CSI/.ipynb_checkpoints/eval-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5dba5871",
   "metadata": {},
   "source": [
    "# In-Distribution = hem"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de7c031b",
   "metadata": {},
   "source": [
    "## combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "7c21eb48",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3581 3581 3581 3581\n",
      "weight_sim:\t0.0152\t0.0130\t0.0105\t0.0135\n",
      "weight_shi:\t-0.0578\t0.1014\t0.1178\t0.1034\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.5853569764733287\n",
      "CNMC               1.9901 +- 0.1085    q0: 1.7212    q10: 1.8609    q20: 1.9042    q30: 1.9291    q40: 1.9549    q50: 1.9800    q60: 2.0013    q70: 2.0351    q80: 2.0786    q90: 2.1427    q100: 2.3180\n",
      "one_class_0        1.9561 +- 0.0829    q0: 1.6679    q10: 1.8555    q20: 1.8869    q30: 1.9128    q40: 1.9357    q50: 1.9564    q60: 1.9747    q70: 1.9978    q80: 2.0224    q90: 2.0600    q100: 2.2041\n",
      "[one_class_0 CSI 0.5854] [one_class_0 best 0.5854] \n",
      "[one_class_mean CSI 0.5854] [one_class_mean best 0.5854] \n",
      "0.5854\t0.5854\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : hem\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur_randpers\n",
    "# crop        : 0.08\n",
    "# blur_sigma  : 40\n",
    "# randpers    : 0.8\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --distortion_scale 0.8 --resize_factor 0.08 --blur_sigma 40 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur_randpers --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 1 --load_path \"logs/id_hem/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_randpers_resize_factor0.08_color_dist0.5_one_class_1/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "846efb49",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3581 3581 3581 3581\n",
      "weight_sim:\t0.0109\t0.0072\t0.0133\t0.0129\n",
      "weight_shi:\t0.4840\t0.0844\t0.4048\t0.2004\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.4842849583244716\n",
      "CNMC               1.9963 +- 0.4334    q0: 0.3449    q10: 1.4686    q20: 1.6647    q30: 1.7749    q40: 1.8802    q50: 1.9851    q60: 2.0904    q70: 2.2032    q80: 2.3314    q90: 2.5160    q100: 3.5596\n",
      "one_class_0        2.0168 +- 0.3659    q0: 0.5032    q10: 1.5638    q20: 1.7269    q30: 1.8222    q40: 1.9245    q50: 2.0083    q60: 2.0883    q70: 2.1776    q80: 2.3057    q90: 2.4967    q100: 3.3674\n",
      "[one_class_0 CSI 0.4843] [one_class_0 best 0.4843] \n",
      "[one_class_mean CSI 0.4843] [one_class_mean best 0.4843] \n",
      "0.4843\t0.4843\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : hem\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur_sharp\n",
    "# crop        : 0.08\n",
    "# blur_sigma  : 40\n",
    "# randpers    : 0.8\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --sharpness_factor 128 --resize_factor 0.08 --blur_sigma 40 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur_sharp --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 1 --load_path \"logs/id_hem/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_sharp_resize_factor0.08_color_dist0.5_one_class_1/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "ebf2e296",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3581 3581 3581 3581\n",
      "weight_sim:\t0.0019\t0.0039\t0.0042\t0.0047\n",
      "weight_shi:\t0.0159\t0.3020\t1.0707\t0.5438\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.43598274238142987\n",
      "CNMC               1.9968 +- 0.4243    q0: 1.0210    q10: 1.5387    q20: 1.6392    q30: 1.7221    q40: 1.7914    q50: 1.8964    q60: 2.0368    q70: 2.1923    q80: 2.3638    q90: 2.6239    q100: 3.6290\n",
      "one_class_0        2.0836 +- 0.4325    q0: 1.0885    q10: 1.6040    q20: 1.7218    q30: 1.8127    q40: 1.9018    q50: 1.9885    q60: 2.1022    q70: 2.2500    q80: 2.4798    q90: 2.6977    q100: 3.7788\n",
      "[one_class_0 CSI 0.4360] [one_class_0 best 0.4360] \n",
      "[one_class_mean CSI 0.4360] [one_class_mean best 0.4360] \n",
      "0.4360\t0.4360\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : hem\n",
    "# epoch       : 100\n",
    "# shift_tr    : randpers_sharp\n",
    "# crop        : 0.08\n",
    "# blur_sigma  : 40\n",
    "# randpers    : 0.8\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --distortion_scale 0.8 --resize_factor 0.08 --sharpness_factor 128 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type randpers_sharp --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 1 --load_path \"logs/id_hem/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_randpers_sharp_resize_factor0.08_color_dist0.5_one_class_1/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "a7b553d3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3581 3581 3581 3581\n",
      "weight_sim:\t0.0011\t0.0008\t0.0009\t0.0009\n",
      "weight_shi:\t-0.0836\t0.1015\t0.0813\t0.0787\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.5724992151024417\n",
      "CNMC               2.0160 +- 0.0836    q0: 1.8554    q10: 1.9224    q20: 1.9466    q30: 1.9663    q40: 1.9854    q50: 2.0042    q60: 2.0232    q70: 2.0465    q80: 2.0759    q90: 2.1259    q100: 2.3440\n",
      "one_class_0        1.9930 +- 0.0670    q0: 1.8047    q10: 1.9141    q20: 1.9399    q30: 1.9557    q40: 1.9704    q50: 1.9843    q60: 2.0012    q70: 2.0244    q80: 2.0475    q90: 2.0793    q100: 2.2942\n",
      "[one_class_0 CSI 0.5725] [one_class_0 best 0.5725] \n",
      "[one_class_mean CSI 0.5725] [one_class_mean best 0.5725] \n",
      "0.5725\t0.5725\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : hem\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur_randpers_sharp\n",
    "# crop        : 0.08\n",
    "# blur_sigma  : 40\n",
    "# randpers    : 0.8\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --sharpness_factor 128 --distortion_scale 0.8 --resize_factor 0.08 --blur_sigma 40 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur_randpers_sharp --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 1 --load_path \"logs/id_hem/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_randpers_sharp_resize_factor0.08_color_dist0.5_one_class_1/last.model\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5d5f05f",
   "metadata": {},
   "source": [
    "## sharp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "13c15d92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3581 3581 3581 3581\n",
      "weight_sim:\t0.0082\t0.0048\t0.0035\t0.0035\n",
      "weight_shi:\t-0.0162\t0.0291\t0.0264\t0.0261\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.46516067612594825\n",
      "CNMC               2.0611 +- 0.2843    q0: 1.4233    q10: 1.7048    q20: 1.8158    q30: 1.8910    q40: 1.9831    q50: 2.0498    q60: 2.1209    q70: 2.1990    q80: 2.3022    q90: 2.4508    q100: 3.0255\n",
      "one_class_0        2.0896 +- 0.2109    q0: 1.5218    q10: 1.8407    q20: 1.9143    q30: 1.9720    q40: 2.0252    q50: 2.0691    q60: 2.1174    q70: 2.1761    q80: 2.2568    q90: 2.3717    q100: 2.9418\n",
      "[one_class_0 CSI 0.4652] [one_class_0 best 0.4652] \n",
      "[one_class_mean CSI 0.4652] [one_class_mean best 0.4652] \n",
      "0.4652\t0.4652\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : hem\n",
    "# epoch       : 100\n",
    "# shift_tr    : sharp\n",
    "# crop        : 0.08\n",
    "# sharpness   : 4096\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --sharpness_factor 4096 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type sharp --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 1 --load_path \"logs/id_hem/sharp/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_sharp_resize_factor0.08_color_dist0.5_sharpness_factor4096.0_one_class_1/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "25951e79",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3581 3581 3581 3581\n",
      "weight_sim:\t0.0095\t0.0075\t0.0068\t0.0072\n",
      "weight_shi:\t-0.0480\t0.0769\t0.0704\t0.0693\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.4025752235692077\n",
      "CNMC               1.9780 +- 0.1552    q0: 1.6133    q10: 1.7905    q20: 1.8304    q30: 1.8774    q40: 1.9265    q50: 1.9698    q60: 2.0166    q70: 2.0610    q80: 2.1123    q90: 2.1776    q100: 2.5595\n",
      "one_class_0        2.0255 +- 0.1272    q0: 1.6800    q10: 1.8659    q20: 1.9179    q30: 1.9585    q40: 1.9884    q50: 2.0210    q60: 2.0530    q70: 2.0845    q80: 2.1202    q90: 2.1844    q100: 2.7354\n",
      "[one_class_0 CSI 0.4026] [one_class_0 best 0.4026] \n",
      "[one_class_mean CSI 0.4026] [one_class_mean best 0.4026] \n",
      "0.4026\t0.4026\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : hem\n",
    "# epoch       : 100\n",
    "# shift_tr    : sharp\n",
    "# crop        : 0.08\n",
    "# sharpness   : 2048\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --sharpness_factor 2048 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type sharp --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 1 --load_path \"logs/id_hem/sharp/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_sharp_resize_factor0.08_color_dist0.5_sharpness_factor2048.0_one_class_1/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "4fc12b02",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3581 3581 3581 3581\n",
      "weight_sim:\t0.0089\t0.0059\t0.0064\t0.0063\n",
      "weight_shi:\t-0.0361\t0.0765\t0.0742\t0.0727\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.4227749420188578\n",
      "CNMC               2.0207 +- 0.1832    q0: 1.5627    q10: 1.7726    q20: 1.8618    q30: 1.9226    q40: 1.9811    q50: 2.0217    q60: 2.0627    q70: 2.1123    q80: 2.1832    q90: 2.2560    q100: 2.6861\n",
      "one_class_0        2.0632 +- 0.1200    q0: 1.6644    q10: 1.9104    q20: 1.9645    q30: 2.0071    q40: 2.0386    q50: 2.0633    q60: 2.0887    q70: 2.1181    q80: 2.1534    q90: 2.2160    q100: 2.5742\n",
      "[one_class_0 CSI 0.4228] [one_class_0 best 0.4228] \n",
      "[one_class_mean CSI 0.4228] [one_class_mean best 0.4228] \n",
      "0.4228\t0.4228\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : hem\n",
    "# epoch       : 100\n",
    "# shift_tr    : sharp\n",
    "# crop        : 0.08\n",
    "# sharpness   : 1024\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --sharpness_factor 1024 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type sharp --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 1 --load_path \"logs/id_hem/sharp/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_sharp_resize_factor0.08_color_dist0.5_sharpness_factor1024.0_one_class_1/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "99698eb6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3581 3581 3581 3581\n",
      "weight_sim:\t0.0067\t0.0032\t0.0035\t0.0038\n",
      "weight_shi:\t-0.0499\t0.0682\t0.0675\t0.0722\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.3898554522529092\n",
      "CNMC               1.9663 +- 0.1069    q0: 1.6970    q10: 1.8437    q20: 1.8799    q30: 1.9043    q40: 1.9235    q50: 1.9537    q60: 1.9831    q70: 2.0145    q80: 2.0505    q90: 2.1087    q100: 2.5004\n",
      "one_class_0        2.0038 +- 0.0970    q0: 1.7568    q10: 1.8902    q20: 1.9239    q30: 1.9445    q40: 1.9657    q50: 1.9897    q60: 2.0196    q70: 2.0483    q80: 2.0868    q90: 2.1398    q100: 2.3773\n",
      "[one_class_0 CSI 0.3899] [one_class_0 best 0.3899] \n",
      "[one_class_mean CSI 0.3899] [one_class_mean best 0.3899] \n",
      "0.3899\t0.3899\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : hem\n",
    "# epoch       : 100\n",
    "# shift_tr    : sharp\n",
    "# crop        : 0.08\n",
    "# sharpness   : 512\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --sharpness_factor 512 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type sharp --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 1 --load_path \"logs/id_hem/sharp/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_sharp_resize_factor0.08_color_dist0.5_sharpness_factor512.0_one_class_1/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "01e6d61a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3581 3581 3581 3581\n",
      "weight_sim:\t0.0053\t0.0084\t0.0092\t0.0087\n",
      "weight_shi:\t0.4300\t0.0647\t0.0695\t0.0685\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.5784846919656873\n",
      "CNMC               2.1270 +- 0.9610    q0: -0.9326    q10: 0.9101    q20: 1.3880    q30: 1.6010    q40: 1.8639    q50: 2.1067    q60: 2.3374    q70: 2.5413    q80: 2.8893    q90: 3.3775    q100: 5.1585\n",
      "one_class_0        1.8950 +- 0.7309    q0: -0.2104    q10: 1.0100    q20: 1.3020    q30: 1.4936    q40: 1.6684    q50: 1.8373    q60: 2.0139    q70: 2.2017    q80: 2.4870    q90: 2.8570    q100: 4.5441\n",
      "[one_class_0 CSI 0.5785] [one_class_0 best 0.5785] \n",
      "[one_class_mean CSI 0.5785] [one_class_mean best 0.5785] \n",
      "0.5785\t0.5785\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : hem\n",
    "# epoch       : 100\n",
    "# shift_tr    : sharp\n",
    "# crop        : 0.08\n",
    "# sharpness   : 256\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --sharpness_factor 256 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type sharp --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 1 --load_path \"logs/id_hem/sharp/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_sharp_resize_factor0.08_color_dist0.5_sharpness_factor256.0_one_class_1/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "65cc4fcd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3581 3581 3581 3581\n",
      "weight_sim:\t0.0089\t0.0063\t0.0075\t0.0065\n",
      "weight_shi:\t-0.0184\t0.0363\t0.0371\t0.0371\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.3679688370350115\n",
      "CNMC               1.9800 +- 0.0919    q0: 1.7207    q10: 1.8629    q20: 1.8911    q30: 1.9241    q40: 1.9548    q50: 1.9755    q60: 2.0034    q70: 2.0354    q80: 2.0631    q90: 2.1071    q100: 2.2242\n",
      "one_class_0        2.0217 +- 0.0794    q0: 1.7727    q10: 1.9194    q20: 1.9543    q30: 1.9779    q40: 1.9999    q50: 2.0212    q60: 2.0423    q70: 2.0650    q80: 2.0906    q90: 2.1259    q100: 2.2548\n",
      "[one_class_0 CSI 0.3680] [one_class_0 best 0.3680] \n",
      "[one_class_mean CSI 0.3680] [one_class_mean best 0.3680] \n",
      "0.3680\t0.3680\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : hem\n",
    "# epoch       : 100\n",
    "# shift_tr    : sharp\n",
    "# crop        : 0.08\n",
    "# sharpness   : 150\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --sharpness_factor 150 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type sharp --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 1 --load_path \"logs/id_hem/sharp/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_sharp_resize_factor0.08_color_dist0.5_sharpness_factor150.0_one_class_1/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e13b48db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3581 3581 3581 3581\n",
      "weight_sim:\t0.0083\t0.0053\t0.0056\t0.0053\n",
      "weight_shi:\t-0.1256\t0.0869\t0.0823\t0.0921\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.4935733347512128\n",
      "CNMC               2.0389 +- 0.1184    q0: 1.7300    q10: 1.8883    q20: 1.9358    q30: 1.9771    q40: 2.0079    q50: 2.0342    q60: 2.0657    q70: 2.0974    q80: 2.1469    q90: 2.1945    q100: 2.5086\n",
      "one_class_0        2.0418 +- 0.0930    q0: 1.7624    q10: 1.9334    q20: 1.9610    q30: 1.9867    q40: 2.0125    q50: 2.0354    q60: 2.0608    q70: 2.0915    q80: 2.1163    q90: 2.1599    q100: 2.3964\n",
      "[one_class_0 CSI 0.4936] [one_class_0 best 0.4936] \n",
      "[one_class_mean CSI 0.4936] [one_class_mean best 0.4936] \n",
      "0.4936\t0.4936\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : hem\n",
    "# epoch       : 100\n",
    "# shift_tr    : sharp\n",
    "# crop        : 0.08\n",
    "# sharpness   : 140\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --sharpness_factor 140 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type sharp --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 1 --load_path \"logs/id_hem/sharp/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_sharp_resize_factor0.08_color_dist0.5_sharpness_factor140.0_one_class_1/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "29cf690f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3581 3581 3581 3581\n",
      "weight_sim:\t0.0045\t0.0043\t0.0070\t0.0053\n",
      "weight_shi:\t-0.0813\t0.0676\t0.0710\t0.0626\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.4419042880725954\n",
      "CNMC               2.0129 +- 0.1195    q0: 1.5835    q10: 1.8643    q20: 1.9252    q30: 1.9581    q40: 1.9900    q50: 2.0157    q60: 2.0378    q70: 2.0653    q80: 2.1055    q90: 2.1542    q100: 2.4828\n",
      "one_class_0        2.0362 +- 0.1010    q0: 1.6891    q10: 1.9098    q20: 1.9486    q30: 1.9817    q40: 2.0087    q50: 2.0351    q60: 2.0576    q70: 2.0853    q80: 2.1206    q90: 2.1651    q100: 2.3927\n",
      "[one_class_0 CSI 0.4419] [one_class_0 best 0.4419] \n",
      "[one_class_mean CSI 0.4419] [one_class_mean best 0.4419] \n",
      "0.4419\t0.4419\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : hem\n",
    "# epoch       : 100\n",
    "# shift_tr    : sharp\n",
    "# crop        : 0.08\n",
    "# sharpness   : 130\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --sharpness_factor 130 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type sharp --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 1 --load_path \"logs/id_hem/sharp/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_sharp_resize_factor0.08_color_dist0.5_sharpness_factor130.0_one_class_1/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "dfaa2119",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3581 3581 3581 3581\n",
      "weight_sim:\t0.0130\t0.0032\t0.0032\t0.0028\n",
      "weight_shi:\t-0.0796\t0.1288\t0.1192\t0.1286\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.47003337080586194\n",
      "CNMC               1.9969 +- 0.1287    q0: 1.5279    q10: 1.8373    q20: 1.9073    q30: 1.9387    q40: 1.9663    q50: 1.9928    q60: 2.0270    q70: 2.0640    q80: 2.0993    q90: 2.1517    q100: 2.4161\n",
      "one_class_0        2.0110 +- 0.1133    q0: 1.6407    q10: 1.8709    q20: 1.9143    q30: 1.9536    q40: 1.9857    q50: 2.0121    q60: 2.0374    q70: 2.0675    q80: 2.1043    q90: 2.1554    q100: 2.3778\n",
      "[one_class_0 CSI 0.4700] [one_class_0 best 0.4700] \n",
      "[one_class_mean CSI 0.4700] [one_class_mean best 0.4700] \n",
      "0.4700\t0.4700\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : hem\n",
    "# epoch       : 100\n",
    "# shift_tr    : sharp\n",
    "# crop        : 0.08\n",
