this.pause_animation();\n",
              "    this.direction = 1;\n",
              "    var t = this;\n",
              "    if (!this.timer) this.timer = setInterval(function() {\n",
              "        t.anim_step_forward();\n",
              "    }, this.interval);\n",
              "  }\n",
              "\n",
              "  Animation.prototype.reverse_animation = function()\n",
              "  {\n",
              "    this.pause_animation();\n",
              "    this.direction = -1;\n",
              "    var t = this;\n",
              "    if (!this.timer) this.timer = setInterval(function() {\n",
              "        t.anim_step_reverse();\n",
              "    }, this.interval);\n",
              "  }\n",
              "</script>\n",
              "\n",
              "<style>\n",
              ".animation {\n",
              "    display: inline-block;\n",
              "    text-align: center;\n",
              "}\n",
              "input[type=range].anim-slider {\n",
              "    width: 374px;\n",
              "    margin-left: auto;\n",
              "    margin-right: auto;\n",
              "}\n",
              ".anim-buttons {\n",
              "    margin: 8px 0px;\n",
              "}\n",
              ".anim-buttons button {\n",
              "    padding: 0;\n",
              "    width: 36px;\n",
              "}\n",
              ".anim-state label {\n",
              "    margin-right: 8px;\n",
              "}\n",
              ".anim-state input {\n",
              "    margin: 0;\n",
              "    vertical-align: middle;\n",
              "}\n",
              "</style>\n",
              "\n",
              "<div class=\"animation\">\n",
              "  <img id=\"_anim_img5db6d31a93e54c7b95bfbb29554dd0e2\">\n",
              "  <div class=\"anim-controls\">\n",
              "    <input id=\"_anim_slider5db6d31a93e54c7b95bfbb29554dd0e2\" type=\"range\" class=\"anim-slider\"\n",
              "           name=\"points\" min=\"0\" max=\"1\" step=\"1\" value=\"0\"\n",
              "           oninput=\"anim5db6d31a93e54c7b95bfbb29554dd0e2.set_frame(parseInt(this.value));\">\n",
              "    <div class=\"anim-buttons\">\n",
              "      <button title=\"Decrease speed\" aria-label=\"Decrease speed\" onclick=\"anim5db6d31a93e54c7b95bfbb29554dd0e2.slower()\">\n",
              "          <i class=\"fa fa-minus\"></i></button>\n",
              "      <button title=\"First frame\" aria-label=\"First frame\" onclick=\"anim5db6d31a93e54c7b95bfbb29554dd0e2.first_frame()\">\n",
              "        <i class=\"fa fa-fast-backward\"></i></button>\n",
              "      <button title=\"Previous frame\" aria-label=\"Previous frame\" onclick=\"anim5db6d31a93e54c7b95bfbb29554dd0e2.previous_frame()\">\n",
              "          <i class=\"fa fa-step-backward\"></i></button>\n",
              "      <button title=\"Play backwards\" aria-label=\"Play backwards\" onclick=\"anim5db6d31a93e54c7b95bfbb29554dd0e2.reverse_animation()\">\n",
              "          <i class=\"fa fa-play fa-flip-horizontal\"></i></button>\n",
              "      <button title=\"Pause\" aria-label=\"Pause\" onclick=\"anim5db6d31a93e54c7b95bfbb29554dd0e2.pause_animation()\">\n",
              "          <i class=\"fa fa-pause\"></i></button>\n",
              "      <button title=\"Play\" aria-label=\"Play\" onclick=\"anim5db6d31a93e54c7b95bfbb29554dd0e2.play_animation()\">\n",
              "          <i class=\"fa fa-play\"></i></button>\n",
              "      <button title=\"Next frame\" aria-label=\"Next frame\" onclick=\"anim5db6d31a93e54c7b95bfbb29554dd0e2.next_frame()\">\n",
              "          <i class=\"fa fa-step-forward\"></i></button>\n",
              "      <button title=\"Last frame\" aria-label=\"Last frame\" onclick=\"anim5db6d31a93e54c7b95bfbb29554dd0e2.last_frame()\">\n",
              "          <i class=\"fa fa-fast-forward\"></i></button>\n",
              "      <button title=\"Increase speed\" aria-label=\"Increase speed\" onclick=\"anim5db6d31a93e54c7b95bfbb29554dd0e2.faster()\">\n",
              "          <i class=\"fa fa-plus\"></i></button>\n",
              "    </div>\n",
              "    <form title=\"Repetition mode\" aria-label=\"Repetition mode\" action=\"#n\" name=\"_anim_loop_select5db6d31a93e54c7b95bfbb29554dd0e2\"\n",
              "          class=\"anim-state\">\n",
              "      <input type=\"radio\" name=\"state\" value=\"once\" id=\"_anim_radio1_5db6d31a93e54c7b95bfbb29554dd0e2\"\n",
              "             >\n",
              "      <label for=\"_anim_radio1_5db6d31a93e54c7b95bfbb29554dd0e2\">Once</label>\n",
              "      <input type=\"radio\" name=\"state\" value=\"loop\" id=\"_anim_radio2_5db6d31a93e54c7b95bfbb29554dd0e2\"\n",
              "             checked>\n",
              "      <label for=\"_anim_radio2_5db6d31a93e54c7b95bfbb29554dd0e2\">Loop</label>\n",
              "      <input type=\"radio\" name=\"state\" value=\"reflect\" id=\"_anim_radio3_5db6d31a93e54c7b95bfbb29554dd0e2\"\n",
              "             >\n",
              "      <label for=\"_anim_radio3_5db6d31a93e54c7b95bfbb29554dd0e2\">Reflect</label>\n",
              "    </form>\n",
              "  </div>\n",
              "</div>\n",
              "\n",
              "\n",
              "<script language=\"javascript\">\n",
              "  /* Instantiate the Animation class. */\n",
              "  /* The IDs given should match those used in the template above. */\n",
              "  (function() {\n",
              "    var img_id = \"_anim_img5db6d31a93e54c7b95bfbb29554dd0e2\";\n",
              "    var slider_id = \"_anim_slider5db6d31a93e54c7b95bfbb29554dd0e2\";\n",
              "    var loop_select_id = \"_anim_loop_select5db6d31a93e54c7b95bfbb29554dd0e2\";\n",
              "    var frames = new Array(1);\n",
              "    \n",
              "  frames[0] = \"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAoAAAAHgCAYAAAA10dzkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90\\\n",
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              "\"\n",
              "\n",
              "\n",
              "    /* set a timeout to make sure all the above elements are created before\n",
              "       the object is initialized. */\n",
              "    setTimeout(function() {\n",
              "        anim5db6d31a93e54c7b95bfbb29554dd0e2 = new Animation(frames, img_id, slider_id, 100.0,\n",
              "                                 loop_select_id);\n",
              "    }, 0);\n",
              "  })()\n",
              "</script>\n"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "# Animate the frames\n",
        "animate_frames(filter_nans(temp[:,LHAND]),edges=edges)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201
        },
        "id": "mWgeoL42qAFQ",
        "outputId": "e4c2e47e-0355-4da8-aa52-753350d937ed"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/matplotlib/animation.py:1740: UserWarning: Can not start iterating the frames for the initial draw. This can be caused by passing in a 0 length sequence for *frames*.\n",
            "\n",
            "If you passed *frames* as a generator it may be exhausted due to a previous display or save.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<link rel=\"stylesheet\"\n",
              "href=\"https://maxcdn.bootstrapcdn.com/font-awesome/4.4.0/css/font-awesome.min.css\">\n",
              "<script language=\"javascript\">\n",
              "  function isInternetExplorer() {\n",
              "    ua = navigator.userAgent;\n",
              "    /* MSIE used to detect old browsers and Trident used to newer ones*/\n",
              "    return ua.indexOf(\"MSIE \") > -1 || ua.indexOf(\"Trident/\") > -1;\n",
              "  }\n",
              "\n",
              "  /* Define the Animation class */\n",
              "  function Animation(frames, img_id, slider_id, interval, loop_select_id){\n",
              "    this.img_id = img_id;\n",
              "    this.slider_id = slider_id;\n",
              "    this.loop_select_id = loop_select_id;\n",
              "    this.interval = interval;\n",
              "    this.current_frame = 0;\n",
              "    this.direction = 0;\n",
              "    this.timer = null;\n",
              "    this.frames = new Array(frames.length);\n",
              "\n",
              "    for (var i=0; i<frames.length; i++)\n",
              "    {\n",
              "     this.frames[i] = new Image();\n",
              "     this.frames[i].src = frames[i];\n",
              "    }\n",
              "    var slider = document.getElementById(this.slider_id);\n",
              "    slider.max = this.frames.length - 1;\n",
              "    if (isInternetExplorer()) {\n",
              "        // switch from oninput to onchange because IE <= 11 does not conform\n",
              "        // with W3C specification. It ignores oninput and onchange behaves\n",
              "        // like oninput. In contrast, Microsoft Edge behaves correctly.\n",
              "        slider.setAttribute('onchange', slider.getAttribute('oninput'));\n",
              "        slider.setAttribute('oninput', null);\n",
              "    }\n",
              "    this.set_frame(this.current_frame);\n",
              "  }\n",
              "\n",
              "  Animation.prototype.get_loop_state = function(){\n",
              "    var button_group = document[this.loop_select_id].state;\n",
              "    for (var i = 0; i < button_group.length; i++) {\n",
              "        var button = button_group[i];\n",
              "        if (button.checked) {\n",
              "            return button.value;\n",
              "        }\n",
              "    }\n",
              "    return undefined;\n",
              "  }\n",
              "\n",
              "  Animation.prototype.set_frame = function(frame){\n",
              "    this.current_frame = frame;\n",
              "    document.getElementById(this.img_id).src =\n",
              "            this.frames[this.current_frame].src;\n",
              "    document.getElementById(this.slider_id).value = this.current_frame;\n",
              "  }\n",
              "\n",
              "  Animation.prototype.next_frame = function()\n",
              "  {\n",
              "    this.set_frame(Math.min(this.frames.length - 1, this.current_frame + 1));\n",
              "  }\n",
              "\n",
              "  Animation.prototype.previous_frame = function()\n",
              "  {\n",
              "    this.set_frame(Math.max(0, this.current_frame - 1));\n",
              "  }\n",
              "\n",
              "  Animation.prototype.first_frame = function()\n",
              "  {\n",
              "    this.set_frame(0);\n",
              "  }\n",
              "\n",
              "  Animation.prototype.last_frame = function()\n",
              "  {\n",
              "    this.set_frame(this.frames.length - 1);\n",
              "  }\n",
              "\n",
              "  Animation.prototype.slower = function()\n",
              "  {\n",
              "    this.interval /= 0.7;\n",
              "    if(this.direction > 0){this.play_animation();}\n",
              "    else if(this.direction < 0){this.reverse_animation();}\n",
              "  }\n",
              "\n",
              "  Animation.prototype.faster = function()\n",
              "  {\n",
              "    this.interval *= 0.7;\n",
              "    if(this.direction > 0){this.play_animation();}\n",
              "    else if(this.direction < 0){this.reverse_animation();}\n",
              "  }\n",
              "\n",
              "  Animation.prototype.anim_step_forward = function()\n",
              "  {\n",
              "    this.current_frame += 1;\n",
              "    if(this.current_frame < this.frames.length){\n",
              "      this.set_frame(this.current_frame);\n",
              "    }else{\n",
              "      var loop_state = this.get_loop_state();\n",
              "      if(loop_state == \"loop\"){\n",
              "        this.first_frame();\n",
              "      }else if(loop_state == \"reflect\"){\n",
              "        this.last_frame();\n",
              "        this.reverse_animation();\n",
              "      }else{\n",
              "        this.pause_animation();\n",
              "        this.last_frame();\n",
              "      }\n",
              "    }\n",
              "  }\n",
              "\n",
              "  Animation.prototype.anim_step_reverse = function()\n",
              "  {\n",
              "    this.current_frame -= 1;\n",
              "    if(this.current_frame >= 0){\n",
              "      this.set_frame(this.current_frame);\n",
              "    }else{\n",
              "      var loop_state = this.get_loop_state();\n",
              "      if(loop_state == \"loop\"){\n",
              "        this.last_frame();\n",
              "      }else if(loop_state == \"reflect\"){\n",
              "        this.first_frame();\n",
              "        this.play_animation();\n",
              "      }else{\n",
              "        this.pause_animation();\n",
              "        this.first_frame();\n",
              "      }\n",
              "    }\n",
              "  }\n",
              "\n",
              "  Animation.prototype.pause_animation = function()\n",
              "  {\n",
              "    this.direction = 0;\n",
              "    if (this.timer){\n",
              "      clearInterval(this.timer);\n",
              "      this.timer = null;\n",
              "    }\n",
              "  }\n",
              "\n",
              "  Animation.prototype.play_animation = function()\n",
              "  {\n",
              "    this.pause_animation();\n",
              "    this.direction = 1;\n",
              "    var t = this;\n",
              "    if (!this.timer) this.timer = setInterval(function() {\n",
              "        t.anim_step_forward();\n",
              "    }, this.interval);\n",
              "  }\n",
              "\n",
              "  Animation.prototype.reverse_animation = function()\n",
              "  {\n",
              "    this.pause_animation();\n",
              "    this.direction = -1;\n",
              "    var t = this;\n",
              "    if (!this.timer) this.timer = setInterval(function() {\n",
              "        t.anim_step_reverse();\n",
              "    }, this.interval);\n",
              "  }\n",
              "</script>\n",
              "\n",
              "<style>\n",
              ".animation {\n",
              "    display: inline-block;\n",
              "    text-align: center;\n",
              "}\n",
              "input[type=range].anim-slider {\n",
              "    width: 374px;\n",
              "    margin-left: auto;\n",
              "    margin-right: auto;\n",
              "}\n",
              ".anim-buttons {\n",
              "    margin: 8px 0px;\n",
              "}\n",
              ".anim-buttons button {\n",
              "    padding: 0;\n",
              "    width: 36px;\n",
              "}\n",
              ".anim-state label {\n",
