10_02_try_cbam_30epochs.ipynb

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    "colab": {
      "name": "10_02_try_cbam_30epochs.ipynb",
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      "machine_shape": "hm"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x_-LdXOGYWjA"
      },
      "source": [
        "train, validation은 일단 나눔. 그리고 cbam적용하는 것이 목표임."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fFScTM2ibuZ-",
        "outputId": "0de25d8d-99cf-4b01-a109-54397b3a18a8"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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        "id": "ag06Xx-tb_aP",
        "outputId": "4aa7d3bf-aceb-4e95-fd95-effe5b8cb08a"
      },
      "source": [
        "!pip install torchaudio\n",
        "!pip install skorch"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: torchaudio in /usr/local/lib/python3.7/dist-packages (0.9.1)\n",
            "Requirement already satisfied: torch==1.9.1 in /usr/local/lib/python3.7/dist-packages (from torchaudio) (1.9.1)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch==1.9.1->torchaudio) (3.7.4.3)\n",
            "Requirement already satisfied: skorch in /usr/local/lib/python3.7/dist-packages (0.10.0)\n",
            "Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from skorch) (1.4.1)\n",
            "Requirement already satisfied: scikit-learn>=0.19.1 in /usr/local/lib/python3.7/dist-packages (from skorch) (0.22.2.post1)\n",
            "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.7/dist-packages (from skorch) (1.19.5)\n",
            "Requirement already satisfied: tabulate>=0.7.7 in /usr/local/lib/python3.7/dist-packages (from skorch) (0.8.9)\n",
            "Requirement already satisfied: tqdm>=4.14.0 in /usr/local/lib/python3.7/dist-packages (from skorch) (4.62.3)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.19.1->skorch) (1.0.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AkCLAqCYbzBs"
      },
      "source": [
        "import pandas as pd\n",
        "from pathlib import Path\n",
        "import math, random\n",
        "import torch\n",
        "import torchaudio\n",
        "from torchaudio import transforms\n",
        "from IPython.display import Audio\n",
        "from torch.utils.data import DataLoader, Dataset, random_split\n",
        "import torchaudio\n",
        "import torchvision\n",
        "from torch.utils.data import random_split\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "from torch.nn import init\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import numpy as np\n",
        "import sklearn.metrics as metrics\n",
        "from torch.utils.data import Subset"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "oSt87ytIb7Eo",
        "outputId": "0d2b8b6c-1646-4e7d-d63c-6a4a6e9c8b79"
      },
      "source": [
        "Catholic_file = '/content/drive/MyDrive/breath_data/breath_v2.2/aug_train_v2.2.csv'\n",
        "df = pd.read_csv(Catholic_file)\n",
        "df"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
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      "source": [
        "df = df.sample(frac=1)"
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      "execution_count": 5,
      "outputs": []
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      "source": [
        "df"
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      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
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        "mean = df.old.mean()\n",
        "std = df.old.std()\n",
        "df.old = (df.old - mean) / std"
      ],
      "execution_count": 7,
      "outputs": []
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              "\n",
              "[1610 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "D5ylVOdPcKas",
        "outputId": "b9af8e3b-4754-45c2-a39d-e8b663b57086"
      },
      "source": [
        "df['relative_path'] = '/' + df['filename'].astype(str)\n",
        "df = df[['relative_path', 'class', 'sex', 'old']]\n",
        "df.head()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>relative_path</th>\n",
              "      <th>class</th>\n",
              "      <th>sex</th>\n",
              "      <th>old</th>\n",
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              "  </thead>\n",
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            "text/plain": [
              "       relative_path  class  sex       old\n",
              "1233  /ts_0032-3.wav      0    1 -0.639293\n",
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            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0nX3Nio1cL6x"
      },
      "source": [
        "data_path = '/content/drive/MyDrive/breath_data/breath_v2.2/aug_train_v2.2'"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F1fDIF4EcNVI"
      },
      "source": [