    "# sharpness   : 120\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --sharpness_factor 120 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type sharp --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 1 --load_path \"logs/id_hem/sharp/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_sharp_resize_factor0.08_color_dist0.5_sharpness_factor120.0_one_class_1/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e3eecf30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3581 3581 3581 3581\n",
      "weight_sim:\t0.0091\t0.0036\t0.0042\t0.0040\n",
      "weight_shi:\t0.2410\t0.5432\t0.2487\t0.3103\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.5940902277722075\n",
      "CNMC               2.0988 +- 0.5314    q0: 1.0497    q10: 1.4954    q20: 1.6747    q30: 1.8162    q40: 1.9078    q50: 2.0314    q60: 2.1299    q70: 2.2698    q80: 2.4594    q90: 2.8458    q100: 4.3420\n",
      "one_class_0        1.9324 +- 0.3714    q0: 1.0642    q10: 1.5287    q20: 1.6460    q30: 1.7249    q40: 1.8095    q50: 1.8731    q60: 1.9535    q70: 2.0458    q80: 2.1797    q90: 2.4138    q100: 3.6869\n",
      "[one_class_0 CSI 0.5941] [one_class_0 best 0.5941] \n",
      "[one_class_mean CSI 0.5941] [one_class_mean best 0.5941] \n",
      "0.5941\t0.5941\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : hem\n",
    "# epoch       : 100\n",
    "# shift_tr    : sharp\n",
    "# crop        : 0.08\n",
    "# sharpness   : 128\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --sharpness_factor 128 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type sharp --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 1 --load_path \"logs/id_hem/sharp/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_sharp_resize_factor0.08_color_dist0.5_sharpness_factor128.0_one_class_1/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d7d86bff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3581 3581 3581 3581\n",
      "weight_sim:\t0.0077\t0.0039\t0.0057\t0.0045\n",
      "weight_shi:\t-0.0543\t0.1223\t0.1116\t0.1079\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.4230414273995078\n",
      "CNMC               1.9898 +- 0.1931    q0: 1.3874    q10: 1.7331    q20: 1.8325    q30: 1.9150    q40: 1.9682    q50: 2.0080    q60: 2.0511    q70: 2.1020    q80: 2.1509    q90: 2.2176    q100: 2.4975\n",
      "one_class_0        2.0442 +- 0.1594    q0: 1.5034    q10: 1.8378    q20: 1.9120    q30: 1.9673    q40: 2.0118    q50: 2.0508    q60: 2.0920    q70: 2.1314    q80: 2.1747    q90: 2.2473    q100: 2.5530\n",
      "[one_class_0 CSI 0.4230] [one_class_0 best 0.4230] \n",
      "[one_class_mean CSI 0.4230] [one_class_mean best 0.4230] \n",
      "0.4230\t0.4230\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : hem\n",
    "# epoch       : 100\n",
    "# shift_tr    : sharp\n",
    "# crop        : 0.08\n",
    "# sharpness   : 100\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --sharpness_factor 100 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type sharp --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 1 --load_path \"logs/id_hem/sharp/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_sharp_resize_factor0.08_color_dist0.5_sharpness_factor100.0_one_class_1/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d60476b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3581 3581 3581 3581\n",
      "weight_sim:\t0.0071\t0.0035\t0.0055\t0.0033\n",
      "weight_shi:\t-0.7731\t-0.4426\t3.0750\t-1.0296\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.3861885880958892\n",
      "CNMC               1.8504 +- 1.7281    q0: -3.9915    q10: -0.3957    q20: 0.3727    q30: 1.0483    q40: 1.6967    q50: 2.0760    q60: 2.4763    q70: 2.8382    q80: 3.3079    q90: 3.8465    q100: 5.6520\n",
      "one_class_0        2.5429 +- 1.3399    q0: -4.5019    q10: 0.9296    q20: 1.4679    q30: 1.9042    q40: 2.2539    q50: 2.5979    q60: 2.9289    q70: 3.3053    q80: 3.6585    q90: 4.1959    q100: 6.6848\n",
      "[one_class_0 CSI 0.3862] [one_class_0 best 0.3862] \n",
      "[one_class_mean CSI 0.3862] [one_class_mean best 0.3862] \n",
      "0.3862\t0.3862\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : hem\n",
    "# epoch       : 100\n",
    "# shift_tr    : sharp\n",
    "# crop        : 0.08\n",
    "# sharpness   : 80\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --sharpness_factor 80 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type sharp --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 1 --load_path \"logs/id_hem/sharp/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_sharp_resize_factor0.08_color_dist0.5_sharpness_factor80.0_one_class_1/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b367669a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3581 3581 3581 3581\n",
      "weight_sim:\t0.0083\t0.0112\t0.0076\t0.0136\n",
      "weight_shi:\t-0.0567\t0.1140\t0.0842\t0.1028\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.376367240907848\n",
      "CNMC               1.9968 +- 0.0768    q0: 1.7939    q10: 1.9005    q20: 1.9341    q30: 1.9569    q40: 1.9751    q50: 1.9937    q60: 2.0157    q70: 2.0367    q80: 2.0629    q90: 2.0964    q100: 2.2761\n",
      "one_class_0        2.0289 +- 0.0677    q0: 1.8223    q10: 1.9439    q20: 1.9701    q30: 1.9928    q40: 2.0111    q50: 2.0279    q60: 2.0448    q70: 2.0625    q80: 2.0815    q90: 2.1167    q100: 2.3343\n",
      "[one_class_0 CSI 0.3764] [one_class_0 best 0.3764] \n",
      "[one_class_mean CSI 0.3764] [one_class_mean best 0.3764] \n",
      "0.3764\t0.3764\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : hem\n",
    "# epoch       : 100\n",
    "# shift_tr    : sharp\n",
    "# crop        : 0.08\n",
    "# sharpness   : 64\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --sharpness_factor 64 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type sharp --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 1 --load_path \"logs/id_hem/sharp/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_sharp_resize_factor0.08_color_dist0.5_sharpness_factor64.0_one_class_1/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "dce638a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3581 3581 3581 3581\n",
      "weight_sim:\t0.0150\t0.0058\t0.0129\t0.0054\n",
      "weight_shi:\t-0.0982\t0.1165\t0.1059\t0.0929\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.4102051874132815\n",
      "CNMC               1.9836 +- 0.2025    q0: 1.4858    q10: 1.7023    q20: 1.8012    q30: 1.8770    q40: 1.9428    q50: 2.0013    q60: 2.0495    q70: 2.0936    q80: 2.1462    q90: 2.2425    q100: 2.5144\n",
      "one_class_0        2.0499 +- 0.1863    q0: 1.5414    q10: 1.8119    q20: 1.8950    q30: 1.9488    q40: 1.9984    q50: 2.0487    q60: 2.0962    q70: 2.1512    q80: 2.2168    q90: 2.2846    q100: 2.6101\n",
      "[one_class_0 CSI 0.4102] [one_class_0 best 0.4102] \n",
      "[one_class_mean CSI 0.4102] [one_class_mean best 0.4102] \n",
      "0.4102\t0.4102\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : hem\n",
    "# epoch       : 100\n",
    "# shift_tr    : sharp\n",
    "# crop        : 0.08\n",
    "# sharpness   : 32\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --sharpness_factor 32 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type sharp --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 1 --load_path \"logs/id_hem/sharp/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_sharp_resize_factor0.08_color_dist0.5_sharpness_factor32.0_one_class_1/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "28387a64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3581 3581 3581 3581\n",
      "weight_sim:\t0.0088\t0.0070\t0.0079\t0.0070\n",
      "weight_shi:\t-0.0517\t0.1752\t0.1985\t0.2796\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.4567964532758079\n",
      "CNMC               2.0252 +- 0.2349    q0: 1.3683    q10: 1.7184    q20: 1.8035    q30: 1.8906    q40: 1.9540    q50: 2.0153    q60: 2.0821    q70: 2.1619    q80: 2.2506    q90: 2.3384    q100: 2.6113\n",
      "one_class_0        2.0617 +- 0.2072    q0: 1.5917    q10: 1.7983    q20: 1.8819    q30: 1.9423    q40: 1.9904    q50: 2.0477    q60: 2.1076    q70: 2.1646    q80: 2.2533    q90: 2.3431    q100: 2.6298\n",
      "[one_class_0 CSI 0.4568] [one_class_0 best 0.4568] \n",
      "[one_class_mean CSI 0.4568] [one_class_mean best 0.4568] \n",
      "0.4568\t0.4568\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : hem\n",
    "# epoch       : 100\n",
    "# shift_tr    : sharp\n",
    "# crop        : 0.08\n",
    "# sharpness   : 16\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --sharpness_factor 16 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type sharp --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 1 --load_path \"logs/id_hem/sharp/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_sharp_resize_factor0.08_color_dist0.5_sharpness_factor16.0_one_class_1/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "424cd4b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3581 3581 3581 3581\n",
      "weight_sim:\t0.0167\t0.0112\t0.0119\t0.0098\n",
      "weight_shi:\t-0.1065\t0.1467\t0.1401\t0.1203\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.4809307872269316\n",
      "CNMC               2.0080 +- 0.2589    q0: 1.4062    q10: 1.6813    q20: 1.7879    q30: 1.8753    q40: 1.9309    q50: 1.9964    q60: 2.0601    q70: 2.1217    q80: 2.2165    q90: 2.3636    q100: 3.0258\n",
      "one_class_0        2.0288 +- 0.2475    q0: 1.4597    q10: 1.7282    q20: 1.8162    q30: 1.8799    q40: 1.9422    q50: 1.9987    q60: 2.0671    q70: 2.1423    q80: 2.2336    q90: 2.3568    q100: 3.5008\n",
      "[one_class_0 CSI 0.4809] [one_class_0 best 0.4809] \n",
      "[one_class_mean CSI 0.4809] [one_class_mean best 0.4809] \n",
      "0.4809\t0.4809\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : hem\n",
    "# epoch       : 100\n",
    "# shift_tr    : sharp\n",
    "# crop        : 0.08\n",
    "# sharpness   : 8\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --sharpness_factor 8 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type sharp --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 1 --load_path \"logs/id_hem/sharp/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_sharp_resize_factor0.08_color_dist0.5_sharpness_factor8.0_one_class_1/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b30452ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3581 3581 3581 3581\n",
      "weight_sim:\t0.0033\t0.0030\t0.0025\t0.0028\n",
      "weight_shi:\t-0.0191\t0.0502\t0.0455\t0.0473\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.4414099292073041\n",
      "CNMC               1.9717 +- 0.1514    q0: 1.4512    q10: 1.7885    q20: 1.8443    q30: 1.8923    q40: 1.9275    q50: 1.9674    q60: 2.0006    q70: 2.0525    q80: 2.0992    q90: 2.1705    q100: 2.4112\n",
      "one_class_0        1.9985 +- 0.1198    q0: 1.6117    q10: 1.8478    q20: 1.9013    q30: 1.9366    q40: 1.9671    q50: 1.9967    q60: 2.0257    q70: 2.0616    q80: 2.0994    q90: 2.1570    q100: 2.3699\n",
      "[one_class_0 CSI 0.4414] [one_class_0 best 0.4414] \n",
      "[one_class_mean CSI 0.4414] [one_class_mean best 0.4414] \n",
      "0.4414\t0.4414\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : hem\n",
    "# epoch       : 100\n",
    "# shift_tr    : sharp\n",
    "# crop        : 0.08\n",
    "# sharpness   : 5\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --sharpness_factor 5 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type sharp --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 1 --load_path \"logs/id_hem/sharp/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_sharp_resize_factor0.08_color_dist0.5_sharpness_factor5.0_one_class_1/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "61511c88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3581 3581 3581 3581\n",
      "weight_sim:\t0.0048\t0.0048\t0.0033\t0.0040\n",
      "weight_shi:\t-0.0216\t0.0573\t0.0466\t0.0474\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.3330541883146477\n",
      "CNMC               1.9613 +- 0.1613    q0: 1.4001    q10: 1.7205    q20: 1.8317    q30: 1.8884    q40: 1.9420    q50: 1.9740    q60: 2.0204    q70: 2.0591    q80: 2.1023    q90: 2.1507    q100: 2.3497\n",
      "one_class_0        2.0506 +- 0.1298    q0: 1.5709    q10: 1.8729    q20: 1.9474    q30: 2.0023    q40: 2.0388    q50: 2.0668    q60: 2.0970    q70: 2.1272    q80: 2.1610    q90: 2.1981    q100: 2.4918\n",
      "[one_class_0 CSI 0.3331] [one_class_0 best 0.3331] \n",
      "[one_class_mean CSI 0.3331] [one_class_mean best 0.3331] \n",
      "0.3331\t0.3331\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : hem\n",
    "# epoch       : 100\n",
    "# shift_tr    : sharp\n",
    "# crop        : 0.08\n",
    "# sharpness   : 4\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --sharpness_factor 4 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type sharp --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 1 --load_path \"logs/id_hem/sharp/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_sharp_resize_factor0.08_color_dist0.5_sharpness_factor4.0_one_class_1/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9aa87298",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3581 3581 3581 3581\n",
      "weight_sim:\t0.0025\t0.0027\t0.0023\t0.0026\n",
      "weight_shi:\t-0.0247\t0.0743\t0.0809\t0.0786\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.32097499468295204\n",
      "CNMC               1.9756 +- 0.0921    q0: 1.6810    q10: 1.8631    q20: 1.9010    q30: 1.9281    q40: 1.9493    q50: 1.9693    q60: 1.9932    q70: 2.0205    q80: 2.0561    q90: 2.0987    q100: 2.2881\n",
      "one_class_0        2.0291 +- 0.0715    q0: 1.8113    q10: 1.9469    q20: 1.9690    q30: 1.9889    q40: 2.0060    q50: 2.0221    q60: 2.0372    q70: 2.0598    q80: 2.0847    q90: 2.1253    q100: 2.3137\n",
      "[one_class_0 CSI 0.3210] [one_class_0 best 0.3210] \n",
      "[one_class_mean CSI 0.3210] [one_class_mean best 0.3210] \n",
      "0.3210\t0.3210\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : hem\n",
    "# epoch       : 100\n",
    "# shift_tr    : sharp\n",
    "# crop        : 0.08\n",
    "# sharpness   : 3\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --sharpness_factor 3 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type sharp --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 1 --load_path \"logs/id_hem/sharp/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_sharp_resize_factor0.08_color_dist0.5_sharpness_factor3.0_one_class_1/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ed261f4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3581 3581 3581 3581\n",
      "weight_sim:\t0.0016\t0.0017\t0.0018\t0.0018\n",
      "weight_shi:\t-0.0191\t0.0634\t0.0692\t0.0681\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.5479179959286604\n",
      "CNMC               2.0439 +- 0.2491    q0: 1.3671    q10: 1.7149    q20: 1.8289    q30: 1.9114    q40: 1.9969    q50: 2.0563    q60: 2.1157    q70: 2.1758    q80: 2.2482    q90: 2.3551    q100: 2.6738\n",
      "one_class_0        2.0114 +- 0.1906    q0: 1.4681    q10: 1.7626    q20: 1.8467    q30: 1.9083    q40: 1.9608    q50: 2.0043    q60: 2.0576    q70: 2.1069    q80: 2.1710    q90: 2.2558    q100: 2.5480\n",
      "[one_class_0 CSI 0.5479] [one_class_0 best 0.5479] \n",
      "[one_class_mean CSI 0.5479] [one_class_mean best 0.5479] \n",
      "0.5479\t0.5479\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : hem\n",
    "# epoch       : 100\n",
    "# shift_tr    : sharp\n",
    "# crop        : 0.08\n",
    "# sharpness   : 2\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --sharpness_factor 2 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type sharp --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 1 --load_path \"logs/id_hem/sharp/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_sharp_resize_factor0.08_color_dist0.5_sharpness_factor2.0_one_class_1/last.model\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f347111",
   "metadata": {},
   "source": [
    "## randpers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6954e9f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3581 3581 3581 3581\n",
      "weight_sim:\t0.0027\t0.0028\t0.0028\t0.0029\n",
      "weight_shi:\t0.0396\t-0.1267\t-0.1178\t-0.1344\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.35701318627897793\n",
      "CNMC               1.9933 +- 0.0426    q0: 1.7637    q10: 1.9470    q20: 1.9683    q30: 1.9825    q40: 1.9896    q50: 1.9981    q60: 2.0059    q70: 2.0119    q80: 2.0228    q90: 2.0391    q100: 2.1039\n",
      "one_class_0        2.0107 +- 0.0300    q0: 1.8398    q10: 1.9753    q20: 1.9909    q30: 2.0006    q40: 2.0073    q50: 2.0134    q60: 2.0197    q70: 2.0245    q80: 2.0323    q90: 2.0429    q100: 2.0991\n",
      "[one_class_0 CSI 0.3570] [one_class_0 best 0.3570] \n",
      "[one_class_mean CSI 0.3570] [one_class_mean best 0.3570] \n",
      "0.3570\t0.3570\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : hem\n",
    "# epoch       : 100\n",
    "# shift_tr    : randpers\n",
    "# crop        : 0.08\n",
    "# randpers    : 0.95\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --distortion_scale 0.95 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type randpers --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 1 --load_path \"logs/id_hem/randpers/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_randpers_resize_factor0.08_color_dist0.5_distortion_scale0.95_one_class_1/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7ef390e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3581 3581 3581 3581\n",
      "weight_sim:\t0.0079\t0.0098\t0.0115\t0.0104\n",
      "weight_shi:\t-0.2285\t-6.8399\t0.4918\t0.3229\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.5641122049038374\n",
      "CNMC               1.9712 +- 0.8313    q0: -4.4253    q10: 0.9802    q20: 1.5045    q30: 1.8002    q40: 1.9917    q50: 2.1173    q60: 2.2353    q70: 2.3608    q80: 2.5048    q90: 2.7707    q100: 4.2407\n",
      "one_class_0        1.9180 +- 0.6218    q0: -3.1624    q10: 1.2584    q20: 1.5541    q30: 1.7231    q40: 1.8312    q50: 1.9474    q60: 2.0646    q70: 2.2017    q80: 2.3455    q90: 2.6130    q100: 4.2616\n",
      "[one_class_0 CSI 0.5641] [one_class_0 best 0.5641] \n",
      "[one_class_mean CSI 0.5641] [one_class_mean best 0.5641] \n",
      "0.5641\t0.5641\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : hem\n",
    "# epoch       : 100\n",
    "# shift_tr    : randpers\n",
    "# crop        : 0.08\n",
