UWB-VDO-classification.ipynb

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   "source": [
    "# !pip install gdown --upgrade\n",
    "# !pip install torchinfo\n",
    "# !pip install torchviz\n",
    "# !pip install wandb\n",
    "# !pip install opencv-python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "executionInfo": {
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    "id": "-b5XdR_fQXUz",
    "outputId": "65d019d6-2b33-4ad6-ffa2-2cc58ef8e3fd"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pickle\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import urllib\n",
    "import wandb\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torchvision.transforms as transforms\n",
    "\n",
    "from sklearn import preprocessing\n",
    "from torch.utils.data import Dataset\n",
    "from torch.utils.data import DataLoader\n",
    "from torchinfo import summary\n",
    "from tqdm.notebook import tqdm\n",
    "import scipy.signal as sig\n",
    "import cv2\n",
    "\n",
    "torch.__version__ # 1.10.0+cu111"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
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     "timestamp": 1678127270948,
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    },
    "id": "ZwD4PnvESYdK",
    "outputId": "1c04f047-4ff8-4ce4-c123-e79524ac10f5"
   },
   "outputs": [],
   "source": [
    "!wandb login"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "wandb.login()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 32,
     "status": "ok",
     "timestamp": 1678127275281,
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    "id": "P9T8UtrjN6WG"
   },
   "outputs": [],
   "source": [
    "# ! gdown --fuzzy https://drive.google.com/file/d/1iVah8T28-ib5YJUIyjI-sSbD7DiEfkuV/view?usp=share_link"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {
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     "timestamp": 1678127275281,
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      "userId": "09050307253439327803"
     },
     "user_tz": -420
    },
    "id": "FwKpcCNbRsGl"
   },
   "outputs": [],
   "source": [
    "# !unzip /content/uwb-pose-prediction.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "executionInfo": {
     "elapsed": 32,
     "status": "ok",
     "timestamp": 1678127275282,
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    "outputId": "fba3f86c-c5fc-49b5-c7bb-f62b846c5470",
    "tags": []
   },
   "outputs": [],
   "source": [
    "annot_df = pd.read_csv('annotations.csv')\n",
    "annot_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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     "base_uri": "https://localhost:8080/",
     "height": 269
    },
    "executionInfo": {
     "elapsed": 31,
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     "timestamp": 1678127275282,
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     },
     "user_tz": -420
    },
    "id": "iIDf0ultUTuc",
    "outputId": "888ef503-e2db-4e72-b867-2cb23e350cbc",
    "tags": []
   },
   "outputs": [],
   "source": [
    "class_df = pd.read_csv('classes.csv')\n",
    "class_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 29,
     "status": "ok",
     "timestamp": 1678127275283,
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      "userId": "09050307253439327803"
     },
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    },
    "id": "RbLIOQmpX-Za",
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   },
   "outputs": [],
   "source": [
    "#Range time data\n",
    "def range_time(IQ_data, overlap = 1): \n",
    "    n_rd_history = 256\n",
    "    frame = []\n",
    "    frames = []\n",
    "    overlap_count = 0\n",
    "    check = 0\n",
    "    for iqini in IQ_data:\n",
    "        if len(frame)<n_rd_history:\n",
    "            frame.append(iqini)\n",
    "        else:  \n",
    "            if check == 0:\n",
    "                frame1 = np.array(frame)\n",
    "                frames.append(np.copy(frame1))\n",
