run_resnet_suicidality_2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "eebc02cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\n__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',\\n           'resnet152', 'resnext50_32x4d', 'resnext101_32x8d',\\n           'wide_resnet50_2', 'wide_resnet101_2']\\n\""
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## =================================== ##\n",
    "## ======= ResNet ======= ##\n",
    "## =================================== ##\n",
    "\n",
    "'''\n",
    "__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',\n",
    "           'resnet152', 'resnext50_32x4d', 'resnext101_32x8d',\n",
    "           'wide_resnet50_2', 'wide_resnet101_2']\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "eb3e052b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import os\n",
    "from os import listdir\n",
    "from os.path import isfile, join\n",
    "from tqdm.auto import tqdm ##progress\n",
    "import time\n",
    "import math\n",
    "import random\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "import torch.nn.functional as F\n",
    "from torchvision import transforms, utils\n",
    "import torch.optim as optim\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import seaborn as sns\n",
    "\n",
    "import random\n",
    "import hashlib\n",
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f3051706",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/connectome/lumierej'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1c146925",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/connectome/dhkdgmlghks/3DCNN/data/3.demo_qc/ABCD_suicide_case.csv\n",
      "/home/connectome/dhkdgmlghks/3DCNN/data/3.demo_qc/ABCD_phenotype_total.csv\n",
      "/home/connectome/dhkdgmlghks/3DCNN/data/3.demo_qc/ABCD_suicide_control.csv\n"
     ]
    }
   ],
   "source": [
    "dirname = '/home/connectome/dhkdgmlghks/3DCNN/data/3.demo_qc'\n",
    "\n",
    "def search(dirname):\n",
    "    filenames = os.listdir(dirname)\n",
    "    for filename in filenames:\n",
    "        full_filename = os.path.join(dirname, filename)\n",
    "        print(full_filename)\n",
    "        \n",
    "search(dirname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f5b71475",
   "metadata": {},
   "outputs": [],
   "source": [
    "subject_dir = '/home/connectome/dhkdgmlghks/3DCNN/data/3.demo_qc'\n",
    "os.chdir(subject_dir)\n",
    "subject_data = pd.read_csv('ABCD_phenotype_total.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "839c067e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# data = pd.DataFrame(subject_data)\n",
    "# os.chdir('/home/connectome/lumierej')\n",
    "# data.to_csv('/home/connectome/lumierej')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b07d67d9",
   "metadata": {},
   "outputs": [
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       "        subjectkey    age  sex  race.ethnicity  high.educ  income  married  \\\n",
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       "\n",
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       "2       22.0    56.5    82.8  ...                       0.0   \n",
       "3        7.0    57.5    76.8  ...                       0.0   \n",
       "4       20.0    56.5    91.5  ...                       0.0   \n",
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       "\n",
       "   totalBIS  totalBAS  PPSP_frequency  PPSP_distress  \n",
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       "[5 rows x 118 columns]"
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     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subject_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b6728851",
   "metadata": {},
   "outputs": [
    {
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       "        subjectkey  Bipolar.I.Disorder.x  Bipolar.II.Disorder.x  \\\n",
       "0  NDARINV007W6H7B                     0                      0   \n",
       "1  NDARINV00CY2MDM                     0                      0   \n",
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       "4  NDARINV00U4FTRU                     1                      1   \n",
       "\n",
       "   Unspecified.Bipolar.and.Related.Disorder.x  Major.Depressive.Disorder.x  \\\n",
       "0                                           0                            0   \n",
       "1                                           0                            0   \n",
       "2                                           0                            0   \n",
