07_29_f1_final.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "37f2b102",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pathlib import Path"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bb59e3f",
   "metadata": {},
   "source": [
    "### Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "95b46278",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0001-1.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0001-2.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0001-3.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0001-4.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0002-1.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1465</th>\n",
       "      <td>minus_0614-3.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1466</th>\n",
       "      <td>minus_0614-4.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1467</th>\n",
       "      <td>minus_0615-1.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1468</th>\n",
       "      <td>minus_0615-2.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1469</th>\n",
       "      <td>minus_0615-4.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1470 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              filename  category  class\n",
       "0           0001-1.wav  wheezing      1\n",
       "1           0001-2.wav   healthy      0\n",
       "2           0001-3.wav  wheezing      1\n",
       "3           0001-4.wav  wheezing      1\n",
       "4           0002-1.wav   healthy      0\n",
       "...                ...       ...    ...\n",
       "1465  minus_0614-3.wav  wheezing      1\n",
       "1466  minus_0614-4.wav  wheezing      1\n",
       "1467  minus_0615-1.wav  wheezing      1\n",
       "1468  minus_0615-2.wav  wheezing      1\n",
       "1469  minus_0615-4.wav   healthy      0\n",
       "\n",
       "[1470 rows x 3 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Catholic_file = 'breathdata/augment_train.csv'\n",
    "df = pd.read_csv(Catholic_file)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c39c7bdf",
   "metadata": {},
   "outputs": [
    {
     "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>relative_path</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>/0001-1.wav</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>/0001-2.wav</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>/0001-3.wav</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>/0001-4.wav</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>/0002-1.wav</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  relative_path  class\n",
       "0   /0001-1.wav      1\n",
       "1   /0001-2.wav      0\n",
       "2   /0001-3.wav      1\n",
       "3   /0001-4.wav      1\n",
       "4   /0002-1.wav      0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['relative_path'] = '/' + df['filename'].astype(str)\n",
    "df = df[['relative_path', 'class']]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "388f1a63",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = 'breathdata/train'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7837d1cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math, random\n",
    "import torch\n",
    "import torchaudio\n",
    "from torchaudio import transforms\n",
    "from IPython.display import Audio\n",
    "\n",
    "class Breath_sound_Util():\n",
    "  \n",
    "  def open(audio_file):\n",
    "    sig, sr = torchaudio.load(audio_file)\n",
    "    sig = torchaudio.functional.gain(sig, gain_db=5.0)\n",
    "    sig = torchaudio.functional.lowpass_biquad(sig, sr, cutoff_freq=3000)\n",
    "    sig = torchaudio.functional.highpass_biquad(sig, sr, cutoff_freq=2000)\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",
    "## 형 여기 time shift도 빼는게 좋지않으까요?? 아 이미 되잇는거니가 2번하게대지??네ㅇㅋ\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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e36fc9a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader, Dataset, random_split\n",
    "import torchaudio\n",
    "import torchvision\n",
    "\n",
    "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",
    "    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",
    "\n",
    "    \n",
    "    return aug_sgram, class_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d22fd96e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import random_split\n",
    "\n",
    "brds = breathDS(df, data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "240180e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dl = torch.utils.data.DataLoader(brds, batch_size=4, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4d18e509",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda', index=0)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.nn import init\n",
    "\n",
    "# --------------------------------------------------------------\n",
    "# 호흡음의 Healthy, Wheezing을 판단하는 Binary Classification Model\n",
    "# --------------------------------------------------------------\n",
    "\n",
    "class WhoWheezing(nn.Module):\n",
    "\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        conv_layers = []\n",
    "        self.conv1 = nn.Conv2d(1, 8, kernel_size=(3, 3), stride=(2, 2), padding=(2, 2))\n",
    "        self.relu1 = nn.ReLU()\n",
    "        self.bn1 = nn.BatchNorm2d(8)\n",
    "        init.kaiming_normal_(self.conv1.weight, a=0.2)\n",
    "        self.conv1.bias.data.zero_()\n",
    "        conv_layers += [self.conv1, self.relu1, self.bn1]\n",
    "\n",
    "        self.conv2 = nn.Conv2d(8, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
    "        self.relu2 = nn.ReLU()\n",
    "        self.bn2 = nn.BatchNorm2d(16)\n",
    "        init.kaiming_normal_(self.conv1.weight, a=0.2)\n",
    "        self.conv2.bias.data.zero_()\n",
