leaves_classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\envs\\torchgpu\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "C:\\ProgramData\\Anaconda3\\envs\\torchgpu\\lib\\site-packages\\numpy\\.libs\\libopenblas.WCDJNK7YVMPZQ2ME2ZZHJJRJ3JIKNDB7.gfortran-win_amd64.dll\n",
      "C:\\ProgramData\\Anaconda3\\envs\\torchgpu\\lib\\site-packages\\numpy\\.libs\\libopenblas.XWYDX2IKJW2NMTWSFYNGFUWKQU3LYTCZ.gfortran-win_amd64.dll\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n"
     ]
    }
   ],
   "source": [
    "# 首先导入包\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from torchvision import transforms\n",
    "from PIL import Image\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import torchvision.models as models\n",
    "# This is for the progress bar.\n",
    "from tqdm import tqdm\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>image</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>images/0.jpg</td>\n",
       "      <td>maclura_pomifera</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>images/1.jpg</td>\n",
       "      <td>maclura_pomifera</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>images/2.jpg</td>\n",
       "      <td>maclura_pomifera</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>images/3.jpg</td>\n",
       "      <td>maclura_pomifera</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>images/4.jpg</td>\n",
       "      <td>maclura_pomifera</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          image             label\n",
       "0  images/0.jpg  maclura_pomifera\n",
       "1  images/1.jpg  maclura_pomifera\n",
       "2  images/2.jpg  maclura_pomifera\n",
       "3  images/3.jpg  maclura_pomifera\n",
       "4  images/4.jpg  maclura_pomifera"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels_dataframe = pd.read_csv(r'train.csv')\n",
    "labels_dataframe.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>image</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>18353</td>\n",
       "      <td>18353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>18353</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>images/0.jpg</td>\n",
       "      <td>maclura_pomifera</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>353</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               image             label\n",
       "count          18353             18353\n",
       "unique         18353               176\n",
       "top     images/0.jpg  maclura_pomifera\n",
       "freq               1               353"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels_dataframe.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x2160 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def barw(ax): \n",
    "    \n",
    "    for p in ax.patches:\n",
    "        val = p.get_width() #height of the bar\n",
    "        x = p.get_x()+ p.get_width() # x- position \n",
    "        y = p.get_y() + p.get_height()/2 #y-position\n",
    "        ax.annotate(round(val,2),(x,y))\n",
    "        \n",
    "#finding top leaves\n",
    "\n",
    "plt.figure(figsize = (15,30))\n",
    "ax0 =sns.countplot(y=labels_dataframe['label'],order=labels_dataframe['label'].value_counts().index)\n",
    "barw(ax0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "176\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['abies_concolor',\n",
       " 'abies_nordmanniana',\n",
       " 'acer_campestre',\n",
       " 'acer_ginnala',\n",
       " 'acer_griseum',\n",
       " 'acer_negundo',\n",
       " 'acer_palmatum',\n",
       " 'acer_pensylvanicum',\n",
       " 'acer_platanoides',\n",
       " 'acer_pseudoplatanus']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 把label文件排个序\n",
    "leaves_labels = sorted(list(set(labels_dataframe['label'])))\n",
    "n_classes = len(leaves_labels)\n",
    "print(n_classes)\n",
    "leaves_labels[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'abies_concolor': 0,\n",
       " 'abies_nordmanniana': 1,\n",
       " 'acer_campestre': 2,\n",
       " 'acer_ginnala': 3,\n",
       " 'acer_griseum': 4,\n",
       " 'acer_negundo': 5,\n",
       " 'acer_palmatum': 6,\n",
       " 'acer_pensylvanicum': 7,\n",
       " 'acer_platanoides': 8,\n",
       " 'acer_pseudoplatanus': 9,\n",
       " 'acer_rubrum': 10,\n",
       " 'acer_saccharinum': 11,\n",
       " 'acer_saccharum': 12,\n",
       " 'aesculus_flava': 13,\n",
       " 'aesculus_glabra': 14,\n",
       " 'aesculus_hippocastamon': 15,\n",
       " 'aesculus_pavi': 16,\n",
       " 'ailanthus_altissima': 17,\n",
       " 'albizia_julibrissin': 18,\n",
       " 'amelanchier_arborea': 19,\n",
       " 'amelanchier_canadensis': 20,\n",
       " 'amelanchier_laevis': 21,\n",
       " 'asimina_triloba': 22,\n",
       " 'betula_alleghaniensis': 23,\n",
       " 'betula_jacqemontii': 24,\n",
       " 'betula_lenta': 25,\n",
       " 'betula_nigra': 26,\n",
       " 'betula_populifolia': 27,\n",
       " 'broussonettia_papyrifera': 28,\n",
       " 'carpinus_betulus': 29,\n",
       " 'carpinus_caroliniana': 30,\n",
       " 'carya_cordiformis': 31,\n",
       " 'carya_glabra': 32,\n",
       " 'carya_ovata': 33,\n",
       " 'carya_tomentosa': 34,\n",
       " 'castanea_dentata': 35,\n",
       " 'catalpa_bignonioides': 36,\n",
       " 'catalpa_speciosa': 37,\n",
       " 'cedrus_atlantica': 38,\n",
       " 'cedrus_deodara': 39,\n",
       " 'cedrus_libani': 40,\n",
       " 'celtis_occidentalis': 41,\n",
       " 'celtis_tenuifolia': 42,\n",
       " 'cercidiphyllum_japonicum': 43,\n",
       " 'cercis_canadensis': 44,\n",
       " 'chamaecyparis_pisifera': 45,\n",
       " 'chamaecyparis_thyoides': 46,\n",