    "                check = 1\n",
    "\n",
    "            if overlap_count < overlap:\n",
    "                frame.append(iqini)\n",
    "                overlap_count = overlap_count+1\n",
    "\n",
    "            if overlap_count == overlap:\n",
    "                frame = frame[overlap_count::]\n",
    "                frame1 = np.array(frame)\n",
    "                frames.append(np.copy(frame1))\n",
    "                overlap_count = 0\n",
    "    return np.stack(frames)\n",
    "\n",
    "import scipy.fftpack as fft\n",
    "#Range frequency data\n",
    "def range_frequency(datas):\n",
    "    Range_frequency_frame = []\n",
    "    for data in datas:\n",
    "        jitter = 1e-10\n",
    "        # noise_threshold = -65\n",
    "        dB = True\n",
    "        #rd_history = np.hanning(n_rd_history)[:, None] * np.array(data)\n",
    "        # Range-Doppler\n",
    "        rd = fft.fft(data, axis=0)\n",
    "        rd = fft.fftshift(rd, axes=0)\n",
    "        rd = abs(rd)\n",
    "        if dB:\n",
    "            rd = 20 * np.log10(rd+jitter)\n",
    "            # rd[rd < noise_threshold] = noise_threshold\n",
    "        Range_frequency_frame.append(rd)\n",
    "    return np.stack(Range_frequency_frame)\n",
    "\n",
    "def get_time_vdo(data):\n",
    "    output = np.abs(range_time(data, overlap=100))\n",
    "    return output.reshape(1,1,-1,256,56) #(batch, channel, timestep, height, width)\n",
    "\n",
    "def get_freq_vdo(data):\n",
    "    # output = np.load(f'{data_path}{data_id}')\n",
    "    output = range_time(data, overlap=100)\n",
    "    output = range_frequency(output)\n",
    "    return output.reshape(1,1,-1,256,56) #(batch, channel, timestep, height, width)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp = get_time_vdo(np.load('train/train/001b0660-4c6e-4d07-8cd5-cd63578512f7.npy'))\n",
    "print(tmp.shape)\n",
    "print(torch.tensor(tmp[0][0][0]).unsqueeze(-1).shape)\n",
    "plt.imshow(torch.tensor(tmp[0][0][0]).unsqueeze(-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 27,
     "status": "ok",
     "timestamp": 1678127275283,
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      "userId": "09050307253439327803"
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    "id": "qTKMTObXaVwa",
    "outputId": "3c2657b3-338b-417c-b3b6-2aed0dd956c1"
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   "outputs": [],
   "source": [
    "tmp = get_freq_vdo(np.load('train/train/001b0660-4c6e-4d07-8cd5-cd63578512f7.npy'))\n",
    "print(tmp.shape)\n",
    "print(torch.tensor(tmp[0][0][0]).unsqueeze(-1).shape)\n",
    "plt.imshow(torch.tensor(tmp[0][0][0]).unsqueeze(-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
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     "status": "ok",
     "timestamp": 1678127275283,
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     },
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    },
    "id": "Tx18ZfToedJ7"
   },
   "outputs": [],
   "source": [
    "from pandas.core.common import random_state\n",
    "def train_test_split_df(df, test_size=0.1):\n",
    "    test_df = df.sample(frac = test_size, random_state = 42)\n",
    "    train_df = df.drop(test_df.index)\n",
    "    return train_df, test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
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    "executionInfo": {
     "elapsed": 28,
     "status": "ok",
     "timestamp": 1678127275288,
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      "userId": "09050307253439327803"
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    },
    "id": "tPkPxeHmgzUL",
    "outputId": "0fcc4fa1-898c-485c-8a30-90b475f8100c"
   },
   "outputs": [],
   "source": [
    "train_df, val_df = train_test_split_df(annot_df, test_size=0.2)\n",
    "print(train_df.shape, val_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "executionInfo": {
     "elapsed": 25,
     "status": "ok",
     "timestamp": 1678127275289,
     "user": {