    "# randpers    : 0.9\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --distortion_scale 0.9 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type randpers --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 1 --load_path \"logs/id_hem/randpers/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_randpers_resize_factor0.08_color_dist0.5_distortion_scale0.9_one_class_1/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1205e882",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3581 3581 3581 3581\n",
      "weight_sim:\t0.0034\t0.0047\t0.0029\t0.0041\n",
      "weight_shi:\t0.1303\t-0.3875\t-0.1777\t-0.3820\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.34714879632161555\n",
      "CNMC               1.9226 +- 0.3041    q0: 0.5031    q10: 1.4958    q20: 1.7161    q30: 1.8443    q40: 1.9245    q50: 1.9879    q60: 2.0373    q70: 2.0889    q80: 2.1566    q90: 2.2287    q100: 2.5521\n",
      "one_class_0        2.0685 +- 0.2043    q0: 1.1682    q10: 1.8203    q20: 1.9473    q30: 2.0089    q40: 2.0513    q50: 2.0872    q60: 2.1284    q70: 2.1701    q80: 2.2162    q90: 2.2834    q100: 2.6224\n",
      "[one_class_0 CSI 0.3471] [one_class_0 best 0.3471] \n",
      "[one_class_mean CSI 0.3471] [one_class_mean best 0.3471] \n",
      "0.3471\t0.3471\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : hem\n",
    "# epoch       : 100\n",
    "# shift_tr    : randpers\n",
    "# crop        : 0.08\n",
    "# randpers    : 0.85\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --distortion_scale 0.85 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type randpers --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 1 --load_path \"logs/id_hem/randpers/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_randpers_resize_factor0.08_color_dist0.5_distortion_scale0.85_one_class_1/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8887546c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3581 3581 3581 3581\n",
      "weight_sim:\t0.0020\t0.0037\t0.0026\t0.0039\n",
      "weight_shi:\t0.1393\t2.5299\t-1.4218\t1.2437\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.6913884078226435\n",
      "CNMC               2.0752 +- 0.3123    q0: 0.7320    q10: 1.7083    q20: 1.8297    q30: 1.9220    q40: 2.0047    q50: 2.0690    q60: 2.1391    q70: 2.2162    q80: 2.3010    q90: 2.4549    q100: 3.2842\n",
      "one_class_0        1.8917 +- 0.2289    q0: 0.7422    q10: 1.6150    q20: 1.7197    q30: 1.7818    q40: 1.8380    q50: 1.8923    q60: 1.9400    q70: 1.9926    q80: 2.0616    q90: 2.1731    q100: 2.9070\n",
      "[one_class_0 CSI 0.6914] [one_class_0 best 0.6914] \n",
      "[one_class_mean CSI 0.6914] [one_class_mean best 0.6914] \n",
      "0.6914\t0.6914\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : hem\n",
    "# epoch       : 100\n",
    "# shift_tr    : randpers\n",
    "# crop        : 0.08\n",
    "# randpers    : 0.8\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --distortion_scale 0.8 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type randpers --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 1 --load_path \"logs/id_hem/randpers/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_randpers_resize_factor0.08_color_dist0.5_distortion_scale0.8_one_class_1/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b65d2295",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3581 3581 3581 3581\n",
      "weight_sim:\t0.0080\t0.0036\t0.0038\t0.0054\n",
      "weight_shi:\t-0.0669\t-0.5647\t-0.7888\t0.5885\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.35124039133473095\n",
      "CNMC               1.9592 +- 0.1769    q0: 1.1540    q10: 1.7506    q20: 1.8287    q30: 1.8809    q40: 1.9227    q50: 1.9622    q60: 1.9930    q70: 2.0505    q80: 2.0959    q90: 2.1715    q100: 2.7656\n",
      "one_class_0        2.0376 +- 0.1423    q0: 1.0198    q10: 1.8838    q20: 1.9409    q30: 1.9806    q40: 2.0144    q50: 2.0407    q60: 2.0708    q70: 2.0989    q80: 2.1348    q90: 2.1967    q100: 2.6480\n",
      "[one_class_0 CSI 0.3512] [one_class_0 best 0.3512] \n",
      "[one_class_mean CSI 0.3512] [one_class_mean best 0.3512] \n",
      "0.3512\t0.3512\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : hem\n",
    "# epoch       : 100\n",
    "# shift_tr    : randpers\n",
    "# crop        : 0.08\n",
    "# randpers    : 0.75\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --distortion_scale 0.75 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type randpers --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 1 --load_path \"logs/id_hem/randpers/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_randpers_resize_factor0.08_color_dist0.5_distortion_scale0.75_one_class_1/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "2a818378",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3581 3581 3581 3581\n",
      "weight_sim:\t0.0034\t0.0037\t0.0024\t0.0028\n",
      "weight_shi:\t0.5181\t-2.5612\t-0.2828\t-0.4473\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.3594818156959256\n",
      "CNMC               1.5977 +- 1.3081    q0: -2.9399    q10: -0.2131    q20: 0.5889    q30: 1.0500    q40: 1.4828    q50: 1.7567    q60: 2.0337    q70: 2.3642    q80: 2.6741    q90: 3.1919    q100: 4.5132\n",
      "one_class_0        2.2261 +- 1.0824    q0: -1.7685    q10: 0.7646    q20: 1.4013    q30: 1.7643    q40: 2.0621    q50: 2.3193    q60: 2.5929    q70: 2.8838    q80: 3.1160    q90: 3.5429    q100: 5.0474\n",
      "[one_class_0 CSI 0.3595] [one_class_0 best 0.3595] \n",
      "[one_class_mean CSI 0.3595] [one_class_mean best 0.3595] \n",
      "0.3595\t0.3595\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : hem\n",
    "# epoch       : 100\n",
    "# shift_tr    : randpers\n",
    "# crop        : 0.08\n",
    "# randpers    : 0.6\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --distortion_scale 0.6 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type randpers --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 1 --load_path \"logs/id_hem/randpers/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_randpers_resize_factor0.08_color_dist0.5_distortion_scale0.6_one_class_1/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "09a15dda",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3581 3581 3581 3581\n",
      "weight_sim:\t0.0043\t0.0115\t0.0075\t0.0087\n",
      "weight_shi:\t12.1609\t0.3968\t2.0101\t0.4812\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.36039204367068733\n",
      "CNMC               1.4531 +- 7.5510    q0: -28.7074    q10: -8.1564    q20: -3.7419    q30: -1.3692    q40: 0.5754    q50: 2.0721    q60: 3.7007    q70: 5.1638    q80: 7.3805    q90: 10.4019    q100: 20.0002\n",
      "one_class_0        4.8084 +- 5.1144    q0: -14.2655    q10: -1.2285    q20: 1.1701    q30: 2.5734    q40: 3.7262    q50: 4.8972    q60: 5.9430    q70: 7.0902    q80: 8.7584    q90: 11.3302    q100: 19.8412\n",
      "[one_class_0 CSI 0.3604] [one_class_0 best 0.3604] \n",
      "[one_class_mean CSI 0.3604] [one_class_mean best 0.3604] \n",
      "0.3604\t0.3604\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : hem\n",
    "# epoch       : 100\n",
    "# shift_tr    : randpers\n",
    "# crop        : 0.08\n",
    "# randpers    : 0.3\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --distortion_scale 0.3 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type randpers --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 1 --load_path \"logs/id_hem/randpers/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_randpers_resize_factor0.08_color_dist0.5_distortion_scale0.3_one_class_1/last.model\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47013663",
   "metadata": {},
   "source": [
    "## blur"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "958ecba3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3581 3581 3581 3581\n",
      "weight_sim:\t0.0038\t0.0072\t0.0039\t0.0044\n",
      "weight_shi:\t-0.1658\t0.1714\t0.2799\t0.2838\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.4812004375170905\n",
      "CNMC               1.9384 +- 0.2410    q0: 1.5304    q10: 1.7013    q20: 1.7508    q30: 1.7894    q40: 1.8299    q50: 1.8827    q60: 1.9243    q70: 1.9999    q80: 2.0841    q90: 2.2637    q100: 2.8485\n",
      "one_class_0        1.9219 +- 0.1651    q0: 1.5451    q10: 1.7386    q20: 1.7816    q30: 1.8174    q40: 1.8548    q50: 1.8945    q60: 1.9407    q70: 1.9846    q80: 2.0549    q90: 2.1446    q100: 2.6371\n",
      "[one_class_0 CSI 0.4812] [one_class_0 best 0.4812] \n",
      "[one_class_mean CSI 0.4812] [one_class_mean best 0.4812] \n",
      "0.4812\t0.4812\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : hem\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.08\n",
    "# blur_sigma : 180\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --blur_sigma 180 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 1 --load_path \"logs/id_hem/blur/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_resize_factor0.08_color_dist0.5_blur_sigma180.0_one_class_1/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "a3f7ef72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3581 3581 3581 3581\n",
      "weight_sim:\t0.0025\t0.0058\t0.0024\t0.0029\n",
      "weight_shi:\t-0.0568\t0.0831\t0.1701\t0.1303\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.6192537902956279\n",
      "CNMC               2.0247 +- 0.0991    q0: 1.8346    q10: 1.9155    q20: 1.9404    q30: 1.9652    q40: 1.9879    q50: 2.0067    q60: 2.0291    q70: 2.0625    q80: 2.1019    q90: 2.1563    q100: 2.4786\n",
      "one_class_0        1.9853 +- 0.0765    q0: 1.7917    q10: 1.9064    q20: 1.9276    q30: 1.9429    q40: 1.9598    q50: 1.9743    q60: 1.9887    q70: 2.0055    q80: 2.0343    q90: 2.0845    q100: 2.3701\n",
      "[one_class_0 CSI 0.6193] [one_class_0 best 0.6193] \n",
      "[one_class_mean CSI 0.6193] [one_class_mean best 0.6193] \n",
      "0.6193\t0.6193\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : hem\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.08\n",
    "# blur_sigma : 120\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --blur_sigma 120 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 1 --load_path \"logs/id_hem/blur/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_resize_factor0.08_color_dist0.5_blur_sigma120.0_one_class_1/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "2f2a8808",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3581 3581 3581 3581\n",
      "weight_sim:\t0.0030\t0.0043\t0.0026\t0.0028\n",
      "weight_shi:\t-0.0889\t0.1756\t0.3138\t0.2610\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.5952460527248604\n",
      "CNMC               2.0008 +- 0.0139    q0: 1.9745    q10: 1.9866    q20: 1.9900    q30: 1.9930    q40: 1.9955    q50: 1.9986    q60: 2.0019    q70: 2.0048    q80: 2.0099    q90: 2.0166    q100: 2.0896\n",
      "one_class_0        1.9964 +- 0.0119    q0: 1.9575    q10: 1.9833    q20: 1.9872    q30: 1.9899    q40: 1.9925    q50: 1.9948    q60: 1.9973    q70: 2.0007    q80: 2.0051    q90: 2.0119    q100: 2.0732\n",
      "[one_class_0 CSI 0.5952] [one_class_0 best 0.5952] \n",
      "[one_class_mean CSI 0.5952] [one_class_mean best 0.5952] \n",
      "0.5952\t0.5952\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : hem\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.08\n",
    "# blur_sigma : 110\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --blur_sigma 110 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 1 --load_path \"logs/id_hem/blur/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_resize_factor0.08_color_dist0.5_blur_sigma110.0_one_class_1/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "08a6959c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3581 3581 3581 3581\n",
      "weight_sim:\t0.0067\t0.0043\t0.0047\t0.0049\n",
      "weight_shi:\t-0.0583\t0.0915\t0.2051\t0.1700\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.633952896018797\n",
      "CNMC               1.9904 +- 0.1692    q0: 1.5494    q10: 1.7849    q20: 1.8442    q30: 1.9005    q40: 1.9430    q50: 1.9786    q60: 2.0203    q70: 2.0602    q80: 2.1209    q90: 2.2064    q100: 2.5511\n",
      "one_class_0        1.9167 +- 0.1098    q0: 1.5787    q10: 1.7796    q20: 1.8329    q30: 1.8646    q40: 1.8922    q50: 1.9150    q60: 1.9402    q70: 1.9662    q80: 1.9991    q90: 2.0546    q100: 2.2965\n",
      "[one_class_0 CSI 0.6340] [one_class_0 best 0.6340] \n",
      "[one_class_mean CSI 0.6340] [one_class_mean best 0.6340] \n",
      "0.6340\t0.6340\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : hem\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.08\n",
    "# blur_sigma : 105\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --blur_sigma 105 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 1 --load_path \"logs/id_hem/blur/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_resize_factor0.08_color_dist0.5_blur_sigma105.0_one_class_1/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "a4a4eee1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3581 3581 3581 3581\n",
      "weight_sim:\t0.0091\t0.0045\t0.0091\t0.0070\n",
      "weight_shi:\t-0.0676\t0.0975\t0.1849\t0.1972\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.5476483456385015\n",
      "CNMC               1.9769 +- 0.2661    q0: 1.3171    q10: 1.6312    q20: 1.7272    q30: 1.8319    q40: 1.9118    q50: 1.9776    q60: 2.0460    q70: 2.1125    q80: 2.1924    q90: 2.3278    q100: 2.8173\n",
      "one_class_0        1.9268 +- 0.2241    q0: 1.2928    q10: 1.6165    q20: 1.7138    q30: 1.8152    q40: 1.8859    q50: 1.9465    q60: 2.0048    q70: 2.0580    q80: 2.1179    q90: 2.1973    q100: 2.5704\n",
      "[one_class_0 CSI 0.5476] [one_class_0 best 0.5476] \n",
      "[one_class_mean CSI 0.5476] [one_class_mean best 0.5476] \n",
      "0.5476\t0.5476\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : hem\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.08\n",
    "# blur_sigma : 100\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --blur_sigma 100 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 1 --load_path \"logs/id_hem/blur/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_resize_factor0.08_color_dist0.5_blur_sigma100.0_one_class_1/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "8f0ceb15",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3581 3581 3581 3581\n",
      "weight_sim:\t0.0018\t0.0028\t0.0016\t0.0018\n",
      "weight_shi:\t-0.2029\t0.1970\t1.0597\t0.4185\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.5393334953767002\n",
      "CNMC               1.9749 +- 0.3046    q0: 1.1986    q10: 1.5960    q20: 1.7089    q30: 1.8060    q40: 1.8905    q50: 1.9696    q60: 2.0356    q70: 2.1233    q80: 2.2217    q90: 2.3752    q100: 3.1061\n",
      "one_class_0        1.9275 +- 0.2387    q0: 1.1897    q10: 1.6222    q20: 1.7266    q30: 1.8084    q40: 1.8732    q50: 1.9343    q60: 1.9958    q70: 2.0464    q80: 2.1172    q90: 2.2327    q100: 2.6893\n",
      "[one_class_0 CSI 0.5393] [one_class_0 best 0.5393] \n",
      "[one_class_mean CSI 0.5393] [one_class_mean best 0.5393] \n",
      "0.5393\t0.5393\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : hem\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.08\n",
    "# blur_sigma : 95\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --blur_sigma 95 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 1 --load_path \"logs/id_hem/blur/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_resize_factor0.08_color_dist0.5_blur_sigma95.0_one_class_1/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "7d89e279",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3581 3581 3581 3581\n",
      "weight_sim:\t0.0075\t0.0041\t0.0071\t0.0059\n",
      "weight_shi:\t-0.0360\t0.0714\t0.1079\t0.0991\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.5292488277175179\n",
      "CNMC               1.9774 +- 0.1920    q0: 1.5068    q10: 1.7357    q20: 1.8106    q30: 1.8745    q40: 1.9206    q50: 1.9753    q60: 2.0197    q70: 2.0694    q80: 2.1299    q90: 2.2192    q100: 2.6272\n",
      "one_class_0        1.9507 +- 0.1545    q0: 1.4789    q10: 1.7436    q20: 1.8103    q30: 1.8750    q40: 1.9239    q50: 1.9683    q60: 2.0050    q70: 2.0411    q80: 2.0780    q90: 2.1379    q100: 2.3968\n",
      "[one_class_0 CSI 0.5292] [one_class_0 best 0.5292] \n",
      "[one_class_mean CSI 0.5292] [one_class_mean best 0.5292] \n",
      "0.5292\t0.5292\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : hem\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.08\n",
    "# blur_sigma : 90\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --blur_sigma 90 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 1 --load_path \"logs/id_hem/blur/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_resize_factor0.08_color_dist0.5_blur_sigma90.0_one_class_1/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "ebb47e6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3581 3581 3581 3581\n",
      "weight_sim:\t0.0050\t0.0117\t0.0038\t0.0049\n",
      "weight_shi:\t-0.2427\t0.2328\t1.3692\t0.7248\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.557083573866456\n",
      "CNMC               2.0005 +- 0.1926    q0: 1.3343    q10: 1.8135    q20: 1.8631    q30: 1.9060    q40: 1.9410    q50: 1.9699    q60: 2.0110    q70: 2.0589    q80: 2.1204    q90: 2.2186    q100: 3.1284\n",
      "one_class_0        1.9634 +- 0.1487    q0: 1.4064    q10: 1.8025    q20: 1.8544    q30: 1.8886    q40: 1.9192    q50: 1.9463    q60: 1.9749    q70: 2.0096    q80: 2.0594    q90: 2.1522    q100: 2.5877\n",
      "[one_class_0 CSI 0.5571] [one_class_0 best 0.5571] \n",
      "[one_class_mean CSI 0.5571] [one_class_mean best 0.5571] \n",
      "0.5571\t0.5571\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : hem\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.08\n",
    "# blur_sigma : 80\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --blur_sigma 80 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 1 --load_path \"logs/id_hem/blur/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_resize_factor0.08_color_dist0.5_blur_sigma80.0_one_class_1/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "7d6e0050",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3581 3581 3581 3581\n",
      "weight_sim:\t0.0062\t0.0053\t0.0066\t0.0062\n",
      "weight_shi:\t-0.0434\t0.0771\t0.1221\t0.1065\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.5821104122990916\n",
      "CNMC               1.9984 +- 0.0869    q0: 1.7841    q10: 1.8832    q20: 1.9216    q30: 1.9505    q40: 1.9768    q50: 1.9991    q60: 2.0230    q70: 2.0443    q80: 2.0710    q90: 2.1126    q100: 2.2334\n",
      "one_class_0        1.9740 +- 0.0685    q0: 1.7594    q10: 1.8780    q20: 1.9143    q30: 1.9428    q40: 1.9641    q50: 1.9808    q60: 1.9973    q70: 2.0131    q80: 2.0305    q90: 2.0551    q100: 2.1770\n",
      "[one_class_0 CSI 0.5821] [one_class_0 best 0.5821] \n",
      "[one_class_mean CSI 0.5821] [one_class_mean best 0.5821] \n",
