10_12_mfcc_spectrogram_stacking_.ipynb

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  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "535bfe53",
   "metadata": {},
   "outputs": [],
   "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\n",
    "from torchcontrib.optim import SWA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "36bf1532",
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   "outputs": [
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   "source": [
    "Catholic_file = 'aug_train_v2.2.csv'\n",
    "df = pd.read_csv(Catholic_file)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "357008ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.sample(frac=1)"
   ]
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  {
   "cell_type": "code",
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   "id": "7e4f551a",
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       "              filename      category  class  sex  old\n",
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   "source": [
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  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "27798019",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean = df.old.mean()\n",
    "std = df.old.std()\n",
    "df.old = (df.old - mean) / std"
   ]
  },
  {
   "cell_type": "code",
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       "              filename      category  class  sex       old\n",
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       "...                ...           ...    ...  ...       ...\n",
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       "1593     wn_0614-3.wav      wheezing      1    0 -0.021489\n",
       "\n",
       "[1610 rows x 5 columns]"
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     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5c01bb8e",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "         relative_path  class  sex       old\n",
       "72         /0029-2.wav      0    0 -1.257097\n",
       "56         /0023-3.wav      0    0 -0.021489\n",
       "891  /minus_0603-1.wav      1    1  0.596315\n",
       "366     /cp_0535-2.wav      0    0  1.523021\n",
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      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['relative_path'] = '/' + df['filename'].astype(str)\n",
    "df = df[['relative_path', 'class', 'sex', 'old']]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7fd5c2d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = 'aug_train_v2.2'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "65ee21f5",
   "metadata": {},
   "outputs": [],
   "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=2048, hop_len=512):\n",
    "    sig,sr = aud\n",
    "    top_db = 80\n",
    "    \n",
    "    spec = torchaudio.transforms.MelSpectrogram(sr, n_fft=n_fft, hop_length=hop_len, n_mels=n_mels)(sig)\n",
    "    spec = torchaudio.transforms.AmplitudeToDB(top_db=top_db)(spec)\n",
    "    return (spec)\n",
    "\n",
    "  def mfcc(aud, n_mfcc=64, melkwargs={\"n_fft\": 2048, \"hop_length\": 512, \"power\": 2}): #melkwargs={\"n_fft\": 2048, \"hop_length\": 512, \"power\": 2} #n_mfcc=64, n_fft=2048, hop_len=512\n",
    "    sig,sr = aud\n",
    "    top_db = 80\n",
    "    mfcc = torchaudio.transforms.MFCC(sr, n_mfcc=64, melkwargs={\"n_fft\": 2048, \"hop_length\": 512, \"power\": 2})(sig)\n",
    "    mfcc = torchaudio.transforms.AmplitudeToDB(top_db=top_db)(mfcc)\n",
    "    return (mfcc)\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 = torchaudio.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 = torchaudio.transforms.TimeMasking(time_mask_param)(aug_spec, mask_value)\n",
    "\n",
    "    return aug_spec\n",
    "    print(aug_spec)\n",
    "    \n",
    "  def mfcc_augment(mfcc, max_mask_pct=0.1, n_freq_masks=1, n_time_masks=1):\n",
    "    _, n_mels, n_steps = mfcc.shape\n",
    "    mask_value = mfcc.mean()\n",
    "    aug_mfcc = mfcc\n",
    "\n",
    "    freq_mask_param = max_mask_pct * n_mels\n",
    "    for _ in range(n_freq_masks):\n",
    "      aug_spec = torchaudio.transforms.FrequencyMasking(freq_mask_param)(aug_mfcc, mask_value)\n",
    "\n",
    "    time_mask_param = max_mask_pct * n_steps\n",
    "    for _ in range(n_time_masks):\n",
    "      aug_spec = torchaudio.transforms.TimeMasking(time_mask_param)(aug_mfcc, mask_value)\n",
    "\n",
    "    return aug_mfcc\n",
    "    print(aug_mfcc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8253cc66",
   "metadata": {},
   "outputs": [],
   "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 = 22050\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=2048, hop_len=512)\n",
    "    mfcc = Breath_sound_Util.mfcc(dur_aud, n_mfcc=64, melkwargs={\"n_fft\": 2048, \"hop_length\": 512, \"power\": 2})\n",
    "    aug_sgram = Breath_sound_Util.spectro_augment(sgram, max_mask_pct=0.1, n_freq_masks=2, n_time_masks=2)\n",
    "    aug_mfcc = Breath_sound_Util.mfcc_augment(mfcc, 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",
    "    aug_sgram = torch.cat((aug_sgram, aug_mfcc), 0)\n",
    "    \n",
    "    return aug_sgram, tabular, class_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7434cae4",
   "metadata": {},
   "outputs": [],
   "source": [