      "displayName": "Rattapon Sanguantrakool",
      "userId": "09050307253439327803"
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    },
    "id": "xtHTq66jg9nt",
    "outputId": "4aaa3d28-8118-4d17-811a-71241c0b1585"
   },
   "outputs": [],
   "source": [
    "plt.bar(train_df['class'].value_counts().index, height=train_df['class'].value_counts().values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "executionInfo": {
     "elapsed": 514,
     "status": "ok",
     "timestamp": 1678127275781,
     "user": {
      "displayName": "Rattapon Sanguantrakool",
      "userId": "09050307253439327803"
     },
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    },
    "id": "ugF-wq7ThLP2",
    "outputId": "0ed12321-5356-4167-95ce-d13358d24e55"
   },
   "outputs": [],
   "source": [
    "plt.bar(val_df['class'].value_counts().index, height=val_df['class'].value_counts().values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 928,
     "status": "ok",
     "timestamp": 1678127278974,
     "user": {
      "displayName": "Rattapon Sanguantrakool",
      "userId": "09050307253439327803"
     },
     "user_tz": -420
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    "id": "N_Ulok3OUa7W"
   },
   "outputs": [],
   "source": [
    "def read_data(df, main_path='train/train/'):\n",
    "    # features = np.array([], dtype=np.float32).reshape(1,2560,56)\n",
    "    # labels = np.array([], dtype=np.float32)\n",
    "    features = np.empty((0,1,24,256,56))\n",
    "    labels = np.array([], dtype=np.float32)\n",
    "    for i in tqdm(range(len(df))):\n",
    "        feature_tmp = np.load(f\"{main_path}{df.iloc[i]['id']}.npy\")\n",
    "        if feature_tmp.shape != (2560,56):\n",
    "            continue\n",
    "        feature_tmp = get_time_vdo(feature_tmp)\n",
    "        # feature_tmp = get_freq_vdo(feature_tmp)\n",
    "        features = np.concatenate((features, feature_tmp), axis=0)\n",
    "        labels = np.concatenate((labels, df.iloc[i]['class'].reshape(1)), axis=0)\n",
    "\n",
    "    return features, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 67140,
     "status": "ok",
     "timestamp": 1678127346091,
     "user": {
      "displayName": "Rattapon Sanguantrakool",
      "userId": "09050307253439327803"
     },
     "user_tz": -420
    },
    "id": "jux7vC5IbIZo",
    "outputId": "9b3e9770-f279-4419-da26-9e9830b6cb30",
    "tags": []
   },
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "x_train, y_train = read_data(train_df)\n",
    "x_val, y_val = read_data(val_df)\n",
    "\n",
    "print('Before SMOTE')\n",
    "print(x_train.shape, y_train.shape)\n",
    "print(x_val.shape, y_val.shape)\n",
    "\n",
    "train_size = x_train.shape[0]\n",
    "sm = SMOTE(random_state=42)\n",
    "x_train, y_train = sm.fit_resample(x_train.reshape(train_size,-1),y_train)\n",
    "\n",
    "train_size = x_train.shape[0]\n",
    "x_train = x_train.reshape(train_size,1,-1,256,56) #(batch, channel, timestep, height, width)\n",
    "\n",
    "print('After SMOTE')\n",
    "print(x_train.shape, y_train.shape)\n",
    "print(x_val.shape, y_val.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('x_train.npy', x_train)\n",
    "np.save('y_train.npy', y_train)\n",
    "np.save('x_val.npy', x_val)\n",
    "np.save('y_val.npy', y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = np.load('x_train.npy')\n",
    "y_train = np.load('y_train.npy')\n",
    "x_val = np.load('x_val.npy')\n",
    "y_val = np.load('y_val.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PackPathway(torch.nn.Module):\n",
    "    \"\"\"\n",
    "    Transform for converting video frames as a list of tensors.\n",
    "    \"\"\"\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "\n",
    "    def forward(self, frames: torch.Tensor):\n",
    "        alpha = 1\n",
    "        fast_pathway = frames\n",
    "        # Perform temporal sampling from the fast pathway.\n",
    "        slow_pathway = torch.index_select(\n",
    "            frames,\n",
    "            1,\n",