       "3                                           0                            0   \n",
       "4                                           1                            0   \n",
       "\n",
       "   Persistent.Depressive.Disorder.x  Unspecified.Depressive.Disorder.x  \\\n",
       "0                                 0                                  0   \n",
       "1                                 0                                  0   \n",
       "2                                 0                                  0   \n",
       "3                                 0                                  0   \n",
       "4                                 0                                  0   \n",
       "\n",
       "   Social.Anxiety.Disorder.x  Generalized.Anxiety.Disorder.x  \\\n",
       "0                          0                               1   \n",
       "1                          0                               0   \n",
       "2                          0                               0   \n",
       "3                          0                               1   \n",
       "4                          1                               1   \n",
       "\n",
       "   SelfInjuriousBehaviorwithoutsuicidalintent.x  ...  \\\n",
       "0                                             1  ...   \n",
       "1                                             0  ...   \n",
       "2                                             0  ...   \n",
       "3                                             0  ...   \n",
       "4                                             0  ...   \n",
       "\n",
       "   Unspecified.Substance.Related.Disorder.x  Stimulant.Use.Disorder.x  \\\n",
       "0                                         0                         0   \n",
       "1                                         0                         0   \n",
       "2                                         0                         0   \n",
       "3                                         0                         0   \n",
       "4                                         0                         0   \n",
       "\n",
       "   Substance.Use.Disorder.x  Other.Drugs.Use.Disorder.x  \\\n",
       "0                         0                           0   \n",
       "1                         0                           0   \n",
       "2                         0                           0   \n",
       "3                         0                           0   \n",
       "4                         0                           0   \n",
       "\n",
       "   Sedative.Hypnotic.or.Anxiolytic.Use.Disorder.x  \\\n",
       "0                                               0   \n",
       "1                                               0   \n",
       "2                                               0   \n",
       "3                                               0   \n",
       "4                                               0   \n",
       "\n",
       "   Other.Specified.Trauma.and.Stressor.Related.Disorder.x  \\\n",
       "0                                                  0        \n",
       "1                                                  0        \n",
       "2                                                  0        \n",
       "3                                                  0        \n",
       "4                                                  1        \n",
       "\n",
       "   Post.Traumatic.Stress.Disorder.x  HOMICIDAL.IDEATION.AND.BEHAVIOR.x  \\\n",
       "0                                 0                                  0   \n",
       "1                                 0                                  0   \n",
       "2                                 0                                  0   \n",
       "3                                 0                                  0   \n",
       "4                                 0                                  0   \n",
       "\n",
       "   SuicidalideationAll  SuicideAll  \n",
       "0                    1           1  \n",
       "1                    1           1  \n",
       "2                    1           1  \n",
       "3                    1           1  \n",
       "4                    1           1  \n",
       "\n",
       "[5 rows x 45 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1 = pd.read_csv('ABCD_suicide_case.csv')\n",
    "data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "efc195a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subjectkey</th>\n",
       "      <th>Bipolar.I.Disorder.x</th>\n",
