model_main_cp.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import psutil\n",
    "import gc\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from tqdm import tqdm\n",
    "\n",
    "import torch\n",
    "from torch.utils import data\n",
    "import time\n",
    "\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = '/scratch/users/neuroimage/conda/data/preprocessed/imgsss'\n",
    "meta_data_path = '/scratch/users/neuroimage/conda/data/ADNI1_Complete_2Yr_3T_4_18_2023.csv'\n",
    "model_dir = '/scratch/users/neuroimage/conda/data/models'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "import io\n",
    "import nibabel as nib\n",
    "from io import BytesIO\n",
    "# import matplotlib.pyplot as plt\n",
    "import nilearn as nil # https://nilearn.github.io/stable/quickstart.html\n",
    "from nilearn import plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Image Data ID</th>\n",
       "      <th>Subject</th>\n",
       "      <th>Group</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Visit</th>\n",
       "      <th>Modality</th>\n",
       "      <th>Description</th>\n",
       "      <th>Type</th>\n",
       "      <th>Acq Date</th>\n",
       "      <th>Format</th>\n",
       "      <th>Downloaded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I205567</td>\n",
       "      <td>136_S_1227</td>\n",
       "      <td>MCI</td>\n",
       "      <td>F</td>\n",
       "      <td>66</td>\n",
       "      <td>m18</td>\n",
       "      <td>MRI</td>\n",
       "      <td>MPR; ; N3; Scaled</td>\n",
       "      <td>Processed</td>\n",
       "      <td>9/29/2008</td>\n",
       "      <td>NiFTI</td>\n",
       "      <td>4/12/2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I66824</td>\n",
       "      <td>136_S_1227</td>\n",
       "      <td>MCI</td>\n",
       "      <td>F</td>\n",
       "      <td>65</td>\n",
       "      <td>bl</td>\n",
       "      <td>MRI</td>\n",
       "      <td>MPR; ; N3; Scaled</td>\n",
       "      <td>Processed</td>\n",
       "      <td>2/21/2007</td>\n",
       "      <td>NiFTI</td>\n",
       "      <td>4/12/2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I79080</td>\n",
       "      <td>136_S_1227</td>\n",
       "      <td>MCI</td>\n",
       "      <td>F</td>\n",
       "      <td>65</td>\n",
       "      <td>m06</td>\n",
       "      <td>MRI</td>\n",
       "      <td>MPR; ; N3; Scaled</td>\n",
       "      <td>Processed</td>\n",
       "      <td>9/11/2007</td>\n",
       "      <td>NiFTI</td>\n",
       "      <td>4/12/2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>I143856</td>\n",
       "      <td>136_S_1227</td>\n",
       "      <td>MCI</td>\n",
       "      <td>F</td>\n",
       "      <td>67</td>\n",
       "      <td>m24</td>\n",
       "      <td>MRI</td>\n",
       "      <td>MPR; ; N3; Scaled</td>\n",
       "      <td>Processed</td>\n",
       "      <td>3/23/2009</td>\n",
       "      <td>NiFTI</td>\n",
       "      <td>4/12/2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I99265</td>\n",
       "      <td>136_S_1227</td>\n",
       "      <td>MCI</td>\n",
       "      <td>F</td>\n",
       "      <td>66</td>\n",
       "      <td>m12</td>\n",
       "      <td>MRI</td>\n",
       "      <td>MPR; ; N3; Scaled</td>\n",
       "      <td>Processed</td>\n",
       "      <td>3/06/2008</td>\n",
       "      <td>NiFTI</td>\n",
       "      <td>4/12/2023</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Image Data ID     Subject Group Sex  Age Visit Modality        Description   \n",
       "0       I205567  136_S_1227   MCI   F   66   m18      MRI  MPR; ; N3; Scaled  \\\n",
       "1        I66824  136_S_1227   MCI   F   65    bl      MRI  MPR; ; N3; Scaled   \n",
       "2        I79080  136_S_1227   MCI   F   65   m06      MRI  MPR; ; N3; Scaled   \n",
       "3       I143856  136_S_1227   MCI   F   67   m24      MRI  MPR; ; N3; Scaled   \n",
       "4        I99265  136_S_1227   MCI   F   66   m12      MRI  MPR; ; N3; Scaled   \n",
       "\n",
       "        Type   Acq Date Format Downloaded  \n",
       "0  Processed  9/29/2008  NiFTI  4/12/2023  \n",
       "1  Processed  2/21/2007  NiFTI  4/12/2023  \n",
       "2  Processed  9/11/2007  NiFTI  4/12/2023  \n",
       "3  Processed  3/23/2009  NiFTI  4/12/2023  \n",
       "4  Processed  3/06/2008  NiFTI  4/12/2023  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(meta_data_path)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Image Data ID</th>\n",
       "      <th>Subject</th>\n",
       "      <th>Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(I205567, 136_S_1227)</th>\n",
       "      <td>I205567</td>\n",
       "      <td>136_S_1227</td>\n",
       "      <td>MCI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(I66824, 136_S_1227)</th>\n",
       "      <td>I66824</td>\n",
       "      <td>136_S_1227</td>\n",
       "      <td>MCI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(I79080, 136_S_1227)</th>\n",
       "      <td>I79080</td>\n",
       "      <td>136_S_1227</td>\n",
       "      <td>MCI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(I143856, 136_S_1227)</th>\n",
       "      <td>I143856</td>\n",
       "      <td>136_S_1227</td>\n",
       "      <td>MCI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(I99265, 136_S_1227)</th>\n",
       "      <td>I99265</td>\n",
       "      <td>136_S_1227</td>\n",
       "      <td>MCI</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Image Data ID     Subject Group\n",
       "(I205567, 136_S_1227)       I205567  136_S_1227   MCI\n",
       "(I66824, 136_S_1227)         I66824  136_S_1227   MCI\n",
       "(I79080, 136_S_1227)         I79080  136_S_1227   MCI\n",
       "(I143856, 136_S_1227)       I143856  136_S_1227   MCI\n",
       "(I99265, 136_S_1227)         I99265  136_S_1227   MCI"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new = df[['Image Data ID', 'Subject', 'Group']].set_index(df[['Image Data ID', 'Subject', 'Group']].apply(lambda x: (x['Image Data ID'], x['Subject']), axis=1))\n",
    "df_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# random shuffle\n",
    "np.random.seed(0)\n",
    "random_index = np.random.permutation(df_new.index)\n",
    "df_new = df_new.loc[random_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(434, 3)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_dict = df_new['Group'].to_dict()\n",
    "# df_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train-test-split here, 70%, 10%, 20%\n",
    "df_len = len(df_new)\n",
    "train_test_assign = [\"train\"] * round(0.7*df_len) + [\"val\"] * (round(0.8*df_len) - round(0.7*df_len)) + [\"test\"] * (df_len - round(0.8*df_len))\n",
    "df_new['train_test_assign'] = train_test_assign\n",
    "df_train_test_dict = df_new['train_test_assign'].to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# train-test-split here, 70%, 10%, 20%\n",
    "# df_len = len(df_new)\n",
    "# df_train = df_new[:round(0.7*df_len)]\n",
    "# df_val = df_new[round(0.7*df_len):round(0.8*df_len)]\n",
    "# df_test = df_new[round(0.8*df_len):]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(121, 145, 121)\n",
      "smt-031_S_1066-I90916-gm.nii.gz\n",
      "[[0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
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      " ...\n",
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    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# generate plot\n",
    "for filename in os.listdir(data_path):\n",
    "    file_path = os.path.join(data_path, filename)\n",
    "    if os.path.isfile(file_path) and filename.endswith('nii.gz') and filename.startswith('smt'):\n",
    "        split_str = filename.split('-')\n",
    "        # print(split_str)\n",
    "        img_id = split_str[2]\n",
    "        subject = split_str[1]\n",
    "        assert (img_id, subject) in df_dict\n",
    "        class_name = df_dict[(img_id, subject)]\n",
    "        # print(type(category))\n",
    "        # print(os.path.join(data_path, filename))\n",
    "        img = nib.load(os.path.join(data_path, filename))\n",
    "        # print(img)\n",
    "        \n",
    "        data = img.get_fdata()\n",
    "        print(data.shape)\n",
    "        print(filename)\n",
    "        plt.imshow(data[60,:,:], cmap='gray')\n",
    "        print(data[60,:,:])\n",
    "        # for i in range(120):\n",
    "        #     plt.imshow(data[:,:,i], cmap='gray')\n",
    "        #     plt.show()\n",
    "        break\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(145, 121)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample = data[60,:,:]\n",
    "sample.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f7ca15bf110>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "elastic_transform = transforms.ElasticTransform(alpha=50.0, sigma=10.0)\n",
    "img = Image.fromarray(sample)\n",
    "augmented_image = elastic_transform(img)\n",
    "plt.imshow(np.array(augmented_image), cmap = 'gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. generate 2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_dataset_2D(out_dir):\n",
    "    # pair the image with labels and put them into respective folder for gm and wm, generate all the 2-D images\n",
    "    \n",
    "    for filename in os.listdir(data_path):\n",
    "        file_path = os.path.join(data_path, filename)\n",
    "        if os.path.isfile(file_path) and filename.endswith('nii.gz') and filename.startswith('smt'):\n",
    "            split_str = filename.split('-')\n",
    "            # print(split_str)\n",
    "            img_id = split_str[2]\n",
    "            subject = split_str[1]\n",
    "            assert (img_id, subject) in df_dict\n",
    "            class_name = df_dict[(img_id, subject)]\n",
    "            # print(type(category))\n",
    "            # print(os.path.join(data_path, filename))\n",
    "            img = nib.load(os.path.join(data_path, filename))\n",
    "            # print(img)\n",
    "            \n",
    "            data = img.get_fdata()\n",
    "            \n",
    "            z_shape = data.shape[0]\n",
    "            \n",
    "            i = z_shape // 2\n",
    "            np_array = data[i,:,:]\n",
    "            # plt.imshow(np_array, cmap='gray')\n",
    "            # print(i, np_array)\n",
    "            img = Image.fromarray(np_array)\n",
    "            # plt.imshow(np.array(img), cmap='gray')\n",
    "            # plt.show()\n",
    "            folder_name = filename.split('.')[0]\n",
    "            filename_new = folder_name + f'_{str(i)}.tiff'\n",
    "            # print(filename, filename_new)\n",
    "            gm_wm = filename.split('.')[0].split('-')[-1]\n",
    "            # print(os.path.join(out_dir, gm_wm, class_name, folder_name, filename_new))\n",
    "            train_test = df_train_test_dict[(img_id, subject)]\n",
    "            if not os.path.exists(os.path.join(out_dir, train_test, gm_wm, class_name, folder_name)):\n",
    "                os.makedirs(os.path.join(out_dir, train_test, gm_wm, class_name, folder_name))\n",
    "            # print(os.path.join(out_dir, train_test, gm_wm, class_name, folder_name))\n",
    "            # np.save(os.path.join(out_dir,train_test, gm_wm, class_name, folder_name, filename_new), np_array)\n",
    "            img.save(os.path.join(out_dir,train_test, gm_wm, class_name, folder_name, filename_new))\n",
    "\n",
    "            # for i in range(z_shape):\n",
    "            #     np_array = data[i,:,:]\n",