              "    margin-right: 8px;\n",
              "}\n",
              ".anim-state input {\n",
              "    margin: 0;\n",
              "    vertical-align: middle;\n",
              "}\n",
              "</style>\n",
              "\n",
              "<div class=\"animation\">\n",
              "  <img id=\"_anim_img5fee4975ddf145f7b0167388e1a00518\">\n",
              "  <div class=\"anim-controls\">\n",
              "    <input id=\"_anim_slider5fee4975ddf145f7b0167388e1a00518\" type=\"range\" class=\"anim-slider\"\n",
              "           name=\"points\" min=\"0\" max=\"1\" step=\"1\" value=\"0\"\n",
              "           oninput=\"anim5fee4975ddf145f7b0167388e1a00518.set_frame(parseInt(this.value));\">\n",
              "    <div class=\"anim-buttons\">\n",
              "      <button title=\"Decrease speed\" aria-label=\"Decrease speed\" onclick=\"anim5fee4975ddf145f7b0167388e1a00518.slower()\">\n",
              "          <i class=\"fa fa-minus\"></i></button>\n",
              "      <button title=\"First frame\" aria-label=\"First frame\" onclick=\"anim5fee4975ddf145f7b0167388e1a00518.first_frame()\">\n",
              "        <i class=\"fa fa-fast-backward\"></i></button>\n",
              "      <button title=\"Previous frame\" aria-label=\"Previous frame\" onclick=\"anim5fee4975ddf145f7b0167388e1a00518.previous_frame()\">\n",
              "          <i class=\"fa fa-step-backward\"></i></button>\n",
              "      <button title=\"Play backwards\" aria-label=\"Play backwards\" onclick=\"anim5fee4975ddf145f7b0167388e1a00518.reverse_animation()\">\n",
              "          <i class=\"fa fa-play fa-flip-horizontal\"></i></button>\n",
              "      <button title=\"Pause\" aria-label=\"Pause\" onclick=\"anim5fee4975ddf145f7b0167388e1a00518.pause_animation()\">\n",
              "          <i class=\"fa fa-pause\"></i></button>\n",
              "      <button title=\"Play\" aria-label=\"Play\" onclick=\"anim5fee4975ddf145f7b0167388e1a00518.play_animation()\">\n",
              "          <i class=\"fa fa-play\"></i></button>\n",
              "      <button title=\"Next frame\" aria-label=\"Next frame\" onclick=\"anim5fee4975ddf145f7b0167388e1a00518.next_frame()\">\n",
              "          <i class=\"fa fa-step-forward\"></i></button>\n",
              "      <button title=\"Last frame\" aria-label=\"Last frame\" onclick=\"anim5fee4975ddf145f7b0167388e1a00518.last_frame()\">\n",
              "          <i class=\"fa fa-fast-forward\"></i></button>\n",
              "      <button title=\"Increase speed\" aria-label=\"Increase speed\" onclick=\"anim5fee4975ddf145f7b0167388e1a00518.faster()\">\n",
              "          <i class=\"fa fa-plus\"></i></button>\n",
              "    </div>\n",
              "    <form title=\"Repetition mode\" aria-label=\"Repetition mode\" action=\"#n\" name=\"_anim_loop_select5fee4975ddf145f7b0167388e1a00518\"\n",
              "          class=\"anim-state\">\n",
              "      <input type=\"radio\" name=\"state\" value=\"once\" id=\"_anim_radio1_5fee4975ddf145f7b0167388e1a00518\"\n",
              "             >\n",
              "      <label for=\"_anim_radio1_5fee4975ddf145f7b0167388e1a00518\">Once</label>\n",
              "      <input type=\"radio\" name=\"state\" value=\"loop\" id=\"_anim_radio2_5fee4975ddf145f7b0167388e1a00518\"\n",
              "             checked>\n",
              "      <label for=\"_anim_radio2_5fee4975ddf145f7b0167388e1a00518\">Loop</label>\n",
              "      <input type=\"radio\" name=\"state\" value=\"reflect\" id=\"_anim_radio3_5fee4975ddf145f7b0167388e1a00518\"\n",
              "             >\n",
              "      <label for=\"_anim_radio3_5fee4975ddf145f7b0167388e1a00518\">Reflect</label>\n",
              "    </form>\n",
              "  </div>\n",
              "</div>\n",
              "\n",
              "\n",
              "<script language=\"javascript\">\n",
              "  /* Instantiate the Animation class. */\n",
              "  /* The IDs given should match those used in the template above. */\n",
              "  (function() {\n",
              "    var img_id = \"_anim_img5fee4975ddf145f7b0167388e1a00518\";\n",
              "    var slider_id = \"_anim_slider5fee4975ddf145f7b0167388e1a00518\";\n",
              "    var loop_select_id = \"_anim_loop_select5fee4975ddf145f7b0167388e1a00518\";\n",
              "    var frames = new Array(0);\n",
              "    \n",
              "\n",
              "\n",
              "    /* set a timeout to make sure all the above elements are created before\n",
              "       the object is initialized. */\n",
              "    setTimeout(function() {\n",
              "        anim5fee4975ddf145f7b0167388e1a00518 = new Animation(frames, img_id, slider_id, 100.0,\n",
              "                                 loop_select_id);\n",
              "    }, 0);\n",
              "  })()\n",
              "</script>\n"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "animate_frames(filter_nans(augment_fn(temp,always=True).numpy()[:,RHAND]),edges=edges)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 579
        },
        "id": "PTiGFv8aqAFQ",
        "outputId": "52310823-9581-4aa5-b319-526ac6bb6aa2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<link rel=\"stylesheet\"\n",
              "href=\"https://maxcdn.bootstrapcdn.com/font-awesome/4.4.0/css/font-awesome.min.css\">\n",
              "<script language=\"javascript\">\n",
              "  function isInternetExplorer() {\n",
              "    ua = navigator.userAgent;\n",
              "    /* MSIE used to detect old browsers and Trident used to newer ones*/\n",
              "    return ua.indexOf(\"MSIE \") > -1 || ua.indexOf(\"Trident/\") > -1;\n",
              "  }\n",
              "\n",
              "  /* Define the Animation class */\n",
              "  function Animation(frames, img_id, slider_id, interval, loop_select_id){\n",
              "    this.img_id = img_id;\n",
              "    this.slider_id = slider_id;\n",
              "    this.loop_select_id = loop_select_id;\n",
              "    this.interval = interval;\n",
              "    this.current_frame = 0;\n",
              "    this.direction = 0;\n",
              "    this.timer = null;\n",
              "    this.frames = new Array(frames.length);\n",
              "\n",
              "    for (var i=0; i<frames.length; i++)\n",
              "    {\n",
              "     this.frames[i] = new Image();\n",
              "     this.frames[i].src = frames[i];\n",
              "    }\n",
              "    var slider = document.getElementById(this.slider_id);\n",
              "    slider.max = this.frames.length - 1;\n",
              "    if (isInternetExplorer()) {\n",
              "        // switch from oninput to onchange because IE <= 11 does not conform\n",
              "        // with W3C specification. It ignores oninput and onchange behaves\n",
              "        // like oninput. In contrast, Microsoft Edge behaves correctly.\n",
              "        slider.setAttribute('onchange', slider.getAttribute('oninput'));\n",
              "        slider.setAttribute('oninput', null);\n",
              "    }\n",
              "    this.set_frame(this.current_frame);\n",
              "  }\n",
              "\n",
              "  Animation.prototype.get_loop_state = function(){\n",
              "    var button_group = document[this.loop_select_id].state;\n",
              "    for (var i = 0; i < button_group.length; i++) {\n",
              "        var button = button_group[i];\n",
              "        if (button.checked) {\n",
              "            return button.value;\n",
              "        }\n",
              "    }\n",
              "    return undefined;\n",
              "  }\n",
              "\n",
              "  Animation.prototype.set_frame = function(frame){\n",
              "    this.current_frame = frame;\n",
              "    document.getElementById(this.img_id).src =\n",
              "            this.frames[this.current_frame].src;\n",
              "    document.getElementById(this.slider_id).value = this.current_frame;\n",
              "  }\n",
              "\n",
              "  Animation.prototype.next_frame = function()\n",
              "  {\n",
              "    this.set_frame(Math.min(this.frames.length - 1, this.current_frame + 1));\n",
              "  }\n",
              "\n",
              "  Animation.prototype.previous_frame = function()\n",
              "  {\n",
              "    this.set_frame(Math.max(0, this.current_frame - 1));\n",
              "  }\n",
              "\n",
              "  Animation.prototype.first_frame = function()\n",
              "  {\n",
              "    this.set_frame(0);\n",
              "  }\n",
              "\n",
              "  Animation.prototype.last_frame = function()\n",
              "  {\n",
              "    this.set_frame(this.frames.length - 1);\n",
              "  }\n",
              "\n",
              "  Animation.prototype.slower = function()\n",
              "  {\n",
              "    this.interval /= 0.7;\n",
              "    if(this.direction > 0){this.play_animation();}\n",
              "    else if(this.direction < 0){this.reverse_animation();}\n",
              "  }\n",
              "\n",
              "  Animation.prototype.faster = function()\n",
              "  {\n",
              "    this.interval *= 0.7;\n",
              "    if(this.direction > 0){this.play_animation();}\n",
              "    else if(this.direction < 0){this.reverse_animation();}\n",
              "  }\n",
              "\n",
              "  Animation.prototype.anim_step_forward = function()\n",
              "  {\n",
              "    this.current_frame += 1;\n",
              "    if(this.current_frame < this.frames.length){\n",
              "      this.set_frame(this.current_frame);\n",
              "    }else{\n",
              "      var loop_state = this.get_loop_state();\n",
              "      if(loop_state == \"loop\"){\n",
              "        this.first_frame();\n",
              "      }else if(loop_state == \"reflect\"){\n",
              "        this.last_frame();\n",
              "        this.reverse_animation();\n",
              "      }else{\n",
              "        this.pause_animation();\n",
              "        this.last_frame();\n",
              "      }\n",
              "    }\n",
              "  }\n",
              "\n",
              "  Animation.prototype.anim_step_reverse = function()\n",
              "  {\n",
              "    this.current_frame -= 1;\n",
              "    if(this.current_frame >= 0){\n",
              "      this.set_frame(this.current_frame);\n",
              "    }else{\n",
              "      var loop_state = this.get_loop_state();\n",
              "      if(loop_state == \"loop\"){\n",
              "        this.last_frame();\n",
              "      }else if(loop_state == \"reflect\"){\n",
              "        this.first_frame();\n",
              "        this.play_animation();\n",
              "      }else{\n",
              "        this.pause_animation();\n",
              "        this.first_frame();\n",
              "      }\n",
              "    }\n",
              "  }\n",
              "\n",
              "  Animation.prototype.pause_animation = function()\n",
              "  {\n",
              "    this.direction = 0;\n",
              "    if (this.timer){\n",
              "      clearInterval(this.timer);\n",
              "      this.timer = null;\n",
              "    }\n",
              "  }\n",
              "\n",
              "  Animation.prototype.play_animation = function()\n",
              "  {\n",
              "    this.pause_animation();\n",
              "    this.direction = 1;\n",
              "    var t = this;\n",
              "    if (!this.timer) this.timer = setInterval(function() {\n",
              "        t.anim_step_forward();\n",
              "    }, this.interval);\n",
              "  }\n",
              "\n",
              "  Animation.prototype.reverse_animation = function()\n",
              "  {\n",
              "    this.pause_animation();\n",
              "    this.direction = -1;\n",
              "    var t = this;\n",
              "    if (!this.timer) this.timer = setInterval(function() {\n",
              "        t.anim_step_reverse();\n",
              "    }, this.interval);\n",
              "  }\n",
              "</script>\n",
              "\n",
              "<style>\n",
              ".animation {\n",
              "    display: inline-block;\n",
              "    text-align: center;\n",
              "}\n",
              "input[type=range].anim-slider {\n",
              "    width: 374px;\n",
              "    margin-left: auto;\n",
              "    margin-right: auto;\n",
              "}\n",
              ".anim-buttons {\n",
              "    margin: 8px 0px;\n",
              "}\n",
              ".anim-buttons button {\n",
              "    padding: 0;\n",
              "    width: 36px;\n",
              "}\n",
              ".anim-state label {\n",
              "    margin-right: 8px;\n",
              "}\n",
              ".anim-state input {\n",
              "    margin: 0;\n",
              "    vertical-align: middle;\n",
              "}\n",
              "</style>\n",
              "\n",
              "<div class=\"animation\">\n",
              "  <img id=\"_anim_img4b7e158cac504d9c908ec92fd71089fe\">\n",
              "  <div class=\"anim-controls\">\n",
              "    <input id=\"_anim_slider4b7e158cac504d9c908ec92fd71089fe\" type=\"range\" class=\"anim-slider\"\n",
              "           name=\"points\" min=\"0\" max=\"1\" step=\"1\" value=\"0\"\n",
              "           oninput=\"anim4b7e158cac504d9c908ec92fd71089fe.set_frame(parseInt(this.value));\">\n",
              "    <div class=\"anim-buttons\">\n",
              "      <button title=\"Decrease speed\" aria-label=\"Decrease speed\" onclick=\"anim4b7e158cac504d9c908ec92fd71089fe.slower()\">\n",
              "          <i class=\"fa fa-minus\"></i></button>\n",
              "      <button title=\"First frame\" aria-label=\"First frame\" onclick=\"anim4b7e158cac504d9c908ec92fd71089fe.first_frame()\">\n",
              "        <i class=\"fa fa-fast-backward\"></i></button>\n",
              "      <button title=\"Previous frame\" aria-label=\"Previous frame\" onclick=\"anim4b7e158cac504d9c908ec92fd71089fe.previous_frame()\">\n",
              "          <i class=\"fa fa-step-backward\"></i></button>\n",
              "      <button title=\"Play backwards\" aria-label=\"Play backwards\" onclick=\"anim4b7e158cac504d9c908ec92fd71089fe.reverse_animation()\">\n",
              "          <i class=\"fa fa-play fa-flip-horizontal\"></i></button>\n",
              "      <button title=\"Pause\" aria-label=\"Pause\" onclick=\"anim4b7e158cac504d9c908ec92fd71089fe.pause_animation()\">\n",
              "          <i class=\"fa fa-pause\"></i></button>\n",
              "      <button title=\"Play\" aria-label=\"Play\" onclick=\"anim4b7e158cac504d9c908ec92fd71089fe.play_animation()\">\n",