    "            torch.linspace(\n",
    "                0, frames.shape[1] - 1, frames.shape[1] // alpha\n",
    "            ).long(),\n",
    "        )\n",
    "        frame_list = [slow_pathway, fast_pathway]\n",
    "        return frame_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1678127346092,
     "user": {
      "displayName": "Rattapon Sanguantrakool",
      "userId": "09050307253439327803"
     },
     "user_tz": -420
    },
    "id": "zB4mH7dXbVeJ"
   },
   "outputs": [],
   "source": [
    "class UWBDataset(Dataset):\n",
    "    def __init__(self, x, y):\n",
    "        self.x = x.astype(np.float32)\n",
    "        self.y = y.astype(np.float32)\n",
    "        print(self.x.shape)\n",
    "        print(self.y.shape)\n",
    "        self.resize = transforms.Resize((256,256))\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        x = self.x[index] # Retrieve feature data\n",
    "        \n",
    "        frames_resize = []\n",
    "        for frame in x:\n",
    "            frame = self.resize(torch.tensor(frame))\n",
    "            frames_resize.append(frame)\n",
    "        if len(frames_resize) > 0:\n",
    "            frames_resize = torch.stack(frames_resize)\n",
    "        y = self.y[index] # Retrieve target\n",
    "        \n",
    "        # pp = PackPathway()\n",
    "        # frames_resize = pp(frames_resize)\n",
    "        return frames_resize, y\n",
    "\n",
    "    def __len__(self):\n",
    "        return self.x.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 702,
     "status": "ok",
     "timestamp": 1678127346786,
     "user": {
      "displayName": "Rattapon Sanguantrakool",
      "userId": "09050307253439327803"
     },
     "user_tz": -420
    },
    "id": "vvqtHQPKiFAy",
    "outputId": "0d69eb22-be0f-47b4-8ac5-3ae8715b3de1"
   },
   "outputs": [],
   "source": [
    "train_dataset = UWBDataset(x_train, y_train)\n",
    "val_dataset = UWBDataset(x_val, y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1678127346788,
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      "userId": "09050307253439327803"
     },
     "user_tz": -420
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    "id": "76lhjFTrjH_d"
   },
   "outputs": [],
   "source": [
    "train_loader = DataLoader(train_dataset, batch_size=4, shuffle=True, pin_memory=True)\n",
    "val_loader = DataLoader(val_dataset, batch_size=4, shuffle=False, pin_memory=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "resize = transforms.Resize((256,256))\n",
    "plt.imshow(resize(torch.tensor(x_train[0][0][0]).unsqueeze(0)).permute(1,2,0))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "executionInfo": {
     "elapsed": 3710,
     "status": "ok",
     "timestamp": 1678127350494,
     "user": {
      "displayName": "Rattapon Sanguantrakool",
      "userId": "09050307253439327803"
     },
     "user_tz": -420
    },
    "id": "X1X0OKA5kOYo",
    "outputId": "8c24cb45-3c21-435a-acab-e064e8a0ad06"
   },
   "outputs": [],
   "source": [
    "for i in train_loader:\n",
    "    # print(i[0][0].shape)\n",
    "    print(i[0][0][0][0].unsqueeze(-1).shape)\n",
    "    plt.imshow(i[0][0][0][0].unsqueeze(-1))\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
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    "executionInfo": {
     "elapsed": 3845,
     "status": "ok",
     "timestamp": 1678127358007,
     "user": {
      "displayName": "Rattapon Sanguantrakool",
      "userId": "09050307253439327803"
     },
     "user_tz": -420
    },
    "id": "kGdLWg-ToVOS",
    "outputId": "2de89707-f58f-4a6e-b438-fe31c586c201",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# !pip install timm\n",
    "# !pip install torchinfo\n",
    "# !pip install pytorchvideo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 1152,
     "status": "ok",
     "timestamp": 1678127360620,
     "user": {
      "displayName": "Rattapon Sanguantrakool",
      "userId": "09050307253439327803"
     },
     "user_tz": -420
    },