       "      <th>Bipolar.II.Disorder.x</th>\n",
       "      <th>Unspecified.Bipolar.and.Related.Disorder.x</th>\n",
       "      <th>Major.Depressive.Disorder.x</th>\n",
       "      <th>Persistent.Depressive.Disorder.x</th>\n",
       "      <th>Unspecified.Depressive.Disorder.x</th>\n",
       "      <th>Social.Anxiety.Disorder.x</th>\n",
       "      <th>Generalized.Anxiety.Disorder.x</th>\n",
       "      <th>SelfInjuriousBehaviorwithoutsuicidalintent.x</th>\n",
       "      <th>...</th>\n",
       "      <th>Unspecified.Substance.Related.Disorder.x</th>\n",
       "      <th>Stimulant.Use.Disorder.x</th>\n",
       "      <th>Substance.Use.Disorder.x</th>\n",
       "      <th>Other.Drugs.Use.Disorder.x</th>\n",
       "      <th>Sedative.Hypnotic.or.Anxiolytic.Use.Disorder.x</th>\n",
       "      <th>Other.Specified.Trauma.and.Stressor.Related.Disorder.x</th>\n",
       "      <th>Post.Traumatic.Stress.Disorder.x</th>\n",
       "      <th>HOMICIDAL.IDEATION.AND.BEHAVIOR.x</th>\n",
       "      <th>SuicidalideationAll</th>\n",
       "      <th>SuicideAll</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NDARINV005V6D2C</td>\n",
       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>NDARINV014RTM1V</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NDARINV01D03VR7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NDARINV01NAYMZH</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NDARINV01RGTWD2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>...</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        subjectkey  Bipolar.I.Disorder.x  Bipolar.II.Disorder.x  \\\n",
       "0  NDARINV005V6D2C                     0                      0   \n",
       "1  NDARINV014RTM1V                     0                      0   \n",
       "2  NDARINV01D03VR7                     0                      0   \n",
       "3  NDARINV01NAYMZH                     0                      0   \n",
       "4  NDARINV01RGTWD2                     0                      0   \n",
       "\n",
       "   Unspecified.Bipolar.and.Related.Disorder.x  Major.Depressive.Disorder.x  \\\n",
       "0                                           0                            0   \n",
       "1                                           0                            0   \n",
       "2                                           0                            0   \n",
       "3                                           0                            0   \n",
       "4                                           0                            0   \n",
       "\n",
       "   Persistent.Depressive.Disorder.x  Unspecified.Depressive.Disorder.x  \\\n",
       "0                                 0                                  0   \n",
       "1                                 0                                  0   \n",
       "2                                 0                                  0   \n",
       "3                                 0                                  0   \n",
       "4                                 0                                  0   \n",
       "\n",
       "   Social.Anxiety.Disorder.x  Generalized.Anxiety.Disorder.x  \\\n",
       "0                          0                               0   \n",
       "1                          0                               0   \n",
       "2                          0                               0   \n",
       "3                          0                               0   \n",
       "4                          0                               0   \n",
       "\n",
       "   SelfInjuriousBehaviorwithoutsuicidalintent.x  ...  \\\n",
       "0                                             0  ...   \n",
       "1                                             0  ...   \n",
       "2                                             0  ...   \n",
       "3                                             0  ...   \n",
       "4                                             0  ...   \n",
       "\n",
       "   Unspecified.Substance.Related.Disorder.x  Stimulant.Use.Disorder.x  \\\n",
       "0                                         0                         0   \n",
       "1                                         0                         0   \n",
       "2                                         0                         0   \n",
       "3                                         0                         0   \n",
       "4                                         0                         0   \n",
       "\n",
       "   Substance.Use.Disorder.x  Other.Drugs.Use.Disorder.x  \\\n",