              "          <i class=\"fa fa-play\"></i></button>\n",
              "      <button title=\"Next frame\" aria-label=\"Next frame\" onclick=\"anim4b7e158cac504d9c908ec92fd71089fe.next_frame()\">\n",
              "          <i class=\"fa fa-step-forward\"></i></button>\n",
              "      <button title=\"Last frame\" aria-label=\"Last frame\" onclick=\"anim4b7e158cac504d9c908ec92fd71089fe.last_frame()\">\n",
              "          <i class=\"fa fa-fast-forward\"></i></button>\n",
              "      <button title=\"Increase speed\" aria-label=\"Increase speed\" onclick=\"anim4b7e158cac504d9c908ec92fd71089fe.faster()\">\n",
              "          <i class=\"fa fa-plus\"></i></button>\n",
              "    </div>\n",
              "    <form title=\"Repetition mode\" aria-label=\"Repetition mode\" action=\"#n\" name=\"_anim_loop_select4b7e158cac504d9c908ec92fd71089fe\"\n",
              "          class=\"anim-state\">\n",
              "      <input type=\"radio\" name=\"state\" value=\"once\" id=\"_anim_radio1_4b7e158cac504d9c908ec92fd71089fe\"\n",
              "             >\n",
              "      <label for=\"_anim_radio1_4b7e158cac504d9c908ec92fd71089fe\">Once</label>\n",
              "      <input type=\"radio\" name=\"state\" value=\"loop\" id=\"_anim_radio2_4b7e158cac504d9c908ec92fd71089fe\"\n",
              "             checked>\n",
              "      <label for=\"_anim_radio2_4b7e158cac504d9c908ec92fd71089fe\">Loop</label>\n",
              "      <input type=\"radio\" name=\"state\" value=\"reflect\" id=\"_anim_radio3_4b7e158cac504d9c908ec92fd71089fe\"\n",
              "             >\n",
              "      <label for=\"_anim_radio3_4b7e158cac504d9c908ec92fd71089fe\">Reflect</label>\n",
              "    </form>\n",
              "  </div>\n",
              "</div>\n",
              "\n",
              "\n",
              "<script language=\"javascript\">\n",
              "  /* Instantiate the Animation class. */\n",
              "  /* The IDs given should match those used in the template above. */\n",
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              "    var loop_select_id = \"_anim_loop_select4b7e158cac504d9c908ec92fd71089fe\";\n",
              "    var frames = new Array(6);\n",
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              "AAAASUVORK5CYII=\\\n",
              "\"\n",
              "\n",
              "\n",
              "    /* set a timeout to make sure all the above elements are created before\n",
              "       the object is initialized. */\n",
              "    setTimeout(function() {\n",
              "        anim4b7e158cac504d9c908ec92fd71089fe = new Animation(frames, img_id, slider_id, 100.0,\n",
              "                                 loop_select_id);\n",
              "    }, 0);\n",
              "  })()\n",
              "</script>\n"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "animate_frames(filter_nans(temp[:,POINT_LANDMARKS]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 579
        },
        "id": "AAoOqgRKqAFQ",
        "outputId": "982bb350-76fd-4a74-c396-c0f76ddbe141"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<link rel=\"stylesheet\"\n",
              "href=\"https://maxcdn.bootstrapcdn.com/font-awesome/4.4.0/css/font-awesome.min.css\">\n",
              "<script language=\"javascript\">\n",
              "  function isInternetExplorer() {\n",
              "    ua = navigator.userAgent;\n",
              "    /* MSIE used to detect old browsers and Trident used to newer ones*/\n",
              "    return ua.indexOf(\"MSIE \") > -1 || ua.indexOf(\"Trident/\") > -1;\n",
              "  }\n",
              "\n",
              "  /* Define the Animation class */\n",
              "  function Animation(frames, img_id, slider_id, interval, loop_select_id){\n",
              "    this.img_id = img_id;\n",
              "    this.slider_id = slider_id;\n",
              "    this.loop_select_id = loop_select_id;\n",
              "    this.interval = interval;\n",
              "    this.current_frame = 0;\n",
              "    this.direction = 0;\n",
              "    this.timer = null;\n",
              "    this.frames = new Array(frames.length);\n",
              "\n",
              "    for (var i=0; i<frames.length; i++)\n",
              "    {\n",
              "     this.frames[i] = new Image();\n",
              "     this.frames[i].src = frames[i];\n",
              "    }\n",
              "    var slider = document.getElementById(this.slider_id);\n",
              "    slider.max = this.frames.length - 1;\n",
              "    if (isInternetExplorer()) {\n",
              "        // switch from oninput to onchange because IE <= 11 does not conform\n",
              "        // with W3C specification. It ignores oninput and onchange behaves\n",
              "        // like oninput. In contrast, Microsoft Edge behaves correctly.\n",
              "        slider.setAttribute('onchange', slider.getAttribute('oninput'));\n",
              "        slider.setAttribute('oninput', null);\n",
              "    }\n",
              "    this.set_frame(this.current_frame);\n",
              "  }\n",
              "\n",
              "  Animation.prototype.get_loop_state = function(){\n",
              "    var button_group = document[this.loop_select_id].state;\n",
              "    for (var i = 0; i < button_group.length; i++) {\n",
              "        var button = button_group[i];\n",
              "        if (button.checked) {\n",
              "            return button.value;\n",
              "        }\n",
              "    }\n",
              "    return undefined;\n",
              "  }\n",
              "\n",
              "  Animation.prototype.set_frame = function(frame){\n",
              "    this.current_frame = frame;\n",
              "    document.getElementById(this.img_id).src =\n",
              "            this.frames[this.current_frame].src;\n",
              "    document.getElementById(this.slider_id).value = this.current_frame;\n",
              "  }\n",
              "\n",
              "  Animation.prototype.next_frame = function()\n",
              "  {\n",
              "    this.set_frame(Math.min(this.frames.length - 1, this.current_frame + 1));\n",
              "  }\n",
              "\n",
              "  Animation.prototype.previous_frame = function()\n",
              "  {\n",
              "    this.set_frame(Math.max(0, this.current_frame - 1));\n",
              "  }\n",
              "\n",
              "  Animation.prototype.first_frame = function()\n",
              "  {\n",
              "    this.set_frame(0);\n",
              "  }\n",
              "\n",
              "  Animation.prototype.last_frame = function()\n",
              "  {\n",
              "    this.set_frame(this.frames.length - 1);\n",
              "  }\n",
              "\n",
              "  Animation.prototype.slower = function()\n",
              "  {\n",
              "    this.interval /= 0.7;\n",
              "    if(this.direction > 0){this.play_animation();}\n",
              "    else if(this.direction < 0){this.reverse_animation();}\n",
              "  }\n",
              "\n",
              "  Animation.prototype.faster = function()\n",
              "  {\n",
              "    this.interval *= 0.7;\n",
              "    if(this.direction > 0){this.play_animation();}\n",
              "    else if(this.direction < 0){this.reverse_animation();}\n",
              "  }\n",
              "\n",
              "  Animation.prototype.anim_step_forward = function()\n",
              "  {\n",
              "    this.current_frame += 1;\n",
              "    if(this.current_frame < this.frames.length){\n",
              "      this.set_frame(this.current_frame);\n",
              "    }else{\n",
              "      var loop_state = this.get_loop_state();\n",
              "      if(loop_state == \"loop\"){\n",
              "        this.first_frame();\n",
              "      }else if(loop_state == \"reflect\"){\n",
              "        this.last_frame();\n",
              "        this.reverse_animation();\n",
              "      }else{\n",
              "        this.pause_animation();\n",
              "        this.last_frame();\n",
              "      }\n",
              "    }\n",
              "  }\n",
              "\n",
              "  Animation.prototype.anim_step_reverse = function()\n",
              "  {\n",
              "    this.current_frame -= 1;\n",
              "    if(this.current_frame >= 0){\n",
              "      this.set_frame(this.current_frame);\n",
              "    }else{\n",
              "      var loop_state = this.get_loop_state();\n",
              "      if(loop_state == \"loop\"){\n",
              "        this.last_frame();\n",
              "      }else if(loop_state == \"reflect\"){\n",
              "        this.first_frame();\n",
              "        this.play_animation();\n",
              "      }else{\n",
              "        this.pause_animation();\n",
              "        this.first_frame();\n",
              "      }\n",
              "    }\n",
              "  }\n",
              "\n",
              "  Animation.prototype.pause_animation = function()\n",
              "  {\n",
              "    this.direction = 0;\n",
              "    if (this.timer){\n",
              "      clearInterval(this.timer);\n",
              "      this.timer = null;\n",
              "    }\n",
              "  }\n",
              "\n",
              "  Animation.prototype.play_animation = function()\n",
              "  {\n",
              "    this.pause_animation();\n",
              "    this.direction = 1;\n",
              "    var t = this;\n",
              "    if (!this.timer) this.timer = setInterval(function() {\n",
              "        t.anim_step_forward();\n",
              "    }, this.interval);\n",
              "  }\n",
              "\n",
              "  Animation.prototype.reverse_animation = function()\n",
              "  {\n",
              "    this.pause_animation();\n",
              "    this.direction = -1;\n",
              "    var t = this;\n",
              "    if (!this.timer) this.timer = setInterval(function() {\n",
              "        t.anim_step_reverse();\n",
              "    }, this.interval);\n",
              "  }\n",
              "</script>\n",
              "\n",
              "<style>\n",
              ".animation {\n",
              "    display: inline-block;\n",
              "    text-align: center;\n",
              "}\n",
              "input[type=range].anim-slider {\n",
              "    width: 374px;\n",
              "    margin-left: auto;\n",
              "    margin-right: auto;\n",
              "}\n",
              ".anim-buttons {\n",
              "    margin: 8px 0px;\n",
              "}\n",
              ".anim-buttons button {\n",
              "    padding: 0;\n",
              "    width: 36px;\n",
              "}\n",
              ".anim-state label {\n",
              "    margin-right: 8px;\n",
              "}\n",
              ".anim-state input {\n",
              "    margin: 0;\n",
              "    vertical-align: middle;\n",
              "}\n",
              "</style>\n",
              "\n",
              "<div class=\"animation\">\n",
              "  <img id=\"_anim_img0e4f9fee14714788bd8b2cae1dd48cfb\">\n",
              "  <div class=\"anim-controls\">\n",
              "    <input id=\"_anim_slider0e4f9fee14714788bd8b2cae1dd48cfb\" type=\"range\" class=\"anim-slider\"\n",
              "           name=\"points\" min=\"0\" max=\"1\" step=\"1\" value=\"0\"\n",
              "           oninput=\"anim0e4f9fee14714788bd8b2cae1dd48cfb.set_frame(parseInt(this.value));\">\n",
              "    <div class=\"anim-buttons\">\n",
              "      <button title=\"Decrease speed\" aria-label=\"Decrease speed\" onclick=\"anim0e4f9fee14714788bd8b2cae1dd48cfb.slower()\">\n",
              "          <i class=\"fa fa-minus\"></i></button>\n",
              "      <button title=\"First frame\" aria-label=\"First frame\" onclick=\"anim0e4f9fee14714788bd8b2cae1dd48cfb.first_frame()\">\n",
              "        <i class=\"fa fa-fast-backward\"></i></button>\n",
              "      <button title=\"Previous frame\" aria-label=\"Previous frame\" onclick=\"anim0e4f9fee14714788bd8b2cae1dd48cfb.previous_frame()\">\n",
              "          <i class=\"fa fa-step-backward\"></i></button>\n",
              "      <button title=\"Play backwards\" aria-label=\"Play backwards\" onclick=\"anim0e4f9fee14714788bd8b2cae1dd48cfb.reverse_animation()\">\n",
              "          <i class=\"fa fa-play fa-flip-horizontal\"></i></button>\n",
              "      <button title=\"Pause\" aria-label=\"Pause\" onclick=\"anim0e4f9fee14714788bd8b2cae1dd48cfb.pause_animation()\">\n",
              "          <i class=\"fa fa-pause\"></i></button>\n",
              "      <button title=\"Play\" aria-label=\"Play\" onclick=\"anim0e4f9fee14714788bd8b2cae1dd48cfb.play_animation()\">\n",
              "          <i class=\"fa fa-play\"></i></button>\n",
              "      <button title=\"Next frame\" aria-label=\"Next frame\" onclick=\"anim0e4f9fee14714788bd8b2cae1dd48cfb.next_frame()\">\n",
              "          <i class=\"fa fa-step-forward\"></i></button>\n",
              "      <button title=\"Last frame\" aria-label=\"Last frame\" onclick=\"anim0e4f9fee14714788bd8b2cae1dd48cfb.last_frame()\">\n",
              "          <i class=\"fa fa-fast-forward\"></i></button>\n",
              "      <button title=\"Increase speed\" aria-label=\"Increase speed\" onclick=\"anim0e4f9fee14714788bd8b2cae1dd48cfb.faster()\">\n",
              "          <i class=\"fa fa-plus\"></i></button>\n",
              "    </div>\n",
              "    <form title=\"Repetition mode\" aria-label=\"Repetition mode\" action=\"#n\" name=\"_anim_loop_select0e4f9fee14714788bd8b2cae1dd48cfb\"\n",
              "          class=\"anim-state\">\n",
              "      <input type=\"radio\" name=\"state\" value=\"once\" id=\"_anim_radio1_0e4f9fee14714788bd8b2cae1dd48cfb\"\n",
              "             >\n",
              "      <label for=\"_anim_radio1_0e4f9fee14714788bd8b2cae1dd48cfb\">Once</label>\n",
              "      <input type=\"radio\" name=\"state\" value=\"loop\" id=\"_anim_radio2_0e4f9fee14714788bd8b2cae1dd48cfb\"\n",
              "             checked>\n",
              "      <label for=\"_anim_radio2_0e4f9fee14714788bd8b2cae1dd48cfb\">Loop</label>\n",
              "      <input type=\"radio\" name=\"state\" value=\"reflect\" id=\"_anim_radio3_0e4f9fee14714788bd8b2cae1dd48cfb\"\n",
              "             >\n",
              "      <label for=\"_anim_radio3_0e4f9fee14714788bd8b2cae1dd48cfb\">Reflect</label>\n",
              "    </form>\n",
              "  </div>\n",
              "</div>\n",
              "\n",
              "\n",
              "<script language=\"javascript\">\n",
              "  /* Instantiate the Animation class. */\n",
              "  /* The IDs given should match those used in the template above. */\n",
              "  (function() {\n",