    "            #     img = Image.fromarray(np.uint8(np_array))\n",
    "            #     folder_name = filename.split('.')[0]\n",
    "            #     filename_new = folder_name + f'_{str(i)}.jpg'\n",
    "            #     # print(filename, filename_new)\n",
    "            #     gm_wm = filename.split('.')[0].split('-')[-1]\n",
    "            #     # print(os.path.join(out_dir, gm_wm, class_name, folder_name, filename_new))\n",
    "            #     if not os.path.exists(os.path.join(out_dir, gm_wm, class_name, folder_name)):\n",
    "            #         os.makedirs(os.path.join(out_dir, gm_wm, class_name, folder_name))\n",
    "            #     # print(os.path.join(out_dir, class_name, filename_new)) \n",
    "            #     img.save(os.path.join(out_dir, gm_wm, class_name, folder_name, filename_new))\n",
    "            print('finish', filename_new)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# pair the image with labels and put them into respective folder for gm and wm, generate all the 2-D images\n",
    "out_dir_single = '/scratch/users/neuroimage/conda/data/paired_data/2D_single'\n",
    "generate_dataset_2D(out_dir_single)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sample\n",
    "filename = 'pp-051_S_1331-I82509-gm.nii.gz'\n",
    "img = nib.load(os.path.join(data_path, filename))\n",
    "\n",
    "data = img.get_fdata()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Preprocess 3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.0\n",
      "1 0.0\n",
      "2 0.0\n",
      "3 0.0\n",
      "4 0.0\n",
      "5 0.0\n",
      "6 0.0\n",
      "7 0.0\n",
      "8 0.0\n",
      "9 0.0002279851809632374\n",
      "10 0.0018808777429467085\n",
      "11 0.015161014534055287\n",
      "12 0.04109432886862354\n",
      "13 0.05939013964092334\n",
      "14 0.07700199487033343\n",
      "15 0.09723567968082075\n",
      "16 0.11718438301510402\n",
      "17 0.13559418637788545\n",
      "18 0.15457395269307495\n",
      "19 0.171900826446281\n",
      "20 0.1883157594756341\n",
      "21 0.20381875178113423\n",
      "22 0.22034767740096894\n",
      "23 0.2358506697064691\n",
      "24 0.25317754345967514\n",
      "25 0.27153035052721575\n",
      "26 0.28805927614705046\n",
      "27 0.30504417212881163\n",
      "28 0.3225990310629809\n",
      "29 0.33639213451125677\n",
      "30 0.3494442861214021\n",
      "31 0.3634653747506412\n",
      "32 0.3760045597036193\n",
      "33 0.3870618409803363\n",
      "34 0.3986320889142206\n",
      "35 0.4102593331433457\n",
      "36 0.4261043032202907\n",
      "37 0.4380165289256198\n",
      "38 0.45095468794528354\n",
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      "45 0.5077799943003705\n",
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      "49 0.5408948418352807\n",
      "50 0.548874323168994\n",
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      "52 0.5606155599886007\n",
      "53 0.5655742376745512\n",
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      "56 0.5793673411228271\n",
      "57 0.5857509261897976\n",
      "58 0.5918495297805643\n",
      "59 0.5973781704189227\n",
      "60 0.6014249073810203\n",
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      "67 0.6256483328583642\n",
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      "72 0.6271302365346253\n",
      "73 0.6254773439726418\n",
      "74 0.6261612995155315\n",
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      "92 0.5385579937304075\n",
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      "94 0.5308634938728982\n",
      "95 0.5231119977201482\n",
      "96 0.5189512681675691\n",
      "97 0.5093188942718724\n",
      "98 0.5011684240524366\n",
      "99 0.4878312909660872\n",
      "100 0.47215730977486464\n",
      "101 0.4596181248218866\n",
      "102 0.44388714733542317\n",
      "103 0.4267312624679396\n",
      "104 0.4088344257623254\n",
      "105 0.3884867483613565\n",
      "106 0.3714448560843545\n",
      "107 0.35018523795953266\n",
      "108 0.3276717013394129\n",
      "109 0.30076944998575095\n",
      "110 0.2634938728982616\n",
      "111 0.2226275292106013\n",
      "112 0.18449700769449986\n",
      "113 0.15759475634083783\n",
      "114 0.1317184383015104\n",
      "115 0.10475919065260758\n",
      "116 0.07911085779424337\n",
      "117 0.04821886577372471\n",
      "118 0.024964377315474493\n",
      "119 0.016984895981761186\n",
      "120 0.009917355371900827\n"
     ]
    }
   ],
   "source": [
    "def generate_dataset_2D_multiple(out_dir):\n",
    "    # pair the image with labels and put them into respective folder for gm and wm, generate all the 2-D images\n",
    "    \n",
    "    for filename in os.listdir(data_path):\n",
    "        file_path = os.path.join(data_path, filename)\n",
    "        if os.path.isfile(file_path) and filename.endswith('nii.gz') and filename.startswith('smt'):\n",
    "            split_str = filename.split('-')\n",
    "            # print(split_str)\n",
    "            img_id = split_str[2]\n",
    "            subject = split_str[1]\n",
    "            assert (img_id, subject) in df_dict\n",
    "            class_name = df_dict[(img_id, subject)]\n",
    "            # print(type(category))\n",
    "            # print(os.path.join(data_path, filename))\n",
    "            img = nib.load(os.path.join(data_path, filename))\n",
    "            # print(img)\n",
    "            \n",
    "            data = img.get_fdata()\n",
    "            \n",
    "            z_shape = data.shape[0]\n",
    "            \n",
    "            for i in range(z_shape):\n",
    "                np_array = data[i,:,:]\n",
    "                non_zero_count = np.count_nonzero(np_array)\n",
    "                zero_count = np_array.size - non_zero_count\n",
    "                print(i, non_zero_count / np_array.size)\n",
    "                # if i == 40 or i == 80:\n",
    "                #     img = Image.fromarray(np_array)\n",
    "                #     plt.imshow(np.array(img), cmap='gray')\n",
    "                #     plt.show()\n",
    "                continue\n",
    "                img = Image.fromarray(np_array)\n",
    "                folder_name = filename.split('.')[0]\n",
    "                \n",
    "                \n",
    "\n",
    "                filename_new = folder_name + f'_{str(i)}.jpg'\n",
    "                # print(filename, filename_new)\n",
    "                gm_wm = filename.split('.')[0].split('-')[-1]\n",
    "                # print(os.path.join(out_dir, gm_wm, class_name, folder_name, filename_new))\n",
    "                if not os.path.exists(os.path.join(out_dir, gm_wm, class_name, folder_name)):\n",
    "                    os.makedirs(os.path.join(out_dir, gm_wm, class_name, folder_name))\n",
    "                # print(os.path.join(out_dir, class_name, filename_new)) \n",
    "                img.save(os.path.join(out_dir, gm_wm, class_name, folder_name, filename_new))\n",
    "            # print('finish', filename_new)\n",
    "            break\n",
    "out_dir_mul = '/scratch/users/neuroimage/conda/data/paired_data/2D_mul'\n",
    "generate_dataset_2D_multiple(out_dir_mul)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "finish\n",
      "finish\n",
      "finish\n",
      "finish\n",
      "finish\n",
      "finish\n",
      "finish\n",
      "finish\n",
      "finish\n"
     ]
    }
   ],
   "source": [
    "def calculate_nonzero_percentage():\n",
    "    # pair the image with labels and put them into respective folder for gm and wm, generate all the 2-D images\n",
    "    large_lst = []\n",
    "    for filename in os.listdir(data_path):\n",
    "        file_path = os.path.join(data_path, filename)\n",
    "        if os.path.isfile(file_path) and filename.endswith('nii.gz') and filename.startswith('smt'):\n",
    "            # split_str = filename.split('-')\n",
    "            # # print(split_str)\n",
    "            # img_id = split_str[2]\n",
    "            # subject = split_str[1]\n",
    "            # assert (img_id, subject) in df_dict\n",
    "            # class_name = df_dict[(img_id, subject)]\n",
    "            # print(type(category))\n",
    "            # print(os.path.join(data_path, filename))\n",
    "            img = nib.load(os.path.join(data_path, filename))\n",
    "            # print(img)\n",
    "            \n",
    "            data = img.get_fdata()\n",
    "            \n",
    "            z_shape = data.shape[0]\n",
    "            \n",
    "            lst = []\n",
    "            for i in range(z_shape):\n",
    "                np_array = data[i,:,:]\n",
    "                non_zero_count = np.count_nonzero(np_array)\n",
    "                lst.append(non_zero_count / np_array.size)\n",
    "            large_lst.append(lst)\n",
    "            print(\"finish\")\n",
    "    return np.array(large_lst)\n",
    "            \n",
    "\n",
    "non_zero_count_arr = calculate_nonzero_percentage()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save(\"non_zero_count_arr.npy\", non_zero_count_arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(868, 121)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "non_zero_count_arr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "arr_flat = non_zero_count_arr.flatten()\n",
    "\n",
    "plt.hist(arr_flat)\n",
    "\n",
    "plt.title(\"Distribution of None-zero pixel percentage\")\n",
    "plt.xlabel(\"Value\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "96545 0.919231062192939\n"
     ]
    }
   ],
   "source": [
    "non_zero_val = np.count_nonzero(arr_flat)\n",
    "print(non_zero_val, non_zero_val/non_zero_count_arr.size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "89228 0.8495639258102601\n"
     ]
    }
   ],
   "source": [
    "valid_val =(arr_flat > 0.1).sum()\n",
    "print(valid_val, valid_val/non_zero_count_arr.size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "finish smt-031_S_1066-I90916-gm.nii.gz\n",
      "finish smt-130_S_0886-I65514-wm.nii.gz\n",
      "finish smt-027_S_0307-I83030-wm.nii.gz\n",
      "finish smt-136_S_0426-I120441-gm.nii.gz\n",
      "finish smt-136_S_0086-I67781-gm.nii.gz\n",
      "finish smt-016_S_1121-I120392-wm.nii.gz\n",
      "finish smt-067_S_0607-I89123-wm.nii.gz\n",
      "finish smt-023_S_1046-I87518-wm.nii.gz\n",
      "finish smt-100_S_0015-I63475-wm.nii.gz\n",
      "finish smt-116_S_1249-I80700-gm.nii.gz\n",
      "finish smt-002_S_0559-I118679-gm.nii.gz\n",
      "finish smt-023_S_0916-I64827-wm.nii.gz\n",
      "finish smt-005_S_0572-I120460-gm.nii.gz\n",
      "finish smt-037_S_0303-I105924-wm.nii.gz\n",
      "finish smt-136_S_0426-I40378-wm.nii.gz\n",
      "finish smt-031_S_0830-I102502-wm.nii.gz\n",
      "finish smt-023_S_0058-I118837-wm.nii.gz\n",
      "finish smt-002_S_1018-I97022-gm.nii.gz\n",
      "finish smt-016_S_1121-I96239-wm.nii.gz\n",
      "finish smt-027_S_0404-I47545-gm.nii.gz\n",
      "finish smt-051_S_1123-I58054-wm.nii.gz\n",
      "finish smt-023_S_0926-I64995-wm.nii.gz\n",
      "finish smt-002_S_1268-I92226-gm.nii.gz\n",
      "finish smt-023_S_0030-I118835-gm.nii.gz\n",
      "finish smt-023_S_0388-I118871-gm.nii.gz\n",
      "finish smt-023_S_0058-I118841-wm.nii.gz\n",
      "finish smt-031_S_1209-I92205-gm.nii.gz\n",
      "finish smt-016_S_1117-I90866-wm.nii.gz\n",
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     ]
    }
   ],
   "source": [
    "def generate_dataset_2D_mul(out_dir):\n",
    "    # pair the image with labels and put them into respective folder for gm and wm, generate all the 2-D images\n",