       " 'chionanthus_retusus': 47,\n",
       " 'chionanthus_virginicus': 48,\n",
       " 'cladrastis_lutea': 49,\n",
       " 'cornus_florida': 50,\n",
       " 'cornus_kousa': 51,\n",
       " 'cornus_mas': 52,\n",
       " 'crataegus_crus-galli': 53,\n",
       " 'crataegus_laevigata': 54,\n",
       " 'crataegus_phaenopyrum': 55,\n",
       " 'crataegus_pruinosa': 56,\n",
       " 'crataegus_viridis': 57,\n",
       " 'cryptomeria_japonica': 58,\n",
       " 'diospyros_virginiana': 59,\n",
       " 'eucommia_ulmoides': 60,\n",
       " 'evodia_daniellii': 61,\n",
       " 'fagus_grandifolia': 62,\n",
       " 'ficus_carica': 63,\n",
       " 'fraxinus_nigra': 64,\n",
       " 'fraxinus_pennsylvanica': 65,\n",
       " 'ginkgo_biloba': 66,\n",
       " 'gleditsia_triacanthos': 67,\n",
       " 'gymnocladus_dioicus': 68,\n",
       " 'halesia_tetraptera': 69,\n",
       " 'ilex_opaca': 70,\n",
       " 'juglans_cinerea': 71,\n",
       " 'juglans_nigra': 72,\n",
       " 'juniperus_virginiana': 73,\n",
       " 'koelreuteria_paniculata': 74,\n",
       " 'larix_decidua': 75,\n",
       " 'liquidambar_styraciflua': 76,\n",
       " 'liriodendron_tulipifera': 77,\n",
       " 'maclura_pomifera': 78,\n",
       " 'magnolia_acuminata': 79,\n",
       " 'magnolia_denudata': 80,\n",
       " 'magnolia_grandiflora': 81,\n",
       " 'magnolia_macrophylla': 82,\n",
       " 'magnolia_stellata': 83,\n",
       " 'magnolia_tripetala': 84,\n",
       " 'magnolia_virginiana': 85,\n",
       " 'malus_baccata': 86,\n",
       " 'malus_coronaria': 87,\n",
       " 'malus_floribunda': 88,\n",
       " 'malus_hupehensis': 89,\n",
       " 'malus_pumila': 90,\n",
       " 'metasequoia_glyptostroboides': 91,\n",
       " 'morus_alba': 92,\n",
       " 'morus_rubra': 93,\n",
       " 'nyssa_sylvatica': 94,\n",
       " 'ostrya_virginiana': 95,\n",
       " 'oxydendrum_arboreum': 96,\n",
       " 'paulownia_tomentosa': 97,\n",
       " 'phellodendron_amurense': 98,\n",
       " 'picea_abies': 99,\n",
       " 'picea_orientalis': 100,\n",
       " 'picea_pungens': 101,\n",
       " 'pinus_bungeana': 102,\n",
       " 'pinus_cembra': 103,\n",
       " 'pinus_densiflora': 104,\n",
       " 'pinus_echinata': 105,\n",
       " 'pinus_flexilis': 106,\n",
       " 'pinus_koraiensis': 107,\n",
       " 'pinus_nigra': 108,\n",
       " 'pinus_parviflora': 109,\n",
       " 'pinus_peucea': 110,\n",
       " 'pinus_pungens': 111,\n",
       " 'pinus_resinosa': 112,\n",
       " 'pinus_rigida': 113,\n",
       " 'pinus_strobus': 114,\n",
       " 'pinus_sylvestris': 115,\n",
       " 'pinus_taeda': 116,\n",
       " 'pinus_thunbergii': 117,\n",
       " 'pinus_virginiana': 118,\n",
       " 'pinus_wallichiana': 119,\n",
       " 'platanus_acerifolia': 120,\n",
       " 'platanus_occidentalis': 121,\n",
       " 'populus_deltoides': 122,\n",
       " 'populus_grandidentata': 123,\n",
       " 'populus_tremuloides': 124,\n",
       " 'prunus_pensylvanica': 125,\n",
       " 'prunus_sargentii': 126,\n",
       " 'prunus_serotina': 127,\n",
       " 'prunus_serrulata': 128,\n",
       " 'prunus_subhirtella': 129,\n",
       " 'prunus_virginiana': 130,\n",
       " 'prunus_yedoensis': 131,\n",
       " 'pseudolarix_amabilis': 132,\n",
       " 'ptelea_trifoliata': 133,\n",
       " 'pyrus_calleryana': 134,\n",
       " 'quercus_acutissima': 135,\n",
       " 'quercus_alba': 136,\n",
       " 'quercus_bicolor': 137,\n",
       " 'quercus_cerris': 138,\n",
       " 'quercus_coccinea': 139,\n",
       " 'quercus_imbricaria': 140,\n",
       " 'quercus_macrocarpa': 141,\n",
       " 'quercus_marilandica': 142,\n",
       " 'quercus_michauxii': 143,\n",
       " 'quercus_montana': 144,\n",
       " 'quercus_muehlenbergii': 145,\n",
       " 'quercus_nigra': 146,\n",
       " 'quercus_palustris': 147,\n",
       " 'quercus_phellos': 148,\n",
       " 'quercus_robur': 149,\n",
       " 'quercus_shumardii': 150,\n",
       " 'quercus_stellata': 151,\n",
       " 'quercus_velutina': 152,\n",
       " 'quercus_virginiana': 153,\n",
       " 'robinia_pseudo-acacia': 154,\n",
       " 'salix_babylonica': 155,\n",
       " 'salix_caroliniana': 156,\n",
       " 'salix_matsudana': 157,\n",
       " 'salix_nigra': 158,\n",
       " 'sassafras_albidum': 159,\n",
       " 'staphylea_trifolia': 160,\n",
       " 'stewartia_pseudocamellia': 161,\n",
       " 'styrax_japonica': 162,\n",
       " 'taxodium_distichum': 163,\n",
       " 'tilia_americana': 164,\n",
       " 'tilia_cordata': 165,\n",
       " 'tilia_europaea': 166,\n",
       " 'tilia_tomentosa': 167,\n",
       " 'tsuga_canadensis': 168,\n",
       " 'ulmus_americana': 169,\n",
       " 'ulmus_glabra': 170,\n",
       " 'ulmus_parvifolia': 171,\n",
       " 'ulmus_procera': 172,\n",
       " 'ulmus_pumila': 173,\n",
       " 'ulmus_rubra': 174,\n",
       " 'zelkova_serrata': 175}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_to_num = dict(zip(leaves_labels, range(n_classes)))\n",
    "class_to_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_to_class = {v : k for k, v in class_to_num.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LeavesData(Dataset):\n",
    "    def __init__(self, csv_path, file_path, mode='train', valid_ratio=0.2, resize_height=256, resize_width=256):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            csv_path (string): csv 文件路径\n",
    "            img_path (string): 图像文件所在路径\n",
    "            mode (string): 训练模式还是测试模式\n",
    "            valid_ratio (float): 验证集比例\n",
    "        \"\"\"\n",
    "        \n",
    "        # 需要调整后的照片尺寸，我这里每张图片的大小尺寸不一致#\n",
    "        self.resize_height = resize_height\n",