    "        conv_layers += [self.conv2, self.relu2, self.bn2]\n",
    "\n",
    "        self.conv3 = nn.Conv2d(16, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
    "        self.relu3 = nn.ReLU()\n",
    "        self.bn3 = nn.BatchNorm2d(32)\n",
    "        init.kaiming_normal_(self.conv1.weight, a=0.2)\n",
    "        self.conv3.bias.data.zero_()\n",
    "        conv_layers += [self.conv3, self.relu3, self.bn3]\n",
    "\n",
    "        self.conv4 = nn.Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
    "        self.relu4 = nn.ReLU()\n",
    "        self.bn4 = nn.BatchNorm2d(64)\n",
    "        init.kaiming_normal_(self.conv1.weight, a=0.2)\n",
    "        self.conv4.bias.data.zero_()\n",
    "        conv_layers += [self.conv4, self.relu4, self.bn4]\n",
    "\n",
    "        self.ap = nn.AdaptiveAvgPool2d(output_size=1)\n",
    "        self.dropout = nn.Dropout(0.4)\n",
    "        self.lin = nn.Linear(in_features=64, out_features=2)\n",
    "        self.dropout = nn.Dropout(0.4)\n",
    "        self.sigmoid = nn.Sigmoid()\n",
    "\n",
    "        self.conv = nn.Sequential(*conv_layers)\n",
    " \n",
    "    def forward(self, x):\n",
    "        x = self.conv(x)\n",
    "        x = self.ap(x)\n",
    "        x = x.view(x.shape[0], -1)\n",
    "        x = self.lin(x)\n",
    "        x = self.dropout(x)\n",
    "        x = self.sigmoid(x)\n",
    "        return x\n",
    "\n",
    "Model1 = WhoWheezing()\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": 10,
   "id": "82f40524",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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      "  2%|▏         | 1/50 [00:35<29:21, 35.95s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0, Loss: 0.6419, Accuracy: 0.6925\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\r",
      "  4%|▍         | 2/50 [01:11<28:36, 35.76s/it]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1, Loss: 0.5869, Accuracy: 0.7782\n"
     ]
    },
    {
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch: 2, Loss: 0.5696, Accuracy: 0.8027\n"
     ]
    },
    {
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    {
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     "text": [
      "Epoch: 3, Loss: 0.5528, Accuracy: 0.8122\n"
     ]
    },
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    {
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     "text": [
      "Epoch: 4, Loss: 0.5463, Accuracy: 0.8102\n"
     ]
    },
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    {
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     "text": [
      "Epoch: 5, Loss: 0.5392, Accuracy: 0.8259\n"
     ]
    },
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    {
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     "text": [
      "Epoch: 6, Loss: 0.5363, Accuracy: 0.8252\n"
     ]
    },
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    {
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     "output_type": "stream",
     "text": [
      "Epoch: 7, Loss: 0.5294, Accuracy: 0.8415\n"
     ]
    },
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch: 8, Loss: 0.5356, Accuracy: 0.8224\n"
     ]
    },
    {
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    },
    {
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     "text": [
      "Epoch: 9, Loss: 0.5421, Accuracy: 0.8293\n"
     ]
    },
    {
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch: 10, Loss: 0.5280, Accuracy: 0.8435\n"
     ]
    },
    {
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    {
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     "text": [
      "Epoch: 11, Loss: 0.5252, Accuracy: 0.8463\n"
     ]
    },
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     "text": [
      "Epoch: 12, Loss: 0.5216, Accuracy: 0.8537\n"
     ]
    },
    {
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      "\r",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 13, Loss: 0.5291, Accuracy: 0.8469\n"
     ]
    },
    {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 14, Loss: 0.5447, Accuracy: 0.8265\n"
     ]
    },
    {
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      "\r",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 15, Loss: 0.5347, Accuracy: 0.8299\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\r",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 16, Loss: 0.5225, Accuracy: 0.8469\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 36%|███▌      | 18/50 [10:41<19:00, 35.63s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 17, Loss: 0.5247, Accuracy: 0.8558\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 38%|███▊      | 19/50 [11:16<18:22, 35.56s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 18, Loss: 0.5248, Accuracy: 0.8510\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 40%|████      | 20/50 [11:52<17:45, 35.51s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 19, Loss: 0.5197, Accuracy: 0.8537\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 42%|████▏     | 21/50 [12:27<17:06, 35.40s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 20, Loss: 0.5170, Accuracy: 0.8599\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 44%|████▍     | 22/50 [13:02<16:28, 35.31s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 21, Loss: 0.5160, Accuracy: 0.8551\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 46%|████▌     | 23/50 [13:37<15:51, 35.26s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 22, Loss: 0.5209, Accuracy: 0.8476\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 48%|████▊     | 24/50 [14:12<15:15, 35.23s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 23, Loss: 0.5144, Accuracy: 0.8483\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 