    "    \n",
    "    for filename in os.listdir(data_path):\n",
    "        file_path = os.path.join(data_path, filename)\n",
    "        if os.path.isfile(file_path) and filename.endswith('nii.gz') and filename.startswith('smt'):\n",
    "            split_str = filename.split('-')\n",
    "            # print(split_str)\n",
    "            img_id = split_str[2]\n",
    "            subject = split_str[1]\n",
    "            assert (img_id, subject) in df_dict\n",
    "            class_name = df_dict[(img_id, subject)]\n",
    "            # print(type(category))\n",
    "            # print(os.path.join(data_path, filename))\n",
    "            img = nib.load(os.path.join(data_path, filename))\n",
    "            # print(img)\n",
    "            \n",
    "            data = img.get_fdata()\n",
    "            \n",
    "            z_shape = data.shape[0]\n",
    "            \n",
    "            for i in range(40,80):\n",
    "                np_array = data[i,:,:]\n",
    "                # plt.imshow(np_array, cmap='gray')\n",
    "                # print(i, np_array)\n",
    "                img = Image.fromarray(np_array)\n",
    "                # plt.imshow(np.array(img), cmap='gray')\n",
    "                # plt.show()\n",
    "                folder_name = filename.split('.')[0]\n",
    "                filename_new = folder_name + f'_{str(i)}.tiff'\n",
    "                # print(filename, filename_new)\n",
    "                gm_wm = filename.split('.')[0].split('-')[-1]\n",
    "                # print(os.path.join(out_dir, gm_wm, class_name, folder_name, filename_new))\n",
    "                train_test = df_train_test_dict[(img_id, subject)]\n",
    "                if not os.path.exists(os.path.join(out_dir, train_test, gm_wm, class_name, folder_name)):\n",
    "                    os.makedirs(os.path.join(out_dir, train_test, gm_wm, class_name, folder_name))\n",
    "                # print(os.path.join(out_dir, train_test, gm_wm, class_name, folder_name))\n",
    "                # np.save(os.path.join(out_dir,train_test, gm_wm, class_name, folder_name, filename_new), np_array)\n",
    "                img.save(os.path.join(out_dir,train_test, gm_wm, class_name, folder_name, filename_new))\n",
    "\n",
    "            # for i in range(z_shape):\n",
    "            #     np_array = data[i,:,:]\n",
    "            #     img = Image.fromarray(np.uint8(np_array))\n",
    "            #     folder_name = filename.split('.')[0]\n",
    "            #     filename_new = folder_name + f'_{str(i)}.jpg'\n",
    "            #     # print(filename, filename_new)\n",
    "            #     gm_wm = filename.split('.')[0].split('-')[-1]\n",
    "            #     # print(os.path.join(out_dir, gm_wm, class_name, folder_name, filename_new))\n",
    "            #     if not os.path.exists(os.path.join(out_dir, gm_wm, class_name, folder_name)):\n",
    "            #         os.makedirs(os.path.join(out_dir, gm_wm, class_name, folder_name))\n",
    "            #     # print(os.path.join(out_dir, class_name, filename_new)) \n",
    "            #     img.save(os.path.join(out_dir, gm_wm, class_name, folder_name, filename_new))\n",
    "            print('finish', filename)\n",
    "out_dir_mul = '/scratch/users/neuroimage/conda/data/paired_data/2D_mul'\n",
    "generate_dataset_2D_mul(out_dir_mul)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model and Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision import models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchvision import datasets, transforms\n",
    "def tiff_loader(path: str) -> Image.Image:\n",
    "    # open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835)\n",
    "    with open(path, \"rb\") as f:\n",
    "        img = Image.open(f)\n",
    "        # print(np.array(img).shape)\n",
    "        # plt.imshow(np.array(img),cmap='gray')\n",
    "        # plt.show()\n",
    "        img.load()\n",
    "        \n",
    "        return img # .convert('RGB')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "def set_all_seeds(seed):\n",
    "    os.environ[\"PL_GLOBAL_SEED\"] = str(seed)\n",
    "    random.seed(seed)\n",
    "    np.random.seed(seed)\n",
    "    torch.manual_seed(seed)\n",
    "    torch.cuda.manual_seed_all(seed)\n",
    "RANDOM_SEED = 123\n",
    "NUM_EPOCHS = 50\n",
    "DEVICE = torch.device('cuda:2' if torch.cuda.is_available() else 'cpu')\n",
    "set_all_seeds(RANDOM_SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# helper function\n",
    "from itertools import product\n",
    "def compute_accuracy(model, data_loader, device, repeat = False):\n",
    "\n",
    "    with torch.no_grad():\n",
    "\n",
    "        correct_pred, num_examples = 0, 0\n",
    "\n",
    "        for i, (features, targets) in enumerate(data_loader):\n",
    "            # print(features[0,:,:,:].transpose(0,1).transpose(1,2).shape)\n",
    "            # plt.imshow(features[0,:,:,:].transpose(0,1).transpose(1,2))\n",
    "            # plt.show()\n",
    "            if repeat:\n",
    "                features = features.repeat(1,3,1,1)\n",
    "            features = features.to(device)\n",
    "            # torch.set_printoptions(precision=3)\n",
    "            # torch.set_printoptions(threshold=float(\"inf\"))\n",
    "            \n",
    "            targets = targets.float().to(device)\n",
    "\n",
    "            logits = model(features)\n",
    "            _, predicted_labels = torch.max(logits, 1)\n",
    "            # print(a, predicted_labels)\n",
    "            # print(targets, predicted_labels)\n",
    "\n",
    "            num_examples += targets.size(0)\n",
    "            correct_pred += (predicted_labels == targets).sum()\n",
    "    return correct_pred.float()/num_examples * 100\n",
    "# feat = compute_accuracy(model, train_loader, DEVICE)\n",
    "# print(compute_accuracy(model, val_loader, DEVICE))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model 1. simple resnet 18/50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 31\n",
    "\n",
    "train_transform = transforms.Compose([\n",
    "    transforms.Resize((224,224)),\n",
    "    transforms.RandomResizedCrop(224),\n",
    "    # transforms.ColorJitter(contrast=0.4),\n",
    "    transforms.ElasticTransform(alpha=70.0, sigma=7.0),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize([0.2170], [0.2997])  # wm\n",
    "    # transforms.Normalize([0.1690], [0.2304]) # gm\n",
    "])\n",
    "test_transform = transforms.Compose([\n",
    "    transforms.Resize((224,224)),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize([0.2170], [0.2997])  # wm\n",
    "    # transforms.Normalize([0.1690], [0.2304]) # gm\n",
    "])\n",
    "\n",
    "out_dir_single = '/scratch/users/neuroimage/conda/data/paired_data/2D_single'\n",
    "train_path = os.path.join(out_dir_single, 'train', 'wm')\n",
    "train_dataset = datasets.ImageFolder(train_path, transform=train_transform, loader=tiff_loader)\n",
    "train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=1)\n",
    "\n",
    "val_path = os.path.join(out_dir_single, 'val', 'wm')\n",
    "val_dataset = datasets.ImageFolder(val_path, transform=test_transform, loader=tiff_loader)\n",
    "val_loader = torch.utils.data.DataLoader(val_dataset,  shuffle=True)\n",
    "\n",
    "test_path = os.path.join(out_dir_single, 'test', 'wm')\n",
    "test_dataset = datasets.ImageFolder(test_path, transform=test_transform, loader=tiff_loader)\n",
    "test_loader = torch.utils.data.DataLoader(test_dataset,  shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean: tensor([-9.1694e-05])\n",
      "Std: tensor([1.0000])\n"
     ]
    }
   ],
   "source": [
    "train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=len(train_dataset.imgs), shuffle=False, num_workers=1)\n",
    "\n",
    "for data in train_loader:\n",
    "    pixels = data[0]\n",
    "    # pixels.append(data[0].squeeze())\n",
    "\n",
    "\n",
    "mean = torch.mean(pixels, dim=(0, 2, 3))\n",
    "std = torch.std(pixels, dim=(0, 2, 3))\n",
    "\n",
    "print('Mean:', mean)\n",
    "print('Std:', std)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ResNet(\n",
       "  (conv1): Conv2d(1, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (relu): ReLU(inplace=True)\n",
       "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "  (layer1): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (layer2): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (3): Bottleneck(\n",
       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (layer3): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (3): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (4): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (5): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (layer4): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
       "  (fc): Linear(in_features=2048, out_features=3, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "# resnet\n",
    "model = models.resnet50(weights=models.ResNet50_Weights.IMAGENET1K_V2)\n",
    "num_ftrs = model.fc.in_features\n",
    "model.fc = nn.Linear(num_ftrs, len(train_dataset.classes), bias=True)\n",
    "\n",
    "# path = model_dir + \"/resnet50_wd0.001_normalize/resnet50_05012343/resnet50_05012343_epoch32.pth\"\n",
    "model.conv1 = nn.Conv2d(1, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
    "path = \"/scratch/users/neuroimage/conda/data/models/resnet50_wd0.001_normalize_layer34_lr0.0005_aug/resnet50_05030801/resnet50_05030801_epoch4.pth\"\n",
    "model.load_state_dict(torch.load(path))\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([64, 1, 7, 7])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conv1_weight = model.state_dict()['conv1.weight']\n",
    "conv1_weight_agg = conv1_weight.sum(axis=1, keepdim=True)\n",
    "conv1_weight_agg.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "for name, param in model.named_parameters():\n",
    "    if 'layer4' in name or 'layer3' in name or 'layer2' in name:\n",
    "        param.requires_grad = True \n",
    "    elif 'fc' in name:\n",
    "        param.requires_grad = True \n",
    "    else:\n",
    "        param.requires_grad = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.fc.requires_grad = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.conv1.weight.data = conv1_weight_agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = model.to(device)\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "def train_model(model, num_epochs, train_loader,\n",
    "                valid_loader, test_loader, optimizer,\n",
    "                device, logging_interval=50,\n",
    "                scheduler=None,\n",
    "                scheduler_on='valid_acc', model_path = None, save_interval = 10, timestamp = None):\n",
    "\n",
    "    start_time = time.time()\n",
    "    minibatch_loss_list, train_acc_list, valid_acc_list = [], [], []\n",
    "    \n",
    "    for epoch in tqdm(range(num_epochs)):\n",
    "\n",
    "        model.train()\n",
    "        for batch_idx, (features, targets) in enumerate(train_loader):\n",
    "            # features = features.repeat(1,3,1,1).to(device)\n",
    "            features = features.to(device)\n",
    "            targets = targets.to(device)\n",
    "\n",
    "            # ## FORWARD AND BACK PROP\n",
    "            logits = model(features)\n",
    "            loss = torch.nn.functional.cross_entropy(logits, targets)\n",
    "            optimizer.zero_grad()\n",