        "class Breath_sound_Util():\n",
        "  \n",
        "  def open(audio_file):\n",
        "    sig, sr = torchaudio.load(audio_file)\n",
        "    \n",
        "    return (sig, sr)\n",
        "\n",
        "  def resample(aud, newsr):\n",
        "    sig, sr = aud\n",
        "    \n",
        "    if (sr == newsr):\n",
        "     return aud\n",
        "\n",
        "    num_channels = sig.shape[0]\n",
        "    resig = torchaudio.transforms.Resample(sr, newsr)(sig[:1,:])\n",
        "    if (num_channels > 1):\n",
        "      retwo = torchaudio.transforms.Resample(sr, newsr)(sig[1:,:])\n",
        "      resig = torch.cat([resig, retwo])\n",
        "\n",
        "    return ((resig, newsr))\n",
        "  \n",
        "\n",
        "  def pad(aud, max_ms):\n",
        "    sig, sr = aud\n",
        "    num_rows, sig_len = sig.shape\n",
        "    max_len = sr//1000 * max_ms\n",
        "   \n",
        "    if (sig_len > max_len):\n",
        "      sig = sig[:,:max_len]\n",
        " \n",
        "    elif (sig_len < max_len):\n",
        "\n",
        "      repeated = []\n",
        "      repeated.append(sig)\n",
        "      required_len = max_len - sig_len\n",
        "\n",
        "      while required_len > sig_len : \n",
        "        repeated.append(sig)\n",
        "        require_len -= sig_len\n",
        "      repeated.append(sig[:, :required_len])\n",
        " \n",
        "      sig = torch.cat(repeated, 1)\n",
        "\n",
        "    return (sig, sr)\n",
        "\n",
        "\n",
        "  def spectro_gram(aud, n_mels=64, n_fft=1024, hop_len=None):\n",
        "    sig,sr = aud\n",
        "    top_db = 80\n",
        "    \n",
        "    spec = transforms.MelSpectrogram(sr, n_fft=n_fft, hop_length=hop_len, n_mels=n_mels)(sig)\n",
        "    spec = transforms.AmplitudeToDB(top_db=top_db)(spec)\n",
        "    return (spec)\n",
        "\n",
        "  def spectro_augment(spec, max_mask_pct=0.1, n_freq_masks=1, n_time_masks=1):\n",
        "    _, n_mels, n_steps = spec.shape\n",
        "    mask_value = spec.mean()\n",
        "    aug_spec = spec\n",
        "\n",
        "    freq_mask_param = max_mask_pct * n_mels\n",
        "    for _ in range(n_freq_masks):\n",
        "      aug_spec = transforms.FrequencyMasking(freq_mask_param)(aug_spec, mask_value)\n",
        "\n",
        "    time_mask_param = max_mask_pct * n_steps\n",
        "    for _ in range(n_time_masks):\n",
        "      aug_spec = transforms.TimeMasking(time_mask_param)(aug_spec, mask_value)\n",
        "\n",
        "    return aug_spec\n",
        "    print(aug_spec)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UvNuQ9Irmkqd"
      },
      "source": [
        "class breathDS(Dataset):\n",
        "    \n",
        "  def __init__(self, df, data_path):\n",
        "    self.df = df\n",
        "    self.data_path = str(data_path)\n",
        "    self.duration = 4000\n",
        "    self.sr = 48000\n",
        "            \n",
        "  def __len__(self):\n",
        "    return len(self.df)    \n",
        "    \n",
        "  def __getitem__(self, idx):\n",
        "    audio_file = self.data_path + self.df.loc[idx, 'relative_path']\n",
        "    class_id = self.df.loc[idx, 'class']\n",
        "    \n",
        "    aud = Breath_sound_Util.open(audio_file)\n",
        "    reaud = Breath_sound_Util.resample(aud, self.sr)\n",
        "    dur_aud = Breath_sound_Util.pad(reaud, self.duration)\n",
        "    sgram = Breath_sound_Util.spectro_gram(dur_aud, n_mels=64, n_fft=1024, hop_len=None)\n",
        "    aug_sgram = Breath_sound_Util.spectro_augment(sgram, max_mask_pct=0.1, n_freq_masks=2, n_time_masks=2)\n",
        "    #여기부터 나이, 성별변수를 집어넣는 과정임\n",
        "    x = self.df.loc[idx, 'old']\n",
        "    y = self.df.loc[idx, 'sex']\n",
        "    x = torch.from_numpy(np.asarray(x).reshape((1,)))\n",
        "    y = torch.from_numpy(np.asarray(y).reshape((1,)))\n",
        "    tabular = torch.cat((x, y), 0)\n",
        "    tabular = tabular.float()\n",
        "    #print(tabular)\n",
        "    #x1 = [\"sex\", \"old\"]\n",
        "    #x2 = x2.\n",
        "    #x2 = x2.iloc[idx].values\n",
        "    \n",
        "    \n",
        "    return aug_sgram, class_id"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O8DWr7l9reS3"
      },
      "source": [
        "brds = breathDS(df, data_path)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hexvLjRtZdMa",
        "outputId": "af4b49c4-4503-4217-ba79-0140c288eb3a"
      },
      "source": [
        "num_items = len(brds)\n",
        "print(len(brds))\n",
        "num_train = round(num_items * 0.9)\n",
        "num_val = num_items - num_train\n",
        "train_ds, val_ds = random_split(brds, [num_train, num_val])\n",
        "\n",
        "train_dl = torch.utils.data.DataLoader(train_ds, batch_size=16, shuffle=True)\n",