    "        self.resize_width = resize_width\n",
    "\n",
    "        self.file_path = file_path\n",
    "        self.mode = mode\n",
    "\n",
    "        # 读取 csv 文件\n",
    "        # 利用pandas读取csv文件\n",
    "        self.data_info = pd.read_csv(csv_path, header=None)  #header=None是去掉表头部分\n",
    "        # 计算 length\n",
    "        self.data_len = len(self.data_info.index) - 1\n",
    "        self.train_len = int(self.data_len * (1 - valid_ratio))\n",
    "        \n",
    "        if mode == 'train':\n",
    "            # 第一列包含图像文件的名称\n",
    "            self.train_image = np.asarray(self.data_info.iloc[1:self.train_len, 0])  #self.data_info.iloc[1:,0]表示读取第一列，从第二行开始到train_len\n",
    "            # 第二列是图像的 label\n",
    "            self.train_label = np.asarray(self.data_info.iloc[1:self.train_len, 1])\n",
    "            self.image_arr = self.train_image \n",
    "            self.label_arr = self.train_label\n",
    "        elif mode == 'valid':\n",
    "            self.valid_image = np.asarray(self.data_info.iloc[self.train_len:, 0])  \n",
    "            self.valid_label = np.asarray(self.data_info.iloc[self.train_len:, 1])\n",
    "            self.image_arr = self.valid_image\n",
    "            self.label_arr = self.valid_label\n",
    "        elif mode == 'test':\n",
    "            self.test_image = np.asarray(self.data_info.iloc[1:, 0])\n",
    "            self.image_arr = self.test_image\n",
    "            \n",
    "        self.real_len = len(self.image_arr)\n",
    "\n",
    "        print('Finished reading the {} set of Leaves Dataset ({} samples found)'\n",
    "              .format(mode, self.real_len))\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        # 从 image_arr中得到索引对应的文件名\n",
    "        single_image_name = self.image_arr[index]\n",
    "\n",
    "        # 读取图像文件\n",
    "        img_as_img = Image.open(self.file_path + single_image_name)\n",
    "\n",
    "        #如果需要将RGB三通道的图片转换成灰度图片可参考下面两行\n",
    "#         if img_as_img.mode != 'L':\n",
    "#             img_as_img = img_as_img.convert('L')\n",
    "\n",
    "        #设置好需要转换的变量，还可以包括一系列的nomarlize等等操作\n",
    "        if self.mode == 'train':\n",
    "            transform = transforms.Compose([\n",
    "                transforms.Resize((224, 224)),\n",
    "                transforms.RandomHorizontalFlip(p=0.5),   #随机水平翻转 选择一个概率\n",
    "                transforms.ToTensor()\n",
    "            ])\n",
    "        else:\n",
    "            # valid和test不做数据增强\n",
    "            transform = transforms.Compose([\n",
    "                transforms.Resize((224, 224)),\n",
    "                transforms.ToTensor()\n",
    "            ])\n",
    "        \n",
    "        img_as_img = transform(img_as_img)\n",
    "        \n",
    "        if self.mode == 'test':\n",
    "            return img_as_img\n",
    "        else:\n",
    "            # 得到图像的 string label\n",
    "            label = self.label_arr[index]\n",
    "            # number label\n",
    "            number_label = class_to_num[label]\n",
    "\n",
    "            return img_as_img, number_label  #返回每一个index对应的图片数据和对应的label\n",
    "\n",
    "    def __len__(self):\n",
    "        return self.real_len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished reading the train set of Leaves Dataset (14681 samples found)\n",
      "Finished reading the valid set of Leaves Dataset (3672 samples found)\n",
      "Finished reading the test set of Leaves Dataset (8800 samples found)\n",
      "<__main__.LeavesData object at 0x0000027A46B76E80>\n",
      "<__main__.LeavesData object at 0x0000027A46B30610>\n",
      "<__main__.LeavesData object at 0x0000027A46B30430>\n"
     ]
    }
   ],
   "source": [
    "train_path = 'train.csv'\n",
    "test_path = 'test.csv'\n",
    "# csv文件中已经images的路径了，因此这里只到上一级目录\n",
    "img_path = ''\n",
    "\n",
    "train_dataset = LeavesData(train_path, img_path, mode='train')\n",
    "val_dataset = LeavesData(train_path, img_path, mode='valid')\n",
    "test_dataset = LeavesData(test_path, img_path, mode='test')\n",
    "print(train_dataset)\n",
    "print(val_dataset)\n",
    "print(test_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_loader = torch.utils.data.DataLoader(\n",
    "        dataset=train_dataset,\n",
    "        batch_size=8, \n",
    "        shuffle=False,\n",
    "        num_workers=4\n",
    "    )\n",
    "\n",
    "val_loader = torch.utils.data.DataLoader(\n",
    "        dataset=val_dataset,\n",
    "        batch_size=8, \n",
    "        shuffle=False,\n",
    "        num_workers=4\n",
    "    )\n",
    "test_loader = torch.utils.data.DataLoader(\n",
    "        dataset=test_dataset,\n",
    "        batch_size=8, \n",
    "        shuffle=False,\n",
    "        num_workers=4\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda\n"
     ]
    }
   ],
   "source": [
    "# 看一下是在cpu还是GPU上\n",
    "def get_device():\n",
    "    return 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "\n",
    "device = get_device()\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 是否要冻住模型的前面一些层\n",
    "def set_parameter_requires_grad(model, feature_extracting):\n",
    "    if feature_extracting:\n",
    "        model = model\n",
    "        for param in model.parameters():\n",
    "            param.requires_grad = False\n",
    "# efficientnet_b0模型\n",
    "def res_model(num_classes, feature_extract = False, use_pretrained=True):\n",
    "\n",
    "    model_ft = models.efficientnet_b0(pretrained=use_pretrained)\n",
    "    set_parameter_requires_grad(model_ft, feature_extract)\n",