              "    var img_id = \"_anim_img0e4f9fee14714788bd8b2cae1dd48cfb\";\n",
              "    var slider_id = \"_anim_slider0e4f9fee14714788bd8b2cae1dd48cfb\";\n",
              "    var loop_select_id = \"_anim_loop_select0e4f9fee14714788bd8b2cae1dd48cfb\";\n",
              "    var frames = new Array(2);\n",
              "    \n",
              "  frames[0] = \"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAoAAAAHgCAYAAAA10dzkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90\\\n",
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              "ETEYBUARERERg1EAFBERETEYBUARERERg1EAFBERETEYBUARERERg1EAFBERETEYBUARERERg1EA\\\n",
              "FBERETEYBUARERERg1EAFBERETEYBUARERERg1EAFBERETEYBUARERERg1EAFBERETEYBUARERER\\\n",
              "g1EAFBERETEYBUARERERg1EAFBERETGY/weE1Q4QeTEz4gAAAABJRU5ErkJggg==\\\n",
              "\"\n",
              "\n",
              "\n",
              "    /* set a timeout to make sure all the above elements are created before\n",
              "       the object is initialized. */\n",
              "    setTimeout(function() {\n",
              "        anim0e4f9fee14714788bd8b2cae1dd48cfb = new Animation(frames, img_id, slider_id, 100.0,\n",
              "                                 loop_select_id);\n",
              "    }, 0);\n",
              "  })()\n",
              "</script>\n"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "animate_frames(filter_nans(augment_fn(temp,always=True).numpy()[:,POINT_LANDMARKS]), idxs=POINT_LANDMARKS)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "zaIHc89AqAFQ"
      },
      "outputs": [],
      "source": [
        "class ECA(tf.keras.layers.Layer):\n",
        "    def __init__(self, kernel_size=5, **kwargs):\n",
        "        super().__init__(**kwargs)\n",
        "        self.supports_masking = True\n",
        "        self.kernel_size = kernel_size\n",
        "        self.conv = tf.keras.layers.Conv1D(1, kernel_size=kernel_size, strides=1, padding=\"same\", use_bias=False)\n",
        "\n",
        "    def call(self, inputs, mask=None):\n",
        "        nn = tf.keras.layers.GlobalAveragePooling1D()(inputs, mask=mask)\n",
        "        nn = tf.expand_dims(nn, -1)\n",
        "        nn = self.conv(nn)\n",
        "        nn = tf.squeeze(nn, -1)\n",
        "        nn = tf.nn.sigmoid(nn)\n",
        "        nn = nn[:,None,:]\n",
        "        return inputs * nn\n",
        "\n",
        "class LateDropout(tf.keras.layers.Layer):\n",
        "    def __init__(self, rate, noise_shape=None, start_step=0, **kwargs):\n",
        "        super().__init__(**kwargs)\n",
        "        self.supports_masking = True\n",
        "        self.rate = rate\n",
        "        self.start_step = start_step\n",
        "        self.dropout = tf.keras.layers.Dropout(rate, noise_shape=noise_shape)\n",
        "      \n",
        "    def build(self, input_shape):\n",
        "        super().build(input_shape)\n",
        "        agg = tf.VariableAggregation.ONLY_FIRST_REPLICA\n",
        "        self._train_counter = tf.Variable(0, dtype=\"int64\", aggregation=agg, trainable=False)\n",
        "\n",
        "    def call(self, inputs, training=False):\n",
        "        x = tf.cond(self._train_counter < self.start_step, lambda:inputs, lambda:self.dropout(inputs, training=training))\n",
        "        if training:\n",
        "          self._train_counter.assign_add(1)\n",
        "        return x\n",
        "\n",
        "class CausalDWConv1D(tf.keras.layers.Layer):\n",
        "    def __init__(self, \n",
        "        kernel_size=17,\n",
        "        dilation_rate=1,\n",
        "        use_bias=False,\n",
        "        depthwise_initializer='glorot_uniform',\n",
        "        name='', **kwargs):\n",
        "        super().__init__(name=name,**kwargs)\n",
        "        self.causal_pad = tf.keras.layers.ZeroPadding1D((dilation_rate*(kernel_size-1),0),name=name + '_pad')\n",
        "        self.dw_conv = tf.keras.layers.DepthwiseConv1D(\n",
        "                            kernel_size,\n",
        "                            strides=1,\n",
        "                            dilation_rate=dilation_rate,\n",
        "                            padding='valid',\n",
        "                            use_bias=use_bias,\n",
        "                            depthwise_initializer=depthwise_initializer,\n",
        "                            name=name + '_dwconv')\n",
        "        self.supports_masking = True\n",
        "        \n",
        "    def call(self, inputs):\n",
        "        x = self.causal_pad(inputs)\n",
        "        x = self.dw_conv(x)\n",
        "        return x\n",
        "\n",
        "def Conv1DBlock(channel_size,\n",
        "          kernel_size,\n",
        "          dilation_rate=1,\n",
        "          drop_rate=0.0,\n",
        "          expand_ratio=2,\n",
        "          se_ratio=0.25,\n",
        "          activation='swish',\n",
        "          name=None):\n",
        "    '''\n",
        "    efficient conv1d block, @hoyso48\n",
        "    '''\n",
        "    if name is None:\n",
        "        name = str(tf.keras.backend.get_uid(\"mbblock\"))\n",
        "    # Expansion phase\n",
        "    def apply(inputs):\n",
        "        channels_in = tf.keras.backend.int_shape(inputs)[-1]\n",
        "        channels_expand = channels_in * expand_ratio\n",
        "\n",
        "        skip = inputs\n",
        "\n",
        "        x = tf.keras.layers.Dense(\n",
        "            channels_expand,\n",
        "            use_bias=True,\n",
        "            activation=activation,\n",
        "            name=name + '_expand_conv')(inputs)s\n",
        "\n",
        "        # Depthwise Convolution\n",
        "        x = CausalDWConv1D(kernel_size,\n",
        "            dilation_rate=dilation_rate,\n",
        "            use_bias=False,\n",
        "            name=name + '_dwconv')(x)\n",
        "\n",
        "        x = tf.keras.layers.BatchNormalization(momentum=0.95, name=name + '_bn')(x)\n",
        "\n",
        "        x  = ECA()(x)\n",
        "\n",
        "        x = tf.keras.layers.Dense(\n",
        "            channel_size,\n",
        "            use_bias=True,\n",
        "            name=name + '_project_conv')(x)\n",
        "\n",
        "        if drop_rate > 0:\n",
        "            x = tf.keras.layers.Dropout(drop_rate, noise_shape=(None,1,1), name=name + '_drop')(x)\n",
        "\n",
        "        if (channels_in == channel_size):\n",
        "            x = tf.keras.layers.add([x, skip], name=name + '_add')\n",
        "        return x\n",
        "\n",
        "    return apply"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "8hPmJX0YqAFR"
      },
      "outputs": [],
      "source": [
        "class MultiHeadSelfAttention(tf.keras.layers.Layer):\n",
        "    def __init__(self, dim=256, num_heads=4, dropout=0, **kwargs):\n",
        "        super().__init__(**kwargs)\n",
        "        self.dim = dim\n",
        "        self.scale = self.dim ** -0.5\n",
        "        self.num_heads = num_heads\n",
        "        self.qkv = tf.keras.layers.Dense(3 * dim, use_bias=False)\n",
        "        self.drop1 = tf.keras.layers.Dropout(dropout)\n",
        "        self.proj = tf.keras.layers.Dense(dim, use_bias=False)\n",
        "        self.supports_masking = True\n",
        "\n",
        "    def call(self, inputs, mask=None):\n",
        "        qkv = self.qkv(inputs)\n",
        "        qkv = tf.keras.layers.Permute((2, 1, 3))(tf.keras.layers.Reshape((-1, self.num_heads, self.dim * 3 // self.num_heads))(qkv))\n",
        "        q, k, v = tf.split(qkv, [self.dim // self.num_heads] * 3, axis=-1)\n",
        "\n",
        "        attn = tf.matmul(q, k, transpose_b=True) * self.scale\n",
        "\n",
        "        if mask is not None:\n",
        "            mask = mask[:, None, None, :]\n",
        "\n",
        "        attn = tf.keras.layers.Softmax(axis=-1)(attn, mask=mask)\n",
        "        attn = self.drop1(attn)\n",
        "\n",
        "        x = attn @ v\n",
        "        x = tf.keras.layers.Reshape((-1, self.dim))(tf.keras.layers.Permute((2, 1, 3))(x))\n",
        "        x = self.proj(x)\n",
        "        return x\n",
        "\n",
        "def TransformerBlock(dim=256, num_heads=4, expand=4, attn_dropout=0.2, drop_rate=0.2, activation='swish'):\n",
        "    def apply(inputs):\n",
        "        x = inputs\n",
        "        x = tf.keras.layers.BatchNormalization(momentum=0.95)(x)\n",
        "        x = MultiHeadSelfAttention(dim=dim,num_heads=num_heads,dropout=attn_dropout)(x)\n",
        "        x = tf.keras.layers.Dropout(drop_rate, noise_shape=(None,1,1))(x)\n",
        "        x = tf.keras.layers.Add()([inputs, x])\n",
        "        attn_out = x\n",
        "\n",
        "        x = tf.keras.layers.BatchNormalization(momentum=0.95)(x)\n",
        "        x = tf.keras.layers.Dense(dim*expand, use_bias=False, activation=activation)(x)\n",
        "        x = tf.keras.layers.Dense(dim, use_bias=False)(x)\n",
        "        x = tf.keras.layers.Dropout(drop_rate, noise_shape=(None,1,1))(x)\n",
        "        x = tf.keras.layers.Add()([attn_out, x])\n",
        "        return x\n",
        "    return apply"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KIooIcnSqAFR",
        "outputId": "7f35615c-edf7-423a-af28-cd9629ab2f63"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=float32, numpy=5.839589>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "def get_model(max_len=64, dropout_step=0, dim=192):\n",
        "    inp = tf.keras.Input((max_len,CHANNELS))\n",
        "    x = tf.keras.layers.Masking(mask_value=PAD,input_shape=(max_len,CHANNELS))(inp)\n",
        "    ksize = 17\n",
        "    x = tf.keras.layers.Dense(dim, use_bias=False,name='stem_conv')(x)\n",
        "    x = tf.keras.layers.BatchNormalization(momentum=0.95,name='stem_bn')(x)\n",
        "\n",
        "    x = Conv1DBlock(dim,ksize,drop_rate=0.2)(x)\n",
        "    x = Conv1DBlock(dim,ksize,drop_rate=0.2)(x)\n",
        "    x = Conv1DBlock(dim,ksize,drop_rate=0.2)(x)\n",
        "    x = TransformerBlock(dim,expand=2)(x)\n",
        "\n",
        "    x = Conv1DBlock(dim,ksize,drop_rate=0.2)(x)\n",
        "    x = Conv1DBlock(dim,ksize,drop_rate=0.2)(x)\n",
        "    x = Conv1DBlock(dim,ksize,drop_rate=0.2)(x)\n",
        "    x = TransformerBlock(dim,expand=2)(x)\n",
        "\n",
        "    if dim == 384: #for the 4x sized model\n",
        "        x = Conv1DBlock(dim,ksize,drop_rate=0.2)(x)\n",
        "        x = Conv1DBlock(dim,ksize,drop_rate=0.2)(x)\n",
        "        x = Conv1DBlock(dim,ksize,drop_rate=0.2)(x)\n",
        "        x = TransformerBlock(dim,expand=2)(x)\n",
        "\n",
        "        x = Conv1DBlock(dim,ksize,drop_rate=0.2)(x)\n",
        "        x = Conv1DBlock(dim,ksize,drop_rate=0.2)(x)\n",
        "        x = Conv1DBlock(dim,ksize,drop_rate=0.2)(x)\n",
        "        x = TransformerBlock(dim,expand=2)(x)\n",
        "\n",
        "    x = tf.keras.layers.Dense(dim*2,activation=None,name='top_conv')(x)\n",
        "    x = tf.keras.layers.GlobalAveragePooling1D()(x)\n",
        "    x = LateDropout(0.8, start_step=dropout_step)(x)\n",
        "    x = tf.keras.layers.Dense(NUM_CLASSES,name='classifier')(x)\n",
        "    return tf.keras.Model(inp, x)\n",
        "\n",
        "model = get_model()\n",
        "y = model(temp_train[0])\n",
        "tf.keras.losses.CategoricalCrossentropy(from_logits=True)(temp_train[1],y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "Nui7lZmUqAFR"
      },
      "outputs": [],
      "source": [
        "#check supports_masking\n",
        "for x in model.layers:\n",
        "    if not x.supports_masking:\n",
        "        print(x.supports_masking, x.name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "BoGUEL6-oEWO"
      },
      "outputs": [],
      "source": [
        "def train_fold(CFG, fold, train_files, valid_files=None, strategy=STRATEGY, summary=True):\n",
        "    seed_everything(CFG.seed)\n",
        "    tf.keras.backend.clear_session()\n",
        "    gc.collect()\n",
        "    tf.config.optimizer.set_jit(True)\n",
        "        \n",
        "    if CFG.fp16:\n",
        "        try:\n",
        "            policy = mixed_precision.Policy('mixed_bfloat16')\n",
        "            mixed_precision.set_global_policy(policy)\n",
        "        except:\n",
        "            policy = mixed_precision.Policy('mixed_float16')\n",
        "            mixed_precision.set_global_policy(policy)\n",
        "    else:\n",
        "        policy = mixed_precision.Policy('float32')\n",
        "        mixed_precision.set_global_policy(policy)\n",
        "\n",
        "    if fold != 'all':\n",
        "        train_ds = get_tfrec_dataset(train_files, batch_size=CFG.batch_size, max_len=CFG.max_len, drop_remainder=True, augment=True, repeat=True, shuffle=32768)\n",
        "        valid_ds = get_tfrec_dataset(valid_files, batch_size=CFG.batch_size, max_len=CFG.max_len, drop_remainder=False, repeat=False, shuffle=False)\n",
        "    else:\n",
        "        train_ds = get_tfrec_dataset(train_files, batch_size=CFG.batch_size, max_len=CFG.max_len, drop_remainder=False, augment=True, repeat=True, shuffle=32768)\n",
        "        valid_ds = None\n",
        "        valid_files = []\n",
        "    \n",
        "    num_train = count_data_items(train_files)\n",
        "    num_valid = count_data_items(valid_files)\n",
        "    steps_per_epoch = num_train//CFG.batch_size\n",
        "    with strategy.scope():\n",
        "        dropout_step = CFG.dropout_start_epoch * steps_per_epoch\n",
        "        model = get_model(max_len=CFG.max_len, dropout_step=dropout_step, dim=CFG.dim)\n",
        "\n",
        "        schedule = OneCycleLR(CFG.lr, CFG.epoch, warmup_epochs=CFG.epoch*CFG.warmup, steps_per_epoch=steps_per_epoch, resume_epoch=CFG.resume, decay_epochs=CFG.epoch, lr_min=CFG.lr_min, decay_type=CFG.decay_type, warmup_type='linear')\n",
        "        decay_schedule = OneCycleLR(CFG.lr*CFG.weight_decay, CFG.epoch, warmup_epochs=CFG.epoch*CFG.warmup, steps_per_epoch=steps_per_epoch, resume_epoch=CFG.resume, decay_epochs=CFG.epoch, lr_min=CFG.lr_min*CFG.weight_decay, decay_type=CFG.decay_type, warmup_type='linear')\n",
        "                \n",
        "        awp_step = CFG.awp_start_epoch * steps_per_epoch\n",
        "        if CFG.fgm:\n",
        "            model = FGM(model.input, model.output, delta=CFG.awp_lambda, eps=0., start_step=awp_step)\n",
        "        elif CFG.awp:\n",
        "            model = AWP(model.input, model.output, delta=CFG.awp_lambda, eps=0., start_step=awp_step)\n",
        "\n",
        "        opt = tfa.optimizers.RectifiedAdam(learning_rate=schedule, weight_decay=decay_schedule, sma_threshold=4)#, clipvalue=1.)\n",
        "        opt = tfa.optimizers.Lookahead(opt,sync_period=5)\n",
        "\n",
        "        model.compile(\n",
        "            optimizer=opt,\n",