    "id": "jpMfAzz7lOmc"
   },
   "outputs": [],
   "source": [
    "import timm\n",
    "import os\n",
    "from torchinfo import summary\n",
    "import pytorchvideo\n",
    "# os.mkdir('/kaggle/working/output')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.hub.list(\"facebookresearch/pytorchvideo\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_name = \"efficient_x3d_s\"\n",
    "model = torch.hub.load(\"facebookresearch/pytorchvideo\", model=model_name, pretrained=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "model.projection.model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 583,
     "status": "ok",
     "timestamp": 1678129095744,
     "user": {
      "displayName": "Rattapon Sanguantrakool",
      "userId": "09050307253439327803"
     },
     "user_tz": -420
    },
    "id": "XzHgz8O8MV2z"
   },
   "outputs": [],
   "source": [
    "class MyModel(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(MyModel, self).__init__()\n",
    "        self.model = torch.hub.load(\"facebookresearch/pytorchvideo\", model=model_name, pretrained=True)\n",
    "#         self.model.blocks[0].conv = torch.nn.Conv3d(1, 64, kernel_size=(1, 7, 7), stride=(1, 2, 2), padding=(0, 3, 3), bias=False)\n",
    "#         self.model.blocks[-1].proj = torch.nn.Linear(in_features=2048, out_features=7, bias=True)\n",
    "        self.model.s1.pathway0_stem_conv_xy.kernel.conv = torch.nn.Conv3d(1, 24, kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1), bias=False)\n",
    "        self.model.projection.model = torch.nn.Linear(in_features=2048, out_features=7, bias=True)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.model(x)\n",
    "        # x = nn.Softmax(dim=1)(x) # Softmax layer added to output\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 949,
     "status": "ok",
     "timestamp": 1678129098516,
     "user": {
      "displayName": "Rattapon Sanguantrakool",
      "userId": "09050307253439327803"
     },
     "user_tz": -420
    },
    "id": "N3udVmetugzw",
    "outputId": "fadba463-f787-4abd-d423-465e8b37b601"
   },
   "outputs": [],
   "source": [
    "model = MyModel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 659,
     "status": "ok",
     "timestamp": 1678129100666,
     "user": {
      "displayName": "Rattapon Sanguantrakool",
      "userId": "09050307253439327803"
     },
     "user_tz": -420
    },
    "id": "GLofpqtln8wR",
    "outputId": "acfc61b6-4e8d-4916-edc6-8f7ce261b7f3",
    "tags": []
   },
   "outputs": [],
   "source": [
    "summary(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 577,
     "status": "ok",
     "timestamp": 1678129128346,
     "user": {
      "displayName": "Rattapon Sanguantrakool",
      "userId": "09050307253439327803"
     },
     "user_tz": -420
    },
    "id": "xoQa20sX0T-d"
   },
   "outputs": [],
   "source": [
    "model = model.cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "for i in train_loader:\n",
    "    # print(i[0][0].shape)\n",
    "    model.eval()\n",
    "    with torch.no_grad():\n",
    "        print(model(i[0].to(device)))\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "run_name = f'{model_name}-01'\n",
    "result_path = f'vdo-results/{run_name}'\n",
    "if not os.path.exists(result_path):\n",
    "    os.mkdir(result_path)\n",
    "\n",
    "config = {\n",
    "    'architecture': model_name,\n",
    "    'lr': 0.1,\n",
    "    'scheduler_factor': 0.2,\n",
    "    'scheduler_patience': 3,\n",
    "    'early_stop_patience' : 6,\n",
    "    'scheduler_min_lr': 1e-4,\n",
    "    'epochs': 200,\n",
    "    'batch_size': 8,\n",
    "    'model_path': f'{result_path}/model.pth.tar',\n",
    "    'best_model_path': f'{result_path}/best_model.pth'\n",
    "}\n",
    "\n",
    "train_loader = DataLoader(train_dataset, batch_size=config['batch_size'], shuffle=True, pin_memory=True)\n",
    "val_loader = DataLoader(val_dataset, batch_size=config['batch_size'], shuffle=False, pin_memory=True)\n",
    "\n",
    "# Model\n",