        "val_dl = torch.utils.data.DataLoader(val_ds, batch_size=16, shuffle=True)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1610\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YUGpbgkAZda7"
      },
      "source": [
        "#train_dl = DataLoader(brds, batch_size=16, shuffle=True)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EINH3YPeq28K"
      },
      "source": [
        "def conv3x3(in_planes, out_planes, stride=1):\n",
        "    \"3x3 convolution with padding\"\n",
        "    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,\n",
        "                     padding=1, bias=False)\n",
        "\n",
        "class ChannelAttention(nn.Module):\n",
        "    def __init__(self, in_planes, ratio=16):\n",
        "        super(ChannelAttention, self).__init__()\n",
        "        self.avg_pool = nn.AdaptiveAvgPool2d(1)\n",
        "        self.max_pool = nn.AdaptiveMaxPool2d(1)\n",
        "           \n",
        "        self.fc = nn.Sequential(nn.Conv2d(in_planes, in_planes // 16, 1, bias=False),\n",
        "                               nn.ReLU(),\n",
        "                               nn.Conv2d(in_planes // 16, in_planes, 1, bias=False))\n",
        "        self.sigmoid = nn.Sigmoid()\n",
        "\n",
        "    def forward(self, x):\n",
        "        avg_out = self.fc(self.avg_pool(x))\n",
        "        max_out = self.fc(self.max_pool(x))\n",
        "        out = avg_out + max_out\n",
        "        return self.sigmoid(out)\n",
        "\n",
        "class SpatialAttention(nn.Module):\n",
        "    def __init__(self, kernel_size=7):\n",
        "        super(SpatialAttention, self).__init__()\n",
        "\n",
        "        self.conv1 = nn.Conv2d(2, 1, kernel_size, padding=kernel_size//2, bias=False)\n",
        "        self.sigmoid = nn.Sigmoid()\n",
        "\n",
        "    def forward(self, x):\n",
        "        avg_out = torch.mean(x, dim=1, keepdim=True)\n",
        "        max_out, _ = torch.max(x, dim=1, keepdim=True)\n",
        "        x = torch.cat([avg_out, max_out], dim=1)\n",
        "        x = self.conv1(x)\n",
        "        return self.sigmoid(x)\n",
        "\n",
        "class BasicBlock(nn.Module):\n",
        "    expansion = 1\n",
        "\n",
        "    def __init__(self, inplanes, planes, stride=1, downsample=None):\n",
        "        super(BasicBlock, self).__init__()\n",
        "        self.conv1 = conv3x3(inplanes, planes, stride)\n",
        "        self.bn1 = nn.BatchNorm2d(planes)\n",
        "        self.relu = nn.ReLU(inplace=True)\n",
        "        self.conv2 = conv3x3(planes, planes)\n",
        "        self.bn2 = nn.BatchNorm2d(planes)\n",
        "\n",
        "        self.ca = ChannelAttention(planes)\n",
        "        self.sa = SpatialAttention()\n",
        "\n",
        "        self.downsample = downsample\n",
        "        self.stride = stride\n",
        "\n",
        "    def forward(self, x):\n",
        "        residual = x\n",
        "\n",
        "        out = self.conv1(x)\n",
        "        out = self.bn1(out)\n",
        "        out = self.relu(out)\n",
        "\n",
        "        out = self.conv2(out)\n",
        "        out = self.bn2(out)\n",
        "\n",
        "        out = self.ca(out) * out\n",
        "        out = self.sa(out) * out\n",
        "\n",
        "        if self.downsample is not None:\n",
        "            residual = self.downsample(x)\n",
        "\n",
        "        out += residual\n",
        "        out = self.relu(out)\n",
        "\n",
        "        return out"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aqrBgNrhq2-T"
      },
      "source": [
        "class ResNet(nn.Module):\n",
        "\n",
        "    def __init__(self, block, layers, num_classes=2):\n",
        "        self.inplanes = 64\n",
        "        super(ResNet, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3,\n",
        "                               bias=False)\n",
        "        self.bn1 = nn.BatchNorm2d(64)\n",
        "        self.relu = nn.ReLU(inplace=True)\n",
        "        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n",
        "        self.layer1 = self._make_layer(block, 64, layers[0])\n",
        "        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)\n",
        "        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)\n",
        "        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)\n",
        "        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))\n",
        "        self.fc = nn.Linear(512 * block.expansion, num_classes)\n",
        "\n",
        "        for m in self.modules():\n",
        "            if isinstance(m, nn.Conv2d):\n",
        "                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\n",