        "            loss=[tf.keras.losses.CategoricalCrossentropy(from_logits=True,label_smoothing=0.1)],# [tf.keras.losses.CategoricalCrossentropy(from_logits=True)]\n",
        "            metrics=[\n",
        "                [\n",
        "                tf.keras.metrics.CategoricalAccuracy(),\n",
        "                ],\n",
        "            ],\n",
        "            steps_per_execution=steps_per_epoch,\n",
        "        )\n",
        "    \n",
        "    if summary:\n",
        "        print()\n",
        "        model.summary()\n",
        "        print()\n",
        "        print(train_ds, valid_ds)\n",
        "        print()\n",
        "        schedule.plot()\n",
        "        print()\n",
        "        init=False\n",
        "    print(f'---------fold{fold}---------')\n",
        "    print(f'train:{num_train} valid:{num_valid}')\n",
        "    print()\n",
        "    \n",
        "    if CFG.resume:\n",
        "        print(f'resume from epoch{CFG.resume}')\n",
        "        model.load_weights(f'{CFG.output_dir}/{CFG.comment}-fold{fold}-last.h5')\n",
        "        if train_ds is not None:\n",
        "            model.evaluate(train_ds.take(steps_per_epoch))\n",
        "        if valid_ds is not None:\n",
        "            model.evaluate(valid_ds)\n",
        "\n",
        "    logger = tf.keras.callbacks.CSVLogger(f'{CFG.output_dir}/{CFG.comment}-fold{fold}-logs.csv')\n",
        "    sv_loss = tf.keras.callbacks.ModelCheckpoint(f'{CFG.output_dir}/{CFG.comment}-fold{fold}-best.h5', monitor='val_loss', verbose=0, save_best_only=True,\n",
        "                save_weights_only=True, mode='min', save_freq='epoch')\n",
        "    snap = Snapshot(f'{CFG.output_dir}/{CFG.comment}-fold{fold}', CFG.snapshot_epochs)\n",
        "    swa = SWA(f'{CFG.output_dir}/{CFG.comment}-fold{fold}', CFG.swa_epochs, strategy=strategy, train_ds=train_ds, valid_ds=valid_ds, valid_steps=-(num_valid//-CFG.batch_size))\n",
        "    callbacks = []\n",
        "    if CFG.save_output:\n",
        "        callbacks.append(logger)\n",
        "        callbacks.append(snap)\n",
        "        callbacks.append(swa)\n",
        "        if fold != 'all':\n",
        "            callbacks.append(sv_loss)\n",
        "        \n",
        "    history = model.fit(\n",
        "        train_ds,\n",
        "        epochs=CFG.epoch-CFG.resume,\n",
        "        steps_per_epoch=steps_per_epoch,\n",
        "        callbacks=callbacks,\n",
        "        validation_data=valid_ds,\n",
        "        verbose=CFG.verbose,\n",
        "        validation_steps=-(num_valid//-CFG.batch_size)\n",
        "    )\n",
        "\n",
        "    if CFG.save_output:\n",
        "        try:\n",
        "            model.load_weights(f'{CFG.output_dir}/{CFG.comment}-fold{fold}-best.h5')\n",
        "        except:\n",
        "            pass\n",
        "    if fold != 'all':\n",
        "        cv = model.evaluate(valid_ds,verbose=CFG.verbose,steps=-(num_valid//-CFG.batch_size))\n",
        "    else:\n",
        "        cv = None\n",
        "\n",
        "    return model, cv, history\n",
        "\n",
        "def train_folds(CFG, folds, strategy=STRATEGY, summary=True):\n",
        "    for fold in folds:\n",
        "        if fold != 'all':\n",
        "            all_files = TRAIN_FILENAMES\n",
        "            train_files = [x for x in all_files if f'fold{fold}' not in x]\n",
        "            valid_files = [x for x in all_files if f'fold{fold}' in x]\n",
        "        else:\n",
        "            train_files = TRAIN_FILENAMES\n",
        "            valid_files = None\n",
        "        \n",
        "        train_fold(CFG, fold, train_files, valid_files, strategy=strategy, summary=summary)\n",
        "    return"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "WYUI6mAlqAFR"
      },
      "outputs": [],
      "source": [
        "class CFG:\n",
        "    n_splits = 5\n",
        "    save_output = True\n",
        "    output_dir = '.'\n",
        "    \n",
        "    seed = 42\n",
        "    verbose = 2 #0) silent 1) progress bar 2) one line per epoch\n",
        "    \n",
        "    max_len = 384\n",
        "    replicas = 8\n",
        "    lr = 5e-4 * replicas\n",
        "    weight_decay = 0.1\n",
        "    lr_min = 1e-6\n",
        "    epoch = 300 #400\n",
        "    warmup = 0\n",
        "    batch_size = 64 * replicas\n",
        "    snapshot_epochs = []\n",
        "    swa_epochs = [] #list(range(epoch//2,epoch+1))\n",
        "    \n",
        "    fp16 = True\n",
        "    fgm = False\n",
        "    awp = True\n",
        "    awp_lambda = 0.2\n",
        "    awp_start_epoch = 15\n",
        "    dropout_start_epoch = 15\n",
        "    resume = 0\n",
        "    decay_type = 'cosine'\n",
        "    dim = 192\n",
        "    comment = f'islr-fp16-192-8-seed{seed}'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "V8T5GhYPqAFR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "2fd8b309-f38e-476e-fbb8-8c3646ee78ff"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Model: \"awp\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_1 (InputLayer)           [(None, 384, 708)]   0           []                               \n",
            "                                                                                                  \n",
            " masking (Masking)              (None, 384, 708)     0           ['input_1[0][0]']                \n",
            "                                                                                                  \n",
            " stem_conv (Dense)              (None, 384, 192)     135936      ['masking[0][0]']                \n",
            "                                                                                                  \n",
            " stem_bn (BatchNormalization)   (None, 384, 192)     768         ['stem_conv[0][0]']              \n",
            "                                                                                                  \n",
            " 1_expand_conv (Dense)          (None, 384, 384)     74112       ['stem_bn[0][0]']                \n",
            "                                                                                                  \n",
            " 1_dwconv (CausalDWConv1D)      (None, 384, 384)     6528        ['1_expand_conv[0][0]']          \n",
            "                                                                                                  \n",
            " 1_bn (BatchNormalization)      (None, 384, 384)     1536        ['1_dwconv[0][0]']               \n",
            "                                                                                                  \n",
            " eca (ECA)                      (None, 384, 384)     5           ['1_bn[0][0]']                   \n",
            "                                                                                                  \n",
            " 1_project_conv (Dense)         (None, 384, 192)     73920       ['eca[0][0]']                    \n",
            "                                                                                                  \n",
            " 1_drop (Dropout)               (None, 384, 192)     0           ['1_project_conv[0][0]']         \n",
            "                                                                                                  \n",
            " 1_add (Add)                    (None, 384, 192)     0           ['1_drop[0][0]',                 \n",
            "                                                                  'stem_bn[0][0]']                \n",
            "                                                                                                  \n",
            " 2_expand_conv (Dense)          (None, 384, 384)     74112       ['1_add[0][0]']                  \n",
            "                                                                                                  \n",
            " 2_dwconv (CausalDWConv1D)      (None, 384, 384)     6528        ['2_expand_conv[0][0]']          \n",
            "                                                                                                  \n",
            " 2_bn (BatchNormalization)      (None, 384, 384)     1536        ['2_dwconv[0][0]']               \n",
            "                                                                                                  \n",
            " eca_1 (ECA)                    (None, 384, 384)     5           ['2_bn[0][0]']                   \n",
            "                                                                                                  \n",
            " 2_project_conv (Dense)         (None, 384, 192)     73920       ['eca_1[0][0]']                  \n",
            "                                                                                                  \n",
            " 2_drop (Dropout)               (None, 384, 192)     0           ['2_project_conv[0][0]']         \n",
            "                                                                                                  \n",
            " 2_add (Add)                    (None, 384, 192)     0           ['2_drop[0][0]',                 \n",
            "                                                                  '1_add[0][0]']                  \n",
            "                                                                                                  \n",
            " 3_expand_conv (Dense)          (None, 384, 384)     74112       ['2_add[0][0]']                  \n",
            "                                                                                                  \n",
            " 3_dwconv (CausalDWConv1D)      (None, 384, 384)     6528        ['3_expand_conv[0][0]']          \n",
            "                                                                                                  \n",
            " 3_bn (BatchNormalization)      (None, 384, 384)     1536        ['3_dwconv[0][0]']               \n",
            "                                                                                                  \n",
            " eca_2 (ECA)                    (None, 384, 384)     5           ['3_bn[0][0]']                   \n",
            "                                                                                                  \n",
            " 3_project_conv (Dense)         (None, 384, 192)     73920       ['eca_2[0][0]']                  \n",
            "                                                                                                  \n",
            " 3_drop (Dropout)               (None, 384, 192)     0           ['3_project_conv[0][0]']         \n",
            "                                                                                                  \n",
            " 3_add (Add)                    (None, 384, 192)     0           ['3_drop[0][0]',                 \n",
            "                                                                  '2_add[0][0]']                  \n",
            "                                                                                                  \n",
            " batch_normalization (BatchNorm  (None, 384, 192)    768         ['3_add[0][0]']                  \n",
            " alization)                                                                                       \n",
            "                                                                                                  \n",
            " multi_head_self_attention (Mul  (None, 384, 192)    147456      ['batch_normalization[0][0]']    \n",
            " tiHeadSelfAttention)                                                                             \n",
            "                                                                                                  \n",
            " dropout_1 (Dropout)            (None, 384, 192)     0           ['multi_head_self_attention[0][0]\n",
            "                                                                 ']                               \n",
            "                                                                                                  \n",
            " add (Add)                      (None, 384, 192)     0           ['3_add[0][0]',                  \n",
            "                                                                  'dropout_1[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_1 (BatchNo  (None, 384, 192)    768         ['add[0][0]']                    \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " dense_2 (Dense)                (None, 384, 384)     73728       ['batch_normalization_1[0][0]']  \n",
            "                                                                                                  \n",
            " dense_3 (Dense)                (None, 384, 192)     73728       ['dense_2[0][0]']                \n",
            "                                                                                                  \n",
            " dropout_2 (Dropout)            (None, 384, 192)     0           ['dense_3[0][0]']                \n",
            "                                                                                                  \n",
            " add_1 (Add)                    (None, 384, 192)     0           ['add[0][0]',                    \n",
            "                                                                  'dropout_2[0][0]']              \n",
            "                                                                                                  \n",
            " 4_expand_conv (Dense)          (None, 384, 384)     74112       ['add_1[0][0]']                  \n",
            "                                                                                                  \n",
            " 4_dwconv (CausalDWConv1D)      (None, 384, 384)     6528        ['4_expand_conv[0][0]']          \n",
            "                                                                                                  \n",
            " 4_bn (BatchNormalization)      (None, 384, 384)     1536        ['4_dwconv[0][0]']               \n",
            "                                                                                                  \n",
            " eca_3 (ECA)                    (None, 384, 384)     5           ['4_bn[0][0]']                   \n",
            "                                                                                                  \n",
            " 4_project_conv (Dense)         (None, 384, 192)     73920       ['eca_3[0][0]']                  \n",
            "                                                                                                  \n",
            " 4_drop (Dropout)               (None, 384, 192)     0           ['4_project_conv[0][0]']         \n",
            "                                                                                                  \n",
            " 4_add (Add)                    (None, 384, 192)     0           ['4_drop[0][0]',                 \n",
            "                                                                  'add_1[0][0]']                  \n",