    "model = model.to(device)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=config['lr'])\n",
    "scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(\n",
    "    optimizer, \n",
    "    'min', \n",
    "    factor=config['scheduler_factor'], \n",
    "    patience=config['scheduler_patience'],\n",
    "    min_lr=config['scheduler_min_lr']\n",
    ")"
   ]
  },
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   "execution_count": null,
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    "colab": {
     "base_uri": "https://localhost:8080/",
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    },
    "executionInfo": {
     "elapsed": 670126,
     "status": "ok",
     "timestamp": 1678129839367,
     "user": {
      "displayName": "Rattapon Sanguantrakool",
      "userId": "09050307253439327803"
     },
     "user_tz": -420
    },
    "id": "WIRrJIbpwfyw",
    "outputId": "448af52a-10f3-49d4-fcc3-b7b30843b310",
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_losses = []\n",
    "val_losses = []\n",
    "learning_rates = []\n",
    "loss_fn = nn.CrossEntropyLoss()\n",
    "early_stop_counter = 0\n",
    "best_val_loss = np.inf\n",
    "\n",
    "# Start wandb run\n",
    "wandb.init(\n",
    "    project=run_name,\n",
    "    config=config,\n",
    ")\n",
    "\n",
    "# Log parameters and gradients\n",
    "wandb.watch(model, log='all')\n",
    "\n",
    "for epoch in range(config['epochs']):  # loop over the dataset multiple times\n",
    "    \n",
    "    # Training\n",
    "    train_loss = []\n",
    "    current_lr = optimizer.param_groups[0]['lr']\n",
    "    learning_rates.append(current_lr)\n",
    "\n",
    "    # Flag model as training. Some layers behave differently in training and\n",
    "    # inference modes, such as dropout, BN, etc.\n",
    "    model.train()\n",
    "\n",
    "    print(f\"Training epoch {epoch+1}...\")\n",
    "    print(f\"Current LR: {current_lr}\")\n",
    "\n",
    "    for i, (inputs, y_true) in enumerate(tqdm(train_loader)):\n",
    "        # Transfer data from cpu to gpu\n",
    "        inputs = inputs.to(device)\n",
    "        y_true = y_true.to(device)\n",
    "        y_true = y_true.to(torch.int64)\n",
    "\n",
    "        # Reset the gradient\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        # Predict\n",
    "        y_pred = model(inputs)\n",
    "\n",
    "        # Calculate loss\n",
    "        loss = loss_fn(y_pred, y_true)\n",
    "\n",
    "        # Compute gradient\n",
    "        loss.backward()\n",
    "        \n",
    "        # Update parameters\n",
    "        optimizer.step()\n",
    "\n",
    "        # Log stuff\n",
    "        train_loss.append(loss)\n",
    "        \n",
    "    avg_train_loss = torch.stack(train_loss).mean().item()\n",
    "    train_losses.append(avg_train_loss)\n",
    "\n",
    "    print(f\"Epoch {epoch+1} train loss: {avg_train_loss:.4f}\")\n",
    "    \n",
    "    # Validation\n",
    "    model.eval()\n",
    "    with torch.no_grad(): # No gradient is required during validation\n",
    "        print(f\"Validating epoch {epoch+1}\")\n",
    "        val_loss = []\n",
    "        for i, (inputs, y_true) in enumerate(tqdm(val_loader)):\n",
    "            # Transfer data from cpu to gpu\n",
    "            inputs = inputs.to(device)\n",
    "            y_true = y_true.to(device)\n",
    "            y_true = y_true.to(torch.int64)\n",
    "            \n",
    "            # Predict\n",
    "            y_pred = model(inputs)\n",
    "\n",
    "            # Calculate loss\n",
    "            loss = loss_fn(y_pred, y_true)\n",
    "\n",
    "            # Log stuff\n",
    "            val_loss.append(loss)\n",
    "        \n",
    "        avg_val_loss = torch.stack(val_loss).mean().item()\n",
    "        val_losses.append(avg_val_loss)\n",
    "\n",
    "        print(f\"Epoch {epoch+1} val loss: {avg_val_loss:.4f}\")\n",
    "\n",
    "        # LR adjustment with scheduler\n",
    "        scheduler.step(avg_val_loss)\n",
