07_26_breath_new.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "922757dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pathlib import Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6b2ba900",
   "metadata": {},
   "outputs": [
    {
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       "       filename  category  class\n",
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       "206  0600-2.wav  wheezing      1\n",
       "207  0600-3.wav  wheezing      1\n",
       "208  0602-3.wav  wheezing      1\n",
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       "210  0604-1.wav  wheezing      1\n",
       "\n",
       "[211 rows x 3 columns]"
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     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Catholic_file = 'data/data_train.csv'\n",
    "df = pd.read_csv(Catholic_file)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2d14c621",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>relative_path</th>\n",
       "      <th>class</th>\n",
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       "  relative_path  class\n",
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     "execution_count": 3,
     "metadata": {},
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   "source": [
    "\n",
    "df['relative_path'] = '/' + df['filename'].astype(str)\n",
    "df = df[['relative_path', 'class']]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "50e90508",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = 'data/train'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "448daacf",
   "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.dither(sig)\n",
    "    sig = torchaudio.functional.lowpass_biquad(sig, sr, cutoff_freq=3000)\n",
    "    sig = torchaudio.functional.highpass_biquad(sig, sr, cutoff_freq=2000)\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",
    "  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 time_shift(aud, shift_limit):\n",
    "    sig,sr = aud\n",
    "    _, sig_len = sig.shape\n",
    "    shift_amt = int(random.random() * shift_limit * sig_len)\n",
    "    return (sig.roll(shift_amt), 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",
    "  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": "cb3d5b48",
   "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",
    "    self.shift_pct = 0.4\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",
    "    shift_aud = Breath_sound_Util.time_shift(dur_aud, self.shift_pct)\n",
    "    sgram = Breath_sound_Util.spectro_gram(shift_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": "5eb9b030",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import random_split\n",
    "\n",
    "brds = breathDS(df, data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "aa37eba5",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "train_dl = torch.utils.data.DataLoader(brds, batch_size=16, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c5294abd",
   "metadata": {},
   "outputs": [
    {
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       "      <th>1</th>\n",
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       "      <td>healthy</td>\n",
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       "      <th>2</th>\n",
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       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
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       "      <th>3</th>\n",
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       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>0042-1.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0042-2.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0042-3.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0042-4.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0043-1.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0043-2.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0043-3.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0043-4.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0044-2.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0044-3.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0044-4.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0063-2.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0063-3.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0070-1.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0108-2.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0261-1.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0411-4.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0515-2.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0533-3.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0533-4.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0535-4.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0588-1.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0588-4.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0592-2.