    "brds = breathDS(df, data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1f090449",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dl = DataLoader(brds, batch_size=32, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a53f9286",
   "metadata": {},
   "outputs": [],
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3f6ac7ac",
   "metadata": {},
   "outputs": [],
   "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(2, 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_tab = nn.Linear(2, 8)\n",
    "        self.fc_tab2 = nn.Linear(8, 16)\n",
    "        self.fc = nn.Linear((512 * block.expansion)+16, 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, x2):\n",
    "        \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",
    "        x2 = self.fc_tab(x2)\n",
    "        x2 = self.fc_tab2(x2)\n",
    "        x = torch.cat((x, x2), 1)\n",
    "        x = self.fc(x)\n",
    "\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3e8ecd48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda', index=0)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "75b5cbe2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def training(model, train_dl, num_epochs):\n",
    "  criterion = nn.CrossEntropyLoss()\n",
    "  #optimizer = torch.optim.Adam(model.parameters(),lr=0.001)\n",
    "  base_optimizer = torch.optim.Adam(model.parameters(),lr=0.0005)\n",
    "  optimizer = SWA(base_optimizer, swa_start=10, swa_freq=5, swa_lr=0.01)\n",
    "  #scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer, max_lr=0.01,\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, x2, labels = data[0].to(device), data[1].to(device), data[2].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",
    "        output = model(x, x2)\n",
    "        loss = criterion(output, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        #scheduler.step()\n",
    "        running_loss += loss.item()\n",
    "        _, prediction = torch.max(output,1)\n",
    "        correct_prediction += (prediction == labels).sum().item()\n",
    "        total_prediction += prediction.shape[0]\n",
    "\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}')\n",
    "\n",
    "  print('Finished Training')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "daac1a82",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0, Loss: 0.4478, Accuracy: 0.8398\n",
      "Epoch: 1, Loss: 0.3872, Accuracy: 0.8652\n",
      "Epoch: 2, Loss: 0.3336, Accuracy: 0.8708\n",
      "Epoch: 3, Loss: 0.3603, Accuracy: 0.8578\n",
      "Epoch: 4, Loss: 0.3103, Accuracy: 0.8795\n",
      "Finished Training\n"
     ]
    }
   ],
   "source": [
    "num_epochs=5\n",
    "training(Model1, train_dl, num_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "4fd32d3c",
   "metadata": {},
   "outputs": [
    {
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       "      <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",
       "3   0004-4.wav  non-wheezing      0    0    1\n",
       "4   0006-1.wav  non-wheezing      0    1    1\n",
       "5   0007-1.wav  non-wheezing      0    0    7\n",
       "6   0008-1.wav  non-wheezing      0    0    2\n",
       "7   0009-1.wav  non-wheezing      0    0    3\n",
       "8   0010-3.wav  non-wheezing      0    1    3\n",
       "9   0012-1.wav  non-wheezing      0    0    2\n",
       "10  0012-4.wav  non-wheezing      0    0    2\n",
       "11  0014-2.wav  non-wheezing      0    0    2\n",
       "12  0014-3.wav  non-wheezing      0    0    2\n",
       "13  0015-4.wav  non-wheezing      0    1    2\n",
       "14  0018-1.wav  non-wheezing      0    0    1\n",
       "15  0019-4.wav  non-wheezing      0    0    1\n",
       "16  0021-3.wav  non-wheezing      0    0    0\n",
       "17  0022-2.wav  non-wheezing      0    0    3\n",
       "18  0023-2.wav  non-wheezing      0    0    4\n",
       "19  0024-4.wav  non-wheezing      0    0    0\n",
       "20  0026-3.wav  non-wheezing      0    0    7\n",
       "21  0027-1.wav  non-wheezing      0    0    1\n",
       "22  0028-1.wav  non-wheezing      0    1    3\n",
       "23  0029-3.wav  non-wheezing      0    0    0\n",
       "24  0031-4.wav  non-wheezing      0    0    1\n",
       "25  0032-4.wav  non-wheezing      0    1    2\n",
       "26  0033-3.wav  non-wheezing      0    1    5\n",
       "27  0035-1.wav  non-wheezing      0    1    3\n",
       "28  0036-2.wav  non-wheezing      0    0    5\n",
       "29  0037-1.wav  non-wheezing      0    1    2\n",
       "30  0038-3.wav  non-wheezing      0    0    6\n",
       "31  0039-4.wav  non-wheezing      0    0    3\n",
       "32  0610-3.wav  non-wheezing      0    0    7\n",
       "33  0539-2.wav  non-wheezing      0    0    4\n",
       "34  0261-4.wav  non-wheezing      0    0    7\n",
       "35  0043-1.wav  non-wheezing      0    0    1\n",
       "36  0001-4.wav      wheezing      1    0    0\n",
       "37  0107-3.wav      wheezing      1    0    8\n",
       "38  0411-1.wav      wheezing      1    0    8\n",
       "39  0526-4.wav      wheezing      1    0    4\n",
       "40  0535-3.wav      wheezing      1    0    9\n",
       "41  0538-4.wav      wheezing      1    0    1\n",
       "42  0549-2.wav      wheezing      1    0    5\n",
       "43  0559-4.wav      wheezing      1    0   11\n",
       "44  0063-3.wav      wheezing      1    1   11\n",