50%|█████     | 25/50 [14:47<14:39, 35.19s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 24, Loss: 0.5200, Accuracy: 0.8537\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 52%|█████▏    | 26/50 [15:23<14:04, 35.18s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 25, Loss: 0.5211, Accuracy: 0.8463\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 54%|█████▍    | 27/50 [15:58<13:30, 35.22s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 26, Loss: 0.5201, Accuracy: 0.8537\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 56%|█████▌    | 28/50 [16:33<12:56, 35.28s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 27, Loss: 0.5206, Accuracy: 0.8524\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 58%|█████▊    | 29/50 [17:09<12:21, 35.33s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 28, Loss: 0.5143, Accuracy: 0.8558\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 60%|██████    | 30/50 [17:44<11:46, 35.35s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 29, Loss: 0.5181, Accuracy: 0.8510\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 62%|██████▏   | 31/50 [18:20<11:12, 35.38s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 30, Loss: 0.5233, Accuracy: 0.8531\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 64%|██████▍   | 32/50 [18:55<10:38, 35.48s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 31, Loss: 0.5137, Accuracy: 0.8517\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 66%|██████▌   | 33/50 [19:31<10:04, 35.56s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 32, Loss: 0.5213, Accuracy: 0.8592\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 68%|██████▊   | 34/50 [20:07<09:30, 35.64s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 33, Loss: 0.5098, Accuracy: 0.8646\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 70%|███████   | 35/50 [20:43<08:55, 35.68s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 34, Loss: 0.5244, Accuracy: 0.8435\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 72%|███████▏  | 36/50 [21:18<08:20, 35.73s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 35, Loss: 0.5129, Accuracy: 0.8633\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 74%|███████▍  | 37/50 [21:54<07:44, 35.76s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 36, Loss: 0.5120, Accuracy: 0.8646\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 76%|███████▌  | 38/50 [22:30<07:09, 35.77s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 37, Loss: 0.5143, Accuracy: 0.8653\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 78%|███████▊  | 39/50 [23:06<06:33, 35.78s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 38, Loss: 0.5148, Accuracy: 0.8490\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 80%|████████  | 40/50 [23:42<05:57, 35.78s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 39, Loss: 0.5147, Accuracy: 0.8612\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 82%|████████▏ | 41/50 [24:18<05:22, 35.80s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 40, Loss: 0.5203, Accuracy: 0.8612\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 84%|████████▍ | 42/50 [24:53<04:46, 35.80s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 41, Loss: 0.5177, Accuracy: 0.8510\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 86%|████████▌ | 43/50 [25:29<04:10, 35.79s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 42, Loss: 0.5102, Accuracy: 0.8633\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 88%|████████▊ | 44/50 [26:05<03:34, 35.79s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 43, Loss: 0.5114, Accuracy: 0.8721\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 90%|█████████ | 45/50 [26:41<02:59, 35.80s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 44, Loss: 0.5031, Accuracy: 0.8789\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 92%|█████████▏| 46/50 [27:17<02:23, 35.82s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 45, Loss: 0.5151, Accuracy: 0.8626\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 94%|█████████▍| 47/50 [27:53<01:47, 35.87s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 46, Loss: 0.5034, Accuracy: 0.8701\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 96%|█████████▌| 48/50 [28:28<01:11, 35.85s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 47, Loss: 0.5109, Accuracy: 0.8646\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 98%|█████████▊| 49/50 [29:04<00:35, 35.85s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 48, Loss: 0.5153, Accuracy: 0.8510\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [29:40<00:00, 35.61s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 49, Loss: 0.5021, Accuracy: 0.8741\n",
      "Finished Training\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import numpy as np\n",
    "import sklearn.metrics as metrics\n",
    "from tqdm import tqdm\n",
    "\n",
    "def training(model, train_dl, num_epochs):\n",
    "  criterion = nn.CrossEntropyLoss()\n",
    "  optimizer = torch.optim.Adam(model.parameters(),lr=0.001)\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 tqdm(range(num_epochs)):\n",
    "    running_loss = 0.0\n",
    "    correct_prediction = 0\n",
    "    total_prediction = 0\n",
    "\n",
    "    for i, data in enumerate(train_dl):\n",
    "        inputs, labels = data[0].to(device), data[1].to(device)\n",
    "        inputs_m, inputs_s = inputs.mean(), inputs.std()\n",
    "        inputs = (inputs - inputs_m) / inputs_s\n",
    "        optimizer.zero_grad()\n",
    "        outputs = model(inputs)\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",
    "\n",
    "    num_batches = len(train_dl)\n",
    "    avg_loss = running_loss / num_batches\n",