    "    num_ftrs = model_ft.classifier[1].in_features\n",
    "    model_ft.classifier[1] = nn.Linear(num_ftrs, num_classes)\n",
    "\n",
    "    return model_ft"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'res_model' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\ADMINI~1\\AppData\\Local\\Temp/ipykernel_6168/3998639110.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mres_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m176\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'res_model' is not defined"
     ]
    }
   ],
   "source": [
    "model = res_model(176)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EfficientNet(\n",
       "  (features): Sequential(\n",
       "    (0): ConvNormActivation(\n",
       "      (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (2): SiLU(inplace=True)\n",
       "    )\n",
       "    (1): Sequential(\n",
       "      (0): MBConv(\n",
       "        (block): Sequential(\n",
       "          (0): ConvNormActivation(\n",
       "            (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
       "            (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): SiLU(inplace=True)\n",
       "          )\n",
       "          (1): SqueezeExcitation(\n",
       "            (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
       "            (fc1): Conv2d(32, 8, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (fc2): Conv2d(8, 32, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (activation): SiLU(inplace=True)\n",
       "            (scale_activation): Sigmoid()\n",
       "          )\n",
       "          (2): ConvNormActivation(\n",
       "            (0): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (stochastic_depth): StochasticDepth(p=0.0, mode=row)\n",
       "      )\n",
       "    )\n",
       "    (2): Sequential(\n",
       "      (0): MBConv(\n",
       "        (block): Sequential(\n",
       "          (0): ConvNormActivation(\n",
       "            (0): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): SiLU(inplace=True)\n",
       "          )\n",
       "          (1): ConvNormActivation(\n",
       "            (0): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=96, bias=False)\n",
       "            (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): SiLU(inplace=True)\n",
       "          )\n",
       "          (2): SqueezeExcitation(\n",
       "            (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
       "            (fc1): Conv2d(96, 4, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (fc2): Conv2d(4, 96, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (activation): SiLU(inplace=True)\n",
       "            (scale_activation): Sigmoid()\n",
       "          )\n",
       "          (3): ConvNormActivation(\n",
       "            (0): Conv2d(96, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (stochastic_depth): StochasticDepth(p=0.0125, mode=row)\n",
       "      )\n",
       "      (1): MBConv(\n",
       "        (block): Sequential(\n",
       "          (0): ConvNormActivation(\n",
       "            (0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): SiLU(inplace=True)\n",
       "          )\n",
       "          (1): ConvNormActivation(\n",
       "            (0): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)\n",
       "            (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): SiLU(inplace=True)\n",
       "          )\n",
       "          (2): SqueezeExcitation(\n",
       "            (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
       "            (fc1): Conv2d(144, 6, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (fc2): Conv2d(6, 144, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (activation): SiLU(inplace=True)\n",
       "            (scale_activation): Sigmoid()\n",
       "          )\n",
       "          (3): ConvNormActivation(\n",
       "            (0): Conv2d(144, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (stochastic_depth): StochasticDepth(p=0.025, mode=row)\n",
       "      )\n",
       "    )\n",
       "    (3): Sequential(\n",
       "      (0): MBConv(\n",
       "        (block): Sequential(\n",
       "          (0): ConvNormActivation(\n",
       "            (0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): SiLU(inplace=True)\n",
       "          )\n",
       "          (1): ConvNormActivation(\n",
       "            (0): Conv2d(144, 144, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=144, bias=False)\n",
       "            (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): SiLU(inplace=True)\n",
       "          )\n",
       "          (2): SqueezeExcitation(\n",
       "            (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
       "            (fc1): Conv2d(144, 6, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (fc2): Conv2d(6, 144, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (activation): SiLU(inplace=True)\n",
       "            (scale_activation): Sigmoid()\n",
       "          )\n",
       "          (3): ConvNormActivation(\n",
       "            (0): Conv2d(144, 40, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): BatchNorm2d(40, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (stochastic_depth): StochasticDepth(p=0.037500000000000006, mode=row)\n",
       "      )\n",
       "      (1): MBConv(\n",
       "        (block): Sequential(\n",
       "          (0): ConvNormActivation(\n",
       "            (0): Conv2d(40, 240, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): BatchNorm2d(240, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): SiLU(inplace=True)\n",