    "\n",
    "            loss.backward()\n",
    "\n",
    "            # ## UPDATE MODEL PARAMETERS\n",
    "            optimizer.step()\n",
    "            # print(model.fc.weight.grad)\n",
    "\n",
    "            # ## LOGGING\n",
    "            minibatch_loss_list.append(loss.item())\n",
    "            if not batch_idx % logging_interval:\n",
    "                print(f'Epoch: {epoch+1:03d}/{num_epochs:03d} '\n",
    "                      f'| Batch {batch_idx:04d}/{len(train_loader):04d} '\n",
    "                      f'| Loss: {loss:.4f}')\n",
    "\n",
    "        model.eval()\n",
    "        with torch.no_grad():  # save memory during inference\n",
    "            train_acc = compute_accuracy(model, train_loader, device=device)\n",
    "            valid_acc = compute_accuracy(model, valid_loader, device=device)\n",
    "            print(f'Epoch: {epoch+1:03d}/{num_epochs:03d} '\n",
    "                  f'| Train: {train_acc :.2f}% '\n",
    "                  f'| Validation: {valid_acc :.2f}%')\n",
    "            train_acc_list.append(train_acc.item())\n",
    "            valid_acc_list.append(valid_acc.item())\n",
    "\n",
    "        elapsed = (time.time() - start_time)/60\n",
    "        print(f'Time elapsed: {elapsed:.2f} min')\n",
    "        \n",
    "        if scheduler is not None:\n",
    "\n",
    "            if scheduler_on == 'valid_acc':\n",
    "                scheduler.step(valid_acc_list[-1])\n",
    "            elif scheduler_on == 'minibatch_loss':\n",
    "                scheduler.step(minibatch_loss_list[-1])\n",
    "            else:\n",
    "                raise ValueError(f'Invalid `scheduler_on` choice.')\n",
    "        \n",
    "        if epoch % save_interval == 0:\n",
    "            model_prefix = \"resnet50\"\n",
    "            model_name = f\"{model_prefix}_{timestamp}_epoch{epoch}.pth\"\n",
    "            torch.save(model.state_dict(), os.path.join(model_path, model_name))\n",
    "            \n",
    "            test_acc = compute_accuracy(model, test_loader, device=device)\n",
    "            print(f'Test accuracy {test_acc :.2f}%')\n",
    "            \n",
    "\n",
    "    elapsed = (time.time() - start_time)/60\n",
    "    print(f'Total Training Time: {elapsed:.2f} min')\n",
    "\n",
    "    test_acc = compute_accuracy(model, test_loader, device=device)\n",
    "    print(f'Test accuracy {test_acc :.2f}%')\n",
    "\n",
    "    return minibatch_loss_list, train_acc_list, valid_acc_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "import copy\n",
    "model = copy.deepcopy(model4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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    },
    {
     "name": "stdout",
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     "text": [
      "Epoch: 001/050 | Batch 0000/0010 | Loss: 0.0902\n",
      "Epoch: 001/050 | Batch 0001/0010 | Loss: 0.2066\n",
      "Epoch: 001/050 | Batch 0002/0010 | Loss: 0.3315\n",
      "Epoch: 001/050 | Batch 0003/0010 | Loss: 0.1659\n",
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      "Epoch: 001/050 | Batch 0005/0010 | Loss: 0.3094\n",
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      "Epoch: 001/050 | Batch 0009/0010 | Loss: 0.2061\n",
      "Epoch: 001/050 | Train: 90.79% | Validation: 81.40%\n",
      "Time elapsed: 3.67 min\n"
     ]
    },
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    },
    {
     "name": "stdout",
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     "text": [
      "Test accuracy 91.95%\n",
      "Epoch: 002/050 | Batch 0000/0010 | Loss: 0.3653\n",
      "Epoch: 002/050 | Batch 0001/0010 | Loss: 0.1835\n",
      "Epoch: 002/050 | Batch 0002/0010 | Loss: 0.2461\n",
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      "Epoch: 002/050 | Batch 0009/0010 | Loss: 0.4863\n",
      "Epoch: 002/050 | Train: 85.20% | Validation: 90.70%\n",
      "Time elapsed: 8.08 min\n"
     ]
    },
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    },
    {
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      "Test accuracy 83.91%\n",
      "Epoch: 003/050 | Batch 0000/0010 | Loss: 0.2351\n",
      "Epoch: 003/050 | Batch 0001/0010 | Loss: 0.4844\n",
      "Epoch: 003/050 | Batch 0002/0010 | Loss: 0.2895\n",
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      "Epoch: 003/050 | Train: 88.16% | Validation: 67.44%\n",
      "Time elapsed: 12.55 min\n"
     ]
    },
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      "Test accuracy 81.61%\n",
      "Epoch: 004/050 | Batch 0000/0010 | Loss: 0.5209\n",
      "Epoch: 004/050 | Batch 0001/0010 | Loss: 0.2202\n",
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      "Epoch: 004/050 | Train: 89.47% | Validation: 81.40%\n",
      "Time elapsed: 16.92 min\n"
     ]
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      "Test accuracy 86.21%\n",
      "Epoch: 005/050 | Batch 0000/0010 | Loss: 0.2023\n",
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      "Epoch: 005/050 | Train: 88.49% | Validation: 79.07%\n",
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      "Test accuracy 86.21%\n",
      "Epoch: 006/050 | Batch 0000/0010 | Loss: 0.1986\n",
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      "Epoch: 006/050 | Train: 91.78% | Validation: 81.40%\n",
      "Time elapsed: 25.62 min\n"
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      "Test accuracy 88.51%\n",
      "Epoch: 007/050 | Batch 0000/0010 | Loss: 0.1574\n",
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      "Epoch: 007/050 | Train: 91.12% | Validation: 88.37%\n",
      "Time elapsed: 30.11 min\n"
     ]
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      "Test accuracy 90.80%\n",
      "Epoch: 008/050 | Batch 0000/0010 | Loss: 0.0867\n",
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      "Epoch: 008/050 | Train: 91.12% | Validation: 86.05%\n",
      "Time elapsed: 34.57 min\n"
     ]
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      "Test accuracy 91.95%\n",
      "Epoch: 009/050 | Batch 0000/0010 | Loss: 0.1502\n",
      "Epoch: 009/050 | Batch 0001/0010 | Loss: 0.2515\n",
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      "Epoch: 009/050 | Train: 88.82% | Validation: 86.05%\n",
      "Time elapsed: 38.88 min\n"
     ]
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      "Test accuracy 87.36%\n",
      "Epoch: 010/050 | Batch 0000/0010 | Loss: 0.1644\n",
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      "Epoch: 010/050 | Train: 90.46% | Validation: 79.07%\n",
      "Time elapsed: 43.25 min\n"
     ]
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      "Test accuracy 91.95%\n",
      "Epoch: 011/050 | Batch 0000/0010 | Loss: 0.3549\n",
      "Epoch: 011/050 | Batch 0001/0010 | Loss: 0.2571\n",
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      "Epoch: 011/050 | Batch 0009/0010 | Loss: 0.2923\n",
      "Epoch: 011/050 | Train: 90.13% | Validation: 72.09%\n",
      "Time elapsed: 47.61 min\n",
      "Epoch 00011: reducing learning rate of group 0 to 4.0000e-05.\n"
     ]
    },
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    },
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     "text": [
      "Test accuracy 77.01%\n",
      "Epoch: 012/050 | Batch 0000/0010 | Loss: 0.2402\n",
      "Epoch: 012/050 | Batch 0001/0010 | Loss: 0.1937\n",
      "Epoch: 012/050 | Batch 0002/0010 | Loss: 0.1713\n",
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      "Epoch: 012/050 | Train: 91.12% | Validation: 79.07%\n",
      "Time elapsed: 52.00 min\n"
     ]
    },
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     "text": [
      "Test accuracy 80.46%\n",
      "Epoch: 013/050 | Batch 0000/0010 | Loss: 0.2148\n",
      "Epoch: 013/050 | Batch 0001/0010 | Loss: 0.1343\n",
      "Epoch: 013/050 | Batch 0002/0010 | Loss: 0.1726\n",
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      "Epoch: 013/050 | Batch 0009/0010 | Loss: 0.2639\n",
      "Epoch: 013/050 | Train: 92.43% | Validation: 83.72%\n",
      "Time elapsed: 56.58 min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      " 26%|██▌       | 13/50 [56:49<2:44:46, 267.20s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy 88.51%\n",
      "Epoch: 014/050 | Batch 0000/0010 | Loss: 0.1762\n",
      "Epoch: 014/050 | Batch 0001/0010 | Loss: 0.2656\n",
      "Epoch: 014/050 | Batch 0002/0010 | Loss: 0.0918\n",
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      "Epoch: 014/050 | Train: 94.08% | Validation: 83.72%\n",
      "Time elapsed: 61.05 min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      " 28%|██▊       | 14/50 [1:01:14<2:39:49, 266.38s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy 91.95%\n",
      "Epoch: 015/050 | Batch 0000/0010 | Loss: 0.2654\n",
      "Epoch: 015/050 | Batch 0001/0010 | Loss: 0.1531\n",
      "Epoch: 015/050 | Batch 0002/0010 | Loss: 0.1460\n",
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      "Epoch: 015/050 | Train: 94.41% | Validation: 83.72%\n",
      "Time elapsed: 65.56 min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      " 30%|███       | 15/50 [1:05:48<2:36:43, 268.67s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy 93.10%\n",
      "Epoch: 016/050 | Batch 0000/0010 | Loss: 0.3481\n",
      "Epoch: 016/050 | Batch 0001/0010 | Loss: 0.2669\n",
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      "Epoch: 016/050 | Train: 92.76% | Validation: 86.05%\n",
      "Time elapsed: 70.15 min\n"
     ]
    },
    {
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      " 32%|███▏      | 16/50 [1:10:23<2:33:20, 270.62s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy 93.10%\n",
      "Epoch: 017/050 | Batch 0000/0010 | Loss: 0.0679\n",
      "Epoch: 017/050 | Batch 0001/0010 | Loss: 0.1270\n",
      "Epoch: 017/050 | Batch 0002/0010 | Loss: 0.1482\n",
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      "Epoch: 017/050 | Train: 92.43% | Validation: 88.37%\n",
      "Time elapsed: 74.63 min\n"
     ]
    },
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      " 34%|███▍      | 17/50 [1:14:49<2:28:03, 269.19s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy 91.95%\n",
      "Epoch: 018/050 | Batch 0000/0010 | Loss: 0.0621\n",
      "Epoch: 018/050 | Batch 0001/0010 | Loss: 0.1906\n",
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      "Epoch: 018/050 | Train: 95.07% | Validation: 86.05%\n",
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     ]
    },
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      " 36%|███▌      | 18/50 [1:19:20<2:23:59, 269.98s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy 93.10%\n",
      "Epoch: 019/050 | Batch 0000/0010 | Loss: 0.2744\n",
      "Epoch: 019/050 | Batch 0001/0010 | Loss: 0.2420\n",
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      "Epoch: 019/050 | Train: 92.43% | Validation: 86.05%\n",
      "Time elapsed: 83.45 min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      " 38%|███▊      | 19/50 [1:23:38<2:17:35, 266.30s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy 90.80%\n",
      "Epoch: 020/050 | Batch 0000/0010 | Loss: 0.1640\n",
      "Epoch: 020/050 | Batch 0001/0010 | Loss: 0.0409\n",
      "Epoch: 020/050 | Batch 0002/0010 | Loss: 0.1422\n",
      "Epoch: 020/050 | Batch 0003/0010 | Loss: 0.2208\n",
      "Epoch: 020/050 | Batch 0004/0010 | Loss: 0.0615\n",
      "Epoch: 020/050 | Batch 0005/0010 | Loss: 0.2224\n",
      "Epoch: 020/050 | Batch 0006/0010 | Loss: 0.1463\n",