       "0                         0                           0   \n",
       "1                         0                           0   \n",
       "2                         0                           0   \n",
       "3                         0                           0   \n",
       "4                         0                           0   \n",
       "\n",
       "   Sedative.Hypnotic.or.Anxiolytic.Use.Disorder.x  \\\n",
       "0                                               0   \n",
       "1                                               0   \n",
       "2                                               0   \n",
       "3                                               0   \n",
       "4                                               0   \n",
       "\n",
       "   Other.Specified.Trauma.and.Stressor.Related.Disorder.x  \\\n",
       "0                                                  0        \n",
       "1                                                  0        \n",
       "2                                                  0        \n",
       "3                                                  0        \n",
       "4                                                  0        \n",
       "\n",
       "   Post.Traumatic.Stress.Disorder.x  HOMICIDAL.IDEATION.AND.BEHAVIOR.x  \\\n",
       "0                                 0                                  0   \n",
       "1                                 0                                  0   \n",
       "2                                 0                                  0   \n",
       "3                                 0                                  0   \n",
       "4                                 0                                  0   \n",
       "\n",
       "   SuicidalideationAll  SuicideAll  \n",
       "0                    0           0  \n",
       "1                    0           0  \n",
       "2                    0           0  \n",
       "3                    0           0  \n",
       "4                    0           0  \n",
       "\n",
       "[5 rows x 45 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2 = pd.read_csv('ABCD_suicide_control.csv')\n",
    "data2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ace14d14",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix, roc_auc_score\n",
    "\n",
    "import monai ##monai: medical open network for AI\n",
    "from monai.data import CSVSaver, ImageDataset, DistributedWeightedRandomSampler\n",
    "from monai.transforms import AddChannel, Compose, RandRotate90, Resize, ScaleIntensity, Flip, ToTensor\n",
    "from monai.utils import set_determinism\n",
    "from monai.apps import CrossValidation\n",
    "\n",
    "import argparse\n",
    "from copy import deepcopy ## add deepcopy for args\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7bb427b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import resnet3d\n",
    "from DataSetMaker import DataSetMaker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9243d6ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "#python3 run_resnet3d_gpu.py --model densenet121 --optim Adam --lr 1e-6 --epoch 100 --exp_name test --gpu_ids 6 7 --train_batch_size 16 --val_batch_size 16 --test_batch_size 16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e9861077",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "_StoreAction(option_strings=['--gpu_ids'], dest='gpu_ids', nargs='*', const=None, default=None, type=<class 'int'>, choices=None, help='', metavar=None)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## ========= Argument Setting ========= ##\n",
    "parser = argparse.ArgumentParser()\n",
    "\n",
    "#parser.add_argument(\"--GPU_NUM\",default=1,type=int,required=True,help='')\n",
    "parser.add_argument(\"--model\",required=True,type=str,choices=['resnet50', 'resnet101', 'resnet152'],help='')\n",
    "parser.add_argument(\"--val_size\",default=0.1,type=float,required=False,help='')\n",
    "parser.add_argument(\"--test_size\",default=0.1,type=float,required=False,help='')\n",
    "parser.add_argument(\"--resize\",default=(96,96,96),required=False,help='')\n",
    "parser.add_argument(\"--train_batch_size\",default=32,type=int,required=False,help='')\n",
    "parser.add_argument(\"--val_batch_size\",default=8,type=int,required=False,help='')\n",
    "parser.add_argument(\"--test_batch_size\",default=1,type=int,required=False,help='')\n",
    "parser.add_argument(\"--optim\",type=str,required=True,help='', choices=['Adam','SGD'])\n",