    "    acc = correct_prediction/total_prediction\n",
    "    print(f'Epoch: {epoch}, Loss: {avg_loss:.4f}, Accuracy: {acc:.4f}')\n",
    "\n",
    "  print('Finished Training')\n",
    "    \n",
    "num_epochs=50\n",
    "training(Model1, train_dl, num_epochs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce49d2ed",
   "metadata": {},
   "source": [
    "### Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "704695cc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0034-1.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0036-2.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0036-3.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0036-4.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0037-1.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0037-3.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0037-4.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0038-3.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0041-3.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0042-4.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0043-3.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0044-3.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0044-4.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0063-2.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0063-3.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0070-1.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0107-1.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0526-3.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0533-2.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0534-4.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0559-4.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0581-4.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0588-1.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0588-4.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0593-3.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0594-2.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0597-2.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0600-1.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0602-2.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0611-4.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>0612-1.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>0613-2.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>0614-1.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      filename  category  class\n",
       "0   0003-1.wav   healthy      0\n",
       "1   0004-1.wav   healthy      0\n",
       "2   0007-1.wav   healthy      0\n",
       "3   0009-1.wav   healthy      0\n",
       "4   0009-4.wav   healthy      0\n",
       "5   0012-2.wav   healthy      0\n",
       "6   0013-2.wav   healthy      0\n",
       "7   0015-1.wav   healthy      0\n",
       "8   0020-1.wav  wheezing      1\n",
       "9   0020-3.wav  wheezing      1\n",
       "10  0020-4.wav  wheezing      1\n",
       "11  0021-2.wav   healthy      0\n",
       "12  0023-1.wav   healthy      0\n",
       "13  0024-3.wav   healthy      0\n",
       "14  0025-3.wav  wheezing      1\n",
       "15  0029-2.wav   healthy      0\n",
       "16  0030-3.wav   healthy      0\n",
       "17  0030-4.wav   healthy      0\n",
       "18  0031-1.wav   healthy      0\n",
       "19  0032-2.wav   healthy      0\n",
       "20  0034-1.wav   healthy      0\n",
       "21  0036-2.wav   healthy      0\n",
       "22  0036-3.wav   healthy      0\n",
       "23  0036-4.wav   healthy      0\n",
       "24  0037-1.wav   healthy      0\n",
       "25  0037-3.wav   healthy      0\n",
       "26  0037-4.wav   healthy      0\n",
       "27  0038-3.wav   healthy      0\n",
       "28  0041-3.wav   healthy      0\n",
       "29  0042-4.wav   healthy      0\n",
       "30  0043-3.wav   healthy      0\n",
       "31  0044-3.wav   healthy      0\n",
       "32  0044-4.wav   healthy      0\n",
       "33  0063-2.wav   healthy      0\n",
       "34  0063-3.wav   healthy      0\n",
       "35  0070-1.wav   healthy      0\n",
       "36  0107-1.wav  wheezing      1\n",
       "37  0526-3.wav  wheezing      1\n",
       "38  0533-2.wav  wheezing      1\n",
       "39  0534-4.wav  wheezing      1\n",
       "40  0559-4.wav  wheezing      1\n",
       "41  0581-4.wav  wheezing      1\n",
       "42  0588-1.wav   healthy      0\n",
       "43  0588-4.wav   healthy      0\n",
       "44  0593-3.wav  wheezing      1\n",
       "45  0594-2.wav  wheezing      1\n",
       "46  0597-2.wav  wheezing      1\n",
       "47  0600-1.wav  wheezing      1\n",
       "48  0602-2.wav   healthy      0\n",
       "49  0611-4.wav  wheezing      1\n",
       "50  0612-1.wav  wheezing      1\n",
       "51  0613-2.wav  wheezing      1\n",
       "52  0614-1.wav   healthy      0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Catholic_file1 = 'breathdata/test.csv'\n",
    "df1 = pd.read_csv(Catholic_file1)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0f7e33fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>relative_path</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>/0003-1.wav</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>/0004-1.wav</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>/0007-1.wav</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>/0009-1.wav</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>/0009-4.wav</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  relative_path  class\n",
       "0   /0003-1.wav      0\n",
       "1   /0004-1.wav      0\n",
       "2   /0007-1.wav      0\n",
       "3   /0009-1.wav      0\n",
       "4   /0009-4.wav      0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['relative_path'] = '/' + df1['filename'].astype(str)\n",