        "                m.weight.data.normal_(0, math.sqrt(2. / n))\n",
        "            elif isinstance(m, nn.BatchNorm2d):\n",
        "                m.weight.data.fill_(1)\n",
        "                m.bias.data.zero_()\n",
        "\n",
        "    def _make_layer(self, block, planes, blocks, stride=1):\n",
        "        downsample = None\n",
        "        if stride != 1 or self.inplanes != planes * block.expansion:\n",
        "            downsample = nn.Sequential(\n",
        "                nn.Conv2d(self.inplanes, planes * block.expansion,\n",
        "                          kernel_size=1, stride=stride, bias=False),\n",
        "                nn.BatchNorm2d(planes * block.expansion),\n",
        "            )\n",
        "\n",
        "        layers = []\n",
        "        layers.append(block(self.inplanes, planes, stride, downsample))\n",
        "        self.inplanes = planes * block.expansion\n",
        "        for i in range(1, blocks):\n",
        "            layers.append(block(self.inplanes, planes))\n",
        "\n",
        "        return nn.Sequential(*layers)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.conv1(x)\n",
        "        x = self.bn1(x)\n",
        "        x = self.relu(x)\n",
        "        x = self.maxpool(x)\n",
        "\n",
        "        x = self.layer1(x)\n",
        "        x = self.layer2(x)\n",
        "        x = self.layer3(x)\n",
        "        x = self.layer4(x)\n",
        "\n",
        "        x = self.avgpool(x)\n",
        "        x = x.view(x.size(0), -1)\n",
        "        x = self.fc(x)\n",
        "\n",
        "        return x\n"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kLuxio_2q3AG",
        "outputId": "c72d08af-2eda-420f-c5b0-c2450a64065f"
      },
      "source": [
        "Model1 = ResNet(BasicBlock, [2, 2, 2, 2])\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "Model1 = Model1.to(device)\n",
        "next(Model1.parameters()).device"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cuda', index=0)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZcjmZ-rmr-lG"
      },
      "source": [
        "def training(model, train_dl, val_dl, num_epochs):\n",
        "  criterion = nn.CrossEntropyLoss()\n",
        "  optimizer = torch.optim.Adam(model.parameters(),lr=0.0001)\n",
        "  scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer, max_lr=0.001,\n",
        "                                                steps_per_epoch=int(len(train_dl)),\n",
        "                                                epochs=num_epochs,\n",
        "                                                anneal_strategy='linear')\n",
        "\n",
        "  for epoch in range(num_epochs):\n",
        "    running_loss = 0.0\n",
        "    correct_prediction = 0\n",
        "    total_prediction = 0\n",
        "    correct_prediction_1 = 0\n",
        "    total_prediction_1 = 0\n",
        "\n",
        "    for i, data in enumerate(train_dl):\n",
        "        x, labels = data[0].to(device), data[1].to(device)\n",
        "        #inputs, inputs2, labels = data[0].to(device), data[1].to(device), data[2].to(device)\n",
        "        #여기에도 inputs2를 추가시켜줫음. 이제 data[1]이 가리키는건 breathDS의 tablular임\n",
        "        x_m, x_s = x.mean(), x.std()\n",
        "        x = (x - x_m) / x_s\n",
        "        optimizer.zero_grad()\n",
        "        outputs = model(x)\n",
        "        loss = criterion(outputs, labels)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        scheduler.step()\n",
        "        running_loss += loss.item()\n",
        "        _, prediction = torch.max(outputs,1)\n",
        "        correct_prediction += (prediction == labels).sum().item()\n",
        "        total_prediction += prediction.shape[0]\n",
        "\n",
        "    for i, data in enumerate(val_dl):\n",
        "        x, labels = data[0].to(device), data[1].to(device)\n",
        "        #inputs, inputs2, labels = data[0].to(device), data[1].to(device), data[2].to(device)\n",
        "        #여기에도 inputs2를 추가시켜줫음. 이제 data[1]이 가리키는건 breathDS의 tablular임\n",
        "        x_m, x_s = x.mean(), x.std()\n",
        "        x = (x - x_m) / x_s\n",
        "        outputs = model(x)\n",
        "        _, prediction = torch.max(outputs,1)\n",
        "        correct_prediction_1 += (prediction == labels).sum().item()\n",
        "        total_prediction_1 += prediction.shape[0]\n",
        "\n",
        "    num_batches = len(train_dl)\n",
        "    avg_loss = running_loss / num_batches\n",
        "    acc = correct_prediction/total_prediction\n",
        "    val_acc = correct_prediction_1/total_prediction_1\n",
        "    print(f'Epoch: {epoch}, Loss: {avg_loss:.4f}, Accuracy: {acc:.4f}, Validation Accuracy: {val_acc:.4f}')\n",
        "\n",