      "0.5821\t0.5821\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : hem\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.08\n",
    "# blur_sigma : 60\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --blur_sigma 60 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 1 --load_path \"logs/id_hem/blur/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_resize_factor0.08_color_dist0.5_blur_sigma60.0_one_class_1/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "df7becce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3581 3581 3581 3581\n",
      "weight_sim:\t0.0045\t0.0051\t0.0033\t0.0041\n",
      "weight_shi:\t-0.1512\t0.2745\t0.6510\t0.4026\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.6940311072625811\n",
      "CNMC               2.0096 +- 0.0626    q0: 1.8488    q10: 1.9389    q20: 1.9597    q30: 1.9741    q40: 1.9884    q50: 2.0031    q60: 2.0185    q70: 2.0342    q80: 2.0520    q90: 2.0853    q100: 2.2770\n",
      "one_class_0        1.9718 +- 0.0446    q0: 1.8450    q10: 1.9219    q20: 1.9383    q30: 1.9495    q40: 1.9584    q50: 1.9680    q60: 1.9769    q70: 1.9880    q80: 2.0015    q90: 2.0244    q100: 2.2023\n",
      "[one_class_0 CSI 0.6940] [one_class_0 best 0.6940] \n",
      "[one_class_mean CSI 0.6940] [one_class_mean best 0.6940] \n",
      "0.6940\t0.6940\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : hem\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.08\n",
    "# blur_sigma : 40\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --blur_sigma 40 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 1 --load_path \"logs/id_hem/blur/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_resize_factor0.08_color_dist0.5_blur_sigma40.0_one_class_1/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "b7036b42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3581 3581 3581 3581\n",
      "weight_sim:\t0.0017\t0.0020\t0.0015\t0.0016\n",
      "weight_shi:\t0.0317\t-0.1164\t-0.0840\t-0.0812\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.39877986408612603\n",
      "CNMC               1.9759 +- 0.1316    q0: 1.4471    q10: 1.8010    q20: 1.8983    q30: 1.9382    q40: 1.9698    q50: 1.9932    q60: 2.0241    q70: 2.0528    q80: 2.0799    q90: 2.1197    q100: 2.2278\n",
      "one_class_0        2.0210 +- 0.0942    q0: 1.5614    q10: 1.8968    q20: 1.9555    q30: 1.9874    q40: 2.0148    q50: 2.0364    q60: 2.0551    q70: 2.0753    q80: 2.0963    q90: 2.1246    q100: 2.2320\n",
      "[one_class_0 CSI 0.3988] [one_class_0 best 0.3988] \n",
      "[one_class_mean CSI 0.3988] [one_class_mean best 0.3988] \n",
      "0.3988\t0.3988\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : hem\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.08\n",
    "# blur_sigma : 20\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --blur_sigma 20 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 1 --load_path \"logs/id_hem/blur/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_resize_factor0.08_color_dist0.5_blur_sigma20.0_one_class_1/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "e7b68654",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3581 3581 3581 3581\n",
      "weight_sim:\t0.0020\t0.0035\t0.0027\t0.0027\n",
      "weight_shi:\t0.1013\t-0.5641\t-0.5419\t-0.3880\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.33394542683235595\n",
      "CNMC               1.9739 +- 0.2803    q0: 1.2318    q10: 1.5933    q20: 1.7359    q30: 1.8196    q40: 1.9141    q50: 1.9863    q60: 2.0640    q70: 2.1425    q80: 2.2339    q90: 2.3203    q100: 2.6037\n",
      "one_class_0        2.1309 +- 0.1875    q0: 1.4830    q10: 1.8910    q20: 1.9714    q30: 2.0347    q40: 2.0807    q50: 2.1311    q60: 2.1754    q70: 2.2372    q80: 2.3011    q90: 2.3743    q100: 2.6831\n",
      "[one_class_0 CSI 0.3339] [one_class_0 best 0.3339] \n",
      "[one_class_mean CSI 0.3339] [one_class_mean best 0.3339] \n",
      "0.3339\t0.3339\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# shift_tr    : blur\n",
    "# id_class    : hem\n",
    "# epoch       : 100\n",
    "# res         : 450px\n",
    "# crop        : 0.08\n",
    "# blur_sigma : 6\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --blur_sigma 6 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 1 --load_path \"logs/id_hem/blur/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_resize_factor0.08_color_dist0.5_blur_sigma6.0_one_class_1/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "5a20ddb8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3581 3581 3581 3581\n",
      "weight_sim:\t0.0029\t0.0062\t0.0034\t0.0030\n",
      "weight_shi:\t0.2169\t2.1291\t-0.6997\t-0.6317\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.5895074388033098\n",
      "CNMC               2.0596 +- 0.2877    q0: 1.0222    q10: 1.6929    q20: 1.8375    q30: 1.9167    q40: 2.0096    q50: 2.0851    q60: 2.1435    q70: 2.2065    q80: 2.2951    q90: 2.4044    q100: 3.0604\n",
      "one_class_0        1.9839 +- 0.2326    q0: 1.1067    q10: 1.6760    q20: 1.7908    q30: 1.8756    q40: 1.9429    q50: 2.0082    q60: 2.0584    q70: 2.1101    q80: 2.1732    q90: 2.2584    q100: 3.0116\n",
      "[one_class_0 CSI 0.5895] [one_class_0 best 0.5895] \n",
      "[one_class_mean CSI 0.5895] [one_class_mean best 0.5895] \n",
      "0.5895\t0.5895\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : hem\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.08\n",
    "# blur_sigma : 4\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --blur_sigma 4 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 1 --load_path \"logs/id_hem/blur/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_resize_factor0.08_color_dist0.5_blur_sigma4.0_one_class_1/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "f014e06d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3581 3581 3581 3581\n",
      "weight_sim:\t0.0047\t0.0065\t0.0046\t0.0045\n",
      "weight_shi:\t0.2645\t-12.1918\t-1.1354\t-0.9111\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.43248488439218546\n",
      "CNMC               1.5307 +- 2.2981    q0: -6.2860    q10: -1.3964    q20: -0.1077    q30: 0.5774    q40: 1.2354    q50: 1.7091    q60: 2.1039    q70: 2.7068    q80: 3.3782    q90: 4.4476    q100: 6.9377\n",
      "one_class_0        2.0424 +- 1.5916    q0: -5.1678    q10: 0.0924    q20: 0.8505    q30: 1.3834    q40: 1.7445    q50: 2.1476    q60: 2.4484    q70: 2.8574    q80: 3.3216    q90: 3.9483    q100: 6.4052\n",
      "[one_class_0 CSI 0.4325] [one_class_0 best 0.4325] \n",
      "[one_class_mean CSI 0.4325] [one_class_mean best 0.4325] \n",
      "0.4325\t0.4325\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : hem\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.08\n",
    "# blur_sigma : 3\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --blur_sigma 3 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 1 --load_path \"logs/id_hem/blur/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_resize_factor0.08_color_dist0.5_blur_sigma3.0_one_class_1/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "469197e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3581 3581 3581 3581\n",
      "weight_sim:\t0.0083\t0.0100\t0.0115\t0.0075\n",
      "weight_shi:\t2.4798\t0.7962\t-4.3631\t-2.5771\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.37173128145920054\n",
      "CNMC               1.8398 +- 0.7107    q0: -1.6740    q10: 0.9741    q20: 1.3853    q30: 1.6153    q40: 1.8033    q50: 1.9486    q60: 2.1053    q70: 2.2304    q80: 2.3601    q90: 2.5741    q100: 3.5645\n",
      "one_class_0        2.1409 +- 0.5323    q0: -0.7020    q10: 1.4665    q20: 1.7704    q30: 1.9274    q40: 2.0616    q50: 2.1799    q60: 2.2918    q70: 2.4188    q80: 2.5430    q90: 2.7544    q100: 3.8279\n",
      "[one_class_0 CSI 0.3717] [one_class_0 best 0.3717] \n",
      "[one_class_mean CSI 0.3717] [one_class_mean best 0.3717] \n",
      "0.3717\t0.3717\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : hem\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.08\n",
    "# blur_sigma : 2\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --blur_sigma 2 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 1 --load_path \"logs/id_hem/blur/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_resize_factor0.08_color_dist0.5_blur_sigma2.0_one_class_1/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "b8ccee0f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3581 3581 3581 3581\n",
      "weight_sim:\t0.0078\t0.0116\t0.0095\t0.0106\n",
      "weight_shi:\t0.1768\t-0.5198\t-0.4439\t-0.3696\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.3392225969475081\n",
      "CNMC               1.9808 +- 0.1438    q0: 1.5735    q10: 1.8230    q20: 1.8637    q30: 1.8908    q40: 1.9230    q50: 1.9577    q60: 1.9928    q70: 2.0435    q80: 2.1120    q90: 2.1900    q100: 2.4139\n",
      "one_class_0        2.0502 +- 0.1152    q0: 1.7554    q10: 1.9134    q20: 1.9501    q30: 1.9799    q40: 2.0064    q50: 2.0376    q60: 2.0668    q70: 2.1043    q80: 2.1520    q90: 2.2170    q100: 2.4357\n",
      "[one_class_0 CSI 0.3392] [one_class_0 best 0.3392] \n",
      "[one_class_mean CSI 0.3392] [one_class_mean best 0.3392] \n",
      "0.3392\t0.3392\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : hem\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.08\n",
    "# blur_sigma : 1.5\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --blur_sigma 1.5 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 1 --load_path \"logs/id_hem/blur/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_resize_factor0.08_color_dist0.5_blur_sigma1.5_one_class_1/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "3ba56d85",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3581 3581 3581 3581\n",
      "weight_sim:\t0.0021\t0.0031\t0.0026\t0.0026\n",
      "weight_shi:\t0.3756\t9.2614\t-0.9536\t-0.8326\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.6796440616169901\n",
      "CNMC               2.2897 +- 0.8188    q0: -0.1330    q10: 1.2176    q20: 1.6025    q30: 1.8640    q40: 2.0833    q50: 2.2961    q60: 2.5135    q70: 2.7109    q80: 2.9502    q90: 3.3024    q100: 4.9905\n",
      "one_class_0        1.8212 +- 0.6476    q0: -0.7580    q10: 1.0215    q20: 1.3188    q30: 1.4821    q40: 1.6494    q50: 1.7908    q60: 1.9457    q70: 2.1147    q80: 2.3124    q90: 2.6421    q100: 4.3585\n",
      "[one_class_0 CSI 0.6796] [one_class_0 best 0.6796] \n",
      "[one_class_mean CSI 0.6796] [one_class_mean best 0.6796] \n",
      "0.6796\t0.6796\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : hem\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.08\n",
    "# blur_sigma : 1\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --blur_sigma 1 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 1 --load_path \"logs/id_hem/blur/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_resize_factor0.08_color_dist0.5_blur_sigma1.0_one_class_1/last.model\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fd03e0e",
   "metadata": {},
   "source": [
    "## other transformations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "beda234d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3581 3581 3581 3581\n",
      "weight_sim:\t0.0022\t0.0048\t0.0029\t0.0028\n",
      "weight_shi:\t-3.2909\t-2.8657\t12.5482\t8.7034\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.5285322922047013\n",
      "CNMC               2.0914 +- 0.5227    q0: 0.3459    q10: 1.3730    q20: 1.6394    q30: 1.8216    q40: 1.9820    q50: 2.1814    q60: 2.2788    q70: 2.3999    q80: 2.5247    q90: 2.7229    q100: 3.6842\n",
      "one_class_0        2.0687 +- 0.3844    q0: 0.9708    q10: 1.5495    q20: 1.7411    q30: 1.8523    q40: 1.9633    q50: 2.0755    q60: 2.1823    q70: 2.2948    q80: 2.4057    q90: 2.5591    q100: 3.3615\n",
      "[one_class_0 CSI 0.5285] [one_class_0 best 0.5285] \n",
      "[one_class_mean CSI 0.5285] [one_class_mean best 0.5285] \n",
      "0.5285\t0.5285\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : hem\n",
    "# epoch       : 100\n",
    "# shift_tr    : rotation\n",
    "# crop        : 0.08\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type rotation --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 1 --load_path \"logs/id_hem/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_rotation_resize_factor0.08_color_dist0.5_one_class_1/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "025aedc5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3581 3581 3581 3581\n",
      "weight_sim:\t0.0030\t0.0034\t0.0036\t0.0040\n",
      "weight_shi:\t-0.0433\t0.5499\t-0.7289\t0.1057\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.4115717953392276\n",
      "CNMC               2.0156 +- 0.1922    q0: 1.5401    q10: 1.7668    q20: 1.8500    q30: 1.9196    q40: 1.9652    q50: 2.0074    q60: 2.0619    q70: 2.1144    q80: 2.1791    q90: 2.2713    q100: 2.6170\n",
      "one_class_0        2.0726 +- 0.1608    q0: 1.6384    q10: 1.8757    q20: 1.9493    q30: 1.9876    q40: 2.0244    q50: 2.0556    q60: 2.0929    q70: 2.1446    q80: 2.2026    q90: 2.3007    q100: 2.7631\n",
      "[one_class_0 CSI 0.4116] [one_class_0 best 0.4116] \n",
      "[one_class_mean CSI 0.4116] [one_class_mean best 0.4116] \n",
      "0.4116\t0.4116\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : hem\n",
    "# epoch       : 100\n",
    "# shift_tr    : cutperm\n",
    "# crop        : 0.08\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type cutperm --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 1 --load_path \"logs/id_hem/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_cutperm_resize_factor0.08_color_dist0.5_one_class_1/last.model\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7def804c",
   "metadata": {},
   "source": [
    "# In-Distribution = ALL"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d826eb3",
   "metadata": {},
   "source": [
    "# Combined shiftings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "ed1501cd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0099\t0.0102\t0.0065\t0.0102\n",
      "weight_shi:\t-0.2651\t0.3988\t-0.4352\t-0.9217\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.39018776775134445\n",
      "CNMC               1.9696 +- 0.3172    q0: 1.0059    q10: 1.5824    q20: 1.7007    q30: 1.7852    q40: 1.8767    q50: 1.9557    q60: 2.0335    q70: 2.1270    q80: 2.2281    q90: 2.3851    q100: 3.0697\n",
      "one_class_1        2.1059 +- 0.3583    q0: 1.1658    q10: 1.6595    q20: 1.8070    q30: 1.9152    q40: 2.0053    q50: 2.0849    q60: 2.1740    q70: 2.2650    q80: 2.3979    q90: 2.5818    q100: 3.4632\n",
      "[one_class_1 CSI 0.3902] [one_class_1 best 0.3902] \n",
      "[one_class_mean CSI 0.3902] [one_class_mean best 0.3902] \n",
      "0.3902\t0.3902\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur_randpers\n",
    "# crop        : 0.08\n",
    "# blur_sigma  : 2\n",
    "# randpers    : 0.75\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --distortion_scale 0.75 --resize_factor 0.08 --blur_sigma 2 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur_randpers --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist0.5/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_randpers_resize_factor0.08_color_dist0.5_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "b471436b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0067\t0.0129\t0.0065\t0.0086\n",
      "weight_shi:\t-0.0850\t0.2249\t0.1729\t0.1702\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.4909736780805963\n",
      "CNMC               2.1838 +- 0.3670    q0: 0.9506    q10: 1.6677    q20: 1.8649    q30: 2.0015    q40: 2.1242    q50: 2.2193    q60: 2.3160    q70: 2.4125    q80: 2.5084    q90: 2.6376    q100: 3.1795\n",
      "one_class_1        2.1670 +- 0.4888    q0: 0.7892    q10: 1.4646    q20: 1.7498    q30: 1.9466    q40: 2.1070    q50: 2.2393    q60: 2.3641    q70: 2.4747    q80: 2.6032    q90: 2.7386    q100: 3.1321\n",
      "[one_class_1 CSI 0.4910] [one_class_1 best 0.4910] \n",
      "[one_class_mean CSI 0.4910] [one_class_mean best 0.4910] \n",
      "0.4910\t0.4910\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur_sharp\n",
    "# crop        : 0.08\n",
    "# blur_sigma  : 2\n",
    "# randpers    : 0.75\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --sharpness_factor 5 --resize_factor 0.08 --blur_sigma 2 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur_sharp --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist0.5/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_sharp_resize_factor0.08_color_dist0.5_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "5c08667d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0058\t0.0071\t0.0060\t0.0060\n",
      "weight_shi:\t-0.0229\t0.0795\t0.0649\t0.0666\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.45186742320663564\n",
      "CNMC               2.0290 +- 0.0922    q0: 1.7841    q10: 1.9115    q20: 1.9486    q30: 1.9769    q40: 2.0011    q50: 2.0200    q60: 2.0479    q70: 2.0788    q80: 2.1081    q90: 2.1556    q100: 2.4408\n",
      "one_class_1        2.0462 +- 0.1034    q0: 1.7679    q10: 1.9185    q20: 1.9546    q30: 1.9876    q40: 2.0171    q50: 2.0410    q60: 2.0679    q70: 2.0985    q80: 2.1328    q90: 2.1914    q100: 2.3683\n",
      "[one_class_1 CSI 0.4519] [one_class_1 best 0.4519] \n",
      "[one_class_mean CSI 0.4519] [one_class_mean best 0.4519] \n",
      "0.4519\t0.4519\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : randpers_sharp\n",
    "# crop        : 0.08\n",
    "# blur_sigma  : 2\n",
    "# randpers    : 0.75\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --sharpness_factor 5 --distortion_scale 0.75 --resize_factor 0.08 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type randpers_sharp --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist0.5/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_randpers_sharp_resize_factor0.08_color_dist0.5_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "e1be886d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0069\t0.0188\t0.0166\t0.0120\n",
      "weight_shi:\t-0.1581\t0.1971\t0.2342\t0.3190\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.32064141322071316\n",
      "CNMC               1.9454 +- 0.0810    q0: 1.7576    q10: 1.8630    q20: 1.8860    q30: 1.9012    q40: 1.9140    q50: 1.9316    q60: 1.9461    q70: 1.9640    q80: 1.9905    q90: 2.0476    q100: 2.4165\n",
      "one_class_1        2.0265 +- 0.1592    q0: 1.7834    q10: 1.8887    q20: 1.9115    q30: 1.9346    q40: 1.9614    q50: 1.9884    q60: 2.0114    q70: 2.0559    q80: 2.1059    q90: 2.2091    q100: 3.1080\n",
      "[one_class_1 CSI 0.3206] [one_class_1 best 0.3206] \n",
      "[one_class_mean CSI 0.3206] [one_class_mean best 0.3206] \n",