    "parser.add_argument(\"--lr\", default=0.01,type=float,required=False,help='')\n",
    "parser.add_argument(\"--momentum\", default=0.5,type=float,required=False,help='')\n",
    "parser.add_argument(\"--weight_decay\",default=0.001,type=float,required=False,help='')\n",
    "parser.add_argument(\"--epoch\",type=int,required=True,help='')\n",
    "parser.add_argument(\"--exp_name\",type=str,required=True,help='')\n",
    "parser.add_argument(\"--gpu_ids\",type=int,nargs='*',required=True,help='')\n",
    "\n",
    "## ==================================== ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b46857cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Experiment is performed on GPU 1\n"
     ]
    }
   ],
   "source": [
    "# ========= GPU Setting ========= ##\n",
    "#GPU_NUM = args.GPU_NUM # enter the number you want to use GPU\n",
    "GPU_NUM=1 # ***\n",
    "device = 'cpu'\n",
    "device = 'cuda:1'\n",
    "device = torch.device(f'cuda:{GPU_NUM}' if torch.cuda.is_available() else 'cpu')\n",
    "\n",
    "torch.cuda.set_device(device)\n",
    "print('Experiment is performed on GPU {}'.format(torch.cuda.current_device()))\n",
    "# ==================================== ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "be049a85",
   "metadata": {},
   "outputs": [],
   "source": [
    "command = \"--model resnet152 --optim Adam --lr 1e-6 --epoch 10 --exp_name test --gpu_ids 0 1 --train_batch_size 1 --val_batch_size 1 --test_batch_size 1\"\n",
    "\n",
    "command = command.split(\" \")\n",
    "args = parser.parse_args(args=command)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bb14f293",
   "metadata": {},
   "outputs": [],
   "source": [
    "#args = parser.parse_args()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d26a44c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** Loading image files as list is completed ***\n",
      "*** Loading control & case subject list is completed ***\n",
      "*** Splitting data into train, val, test is completed ***\n"
     ]
    }
   ],
   "source": [
    "datasetMaker = DataSetMaker(args)\n",
    "partition = datasetMaker.make_dataset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0b2c2232",
   "metadata": {},
   "outputs": [],
   "source": [
    "## ========= Train,Validate, and Test ========= ##\n",
    "# define training step\n",
    "def train(net,partition,optimizer,criterion,args):\n",
    "    trainloader = torch.utils.data.DataLoader(partition['train'],\n",
    "                                             batch_size=args.train_batch_size,\n",
    "                                             shuffle=True,\n",
    "                                             num_workers=2)\n",
    "\n",
    "    net.train()\n",
    "\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    train_loss = 0.0\n",
    "\n",
    "\n",
    "    for i, data in enumerate(trainloader,0):\n",
    "        optimizer.zero_grad() #this code makes {train gradient=0}\n",
    "        image, label = data\n",
    "        image = image.to(f'cuda:{net.device_ids[0]}')\n",
    "        label = label.to(f'cuda:{net.device_ids[0]}')\n",
    "        output = net(image)\n",
    "\n",
    "        loss = criterion(output,label)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        train_loss += loss.item()\n",
    "        _, predicted = torch.max(net(image).data,1)\n",
    "        total += label.size(0)\n",
    "        correct += (predicted == label).sum().item()\n",
    "\n",
    "    train_loss = train_loss / len(trainloader)\n",
    "    train_acc = 100 * correct / total\n",
    "\n",
    "    return net, train_loss, train_acc\n",
    "\n",
    "\n",
    "# define validation step\n",
    "def validate(net,partition,criterion, scheduler,args):\n",
    "    valloader = torch.utils.data.DataLoader(partition['val'],\n",
    "                                           batch_size=args.val_batch_size,\n",
    "                                           shuffle=False,\n",
    "                                           num_workers=2)\n",
    "\n",
    "    net.eval()\n",
    "\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    val_loss = 0.0\n",
    "\n",
    "    with torch.no_grad():\n",
    "\n",
    "        for i, data in enumerate(valloader,0):\n",
    "            image, label = data\n",
    "            image = image.to(f'cuda:{net.device_ids[0]}')\n",
    "            label = label.to(f'cuda:{net.device_ids[0]}')\n",
    "            output = net(image)\n",
    "\n",
    "            loss = criterion(output,label)\n",
    "\n",
    "            val_loss += loss.item()\n",