        "  print('Finished Training')"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mt_EWKp1r-ne",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fe401cff-9290-40bf-af56-d7a866d172f0"
      },
      "source": [
        "num_epochs=30\n",
        "training(Model1, train_dl, val_dl, num_epochs)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 0, Loss: 0.4135, Accuracy: 0.8316, Validation Accuracy: 0.8447\n",
            "Epoch: 1, Loss: 0.3935, Accuracy: 0.8544, Validation Accuracy: 0.8199\n",
            "Epoch: 2, Loss: 0.3913, Accuracy: 0.8585, Validation Accuracy: 0.7950\n",
            "Epoch: 3, Loss: 0.3711, Accuracy: 0.8558, Validation Accuracy: 0.8571\n",
            "Epoch: 4, Loss: 0.3572, Accuracy: 0.8606, Validation Accuracy: 0.8634\n",
            "Epoch: 5, Loss: 0.3696, Accuracy: 0.8592, Validation Accuracy: 0.8447\n",
            "Epoch: 6, Loss: 0.3665, Accuracy: 0.8509, Validation Accuracy: 0.8509\n",
            "Epoch: 7, Loss: 0.3644, Accuracy: 0.8661, Validation Accuracy: 0.8447\n",
            "Epoch: 8, Loss: 0.3599, Accuracy: 0.8578, Validation Accuracy: 0.8075\n",
            "Epoch: 9, Loss: 0.3525, Accuracy: 0.8640, Validation Accuracy: 0.8447\n",
            "Epoch: 10, Loss: 0.3490, Accuracy: 0.8551, Validation Accuracy: 0.8509\n",
            "Epoch: 11, Loss: 0.3234, Accuracy: 0.8709, Validation Accuracy: 0.8137\n",
            "Epoch: 12, Loss: 0.3386, Accuracy: 0.8634, Validation Accuracy: 0.8696\n",
            "Epoch: 13, Loss: 0.3021, Accuracy: 0.8772, Validation Accuracy: 0.8634\n",
            "Epoch: 14, Loss: 0.3239, Accuracy: 0.8703, Validation Accuracy: 0.8509\n",
            "Epoch: 15, Loss: 0.2989, Accuracy: 0.8820, Validation Accuracy: 0.8571\n",
            "Epoch: 16, Loss: 0.2859, Accuracy: 0.8813, Validation Accuracy: 0.8571\n",
            "Epoch: 17, Loss: 0.2823, Accuracy: 0.8903, Validation Accuracy: 0.8447\n",
            "Epoch: 18, Loss: 0.2721, Accuracy: 0.8889, Validation Accuracy: 0.8323\n",
            "Epoch: 19, Loss: 0.2411, Accuracy: 0.8992, Validation Accuracy: 0.8571\n",
            "Epoch: 20, Loss: 0.2301, Accuracy: 0.9103, Validation Accuracy: 0.8944\n",
            "Epoch: 21, Loss: 0.2040, Accuracy: 0.9110, Validation Accuracy: 0.8509\n",
            "Epoch: 22, Loss: 0.2190, Accuracy: 0.9117, Validation Accuracy: 0.8696\n",
            "Epoch: 23, Loss: 0.1651, Accuracy: 0.9400, Validation Accuracy: 0.8820\n",
            "Epoch: 24, Loss: 0.1492, Accuracy: 0.9344, Validation Accuracy: 0.9006\n",
            "Epoch: 25, Loss: 0.1343, Accuracy: 0.9572, Validation Accuracy: 0.8944\n",
            "Epoch: 26, Loss: 0.1138, Accuracy: 0.9551, Validation Accuracy: 0.9317\n",
            "Epoch: 27, Loss: 0.0974, Accuracy: 0.9627, Validation Accuracy: 0.9193\n",
            "Epoch: 28, Loss: 0.0800, Accuracy: 0.9772, Validation Accuracy: 0.9317\n",
            "Epoch: 29, Loss: 0.0651, Accuracy: 0.9793, Validation Accuracy: 0.9255\n",
            "Finished Training\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NfJVKNCdr-ql",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "163ca611-02de-4587-d8cd-d8adf176eca8"
      },
      "source": [
        "Catholic_file1 = '/content/drive/MyDrive/breath_data/breath_v2.2/test_v2.2.csv'\n",
        "df1 = pd.read_csv(Catholic_file1)\n",
        "df1"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>filename</th>\n",
              "      <th>category</th>\n",
              "      <th>class</th>\n",
              "      <th>sex</th>\n",
              "      <th>old</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0001-2.wav</td>\n",
              "      <td>non-wheezing</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0003-1.wav</td>\n",
              "      <td>non-wheezing</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0004-1.wav</td>\n",
              "      <td>non-wheezing</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0004-4.wav</td>\n",
              "      <td>non-wheezing</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0006-1.wav</td>\n",
              "      <td>non-wheezing</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0007-1.wav</td>\n",
              "      <td>non-wheezing</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0008-1.wav</td>\n",
              "      <td>non-wheezing</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0009-1.wav</td>\n",
              "      <td>non-wheezing</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0010-3.wav</td>\n",
              "      <td>non-wheezing</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0012-1.wav</td>\n",
              "      <td>non-wheezing</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0012-4.wav</td>\n",
              "      <td>non-wheezing</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0014-2.wav</td>\n",