       "          )\n",
       "          (1): ConvNormActivation(\n",
       "            (0): Conv2d(240, 240, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=240, bias=False)\n",
       "            (1): BatchNorm2d(240, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): SiLU(inplace=True)\n",
       "          )\n",
       "          (2): SqueezeExcitation(\n",
       "            (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
       "            (fc1): Conv2d(240, 10, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (fc2): Conv2d(10, 240, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (activation): SiLU(inplace=True)\n",
       "            (scale_activation): Sigmoid()\n",
       "          )\n",
       "          (3): ConvNormActivation(\n",
       "            (0): Conv2d(240, 40, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): BatchNorm2d(40, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (stochastic_depth): StochasticDepth(p=0.05, mode=row)\n",
       "      )\n",
       "    )\n",
       "    (4): Sequential(\n",
       "      (0): MBConv(\n",
       "        (block): Sequential(\n",
       "          (0): ConvNormActivation(\n",
       "            (0): Conv2d(40, 240, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): BatchNorm2d(240, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): SiLU(inplace=True)\n",
       "          )\n",
       "          (1): ConvNormActivation(\n",
       "            (0): Conv2d(240, 240, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=240, bias=False)\n",
       "            (1): BatchNorm2d(240, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): SiLU(inplace=True)\n",
       "          )\n",
       "          (2): SqueezeExcitation(\n",
       "            (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
       "            (fc1): Conv2d(240, 10, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (fc2): Conv2d(10, 240, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (activation): SiLU(inplace=True)\n",
       "            (scale_activation): Sigmoid()\n",
       "          )\n",
       "          (3): ConvNormActivation(\n",
       "            (0): Conv2d(240, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): BatchNorm2d(80, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (stochastic_depth): StochasticDepth(p=0.0625, mode=row)\n",
       "      )\n",
       "      (1): MBConv(\n",
       "        (block): Sequential(\n",
       "          (0): ConvNormActivation(\n",
       "            (0): Conv2d(80, 480, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): SiLU(inplace=True)\n",
       "          )\n",
       "          (1): ConvNormActivation(\n",
       "            (0): Conv2d(480, 480, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=480, bias=False)\n",
       "            (1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): SiLU(inplace=True)\n",
       "          )\n",
       "          (2): SqueezeExcitation(\n",
       "            (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
       "            (fc1): Conv2d(480, 20, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (fc2): Conv2d(20, 480, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (activation): SiLU(inplace=True)\n",
       "            (scale_activation): Sigmoid()\n",
       "          )\n",
       "          (3): ConvNormActivation(\n",
       "            (0): Conv2d(480, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): BatchNorm2d(80, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (stochastic_depth): StochasticDepth(p=0.07500000000000001, mode=row)\n",
       "      )\n",
       "      (2): MBConv(\n",
       "        (block): Sequential(\n",
       "          (0): ConvNormActivation(\n",
       "            (0): Conv2d(80, 480, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): SiLU(inplace=True)\n",
       "          )\n",
       "          (1): ConvNormActivation(\n",
       "            (0): Conv2d(480, 480, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=480, bias=False)\n",
       "            (1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): SiLU(inplace=True)\n",
       "          )\n",
       "          (2): SqueezeExcitation(\n",
       "            (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
       "            (fc1): Conv2d(480, 20, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (fc2): Conv2d(20, 480, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (activation): SiLU(inplace=True)\n",
       "            (scale_activation): Sigmoid()\n",
       "          )\n",
       "          (3): ConvNormActivation(\n",
       "            (0): Conv2d(480, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): BatchNorm2d(80, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (stochastic_depth): StochasticDepth(p=0.08750000000000001, mode=row)\n",
       "      )\n",
       "    )\n",
       "    (5): Sequential(\n",
       "      (0): MBConv(\n",
       "        (block): Sequential(\n",
       "          (0): ConvNormActivation(\n",
       "            (0): Conv2d(80, 480, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): SiLU(inplace=True)\n",
       "          )\n",
       "          (1): ConvNormActivation(\n",
       "            (0): Conv2d(480, 480, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=480, bias=False)\n",
       "            (1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): SiLU(inplace=True)\n",
       "          )\n",
       "          (2): SqueezeExcitation(\n",
       "            (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