      "Epoch: 020/050 | Batch 0007/0010 | Loss: 0.2129\n",
      "Epoch: 020/050 | Batch 0008/0010 | Loss: 0.1420\n",
      "Epoch: 020/050 | Batch 0009/0010 | Loss: 0.1437\n"
     ]
    }
   ],
   "source": [
    "optimizer = optim.Adam(model.parameters(), lr=0.0002, weight_decay=0.001)\n",
    "scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer,\n",
    "                                                       factor=0.2,\n",
    "                                                       mode='max',\n",
    "                                                       verbose=True, patience=8)\n",
    "\n",
    "NUM_EPOCHS = 50\n",
    "model_prefix = \"resnet50\"\n",
    "\n",
    "from datetime import datetime\n",
    "timestamp = datetime.now().strftime(\"%m%d%H%M\")\n",
    "model_name_path = f\"{model_prefix}_{timestamp}\"\n",
    "model_path = os.path.join(model_dir, model_prefix + '_wd0.001_normalize_layer234_lr0.0002_aug_alpha70_sigma7', model_name_path)\n",
    "\n",
    "if not os.path.exists(model_path):\n",
    "    os.makedirs(model_path)\n",
    "\n",
    "\n",
    "minibatch_loss_list, train_acc_list, valid_acc_list = train_model(\n",
    "    model=model,\n",
    "    num_epochs=NUM_EPOCHS,\n",
    "    train_loader=train_loader,\n",
    "    valid_loader=val_loader,\n",
    "    test_loader=test_loader,\n",
    "    optimizer=optimizer,\n",
    "    device=DEVICE,\n",
    "    scheduler=scheduler,\n",
    "    scheduler_on='valid_acc',\n",
    "    logging_interval=1,  model_path = model_path, save_interval = 1, timestamp = timestamp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/scratch/users/neuroimage/conda/data/models'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_dir\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "minibatch_loss_list1, train_acc_list1, valid_acc_list1 = minibatch_loss_list, train_acc_list, valid_acc_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy 94.25%\n",
      "Val accuracy 83.72%\n"
     ]
    }
   ],
   "source": [
    "# path = \"dl_models/resnet50_wd0.001_normalize/resnet50_05012343/resnet50_05012343_epoch32.pth\"\n",
    "# model.load_state_dict(torch.load(path)) \n",
    "# path = '/scratch/users/neuroimage/conda/data/models/resnet50_wd0.001_normalize_layer4_lr0.0005_aug/resnet50_05030557/resnet50_05030557_epoch40.pth'\n",
    "# model40 = copy.deepcopy(model)\n",
    "# model40.load_state_dict(torch.load(path)) \n",
    "\n",
    "\n",
    "# path = '/scratch/users/neuroimage/conda/data/models/resnet50_wd0.001_normalize_layer34_lr0.0005_aug/resnet50_05030801/resnet50_05030801_epoch8.pth'\n",
    "# model4 = copy.deepcopy(model)\n",
    "# model4.load_state_dict(torch.load(path)) \n",
    "# model4.eval()\n",
    "model.eval()\n",
    "test_acc = compute_accuracy(model, test_loader, device=device, repeat=False)\n",
    "print(f'Test accuracy {test_acc :.2f}%')\n",
    "\n",
    "val_acc = compute_accuracy(model, val_loader, device=device, repeat=False)\n",
    "print(f'Val accuracy {val_acc :.2f}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "model_prefix = \"resnet50\"\n",
    "\n",
    "# timestamp = datetime.now().strftime(\"%m%d%H%M\")\n",
    "# model_name_path = f\"{model_prefix}_{timestamp}\"\n",
    "# model_path = os.path.join(model_dir, model_prefix, model_name_path)\n",
    "timestamp = datetime.now().strftime(\"%m%d%H%M\")\n",
    "model_name_path = f\"{model_prefix}_{timestamp}\"\n",
    "model_path = os.path.join(model_dir, model_prefix + '_wd0.001_normalize_layer4_lr0.0005', model_name_path)\n",
    "\n",
    "if not os.path.exists(model_path):\n",
    "    os.makedirs(model_path)\n",
    "\n",
    "\n",
    "model_name = f\"{model_prefix}_{timestamp}.pth\"\n",
    "\n",
    "torch.save(model.state_dict(), os.path.join(model_path, model_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/scratch/users/neuroimage/conda/data/models/resnet50_wd0.001_normalize_layer4_lr0.0005/resnet50_05030519/resnet50_05030519.pth'"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.path.join(model_path, model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_training_loss(minibatch_loss_list, num_epochs, iter_per_epoch,\n",
    "                       results_dir=None, averaging_iterations=100):\n",
    "\n",
    "    plt.figure()\n",
    "    ax1 = plt.subplot(1, 1, 1)\n",
    "    ax1.plot(range(len(minibatch_loss_list)),\n",
    "             (minibatch_loss_list), label='Minibatch Loss')\n",
    "\n",
    "    if len(minibatch_loss_list) > 1000:\n",
    "        ax1.set_ylim([\n",
    "            0, np.max(minibatch_loss_list[1000:])*1.5\n",
    "            ])\n",
    "    ax1.set_xlabel('Iterations')\n",
    "    ax1.set_ylabel('Loss')\n",
    "\n",
    "    # ax1.plot(np.convolve(minibatch_loss_list,\n",
    "    #                      np.ones(averaging_iterations,)/averaging_iterations,\n",
    "    #                      mode='valid'),\n",
    "    #          label='Running Average')\n",
    "    ax1.legend()\n",
    "\n",
    "    ###################\n",
    "    # Set scond x-axis\n",
    "    ax2 = ax1.twiny()\n",
    "    newlabel = list(range(num_epochs+1))\n",
    "\n",
    "    newpos = [e*iter_per_epoch for e in newlabel]\n",
    "\n",
    "    ax2.set_xticks(newpos[::10])\n",
    "    ax2.set_xticklabels(newlabel[::10])\n",
    "\n",
    "    ax2.xaxis.set_ticks_position('bottom')\n",
    "    ax2.xaxis.set_label_position('bottom')\n",
    "    ax2.spines['bottom'].set_position(('outward', 45))\n",
    "    ax2.set_xlabel('Epochs')\n",
    "    ax2.set_xlim(ax1.get_xlim())\n",
    "    ###################\n",
    "\n",
    "    plt.tight_layout()\n",
    "\n",
    "    if results_dir is not None:\n",
    "        image_path = os.path.join(results_dir, 'plot_training_loss.png')\n",
    "        plt.savefig(image_path)\n",
    "\n",
    "\n",
    "def plot_accuracy(train_acc_list, valid_acc_list, results_dir):\n",
    "\n",
    "    num_epochs = len(train_acc_list)\n",
    "\n",
    "    plt.plot(np.arange(1, num_epochs+1),\n",
    "             train_acc_list, label='Training')\n",
    "    plt.plot(np.arange(1, num_epochs+1),\n",
    "             valid_acc_list, label='Validation')\n",
    "\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Accuracy')\n",
    "    plt.legend()\n",
    "\n",
    "    # plt.tight_layout()\n",
    "\n",
    "    if results_dir is not None:\n",
    "        image_path = os.path.join(\n",
    "            results_dir, 'plot_acc_training_validation.png')\n",
    "        plt.savefig(image_path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_training_loss(minibatch_loss_list=minibatch_loss_list,\n",
    "                   num_epochs=NUM_EPOCHS,\n",
    "                   iter_per_epoch=len(train_loader),\n",
    "                   results_dir=model_path,\n",
    "                   averaging_iterations=200)\n",
    "plt.show()\n",
    "\n",
    "plot_accuracy(train_acc_list=train_acc_list,\n",
    "              valid_acc_list=valid_acc_list,\n",
    "              results_dir=model_path)\n",
    "plt.ylim([60, 100])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model 2 \"3D\" Resnet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "def compute_accuracy_test(model, data_loader, device):\n",
    "\n",
    "    with torch.no_grad():\n",
    "\n",
    "        correct_pred, num_examples = 0, 0\n",
    "\n",
    "        for i, (features, targets) in enumerate(data_loader):\n",
    "            # print(features[0,:,:,:].transpose(0,1).transpose(1,2).shape)\n",
    "            # plt.imshow(features[0,:,:,:].transpose(0,1).transpose(1,2))\n",
    "            # plt.show()\n",
    "            features = features.repeat(1,3,1,1)\n",
    "            features = features.to(device)\n",
    "            # torch.set_printoptions(precision=3)\n",
    "            # torch.set_printoptions(threshold=float(\"inf\"))\n",
    "            \n",
    "            targets = targets.float().to(device)\n",
    "\n",
    "            logits = model(features)\n",
    "            _, predicted_labels = torch.max(logits, 1)\n",
    "            # print(a, predicted_labels)\n",
    "            # print(targets, predicted_labels)\n",
    "            print(i)\n",
    "            break\n",
    "            # num_examples += targets.size(0)\n",
    "            # correct_pred += (predicted_labels == targets).sum()\n",
    "    return # correct_pred.float()/num_examples * 100\n",
    "compute_accuracy_test(model, val_loader, device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 304\n",
    "\n",
    "data_transform = transforms.Compose([\n",
    "    transforms.Resize((224,224)),\n",
    "    # transforms.RandomResizedCrop(224),\n",
    "    # transforms.RandomHorizontalFlip(),\n",
    "    transforms.ToTensor(),\n",
    "    # transforms.Normalize([0.2170], [0.2997])\n",
    "    # transforms.Normalize([0.1690], [0.2304])\n",
    "])\n",
    "\n",
    "out_dir_single = '/scratch/users/neuroimage/conda/data/paired_data/2D_mul'\n",
    "train_path = os.path.join(out_dir_single, 'train', 'wm')\n",
    "train_dataset = datasets.ImageFolder(train_path, transform=data_transform, loader=tiff_loader)\n",
    "train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=1)\n",
    "\n",
    "val_path = os.path.join(out_dir_single, 'val', 'wm')\n",
    "val_dataset = datasets.ImageFolder(val_path, transform=data_transform, loader=tiff_loader)\n",
    "val_loader = torch.utils.data.DataLoader(val_dataset,  shuffle=True)\n",
    "\n",
    "test_path = os.path.join(out_dir_single, 'test', 'wm')\n",
    "test_dataset = datasets.ImageFolder(test_path, transform=data_transform, loader=tiff_loader)\n",
    "test_loader = torch.utils.data.DataLoader(test_dataset,  shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12160"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_dataset.imgs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "# resnet\n",
    "model = models.resnet50(weights=models.ResNet50_Weights.IMAGENET1K_V2)\n",
    "for param in model.parameters():\n",
    "    param.requires_grad = False\n",
    "# model\n",
    "num_ftrs = model.fc.in_features\n",
    "model.fc = nn.Linear(num_ftrs, len(train_dataset.classes), bias=True)\n",
    "\n",
    "model.fc.requires_grad = True\n",
    "\n",
    "model = model.to(device)\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(model, num_epochs, train_loader,\n",
    "                valid_loader, test_loader, optimizer,\n",
    "                device, logging_interval=50,\n",
    "                scheduler=None,\n",
    "                scheduler_on='valid_acc', model_path = None, save_interval = 10, timestamp = None):\n",
    "\n",
    "    start_time = time.time()\n",
    "    minibatch_loss_list, train_acc_list, valid_acc_list = [], [], []\n",
    "    \n",
    "    for epoch in tqdm(range(num_epochs)):\n",
    "\n",
    "        model.train()\n",
    "        for batch_idx, (features, targets) in enumerate(train_loader):\n",
    "            features = features.repeat(1,3,1,1).to(device)\n",
    "            targets = targets.to(device)\n",
    "\n",
    "            # ## FORWARD AND BACK PROP\n",
    "            logits = model(features)\n",
    "            loss = torch.nn.functional.cross_entropy(logits, targets)\n",
    "            optimizer.zero_grad()\n",
    "\n",
    "            loss.backward()\n",
    "\n",
    "            # ## UPDATE MODEL PARAMETERS\n",
    "            optimizer.step()\n",
    "            # print(model.fc.weight.grad)\n",
    "\n",
    "            # ## LOGGING\n",