      "0.3206\t0.3206\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur_randpers_sharp\n",
    "# crop        : 0.08\n",
    "# blur_sigma  : 2\n",
    "# randpers    : 0.75\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --sharpness_factor 5 --distortion_scale 0.75 --resize_factor 0.08 --blur_sigma 2 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur_randpers_sharp --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist0.5/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_randpers_sharp_resize_factor0.08_color_dist0.5_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8cd9c5a",
   "metadata": {},
   "source": [
    "# Rotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "3f9748c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0058\t0.0091\t0.0059\t0.0061\n",
      "weight_shi:\t-20.2520\t5.6794\t4.4756\t-13.8486\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.6155293247855458\n",
      "CNMC               2.2941 +- 0.4851    q0: 0.6171    q10: 1.6977    q20: 1.9170    q30: 2.0537    q40: 2.1467    q50: 2.2525    q60: 2.3680    q70: 2.5004    q80: 2.6952    q90: 2.9333    q100: 4.0615\n",
      "one_class_1        2.0566 +- 0.6054    q0: 0.2141    q10: 1.2573    q20: 1.5313    q30: 1.7431    q40: 1.8966    q50: 2.0426    q60: 2.2221    q70: 2.3685    q80: 2.6045    q90: 2.8399    q100: 3.9073\n",
      "[one_class_1 CSI 0.6155] [one_class_1 best 0.6155] \n",
      "[one_class_mean CSI 0.6155] [one_class_mean best 0.6155] \n",
      "0.6155\t0.6155\n"
     ]
    }
   ],
   "source": [
    "###### EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : rotation\n",
    "# crop        : 0.08\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type rotation --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist0.5/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_rotation_resize_factor0.08_color_dist0.5_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed7a3ca6",
   "metadata": {},
   "source": [
    "# Cutperm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "47382eef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0033\t0.0040\t0.0048\t0.0059\n",
      "weight_shi:\t-0.0422\t-0.2956\t0.3071\t0.0913\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.5637665967854647\n",
      "CNMC               2.1340 +- 0.2713    q0: 1.5092    q10: 1.8054    q20: 1.8955    q30: 1.9635    q40: 2.0281    q50: 2.0900    q60: 2.1689    q70: 2.2729    q80: 2.3753    q90: 2.5306    q100: 2.8713\n",
      "one_class_1        2.0681 +- 0.3216    q0: 1.3818    q10: 1.6678    q20: 1.7728    q30: 1.8582    q40: 1.9368    q50: 2.0391    q60: 2.1288    q70: 2.2616    q80: 2.3884    q90: 2.5307    q100: 2.8915\n",
      "[one_class_1 CSI 0.5638] [one_class_1 best 0.5638] \n",
      "[one_class_mean CSI 0.5638] [one_class_mean best 0.5638] \n",
      "0.5638\t0.5638\n"
     ]
    }
   ],
   "source": [
    "###### EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : cutperm\n",
    "# crop        : 0.08\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type cutperm --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist0.5/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_cutperm_resize_factor0.08_color_dist0.5_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e338538b",
   "metadata": {},
   "source": [
    "# Rotated Dataset 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "18aa1694",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0089\t0.0071\t0.0082\t0.0060\n",
      "weight_shi:\t-0.0826\t0.1155\t0.1144\t0.1138\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.6829627857280305\n",
      "CNMC               2.0823 +- 0.1590    q0: 1.4861    q10: 1.8867    q20: 1.9512    q30: 1.9962    q40: 2.0434    q50: 2.0844    q60: 2.1284    q70: 2.1653    q80: 2.2150    q90: 2.2784    q100: 2.7066\n",
      "one_class_1        1.9798 +- 0.1471    q0: 1.4589    q10: 1.7996    q20: 1.8601    q30: 1.9145    q40: 1.9503    q50: 1.9828    q60: 2.0164    q70: 2.0541    q80: 2.1007    q90: 2.1670    q100: 2.3931\n",
      "[one_class_1 CSI 0.6830] [one_class_1 best 0.6830] \n",
      "[one_class_mean CSI 0.6830] [one_class_mean best 0.6830] \n",
      "0.6830\t0.6830\n"
     ]
    }
   ],
   "source": [
    "###### EVALUATION\n",
    "# dataset     : CNMC_ROT4\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : sharp\n",
    "# crop        : 0.08\n",
    "# sharp       : 64\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --sharpness_factor 64 --color_distort 0.5 --resize_factor 0.08 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type sharp --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/dataset_rotated_4/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_sharp_resize_factor0.08_color_dist0.5_sharpness_factor64.0_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "95e84b59",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0076\t0.0081\t0.0080\t0.0086\n",
      "weight_shi:\t-0.1382\t1.2588\t2.1567\t0.5287\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.3485907290938738\n",
      "CNMC               1.8551 +- 0.4346    q0: 0.8263    q10: 1.3471    q20: 1.5032    q30: 1.6156    q40: 1.7119    q50: 1.8134    q60: 1.9097    q70: 2.0306    q80: 2.1600    q90: 2.4047    q100: 4.4743\n",
      "one_class_1        2.1133 +- 0.5033    q0: 0.9826    q10: 1.5132    q20: 1.7004    q30: 1.8184    q40: 1.9124    q50: 2.0310    q60: 2.1594    q70: 2.3078    q80: 2.5394    q90: 2.7696    q100: 4.0888\n",
      "[one_class_1 CSI 0.3486] [one_class_1 best 0.3486] \n",
      "[one_class_mean CSI 0.3486] [one_class_mean best 0.3486] \n",
      "0.3486\t0.3486\n"
     ]
    }
   ],
   "source": [
    "###### EVALUATION\n",
    "# dataset     : CNMC_ROT4\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : randpers\n",
    "# crop        : 0.08\n",
    "# randpers    : 0.75\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --distortion_scale 0.75 --color_distort 0.5 --resize_factor 0.08 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type randpers --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/dataset_rotated_4/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_randpers_resize_factor0.08_color_dist0.5_distortion_scale0.75_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "982cf5a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0137\t0.0152\t0.0140\t0.0126\n",
      "weight_shi:\t-0.1440\t0.3135\t0.4775\t0.4211\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.3167783246741409\n",
      "CNMC               1.9366 +- 0.1773    q0: 1.4437    q10: 1.7285    q20: 1.7909    q30: 1.8431    q40: 1.8862    q50: 1.9192    q60: 1.9569    q70: 2.0073    q80: 2.0694    q90: 2.1765    q100: 2.5655\n",
      "one_class_1        2.0674 +- 0.2071    q0: 1.6099    q10: 1.8331    q20: 1.8954    q30: 1.9420    q40: 1.9859    q50: 2.0320    q60: 2.0882    q70: 2.1432    q80: 2.2308    q90: 2.3727    q100: 2.8015\n",
      "[one_class_1 CSI 0.3168] [one_class_1 best 0.3168] \n",
      "[one_class_mean CSI 0.3168] [one_class_mean best 0.3168] \n",
      "0.3168\t0.3168\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC_ROT4\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.08\n",
    "# blur_sigma : 2\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --blur_sigma 2 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/dataset_rotated_4/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_resize_factor0.08_color_dist0.5_blur_sigma2.0_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9c8f555",
   "metadata": {},
   "source": [
    "# Sharpness Factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "ac35a164",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0050\t0.0011\t0.0010\t0.0009\n",
      "weight_shi:\t-0.0433\t0.0726\t0.2303\t0.0769\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.5375965930382118\n",
      "CNMC               1.9981 +- 0.1008    q0: 1.5969    q10: 1.8641    q20: 1.9163    q30: 1.9503    q40: 1.9776    q50: 2.0056    q60: 2.0329    q70: 2.0563    q80: 2.0814    q90: 2.1226    q100: 2.2687\n",
      "one_class_1        1.9867 +- 0.1056    q0: 1.6492    q10: 1.8484    q20: 1.8943    q30: 1.9323    q40: 1.9603    q50: 1.9909    q60: 2.0131    q70: 2.0457    q80: 2.0783    q90: 2.1193    q100: 2.2764\n",
      "[one_class_1 CSI 0.5376] [one_class_1 best 0.5376] \n",
      "[one_class_mean CSI 0.5376] [one_class_mean best 0.5376] \n",
      "0.5376\t0.5376\n"
     ]
    }
   ],
   "source": [
    "###### EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : sharp\n",
    "# crop        : 0.08\n",
    "# sharp       : 4096\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --sharpness_factor 4096 --color_distort 0.5 --resize_factor 0.08 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type sharp --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist0.5/sharp/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_sharp_resize_factor0.08_color_dist0.5_sharpness_factor4096.0_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "49250ae3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0099\t0.0038\t0.0037\t0.0035\n",
      "weight_shi:\t-0.0601\t0.0628\t0.0572\t0.0620\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.5004063743809436\n",
      "CNMC               2.1367 +- 0.2510    q0: 1.4456    q10: 1.8346    q20: 1.9233    q30: 1.9922    q40: 2.0485    q50: 2.1189    q60: 2.1860    q70: 2.2597    q80: 2.3425    q90: 2.4668    q100: 3.2275\n",
      "one_class_1        2.1346 +- 0.3755    q0: 1.1971    q10: 1.6290    q20: 1.8287    q30: 1.9361    q40: 2.0319    q50: 2.1191    q60: 2.2264    q70: 2.3246    q80: 2.4509    q90: 2.6481    q100: 3.1412\n",
      "[one_class_1 CSI 0.5004] [one_class_1 best 0.5004] \n",
      "[one_class_mean CSI 0.5004] [one_class_mean best 0.5004] \n",
      "0.5004\t0.5004\n"
     ]
    }
   ],
   "source": [
    "###### EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : sharp\n",
    "# crop        : 0.08\n",
    "# sharp       : 2048\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --sharpness_factor 2048 --color_distort 0.5 --resize_factor 0.08 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type sharp --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist0.5/sharp/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_sharp_resize_factor0.08_color_dist0.5_sharpness_factor2048.0_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "0bd84a7e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0242\t0.0050\t0.0046\t0.0044\n",
      "weight_shi:\t-0.0828\t0.0645\t0.0669\t0.0596\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.521132733772876\n",
      "CNMC               2.1385 +- 0.2030    q0: 1.5179    q10: 1.8773    q20: 1.9648    q30: 2.0325    q40: 2.0825    q50: 2.1336    q60: 2.1808    q70: 2.2389    q80: 2.3102    q90: 2.3997    q100: 2.7902\n",
      "one_class_1        2.1145 +- 0.2767    q0: 1.3283    q10: 1.7725    q20: 1.8865    q30: 1.9760    q40: 2.0428    q50: 2.1166    q60: 2.1976    q70: 2.2709    q80: 2.3529    q90: 2.4697    q100: 2.8054\n",
      "[one_class_1 CSI 0.5211] [one_class_1 best 0.5211] \n",
      "[one_class_mean CSI 0.5211] [one_class_mean best 0.5211] \n",
      "0.5211\t0.5211\n"
     ]
    }
   ],
   "source": [
    "###### EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : sharp\n",
    "# crop        : 0.08\n",
    "# sharp       : 1024\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --sharpness_factor 1024 --color_distort 0.5 --resize_factor 0.08 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type sharp --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist0.5/sharp/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_sharp_resize_factor0.08_color_dist0.5_sharpness_factor1024.0_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "7084a03f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0059\t0.0055\t0.0051\t0.0051\n",
      "weight_shi:\t-0.0132\t0.0371\t0.0377\t0.0376\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.5495593179999798\n",
      "CNMC               2.0729 +- 0.0917    q0: 1.7983    q10: 1.9554    q20: 1.9947    q30: 2.0222    q40: 2.0461    q50: 2.0659    q60: 2.0930    q70: 2.1220    q80: 2.1540    q90: 2.1973    q100: 2.3686\n",
      "one_class_1        2.0536 +- 0.1203    q0: 1.7288    q10: 1.9078    q20: 1.9506    q30: 1.9885    q40: 2.0186    q50: 2.0481    q60: 2.0860    q70: 2.1206    q80: 2.1548    q90: 2.2130    q100: 2.3754\n",
      "[one_class_1 CSI 0.5496] [one_class_1 best 0.5496] \n",
      "[one_class_mean CSI 0.5496] [one_class_mean best 0.5496] \n",
      "0.5496\t0.5496\n"
     ]
    }
   ],
   "source": [
    "###### EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : sharp\n",
    "# crop        : 0.08\n",
    "# sharp       : 512\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --sharpness_factor 512 --color_distort 0.5 --resize_factor 0.08 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type sharp --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist0.5/sharp/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_sharp_resize_factor0.08_color_dist0.5_sharpness_factor512.0_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "7609406d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0033\t0.0016\t0.0015\t0.0015\n",
      "weight_shi:\t-0.0626\t0.0548\t0.0482\t0.0476\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.5079350611207324\n",
      "CNMC               2.1463 +- 0.2056    q0: 1.5154    q10: 1.8902    q20: 1.9661    q30: 2.0278    q40: 2.0864    q50: 2.1497    q60: 2.1995    q70: 2.2608    q80: 2.3272    q90: 2.4144    q100: 2.8480\n",
      "one_class_1        2.1363 +- 0.2821    q0: 1.3866    q10: 1.7738    q20: 1.8962    q30: 1.9787    q40: 2.0632    q50: 2.1372    q60: 2.2119    q70: 2.2966    q80: 2.3999    q90: 2.5028    q100: 2.7673\n",
      "[one_class_1 CSI 0.5079] [one_class_1 best 0.5079] \n",
      "[one_class_mean CSI 0.5079] [one_class_mean best 0.5079] \n",
      "0.5079\t0.5079\n"
     ]
    }
   ],
   "source": [
    "###### EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : sharp\n",
    "# crop        : 0.08\n",
    "# sharp       : 256\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --sharpness_factor 256 --color_distort 0.5 --resize_factor 0.08 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type sharp --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist0.5/sharp/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_sharp_resize_factor0.08_color_dist0.5_sharpness_factor256.0_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "aad2a734",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0048\t0.0080\t0.0087\t0.0057\n",
      "weight_shi:\t-0.0840\t0.0954\t0.0919\t0.0779\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.5942041645145282\n",
      "CNMC               2.0494 +- 0.1428    q0: 1.7036    q10: 1.8748    q20: 1.9344    q30: 1.9773    q40: 2.0081    q50: 2.0436    q60: 2.0718    q70: 2.1122    q80: 2.1561    q90: 2.2278    q100: 2.7563\n",
      "one_class_1        2.0001 +- 0.1729    q0: 1.5526    q10: 1.7899    q20: 1.8590    q30: 1.9109    q40: 1.9589    q50: 1.9977    q60: 2.0327    q70: 2.0653    q80: 2.1332    q90: 2.2229    q100: 2.6275\n",
      "[one_class_1 CSI 0.5942] [one_class_1 best 0.5942] \n",
      "[one_class_mean CSI 0.5942] [one_class_mean best 0.5942] \n",
      "0.5942\t0.5942\n"
     ]
    }
   ],
   "source": [
    "###### EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : sharp\n",
    "# crop        : 0.08\n",
    "# sharp       : 128\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --sharpness_factor 128 --color_distort 0.5 --resize_factor 0.08 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type sharp --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist0.5/sharp/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_sharp_resize_factor0.08_color_dist0.5_sharpness_factor128.0_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "eceb0082",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0055\t0.0037\t0.0058\t0.0037\n",
      "weight_shi:\t-0.1448\t0.1735\t0.1588\t0.1423\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.7104164767720962\n",
      "CNMC               2.0883 +- 0.1055    q0: 1.7978    q10: 1.9497    q20: 1.9997    q30: 2.0352    q40: 2.0642    q50: 2.0925    q60: 2.1162    q70: 2.1440    q80: 2.1764    q90: 2.2198    q100: 2.4996\n",
      "one_class_1        1.9981 +- 0.1273    q0: 1.6099    q10: 1.8403    q20: 1.8878    q30: 1.9263    q40: 1.9618    q50: 1.9937    q60: 2.0157    q70: 2.0606    q80: 2.1110    q90: 2.1738    q100: 2.4795\n",
      "[one_class_1 CSI 0.7104] [one_class_1 best 0.7104] \n",
      "[one_class_mean CSI 0.7104] [one_class_mean best 0.7104] \n",
      "0.7104\t0.7104\n"
     ]
    }
   ],
   "source": [
    "###### EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : sharp\n",
    "# crop        : 0.08\n",
    "# sharp       : 64\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --sharpness_factor 64 --color_distort 0.5 --resize_factor 0.08 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type sharp --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist0.5/sharp/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_sharp_resize_factor0.08_color_dist0.5_sharpness_factor64.0_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "7c881700",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0028\t0.0085\t0.0044\t0.0097\n",
      "weight_shi:\t-0.0235\t0.0638\t0.0549\t0.0541\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.5937648750746918\n",
      "CNMC               2.0002 +- 0.1702    q0: 1.6494    q10: 1.8157    q20: 1.8583    q30: 1.8929    q40: 1.9291    q50: 1.9695    q60: 2.0094    q70: 2.0576    q80: 2.1345    q90: 2.2419    q100: 2.8225\n",
      "one_class_1        1.9446 +- 0.1597    q0: 1.5613    q10: 1.7697    q20: 1.8122    q30: 1.8531    q40: 1.8816    q50: 1.9188    q60: 1.9583    q70: 2.0091    q80: 2.0737    q90: 2.1568    q100: 2.5480\n",
      "[one_class_1 CSI 0.5938] [one_class_1 best 0.5938] \n",
      "[one_class_mean CSI 0.5938] [one_class_mean best 0.5938] \n",
      "0.5938\t0.5938\n"
     ]
    }
   ],
   "source": [
    "###### EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : sharp\n",
    "# crop        : 0.08\n",
    "# sharp       : 32\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --sharpness_factor 32 --color_distort 0.5 --resize_factor 0.08 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type sharp --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist0.5/sharp/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_sharp_resize_factor0.08_color_dist0.5_sharpness_factor32.0_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "afaa2706",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0030\t0.0048\t0.0042\t0.0054\n",
      "weight_shi:\t-0.0352\t0.0883\t0.0761\t0.0693\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.5747906095868907\n",