       "            (fc1): Conv2d(480, 20, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (fc2): Conv2d(20, 480, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (activation): SiLU(inplace=True)\n",
       "            (scale_activation): Sigmoid()\n",
       "          )\n",
       "          (3): ConvNormActivation(\n",
       "            (0): Conv2d(480, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): BatchNorm2d(112, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (stochastic_depth): StochasticDepth(p=0.1, mode=row)\n",
       "      )\n",
       "      (1): MBConv(\n",
       "        (block): Sequential(\n",
       "          (0): ConvNormActivation(\n",
       "            (0): Conv2d(112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): SiLU(inplace=True)\n",
       "          )\n",
       "          (1): ConvNormActivation(\n",
       "            (0): Conv2d(672, 672, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=672, bias=False)\n",
       "            (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): SiLU(inplace=True)\n",
       "          )\n",
       "          (2): SqueezeExcitation(\n",
       "            (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
       "            (fc1): Conv2d(672, 28, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (fc2): Conv2d(28, 672, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (activation): SiLU(inplace=True)\n",
       "            (scale_activation): Sigmoid()\n",
       "          )\n",
       "          (3): ConvNormActivation(\n",
       "            (0): Conv2d(672, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): BatchNorm2d(112, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (stochastic_depth): StochasticDepth(p=0.1125, mode=row)\n",
       "      )\n",
       "      (2): MBConv(\n",
       "        (block): Sequential(\n",
       "          (0): ConvNormActivation(\n",
       "            (0): Conv2d(112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): SiLU(inplace=True)\n",
       "          )\n",
       "          (1): ConvNormActivation(\n",
       "            (0): Conv2d(672, 672, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=672, bias=False)\n",
       "            (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): SiLU(inplace=True)\n",
       "          )\n",
       "          (2): SqueezeExcitation(\n",
       "            (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
       "            (fc1): Conv2d(672, 28, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (fc2): Conv2d(28, 672, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (activation): SiLU(inplace=True)\n",
       "            (scale_activation): Sigmoid()\n",
       "          )\n",
       "          (3): ConvNormActivation(\n",
       "            (0): Conv2d(672, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): BatchNorm2d(112, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (stochastic_depth): StochasticDepth(p=0.125, mode=row)\n",
       "      )\n",
       "    )\n",
       "    (6): Sequential(\n",
       "      (0): MBConv(\n",
       "        (block): Sequential(\n",
       "          (0): ConvNormActivation(\n",
       "            (0): Conv2d(112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): SiLU(inplace=True)\n",
       "          )\n",
       "          (1): ConvNormActivation(\n",
       "            (0): Conv2d(672, 672, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=672, bias=False)\n",
       "            (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): SiLU(inplace=True)\n",
       "          )\n",
       "          (2): SqueezeExcitation(\n",
       "            (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
       "            (fc1): Conv2d(672, 28, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (fc2): Conv2d(28, 672, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (activation): SiLU(inplace=True)\n",
       "            (scale_activation): Sigmoid()\n",
       "          )\n",
       "          (3): ConvNormActivation(\n",
       "            (0): Conv2d(672, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (stochastic_depth): StochasticDepth(p=0.1375, mode=row)\n",
       "      )\n",
       "      (1): MBConv(\n",
       "        (block): Sequential(\n",
       "          (0): ConvNormActivation(\n",
       "            (0): Conv2d(192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): SiLU(inplace=True)\n",
       "          )\n",
       "          (1): ConvNormActivation(\n",
       "            (0): Conv2d(1152, 1152, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=1152, bias=False)\n",
       "            (1): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): SiLU(inplace=True)\n",
       "          )\n",
       "          (2): SqueezeExcitation(\n",
       "            (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
       "            (fc1): Conv2d(1152, 48, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (fc2): Conv2d(48, 1152, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (activation): SiLU(inplace=True)\n",
       "            (scale_activation): Sigmoid()\n",
       "          )\n",
       "          (3): ConvNormActivation(\n",
       "            (0): Conv2d(1152, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (stochastic_depth): StochasticDepth(p=0.15000000000000002, mode=row)\n",
       "      )\n",
       "      (2): MBConv(\n",
       "        (block): Sequential(\n",
       "          (0): ConvNormActivation(\n",
       "            (0): Conv2d(192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): SiLU(inplace=True)\n",