    "df1 = df1[['relative_path', 'class']]\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7e3c7002",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = 'breathdata/test'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f436caca",
   "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",
    "    aud = torchaudio.transforms.TimeStretch(hop_length=2048, fixed_rate=5000000)\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",
    "#이상한애들 타임시프트랑 / 마스크랑 / 이상한애들 다뺌\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "690e8c8e",
   "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",
    "    aud = Breath_sound_Util1.open(audio_file)\n",
    "    reaud = Breath_sound_Util1.resample(aud, self.sr)\n",
    "    dur_aud = Breath_sound_Util1.pad(reaud, self.duration)\n",
    "    sgram = Breath_sound_Util1.spectro_gram(dur_aud, n_mels=64, n_fft=1024, hop_len=None)\n",
    "\n",
    "    \n",
    "    return sgram, class_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8cc4ee03",
   "metadata": {},
   "outputs": [],
   "source": [
    "brds1 = breathDS1(df1, data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b3e90a0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "val_dl = torch.utils.data.DataLoader(brds1, batch_size=4, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "86577274",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8302, Total items: 53\n",
      "precision: 0.7500, F1: 0.7273\n",
      "recall: 0.7059\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "     healthy       0.86      0.89      0.88        36\n",
      "    wheezing       0.75      0.71      0.73        17\n",
      "\n",
      "    accuracy                           0.83        53\n",
      "   macro avg       0.81      0.80      0.80        53\n",
      "weighted avg       0.83      0.83      0.83        53\n",
      "\n",
      "AUC:0.7973856209150327\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, val_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(val_dl):\n",
    "      \n",
    "      inputs, labels = data[0].to(device), data[1].to(device)\n",
    "      cpu_labels = data[1]\n",
    "      inputs_m, inputs_s = inputs.mean(), inputs.std()\n",
    "      inputs = (inputs - inputs_m) / inputs_s\n",
    "      outputs = model(inputs)\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",
    "\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, val_dl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa5b0cbd",
   "metadata": {},
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef509220",
   "metadata": {},
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "699cfed9",
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  {
   "cell_type": "code",
   "execution_count": null,
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   "metadata": {},
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "138bd498",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5cf2625f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22be6ed0",
   "metadata": {},
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "8d5f311b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8868, Total items: 53\n",
      "precision: 0.8667, F1: 0.8125\n",
      "recall: 0.7647\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "     healthy       0.89      0.94      0.92        36\n",
      "    wheezing       0.87      0.76      0.81        17\n",
      "\n",
      "    accuracy                           0.89        53\n",
      "   macro avg       0.88      0.85      0.87        53\n",
      "weighted avg       0.89      0.89      0.88        53\n",
      "\n",
      "AUC:0.8545751633986928\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, val_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(val_dl):\n",
    "      \n",
    "      inputs, labels = data[0].to(device), data[1].to(device)\n",
    "      cpu_labels = data[1]\n",
    "      inputs_m, inputs_s = inputs.mean(), inputs.std()\n",
    "      inputs = (inputs - inputs_m) / inputs_s\n",
    "      outputs = model(inputs)\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",
    "\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, val_dl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "6e4982e6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8679, Total items: 53\n",
      "precision: 0.8125, F1: 0.7879\n",
      "recall: 0.7647\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "     healthy       0.89      0.92      0.90        36\n",
      "    wheezing       0.81      0.76      0.79        17\n",
      "\n",
      "    accuracy                           0.87        53\n",
      "   macro avg       0.85      0.84      0.85        53\n",
      "weighted avg       0.87      0.87      0.87        53\n",
      "\n",
      "AUC:0.8406862745098038\n",
      "tensor(13, device='cuda:0')\n",
      "tensor(3, device='cuda:0')\n",
      "tensor(33, device='cuda:0')\n",
      "tensor(4, device='cuda:0')\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, val_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(val_dl):\n",
    "      \n",
    "      inputs, labels = data[0].to(device), data[1].to(device)\n",
    "      cpu_labels = data[1]\n",
    "      inputs_m, inputs_s = inputs.mean(), inputs.std()\n",
    "      inputs = (inputs - inputs_m) / inputs_s\n",
    "      outputs = model(inputs)\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",
    "  \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",
    "  print(TP)\n",
    "  print(FP)\n",
    "  print(TN)\n",
    "  print(FN)  \n",
    "  #TP 13개\n",
    "  #FP 3개\n",
    "  #TN 36개\n",
    "  #FN 1개\n",
    "inference(Model1, val_dl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e43e2cb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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