              "      <td>non-wheezing</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0014-3.wav</td>\n",
              "      <td>non-wheezing</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0015-4.wav</td>\n",
              "      <td>non-wheezing</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0018-1.wav</td>\n",
              "      <td>non-wheezing</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0019-4.wav</td>\n",
              "      <td>non-wheezing</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0021-3.wav</td>\n",
              "      <td>non-wheezing</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0022-2.wav</td>\n",
              "      <td>non-wheezing</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0023-2.wav</td>\n",
              "      <td>non-wheezing</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0024-4.wav</td>\n",
              "      <td>non-wheezing</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>0026-3.wav</td>\n",
              "      <td>non-wheezing</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>0027-1.wav</td>\n",
              "      <td>non-wheezing</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>0028-1.wav</td>\n",
              "      <td>non-wheezing</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>0029-3.wav</td>\n",
              "      <td>non-wheezing</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>0031-4.wav</td>\n",
              "      <td>non-wheezing</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>0032-4.wav</td>\n",
              "      <td>non-wheezing</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>0033-3.wav</td>\n",
              "      <td>non-wheezing</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>0035-1.wav</td>\n",
              "      <td>non-wheezing</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>0036-2.wav</td>\n",
              "      <td>non-wheezing</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>0037-1.wav</td>\n",
              "      <td>non-wheezing</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>0038-3.wav</td>\n",
              "      <td>non-wheezing</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>0039-4.wav</td>\n",
              "      <td>non-wheezing</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>0610-3.wav</td>\n",
              "      <td>non-wheezing</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>0539-2.wav</td>\n",
              "      <td>non-wheezing</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>0261-4.wav</td>\n",
              "      <td>non-wheezing</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>0043-1.wav</td>\n",
              "      <td>non-wheezing</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>0001-4.wav</td>\n",
              "      <td>wheezing</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>0107-3.wav</td>\n",
              "      <td>wheezing</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>0411-1.wav</td>\n",
              "      <td>wheezing</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>0526-4.wav</td>\n",
              "      <td>wheezing</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>0535-3.wav</td>\n",
              "      <td>wheezing</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>0538-4.wav</td>\n",
              "      <td>wheezing</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>0549-2.wav</td>\n",
              "      <td>wheezing</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>0559-4.wav</td>\n",
              "      <td>wheezing</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>0063-3.wav</td>\n",
              "      <td>wheezing</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>0533-3.wav</td>\n",
              "      <td>wheezing</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>0614-2.wav</td>\n",
              "      <td>wheezing</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>0606-2.wav</td>\n",
              "      <td>wheezing</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>0600-1.wav</td>\n",
              "      <td>wheezing</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>0590-1.wav</td>\n",
              "      <td>wheezing</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>0568-4.wav</td>\n",
              "      <td>wheezing</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>0594-1.wav</td>\n",
              "      <td>wheezing</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>0609-4.wav</td>\n",
              "      <td>wheezing</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>0615-2.wav</td>\n",
              "      <td>wheezing</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>0603-2.wav</td>\n",
              "      <td>wheezing</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>0588-4.wav</td>\n",