            "                                                                                                  \n",
            " 5_expand_conv (Dense)          (None, 384, 384)     74112       ['4_add[0][0]']                  \n",
            "                                                                                                  \n",
            " 5_dwconv (CausalDWConv1D)      (None, 384, 384)     6528        ['5_expand_conv[0][0]']          \n",
            "                                                                                                  \n",
            " 5_bn (BatchNormalization)      (None, 384, 384)     1536        ['5_dwconv[0][0]']               \n",
            "                                                                                                  \n",
            " eca_4 (ECA)                    (None, 384, 384)     5           ['5_bn[0][0]']                   \n",
            "                                                                                                  \n",
            " 5_project_conv (Dense)         (None, 384, 192)     73920       ['eca_4[0][0]']                  \n",
            "                                                                                                  \n",
            " 5_drop (Dropout)               (None, 384, 192)     0           ['5_project_conv[0][0]']         \n",
            "                                                                                                  \n",
            " 5_add (Add)                    (None, 384, 192)     0           ['5_drop[0][0]',                 \n",
            "                                                                  '4_add[0][0]']                  \n",
            "                                                                                                  \n",
            " 6_expand_conv (Dense)          (None, 384, 384)     74112       ['5_add[0][0]']                  \n",
            "                                                                                                  \n",
            " 6_dwconv (CausalDWConv1D)      (None, 384, 384)     6528        ['6_expand_conv[0][0]']          \n",
            "                                                                                                  \n",
            " 6_bn (BatchNormalization)      (None, 384, 384)     1536        ['6_dwconv[0][0]']               \n",
            "                                                                                                  \n",
            " eca_5 (ECA)                    (None, 384, 384)     5           ['6_bn[0][0]']                   \n",
            "                                                                                                  \n",
            " 6_project_conv (Dense)         (None, 384, 192)     73920       ['eca_5[0][0]']                  \n",
            "                                                                                                  \n",
            " 6_drop (Dropout)               (None, 384, 192)     0           ['6_project_conv[0][0]']         \n",
            "                                                                                                  \n",
            " 6_add (Add)                    (None, 384, 192)     0           ['6_drop[0][0]',                 \n",
            "                                                                  '5_add[0][0]']                  \n",
            "                                                                                                  \n",
            " batch_normalization_2 (BatchNo  (None, 384, 192)    768         ['6_add[0][0]']                  \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " multi_head_self_attention_1 (M  (None, 384, 192)    147456      ['batch_normalization_2[0][0]']  \n",
            " ultiHeadSelfAttention)                                                                           \n",
            "                                                                                                  \n",
            " dropout_4 (Dropout)            (None, 384, 192)     0           ['multi_head_self_attention_1[0][\n",
            "                                                                 0]']                             \n",
            "                                                                                                  \n",
            " add_2 (Add)                    (None, 384, 192)     0           ['6_add[0][0]',                  \n",
            "                                                                  'dropout_4[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_3 (BatchNo  (None, 384, 192)    768         ['add_2[0][0]']                  \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " dense_6 (Dense)                (None, 384, 384)     73728       ['batch_normalization_3[0][0]']  \n",
            "                                                                                                  \n",
            " dense_7 (Dense)                (None, 384, 192)     73728       ['dense_6[0][0]']                \n",
            "                                                                                                  \n",
            " dropout_5 (Dropout)            (None, 384, 192)     0           ['dense_7[0][0]']                \n",
            "                                                                                                  \n",
            " add_3 (Add)                    (None, 384, 192)     0           ['add_2[0][0]',                  \n",
            "                                                                  'dropout_5[0][0]']              \n",
            "                                                                                                  \n",
            " top_conv (Dense)               (None, 384, 384)     74112       ['add_3[0][0]']                  \n",
            "                                                                                                  \n",
            " global_average_pooling1d (Glob  (None, 384)         0           ['top_conv[0][0]']               \n",
            " alAveragePooling1D)                                                                              \n",
            "                                                                                                  \n",
            " late_dropout (LateDropout)     (None, 384)          1           ['global_average_pooling1d[0][0]'\n",
            "                                                                 ]                                \n",
            "                                                                                                  \n",
            " classifier (Dense)             (None, 250)          96250       ['late_dropout[0][0]']           \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 1,836,569\n",
            "Trainable params: 1,830,040\n",
            "Non-trainable params: 6,529\n",
            "__________________________________________________________________________________________________\n",
            "\n",
            "<_PrefetchDataset element_spec=(TensorSpec(shape=(512, 384, 708), dtype=tf.float32, name=None), TensorSpec(shape=(512, 250), dtype=tf.float32, name=None))> <_PrefetchDataset element_spec=(TensorSpec(shape=(None, 384, 708), dtype=tf.float32, name=None), TensorSpec(shape=(None, 250), dtype=tf.float32, name=None))>\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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Pjo6GXq+X1VitVhQWFra4Tft+gWv9clpSWlqKiIiIm78x8lmt3u6yXbvdxeBDROQdlK6ukJKSgilTpmDw4MEYOnQoVqxYgZqaGkydOhUAMHnyZHTv3h0ZGRkAgNmzZ2PUqFFYvnw5xo0bh6ysLBQVFWHdunUAAD8/PyQnJ2PJkiXo3bs3oqOjkZaWhsjISCQmJgIACgsL8cMPPyAhIQFdunTBjz/+iLS0NNxzzz1SMNqwYQNUKhUGDRoEANi8eTM++OADvP/++7f8IZF3a3q765GV36Li4vWQ04hrwWfzi/Ho1z0MSn+PuiNMRERNuBx6xo8fj3PnziE9PR1msxmxsbHIzc2VOiJXVFRAobj+xTB8+HBs3LgRixYtwssvv4zevXtjy5Yt6N+/v1Qzb9481NTUYNq0aaiurkZCQgJyc3Oh0VybVC4oKAibN2/G4sWLUVNTg4iICDz88MNYtGgR1Gq1tJ3XXnsNJ0+ehFKpRExMDLKzsx3O5UPkiH1On7xDVZj+0V6p3d7Ph8PaiYg8m8vz9HgzztNDANDQaMNjq77FIfPlZssCFEBJ2hgGHyIiN9Ih8/QQ+QKlvwJbZ41w2M/nqg349XKO7CIi8kQMPUQOtDas/ezlq3xaOxGRB2LoIWqFfVh7jy6Bsnb709ofXvENausbOunoiIjIFQw9RDehUSmx479H4t67ms/nc+xcDca+/TWDDxGRB2DoIXKCRqVEbrLj+XwqLtZi7Nu84kNE5O4Yeoic1LSfT7cQ+YNuKy5eYQdnIiI3x9BD5KKQQBV2znkQATf89lRa6vjoCiIiN8bQQ9QGIYEqlKSNQaRWLWvnoyuIiNwXQw9RG9mv+NzYwbkRwOAlDD5ERO6GoYfoFtg7OK+d9ICsvUFcu+LDuXyIiNwHQw/RLVL6KzCmrw599SGydvszuziXDxGRe2DoIWoHrT26gnP5EBG5B4YeonZiH9JetGg05/IhInJDDD1E7SwsWMO5fIiI3BBDD1EH4Fw+RETuh6GHqINwLh8iIvfC0EPUgTiXDxGR+2DoIepgrc3lw+BDRHT7MPQQ3QYtzeXDSQyJiG4fhh6i26SluXzskxg+tupbBh8iog7E0EN0G7U2l88h82UcqrR2zoEREfkAhh6iThAWrEHRotHwv6H9iTXf81YXEVEHYegh6iRhwRoUp412eKuLz+siImp/DD1Ench+xcfR87oeZR8fIqJ2xdBD1MlaDj6/IO9QFYMPEVE7YeghcgMtPa9r+kd7eauLiKidMPQQuQn77M3KG34rj52rgYnBh4joljH0ELmRkEAVihY2v9V18sIVBh8iolvE0EPkZuy3um58UOnJC1cw9u2vGXyIiNqIoYfIDdlvdfXsGihrr7hYi7Fv84oPEVFbMPQQuSmNSokvkkc2u+JTcZG3uoiI2qJNoWfNmjXo1asXNBoNDAYD9uzZ02r9pk2bEBMTA41GgwEDBiAnJ0e2XAiB9PR0REREIDAwEEajEUePHpXVPPbYY+jRowc0Gg0iIiLw7LPPorKyUlazf/9+jBgxAhqNBlFRUVi6dGlb3h6R29ColNiRPBIBN/ymso8PEZHrXA492dnZSElJweLFi7F3714MHDgQJpMJZ8+edVi/a9cuTJw4EUlJSSgpKUFiYiISExNRVlYm1SxduhQrV65EZmYmCgsLERwcDJPJhNraWqnmoYcewscff4zy8nJ88skn+PHHH/HUU09Jy61WK8aOHYuePXuiuLgYy5YtwyuvvIJ169a5+haJ3EpIoAolaY77+HACQyIi5/kJIYQrKxgMBgwZMgSrV68GANhsNkRFRWHWrFlYsGBBs/rx48ejpqYG27Ztk9qGDRuG2NhYZGZmQgiByMhIzJkzB3PnzgUAWCwW6HQ6rF+/HhMmTHB4HFu3bkViYiLq6uoQEBCAtWvXYuHChTCbzVCpVACABQsWYMuWLThy5IhT781qtUKr1cJisSA0NNSVj4Wow9XWN8C04hucvHBF1r520gMY01cHpT/vVhORb3L2+9ulv5L19fUoLi6G0Wi8vgGFAkajEQUFBQ7XKSgokNUDgMlkkuqPHz8Os9ksq9FqtTAYDC1u88KFC/joo48wfPhwBAQESPsZOXKkFHjs+ykvL8fFixcdbqeurg5Wq1X2InJXLfXx4QSGRETOcSn0nD9/Ho2NjdDpdLJ2nU4Hs9nscB2z2dxqvf2nM9ucP38+goODceedd6KiogKfffbZTffTdB83ysjIgFarlV5RUVEO64jchb2PDycwJCJynUddD3/ppZdQUlKCHTt2wN/fH5MnT4aLd+dkUlNTYbFYpNepU6fa8WiJOgYnMCQiahuXQk94eDj8/f1RVVUla6+qqoJer3e4jl6vb7Xe/tOZbYaHh+O+++7DmDFjkJWVhZycHOzevbvV/TTdx43UajVCQ0NlLyJP0NoEhuzcTETkmEuhR6VSIS4uDvn5+VKbzWZDfn4+4uPjHa4THx8vqweAvLw8qT46Ohp6vV5WY7VaUVhY2OI27fsFrvXLse/nm2++wdWrV2X76dOnD7p06eLK2yTyCC1NYMinsxMROeby7a2UlBT89a9/xYYNG3D48GFMnz4dNTU1mDp1KgBg8uTJSE1Nlepnz56N3NxcLF++HEeOHMErr7yCoqIizJw5EwDg5+eH5ORkLFmyBFu3bsWBAwcwefJkREZGIjExEQBQWFiI1atXo7S0FCdPnsTOnTsxceJE3HPPPVIw+sMf/gCVSoWkpCQcPHgQ2dnZeOedd5CSknKrnxGR22LnZiIi57kcesaPH4+33noL6enpiI2NRWlpKXJzc6VOwxUVFThz5oxUP3z4cGzcuBHr1q3DwIED8fe//x1btmxB//79pZp58+Zh1qxZmDZtGoYMGYLLly8jNzcXGo0GABAUFITNmzdj9OjR6NOnD5KSknD//ffj66+/hlp97Y+9VqvFjh07cPz4ccTFxWHOnDlIT0/HtGnTbukDInJ37NxMROQcl+fp8Wacp4c8WXVNLQYvyUfDDb/R994VhNzkUZzHh4i8VofM00NE7qulzs3Hzv2CQ5Wcg4qIiKGHyIvYOzffGHx+9+73qK6pbWEtIiLfwNBD5GUc9fFpEMDgJfkMPkTk0xh6iLyQowkMGXyIyNcx9BB5qbBgDYoWNQ8+ca/l4/ylXzrvwIiIOglDD5EXcxR8GgEMfv1LBh8i8jkMPURezh58/G9oN/zlS97qIiKfwtBD5APCgjUoXPiQrK2RfXyIyMcw9BD5iPA7glC08CH4s3MzEfkohh4iHxJ+RxCKHXRuHrwkH5ev1HfegRER3QYMPUQ+xlEfnwYBjOVzuojIyzH0EPmgsGANNr8YL2urtNTxAaVE5NUYeoh8VL/uYbj3rmBZ28kLVxh8iMhrMfQQ+SilvwLbZiWgZ9dAWTuDDxF5K4YeIh+mUSnxRfJIh8Hn0VXfoqHR1klHRkTU/hh6iHxcS8Hn2LlfcKjS2klHRUTU/hh6iEgKPpFataz9d+9+zzl8iMhrMPQQEYBrwWdH8kgom/xV4OSFRORNGHqISBISqELRQseTFzL4EJGnY+ghIhlHT2bnrM1E5A0YeoioGc7aTETeiKGHiBzirM1E5G0YeoioRZy1mYi8CUMPEbWIszYTkTdh6CGiVnHWZiLyFgw9RHRTnLWZiLwBQw8ROYWzNhORp2PoISKncdZmIvJkDD1E5BLO2kxEnoqhh4hcxlmbicgTMfQQUZtw1mYi8jRtCj1r1qxBr169oNFoYDAYsGfPnlbrN23ahJiYGGg0GgwYMAA5OTmy5UIIpKenIyIiAoGBgTAajTh69Ki0/MSJE0hKSkJ0dDQCAwNxzz33YPHixaivr5fV+Pn5NXvt3r27LW+RiJzAWZuJyJO4HHqys7ORkpKCxYsXY+/evRg4cCBMJhPOnj3rsH7Xrl2YOHEikpKSUFJSgsTERCQmJqKsrEyqWbp0KVauXInMzEwUFhYiODgYJpMJtbXX+gccOXIENpsN7733Hg4ePIi3334bmZmZePnll5vt75///CfOnDkjveLi4lx9i0TkAs7aTESewk8IIVxZwWAwYMiQIVi9ejUAwGazISoqCrNmzcKCBQua1Y8fPx41NTXYtm2b1DZs2DDExsYiMzMTQghERkZizpw5mDt3LgDAYrFAp9Nh/fr1mDBhgsPjWLZsGdauXYuffvoJwLUrPdHR0SgpKUFsbKwrb0litVqh1WphsVgQGhrapm0Q+aLa+gaYVnyDkxeuyNp7dg3EF8kjoVEpO+nIiMgXOPv97dKVnvr6ehQXF8NoNF7fgEIBo9GIgoICh+sUFBTI6gHAZDJJ9cePH4fZbJbVaLVaGAyGFrcJXAtGXbt2bdb+2GOPoVu3bkhISMDWrVtdeXtE1EactZmIPIFLoef8+fNobGyETqeTtet0OpjNZofrmM3mVuvtP13Z5rFjx7Bq1So8//zzUltISAiWL1+OTZs2Yfv27UhISEBiYmKrwaeurg5Wq1X2IqK24azNROTuPG701unTp/Hwww/j6aefxnPPPSe1h4eHIyUlRbr99sYbb+CZZ57BsmXLWtxWRkYGtFqt9IqKirodb4HIa3HWZiJyZy6FnvDwcPj7+6OqqkrWXlVVBb1e73AdvV7far39pzPbrKysxEMPPYThw4dj3bp1Nz1eg8GAY8eOtbg8NTUVFotFep06deqm2ySi1nHWZiJyVy6FHpVKhbi4OOTn50ttNpsN+fn5iI+Pd7hOfHy8rB4A8vLypPro6Gjo9XpZjdVqRWFhoWybp0+fxoMPPoi4uDh8+OGHUChufuilpaWIiIhocblarUZoaKjsRUS3jrM2E5E7cnlIRUpKCqZMmYLBgwdj6NChWLFiBWpqajB16lQAwOTJk9G9e3dkZGQAAGbPno1Ro0Zh+fLlGDduHLKyslBUVCRdqfHz80NycjKWLFmC3r17Izo6GmlpaYiMjERiYiKA64GnZ8+eeOutt3Du3DnpeOxXgzZs2ACVSoVBgwYBADZv3owPPvgA77//fts/HSJqM/vkhYOX5KPh32NE7cGnNH0MQgJVnXuARORzXA4948ePx7lz55Ceng6z2YzY2Fjk5uZKHZErKipkV2GGDx+OjRs3YtGiRXj55ZfRu3dvbNmyBf3795dq5s2bh5qaGkybNg3V1dVISEhAbm4uNBoNgGtXho4dO4Zjx47h7rvvlh1P0xH3r732Gk6ePAmlUomYmBhkZ2fjqaeecvUtElE7sQefuNfy0fjvNvuszTvnPMih7ER0W7k8T4834zw9RB1jX8UFPP6ufAoKzuFDRO2lQ+bpISJqC87aTETugKGHiDqc0l+BbbMSHE5eyOBDRLcLQw8R3RactZmIOhtDDxHdNq3N2lxuvtRJR0VEvoKhh4huq5ZmbX7uf//A21xE1KEYeojotnM0a3OlpY79e4ioQzH0EFGnCAlUYcuLw2Vt7NhMRB2JoYeIOk1MhBa9uzUfys6OzUTUERh6iKjTKP0V+MfM5kPZj537BYcqrZ10VETkrRh6iKhTtdSx+Xfvfs+HkxJRu2LoIaJO56hjM5/KTkTtjaGHiNxCSKAKRQtHQ+l3vY3Bh4jaE0MPEbkN+1PZGXyIqCMw9BCRW2kt+Fy+Ut95B0ZEHo+hh4jcjj34+DdpaxDAWM7hQ0S3gKGHiNxSWLAGm1+Ml7Vx1mYiuhUMPUTktvp1D8O9dzWfvPC3q7/j5IVE5DKGHiJyW0p/BbbNaj554dGzNXwqOxG5jKGHiNyaffLCG4NP4prv2LGZiFzC0ENEbs/RrM1XbezYTESuYeghIo+gUSmx7tk4WRs7NhORKxh6iMhjxERoHXZsZvAhImcw9BCRx2ipYzODDxE5g6GHiDxKSx2bT164gkdXfcuh7ETUIoYeIvI4LQWfY+d+waFKaycdFRG5O4YeIvJIjkZ0AcDv3v2eDyclIocYeojIY2lUSuxIHgllk79kDQIY8jofTkpEzTH0EJFHCwlUoWih/KnsV21gx2Yiaoahh4g8nv2p7E2Dz2lLHTs2E5EMQw8ReYWwYA22zBgua2PHZiJqiqGHiLxGTIS22YgudmwmIrs2hZ41a9agV69e0Gg0MBgM2LNnT6v1mzZtQkxMDDQaDQYMGICcnBzZciEE0tPTERERgcDAQBiNRhw9elRafuLECSQlJSE6OhqBgYG45557sHjxYtTXyzsq7t+/HyNGjIBGo0FUVBSWLl3alrdHRB5K6a/A9lkJzTo2D16Sz+BDRK6HnuzsbKSkpGDx4sXYu3cvBg4cCJPJhLNnzzqs37VrFyZOnIikpCSUlJQgMTERiYmJKCsrk2qWLl2KlStXIjMzE4WFhQgODobJZEJt7bU/UkeOHIHNZsN7772HgwcP4u2330ZmZiZefvllaRtWqxVjx45Fz549UVxcjGXLluGVV17BunXrXH2LROTBHHVsZvAhIgDwE0IIV1YwGAwYMmQIVq9eDQCw2WyIiorCrFmzsGDBgmb148ePR01NDbZt2ya1DRs2DLGxscjMzIQQApGRkZgzZw7mzp0LALBYLNDpdFi/fj0mTJjg8DiWLVuGtWvX4qeffgIArF27FgsXLoTZbIZKpQIALFiwAFu2bMGRI0ecem9WqxVarRYWiwWhoaHOfyhE5Haqa2oxeEk+Gpr8hVP6AUWLRiMsWNN5B0ZE7c7Z72+XrvTU19ejuLgYRqPx+gYUChiNRhQUFDhcp6CgQFYPACaTSao/fvw4zGazrEar1cJgMLS4TeBaMOratatsPyNHjpQCj30/5eXluHjxoitvk4i8gKMRXfYrPpzDh8g3uRR6zp8/j8bGRuh0Olm7TqeD2Wx2uI7ZbG613v7TlW0eO3YMq1atwvPPP3/T/TTdx43q6upgtVplLyLyHvbg49+krUEAYzmHD5FP8rjRW6dPn8bDDz+Mp59+Gs8999wtbSsjIwNarVZ6RUVFtdNREpG7CAvWYPOL8bK2Sksdfrv6O87hQ+RjXAo94eHh8Pf3R1VVlay9qqoKer3e4Tp6vb7VevtPZ7ZZWVmJhx56CMOHD2/WQbml/TTdx41SU1NhsVik16lTpxzWEZFn69c9DPfeFSxrO3q2BuXmS510RETUGVwKPSqVCnFxccjPz5fabDYb8vPzER8f73Cd+Ph4WT0A5OXlSfXR0dHQ6/WyGqvVisLCQtk2T58+jQcffBBxcXH48MMPoVDIDz0+Ph7ffPMNrl69KttPnz590KVLF4fHplarERoaKnsRkfdR+iuwbVZCszl8Hl/zHUd0EfkQl29vpaSk4K9//Ss2bNiAw4cPY/r06aipqcHUqVMBAJMnT0ZqaqpUP3v2bOTm5mL58uU4cuQIXnnlFRQVFWHmzJkAAD8/PyQnJ2PJkiXYunUrDhw4gMmTJyMyMhKJiYkArgeeHj164K233sK5c+dgNptlfXX+8Ic/QKVSISkpCQcPHkR2djbeeecdpKSk3MrnQ0Rewv5U9u5h10duNdjYsZnIlyhdXWH8+PE4d+4c0tPTYTabERsbi9zcXKnTcEVFhewqzPDhw7Fx40YsWrQIL7/8Mnr37o0tW7agf//+Us28efNQU1ODadOmobq6GgkJCcjNzYVGc+2PU15eHo4dO4Zjx47h7rvvlh2PfcS9VqvFjh07MGPGDMTFxSE8PBzp6emYNm2a658