    "            minibatch_loss_list.append(loss.item())\n",
    "            if not batch_idx % logging_interval:\n",
    "                print(f'Epoch: {epoch+1:03d}/{num_epochs:03d} '\n",
    "                      f'| Batch {batch_idx:04d}/{len(train_loader):04d} '\n",
    "                      f'| Loss: {loss:.4f}')\n",
    "\n",
    "        model.eval()\n",
    "        with torch.no_grad():  # save memory during inference\n",
    "            train_acc = compute_accuracy(model, train_loader, device=device)\n",
    "            valid_acc = compute_accuracy(model, valid_loader, device=device)\n",
    "            print(f'Epoch: {epoch+1:03d}/{num_epochs:03d} '\n",
    "                  f'| Train: {train_acc :.2f}% '\n",
    "                  f'| Validation: {valid_acc :.2f}%')\n",
    "            train_acc_list.append(train_acc.item())\n",
    "            valid_acc_list.append(valid_acc.item())\n",
    "\n",
    "        elapsed = (time.time() - start_time)/60\n",
    "        print(f'Time elapsed: {elapsed:.2f} min')\n",
    "        \n",
    "        if scheduler is not None:\n",
    "\n",
    "            if scheduler_on == 'valid_acc':\n",
    "                scheduler.step(valid_acc_list[-1])\n",
    "            elif scheduler_on == 'minibatch_loss':\n",
    "                scheduler.step(minibatch_loss_list[-1])\n",
    "            else:\n",
    "                raise ValueError(f'Invalid `scheduler_on` choice.')\n",
    "        \n",
    "        if epoch % save_interval == 0 and epoch > 10:\n",
    "            model_prefix = \"resnet50_3D\"\n",
    "            model_name = f\"{model_prefix}_{timestamp}_epoch{epoch}.pth\"\n",
    "            torch.save(model.state_dict(), os.path.join(model_path, model_name))\n",
    "            test_acc = compute_accuracy(model, test_loader, device=device)\n",
    "            print(f'Test accuracy {test_acc :.2f}%')\n",
    "            \n",
    "\n",
    "    elapsed = (time.time() - start_time)/60\n",
    "    print(f'Total Training Time: {elapsed:.2f} min')\n",
    "\n",
    "    test_acc = compute_accuracy(model, test_loader, device=device)\n",
    "    print(f'Test accuracy {test_acc :.2f}%')\n",
    "\n",
    "    return minibatch_loss_list, train_acc_list, valid_acc_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/50 [02:00<?, ?it/s]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[75], line 20\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mexists(model_path):\n\u001b[1;32m     17\u001b[0m     os\u001b[38;5;241m.\u001b[39mmakedirs(model_path)\n\u001b[0;32m---> 20\u001b[0m minibatch_loss_list, train_acc_list, valid_acc_list \u001b[38;5;241m=\u001b[39m \u001b[43mtrain_model\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     21\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     22\u001b[0m \u001b[43m    \u001b[49m\u001b[43mnum_epochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mNUM_EPOCHS\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     23\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtrain_loader\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrain_loader\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     24\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvalid_loader\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mval_loader\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     25\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtest_loader\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtest_loader\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     26\u001b[0m \u001b[43m    \u001b[49m\u001b[43moptimizer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptimizer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     27\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mDEVICE\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     28\u001b[0m \u001b[43m    \u001b[49m\u001b[43mscheduler\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscheduler\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     29\u001b[0m \u001b[43m    \u001b[49m\u001b[43mscheduler_on\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mvalid_acc\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     30\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlogging_interval\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[43mmodel_path\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mmodel_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msave_interval\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimestamp\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mtimestamp\u001b[49m\u001b[43m)\u001b[49m\n",
      "Cell \u001b[0;32mIn[74], line 18\u001b[0m, in \u001b[0;36mtrain_model\u001b[0;34m(model, num_epochs, train_loader, valid_loader, test_loader, optimizer, device, logging_interval, scheduler, scheduler_on, model_path, save_interval, timestamp)\u001b[0m\n\u001b[1;32m     15\u001b[0m targets \u001b[38;5;241m=\u001b[39m targets\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[1;32m     17\u001b[0m \u001b[38;5;66;03m# ## FORWARD AND BACK PROP\u001b[39;00m\n\u001b[0;32m---> 18\u001b[0m logits \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfeatures\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     19\u001b[0m loss \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mnn\u001b[38;5;241m.\u001b[39mfunctional\u001b[38;5;241m.\u001b[39mcross_entropy(logits, targets)\n\u001b[1;32m     20\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mzero_grad()\n",
      "File \u001b[0;32m/scratch/users/neuroimage/conda/venv/lib/python3.11/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1496\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1497\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1498\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1499\u001b[0m         \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1500\u001b[0m         \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m     \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   1502\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n",
      "File \u001b[0;32m/scratch/users/neuroimage/conda/venv/lib/python3.11/site-packages/torchvision/models/resnet.py:285\u001b[0m, in \u001b[0;36mResNet.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    284\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39mself\u001b[39m, x: Tensor) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Tensor:\n\u001b[0;32m--> 285\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_forward_impl(x)\n",
      "File \u001b[0;32m/scratch/users/neuroimage/conda/venv/lib/python3.11/site-packages/torchvision/models/resnet.py:274\u001b[0m, in \u001b[0;36mResNet._forward_impl\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    271\u001b[0m x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmaxpool(x)\n\u001b[1;32m    273\u001b[0m x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlayer1(x)\n\u001b[0;32m--> 274\u001b[0m x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mlayer2(x)\n\u001b[1;32m    275\u001b[0m x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlayer3(x)\n\u001b[1;32m    276\u001b[0m x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlayer4(x)\n",
      "File \u001b[0;32m/scratch/users/neuroimage/conda/venv/lib/python3.11/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1496\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1497\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1498\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1499\u001b[0m         \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1500\u001b[0m         \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m     \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   1502\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n",
      "File \u001b[0;32m/scratch/users/neuroimage/conda/venv/lib/python3.11/site-packages/torch/nn/modules/container.py:217\u001b[0m, in \u001b[0;36mSequential.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    215\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39minput\u001b[39m):\n\u001b[1;32m    216\u001b[0m     \u001b[39mfor\u001b[39;00m module \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m:\n\u001b[0;32m--> 217\u001b[0m         \u001b[39minput\u001b[39m \u001b[39m=\u001b[39m module(\u001b[39minput\u001b[39;49m)\n\u001b[1;32m    218\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39minput\u001b[39m\n",
      "File \u001b[0;32m/scratch/users/neuroimage/conda/venv/lib/python3.11/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1496\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1497\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1498\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1499\u001b[0m         \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1500\u001b[0m         \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m     \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   1502\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n",
      "File \u001b[0;32m/scratch/users/neuroimage/conda/venv/lib/python3.11/site-packages/torchvision/models/resnet.py:158\u001b[0m, in \u001b[0;36mBottleneck.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    155\u001b[0m out \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mbn3(out)\n\u001b[1;32m    157\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdownsample \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> 158\u001b[0m     identity \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdownsample(x)\n\u001b[1;32m    160\u001b[0m out \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m identity\n\u001b[1;32m    161\u001b[0m out \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mrelu(out)\n",
      "File \u001b[0;32m/scratch/users/neuroimage/conda/venv/lib/python3.11/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1496\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1497\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1498\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1499\u001b[0m         \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1500\u001b[0m         \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m     \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   1502\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n",
      "File \u001b[0;32m/scratch/users/neuroimage/conda/venv/lib/python3.11/site-packages/torch/nn/modules/container.py:217\u001b[0m, in \u001b[0;36mSequential.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    215\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39minput\u001b[39m):\n\u001b[1;32m    216\u001b[0m     \u001b[39mfor\u001b[39;00m module \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m:\n\u001b[0;32m--> 217\u001b[0m         \u001b[39minput\u001b[39m \u001b[39m=\u001b[39m module(\u001b[39minput\u001b[39;49m)\n\u001b[1;32m    218\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39minput\u001b[39m\n",
      "File \u001b[0;32m/scratch/users/neuroimage/conda/venv/lib/python3.11/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1496\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1497\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1498\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1499\u001b[0m         \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1500\u001b[0m         \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m     \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   1502\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n",