    "\n",
    "        # Save checkpoint if val_loss is the best we got\n",
    "        best_val_loss = np.inf if epoch == 0 else min(val_losses[:-1])\n",
    "        if avg_val_loss < best_val_loss:\n",
    "            # Save whatever you want\n",
    "            state = {\n",
    "                'epoch': epoch,\n",
    "                'model': model.state_dict(),\n",
    "                'optimizer': optimizer.state_dict(),\n",
    "                'scheduler': scheduler.state_dict(),\n",
    "                'train_loss': avg_train_loss,\n",
    "                'val_loss': avg_val_loss,\n",
    "                'best_val_loss': best_val_loss,\n",
    "            }\n",
    "            \n",
    "            print(f\"Saving new best model..\")\n",
    "            torch.save(state, config['model_path'])\n",
    "\n",
    "    # Check for early stopping\n",
    "    if avg_val_loss < best_val_loss:\n",
    "        best_val_loss = avg_val_loss\n",
    "        early_stop_counter = 0\n",
    "        torch.save(model.state_dict(), config['best_model_path'])  # save the best model\n",
    "    else:\n",
    "        early_stop_counter += 1\n",
    "        if early_stop_counter == config['early_stop_patience']:\n",
    "            print(f'Early stopping at epoch {epoch}')\n",
    "            break\n",
    "    wandb.log({\n",
    "        'train_loss': avg_train_loss,\n",
    "        'val_loss': avg_val_loss,\n",
    "        'lr': current_lr,\n",
    "    })\n",
    "\n",
    "wandb.finish()\n",
    "print('Finished Training')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpoint = torch.load('vdo-results/efficient_x3d_s-01/best_model.pth')\n",
    "import copy\n",
    "best_model = copy.deepcopy(model)\n",
    "best_model.load_state_dict(checkpoint) # Load weights\n",
    "\n",
    "val_loader = DataLoader(val_dataset, batch_size=1, shuffle=False, pin_memory=True)\n",
    "pred_val_list = []\n",
    "for i in val_loader:\n",
    "    # print(i[0].shape)\n",
    "    # plt.imshow(i[0][0].permute(1, 2, 0))\n",
    "    best_model.eval()\n",
    "    with torch.no_grad():\n",
    "        pred = best_model(i[0].to(device))\n",
    "        pred = np.argmax(pred.to('cpu').numpy(), axis = 1).item()\n",
    "        pred_val_list.append(pred)\n",
    "\n",
    "from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay\n",
    "\n",
    "print(classification_report(y_true=y_val, y_pred=pred_val_list))\n",
    "\n",
    "cm = confusion_matrix(y_true=y_val, y_pred=pred_val_list)\n",
    "print(cm)\n",
    "\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
    "disp.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 40,
     "status": "ok",
     "timestamp": 1678129839370,
     "user": {
      "displayName": "Rattapon Sanguantrakool",
      "userId": "09050307253439327803"
     },
     "user_tz": -420
    },
    "id": "80d2OUi0yE2y",
    "outputId": "7eb4dc84-1b30-4f1a-af76-e1b75d1cfb9e"
   },
   "outputs": [],
   "source": [
    "# checkpoint = torch.load('/content/drive/MyDrive/output/efficientnetv2_s/best_model.pth')\n",
    "# # loaded_model = FeedForwardNN(hidden_size=config['hidden_size']) # Create model object\n",
    "# import copy\n",
    "# test_model = copy.deepcopy(model)\n",
    "# test_model.load_state_dict(checkpoint) # Load weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 25,
     "status": "ok",
     "timestamp": 1678129839371,
     "user": {
      "displayName": "Rattapon Sanguantrakool",
      "userId": "09050307253439327803"
     },
     "user_tz": -420
    },
    "id": "pcz4kteNFBsN"
   },
   "outputs": [],
   "source": [
    "sample_sub = pd.read_csv('sample_submission.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 27,
     "status": "ok",
     "timestamp": 1678129839374,
     "user": {
      "displayName": "Rattapon Sanguantrakool",
      "userId": "09050307253439327803"
     },
     "user_tz": -420
    },
    "id": "tIZK4yL9EX_i",
    "outputId": "81e37e76-522e-4db6-a296-72b75d899e77"