KEXkljUqJL2aPwMA/56Hx30PZ7R2bd855EBqVy38SiciDuDxPjzfjPD1EvmFfxQU8/q58SoyeXQPxRfJIBh8iD9Qh8/QQEXkDRx2bT164AhOHshN5NYYeIvI5LXVsZvAh8m4MPUTkk+wdmx0Fn0dXfcs5fIi8EEMPEfmsloLPsXO/4FAlZ2gn8jYMPUTk0+zBJ1KrlrU/ufZ7DmUn8jIMPUTk8zQqJXYkj4SyyV/Eqzawfw+Rl2HoISICEBKoQtFC+VPZT1vqGHyIvAhDDxHRv4UFa7BlxnBZGzs2E3kPhh4ioiZiIrTo3U0+hw87NhN5B4YeIqImlP4K/GNmQrOOzb9793s+nJTIwzH0EBHdwFHH5gZx7eGkDD5Enouhh4jIAUcdmxl8iDwbQw8RUQvCgjUoWsTgQ+QtGHqIiFrRWvDh5IVEnoWhh4joJuzBx79JW4MAxnIOHyKPwtBDROSEsGANNr8YL2ur5OSFRB6FoYeIyEn9uofh3rvkc/icvHAFv139HScvJPIADD1ERE5S+iuwbVZCs6eyHz1bg3LzpU46KiJyFkMPEZEL7E9lvzH4JK75jh2bidwcQw8RkYvswafprM1XbezYTOTuGHqIiNpAo1Ji3bNxsjZ2bCZybww9RERtFBOhddixmcGHyD0x9BARtVFLHZsZfIjcE0MPEdEtaKlj88kLV/Doqm85lJ3IjTD0EBHdopaCz7Fzv+BQpbWTjoqIbsTQQ0TUDhyN6AKA3737PR9OSuQmGHqIiNqJRqXEjuSRUDb5y8qnshO5D4YeIqJ2FBKoQtFCx09lZ/Ah6lwMPURE7cz+VHZHwYezNhN1HoYeIqIOYA8+/k3aGgRnbSbqTAw9REQdJCxYg80vxsvaOGszUedpU+hZs2YNevXqBY1GA4PBgD179rRav2nTJsTExECj0WDAgAHIycmRLRdCID09HREREQgMDITRaMTRo0dlNa+//jqGDx+OoKAghIWFOdyPn59fs1dWVlZb3iIRUbvo1z3M4azNv139HefwIbrNXA492dnZSElJweLFi7F3714MHDgQJpMJZ8+edVi/a9cuTJw4EUlJSSgpKUFiYiISExNRVlYm1SxduhQrV65EZmYmCgsLERwcDJPJhNra653+6uvr8fTTT2P69OmtHt+HH36IM2fOSK/ExERX3yIRUbtpadbmo2drUG6+1ElHReSb/IQQwpUVDAYDhgwZgtWrVwMAbDYboqKiMGvWLCxYsKBZ/fjx41FTU4Nt27ZJbcOGDUNsbCwyMzMhhEBkZCTmzJmDuXPnAgAsFgt0Oh3Wr1+PCRMmyLa3fv16JCcno7q6uvmb8fPDp59+2uagY7VaodVqYbFYEBoa2qZtEBE5UlvfANOKb3DywhWpTakAihaORliwphOPjMjzOfv97dKVnvr6ehQXF8NoNF7fgEIBo9GIgoICh+sUFBTI6gHAZDJJ9cePH4fZbJbVaLVaGAyGFrfZmhkzZiA8PBxDhw7FBx98ABczHRFRh7BPXtg97HrAabBxRBfR7eRS6Dl//jwaGxuh0+lk7TqdDmaz2eE6ZrO51Xr7T1e22ZI///nP+Pjjj5GXl4cnn3wSL774IlatWtVifV1dHaxWq+xFRNRRNColvpg9Av43DGXniC6i28OrRm+lpaXhV7/6FQYNGoT58+dj3rx5WLZsWYv1GRkZ0Gq10isqKuo2Hi0R+aKQQBU2T+eILqLO4FLoCQ8Ph7+/P6qqqmTtVVVV0Ov1DtfR6/Wt1tt/urJNZxkMBvz888+oq6tzuDw1NRUWi0V6nTp16pb2R0TkjJZGdDH4EHUsl0KPSqVCXFwc8vPzpTabzYb8/HzEx8c7XCc+Pl5WDwB5eXlSfXR0NPR6vazGarWisLCwxW06q7S0FF26dIFarXa4XK1WIzQ0VPYiIupoLY3oOnnhCh5d9S2HshN1EKWrK6SkpGDKlCkYPHgwhg4dihUrVqCmpgZTp04FAEyePBndu3dHRkYGAGD27NkYNWoUli9fjnHjxiErKwtFRUVYt24dgGsjrpKTk7FkyRL07t0b0dHRSEtLQ2RkpGwUVkVFBS5cuICKigo0NjaitLQUAHDvvfciJCQE//jHP1BVVYVhw4ZBo9EgLy8Pf/nLX6QRYURE7sTesfnGEV3Hzv2CQ5VW3B8V1nkHR+StRBusWrVK9OjRQ6hUKjF06FCxe/duadmoUaPElClTZPUff/yxuO+++4RKpRL9+vUT27dvly232WwiLS1N6HQ6oVarxejRo0V5ebmsZsqUKQJAs9eXX34phBDi888/F7GxsSIkJEQEBweLgQMHiszMTNHY2Oj0+7JYLAKAsFgsrn0gRERtdKXuqoj/S57oOX+b9LpnwTZx8fKVzj40Io/h7Pe3y/P0eDPO00NEneHylXrEvpaHhiZ3tZR+QNEizuFD5IwOmaeHiIjaX0igCkULHT+VvbqmtuUVicglDD1ERG7A/lR2Bh+ijsPQQ0TkJloLPpy1mejWMfQQEbkRe/Dxb9LGWZuJ2gdDDxGRmwkL1mDzi5y1mai9MfQQEbkhztpM1P4YeoiI3BBnbSZqfww9RERuyj5r843Bxz5rMxG5hqGHiMiN2YNPpFb+DMHfvfs9h7ITuYihh4jIzWlUSuxIHgllk7/YnMOHyHUMPUREHoCzNhPdOoYeIiIPwVmbiW4NQw8RkQfhrM1EbcfQQ0TkYThrM1HbMPQQEXmglmZt/vXyr3jFh6gFDD1ERB7K0azNlZY6xL6Wxz4+RA4w9BAReaiWZm1usLGPD5EjDD1ERB7MPnnhvXcFydrZx4eoOYYeIiIPp1EpkZs8CmsnPSBr55PZieQYeoiIvIDSX4ExfXV8MjtRKxh6iIi8RGtPZmfwIWLoISLyKi09mf3khSt4dNW3aGi0ddKREXU+hh4iIi/TUvA5du4XHKq0dtJREXU+hh4iIi9kDz6RWrWs/Xfvfs85fMhnMfQQEXkpjUqJHckjoWzyl54PKCVfxtBDROTFQgJVKFrY/AGlca/l4/ylXzrvwIg6AUMPEZGXc/Rk9kYAg1//ksGHfApDDxGRD3D0ZHYAMPzlS97qIp/B0ENE5CPCgjUoXPiQrK2RfXzIhzD0EBH5kPA7glC08CH439DHh8GHfAFDDxGRjwm/IwjFi5p3bmbwIW/XptCzZs0a9OrVCxqNBgaDAXv27Gm1ftOmTYiJiYFGo8GAAQOQk5MjWy6EQHp6OiIiIhAYGAij0YijR4/Kal5//XUMHz4cQUFBCAsLc7ifiooKjBs3DkFBQejWrRteeuklNDRw2nUiohs56txsDz6Xr9R33oERdSCXQ092djZSUlKwePFi7N27FwMHDoTJZMLZs2cd1u/atQsTJ05EUlISSkpKkJiYiMTERJSVlUk1S5cuxcqVK5GZmYnCwkIEBwfDZDKhtvb6/+Oor6/H008/jenTpzvcT2NjI8aNG4f6+nrs2rULGzZswPr165Genu7qWyQi8gmOOjc3CODXy79i8CGv5CeEEK6sYDAYMGTIEKxevRoAYLPZEBUVhVmzZmHBggXN6sePH4+amhps27ZNahs2bBhiY2ORmZkJIQQiIyMxZ84czJ07FwBgsVig0+mwfv16TJgwQba99evXIzk5GdXV1bL2zz//HI8++igqKyuh0+kAAJmZmZg/fz7OnTsHlUp10/dmtVqh1WphsVgQGhrqysdCROSx9lVcwOPvFsjalAqgaOFohAVrOumoiJzn7Pe3S1d66uvrUVxcDKPReH0DCgWMRiMKCgocrlNQUCCrBwCTySTVHz9+HGazWVaj1WphMBha3GZL+xkwYIAUeOz7sVqtOHjwoNPbISLyNf26h+Heu4JlbQ029vEh7+NS6Dl//jwaGxtlwQIAdDodzGazw3XMZnOr9fafrmzTlf003ceN6urqYLVaZS8iIl+j9Fdg26wE3HtXkKydfXzI2/j06K2MjAxotVrpFRUV1dmHRETUKTQqJXKTR2HrjF816+MzdsU3qK3noBDyfC6FnvDwcPj7+6OqqkrWXlVVBb1e73AdvV7far39pyvbdGU/Tfdxo9TUVFgsFul16tQpp/dHRORtlP4K3B8Vhs0vxsvaKy117NxMXsGl0KNSqRAXF4f8/HypzWazIT8/H/Hx8Q7XiY+Pl9UDQF5enlQfHR0NvV4vq7FarSgsLGxxmy3t58CBA7JRZHl5eQgNDUXfvn0drqNWqxEaGip7ERH5Okd9fCotdYh9LY99fMijuXx7KyUlBX/961+xYcMGHD58GNOnT0dNTQ2mTp0KAJg8eTJSU1Ol+tmzZyM3NxfLly/HkSNH8Morr6CoqAgzZ84EAPj5+SE5ORlLlizB1q1bceDAAUyePBmRkZFITEyUtlNRUYHS0lJUVFSgsbERpaWlKC0txeXLlwEAY8eORd++ffHss89i3759+OKLL7Bo0SLMmDEDarX6Vj4jIiKfYu/j07NroKydnZvJ44k2WLVqlejRo4dQqVRi6NChYvfu3dKyUaNGiSlTpsjqP/74Y3HfffcJlUol+vXrJ7Zv3y5bbrPZRFpamtDpdEKtVovRo0eL8vJyWc2UKVMEgGavL7/8Uqo5ceKE+M1vfiMCAwNFeHi4mDNnjrh69arT78tisQgAwmKxOP9hEBF5qSt1V8Xot3aKnvO3yV73LNgmLv1S19mHRyRx9vvb5Xl6vBnn6SEikmtotOFQpRVPrPkejU3au4UEYOecBxESePM50Ig6WofM00NERL6lpc7NZy9fZR8f8jgMPUREdFOcwJC8AUMPERHdVGsTGMa9xuBDnoGhh4iInNLSBIaN4BUf8gwMPURE5LSW+vjYH1nB4EPujKGHiIhc1q97GPrqQ2Rt9ltd+youoKHR1klHRtQyhh4iInKZ0l+BrbNGYOuMX0Hpd729EcDj7xbgsVXfMviQ22HoISKiNrHf6ipaNFoWfADgkPkyDlVaO+fAiFrA0ENERLckLFiDokWjZZ2bAeCJNd/zVhe5FYYeIiK6ZWHBGhSnjXZ4q+vhFd+gtr6h046NyI6hh4iI2oX9is+Nt7qOnavBo+zjQ26AoYeIiNpNy8HnF+QdqmLwoU7F0ENERO0qLFiD0vQx6BYSIGuf/tFe3uqiTsXQQ0RE7S4kUIWdcx6E8oZvmWPnavDr5V/h8pX6zjkw8mkMPURE1CFCAlUoWtj8VlelpY5PaKdOwdBDREQdxn6rK1KrlrU32PigUrr9GHqIiKhD2W913fiEdj6olG43hh4iIupw9ie0r530gKydz+ui24mhh4iIbgulvwJj+uqaPaiUz+ui24Whh4iIbpuWHlQKXHteF+fyoY7E0ENERLdV0weV3vi8Ls7lQx2JoYeIiDqFo+d1AZzLhzoOQw8REXWalh5bwbl8qCMw9BARUafiXD50uzD0EBFRp2ttLh8Oaaf2wtBDRERuoaW5fOxD2tnBmW4VQw8REbmNlubyAa51cB779tcMPtRmDD1ERORWWpvLp+JiLR56iyO7qG0YeoiIyO3Y5/IpTR+DbiEBsmVnrBzZRW3D0ENERG7L3sE54IZvK/vILnZwJlcw9BARkVsLCVShJG0MenTRyNrtHZxN7OdDTmpT6FmzZg169eoFjUYDg8GAPXv2tFq/adMmxMTEQKPRYMCAAcjJyZEtF0IgPT0dERERCAwMhNFoxNGjR2U1Fy5cwKRJkxAaGoqwsDAkJSXh8uXL0vITJ07Az8+v2Wv37t1teYtERORGQgJV2Dn3IYf9fH48/wv7+ZBTXA492dnZSElJweLFi7F3714MHDgQJpMJZ8+edVi/a9cuTJw4EUlJSSgpKUFiYiISExNRVlYm1SxduhQrV65EZmYmCgsLERwcDJPJhNra6/drJ02ahIMHDyIvLw/btm3DN998g2nTpjXb3z//+U+cOXNGesXFxbn6FomIyA219syuM9Y6DPxzHm93Uav8hBDClRUMBgOGDBmC1atXAwBsNhuioqIwa9YsLFiwoFn9+PHjUVNTg23btkltw4YNQ2xsLDIzMyGEQGRkJObMmYO5c+cCACwWC3Q6HdavX48JEybg8OHD6Nu3L3744QcMHjwYAJCbm4tHHnkEP//8MyIjI3HixAlER0ejpKQEsbGxbfowrFYrtFotLBYLQkND27QNIiLqeNU1tRi8JB8NDr7B+upDsHXWCCj92YPDVzj7/e3SfxH19fUoLi6G0Wi8vgGFAkajEQUFBQ7XKSgokNUDgMlkkuqPHz8Os9ksq9FqtTAYDFJNQUEBwsLCpMADAEajEQqFAoWFhbJtP/bYY+jWrRsSEhKwdetWV94eERF5CPujK27s5wMAh8yXkXeoild8qBmXQs/58+fR2NgInU4na9fpdDCbzQ7XMZvNrdbbf96splu3brLlSqUSXbt2lWpCQkKwfPlybNq0Cdu3b0dCQgISExNbDT51dXWwWq2yFxEReYam/XxuvN01/aO9+DX7+dANvObaX3h4OFJSUqTbb2+88QaeeeYZLFu2rMV1MjIyoNVqpVdUVNRtPGIiIrpV9n4+xWnNn9RecfEK+/mQjEuhJzw8HP7+/qiqqpK1V1VVQa/XO1xHr9e3Wm//ebOaGztKNzQ04MKFCy3uF7jW/+jYsWMtLk9NTYXFYpFep06darGWiIjcV1iwBkWLmgefRsFh7XSdS6FHpVIhLi4O+fn5UpvNZkN+fj7i4+MdrhMfHy+rB4C8vDypPjo6Gnq9XlZjtVpRWFgo1cTHx6O6uhrFxcVSzc6dO2Gz2WAwGFo83tLSUkRERLS4XK1WIzQ0VPYiIiLP1Fo/Hw5rJwBQurpCSkoKpkyZgsGDB2Po0KFYsWIFampqMHXqVADA5MmT0b17d2RkZAAAZs+ejVGjRmH58uUYN24csrKyUFRUhHXr1gEA/Pz8kJycjCVLlqB3796Ijo5GWloaIiMjkZiYCAD4z//8Tzz88MN47rnnkJmZiatXr2LmzJmYMGECIiMjAQAbNmyASqXCoEGDAACbN2/GBx98gPfff/+WPyQiIvIM9n4+hyqteGLN92hsssw+rH3z9Hj06x7G0V0+yOXQM378eJw7dw7p6ekwm82IjY1Fbm6u1BG5oqICCsX1/5CGDx+OjRs3YtGiRXj55ZfRu3dvbNmyBf3795dq5s2bh5qaGkybNg3V1dVISEhAbm4uNJrraf2jjz7CzJkzMXr0aCgUCjz55JNYuXKl7Nhee+01nDx5EkqlEjExMcjOzsZTTz3l8odCRESeq2k/nxuHtdtvd90THoTtfxoBjcrlr0HyYC7P0+PNOE8PEZF3uXylHo+s/BYVF5s/nDQiVI28/x6JkEBVJxwZtacOmaeHiIjIk7Q2rP2MtQ73v5qHnP2n2cnZRzD0EBGRV2ttWLsNwIsbS9H/lS9w/tIvnXJ8dPsw9BARkU9obXRXgw0Y/PqXKD5xnnP6eDH26WmCfXqIiLxfQ6MNhyqt+N273zt8dlePLoHI+VMC+/p4EPbpISIicsB+u6vsVRNWjo9ttrzi4hXc/2oer/p4IYYeIiLySRqVEo8N6o6ihQ/B30FfnyczCzmhoZdh6CEiIp8WfkcQ9rXQ1+fUv6/6cISXd2CfnibYp4eIyHc1NNpw4GcLnli7y+FypQLYnfoQwu8Ius1HRjfDPj1EREQuUPorMKhnF5SmjUZEqLrZcvsIr3+UnuJVHw/FKz1N8EoPEREBNx/hpVQAn7zAZ3i5C2e/vxl6mmDoISKipmrrG7DjYBX+lF3qcHlUFw0+/9MIDm/vZLy9RUREdItaG+EFAKcu1rKjswfhlZ4meKWHiIhaUlvfgPzDZ/Gnv5Wg0cFyfz9g1cRY/DpGx6e332a8vdUGDD1ERHQz1TW1GLwk32FfHwBQ+gGfTGd/n9uJt7eIiIg6QFiwBmWvmrBm4qBmDzAFgAYBPP5uAR5a9iXKfr7IWZ3dCK/0NMErPURE5Iqb3fIC2Nn5duCVHiIiog6mUSkxbmAk9i0egygHMzoD7OzsTnilpwle6SEiorZqaLSh3HwJ0/9vESou1jqsUQBIezQGT8dF8cpPO2JH5jZg6CEiolt1s4kN7VZNuB9j+kZwpFc7YOhpA4YeIiJqL/b+PrOzSloMP7zy0z4YetqAoYeIiNqbM52dAWDF7wegj16L3ro7ONTdRQw9bcDQQ0REHcX+SIv//rgUja18896tVSPt0b4Y1acbb305iaGnDRh6iIioozl75ccfwKo/cIZnZzD0tAFDDxER3S7OXvlhv5+bY+hpA4YeIiK63WrrG/BV+Tm8vv0QTlU7Huput/yp/ggNVGNE73Be/WmCoacNGHqIiKiz2Of5eeH/FN00/CgALHykDwz/cSdiIrQ+3/GZoacNGHqIiKizNTTacOzsZRytuoTkrNJW+/0AQHetGgsejoFGpfTZK0AMPW3A0ENERO7E2X4/dv4Alv/+fgSrA3wqADH0tAFDDxERuaPa+gZ8e/Q8Ltc2YO6mfTe9+gNcuwWW+vB96HFnsNcPf2foaQOGHiIicnf2Ie+vbTsI86V6p9bx9wOWPjkAl+oaMbhnmNf1A2LoaQOGHiIi8hT2vj8/nbuMv+Qcws/VdU6va+8HJOAHS+1VPBEb6dHD4Z39/m5TzFuzZg169eoFjUYDg8GAPXv2tFq/adMmxMTEQKPRYMCAAcjJyZEtF0IgPT0dERERCAwMhNFoxNGjR2U1Fy5cwKRJkxAaGoqwsDAkJSXh8uXLspr9+/djxIgR0Gg0iIqKwtKlS9vy9oiIiNye0l+BmIhQPHJ/JL566dfInT0C7z3zAN5+eiD8b7LuaUsdZmXvw5+yS5H22UH0fzUPnxSdxNaS0/hf3/6ELw5Uora+4ba8j9vJ5Ss92dnZmDx5MjIzM2EwGLBixQps2rQJ5eXl6NatW7P6Xbt2YeTIkcjIyMCjjz6KjRs34s0338TevXvRv39/AMCbb76JjIwMbNiwAdHR0UhLS8OBAwdw6NAhaDQaAMBvfvMbnDlzBu+99x6uXr2KqVOnYsiQIdi4cSOAaynvvvvug9FoRGpqKg4cOIA//vGPWLFiBaZNm+bUe+OVHiIi8gb2uX9Onq/Bsh3lrT7tvSX+fsDyp++Hwk+Bc5fr0C1EhQClP3reGeR2zwfrsNtbBoMBQ4YMwerVqwEANpsNUVFRmDVrFhYsWNCsfvz48aipqcG2bduktmHDhiE2NhaZmZkQQiAyMhJz5szB3LlzAQAWiwU6nQ7r16/HhAkTcPjwYfTt2xc//PADBg8eDADIzc3FI488gp9//hmRkZFYu3YtFi5cCLPZDJXq2iW6BQsWYMuWLThy5IhT742hh4iIvE3TTtBv5h52uh9Qa3QhAUh95D8R4H/tmlKjTUjBSKFQNPu3vaajbqU5+/3tUlfu+vp6FBcXIzU1VWpTKBQwGo0oKChwuE5BQQFSUlJkbSaTCVu2bAEAHD9+HGazGUajUVqu1WphMBhQUFCACRMmoKCgAGFhYVLgAQCj0QiFQoHCwkI88cQTKCgowMiRI6XAY9/Pm2++iYsXL6JLly6uvFUiIiKvoFEpMaafHgDw29hIHDt7GSf/VYOGRoEr9Y2Yv3m/U8Phm6q6fBXJH+9v0/Gkf3YQBxaP6ZQ+RC6FnvPnz6OxsRE6nU7WrtPpWryaYjabHdabzWZpub2ttZobb50plUp07dpVVhMdHd1sG/ZljkJPXV0d6uqud/yyWq0O3wMREZE3sPcDiom4fjXk0YER+PboedQ32NBoEzhTfaXNt8ScIQBsP2DG+KE9OmYHrfDeQftOyMjIwKuvvtrZh0FERNRpml4Jspvyq16yIHTuch3uDArAWzvK8bPF+VFijvgBGDdAf9O6juBS6AkPD4e/vz+qqqpk7VVVVdDrHb8BvV7far39Z1VVFSIiImQ1sbGxUs3Zs2dl22hoaMCFCxdk23G0n6b7uFFqaqrs1pvVakVUVJTDWiIiIl/hKAgBwKOx3VFuvoSiExfQNShA1l+nM/v0OMul0KNSqRAXF4f8/HwkJiYCuNaROT8/HzNnznS4Tnx8PPLz85GcnCy15eXlIT4+HgAQHR0NvV6P/Px8KeRYrVYUFhZi+vTp0jaqq6tRXFyMuLg4AMDOnTths9lgMBikmoULF+Lq1asICAiQ9tOnT58W+/Oo1Wqo1WpXPgIiIiKfpfRXoF93Lfp113b2obSNcFFWVpZQq9Vi/fr14tChQ2LatGkiLCxMmM1mIYQQzz77rFiwYIFU//333wulUineeustcfjwYbF48WIREBAgDhw4INW88cYbIiwsTHz22Wdi//794vHHHxfR0dHiypUrUs3DDz8sBg0aJAoLC8V3330nevfuLSZOnCgtr66uFjqdTjz77LOirKxMZGVliaCgIPHee+85/d4sFosAICwWi6sfCxEREXUSZ7+/XQ49QgixatUq0aNHD6FSqcTQoUPF7t27pWWjRo0SU6ZMkdV//PHH4r777hMqlUr069dPbN++XbbcZrOJtLQ0odPphFqtFqNHjxbl5eWymn/9619i4sSJIiQkRISGhoqpU6eKS5cuyWr27dsnEhIShFqtFt27dxdvvPGGS++LoYeIiMjzOPv9zcdQNMF5eoiIiDxPhz6GgoiIiMjTMPQQERGRT2DoISIiIp/A0ENEREQ+gaGHiIiIfAJDDxEREfkEhh4iIiLyCQw9RERE5BMYeoiIiMgnuPTAUW9nn5zaarV28pEQERGRs+zf2zd7yARDTxOXLl0CAERFRXXykRAREZGrLl26BK225SfA89lbTdhsNlRWVuKOO+6An59fu27barUiKioKp06d4nO9PATPmWfiefNMPG+eyV3OmxACly5dQmRkJBSKlnvu8EpPEwqFAnfffXeH7iM0NJS/0B6G58wz8bx5Jp43z+QO5621Kzx27MhMREREPoGhh4iIiHwCQ89tolarsXjxYqjV6s4+FHISz5ln4nnzTDxvnsnTzhs7MhMREZFP4JUeIiIi8gkMPUREROQTGHqIiIjIJzD0EBERkU9g6LkN1qxZg169ekGj0cBgMGDPnj2dfUhe65tvvsFvf/tbREZGws/PD1u2bJEtF0IgPT0dERERCAwMhNFoxNGjR2U1Fy5cwKRJkxAaGoqwsDAkJSXh8uXLspr9+/djxIgR0Gg0iIqKwtKlS5sdy6ZNmxATEwONRoMBAwYgJyen3d+vN8jIyMCQIUNwxx13oFu3bkhMTER5ebmspra2FjNmzMCdd96JkJAQPPnkk6iqqpLVVFRUYNy4cQgKCkK3bt3w0ksvoaGhQVbz1Vdf4YEHHoBarca9996L9evXNzse/r46Z+3atbj//vulSeni4+Px+eefS8t5ztzfG2+8AT8/PyQnJ0ttXn/eBHWorKwsoVKpxAcffCAOHjwonnvuOREWFiaqqqo6+9C8Uk5Ojli4cKHYvHmzACA+/fRT2fI33nhDaLVasWXLFrFv3z7x2GOPiejoaHHlyhWp5uGHHxYDBw4Uu3fvFt9++6249957xcSJE6XlFotF6HQ6MWnSJFFWVib+9re/icDAQPHee+9JNd9//73w9/cXS5cuFYcOHRKLFi0SAQEB4sCBAx3+GXgak8kkPvzwQ1FWViZKS0vFI488Inr06CEuX74s1bzwwgsiKipK5Ofni6KiIjFs2DAxfPhwaXlDQ4Po37+/MBqNoqSkROTk5Ijw8HCRmpoq1fz0008iKChIpKSkiEOHDolVq1YJf39/kZubK9Xw99V5W7duFdu3bxf/7//9P1FeXi5efvllERAQIMrKyoQQPGfubs+ePaJXr17i/vvvF7Nnz5bavf28MfR0sKFDh4oZM2ZI/25sbBSRkZEiIyOjE4/KN9wYemw2m9Dr9WLZsmVSW3V1tVCr1eJvf/ubEEKIQ4cOCQDihx9+kGo+//xz4efnJ06fPi2EEOLdd98VXbp0EXV1dVLN/PnzRZ8+faR///73vxfjxo2THY/BYBDPP/98u75Hb3T27FkBQHz99ddCiGvnKCAgQGzatEmqOXz4sAAgCgoKhBDXwq5CoRBms1mqWbt2rQgNDZXO07x580S/fv1k+xo/frwwmUzSv/n7emu6dOki3n//fZ4zN3fp0iXRu3dvkZeXJ0aNGiWFHl84b7y91YHq6+tRXFwMo9EotSkUChiNRhQUFHTikfmm48ePw2w2y86HVquFwWCQzkdBQQHCwsIwePBgqcZoNEKhUKCwsFCqGTlyJFQqlVRjMplQXl6OixcvSjVN92Ov4Xm/OYvFAgDo2rUrAKC4uBhXr16VfZ4xMTHo0aOH7LwNGDAAOp1OqjGZTLBarTh48KBU09o54e9r2zU2NiIrKws1NTWIj4/nOXNzM2bMwLhx45p9tr5w3vjA0Q50/vx5NDY2yv7jAACdTocjR4500lH5LrPZDAAOz4d9mdlsRrdu3WTLlUolunbtKquJjo5utg37si5dusBsNre6H3LMZrMhOTkZv/rVr9C/f38A1z5TlUqFsLAwWe2N583R521f1lqN1WrFlStXcPHiRf6+uujAgQOIj49HbW0tQkJC8Omnn6Jv374oLS3lOXNTWVlZ2Lt3L3744Ydmy3zhd42hh4jcxowZM1BWVobvvvuusw+FnNCnTx+UlpbCYrHg73//O6ZMmYKvv/66sw+LWnDq1CnMnj0beXl50Gg0nX04nYK3tzpQeHg4/P39m/V8r6qqgl6v76Sj8l32z7y186HX63H27FnZ8oaGBly4cEFW42gbTffRUg3Pe8tmzpyJbdu24csvv8Tdd98ttev1etTX16O6ulpWf+N5a+s5CQ0NRWBgIH9f20ClUuHee+9FXFwcMjIyMHDgQLzzzjs8Z26quLgYZ8+exQMPPAClUgmlUomvv/4aK1euhFKphE6n8/rzxtDTgVQqFeLi4pCfny+12Ww25OfnIz4+vhOPzDdFR0dDr9fLzofVakVhYaF0PuLj41FdXY3i4mKpZufOnbDZbDAYDFLNN998g6tXr0o1eXl56NOnD7p06SLVNN2PvYbnvTkhBGbOnIlPP/0UO3fubHbrMC4uDgEBAbLPs7y8HBUVFbLzduDAAVlgzcvLQ2hoKPr27SvVtHZO+Pt662w2G+rq6njO3NTo0aNx4MABlJaWSq/Bgwdj0qRJ0v/2+vPWod2kSWRlZQm1Wi3Wr18vDh06JKZNmybCwsJkPd+p/Vy6dEmUlJSIkpISAUD8z//8jygpKREnT54UQlwbsh4WFiY+++wzsX//fvH44487HLI+aNAgUVhYKL777jvRu3dv2ZD16upqodPpxLPPPivKyspEVlaWCAoKajZkXalUirfeekscPnxYLF68mEPWWzB9+nSh1WrFV199Jc6cOSO9fvnlF6nmhRdeED169BA7d+4URUVFIj4+XsTHx0vL7cNox44dK0pLS0Vubq646667HA6jfemll8Thw4fFmjVrHA6j5e+rcxYsWCC+/vprcfz4cbF//36xYMEC4efnJ3bs2CGE4DnzFE1Hbwnh/eeNoec2WLVqlejRo4dQqVRi6NChYvfu3Z19SF7ryy+/FACavaZMmSKEuDZsPS0tTeh0OqFWq8Xo0aNFeXm5bBv/+te/xMSJE0VISIgIDQ0VU6dOFZcuXZLV7Nu3TyQkJAi1Wi26d+8u3njjjWbH8vHHH4v77rtPqFQq0a9fP7F9+/YOe9+ezNH5AiA+/PBDqebKlSvixRdfFF26dBFBQUHiiSeeEGfOnJFt58SJE+I3v/mNCAwMFOHh4WLOnDni6tWrspovv/xSxMbGCpVKJf7jP/5Dtg87/r46549//KPo2bOnUKlU4q677hKjR4+WAo8QPGee4sbQ4+3nzU8IITr2WhIRERFR52OfHiIiIvIJDD1ERETkExh6iIiIyCcw9BAREZFPYOghIiIin8DQQ0RERD6BoYeIiIh8AkMPERER+QSGHiIiIvIJDD1ERETkExh6iIiIyCcw9BAREZFP+P8BPpa+0t5uRJ4AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "---------fold0---------\n",
            "train:70611 valid:23866\n",
            "\n",
            "Epoch 1/300\n",
            "137/137 - 408s - loss: 5.0584 - categorical_accuracy: 0.0687 - val_loss: 4.0326 - val_categorical_accuracy: 0.2066 - 408s/epoch - 3s/step\n",
            "Epoch 2/300\n",
            "137/137 - 41s - loss: 4.0519 - categorical_accuracy: 0.2260 - val_loss: 3.4663 - val_categorical_accuracy: 0.3298 - 41s/epoch - 299ms/step\n",
            "Epoch 3/300\n",
            "137/137 - 38s - loss: 3.6254 - categorical_accuracy: 0.3172 - val_loss: 2.9705 - val_categorical_accuracy: 0.4582 - 38s/epoch - 274ms/step\n",
            "Epoch 4/300\n",
            "137/137 - 35s - loss: 3.3191 - categorical_accuracy: 0.3839 - val_loss: 2.8391 - val_categorical_accuracy: 0.4855 - 35s/epoch - 258ms/step\n",
            "Epoch 5/300\n",
            "137/137 - 37s - loss: 3.1039 - categorical_accuracy: 0.4374 - val_loss: 2.5060 - val_categorical_accuracy: 0.5780 - 37s/epoch - 271ms/step\n",
            "Epoch 6/300\n",
            "137/137 - 30s - loss: 2.9420 - categorical_accuracy: 0.4727 - val_loss: 2.6257 - val_categorical_accuracy: 0.5417 - 30s/epoch - 220ms/step\n",
            "Epoch 7/300\n",
            "137/137 - 30s - loss: 2.8267 - categorical_accuracy: 0.5014 - val_loss: 2.6613 - val_categorical_accuracy: 0.5245 - 30s/epoch - 219ms/step\n",