      "CNMC               2.0814 +- 0.1200    q0: 1.7198    q10: 1.9284    q20: 1.9790    q30: 2.0137    q40: 2.0462    q50: 2.0794    q60: 2.1084    q70: 2.1462    q80: 2.1929    q90: 2.2480    q100: 2.3614\n",
      "one_class_1        2.0319 +- 0.1736    q0: 1.4826    q10: 1.8007    q20: 1.8786    q30: 1.9545    q40: 1.9968    q50: 2.0343    q60: 2.0956    q70: 2.1409    q80: 2.1917    q90: 2.2512    q100: 2.3875\n",
      "[one_class_1 CSI 0.5748] [one_class_1 best 0.5748] \n",
      "[one_class_mean CSI 0.5748] [one_class_mean best 0.5748] \n",
      "0.5748\t0.5748\n"
     ]
    }
   ],
   "source": [
    "###### EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : sharp\n",
    "# crop        : 0.08\n",
    "# sharp       : 16\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --sharpness_factor 16 --color_distort 0.5 --resize_factor 0.08 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type sharp --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist0.5/sharp/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_sharp_resize_factor0.08_color_dist0.5_sharpness_factor16.0_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "374eec9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0040\t0.0039\t0.0044\t0.0039\n",
      "weight_shi:\t-0.0360\t0.1191\t0.0847\t0.0773\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.47723417292052783\n",
      "CNMC               2.1169 +- 0.2902    q0: 1.3060    q10: 1.7390    q20: 1.8614    q30: 1.9501    q40: 2.0312    q50: 2.1085    q60: 2.1828    q70: 2.2782    q80: 2.3917    q90: 2.5189    q100: 3.0332\n",
      "one_class_1        2.1411 +- 0.3676    q0: 1.2368    q10: 1.6509    q20: 1.8257    q30: 1.9349    q40: 2.0555    q50: 2.1498    q60: 2.2467    q70: 2.3477    q80: 2.4742    q90: 2.6155    q100: 3.2105\n",
      "[one_class_1 CSI 0.4772] [one_class_1 best 0.4772] \n",
      "[one_class_mean CSI 0.4772] [one_class_mean best 0.4772] \n",
      "0.4772\t0.4772\n"
     ]
    }
   ],
   "source": [
    "###### EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : sharp\n",
    "# crop        : 0.08\n",
    "# sharp       : 8\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --sharpness_factor 8 --color_distort 0.5 --resize_factor 0.08 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type sharp --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist0.5/sharp/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_sharp_resize_factor0.08_color_dist0.5_sharpness_factor8.0_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "2b907319",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0120\t0.0137\t0.0113\t0.0151\n",
      "weight_shi:\t-0.0230\t0.0744\t0.0702\t0.0797\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.7440512360870578\n",
      "CNMC               2.0897 +- 0.1280    q0: 1.6279    q10: 1.9253    q20: 1.9937    q30: 2.0344    q40: 2.0646    q50: 2.0929    q60: 2.1235    q70: 2.1514    q80: 2.1925    q90: 2.2452    q100: 2.5109\n",
      "one_class_1        1.9564 +- 0.1648    q0: 1.2763    q10: 1.7402    q20: 1.8197    q30: 1.8821    q40: 1.9240    q50: 1.9604    q60: 2.0013    q70: 2.0434    q80: 2.0908    q90: 2.1604    q100: 2.4535\n",
      "[one_class_1 CSI 0.7441] [one_class_1 best 0.7441] \n",
      "[one_class_mean CSI 0.7441] [one_class_mean best 0.7441] \n",
      "0.7441\t0.7441\n"
     ]
    }
   ],
   "source": [
    "###### EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : sharp\n",
    "# crop        : 0.08\n",
    "# sharp       : 5\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --sharpness_factor 5 --color_distort 0.5 --resize_factor 0.08 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type sharp --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist0.5/sharp/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_sharp_resize_factor0.08_color_dist0.5_sharpness_factor5.0_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "eadc9f63",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0044\t0.0050\t0.0036\t0.0045\n",
      "weight_shi:\t-0.0174\t0.0520\t0.0435\t0.0457\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.6856960015799229\n",
      "CNMC               2.0337 +- 0.0637    q0: 1.7790    q10: 1.9526    q20: 1.9802    q30: 2.0010    q40: 2.0182    q50: 2.0348    q60: 2.0490    q70: 2.0674    q80: 2.0882    q90: 2.1155    q100: 2.2475\n",
      "one_class_1        1.9842 +- 0.0803    q0: 1.6728    q10: 1.8819    q20: 1.9195    q30: 1.9483    q40: 1.9679    q50: 1.9875    q60: 2.0072    q70: 2.0236    q80: 2.0479    q90: 2.0840    q100: 2.2335\n",
      "[one_class_1 CSI 0.6857] [one_class_1 best 0.6857] \n",
      "[one_class_mean CSI 0.6857] [one_class_mean best 0.6857] \n",
      "0.6857\t0.6857\n"
     ]
    }
   ],
   "source": [
    "###### EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : sharp\n",
    "# crop        : 0.08\n",
    "# sharp       : 4\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --sharpness_factor 4 --color_distort 0.5 --resize_factor 0.08 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type sharp --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist0.5/sharp/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_sharp_resize_factor0.08_color_dist0.5_sharpness_factor4.0_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "66a30bac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0096\t0.0088\t0.0090\t0.0096\n",
      "weight_shi:\t-0.0320\t0.1007\t0.1076\t0.0998\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.49014067389785193\n",
      "CNMC               2.0877 +- 0.2810    q0: 1.3173    q10: 1.7265    q20: 1.8460    q30: 1.9306    q40: 2.0048    q50: 2.0768    q60: 2.1398    q70: 2.2252    q80: 2.3252    q90: 2.4627    q100: 3.0835\n",
      "one_class_1        2.0957 +- 0.3295    q0: 1.1248    q10: 1.6700    q20: 1.8185    q30: 1.9197    q40: 2.0144    q50: 2.0849    q60: 2.1813    q70: 2.2611    q80: 2.3718    q90: 2.5219    q100: 3.0920\n",
      "[one_class_1 CSI 0.4901] [one_class_1 best 0.4901] \n",
      "[one_class_mean CSI 0.4901] [one_class_mean best 0.4901] \n",
      "0.4901\t0.4901\n"
     ]
    }
   ],
   "source": [
    "###### EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : sharp\n",
    "# crop        : 0.08\n",
    "# sharp       : 3\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --sharpness_factor 3 --color_distort 0.5 --resize_factor 0.08 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type sharp --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist0.5/sharp/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_sharp_resize_factor0.08_color_dist0.5_sharpness_factor3.0_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "e8fde266",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0051\t0.0046\t0.0048\t0.0045\n",
      "weight_shi:\t-0.0137\t0.0407\t0.0450\t0.0411\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.4467181154356435\n",
      "CNMC               2.0176 +- 0.0689    q0: 1.7903    q10: 1.9308    q20: 1.9620    q30: 1.9833    q40: 1.9998    q50: 2.0144    q60: 2.0329    q70: 2.0534    q80: 2.0752    q90: 2.1054    q100: 2.2461\n",
      "one_class_1        2.0300 +- 0.0917    q0: 1.7417    q10: 1.9114    q20: 1.9580    q30: 1.9866    q40: 2.0089    q50: 2.0337    q60: 2.0591    q70: 2.0798    q80: 2.1052    q90: 2.1409    q100: 2.2672\n",
      "[one_class_1 CSI 0.4467] [one_class_1 best 0.4467] \n",
      "[one_class_mean CSI 0.4467] [one_class_mean best 0.4467] \n",
      "0.4467\t0.4467\n"
     ]
    }
   ],
   "source": [
    "###### EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : sharp\n",
    "# crop        : 0.08\n",
    "# sharp       : 2\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --sharpness_factor 2 --color_distort 0.5 --resize_factor 0.08 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type sharp --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist0.5/sharp/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_sharp_resize_factor0.08_color_dist0.5_sharpness_factor2.0_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bac55a6b",
   "metadata": {},
   "source": [
    "# Random Perspective"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "acb8e0cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0046\t0.0037\t0.0045\t0.0046\n",
      "weight_shi:\t0.1028\t-0.1896\t-0.2910\t-0.3483\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.6587695844600411\n",
      "CNMC               2.0386 +- 0.2243    q0: 1.1465    q10: 1.7463    q20: 1.8820    q30: 1.9641    q40: 2.0161    q50: 2.0690    q60: 2.1137    q70: 2.1678    q80: 2.2188    q90: 2.2888    q100: 2.5369\n",
      "one_class_1        1.8805 +- 0.3066    q0: 0.7440    q10: 1.4384    q20: 1.6744    q30: 1.7753    q40: 1.8640    q50: 1.9389    q60: 1.9992    q70: 2.0643    q80: 2.1327    q90: 2.2159    q100: 2.4966\n",
      "[one_class_1 CSI 0.6588] [one_class_1 best 0.6588] \n",
      "[one_class_mean CSI 0.6588] [one_class_mean best 0.6588] \n",
      "0.6588\t0.6588\n"
     ]
    }
   ],
   "source": [
    "###### EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : randpers\n",
    "# crop        : 0.08\n",
    "# randper_dist: 0.95\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --distortion_scale 0.95 --color_distort 0.5 --resize_factor 0.08 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type randpers --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist0.5/randpers/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_randpers_resize_factor0.08_color_dist0.5_distortion_scale0.95_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "38406c45",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0036\t0.0043\t0.0044\t0.0045\n",
      "weight_shi:\t0.0940\t-0.3354\t-0.3010\t-0.4613\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.6866549691611218\n",
      "CNMC               2.0701 +- 0.1458    q0: 1.3353    q10: 1.9029    q20: 1.9897    q30: 2.0319    q40: 2.0558    q50: 2.0795    q60: 2.1028    q70: 2.1319    q80: 2.1715    q90: 2.2439    q100: 2.4895\n",
      "one_class_1        1.9579 +- 0.2070    q0: 0.9880    q10: 1.6843    q20: 1.8208    q30: 1.8972    q40: 1.9508    q50: 1.9923    q60: 2.0252    q70: 2.0622    q80: 2.0986    q90: 2.1886    q100: 2.4493\n",
      "[one_class_1 CSI 0.6867] [one_class_1 best 0.6867] \n",
      "[one_class_mean CSI 0.6867] [one_class_mean best 0.6867] \n",
      "0.6867\t0.6867\n"
     ]
    }
   ],
   "source": [
    "###### EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : randpers\n",
    "# crop        : 0.08\n",
    "# randper_dist: 0.9\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --distortion_scale 0.9 --color_distort 0.5 --resize_factor 0.08 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type randpers --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist0.5/randpers/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_randpers_resize_factor0.08_color_dist0.5_distortion_scale0.9_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "79e43776",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0058\t0.0098\t0.0051\t0.0075\n",
      "weight_shi:\t0.7573\t0.7158\t-0.4403\t3.1769\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.6343618023273478\n",
      "CNMC               2.2964 +- 0.4564    q0: 0.6867    q10: 1.7046    q20: 1.9085    q30: 2.0619    q40: 2.1922    q50: 2.3256    q60: 2.4368    q70: 2.5468    q80: 2.6813    q90: 2.8610    q100: 3.7183\n",
      "one_class_1        1.9670 +- 0.7117    q0: -1.6022    q10: 1.0639    q20: 1.4591    q30: 1.7160    q40: 1.8807    q50: 2.0547    q60: 2.2023    q70: 2.3776    q80: 2.5617    q90: 2.7902    q100: 3.2754\n",
      "[one_class_1 CSI 0.6344] [one_class_1 best 0.6344] \n",
      "[one_class_mean CSI 0.6344] [one_class_mean best 0.6344] \n",
      "0.6344\t0.6344\n"
     ]
    }
   ],
   "source": [
    "###### EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : randpers\n",
    "# crop        : 0.08\n",
    "# randper_dist: 0.85\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --distortion_scale 0.85 --color_distort 0.5 --resize_factor 0.08 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type randpers --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist0.5/randpers/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_randpers_resize_factor0.08_color_dist0.5_distortion_scale0.85_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "b5045a90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0044\t0.0059\t0.0035\t0.0046\n",
      "weight_shi:\t0.1149\t-0.5921\t-0.2913\t-0.4212\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.6980790265244736\n",
      "CNMC               2.0586 +- 0.0926    q0: 1.6907    q10: 1.9416    q20: 1.9891    q30: 2.0146    q40: 2.0367    q50: 2.0567    q60: 2.0821    q70: 2.1041    q80: 2.1311    q90: 2.1727    q100: 2.4236\n",
      "one_class_1        1.9890 +- 0.1129    q0: 1.6747    q10: 1.8586    q20: 1.8947    q30: 1.9326    q40: 1.9579    q50: 1.9848    q60: 2.0103    q70: 2.0342    q80: 2.0767    q90: 2.1413    q100: 2.4328\n",
      "[one_class_1 CSI 0.6981] [one_class_1 best 0.6981] \n",
      "[one_class_mean CSI 0.6981] [one_class_mean best 0.6981] \n",
      "0.6981\t0.6981\n"
     ]
    }
   ],
   "source": [
    "###### EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : randpers\n",
    "# crop        : 0.08\n",
    "# randper_dist: 0.8\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --distortion_scale 0.8 --color_distort 0.5 --resize_factor 0.08 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type randpers --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist0.5/randpers/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_randpers_resize_factor0.08_color_dist0.5_distortion_scale0.8_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "5d4659ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0020\t0.0024\t0.0019\t0.0028\n",
      "weight_shi:\t0.0839\t-0.1992\t-0.1714\t-0.2720\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.7161709152411915\n",
      "CNMC               2.0766 +- 0.1562    q0: 1.4380    q10: 1.8760    q20: 1.9670    q30: 2.0275    q40: 2.0724    q50: 2.1054    q60: 2.1342    q70: 2.1645    q80: 2.2012    q90: 2.2452    q100: 2.4108\n",
      "one_class_1        1.9367 +- 0.2053    q0: 1.1357    q10: 1.6437    q20: 1.7718    q30: 1.8741    q40: 1.9299    q50: 1.9756    q60: 2.0202    q70: 2.0576    q80: 2.1027    q90: 2.1710    q100: 2.3247\n",
      "[one_class_1 CSI 0.7162] [one_class_1 best 0.7162] \n",
      "[one_class_mean CSI 0.7162] [one_class_mean best 0.7162] \n",
      "0.7162\t0.7162\n"
     ]
    }
   ],
   "source": [
    "###### EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : randpers\n",
    "# crop        : 0.08\n",
    "# randper_dist: 0.75\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --distortion_scale 0.75 --color_distort 0.5 --resize_factor 0.08 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type randpers --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist0.5/randpers/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_randpers_resize_factor0.08_color_dist0.5_distortion_scale0.75_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "43c01d76",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0049\t0.0060\t0.0045\t0.0072\n",
      "weight_shi:\t-1.4937\t0.4193\t-0.5923\t0.9519\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.272612645459241\n",
      "CNMC               1.3428 +- 1.3765    q0: -2.6822    q10: -0.1317    q20: 0.2430    q30: 0.5766    q40: 0.8777    q50: 1.1651    q60: 1.4791    q70: 1.7858    q80: 2.3163    q90: 3.1566    q100: 7.5281\n",
      "one_class_1        2.6219 +- 1.7311    q0: -1.5026    q10: 0.6189    q20: 1.1608    q30: 1.6420    q40: 2.0065    q50: 2.4546    q60: 2.8590    q70: 3.3026    q80: 3.9411    q90: 5.0125    q100: 8.8420\n",
      "[one_class_1 CSI 0.2726] [one_class_1 best 0.2726] \n",
      "[one_class_mean CSI 0.2726] [one_class_mean best 0.2726] \n",
      "0.2726\t0.2726\n"
     ]
    }
   ],
   "source": [
    "###### EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : randpers\n",
    "# crop        : 0.08\n",
    "# randper_dist: 0.6\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --distortion_scale 0.6 --color_distort 0.5 --resize_factor 0.08 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type randpers --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist0.5/randpers/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_randpers_resize_factor0.08_color_dist0.5_distortion_scale0.6_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "b3c2bb68",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0040\t0.0080\t0.0069\t0.0094\n",
      "weight_shi:\t0.2243\t2.4831\t-1.1810\t8.3228\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.28934109115952156\n",
      "CNMC               1.5171 +- 0.6417    q0: -1.0892    q10: 0.6963    q20: 1.0163    q30: 1.2147    q40: 1.3800    q50: 1.5239    q60: 1.6610    q70: 1.8232    q80: 2.0413    q90: 2.3147    q100: 3.5528\n",
      "one_class_1        2.0698 +- 0.8043    q0: -0.3686    q10: 0.9775    q20: 1.4094    q30: 1.6626    q40: 1.9406    q50: 2.1430    q60: 2.3291    q70: 2.4725    q80: 2.7776    q90: 3.0563    q100: 4.2509\n",
      "[one_class_1 CSI 0.2893] [one_class_1 best 0.2893] \n",
      "[one_class_mean CSI 0.2893] [one_class_mean best 0.2893] \n",
      "0.2893\t0.2893\n"
     ]
    }
   ],
   "source": [
    "###### EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : randpers\n",
    "# crop        : 0.08\n",
    "# randper_dist: 0.3\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --distortion_scale 0.3 --color_distort 0.5 --resize_factor 0.08 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type randpers --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist0.5/randpers/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_randpers_resize_factor0.08_color_dist0.5_distortion_scale0.3_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cfed222",
   "metadata": {},
   "source": [
    "# Color Distortion = 0.8"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "009f41d0",
   "metadata": {},
   "source": [
    "## Examine crop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "0c216c1d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0109\t0.0127\t0.0131\t0.0112\n",
      "weight_shi:\t-0.3601\t0.8696\t0.8266\t1.2633\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.3773116499053059\n",
      "CNMC               1.9315 +- 0.0895    q0: 1.6857    q10: 1.8069    q20: 1.8435    q30: 1.8794    q40: 1.9068    q50: 1.9359    q60: 1.9582    q70: 1.9849    q80: 2.0138    q90: 2.0465    q100: 2.2963\n",
      "one_class_1        1.9880 +- 0.1336    q0: 1.7198    q10: 1.8075    q20: 1.8598    q30: 1.9006    q40: 1.9419    q50: 1.9827    q60: 2.0260    q70: 2.0729    q80: 2.1099    q90: 2.1645    q100: 2.4634\n",