    "            _, predicted = torch.max(output.data,1)\n",
    "            total += label.size(0)\n",
    "            correct += (predicted == label).sum().item()\n",
    "\n",
    "        val_loss = val_loss / len(valloader)\n",
    "        val_acc = 100 * correct / total\n",
    "\n",
    "    scheduler.step(val_acc)\n",
    "    return val_loss, val_acc\n",
    "\n",
    "\n",
    "# define test step\n",
    "def test(net,partition,args):\n",
    "    testloader = torch.utils.data.DataLoader(partition['test'],\n",
    "                                            batch_size=args.test_batch_size,\n",
    "                                            shuffle=False,\n",
    "                                            num_workers=2)\n",
    "\n",
    "    net.eval()\n",
    "\n",
    "    correct = 0\n",
    "    total = 0\n",
    "\n",
    "    cmt = {}\n",
    "    true_positive = 0\n",
    "    true_negative = 0\n",
    "    false_positive = 0\n",
    "    false_negative = 0\n",
    "    \n",
    "    subj_predicted = {}\n",
    "    subj_predicted['label'] = []\n",
    "    subj_predicted['pred'] = []\n",
    "    \n",
    "    for i, data in enumerate(testloader,0):\n",
    "        image, label = data\n",
    "        image = image.to(f'cuda:{net.device_ids[0]}')\n",
    "        label = label.to(f'cuda:{net.device_ids[0]}')\n",
    "        output = net(image)\n",
    "\n",
    "        _, predicted = torch.max(output.data,1)\n",
    "        total += label.size(0)\n",
    "        correct += (predicted == label).sum().item()\n",
    "        \n",
    "        # calculate confusion_matrix\n",
    "        result_cmt = confusion_matrix(label.cpu(), predicted.cpu())\n",
    "\n",
    "        if len(result_cmt) == 1:\n",
    "            if label.item() ==1:\n",
    "                true_positive += 1\n",
    "            else:\n",
    "                true_negative += 1\n",
    "        else:\n",
    "\n",
    "            tn, fp, fn, tp = result_cmt.ravel()\n",
    "            true_positive += int(tp)\n",
    "            true_negative += int(tn)\n",
    "            false_positive += int(fp)\n",
    "            false_negative += int(fn)\n",
    "        \n",
    "        \n",
    "        cmt['true_positive'] = true_positive\n",
    "        cmt['true_negative'] = true_negative\n",
    "        cmt['false_positive'] = false_positive\n",
    "        cmt['false_negative'] = false_negative\n",
    "\n",
    "        # subj_predicted\n",
    "        subj_predicted['label'].append(label.cpu().tolist()[0])\n",
    "        subj_predicted['pred'].append(output.data.cpu().tolist()[0])\n",
    "        #print(subj_predicted)\n",
    "           \n",
    "    test_acc = 100 * correct / total\n",
    "    \n",
    "    return test_acc, cmt, subj_predicted\n",
    "## ============================================ ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "18597c0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "## ========= Experiment =============== ##\n",
    "def experiment(partition, args): #in_channels,out_dim\n",
    "    \n",
    "    if args.model == 'resnet50':\n",
    "        net = resnet3d.resnet3D50()\n",
    "    elif args.model == 'resnet101':\n",
    "        net = resnet3d.resnet3D101()\n",
    "    elif args.model == 'resnet152':\n",
    "        net = resnet3d.resnet3D152()\n",
    "\n",
    "    net = torch.nn.DataParallel(net,device_ids=args.gpu_ids)\n",
    "    net = net.to(f'cuda:{net.device_ids[0]}') #net = net.to(device)\n",
    "        \n",
    "    criterion = nn.CrossEntropyLoss()\n",
    "    if args.optim == 'SGD':\n",
    "        optimizer = optim.SGD(net.parameters(), lr=args.lr, momentum=args.momentum)\n",
    "    elif args.optim == 'Adam':\n",
    "        optimizer = optim.Adam(net.parameters(),lr=args.lr,weight_decay=args.weight_decay)\n",
    "    else:\n",
    "        raise ValueError('In-valid optimizer choice')\n",
    "    scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min')\n",
    "\n",
    "    train_losses = []\n",
    "    train_accs = []\n",
    "    val_losses = []\n",
    "    val_accs = []\n",
    "\n",
    "\n",
    "    for epoch in tqdm(range(args.epoch)):\n",
    "        ts = time.time()\n",
    "        net, train_loss, train_acc = train(net,partition,optimizer,criterion,args)\n",
    "        val_loss, val_acc = validate(net,partition,criterion, scheduler,args)\n",
    "        test_acc = test(net,partition,args)\n",
    "        te = time.time()\n",
    "\n",
    "        train_losses.append(train_loss)\n",