            "Epoch 8/300\n",
            "137/137 - 31s - loss: 2.7211 - categorical_accuracy: 0.5300 - val_loss: 2.4087 - val_categorical_accuracy: 0.6024 - 31s/epoch - 226ms/step\n",
            "Epoch 9/300\n",
            "137/137 - 30s - loss: 2.6574 - categorical_accuracy: 0.5481 - val_loss: 2.3274 - val_categorical_accuracy: 0.6206 - 30s/epoch - 220ms/step\n",
            "Epoch 10/300\n",
            "137/137 - 33s - loss: 2.6009 - categorical_accuracy: 0.5643 - val_loss: 2.1807 - val_categorical_accuracy: 0.6690 - 33s/epoch - 243ms/step\n",
            "Epoch 11/300\n",
            "137/137 - 26s - loss: 2.5476 - categorical_accuracy: 0.5757 - val_loss: 2.2671 - val_categorical_accuracy: 0.6433 - 26s/epoch - 192ms/step\n",
            "Epoch 12/300\n",
            "137/137 - 30s - loss: 2.4949 - categorical_accuracy: 0.5908 - val_loss: 2.3409 - val_categorical_accuracy: 0.6228 - 30s/epoch - 220ms/step\n",
            "Epoch 13/300\n",
            "137/137 - 30s - loss: 2.4584 - categorical_accuracy: 0.6017 - val_loss: 2.1528 - val_categorical_accuracy: 0.6742 - 30s/epoch - 222ms/step\n",
            "Epoch 14/300\n",
            "137/137 - 29s - loss: 2.4229 - categorical_accuracy: 0.6096 - val_loss: 2.1903 - val_categorical_accuracy: 0.6612 - 29s/epoch - 214ms/step\n",
            "Epoch 15/300\n",
            "137/137 - 31s - loss: 2.3886 - categorical_accuracy: 0.6200 - val_loss: 2.0783 - val_categorical_accuracy: 0.6955 - 31s/epoch - 224ms/step\n",
            "Epoch 16/300\n",
            "137/137 - 29s - loss: 4.0842 - categorical_accuracy: 0.1602 - val_loss: 2.4455 - val_categorical_accuracy: 0.6145 - 29s/epoch - 213ms/step\n",
            "Epoch 17/300\n",
            "137/137 - 28s - loss: 3.6816 - categorical_accuracy: 0.2236 - val_loss: 2.3891 - val_categorical_accuracy: 0.6294 - 28s/epoch - 206ms/step\n",
            "Epoch 18/300\n",
            "137/137 - 30s - loss: 3.5485 - categorical_accuracy: 0.2518 - val_loss: 2.2842 - val_categorical_accuracy: 0.6547 - 30s/epoch - 218ms/step\n",
            "Epoch 19/300\n",
            "137/137 - 32s - loss: 3.4748 - categorical_accuracy: 0.2707 - val_loss: 2.2845 - val_categorical_accuracy: 0.6525 - 32s/epoch - 237ms/step\n",
            "Epoch 20/300\n",
            "137/137 - 28s - loss: 3.4196 - categorical_accuracy: 0.2888 - val_loss: 2.2217 - val_categorical_accuracy: 0.6725 - 28s/epoch - 202ms/step\n",
            "Epoch 21/300\n",
            "137/137 - 29s - loss: 3.3748 - categorical_accuracy: 0.2966 - val_loss: 2.2149 - val_categorical_accuracy: 0.6673 - 29s/epoch - 211ms/step\n",
            "Epoch 22/300\n",
            "137/137 - 31s - loss: 3.3393 - categorical_accuracy: 0.3102 - val_loss: 2.2218 - val_categorical_accuracy: 0.6671 - 31s/epoch - 223ms/step\n",
            "Epoch 23/300\n",
            "137/137 - 29s - loss: 3.3082 - categorical_accuracy: 0.3164 - val_loss: 2.1679 - val_categorical_accuracy: 0.6871 - 29s/epoch - 215ms/step\n",
            "Epoch 24/300\n",
            "137/137 - 30s - loss: 3.2672 - categorical_accuracy: 0.3268 - val_loss: 2.1701 - val_categorical_accuracy: 0.6828 - 30s/epoch - 221ms/step\n",
            "Epoch 25/300\n",
            "137/137 - 29s - loss: 3.2488 - categorical_accuracy: 0.3346 - val_loss: 2.1127 - val_categorical_accuracy: 0.6992 - 29s/epoch - 212ms/step\n",
            "Epoch 26/300\n",
            "137/137 - 40s - loss: 3.2238 - categorical_accuracy: 0.3439 - val_loss: 2.1241 - val_categorical_accuracy: 0.6951 - 40s/epoch - 290ms/step\n",
            "Epoch 27/300\n",
            "137/137 - 28s - loss: 3.1970 - categorical_accuracy: 0.3489 - val_loss: 2.1288 - val_categorical_accuracy: 0.6866 - 28s/epoch - 202ms/step\n",
            "Epoch 28/300\n",
            "137/137 - 30s - loss: 3.1802 - categorical_accuracy: 0.3546 - val_loss: 2.0932 - val_categorical_accuracy: 0.7036 - 30s/epoch - 221ms/step\n",
            "Epoch 29/300\n",
            "137/137 - 28s - loss: 3.1644 - categorical_accuracy: 0.3610 - val_loss: 2.0987 - val_categorical_accuracy: 0.6960 - 28s/epoch - 204ms/step\n",
            "Epoch 30/300\n",
            "137/137 - 31s - loss: 3.1429 - categorical_accuracy: 0.3681 - val_loss: 2.0526 - val_categorical_accuracy: 0.7126 - 31s/epoch - 225ms/step\n",
            "Epoch 31/300\n",
            "137/137 - 28s - loss: 3.1182 - categorical_accuracy: 0.3746 - val_loss: 2.0777 - val_categorical_accuracy: 0.7045 - 28s/epoch - 202ms/step\n",
            "Epoch 32/300\n",
            "137/137 - 31s - loss: 3.0966 - categorical_accuracy: 0.3807 - val_loss: 2.1028 - val_categorical_accuracy: 0.6964 - 31s/epoch - 228ms/step\n",
            "Epoch 33/300\n",
            "137/137 - 28s - loss: 3.0827 - categorical_accuracy: 0.3846 - val_loss: 2.0373 - val_categorical_accuracy: 0.7151 - 28s/epoch - 202ms/step\n",
            "Epoch 34/300\n",
            "137/137 - 33s - loss: 3.0675 - categorical_accuracy: 0.3898 - val_loss: 2.0356 - val_categorical_accuracy: 0.7125 - 33s/epoch - 241ms/step\n",
            "Epoch 35/300\n",
            "137/137 - 29s - loss: 3.0612 - categorical_accuracy: 0.3916 - val_loss: 2.0134 - val_categorical_accuracy: 0.7241 - 29s/epoch - 211ms/step\n",
            "Epoch 36/300\n",
            "137/137 - 31s - loss: 3.0437 - categorical_accuracy: 0.3974 - val_loss: 2.0159 - val_categorical_accuracy: 0.7199 - 31s/epoch - 224ms/step\n",
            "Epoch 37/300\n",
            "137/137 - 28s - loss: 3.0276 - categorical_accuracy: 0.4011 - val_loss: 2.0446 - val_categorical_accuracy: 0.7098 - 28s/epoch - 204ms/step\n",
            "Epoch 38/300\n",
            "137/137 - 30s - loss: 3.0115 - categorical_accuracy: 0.4057 - val_loss: 2.0041 - val_categorical_accuracy: 0.7214 - 30s/epoch - 217ms/step\n",
            "Epoch 39/300\n",
            "137/137 - 28s - loss: 2.9973 - categorical_accuracy: 0.4093 - val_loss: 2.0100 - val_categorical_accuracy: 0.7196 - 28s/epoch - 201ms/step\n",
            "Epoch 40/300\n",
            "137/137 - 31s - loss: 2.9778 - categorical_accuracy: 0.4161 - val_loss: 1.9780 - val_categorical_accuracy: 0.7323 - 31s/epoch - 227ms/step\n",
            "Epoch 41/300\n",
            "137/137 - 28s - loss: 2.9652 - categorical_accuracy: 0.4185 - val_loss: 1.9850 - val_categorical_accuracy: 0.7250 - 28s/epoch - 202ms/step\n",
            "Epoch 42/300\n",
            "137/137 - 32s - loss: 2.9675 - categorical_accuracy: 0.4199 - val_loss: 1.9952 - val_categorical_accuracy: 0.7268 - 32s/epoch - 236ms/step\n",
            "Epoch 43/300\n",
            "137/137 - 29s - loss: 2.9534 - categorical_accuracy: 0.4229 - val_loss: 1.9715 - val_categorical_accuracy: 0.7312 - 29s/epoch - 212ms/step\n",
            "Epoch 44/300\n",
            "137/137 - 28s - loss: 2.9340 - categorical_accuracy: 0.4290 - val_loss: 1.9801 - val_categorical_accuracy: 0.7293 - 28s/epoch - 204ms/step\n",
            "Epoch 45/300\n",
            "137/137 - 30s - loss: 2.9191 - categorical_accuracy: 0.4353 - val_loss: 1.9535 - val_categorical_accuracy: 0.7348 - 30s/epoch - 217ms/step\n",
            "Epoch 46/300\n",
            "137/137 - 29s - loss: 2.9198 - categorical_accuracy: 0.4358 - val_loss: 1.9611 - val_categorical_accuracy: 0.7346 - 29s/epoch - 213ms/step\n",
            "Epoch 47/300\n",
            "137/137 - 29s - loss: 2.9090 - categorical_accuracy: 0.4395 - val_loss: 1.9726 - val_categorical_accuracy: 0.7351 - 29s/epoch - 212ms/step\n",
            "Epoch 48/300\n",
            "137/137 - 31s - loss: 2.8917 - categorical_accuracy: 0.4424 - val_loss: 1.9455 - val_categorical_accuracy: 0.7385 - 31s/epoch - 228ms/step\n",
            "Epoch 49/300\n",
            "137/137 - 28s - loss: 2.8932 - categorical_accuracy: 0.4441 - val_loss: 1.9537 - val_categorical_accuracy: 0.7350 - 28s/epoch - 206ms/step\n",
            "Epoch 50/300\n",
            "137/137 - 31s - loss: 2.8876 - categorical_accuracy: 0.4448 - val_loss: 1.9351 - val_categorical_accuracy: 0.7403 - 31s/epoch - 229ms/step\n",
            "Epoch 51/300\n",
            "137/137 - 28s - loss: 2.8686 - categorical_accuracy: 0.4511 - val_loss: 1.9482 - val_categorical_accuracy: 0.7370 - 28s/epoch - 205ms/step\n",
            "Epoch 52/300\n",
            "137/137 - 30s - loss: 2.8677 - categorical_accuracy: 0.4493 - val_loss: 1.9643 - val_categorical_accuracy: 0.7318 - 30s/epoch - 218ms/step\n",
            "Epoch 53/300\n",
            "137/137 - 30s - loss: 2.8512 - categorical_accuracy: 0.4548 - val_loss: 1.9222 - val_categorical_accuracy: 0.7449 - 30s/epoch - 219ms/step\n",
            "Epoch 54/300\n",
            "137/137 - 29s - loss: 2.8423 - categorical_accuracy: 0.4585 - val_loss: 1.9244 - val_categorical_accuracy: 0.7426 - 29s/epoch - 213ms/step\n",
            "Epoch 55/300\n",
            "137/137 - 29s - loss: 2.8329 - categorical_accuracy: 0.4615 - val_loss: 1.9076 - val_categorical_accuracy: 0.7526 - 29s/epoch - 209ms/step\n",
            "Epoch 56/300\n",
            "137/137 - 30s - loss: 2.8283 - categorical_accuracy: 0.4625 - val_loss: 1.9236 - val_categorical_accuracy: 0.7430 - 30s/epoch - 220ms/step\n",
            "Epoch 57/300\n",
            "137/137 - 29s - loss: 2.8290 - categorical_accuracy: 0.4644 - val_loss: 1.9335 - val_categorical_accuracy: 0.7418 - 29s/epoch - 213ms/step\n",
            "Epoch 58/300\n",
            "137/137 - 30s - loss: 2.8221 - categorical_accuracy: 0.4646 - val_loss: 1.9082 - val_categorical_accuracy: 0.7476 - 30s/epoch - 222ms/step\n",
            "Epoch 59/300\n",
            "137/137 - 29s - loss: 2.8049 - categorical_accuracy: 0.4690 - val_loss: 1.9150 - val_categorical_accuracy: 0.7424 - 29s/epoch - 208ms/step\n",
            "Epoch 60/300\n",
            "137/137 - 32s - loss: 2.7992 - categorical_accuracy: 0.4709 - val_loss: 1.8942 - val_categorical_accuracy: 0.7552 - 32s/epoch - 231ms/step\n",
            "Epoch 61/300\n",
            "137/137 - 29s - loss: 2.7951 - categorical_accuracy: 0.4731 - val_loss: 1.8995 - val_categorical_accuracy: 0.7512 - 29s/epoch - 209ms/step\n",
            "Epoch 62/300\n",
            "137/137 - 31s - loss: 2.7863 - categorical_accuracy: 0.4778 - val_loss: 1.9145 - val_categorical_accuracy: 0.7476 - 31s/epoch - 226ms/step\n",
            "Epoch 63/300\n",
            "137/137 - 30s - loss: 2.7715 - categorical_accuracy: 0.4803 - val_loss: 1.8925 - val_categorical_accuracy: 0.7503 - 30s/epoch - 218ms/step\n",
            "Epoch 64/300\n",
            "137/137 - 28s - loss: 2.7662 - categorical_accuracy: 0.4808 - val_loss: 1.9096 - val_categorical_accuracy: 0.7457 - 28s/epoch - 207ms/step\n",
            "Epoch 65/300\n",
            "137/137 - 31s - loss: 2.7735 - categorical_accuracy: 0.4786 - val_loss: 1.8886 - val_categorical_accuracy: 0.7558 - 31s/epoch - 224ms/step\n",
            "Epoch 66/300\n",
            "137/137 - 29s - loss: 2.7623 - categorical_accuracy: 0.4826 - val_loss: 1.8919 - val_categorical_accuracy: 0.7520 - 29s/epoch - 215ms/step\n",
            "Epoch 67/300\n",
            "137/137 - 29s - loss: 2.7506 - categorical_accuracy: 0.4874 - val_loss: 1.8932 - val_categorical_accuracy: 0.7507 - 29s/epoch - 209ms/step\n",
            "Epoch 68/300\n",
            "137/137 - 32s - loss: 2.7420 - categorical_accuracy: 0.4908 - val_loss: 1.8797 - val_categorical_accuracy: 0.7550 - 32s/epoch - 235ms/step\n",
            "Epoch 69/300\n",
            "137/137 - 29s - loss: 2.7296 - categorical_accuracy: 0.4926 - val_loss: 1.8931 - val_categorical_accuracy: 0.7504 - 29s/epoch - 209ms/step\n",
            "Epoch 70/300\n",
            "137/137 - 32s - loss: 2.7345 - categorical_accuracy: 0.4929 - val_loss: 1.8730 - val_categorical_accuracy: 0.7573 - 32s/epoch - 233ms/step\n",
            "Epoch 71/300\n",
            "137/137 - 30s - loss: 2.7311 - categorical_accuracy: 0.4940 - val_loss: 1.8607 - val_categorical_accuracy: 0.7610 - 30s/epoch - 221ms/step\n",
            "Epoch 72/300\n",
            "137/137 - 28s - loss: 2.7232 - categorical_accuracy: 0.4945 - val_loss: 1.8808 - val_categorical_accuracy: 0.7530 - 28s/epoch - 205ms/step\n",
            "Epoch 73/300\n",
            "137/137 - 31s - loss: 2.7145 - categorical_accuracy: 0.4999 - val_loss: 1.8621 - val_categorical_accuracy: 0.7615 - 31s/epoch - 229ms/step\n",
            "Epoch 74/300\n",
            "137/137 - 31s - loss: 2.7053 - categorical_accuracy: 0.4996 - val_loss: 1.8674 - val_categorical_accuracy: 0.7569 - 31s/epoch - 224ms/step\n",
            "Epoch 75/300\n",
            "137/137 - 29s - loss: 2.6981 - categorical_accuracy: 0.5024 - val_loss: 1.8607 - val_categorical_accuracy: 0.7623 - 29s/epoch - 210ms/step\n",
            "Epoch 76/300\n",
            "137/137 - 29s - loss: 2.6953 - categorical_accuracy: 0.5029 - val_loss: 1.8631 - val_categorical_accuracy: 0.7607 - 29s/epoch - 208ms/step\n",
            "Epoch 77/300\n",
            "137/137 - 30s - loss: 2.6909 - categorical_accuracy: 0.5051 - val_loss: 1.8699 - val_categorical_accuracy: 0.7589 - 30s/epoch - 219ms/step\n",
            "Epoch 78/300\n",
            "137/137 - 32s - loss: 2.6950 - categorical_accuracy: 0.5048 - val_loss: 1.8596 - val_categorical_accuracy: 0.7603 - 32s/epoch - 230ms/step\n",
            "Epoch 79/300\n",
            "137/137 - 30s - loss: 2.6781 - categorical_accuracy: 0.5072 - val_loss: 1.8573 - val_categorical_accuracy: 0.7602 - 30s/epoch - 217ms/step\n",
            "Epoch 80/300\n",
            "137/137 - 30s - loss: 2.6804 - categorical_accuracy: 0.5075 - val_loss: 1.8520 - val_categorical_accuracy: 0.7633 - 30s/epoch - 221ms/step\n",
            "Epoch 81/300\n",
            "137/137 - 29s - loss: 2.6627 - categorical_accuracy: 0.5139 - val_loss: 1.8633 - val_categorical_accuracy: 0.7570 - 29s/epoch - 212ms/step\n",
            "Epoch 82/300\n",
            "137/137 - 30s - loss: 2.6594 - categorical_accuracy: 0.5148 - val_loss: 1.8598 - val_categorical_accuracy: 0.7622 - 30s/epoch - 219ms/step\n",
            "Epoch 83/300\n",
            "137/137 - 30s - loss: 2.6615 - categorical_accuracy: 0.5133 - val_loss: 1.8513 - val_categorical_accuracy: 0.7589 - 30s/epoch - 217ms/step\n",
            "Epoch 84/300\n"
          ]
        }
      ],
      "source": [
        "train_folds(CFG, [0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WTJ8eruttPxJ"
      },
      "outputs": [],
      "source": [
        "# CFG.seed = 42\n",
        "# CFG.comment = f'islr-fp16-192-8-seed{CFG.seed}'\n",
        "# train_folds(CFG, ['all'], summary=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "y8IvRT7btP2E"
      },
      "outputs": [],
      "source": [
        "# CFG.seed = 43\n",
        "# CFG.comment = f'islr-fp16-192-8-seed{CFG.seed}'\n",
        "# train_folds(CFG, ['all'], summary=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rHLmUWM4tP9S"
      },
      "outputs": [],
      "source": [
        "# CFG.seed = 44\n",
        "# CFG.comment = f'islr-fp16-192-8-seed{CFG.seed}'\n",
        "# train_folds(CFG, ['all'], summary=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "at4Io1dNtSLh"
      },
      "outputs": [],
      "source": [
        "# CFG.seed = 45\n",
        "# CFG.comment = f'islr-fp16-192-8-seed{CFG.seed}'\n",
        "# train_folds(CFG, ['all'], summary=False)"
      ]
    }
  ]
}