   },
   "outputs": [],
   "source": [
    "sample_sub.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 8086,
     "status": "ok",
     "timestamp": 1678129847435,
     "user": {
      "displayName": "Rattapon Sanguantrakool",
      "userId": "09050307253439327803"
     },
     "user_tz": -420
    },
    "id": "kOHx5Rg7Ewbk"
   },
   "outputs": [],
   "source": [
    "test_data, _ = read_data(sample_sub, main_path = 'test/test/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 17,
     "status": "ok",
     "timestamp": 1678129847437,
     "user": {
      "displayName": "Rattapon Sanguantrakool",
      "userId": "09050307253439327803"
     },
     "user_tz": -420
    },
    "id": "dPHxohaTFhk9",
    "outputId": "d10de28f-f471-4d22-a4d3-effae835323d"
   },
   "outputs": [],
   "source": [
    "test_data = UWBDataset(test_data, _)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 20,
     "status": "ok",
     "timestamp": 1678128997430,
     "user": {
      "displayName": "Rattapon Sanguantrakool",
      "userId": "09050307253439327803"
     },
     "user_tz": -420
    },
    "id": "7jbHuWW3Fr6I"
   },
   "outputs": [],
   "source": [
    "test_loader = DataLoader(test_data, batch_size=1, shuffle=False, pin_memory=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in test_loader:\n",
    "    print(i[0].shape)\n",
    "    plt.imshow(i[0][0].permute(1, 2, 0))\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 5798,
     "status": "ok",
     "timestamp": 1678129855294,
     "user": {
      "displayName": "Rattapon Sanguantrakool",
      "userId": "09050307253439327803"
     },
     "user_tz": -420
    },
    "id": "sUBVRfSGGRCj"
   },
   "outputs": [],
   "source": [
    "# checkpoint = torch.load('results/efficientnetv2_s_smote_2/best_model.pth')\n",
    "# import copy\n",
    "# best_model = copy.deepcopy(model)\n",
    "# best_model.load_state_dict(checkpoint) # Load weights\n",
    "\n",
    "pred_list = []\n",
    "for i in tqdm(test_loader):\n",
    "    # print(i[0].shape)\n",
    "    # plt.imshow(i[0][0].permute(1, 2, 0))\n",
    "    best_model.eval()\n",
    "    with torch.no_grad():\n",
    "        pred = best_model(i[0].to(device))\n",
    "        # print(pred)\n",
    "        # break\n",
    "        pred = np.argmax(pred.to('cpu').numpy(), axis = 1).item()\n",
    "        pred_list.append(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 34,
     "status": "ok",
     "timestamp": 1678129855295,
     "user": {
      "displayName": "Rattapon Sanguantrakool",
      "userId": "09050307253439327803"
     },
     "user_tz": -420
    },
    "id": "qhYLxFtZGdaP"
   },
   "outputs": [],
   "source": [
    "sample_sub['class'] = pred_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 34,
     "status": "ok",
     "timestamp": 1678129855296,
     "user": {
      "displayName": "Rattapon Sanguantrakool",
      "userId": "09050307253439327803"
     },
     "user_tz": -420
    },
    "id": "dqwPR__oH0gW",
    "outputId": "9e778aa2-1595-4f70-8b24-1c59d6588f0e"
   },
   "outputs": [],
   "source": [
    "sample_sub.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 33,
     "status": "ok",
     "timestamp": 1678129855297,
     "user": {
      "displayName": "Rattapon Sanguantrakool",
      "userId": "09050307253439327803"
     },
     "user_tz": -420
    },
    "id": "wpAn7mYIKRC1"
   },
   "outputs": [],
   "source": [
    "sample_sub.to_csv(f'results/efficientnetv2_s_smote_2/{run_name}.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 34,
     "status": "ok",
     "timestamp": 1678129855298,
     "user": {
      "displayName": "Rattapon Sanguantrakool",
      "userId": "09050307253439327803"
     },
     "user_tz": -420
    },
    "id": "9BnImFuOKkp5"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "-zixYfo1RL5_"
   },
   "outputs": [],
   "source": []
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "rLezAia7Qi72"
   },
   "outputs": [],
   "source": []
  }
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