       "45  0533-3.wav      wheezing      1    0    2\n",
       "46  0614-2.wav      wheezing      1    0    4\n",
       "47  0606-2.wav      wheezing      1    1   10\n",
       "48  0600-1.wav      wheezing      1    0    2\n",
       "49  0590-1.wav      wheezing      1    1    2\n",
       "50  0568-4.wav      wheezing      1    0    0\n",
       "51  0594-1.wav      wheezing      1    1    4\n",
       "52  0609-4.wav      wheezing      1    1    6\n",
       "53  0615-2.wav      wheezing      1    1   11\n",
       "54  0603-2.wav      wheezing      1    1    6\n",
       "55  0588-4.wav      wheezing      1    0    1\n",
       "56  0025-3.wav      wheezing      1    1    0"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Catholic_file1 = 'test_v2.2.csv'\n",
    "df1 = pd.read_csv(Catholic_file1)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "164b4f81",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean = df1.old.mean()\n",
    "std = df1.old.std()\n",
    "df1.old = (df1.old - mean) / std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "f04a66b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>/0001-2.wav</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.131772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>/0003-1.wav</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.809217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>/0004-1.wav</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.809217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>/0004-4.wav</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.809217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>/0006-1.wav</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.809217</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  relative_path  class  sex       old\n",
       "0   /0001-2.wav      0    0 -1.131772\n",
       "1   /0003-1.wav      0    0 -0.809217\n",
       "2   /0004-1.wav      0    0 -0.809217\n",
       "3   /0004-4.wav      0    0 -0.809217\n",
       "4   /0006-1.wav      0    1 -0.809217"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['relative_path'] = '/' + df1['filename'].astype(str)\n",
    "df1 = df1[['relative_path', 'class', 'sex', 'old']]\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "78df03ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = 'test_v2.2'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "9f3a0817",
   "metadata": {},
   "outputs": [],
   "source": [
    "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)\n",
    "\n",
    "  def mfcc(aud, n_mfcc=64, melkwargs={\"n_fft\": 2048, \"hop_length\": 512, \"power\": 2}): #melkwargs={\"n_fft\": 2048, \"hop_length\": 512, \"power\": 2} #n_mfcc=64, n_fft=2048, hop_len=512\n",
    "    sig,sr = aud\n",
    "    top_db = 80\n",
    "    mfcc = torchaudio.transforms.MFCC(sr, n_mfcc=64, melkwargs={\"n_fft\": 2048, \"hop_length\": 512, \"power\": 2})(sig)\n",
    "    mfcc = torchaudio.transforms.AmplitudeToDB(top_db=top_db)(mfcc)\n",
    "    return (mfcc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "7ccafcac",
   "metadata": {},
   "outputs": [],
   "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",
    "    mfcc = Breath_sound_Util.mfcc(dur_aud, n_mfcc=64, melkwargs={\"n_fft\": 2048, \"hop_length\": 512, \"power\": 2})\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",
    "    aug_sgram = torch.cat((sgram, mfcc), 0)\n",
    "    \n",
    "    return aug_sgram, tabular, class_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "a0b142a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "brds1 = breathDS1(df1, data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "d04d7419",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_dl = torch.utils.data.DataLoader(brds1, batch_size=32, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "d78978b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(19, device='cuda:0')\n",
      "tensor(2, device='cuda:0')\n",
      "tensor(2, device='cuda:0')\n",
      "tensor(34, device='cuda:0')\n",
      "Accuracy: 0.9298, Total items: 57\n",
      "precision: 0.9048, F1: 0.9048\n",
      "recall: 0.9048\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "     healthy       0.94      0.94      0.94        36\n",
      "    wheezing       0.90      0.90      0.90        21\n",
      "\n",
      "    accuracy                           0.93        57\n",
      "   macro avg       0.92      0.92      0.92        57\n",
      "weighted avg       0.93      0.93      0.93        57\n",
      "\n",
      "AUC:0.9246031746031745\n"
     ]
    }
   ],
   "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, x2, labels = data[0].to(device), data[1].to(device), data[2].to(device)\n",
    "      cpu_labels = data[2]\n",
    "      x_m, x_s = x.mean(), x.std()\n",
    "      x = (x - x_m) / x_s\n",
    "      outputs = model(x, x2)\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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "adced1cb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2116f8c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "1271301e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.onnx as onnx\n",
    "import torchvision.models as models\n",
    "\n",
    "torch.save(Model1.state_dict(), 'best_resnet18.pth')"
   ]
  }
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