              "      <td>wheezing</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>0025-3.wav</td>\n",
              "      <td>wheezing</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      filename      category  class  sex  old\n",
              "0   0001-2.wav  non-wheezing      0    0    0\n",
              "1   0003-1.wav  non-wheezing      0    0    1\n",
              "2   0004-1.wav  non-wheezing      0    0    1\n",
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              "56  0025-3.wav      wheezing      1    1    0"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wPC_GbeVr-s_"
      },
      "source": [
        "mean = df1.old.mean()\n",
        "std = df1.old.std()\n",
        "df1.old = (df1.old - mean) / std"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DchijOyUx-vC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "d648d173-8951-4078-b57d-e0755468bac9"
      },
      "source": [
        "df1['relative_path'] = '/' + df1['filename'].astype(str)\n",
        "df1 = df1[['relative_path', 'class', 'sex', 'old']]\n",
        "df1.head()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>relative_path</th>\n",
              "      <th>class</th>\n",
              "      <th>sex</th>\n",
              "      <th>old</th>\n",
              "    </tr>\n",
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            "text/plain": [
              "  relative_path  class  sex       old\n",
              "0   /0001-2.wav      0    0 -1.131772\n",
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          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8ZntIBUwx-xg"
      },
      "source": [
        "data_path = '/content/drive/MyDrive/breath_data/breath_v2.2/test_v2.2'"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h-SM8nHix-zu"
      },
      "source": [
        "\n",
        "class Breath_sound_Util1():\n",
        "  \n",
        "  def open(audio_file):\n",
        "    sig, sr = torchaudio.load(audio_file)\n",
        "    \n",
        "    return (sig, sr)\n",
        "\n",
        "  def resample(aud, newsr):\n",
        "    sig, sr = aud\n",
        "    \n",
        "    if (sr == newsr):\n",
        "     return aud\n",
        "\n",
        "    num_channels = sig.shape[0]\n",
        "    resig = torchaudio.transforms.Resample(sr, newsr)(sig[:1,:])\n",
        "    if (num_channels > 1):\n",
        "      retwo = torchaudio.transforms.Resample(sr, newsr)(sig[1:,:])\n",
        "      resig = torch.cat([resig, retwo])\n",
        "\n",
        "    return ((resig, newsr))\n",
        "  \n",
        "\n",
        "  def pad(aud, max_ms):\n",
        "    sig, sr = aud\n",
        "    num_rows, sig_len = sig.shape\n",
        "    max_len = sr//1000 * max_ms\n",
        "   \n",
        "    if (sig_len > max_len):\n",
        "      sig = sig[:,:max_len]\n",
        " \n",
        "    elif (sig_len < max_len):\n",
        "\n",
        "      repeated = []\n",
        "      repeated.append(sig)\n",
        "      required_len = max_len - sig_len\n",
        "\n",
        "      while required_len > sig_len : \n",
        "        repeated.append(sig)\n",
        "        require_len -= sig_len\n",
        "      repeated.append(sig[:, :required_len])\n",
        " \n",
        "      sig = torch.cat(repeated, 1)\n",
        "\n",
        "    return (sig, sr)\n",
        "\n",
        "  def spectro_gram(aud, n_mels=64, n_fft=1024, hop_len=None):\n",
        "    sig,sr = aud\n",
        "    top_db = 80\n",
        "    \n",
        "    spec = transforms.MelSpectrogram(sr, n_fft=n_fft, hop_length=hop_len, n_mels=n_mels)(sig)\n",
        "    spec = transforms.AmplitudeToDB(top_db=top_db)(spec)\n",
        "    return (spec)"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3hkM-HV0x-1z"
      },
      "source": [
        "class breathDS1(Dataset):\n",
        "    \n",
        "  def __init__(self, df1, data_path):\n",
        "    self.df1 = df1\n",
        "    self.data_path = str(data_path)\n",
        "    self.duration = 4000\n",
        "    self.sr = 48000\n",
        "            \n",
        "  def __len__(self):\n",
        "    return len(self.df1)    \n",
        "    \n",
        "  def __getitem__(self, idx):\n",
        "    audio_file = self.data_path + self.df1.loc[idx, 'relative_path']\n",
        "    class_id = self.df1.loc[idx, 'class']\n",
        "    \n",
        "    aud = Breath_sound_Util.open(audio_file)\n",
        "    reaud = Breath_sound_Util.resample(aud, self.sr)\n",
        "    dur_aud = Breath_sound_Util.pad(reaud, self.duration)\n",
        "    sgram = Breath_sound_Util.spectro_gram(dur_aud, n_mels=64, n_fft=1024, hop_len=None)\n",
        "    aug_sgram = Breath_sound_Util.spectro_augment(sgram, max_mask_pct=0.1, n_freq_masks=2, n_time_masks=2)\n",
        "    #여기부터 나이, 성별변수를 집어넣는 과정임\n",
        "    x = self.df1.loc[idx, 'old']\n",