    "        train_accs.append(train_acc)\n",
    "        val_losses.append(val_loss)\n",
    "        val_accs.append(val_acc)\n",
    "\n",
    "        print('Epoch {}, ACC(train/val): {:2.2f}/{:2.2f}, Loss(train/val): {:2.2f}/{:2.2f}. Current learning rate {}.Took {:2.2f} sec'.format(epoch,train_acc,val_acc,train_loss,val_loss,optimizer.param_groups[0]['lr'],te-ts))\n",
    "\n",
    "\n",
    "    test_acc, cmt, subj_predicted = test(net,partition,args)\n",
    "\n",
    "    result = {}\n",
    "    result['train_losses'] = train_losses\n",
    "    result['train_accs'] = train_accs\n",
    "    result['val_losses'] = val_losses\n",
    "    result['val_accs'] = val_accs\n",
    "    result['train_acc'] = train_acc\n",
    "    result['val_acc'] = val_acc\n",
    "    result['test_acc'] = test_acc\n",
    "\n",
    "    return vars(args), result, cmt, subj_predicted\n",
    "## ==================================== ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0e4a57f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import gc\n",
    "gc.collect()\n",
    "torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "260d3f8e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1ca07a35de8a4c829ec70016fae65cbe",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0, ACC(train/val): 67.62/66.77, Loss(train/val): 0.66/0.57. Current learning rate 1e-06.Took 861.02 sec\n",
      "Epoch 1, ACC(train/val): 71.90/77.22, Loss(train/val): 0.55/0.52. Current learning rate 1e-06.Took 870.46 sec\n",
      "Epoch 2, ACC(train/val): 82.43/90.51, Loss(train/val): 0.44/0.38. Current learning rate 1e-06.Took 836.22 sec\n",
      "Epoch 3, ACC(train/val): 95.54/69.30, Loss(train/val): 0.35/0.54. Current learning rate 1e-06.Took 843.44 sec\n",
      "Epoch 4, ACC(train/val): 99.82/80.70, Loss(train/val): 0.27/0.44. Current learning rate 1e-06.Took 840.08 sec\n"
     ]
    }
   ],
   "source": [
    "## ========= Run Experiment and saving result ========= ##\n",
    "# define result-saving function\n",
    "def save_exp_result(setting, result, cmt, subj_predicted):\n",
    "    exp_name = setting['exp_name']\n",
    "    \n",
    "    del setting['epoch']\n",
    "    del setting['test_batch_size']\n",
    "\n",
    "    hash_key = hashlib.sha1(str(setting).encode()).hexdigest()[:6]\n",
    "    filename = '/home/connectome/lumierej/3DCNN/{}--{}.json'.format(exp_name, hash_key)\n",
    "    result.update(setting)\n",
    "    result.update(cmt)\n",
    "    result.update(subj_predicted)\n",
    "    \n",
    "    with open(filename, 'w') as f:\n",
    "        json.dump(result, f)\n",
    "\n",
    "# seed number\n",
    "seed = 1234\n",
    "np.random.seed(seed)\n",
    "torch.manual_seed(seed)\n",
    "\n",
    "# Run Experiment and save result\n",
    "setting, result, cmt, subj_predicted = experiment(partition, deepcopy(args))\n",
    "save_exp_result(setting,result, cmt, subj_predicted)\n",
    "## ==================================================== ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f488211",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "torch.cuda.memory_summary(device=None, abbreviated=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0510316f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e7f40fb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a607e5b8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c1c54f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device(f'cuda:1' if torch.cuda.is_available() else 'cpu')\n",
    "torch.cuda.set_device(device)\n",
    "print('Experiment is performed on GPU {}'.format(torch.cuda.current_device()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11beb94d",
   "metadata": {},
   "outputs": [],
   "source": [
    "## ========= Data Preprocessing: image ========= ##\n",
    "## get the current working directory\n",
    "currentPath = os.getcwd()\n",
    "print(currentPath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a319c46c",
   "metadata": {},
   "outputs": [],
   "source": [
    "### get image file names (subject ID + '.npy') as list\n",
    "#base_dir = '/home/connectome/dhkdgmlghks/3DCNN_test'\n",
    "#data_dir = '/home/connectome/dhkdgmlghks/3DCNN_test/preprocessed_masked'\n",
    "#os.chdir(data_dir)\n",
    "image_files = glob.glob('*.npy')\n",
    "image_files = sorted(image_files)\n",
    "#image_files = image_files[:100]\n",
    "print(\"Loading image file names as list is completed\")\n",