       "          )\n",
       "          (1): ConvNormActivation(\n",
       "            (0): Conv2d(1152, 1152, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=1152, bias=False)\n",
       "            (1): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): SiLU(inplace=True)\n",
       "          )\n",
       "          (2): SqueezeExcitation(\n",
       "            (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
       "            (fc1): Conv2d(1152, 48, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (fc2): Conv2d(48, 1152, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (activation): SiLU(inplace=True)\n",
       "            (scale_activation): Sigmoid()\n",
       "          )\n",
       "          (3): ConvNormActivation(\n",
       "            (0): Conv2d(1152, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (stochastic_depth): StochasticDepth(p=0.1625, mode=row)\n",
       "      )\n",
       "      (3): MBConv(\n",
       "        (block): Sequential(\n",
       "          (0): ConvNormActivation(\n",
       "            (0): Conv2d(192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): SiLU(inplace=True)\n",
       "          )\n",
       "          (1): ConvNormActivation(\n",
       "            (0): Conv2d(1152, 1152, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=1152, bias=False)\n",
       "            (1): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): SiLU(inplace=True)\n",
       "          )\n",
       "          (2): SqueezeExcitation(\n",
       "            (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
       "            (fc1): Conv2d(1152, 48, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (fc2): Conv2d(48, 1152, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (activation): SiLU(inplace=True)\n",
       "            (scale_activation): Sigmoid()\n",
       "          )\n",
       "          (3): ConvNormActivation(\n",
       "            (0): Conv2d(1152, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (stochastic_depth): StochasticDepth(p=0.17500000000000002, mode=row)\n",
       "      )\n",
       "    )\n",
       "    (7): Sequential(\n",
       "      (0): MBConv(\n",
       "        (block): Sequential(\n",
       "          (0): ConvNormActivation(\n",
       "            (0): Conv2d(192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): SiLU(inplace=True)\n",
       "          )\n",
       "          (1): ConvNormActivation(\n",
       "            (0): Conv2d(1152, 1152, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1152, bias=False)\n",
       "            (1): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): SiLU(inplace=True)\n",
       "          )\n",
       "          (2): SqueezeExcitation(\n",
       "            (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
       "            (fc1): Conv2d(1152, 48, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (fc2): Conv2d(48, 1152, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (activation): SiLU(inplace=True)\n",
       "            (scale_activation): Sigmoid()\n",
       "          )\n",
       "          (3): ConvNormActivation(\n",
       "            (0): Conv2d(1152, 320, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (stochastic_depth): StochasticDepth(p=0.1875, mode=row)\n",
       "      )\n",
       "    )\n",
       "    (8): ConvNormActivation(\n",
       "      (0): Conv2d(320, 1280, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (1): BatchNorm2d(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (2): SiLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
       "  (classifier): Sequential(\n",
       "    (0): Dropout(p=0.2, inplace=True)\n",
       "    (1): Linear(in_features=1280, out_features=1000, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "learning_rate = 3e-4\n",
    "weight_decay = 1e-3\n",
    "num_epoch = 50\n",
    "model_path = './pre_efficientnet_b0_model.ckpt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Dropout' object has no attribute 'out_features'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\ADMINI~1\\AppData\\Local\\Temp/ipykernel_11800/692098696.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# Initialize a model, and put it on the device specified.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mres_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m176\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdevice\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdevice\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;31m# For the classification task, we use cross-entropy as the measurement of performance.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\ADMINI~1\\AppData\\Local\\Temp/ipykernel_11800/578348590.py\u001b[0m in \u001b[0;36mres_model\u001b[1;34m(num_classes, feature_extract, use_pretrained)\u001b[0m\n\u001b[0;32m     10\u001b[0m     \u001b[0mmodel_ft\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodels\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mefficientnet_b0\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpretrained\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0muse_pretrained\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m     \u001b[0mset_parameter_requires_grad\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel_ft\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeature_extract\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 12\u001b[1;33m     \u001b[0mnum_ftrs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel_ft\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclassifier\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mout_features\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     