      "[one_class_1 CSI 0.3773] [one_class_1 best 0.3773] \n",
      "[one_class_mean CSI 0.3773] [one_class_mean best 0.3773] \n",
      "0.3773\t0.3773\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.5\n",
    "# blur_sigma  : 2\n",
    "# color_dist  : 0.8\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.8 --resize_factor 0.5 --blur_sigma 2 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist0.8/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_resize_factor0.5_color_dist0.8_blur_sigma2.0_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "6320eef5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0076\t0.0076\t0.0074\t0.0074\n",
      "weight_shi:\t0.9058\t0.5362\t0.6368\t-14.1887\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.5917127477491163\n",
      "CNMC               2.8214 +- 1.0315    q0: -1.2486    q10: 1.3987    q20: 1.9532    q30: 2.3459    q40: 2.7058    q50: 2.9513    q60: 3.1875    q70: 3.4447    q80: 3.7111    q90: 4.0288    q100: 5.9040\n",
      "one_class_1        2.3812 +- 1.3314    q0: -2.1268    q10: 0.5397    q20: 1.2181    q30: 1.7456    q40: 2.2246    q50: 2.5793    q60: 2.9176    q70: 3.1949    q80: 3.5267    q90: 3.9124    q100: 4.9106\n",
      "[one_class_1 CSI 0.5917] [one_class_1 best 0.5917] \n",
      "[one_class_mean CSI 0.5917] [one_class_mean best 0.5917] \n",
      "0.5917\t0.5917\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.3\n",
    "# blur_sigma  : 2\n",
    "# color_dist  : 0.8\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.8 --resize_factor 0.3 --blur_sigma 2 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist0.8/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_resize_factor0.3_color_dist0.8_blur_sigma2.0_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "451c90e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0110\t0.0071\t0.0102\t0.0101\n",
      "weight_shi:\t-0.2335\t0.3455\t0.5920\t0.5756\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.4148462107171432\n",
      "CNMC               1.8587 +- 0.2019    q0: 1.3001    q10: 1.5984    q20: 1.6837    q30: 1.7477    q40: 1.8032    q50: 1.8568    q60: 1.9066    q70: 1.9618    q80: 2.0281    q90: 2.1033    q100: 2.4803\n",
      "one_class_1        1.9549 +- 0.3011    q0: 1.3374    q10: 1.5829    q20: 1.6833    q30: 1.7648    q40: 1.8483    q50: 1.9432    q60: 2.0105    q70: 2.0860    q80: 2.2017    q90: 2.3676    q100: 3.0008\n",
      "[one_class_1 CSI 0.4148] [one_class_1 best 0.4148] \n",
      "[one_class_mean CSI 0.4148] [one_class_mean best 0.4148] \n",
      "0.4148\t0.4148\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.02\n",
    "# blur_sigma  : 2\n",
    "# color_dist  : 0.8\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.8 --resize_factor 0.02 --blur_sigma 2 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist0.8/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_resize_factor0.02_color_dist0.8_blur_sigma2.0_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "54fef60e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0055\t0.0047\t0.0063\t0.0070\n",
      "weight_shi:\t-1.5156\t2.2142\t13.3925\t216.9532\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.3877887663435927\n",
      "CNMC               -9.2248 +- 14.9978    q0: -31.8010    q10: -22.8496    q20: -20.5631    q30: -18.8369    q40: -16.1600    q50: -13.7478    q60: -10.1906    q70: -5.6572    q80: 0.0581    q90: 9.1230    q100: 77.4578\n",
      "one_class_1        0.7817 +- 24.0001    q0: -33.6751    q10: -22.8505    q20: -20.2689    q30: -16.3248    q40: -11.6706    q50: -5.3667    q60: 0.3825    q70: 6.7728    q80: 17.5805    q90: 39.7293    q100: 83.7649\n",
      "[one_class_1 CSI 0.3878] [one_class_1 best 0.3878] \n",
      "[one_class_mean CSI 0.3878] [one_class_mean best 0.3878] \n",
      "0.3878\t0.3878\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.008\n",
    "# blur_sigma  : 2\n",
    "# color_dist  : 0.8\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.8 --resize_factor 0.008 --blur_sigma 2 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist0.8/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_resize_factor0.008_color_dist0.8_blur_sigma2.0_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dccb685",
   "metadata": {},
   "source": [
    "## Examine blur_sigma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "0c13892c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0059\t0.0053\t0.0052\t0.0054\n",
      "weight_shi:\t-0.0908\t0.2339\t0.2553\t0.2459\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.46215401209248624\n",
      "CNMC               1.9646 +- 0.0814    q0: 1.7239    q10: 1.8537    q20: 1.8937    q30: 1.9247    q40: 1.9510    q50: 1.9695    q60: 1.9918    q70: 2.0122    q80: 2.0334    q90: 2.0642    q100: 2.1895\n",
      "one_class_1        1.9790 +- 0.1048    q0: 1.6906    q10: 1.8438    q20: 1.8841    q30: 1.9178    q40: 1.9505    q50: 1.9783    q60: 2.0103    q70: 2.0393    q80: 2.0700    q90: 2.1155    q100: 2.2617\n",
      "[one_class_1 CSI 0.4622] [one_class_1 best 0.4622] \n",
      "[one_class_mean CSI 0.4622] [one_class_mean best 0.4622] \n",
      "0.4622\t0.4622\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.08\n",
    "# blur_sigma  : 40\n",
    "# color_dist  : 0.8 \n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.8 --resize_factor 0.08 --blur_sigma 40 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist0.8/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_resize_factor0.08_color_dist0.8_blur_sigma40.0_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "7b24db11",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0172\t0.0135\t0.0226\t0.0192\n",
      "weight_shi:\t-0.0741\t0.1495\t0.1978\t0.1718\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.4793875773503884\n",
      "CNMC               1.9531 +- 0.1138    q0: 1.6474    q10: 1.8092    q20: 1.8542    q30: 1.8878    q40: 1.9235    q50: 1.9532    q60: 1.9802    q70: 2.0102    q80: 2.0447    q90: 2.0976    q100: 2.3859\n",
      "one_class_1        1.9692 +- 0.1523    q0: 1.6030    q10: 1.7796    q20: 1.8318    q30: 1.8785    q40: 1.9130    q50: 1.9548    q60: 1.9983    q70: 2.0455    q80: 2.0996    q90: 2.1848    q100: 2.3561\n",
      "[one_class_1 CSI 0.4794] [one_class_1 best 0.4794] \n",
      "[one_class_mean CSI 0.4794] [one_class_mean best 0.4794] \n",
      "0.4794\t0.4794\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.08\n",
    "# blur_sigma  : 20\n",
    "# color_dist  : 0.8 \n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.8 --resize_factor 0.08 --blur_sigma 20 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist0.8/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_resize_factor0.08_color_dist0.8_blur_sigma20.0_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "352c0a41",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0025\t0.0040\t0.0022\t0.0025\n",
      "weight_shi:\t1.2410\t0.6755\t-1.1582\t-3.5877\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.6594658645520007\n",
      "CNMC               2.1628 +- 0.3004    q0: 0.7838    q10: 1.7714    q20: 1.9641    q30: 2.0468    q40: 2.1213    q50: 2.1830    q60: 2.2509    q70: 2.3213    q80: 2.4001    q90: 2.5014    q100: 3.3713\n",
      "one_class_1        1.9514 +- 0.4284    q0: -0.1104    q10: 1.4546    q20: 1.6765    q30: 1.7941    q40: 1.8892    q50: 2.0071    q60: 2.0917    q70: 2.1785    q80: 2.2711    q90: 2.4356    q100: 2.9090\n",
      "[one_class_1 CSI 0.6595] [one_class_1 best 0.6595] \n",
      "[one_class_mean CSI 0.6595] [one_class_mean best 0.6595] \n",
      "0.6595\t0.6595\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.08\n",
    "# blur_sigma  : 6\n",
    "# color_dist  : 0.8 \n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.8 --resize_factor 0.08 --blur_sigma 6 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist0.8/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_resize_factor0.08_color_dist0.8_blur_sigma6.0_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "d22c485a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0050\t0.0078\t0.0052\t0.0062\n",
      "weight_shi:\t0.4106\t0.4163\t-2.7425\t-3.5688\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.6272255643666637\n",
      "CNMC               2.1200 +- 0.6206    q0: -0.0284    q10: 1.2949    q20: 1.5957    q30: 1.8155    q40: 2.0312    q50: 2.1755    q60: 2.3270    q70: 2.4960    q80: 2.6611    q90: 2.8534    q100: 4.1276\n",
      "one_class_1        1.8269 +- 0.6667    q0: -0.9521    q10: 0.9740    q20: 1.3397    q30: 1.5917    q40: 1.7532    q50: 1.8870    q60: 2.0460    q70: 2.2002    q80: 2.3477    q90: 2.5711    q100: 3.5803\n",
      "[one_class_1 CSI 0.6272] [one_class_1 best 0.6272] \n",
      "[one_class_mean CSI 0.6272] [one_class_mean best 0.6272] \n",
      "0.6272\t0.6272\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.08\n",
    "# blur_sigma  : 4\n",
    "# color_dist  : 0.8 \n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.8 --resize_factor 0.08 --blur_sigma 4 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist0.8/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_resize_factor0.08_color_dist0.8_blur_sigma4.0_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "00a8d2ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0047\t0.0072\t0.0056\t0.0047\n",
      "weight_shi:\t0.3757\t1.6655\t5.1831\t-1.0361\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.49718323053707253\n",
      "CNMC               1.9914 +- 0.1852    q0: 1.3248    q10: 1.7612    q20: 1.8463    q30: 1.9021    q40: 1.9490    q50: 1.9936    q60: 2.0285    q70: 2.0851    q80: 2.1355    q90: 2.2233    q100: 2.6945\n",
      "one_class_1        1.9965 +- 0.2132    q0: 1.1855    q10: 1.7487    q20: 1.8267    q30: 1.8843    q40: 1.9394    q50: 1.9881    q60: 2.0429    q70: 2.1000    q80: 2.1632    q90: 2.2481    q100: 2.8524\n",
      "[one_class_1 CSI 0.4972] [one_class_1 best 0.4972] \n",
      "[one_class_mean CSI 0.4972] [one_class_mean best 0.4972] \n",
      "0.4972\t0.4972\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.08\n",
    "# blur_sigma  : 3\n",
    "# color_dist  : 0.8 \n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.8 --resize_factor 0.08 --blur_sigma 3 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist0.8/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_resize_factor0.08_color_dist0.8_blur_sigma3.0_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "cdab5a91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0019\t0.0026\t0.0019\t0.0022\n",
      "weight_shi:\t0.2520\t-1.0379\t-0.8245\t-0.8299\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.7393317230273752\n",
      "CNMC               2.1093 +- 0.1696    q0: 1.2392    q10: 1.9016    q20: 2.0068    q30: 2.0585    q40: 2.1066    q50: 2.1333    q60: 2.1667    q70: 2.1958    q80: 2.2352    q90: 2.2885    q100: 2.5315\n",
      "one_class_1        1.9282 +- 0.2660    q0: 0.4865    q10: 1.6153    q20: 1.7843    q30: 1.8731    q40: 1.9295    q50: 1.9714    q60: 2.0108    q70: 2.0668    q80: 2.1224    q90: 2.2106    q100: 2.5011\n",
      "[one_class_1 CSI 0.7393] [one_class_1 best 0.7393] \n",
      "[one_class_mean CSI 0.7393] [one_class_mean best 0.7393] \n",
      "0.7393\t0.7393\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.08\n",
    "# blur_sigma  : 2\n",
    "# color_dist  : 0.8 \n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.8 --resize_factor 0.08 --blur_sigma 2 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist0.8/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_resize_factor0.08_color_dist0.8_blur_sigma2.0_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "76bdab2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0044\t0.0059\t0.0046\t0.0046\n",
      "weight_shi:\t0.2676\t-0.5492\t-0.7697\t-0.6319\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.6709070124267007\n",
      "CNMC               2.0490 +- 0.0659    q0: 1.6783    q10: 1.9728    q20: 1.9995    q30: 2.0151    q40: 2.0347    q50: 2.0501    q60: 2.0654    q70: 2.0818    q80: 2.1012    q90: 2.1296    q100: 2.2543\n",
      "one_class_1        1.9948 +- 0.1054    q0: 1.5066    q10: 1.8893    q20: 1.9323    q30: 1.9563    q40: 1.9732    q50: 1.9993    q60: 2.0219    q70: 2.0484    q80: 2.0819    q90: 2.1226    q100: 2.2211\n",
      "[one_class_1 CSI 0.6709] [one_class_1 best 0.6709] \n",
      "[one_class_mean CSI 0.6709] [one_class_mean best 0.6709] \n",
      "0.6709\t0.6709\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.08\n",
    "# blur_sigma  : 1.5\n",
    "# color_dist  : 0.8 \n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.8 --resize_factor 0.08 --blur_sigma 1.5 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist0.8/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_resize_factor0.08_color_dist0.8_blur_sigma1.5_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "0c1efb9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0058\t0.0159\t0.0080\t0.0086\n",
      "weight_shi:\t0.5438\t-2.8363\t-21.1928\t-1.9421\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.6621902186572681\n",
      "CNMC               2.2408 +- 0.8449    q0: -0.8843    q10: 1.1519    q20: 1.6038    q30: 1.9076    q40: 2.1231    q50: 2.3416    q60: 2.5656    q70: 2.7323    q80: 2.9607    q90: 3.2092    q100: 3.9264\n",
      "one_class_1        1.6402 +- 1.1251    q0: -2.9414    q10: 0.0207    q20: 0.8939    q30: 1.3058    q40: 1.6627    q50: 1.9102    q60: 2.1044    q70: 2.2975    q80: 2.5539    q90: 2.8038    q100: 3.7386\n",
      "[one_class_1 CSI 0.6622] [one_class_1 best 0.6622] \n",
      "[one_class_mean CSI 0.6622] [one_class_mean best 0.6622] \n",
      "0.6622\t0.6622\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.08\n",
    "# blur_sigma  : 1\n",
    "# color_dist  : 0.8 \n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.8 --resize_factor 0.08 --blur_sigma 1 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist0.8/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_resize_factor0.08_color_dist0.8_blur_sigma1.0_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f676267b",
   "metadata": {},
   "source": [
    "# Color Distortion = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "744297b9",
   "metadata": {},
   "source": [
    "## Examine crop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "21a87be2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0061\t0.0065\t0.0065\t0.0056\n",
      "weight_shi:\t1.6932\t-31.1268\t15.0080\t-10.2414\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.7102132895816242\n",
      "CNMC               2.2522 +- 0.5226    q0: -0.3755    q10: 1.6169    q20: 1.9232    q30: 2.1160    q40: 2.2220    q50: 2.3245    q60: 2.4225    q70: 2.5144    q80: 2.6312    q90: 2.8060    q100: 3.9139\n",
      "one_class_1        1.8127 +- 0.7110    q0: -1.7832    q10: 0.9329    q20: 1.3309    q30: 1.6150    q40: 1.7793    q50: 1.9225    q60: 2.0429    q70: 2.1887    q80: 2.3378    q90: 2.5668    q100: 3.4155\n",
      "[one_class_1 CSI 0.7102] [one_class_1 best 0.7102] \n",
      "[one_class_mean CSI 0.7102] [one_class_mean best 0.7102] \n",
      "0.7102\t0.7102\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.5\n",
    "# blur_sigma  : 2\n",
    "# color_dist  : 1\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 1 --resize_factor 0.5 --blur_sigma 2 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist1.0/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_2.0_resize_factor_0.5_color_dist1.0_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "8dd1d6d5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0092\t0.0099\t0.0099\t0.0096\n",
      "weight_shi:\t0.5734\t-1.4904\t-1.4266\t-2.6760\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.5938636202513699\n",
      "CNMC               2.0102 +- 0.1072    q0: 1.4470    q10: 1.8844    q20: 1.9436    q30: 1.9761    q40: 2.0084    q50: 2.0343    q60: 2.0578    q70: 2.0757    q80: 2.0944    q90: 2.1139    q100: 2.2014\n",
      "one_class_1        1.9687 +- 0.1365    q0: 1.2909    q10: 1.8035    q20: 1.8848    q30: 1.9370    q40: 1.9730    q50: 2.0035    q60: 2.0287    q70: 2.0532    q80: 2.0725    q90: 2.0980    q100: 2.1942\n",
      "[one_class_1 CSI 0.5939] [one_class_1 best 0.5939] \n",
      "[one_class_mean CSI 0.5939] [one_class_mean best 0.5939] \n",
      "0.5939\t0.5939\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.3\n",
    "# blur_sigma  : 2\n",
    "# color_dist  : 1\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 1 --resize_factor 0.3 --blur_sigma 2 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist1.0/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_2.0_resize_factor_0.3_color_dist1.0_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "80437a6c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0086\t0.0090\t0.0096\t0.0084\n",
      "weight_shi:\t-0.6178\t0.6564\t1.4537\t1.9758\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.40624398667193307\n",
      "CNMC               1.9234 +- 0.1679    q0: 1.2990    q10: 1.6940    q20: 1.7902    q30: 1.8538    q40: 1.9027    q50: 1.9438    q60: 1.9742    q70: 2.0088    q80: 2.0543    q90: 2.1136    q100: 2.6046\n",
      "one_class_1        1.9913 +- 0.2119    q0: 1.3411    q10: 1.7247    q20: 1.8161    q30: 1.8739    q40: 1.9369    q50: 1.9987    q60: 2.0415    q70: 2.0979    q80: 2.1553    q90: 2.2420    q100: 2.6629\n",
      "[one_class_1 CSI 0.4062] [one_class_1 best 0.4062] \n",
      "[one_class_mean CSI 0.4062] [one_class_mean best 0.4062] \n",
      "0.4062\t0.4062\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.02\n",
    "# blur_sigma  : 2\n",
    "# color_dist  : 1\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 1 --resize_factor 0.02 --blur_sigma 2 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist1.0/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_2.0_resize_factor_0.02_color_dist1.0_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "5ee4b03d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0077\t0.0063\t0.0079\t0.0085\n",
      "weight_shi:\t-0.5622\t1.4395\t2.1736\t5.1802\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.40242330791278014\n",
      "CNMC               1.7715 +- 0.3123    q0: 1.0132    q10: 1.4352    q20: 1.5132    q30: 1.5847    q40: 1.6416    q50: 1.7219    q60: 1.7995    q70: 1.8923    q80: 2.0104    q90: 2.1944    q100: 3.1272\n",
      "one_class_1        1.9377 +- 0.4535    q0: 1.0669    q10: 1.4215    q20: 1.5153    q30: 1.6260    q40: 1.7391    q50: 1.8745    q60: 1.9976    q70: 2.1337    q80: 2.2999    q90: 2.5968    q100: 3.2364\n",
      "[one_class_1 CSI 0.4024] [one_class_1 best 0.4024] \n",
      "[one_class_mean CSI 0.4024] [one_class_mean best 0.4024] \n",
      "0.4024\t0.4024\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.008\n",
    "# blur_sigma  : 2\n",