      "File \u001b[0;32m/scratch/users/neuroimage/conda/venv/lib/python3.11/site-packages/torch/nn/modules/batchnorm.py:171\u001b[0m, in \u001b[0;36m_BatchNorm.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    164\u001b[0m     bn_training \u001b[39m=\u001b[39m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mrunning_mean \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m) \u001b[39mand\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mrunning_var \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m)\n\u001b[1;32m    166\u001b[0m \u001b[39m\u001b[39m\u001b[39mr\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m    167\u001b[0m \u001b[39mBuffers are only updated if they are to be tracked and we are in training mode. Thus they only need to be\u001b[39;00m\n\u001b[1;32m    168\u001b[0m \u001b[39mpassed when the update should occur (i.e. in training mode when they are tracked), or when buffer stats are\u001b[39;00m\n\u001b[1;32m    169\u001b[0m \u001b[39mused for normalization (i.e. in eval mode when buffers are not None).\u001b[39;00m\n\u001b[1;32m    170\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m--> 171\u001b[0m \u001b[39mreturn\u001b[39;00m F\u001b[39m.\u001b[39;49mbatch_norm(\n\u001b[1;32m    172\u001b[0m     \u001b[39minput\u001b[39;49m,\n\u001b[1;32m    173\u001b[0m     \u001b[39m# If buffers are not to be tracked, ensure that they won't be updated\u001b[39;49;00m\n\u001b[1;32m    174\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mrunning_mean\n\u001b[1;32m    175\u001b[0m     \u001b[39mif\u001b[39;49;00m \u001b[39mnot\u001b[39;49;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtraining \u001b[39mor\u001b[39;49;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtrack_running_stats\n\u001b[1;32m    176\u001b[0m     \u001b[39melse\u001b[39;49;00m \u001b[39mNone\u001b[39;49;00m,\n\u001b[1;32m    177\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mrunning_var \u001b[39mif\u001b[39;49;00m \u001b[39mnot\u001b[39;49;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtraining \u001b[39mor\u001b[39;49;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtrack_running_stats \u001b[39melse\u001b[39;49;00m \u001b[39mNone\u001b[39;49;00m,\n\u001b[1;32m    178\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mweight,\n\u001b[1;32m    179\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mbias,\n\u001b[1;32m    180\u001b[0m     bn_training,\n\u001b[1;32m    181\u001b[0m     exponential_average_factor,\n\u001b[1;32m    182\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49meps,\n\u001b[1;32m    183\u001b[0m )\n",
      "File \u001b[0;32m/scratch/users/neuroimage/conda/venv/lib/python3.11/site-packages/torch/nn/functional.py:2450\u001b[0m, in \u001b[0;36mbatch_norm\u001b[0;34m(input, running_mean, running_var, weight, bias, training, momentum, eps)\u001b[0m\n\u001b[1;32m   2447\u001b[0m \u001b[39mif\u001b[39;00m training:\n\u001b[1;32m   2448\u001b[0m     _verify_batch_size(\u001b[39minput\u001b[39m\u001b[39m.\u001b[39msize())\n\u001b[0;32m-> 2450\u001b[0m \u001b[39mreturn\u001b[39;00m torch\u001b[39m.\u001b[39;49mbatch_norm(\n\u001b[1;32m   2451\u001b[0m     \u001b[39minput\u001b[39;49m, weight, bias, running_mean, running_var, training, momentum, eps, torch\u001b[39m.\u001b[39;49mbackends\u001b[39m.\u001b[39;49mcudnn\u001b[39m.\u001b[39;49menabled\n\u001b[1;32m   2452\u001b[0m )\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "optimizer = optim.Adam(model.parameters(), lr=0.01, weight_decay=0.0002)\n",
    "scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer,\n",
    "                                                       factor=0.1,\n",
    "                                                       mode='max',\n",
    "                                                       verbose=True)\n",
    "\n",
    "NUM_EPOCHS = 50\n",
    "model_dir = model_path\n",
    "model_prefix = \"resnet50_3D\"\n",
    "\n",
    "from datetime import datetime\n",
    "timestamp = datetime.now().strftime(\"%m%d%H%M\")\n",
    "model_name_path = f\"{model_prefix}_{timestamp}\"\n",
    "model_path = os.path.join(model_dir, model_prefix + '_wd0.0002', model_name_path)\n",
    "\n",
    "if not os.path.exists(model_path):\n",
    "    os.makedirs(model_path)\n",
    "\n",
    "\n",
    "minibatch_loss_list, train_acc_list, valid_acc_list = train_model(\n",
    "    model=model,\n",
    "    num_epochs=NUM_EPOCHS,\n",
    "    train_loader=train_loader,\n",
    "    valid_loader=val_loader,\n",
    "    test_loader=test_loader,\n",
    "    optimizer=optimizer,\n",
    "    device=DEVICE,\n",
    "    scheduler=scheduler,\n",
    "    scheduler_on='valid_acc',\n",
    "    logging_interval=1,  model_path = model_path, save_interval = 5, timestamp = timestamp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some analysis(explainable AI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 1\n",
    "\n",
    "train_transform = transforms.Compose([\n",
    "    transforms.Resize((224,224)),\n",
    "    transforms.RandomResizedCrop(224),\n",
    "    # transforms.ColorJitter(contrast=0.4),\n",
    "    transforms.ElasticTransform(alpha=70.0, sigma=7.0),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize([0.2170], [0.2997])  # wm\n",
    "    # transforms.Normalize([0.1690], [0.2304]) # gm\n",
    "])\n",
    "test_transform = transforms.Compose([\n",
    "    transforms.Resize((224,224)),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize([0.2170], [0.2997])  # wm\n",
    "    # transforms.Normalize([0.1690], [0.2304]) # gm\n",
    "])\n",
    "\n",
    "out_dir_single = '/scratch/users/neuroimage/conda/data/paired_data/2D_single'\n",
    "train_path = os.path.join(out_dir_single, 'train', 'wm')\n",
    "train_dataset = datasets.ImageFolder(train_path, transform=train_transform, loader=tiff_loader)\n",
    "train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=1)\n",
    "\n",
    "val_path = os.path.join(out_dir_single, 'val', 'wm')\n",
    "val_dataset = datasets.ImageFolder(val_path, transform=test_transform, loader=tiff_loader)\n",
    "val_loader = torch.utils.data.DataLoader(val_dataset,  shuffle=True)\n",
    "\n",
    "test_path = os.path.join(out_dir_single, 'test', 'wm')\n",
    "test_dataset = datasets.ImageFolder(test_path, transform=test_transform, loader=tiff_loader)\n",
    "test_loader = torch.utils.data.DataLoader(test_dataset,  shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ResNet(\n",
       "  (conv1): Conv2d(1, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (relu): ReLU(inplace=True)\n",
       "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "  (layer1): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (layer2): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (3): Bottleneck(\n",
       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (layer3): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (3): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (4): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (5): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (layer4): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
       "  (fc): Linear(in_features=2048, out_features=3, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "# resnet\n",
    "model = models.resnet50(weights=models.ResNet50_Weights.IMAGENET1K_V2)\n",
    "num_ftrs = model.fc.in_features\n",
    "model.fc = nn.Linear(num_ftrs, len(train_dataset.classes), bias=True)\n",
    "\n",
    "# path = model_dir + \"/resnet50_wd0.001_normalize/resnet50_05012343/resnet50_05012343_epoch32.pth\"\n",
    "model.conv1 = nn.Conv2d(1, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
    "path = \"/scratch/users/neuroimage/conda/data/models/resnet50_wd0.001_normalize_layer34_lr0.0005_aug/resnet50_05030801/resnet50_05030801_epoch4.pth\"\n",
    "model.load_state_dict(torch.load(path))\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy 94.25%\n",
      "Val accuracy 88.37%\n"
     ]
    }
   ],
   "source": [
    "model.eval()\n",
    "test_acc = compute_accuracy(model, test_loader, device=device, repeat=False)\n",
    "print(f'Test accuracy {test_acc :.2f}%')\n",
    "\n",
    "val_acc = compute_accuracy(model, val_loader, device=device, repeat=False)\n",
    "print(f'Val accuracy {val_acc :.2f}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from torch.autograd import Function\n",
    "from torchvision import models\n",
    "from torchvision import utils\n",
    "import cv2\n",
    "import sys\n",
    "from collections import OrderedDict\n",
    "import numpy as np\n",
    "import argparse\n",
    "import os\n",
    "import torch.nn as nn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.eval()\n",
    "import copy\n",
    "resnet = copy.deepcopy(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "i=0##testing in what\n",
    "image = []\n",
    "class FeatureExtractor():\n",
    "    \"\"\" Class for extracting activations and \n",
    "    registering gradients from targetted intermediate layers \"\"\"\n",
    "    def __init__(self, model, target_layers):\n",
    "        self.model = model\n",
    "        self.target_layers = target_layers\n",
    "        self.gradients = []\n",
    "\n",
    "    def save_gradient(self, grad):\n",
    "        self.gradients.append(grad)\n",
    "\n",
    "    def __call__(self, x):\n",
    "        outputs = []\n",
    "        self.gradients = []\n",
    "        for name, module in self.model._modules.items():##resnet50没有.feature这个特征，直接删除用就可以。\n",
    "            x = module(x)\n",
    "            #print('name=',name)\n",
    "            #print('x.size()=',x.size())\n",
    "            if name in self.target_layers:\n",
    "                x.register_hook(self.save_gradient)\n",
    "                outputs += [x]\n",
    "            #print('outputs.size()=',x.size())\n",
    "        #print('len(outputs)',len(outputs))\n",
    "        return outputs, x\n",
    "\n",
    "class ModelOutputs():\n",
    "    \"\"\" Class for making a forward pass, and getting:\n",
    "    1. The network output.\n",
    "    2. Activations from intermeddiate targetted layers.\n",
    "    3. Gradients from intermeddiate targetted layers. \"\"\"\n",
    "    def __init__(self, model, target_layers,use_cuda):\n",
    "        self.model = model\n",
    "        self.feature_extractor = FeatureExtractor(self.model, target_layers)\n",
    "        self.cuda = use_cuda\n",
    "    def get_gradients(self):\n",
    "        return self.feature_extractor.gradients\n",
    "\n",
    "    def __call__(self, x):\n",
    "        target_activations, output  = self.feature_extractor(x)\n",
    "        output = output.view(output.size(0), -1)\n",
    "        #print('classfier=',output.size())\n",
    "        if self.cuda:\n",
    "            output = output.cpu()\n",
    "            output = resnet.fc(output).cuda()##这里就是为什么我们多加载一个resnet模型进来的原因，因为后面我们命名的model不包含fc层，但是这里又偏偏要使用。#\n",
    "        else:\n",
    "            output = resnet.fc(output)##这里对应use-cuda上更正一些bug,不然用use-cuda的时候会导致类型对不上,这样保证既可以在cpu上运行,gpu上运行也不会出问题.\n",
    "        return target_activations, output\n",
    "\n",
    "\n",
    "class GradCam:\n",
    "    def __init__(self, model, target_layer_names, use_cuda):\n",
    "        self.model = model\n",
    "        self.model.eval()\n",
    "        self.cuda = use_cuda\n",
    "        if self.cuda:\n",
    "            self.model = model.cuda()\n",
    "\n",
    "        self.extractor = ModelOutputs(self.model, target_layer_names, use_cuda)\n",
    "\n",
    "    def forward(self, features):\n",
    "        return self.model(features) \n",