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0599-1.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0599-2.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0599-3.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0602-2.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0603-2.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0614-1.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0615-4.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0606-1.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0606-2.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0607-2.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0607-3.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0609-4.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0611-1.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0611-2.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0611-3.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0611-4.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0612-1.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0613-2.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0613-3.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0614-2.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0614-3.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0614-4.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>0615-1.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>0615-2.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      filename  category  class\n",
       "0   0041-1.wav   healthy      0\n",
       "1   0041-2.wav   healthy      0\n",
       "2   0041-3.wav   healthy      0\n",
       "3   0041-4.wav   healthy      0\n",
       "4   0042-1.wav   healthy      0\n",
       "5   0042-2.wav   healthy      0\n",
       "6   0042-3.wav   healthy      0\n",
       "7   0042-4.wav   healthy      0\n",
       "8   0043-1.wav   healthy      0\n",
       "9   0043-2.wav   healthy      0\n",
       "10  0043-3.wav   healthy      0\n",
       "11  0043-4.wav   healthy      0\n",
       "12  0044-2.wav   healthy      0\n",
       "13  0044-3.wav   healthy      0\n",
       "14  0044-4.wav   healthy      0\n",
       "15  0063-2.wav   healthy      0\n",
       "16  0063-3.wav   healthy      0\n",
       "17  0070-1.wav   healthy      0\n",
       "18  0108-2.wav   healthy      0\n",
       "19  0261-1.wav   healthy      0\n",
       "20  0411-4.wav   healthy      0\n",
       "21  0515-2.wav   healthy      0\n",
       "22  0533-3.wav   healthy      0\n",
       "23  0533-4.wav   healthy      0\n",
       "24  0535-4.wav   healthy      0\n",
       "25  0588-1.wav   healthy      0\n",
       "26  0588-4.wav   healthy      0\n",
       "27  0592-2.wav   healthy      0\n",
       "28  0599-1.wav   healthy      0\n",
       "29  0599-2.wav   healthy      0\n",
       "30  0599-3.wav   healthy      0\n",
       "31  0602-2.wav   healthy      0\n",
       "32  0603-2.wav   healthy      0\n",
       "33  0614-1.wav   healthy      0\n",
       "34  0615-4.wav   healthy      0\n",
       "35  0606-1.wav  wheezing      1\n",
       "36  0606-2.wav  wheezing      1\n",
       "37  0607-2.wav  wheezing      1\n",
       "38  0607-3.wav  wheezing      1\n",
       "39  0609-4.wav  wheezing      1\n",
       "40  0611-1.wav  wheezing      1\n",
       "41  0611-2.wav  wheezing      1\n",
       "42  0611-3.wav  wheezing      1\n",
       "43  0611-4.wav  wheezing      1\n",
       "44  0612-1.wav  wheezing      1\n",
       "45  0613-2.wav  wheezing      1\n",
       "46  0613-3.wav  wheezing      1\n",
       "47  0614-2.wav  wheezing      1\n",
       "48  0614-3.wav  wheezing      1\n",
       "49  0614-4.wav  wheezing      1\n",
       "50  0615-1.wav  wheezing      1\n",
       "51  0615-2.wav  wheezing      1"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Catholic_file1 = 'data/data_val.csv'\n",
    "df1 = pd.read_csv(Catholic_file1)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4537447f",
   "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>/0041-1.wav</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>/0041-2.wav</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>/0041-3.wav</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>/0041-4.wav</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>/0042-1.wav</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  relative_path  class\n",
       "0   /0041-1.wav      0\n",
       "1   /0041-2.wav      0\n",
       "2   /0041-3.wav      0\n",
       "3   /0041-4.wav      0\n",
       "4   /0042-1.wav      0"
      ]
     },
     "execution_count": 10,
     "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": 11,
   "id": "57ec8b19",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = 'data/val'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7c3456d8",
   "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",