    "## ====================================== ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d47c45f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "## ========= Data Preprocessing: target ========= ##\n",
    "### get subject ID and target variables\n",
    "target = 'sex'\n",
    "\n",
    "#subject_dir = '/home/connectome/dhkdgmlghks/3DCNN_test'\n",
    "os.chdir(subject_dir)\n",
    "subject_data = pd.read_csv('ABCD_phenotype_total.csv')\n",
    "subject_data = subject_data.loc[:,['subjectkey',target]]\n",
    "subject_data = subject_data.sort_values(by='subjectkey')\n",
    "subject_data = subject_data.dropna(axis = 0)\n",
    "subject_data = subject_data.reset_index(drop=True) # removing subject have NA values in sex\n",
    "print(\"Loading subject list is completed\")\n",
    "## ====================================== ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "476792c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "## ========= Data Preprocessing ========= ##\n",
    "os.chdir(data_dir) # if I do not change directory here, image data is not loaded\n",
    "# get subject ID and target variables as sorted list\n",
    "\n",
    "imageFiles_labels = []\n",
    "\n",
    "for subjectID in tqdm(image_files):\n",
    "    subjectID = subjectID[:-4] #removing '.npy' for comparing\n",
    "    #print(subjectID)\n",
    "    for i in range(len(subject_data)):\n",
    "        if subjectID == subject_data['subjectkey'][i]:\n",
    "            if subject_data['sex'][i] == 1:\n",
    "                imageFiles_labels.append((subjectID+'.npy',0))\n",
    "            elif subject_data['sex'][i] == 2:\n",
    "                imageFiles_labels.append((subjectID+'.npy',1))\n",
    "            else:\n",
    "                print('NaN value for {}'.format(subjectID))\n",
    "                continue\n",
    "## ====================================== ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f0a55d80",
   "metadata": {},
   "outputs": [],
   "source": [
    "## ========= Split train, val, test========= ##\n",
    "# defining train,val, test set splitting function\n",
    "def partition(imageFiles_labels,args):\n",
    "    random.shuffle(imageFiles_labels)\n",
    "\n",
    "    images = []\n",
    "    labels = []\n",
    "    for imageFile_label in imageFiles_labels:\n",
    "        image, label = imageFile_label\n",
    "        images.append(image)\n",
    "        labels.append(label)\n",
    "\n",
    "    resize = args.resize\n",
    "    train_transform = Compose([ScaleIntensity(),\n",
    "                               AddChannel(),\n",
    "                               Resize(resize),\n",
    "                              ToTensor()])\n",
    "\n",
    "    val_transform = Compose([ScaleIntensity(),\n",
    "                               AddChannel(),\n",
    "                               Resize(resize),\n",
    "                              ToTensor()])\n",
    "\n",
    "    test_transform = Compose([ScaleIntensity(),\n",
    "                               AddChannel(),\n",
    "                               Resize(resize),\n",
    "                              ToTensor()])\n",
    "\n",
    "    num_total = len(images)\n",
    "    num_train = int(num_total*(1 - args.val_size - args.test_size))\n",
    "    #print(num_train)\n",
    "    num_val = int(num_total*args.val_size)\n",
    "    #print(num_val)\n",
    "    num_test = int(num_total*args.test_size)\n",
    "    #print(num_test)\n",
    "\n",
    "    images_train = images[:num_train]\n",
    "    labels_train = labels[:num_train]\n",
    "\n",
    "    images_val = images[num_train:num_train+num_val]\n",
    "    labels_val = labels[num_train:num_train+num_val]\n",
    "\n",
    "    images_test = images[num_total-num_test:]\n",
    "    labels_test = labels[num_total-num_test:]\n",
    "\n",
    "    train_set = ImageDataset(image_files=images_train,labels=labels_train,transform=train_transform)\n",
    "    val_set = ImageDataset(image_files=images_val,labels=labels_val,transform=val_transform)\n",
    "    test_set = ImageDataset(image_files=images_test,labels=labels_test,transform=test_transform)\n",
    "\n",
    "    partition = {}\n",
    "    partition['train'] = train_set\n",
    "    partition['val'] = val_set\n",
    "    partition['test'] = test_set\n",
    "\n",
    "    return partition\n",
    "\n",
    "# split dataset\n",
    "partition = partition(imageFiles_labels,args)\n",
    "print(\"Splitting data to train, val, test is completed\")\n",
    "## ====================================== ##"
   ]
  }
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