13\u001b[0m     \u001b[0mmodel_ft\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSequential\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mLinear\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnum_ftrs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnum_classes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\envs\\torchgpu\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   1175\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mmodules\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1176\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mmodules\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1177\u001b[1;33m         raise AttributeError(\"'{}' object has no attribute '{}'\".format(\n\u001b[0m\u001b[0;32m   1178\u001b[0m             type(self).__name__, name))\n\u001b[0;32m   1179\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'Dropout' object has no attribute 'out_features'"
     ]
    }
   ],
   "source": [
    "# Initialize a model, and put it on the device specified.\n",
    "model = res_model(176)\n",
    "model = model.to(device)\n",
    "model.device = device\n",
    "# For the classification task, we use cross-entropy as the measurement of performance.\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "# Initialize optimizer, you may fine-tune some hyperparameters such as learning rate on your own.\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr = learning_rate, weight_decay=weight_decay)\n",
    "\n",
    "# The number of training epochs.\n",
    "n_epochs = num_epoch\n",
    "\n",
    "best_acc = 0.0\n",
    "for epoch in range(n_epochs):\n",
    "    # ---------- Training ----------\n",
    "    # Make sure the model is in train mode before training.\n",
    "    model.train() \n",
    "    # These are used to record information in training.\n",
    "    train_loss = []\n",
    "    train_accs = []\n",
    "    # Iterate the training set by batches.\n",
    "    for batch in tqdm(train_loader):\n",
    "        # A batch consists of image data and corresponding labels.\n",
    "        imgs, labels = batch\n",
    "        imgs = imgs.to(device)\n",
    "        labels = labels.to(device)\n",
    "        # Forward the data. (Make sure data and model are on the same device.)\n",
    "        logits = model(imgs)\n",
    "        # Calculate the cross-entropy loss.\n",
    "        # We don't need to apply softmax before computing cross-entropy as it is done automatically.\n",
    "        loss = criterion(logits, labels)\n",
    "        \n",
    "        # Gradients stored in the parameters in the previous step should be cleared out first.\n",
    "        optimizer.zero_grad()\n",
    "        # Compute the gradients for parameters.\n",
    "        loss.backward()\n",
    "        # Update the parameters with computed gradients.\n",
    "        optimizer.step()\n",
    "        \n",
    "        # Compute the accuracy for current batch.\n",
    "        acc = (logits.argmax(dim=-1) == labels).float().mean()\n",
    "\n",
    "        # Record the loss and accuracy.\n",
    "        train_loss.append(loss.item())\n",
    "        train_accs.append(acc)\n",
    "        \n",
    "    # The average loss and accuracy of the training set is the average of the recorded values.\n",
    "    train_loss = sum(train_loss) / len(train_loss)\n",
    "    train_acc = sum(train_accs) / len(train_accs)\n",
    "\n",
    "    # Print the information.\n",
    "    print(f\"[ Train | {epoch + 1:03d}/{n_epochs:03d} ] loss = {train_loss:.5f}, acc = {train_acc:.5f}\")\n",
    "    \n",
    "    \n",
    "    # ---------- Validation ----------\n",
    "    # Make sure the model is in eval mode so that some modules like dropout are disabled and work normally.\n",
    "    model.eval()\n",
    "    # These are used to record information in validation.\n",
    "    valid_loss = []\n",
    "    valid_accs = []\n",
    "    \n",
    "    # Iterate the validation set by batches.\n",
    "    for batch in tqdm(val_loader):\n",
    "        imgs, labels = batch\n",
    "        # We don't need gradient in validation.\n",
    "        # Using torch.no_grad() accelerates the forward process.\n",
    "        with torch.no_grad():\n",
    "            logits = model(imgs.to(device))\n",
    "            \n",
    "        # We can still compute the loss (but not the gradient).\n",
    "        loss = criterion(logits, labels.to(device))\n",
    "\n",
    "        # Compute the accuracy for current batch.\n",
    "        acc = (logits.argmax(dim=-1) == labels.to(device)).float().mean()\n",
    "\n",
    "        # Record the loss and accuracy.\n",
    "        valid_loss.append(loss.item())\n",
    "        valid_accs.append(acc)\n",
    "        \n",
    "    # The average loss and accuracy for entire validation set is the average of the recorded values.\n",
    "    valid_loss = sum(valid_loss) / len(valid_loss)\n",
    "    valid_acc = sum(valid_accs) / len(valid_accs)\n",
    "\n",
    "    # Print the information.\n",
    "    print(f\"[ Valid | {epoch + 1:03d}/{n_epochs:03d} ] loss = {valid_loss:.5f}, acc = {valid_acc:.5f}\")\n",
    "    \n",
    "    # if the model improves, save a checkpoint at this epoch\n",
    "    if valid_acc > best_acc:\n",
    "        best_acc = valid_acc\n",
    "        torch.save(model.state_dict(), model_path)\n",
    "        print('saving model with acc {:.3f}'.format(best_acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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