    "    self.shift_pct = 0.4\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_Util.open(audio_file)\n",
    "    reaud = Breath_sound_Util.resample(aud, self.sr)\n",
    "    dur_aud = Breath_sound_Util.pad(reaud, self.duration)\n",
    "    shift_aud = Breath_sound_Util.time_shift(dur_aud, self.shift_pct)\n",
    "    sgram = Breath_sound_Util.spectro_gram(shift_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": 13,
   "id": "87a5d5c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "brds1 = breathDS1(df1, data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "035dafb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "val_dl = torch.utils.data.DataLoader(brds1, batch_size=16, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f3d903e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda', index=0)"
      ]
     },
     "execution_count": 15,
     "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": 16,
   "id": "410ea83c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchcontrib.optim import SWA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1d473616",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0, Loss: 0.6658, Accuracy: 0.7820\n",
      "Epoch: 1, Loss: 0.6258, Accuracy: 0.8246\n",
      "Epoch: 2, Loss: 0.5794, Accuracy: 0.8246\n",
      "Epoch: 3, Loss: 0.5628, Accuracy: 0.8436\n",
      "Epoch: 4, Loss: 0.5634, Accuracy: 0.8483\n",
      "Epoch: 5, Loss: 0.5468, Accuracy: 0.8436\n",
      "Epoch: 6, Loss: 0.5373, Accuracy: 0.8768\n",
      "Epoch: 7, Loss: 0.5313, Accuracy: 0.8768\n",
      "Epoch: 8, Loss: 0.5348, Accuracy: 0.9147\n",
      "Epoch: 9, Loss: 0.5088, Accuracy: 0.8863\n",
      "Epoch: 10, Loss: 0.5192, Accuracy: 0.8863\n",
      "Epoch: 11, Loss: 0.4952, Accuracy: 0.8863\n",
      "Epoch: 12, Loss: 0.5007, Accuracy: 0.8815\n",
      "Epoch: 13, Loss: 0.4773, Accuracy: 0.9147\n",
      "Epoch: 14, Loss: 0.4953, Accuracy: 0.9147\n",
      "Epoch: 15, Loss: 0.4941, Accuracy: 0.8957\n",
      "Epoch: 16, Loss: 0.4933, Accuracy: 0.9100\n",
      "Epoch: 17, Loss: 0.4842, Accuracy: 0.9147\n",
      "Epoch: 18, Loss: 0.4894, Accuracy: 0.9005\n",
      "Epoch: 19, Loss: 0.4890, Accuracy: 0.9336\n",
      "Epoch: 20, Loss: 0.4901, Accuracy: 0.9147\n",
      "Epoch: 21, Loss: 0.4973, Accuracy: 0.9052\n",
      "Epoch: 22, Loss: 0.4890, Accuracy: 0.9052\n",
      "Epoch: 23, Loss: 0.4829, Accuracy: 0.8957\n",
      "Epoch: 24, Loss: 0.4877, Accuracy: 0.9242\n",
      "Epoch: 25, Loss: 0.4876, Accuracy: 0.8957\n",
      "Epoch: 26, Loss: 0.4711, Accuracy: 0.9005\n",
      "Epoch: 27, Loss: 0.5024, Accuracy: 0.9005\n",
      "Epoch: 28, Loss: 0.4861, Accuracy: 0.9336\n",
      "Epoch: 29, Loss: 0.4907, Accuracy: 0.8815\n",
      "Epoch: 30, Loss: 0.5042, Accuracy: 0.9100\n",
      "Epoch: 31, Loss: 0.4958, Accuracy: 0.8910\n",
      "Epoch: 32, Loss: 0.4939, Accuracy: 0.9052\n",
      "Epoch: 33, Loss: 0.4773, Accuracy: 0.9289\n",
      "Epoch: 34, Loss: 0.4561, Accuracy: 0.9052\n",
      "Epoch: 35, Loss: 0.4779, Accuracy: 0.9147\n",
      "Epoch: 36, Loss: 0.4650, Accuracy: 0.9194\n",
      "Epoch: 37, Loss: 0.4822, Accuracy: 0.9384\n",
      "Epoch: 38, Loss: 0.4819, Accuracy: 0.8957\n",
      "Epoch: 39, Loss: 0.4811, Accuracy: 0.9194\n",
      "Epoch: 40, Loss: 0.4727, Accuracy: 0.9336\n",
      "Epoch: 41, Loss: 0.4718, Accuracy: 0.9052\n",
      "Epoch: 42, Loss: 0.4837, Accuracy: 0.9147\n",
      "Epoch: 43, Loss: 0.4876, Accuracy: 0.8910\n",
      "Epoch: 44, Loss: 0.4802, Accuracy: 0.9194\n",
      "Epoch: 45, Loss: 0.4942, Accuracy: 0.9005\n",
      "Epoch: 46, Loss: 0.4728, Accuracy: 0.9052\n",
      "Epoch: 47, Loss: 0.4829, Accuracy: 0.9100\n",
      "Epoch: 48, Loss: 0.4921, Accuracy: 0.9100\n",
      "Epoch: 49, Loss: 0.4831, Accuracy: 0.9052\n",
      "Finished Training\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import numpy as np\n",
    "import sklearn.metrics as metrics\n",
    "\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 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",
    "    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": "code",
   "execution_count": 32,
   "id": "ed2ff541",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8269, Total items: 52\n"
     ]
    }
   ],
   "source": [
    "def inference (model, val_dl):\n",
    "  correct_prediction = 0\n",
    "  total_prediction = 0\n",
    "\n",
    "  with torch.no_grad():\n",
    "    for data in val_dl:\n",
    "      \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",
    "      outputs = model(inputs)\n",
    " \n",
    "      _, prediction = torch.max(outputs,1)\n",
    "      \n",
    "      correct_prediction += (prediction == labels).sum().item()\n",
    "      total_prediction += prediction.shape[0]\n",
    "    \n",
    "  acc = correct_prediction/total_prediction\n",
    "  print(f'Accuracy: {acc:.4f}, Total items: {total_prediction}')\n",
    "  \n",
    "inference(Model1, val_dl)"
   ]
  }
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