        "    y = self.df1.loc[idx, 'sex']\n",
        "    x = torch.from_numpy(np.asarray(x).reshape((1,)))\n",
        "    y = torch.from_numpy(np.asarray(y).reshape((1,)))\n",
        "    tabular = torch.cat((x, y), 0)\n",
        "    tabular = tabular.float()\n",
        "    #print(tabular)\n",
        "    #x1 = [\"sex\", \"old\"]\n",
        "    #x2 = x2.\n",
        "    #x2 = x2.iloc[idx].values\n",
        "    \n",
        "    \n",
        "    return aug_sgram, class_id"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nltFxL49x-4n"
      },
      "source": [
        "brds1 = breathDS1(df1, data_path)"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "847cseg-x-6S"
      },
      "source": [
        "test_dl = torch.utils.data.DataLoader(brds1, batch_size=16, shuffle=True)"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WUTK415Px-9D",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8ef4bfb3-a876-4537-bdee-84a3bcf5d161"
      },
      "source": [
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, classification_report\n",
        "from sklearn.metrics import roc_curve, roc_auc_score\n",
        "\n",
        "def inference (model, test_dl):\n",
        "  correct_prediction = 0\n",
        "  total_prediction = 0\n",
        "  TP = 0 \n",
        "  TN = 0\n",
        "  FN = 0\n",
        "  FP = 0\n",
        "  a = []\n",
        "  b = []\n",
        "  with torch.no_grad():\n",
        "    for i, data in enumerate(test_dl):\n",
        "      \n",
        "      x, labels = data[0].to(device), data[1].to(device)\n",
        "      cpu_labels = data[1]\n",
        "      x_m, x_s = x.mean(), x.std()\n",
        "      x = (x - x_m) / x_s\n",
        "      outputs = model(x)\n",
        " \n",
        "      _, prediction = torch.max(outputs,1)\n",
        "      \n",
        "      cpu_prediction = prediction.to('cpu')\n",
        "      correct_prediction += (prediction == labels).sum().item()\n",
        "      total_prediction += prediction.shape[0]\n",
        "          \n",
        "\n",
        "      TP += ((prediction == 1) & (labels == 1)).sum()\n",
        "# TN predict and label are both 0\n",
        "      TN += ((prediction == 0) & (labels == 0)).sum()\n",
        "# FN    predict 0 label 1\n",
        "      FN += ((prediction == 0) & (labels == 1)).sum()\n",
        "# FP    predict 1 label 0\n",
        "      FP += ((prediction == 1) & (labels == 0)).sum()\n",
        "\n",
        "      c = cpu_labels.tolist()\n",
        "      a.extend(c)\n",
        "\n",
        "      d = cpu_prediction.tolist()\n",
        "      b.extend(d)\n",
        "    \n",
        "    # TP : 휘징인데 휘징이라고 판단(잘함)\n",
        "    # FP : 헬시인데 휘징이라고 판단(못함)\n",
        "    # TN : 헬시인데 헬시라고 판단(잘함)\n",
        "    # FN : 휘징인데 헬시라고 판단(못함)\n",
        "    \n",
        "    # POSITIVE : 1(휘징)이라고 판단\n",
        "    # NEGATIVE : 0(헬시)라고 판단\n",
        "    # TRUE : 정답값 1(휘징)\n",
        "    # FALSE : 정답값 0(헬시)\n",
        "    \n",
        "    # PRECISION : 휘징이라고 판단한 것 중 진짜 휘징\n",
        "    # RECALL : 휘징인데 휘징이라고 잘 판단\n",
        "    \n",
        "\n",
        "  precision = TP / (TP + FP)\n",
        "  recall = TP / (TP + FN)\n",
        "  F1 = 2 * recall * precision / (recall + precision)\n",
        "  print(TP)\n",
        "  print(FP)\n",
        "  print(FN)\n",
        "  print(TN)\n",
        "\n",
        "  acc = correct_prediction/total_prediction\n",
        "  print(f'Accuracy: {acc:.4f}, Total items: {total_prediction}')\n",
        "  print(f'precision: {precision:.4f}, F1: {F1:.4f}')\n",
        "  print(f'recall: {recall:.4f}')\n",
        "  target_names = ['healthy', 'wheezing']\n",
        "  print(classification_report(a, b, target_names = target_names))\n",
        "  print(\"AUC:{}\".format(roc_auc_score(a, b)))\n",
        "\n",
        "inference(Model1, test_dl)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor(19, device='cuda:0')\n",
            "tensor(3, device='cuda:0')\n",
            "tensor(2, device='cuda:0')\n",
            "tensor(33, device='cuda:0')\n",
            "Accuracy: 0.9123, Total items: 57\n",
            "precision: 0.8636, F1: 0.8837\n",
            "recall: 0.9048\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "     healthy       0.94      0.92      0.93        36\n",
            "    wheezing       0.86      0.90      0.88        21\n",
            "\n",
            "    accuracy                           0.91        57\n",
            "   macro avg       0.90      0.91      0.91        57\n",
            "weighted avg       0.91      0.91      0.91        57\n",
            "\n",
            "AUC:0.9107142857142856\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o82PAbaxx--_"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mwyjqEG8zAmg"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}