    "\n",
    "    def __call__(self, features, index = None):\n",
    "        if self.cuda:\n",
    "            features, output = self.extractor(features.cuda())\n",
    "        else:\n",
    "            features, output = self.extractor(features)\n",
    "        # print(features[0].shape, output[0].shape)\n",
    "        if index == None:\n",
    "            index = np.argmax(output.cpu().data.numpy())\n",
    "        one_hot = np.zeros((1, output.size()[-1]), dtype = np.float32)\n",
    "        one_hot[0][index] = 1\n",
    "        one_hot = torch.Tensor(torch.from_numpy(one_hot))\n",
    "        one_hot.requires_grad = True\n",
    "        if self.cuda:\n",
    "            one_hot = torch.sum(one_hot.cuda() * output)\n",
    "        else:\n",
    "            one_hot = torch.sum(one_hot * output)\n",
    "        \n",
    "        self.model.zero_grad()##features和classifier不包含，可以重新加回去试一试，会报错不包含这个对象。\n",
    "        #self.model.zero_grad()\n",
    "        one_hot.backward(retain_graph=True)##这里适配我们的torch0.4及以上，我用的1.0也可以完美兼容。（variable改成graph即可）\n",
    "\n",
    "        grads_val = self.extractor.get_gradients()[-1].cpu().data.numpy()\n",
    "        #print('grads_val',grads_val.shape)\n",
    "        target = features[-1]\n",
    "        target = target.cpu().data.numpy()[0, :]\n",
    "        # print(grads_val.shape)\n",
    "        weights = np.mean(grads_val, axis = (2, 3))[0, :]\n",
    "        #print('weights',weights.shape)\n",
    "        cam = np.zeros(target.shape[1 : ], dtype = np.float32)\n",
    "        #print('cam',cam.shape)\n",
    "        #print('features',features[-1].shape)\n",
    "        #print('target',target.shape)\n",
    "        for i, w in enumerate(weights):\n",
    "            cam += w * target[i, :, :]\n",
    "        # print(cam)\n",
    "        cam = np.maximum(cam, 0)\n",
    "        cam = cv2.resize(cam, (224, 224))\n",
    "        cam = cam - np.min(cam)\n",
    "        cam = cam / np.max(cam)\n",
    "        return cam\n",
    "        \n",
    "class GuidedBackpropReLUModel:\n",
    "    def __init__(self, model, use_cuda):\n",
    "        self.model = model#这里同理，要的是一个完整的网络，不然最后维度会不匹配。\n",
    "        self.model.eval()\n",
    "        self.cuda = use_cuda\n",
    "        if self.cuda:\n",
    "            self.model = model.cuda()\n",
    "        for module in self.model.named_modules():\n",
    "            module[1].register_backward_hook(self.bp_relu)\n",
    "\n",
    "    def bp_relu(self, module, grad_in, grad_out):\n",
    "        if isinstance(module, nn.ReLU):\n",
    "            return (torch.clamp(grad_in[0], min=0.0),)\n",
    "    def forward(self, features):\n",
    "        return self.model(features)\n",
    "\n",
    "    def __call__(self, features, index = None):\n",
    "        if self.cuda:\n",
    "            output = self.forward(features.cuda())\n",
    "        else:\n",
    "            output = self.forward(features)\n",
    "        if index == None:\n",
    "            index = np.argmax(output.cpu().data.numpy())\n",
    "        print(features.grad)\n",
    "        one_hot = np.zeros((1, output.size()[-1]), dtype = np.float32)\n",
    "        one_hot[0][index] = 1\n",
    "        one_hot = torch.from_numpy(one_hot)\n",
    "        one_hot.requires_grad = True\n",
    "        if self.cuda:\n",
    "            one_hot = torch.sum(one_hot.cuda() * output)\n",
    "        else:\n",
    "            one_hot = torch.sum(one_hot * output)\n",
    "        #self.model.classifier.zero_grad()\n",
    "        one_hot.backward(retain_graph=True)\n",
    "        output = features.grad.cpu().data.numpy()\n",
    "        output = output[0,:,:,:]\n",
    "        return output\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_cam_on_image(img, mask,path,name):\n",
    "\t# plt.imshow(mask, cmap='gray')\n",
    "\theatmap = cv2.applyColorMap(np.uint8(255*mask), cv2.COLORMAP_JET)\n",
    "\t# print(heatmap)\n",
    "\theatmap = np.float32(heatmap) / 255\n",
    "\t#plt.imshow(img[0,:,:])\n",
    "\n",
    "\tcam = heatmap + np.float32(img).transpose(1,2,0) # \n",
    "\tcam = cam / np.max(cam)\n",
    "\tplt.imshow(cam)\n",
    "\n",
    "\tcv2.imwrite(f\"{path}/cam_{name}.jpg\", 255 * cam)\n",
    "\n",
    "\theatmap = heatmap / np.max(heatmap)\n",
    "\tcv2.imwrite(f\"{path}/heat_{name}.jpg\", np.uint8(255 * heatmap))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 'AD', 1: 'CN', 2: 'MCI'}"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reversed_dict = {value: key for key, value in test_dataset.class_to_idx.items()}\n",
    "reversed_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "mat1 and mat2 shapes cannot be multiplied (2048x1 and 2048x3)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[14], line 26\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[38;5;66;03m# print('input.size()=',features.size())\u001b[39;00m\n\u001b[1;32m     25\u001b[0m target_index \u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m---> 26\u001b[0m mask \u001b[38;5;241m=\u001b[39m \u001b[43mgrad_cam\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfeatures\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m     27\u001b[0m i\u001b[38;5;241m=\u001b[39mi\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39m \n\u001b[1;32m     28\u001b[0m img \u001b[38;5;241m=\u001b[39m features\u001b[38;5;241m.\u001b[39mrepeat(\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m3\u001b[39m,\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m1\u001b[39m)\u001b[38;5;241m.\u001b[39msqueeze()\n",
      "Cell \u001b[0;32mIn[12], line 69\u001b[0m, in \u001b[0;36mGradCam.__call__\u001b[0;34m(self, features, index)\u001b[0m\n\u001b[1;32m     67\u001b[0m     features, output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mextractor(features\u001b[38;5;241m.\u001b[39mcuda())\n\u001b[1;32m     68\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 69\u001b[0m     features, output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mextractor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfeatures\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     70\u001b[0m \u001b[38;5;66;03m# print(features[0].shape, output[0].shape)\u001b[39;00m\n\u001b[1;32m     71\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m index \u001b[38;5;241m==\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "Cell \u001b[0;32mIn[12], line 41\u001b[0m, in \u001b[0;36mModelOutputs.__call__\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     40\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, x):\n\u001b[0;32m---> 41\u001b[0m     target_activations, output  \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfeature_extractor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     42\u001b[0m     output \u001b[38;5;241m=\u001b[39m output\u001b[38;5;241m.\u001b[39mview(output\u001b[38;5;241m.\u001b[39msize(\u001b[38;5;241m0\u001b[39m), \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m     43\u001b[0m     \u001b[38;5;66;03m#print('classfier=',output.size())\u001b[39;00m\n",
      "Cell \u001b[0;32mIn[12], line 18\u001b[0m, in \u001b[0;36mFeatureExtractor.__call__\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgradients \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m     17\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m name, module \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel\u001b[38;5;241m.\u001b[39m_modules\u001b[38;5;241m.\u001b[39mitems():\u001b[38;5;66;03m##resnet50没有.feature这个特征，直接删除用就可以。\u001b[39;00m\n\u001b[0;32m---> 18\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[43mmodule\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     19\u001b[0m     \u001b[38;5;66;03m#print('name=',name)\u001b[39;00m\n\u001b[1;32m     20\u001b[0m     \u001b[38;5;66;03m#print('x.size()=',x.size())\u001b[39;00m\n\u001b[1;32m     21\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtarget_layers:\n",
      "File \u001b[0;32m/scratch/users/neuroimage/conda/venv/lib/python3.11/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1496\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1497\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1498\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1499\u001b[0m         \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1500\u001b[0m         \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m     \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   1502\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n",
      "File \u001b[0;32m/scratch/users/neuroimage/conda/venv/lib/python3.11/site-packages/torch/nn/modules/linear.py:114\u001b[0m, in \u001b[0;36mLinear.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    113\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39minput\u001b[39m: Tensor) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Tensor:\n\u001b[0;32m--> 114\u001b[0m     \u001b[39mreturn\u001b[39;00m F\u001b[39m.\u001b[39;49mlinear(\u001b[39minput\u001b[39;49m, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mweight, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mbias)\n",
      "\u001b[0;31mRuntimeError\u001b[0m: mat1 and mat2 shapes cannot be multiplied (2048x1 and 2048x3)"
     ]
    }
   ],
   "source": [
    "\"\"\" python grad_cam.py <path_to_image>\n",
    "1. Loads an image with opencv.\n",
    "2. Preprocesses it for VGG19 and converts to a pytorch variable.\n",
    "3. Makes a forward pass to find the category index with the highest score,\n",
    "and computes intermediate activations.\n",
    "Makes the visualization. \"\"\"\n",
    "\n",
    "\n",
    "# Can work with any model, but it assumes that the model has a \n",
    "# feature method, and a classifier method,\n",
    "# as in the VGG models in torchvision.\n",
    "# del model.fc\n",
    "\n",
    "#print(model)\n",
    "grad_cam = GradCam(model, target_layer_names = [\"layer4\"], use_cuda=False)\n",
    "\n",
    "data_loader = train_loader\n",
    "\n",
    "resnet.eval()\n",
    "\n",
    "for i, (features, targets) in enumerate(data_loader):\n",
    "\tfeatures.required_grad = True\n",
    "\t# print('input.size()=',features.size())\n",
    "\n",
    "\ttarget_index =None\n",
    "\tmask = grad_cam(features, None)\n",
    "\ti=i+1 \n",
    "\timg = features.repeat(1,3,1,1).squeeze()\n",
    "\toutput = resnet(features)\n",
    "\tindex = np.argmax(output.cpu().data.numpy())\n",
    "\tpath = f'train_cam/{reversed_dict[targets[0].item()]}/{reversed_dict[index]}/'\n",
    "\tif not os.path.exists(path):\n",
    "\t\tos.makedirs(path)\n",
    "\t# print(path)\n",
    "\t# break\n",
    "\tshow_cam_on_image(img, mask, path, i)\n",
    "\t# gb_model = GuidedBackpropReLUModel(model = copy.deepcopy(resnet), use_cuda=False)\n",
    "\t# gb = gb_model(features, index=target_index)\n",
    "\t# if not os.path.exists('gb'):\n",
    "\t# \tos.mkdir('gb')\n",
    "\t# if not os.path.exists('camgb'):\n",
    "\t# \tos.mkdir('camgb')\n",
    "\t# utils.save_image(torch.from_numpy(gb), 'gb/gb_{}.jpg'.format(i))\n",
    "\t# cam_mask = np.zeros(gb.shape)\n",
    "\t# for j in range(0, gb.shape[0]):\n",
    "\t# \tcam_mask[j, :, :] = mask\n",
    "\t# cam_gb = np.multiply(cam_mask, gb)\n",
    "\t# utils.save_image(torch.from_numpy(cam_gb), 'camgb/cam_gb_{}.jpg'.format(i))\n",
    "\t# break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(224, 224, 3)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.float32(img).transpose(1,2,0).shape"
   ]
  }
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