    "# color_dist  : 1\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 1 --resize_factor 0.008 --blur_sigma 2 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist1.0/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_2.0_resize_factor_0.008_color_dist1.0_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3993fc92",
   "metadata": {},
   "source": [
    "## Examine blur_sigma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "d11c9dcd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0118\t0.0082\t0.0118\t0.0109\n",
      "weight_shi:\t-0.5332\t0.3382\t1.2635\t1.1178\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.513900282563121\n",
      "CNMC               1.8224 +- 0.5573    q0: 0.4695    q10: 1.0940    q20: 1.3195    q30: 1.5008    q40: 1.6572    q50: 1.8032    q60: 1.9693    q70: 2.1209    q80: 2.3099    q90: 2.5687    q100: 3.4860\n",
      "one_class_1        1.8135 +- 0.7140    q0: 0.2666    q10: 0.8849    q20: 1.1651    q30: 1.3949    q40: 1.5485    q50: 1.7703    q60: 1.9382    q70: 2.1803    q80: 2.4559    q90: 2.7728    q100: 4.0059\n",
      "[one_class_1 CSI 0.5139] [one_class_1 best 0.5139] \n",
      "[one_class_mean CSI 0.5139] [one_class_mean best 0.5139] \n",
      "0.5139\t0.5139\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.08\n",
    "# blur_sigma  : 40\n",
    "# color_dist  : 1\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 1 --resize_factor 0.08 --blur_sigma 40 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist1.0/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_40.0_resize_factor_0.08_color_dist1.0_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "b5ffde5e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0083\t0.0108\t0.0081\t0.0091\n",
      "weight_shi:\t-0.0827\t0.1462\t0.2242\t0.2133\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.5305926482950001\n",
      "CNMC               1.9788 +- 0.0509    q0: 1.8514    q10: 1.9121    q20: 1.9387    q30: 1.9537    q40: 1.9657    q50: 1.9801    q60: 1.9904    q70: 2.0027    q80: 2.0193    q90: 2.0412    q100: 2.1780\n",
      "one_class_1        1.9783 +- 0.0729    q0: 1.8180    q10: 1.8943    q20: 1.9205    q30: 1.9377    q40: 1.9541    q50: 1.9673    q60: 1.9856    q70: 2.0070    q80: 2.0322    q90: 2.0712    q100: 2.2575\n",
      "[one_class_1 CSI 0.5306] [one_class_1 best 0.5306] \n",
      "[one_class_mean CSI 0.5306] [one_class_mean best 0.5306] \n",
      "0.5306\t0.5306\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.08\n",
    "# blur_sigma  : 20\n",
    "# color_dist  : 1\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 1 --resize_factor 0.08 --blur_sigma 20 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist1.0/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_20.0_resize_factor_0.08_color_dist1.0_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "46c0a5be",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0121\t0.0090\t0.0108\t0.0115\n",
      "weight_shi:\t-0.1191\t0.1866\t0.3505\t0.3025\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.5126887552031113\n",
      "CNMC               1.9350 +- 0.1456    q0: 1.5801    q10: 1.7447    q20: 1.8072    q30: 1.8557    q40: 1.8955    q50: 1.9330    q60: 1.9678    q70: 2.0099    q80: 2.0570    q90: 2.1285    q100: 2.4351\n",
      "one_class_1        1.9410 +- 0.1925    q0: 1.5534    q10: 1.7034    q20: 1.7718    q30: 1.8241    q40: 1.8672    q50: 1.9158    q60: 1.9602    q70: 2.0200    q80: 2.1069    q90: 2.2114    q100: 2.5473\n",
      "[one_class_1 CSI 0.5127] [one_class_1 best 0.5127] \n",
      "[one_class_mean CSI 0.5127] [one_class_mean best 0.5127] \n",
      "0.5127\t0.5127\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.08\n",
    "# blur_sigma  : 6\n",
    "# color_dist  : 1\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 1 --resize_factor 0.08 --blur_sigma 6 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist1.0/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_6.0_resize_factor_0.08_color_dist1.0_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "9c074889",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0091\t0.0056\t0.0092\t0.0077\n",
      "weight_shi:\t1.7473\t1.4099\t-3.2623\t7.2654\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.3547040683012791\n",
      "CNMC               1.6471 +- 0.5245    q0: 0.2048    q10: 0.9772    q20: 1.1761    q30: 1.3840    q40: 1.5410    q50: 1.6358    q60: 1.7534    q70: 1.8764    q80: 2.0524    q90: 2.3159    q100: 3.4153\n",
      "one_class_1        2.0065 +- 0.7329    q0: 0.2362    q10: 1.0671    q20: 1.3288    q30: 1.5682    q40: 1.7685    q50: 1.9697    q60: 2.1593    q70: 2.3883    q80: 2.6860    q90: 3.0258    q100: 4.4012\n",
      "[one_class_1 CSI 0.3547] [one_class_1 best 0.3547] \n",
      "[one_class_mean CSI 0.3547] [one_class_mean best 0.3547] \n",
      "0.3547\t0.3547\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.08\n",
    "# blur_sigma  : 4\n",
    "# color_dist  : 1\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 1 --resize_factor 0.08 --blur_sigma 4 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist1.0/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_resize_factor0.08_color_dist1.0_blur_sigma4.0_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "99c14a28",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0033\t0.0033\t0.0025\t0.0033\n",
      "weight_shi:\t0.2828\t-1.2986\t-0.7648\t-1.3398\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.5759223812272759\n",
      "CNMC               1.9848 +- 0.2270    q0: 1.1862    q10: 1.7005    q20: 1.7975    q30: 1.8756    q40: 1.9360    q50: 1.9962    q60: 2.0534    q70: 2.1141    q80: 2.1839    q90: 2.2699    q100: 2.5657\n",
      "one_class_1        1.9048 +- 0.2961    q0: 0.9850    q10: 1.4973    q20: 1.6832    q30: 1.7788    q40: 1.8554    q50: 1.9257    q60: 1.9946    q70: 2.0781    q80: 2.1586    q90: 2.2805    q100: 2.6712\n",
      "[one_class_1 CSI 0.5759] [one_class_1 best 0.5759] \n",
      "[one_class_mean CSI 0.5759] [one_class_mean best 0.5759] \n",
      "0.5759\t0.5759\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.08\n",
    "# blur_sigma  : 3\n",
    "# color_dist  : 1\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 1 --resize_factor 0.08 --blur_sigma 3 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist1.0/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_resize_factor0.08_color_dist1.0_blur_sigma3.0_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "bd3e218a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0050\t0.0049\t0.0049\t0.0050\n",
      "weight_shi:\t0.3094\t-1.0241\t-0.9471\t-0.9535\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.5930793556750625\n",
      "CNMC               2.0015 +- 0.1074    q0: 1.5164    q10: 1.8705    q20: 1.9260    q30: 1.9607    q40: 1.9870    q50: 2.0109    q60: 2.0342    q70: 2.0588    q80: 2.0868    q90: 2.1264    q100: 2.2720\n",
      "one_class_1        1.9465 +- 0.1678    q0: 1.2629    q10: 1.7271    q20: 1.8484    q30: 1.9020    q40: 1.9374    q50: 1.9751    q60: 2.0018    q70: 2.0397    q80: 2.0761    q90: 2.1296    q100: 2.2873\n",
      "[one_class_1 CSI 0.5931] [one_class_1 best 0.5931] \n",
      "[one_class_mean CSI 0.5931] [one_class_mean best 0.5931] \n",
      "0.5931\t0.5931\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.08\n",
    "# blur_sigma  : 2\n",
    "# color_dist  : 1\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 1 --resize_factor 0.08 --blur_sigma 2 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist1.0/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_2.0_resize_factor_0.08_color_dist1.0_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "c2f0113b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0151\t0.0114\t0.0140\t0.0143\n",
      "weight_shi:\t0.3904\t-1.7955\t-0.8990\t-1.3060\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.6319476093539534\n",
      "CNMC               2.0984 +- 0.1571    q0: 1.7583    q10: 1.9064    q20: 1.9536    q30: 2.0042    q40: 2.0398    q50: 2.0803    q60: 2.1205    q70: 2.1755    q80: 2.2444    q90: 2.3124    q100: 2.6504\n",
      "one_class_1        2.0194 +- 0.1919    q0: 1.5830    q10: 1.7904    q20: 1.8384    q30: 1.8841    q40: 1.9493    q50: 1.9958    q60: 2.0566    q70: 2.1204    q80: 2.1982    q90: 2.2910    q100: 2.6254\n",
      "[one_class_1 CSI 0.6319] [one_class_1 best 0.6319] \n",
      "[one_class_mean CSI 0.6319] [one_class_mean best 0.6319] \n",
      "0.6319\t0.6319\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.08\n",
    "# blur_sigma  : 1.5\n",
    "# color_dist  : 1\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 1 --resize_factor 0.08 --blur_sigma 1.5 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist1.0/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_resize_factor0.08_color_dist1.0_blur_sigma1.5_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "1a64397f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0835\t0.0834\t0.0843\t0.0839\n",
      "weight_shi:\t0.6194\t-4.8322\t-1.4623\t-2.0319\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.5156447806844306\n",
      "CNMC               2.0671 +- 0.1604    q0: 1.6242    q10: 1.8621    q20: 1.9293    q30: 1.9753    q40: 2.0244    q50: 2.0641    q60: 2.0983    q70: 2.1559    q80: 2.2074    q90: 2.2798    q100: 2.5007\n",
      "one_class_1        2.0520 +- 0.2129    q0: 1.5274    q10: 1.7652    q20: 1.8656    q30: 1.9325    q40: 1.9996    q50: 2.0618    q60: 2.1083    q70: 2.1784    q80: 2.2379    q90: 2.3346    q100: 2.6253\n",
      "[one_class_1 CSI 0.5156] [one_class_1 best 0.5156] \n",
      "[one_class_mean CSI 0.5156] [one_class_mean best 0.5156] \n",
      "0.5156\t0.5156\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.08\n",
    "# blur_sigma  : 1\n",
    "# color_dist  : 1\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 1 --resize_factor 0.08 --blur_sigma 1 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist1.0/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_resize_factor0.08_color_dist1.0_blur_sigma1.0_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1bce058",
   "metadata": {},
   "source": [
    "# Color Distortion = 0.5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65e662af",
   "metadata": {},
   "source": [
    "## Examine crop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "fdaec3de",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0077\t0.0072\t0.0077\t0.0083\n",
      "weight_shi:\t-0.2495\t0.5029\t0.4407\t0.6284\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.4545575962892069\n",
      "CNMC               1.9531 +- 0.0857    q0: 1.5656    q10: 1.8568    q20: 1.8889    q30: 1.9090    q40: 1.9290    q50: 1.9470    q60: 1.9664    q70: 1.9868    q80: 2.0131    q90: 2.0534    q100: 2.4858\n",
      "one_class_1        1.9770 +- 0.1276    q0: 1.5910    q10: 1.8422    q20: 1.8816    q30: 1.9115    q40: 1.9369    q50: 1.9621    q60: 1.9818    q70: 2.0174    q80: 2.0584    q90: 2.1323    q100: 2.7000\n",
      "[one_class_1 CSI 0.4546] [one_class_1 best 0.4546] \n",
      "[one_class_mean CSI 0.4546] [one_class_mean best 0.4546] \n",
      "0.4546\t0.4546\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.5\n",
    "# blur_sigma  : 2\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.5 --blur_sigma 2 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist0.5/blur/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_2.0_resize_factor_0.5_color_dist0.5_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "eaa5ec79",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0069\t0.0088\t0.0090\t0.0079\n",
      "weight_shi:\t2.7516\t0.9415\t1.1553\t-18.6953\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.580519095798013\n",
      "CNMC               2.6811 +- 1.0535    q0: -1.5616    q10: 1.2724    q20: 1.7695    q30: 2.1545    q40: 2.4883    q50: 2.7551    q60: 3.0169    q70: 3.2695    q80: 3.5643    q90: 3.8850    q100: 6.2124\n",
      "one_class_1        2.2993 +- 1.4215    q0: -2.7435    q10: 0.4967    q20: 1.2345    q30: 1.7164    q40: 2.0762    q50: 2.3752    q60: 2.6957    q70: 3.0288    q80: 3.4597    q90: 3.9539    q100: 6.3139\n",
      "[one_class_1 CSI 0.5805] [one_class_1 best 0.5805] \n",
      "[one_class_mean CSI 0.5805] [one_class_mean best 0.5805] \n",
      "0.5805\t0.5805\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.3\n",
    "# blur_sigma  : 2\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.3 --blur_sigma 2 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist0.5/blur/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_2.0_resize_factor_0.3_color_dist0.5_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "4a75f4d4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0074\t0.0080\t0.0073\t0.0077\n",
      "weight_shi:\t-0.8732\t0.8498\t2.4905\t1.5653\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.2671803947781525\n",
      "CNMC               1.8416 +- 0.1772    q0: 1.2265    q10: 1.6388    q20: 1.7083    q30: 1.7532    q40: 1.7915    q50: 1.8242    q60: 1.8580    q70: 1.8990    q80: 1.9656    q90: 2.0775    q100: 2.5900\n",
      "one_class_1        2.0431 +- 0.2719    q0: 1.2846    q10: 1.7272    q20: 1.8128    q30: 1.8857    q40: 1.9439    q50: 2.0013    q60: 2.0829    q70: 2.1644    q80: 2.2684    q90: 2.4103    q100: 2.9156\n",
      "[one_class_1 CSI 0.2672] [one_class_1 best 0.2672] \n",
      "[one_class_mean CSI 0.2672] [one_class_mean best 0.2672] \n",
      "0.2672\t0.2672\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.02\n",
    "# blur_sigma  : 2\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.02 --blur_sigma 2 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist0.5/blur/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_2.0_resize_factor_0.02_color_dist0.5_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "9d31d62a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0046\t0.0037\t0.0035\t0.0047\n",
      "weight_shi:\t0.4014\t-0.7791\t-0.6536\t-1.3711\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.611233276618155\n",
      "CNMC               1.9991 +- 0.2593    q0: 0.9291    q10: 1.6096    q20: 1.7794    q30: 1.9130    q40: 1.9994    q50: 2.0695    q60: 2.1203    q70: 2.1657    q80: 2.2124    q90: 2.2692    q100: 2.5149\n",
      "one_class_1        1.8852 +- 0.3136    q0: 0.6811    q10: 1.4317    q20: 1.6563    q30: 1.7768    q40: 1.8804    q50: 1.9566    q60: 2.0179    q70: 2.0924    q80: 2.1459    q90: 2.2152    q100: 2.4864\n",
      "[one_class_1 CSI 0.6112] [one_class_1 best 0.6112] \n",
      "[one_class_mean CSI 0.6112] [one_class_mean best 0.6112] \n",
      "0.6112\t0.6112\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.008\n",
    "# blur_sigma  : 2\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.008 --blur_sigma 2 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist0.5/blur/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_2.0_resize_factor_0.008_color_dist0.5_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58a14458",
   "metadata": {},
   "source": [
    "## Examine blur_sigma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "c7c2318d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0050\t0.0073\t0.0050\t0.0055\n",
      "weight_shi:\t-0.3869\t0.3100\t0.7499\t0.9321\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.4740262206422994\n",
      "CNMC               1.9842 +- 0.2291    q0: 1.4232    q10: 1.6950    q20: 1.7851    q30: 1.8500    q40: 1.9214    q50: 1.9744    q60: 2.0301    q70: 2.0950    q80: 2.1712    q90: 2.2769    q100: 3.0240\n",
      "one_class_1        2.0169 +- 0.2738    q0: 1.4504    q10: 1.6924    q20: 1.7765    q30: 1.8481    q40: 1.9251    q50: 1.9917    q60: 2.0673    q70: 2.1457    q80: 2.2342    q90: 2.3550    q100: 3.2798\n",
      "[one_class_1 CSI 0.4740] [one_class_1 best 0.4740] \n",
      "[one_class_mean CSI 0.4740] [one_class_mean best 0.4740] \n",
      "0.4740\t0.4740\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.08\n",
    "# blur_sigma  : 40\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --blur_sigma 40 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist0.5/blur/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_40.0_resize_factor_0.08_color_dist0.5_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "dbd4fb10",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0041\t0.0073\t0.0038\t0.0040\n",
      "weight_shi:\t-0.0807\t0.1383\t0.2679\t0.2225\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.3159839323874052\n",
      "CNMC               1.9782 +- 0.0390    q0: 1.8641    q10: 1.9344    q20: 1.9491    q30: 1.9576    q40: 1.9654    q50: 1.9735    q60: 1.9830    q70: 1.9942    q80: 2.0052    q90: 2.0239    q100: 2.1760\n",
      "one_class_1        2.0111 +- 0.0558    q0: 1.8790    q10: 1.9491    q20: 1.9646    q30: 1.9780    q40: 1.9912    q50: 2.0041    q60: 2.0170    q70: 2.0318    q80: 2.0532    q90: 2.0897    q100: 2.2666\n",
      "[one_class_1 CSI 0.3160] [one_class_1 best 0.3160] \n",
      "[one_class_mean CSI 0.3160] [one_class_mean best 0.3160] \n",
      "0.3160\t0.3160\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.08\n",
    "# blur_sigma  : 20\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --blur_sigma 20 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist0.5/blur/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_20.0_resize_factor_0.08_color_dist0.5_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "c0cd8374",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0021\t0.0037\t0.0024\t0.0027\n",
      "weight_shi:\t0.1478\t4.1795\t-0.4613\t-0.5806\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.4508957959874011\n",
      "CNMC               2.0731 +- 0.5687    q0: 0.4702    q10: 1.3853    q20: 1.5945    q30: 1.7650    q40: 1.9267    q50: 2.0493    q60: 2.1848    q70: 2.3330    q80: 2.5050    q90: 2.7946    q100: 4.6939\n",
      "one_class_1        2.1855 +- 0.7534    q0: 0.3032    q10: 1.1734    q20: 1.4954    q30: 1.7768    q40: 1.9835    q50: 2.1717    q60: 2.4165    q70: 2.5852    q80: 2.8103    q90: 3.1495    q100: 4.4871\n",
      "[one_class_1 CSI 0.4509] [one_class_1 best 0.4509] \n",
      "[one_class_mean CSI 0.4509] [one_class_mean best 0.4509] \n",
      "0.4509\t0.4509\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.08\n",
    "# blur_sigma  : 6\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --blur_sigma 6 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist0.5/blur/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_6.0_resize_factor_0.08_color_dist0.5_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "1a733a07",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0019\t0.0025\t0.0018\t0.0018\n",
      "weight_shi:\t0.1207\t-0.4216\t-0.2927\t-0.2699\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.622416167876928\n",
      "CNMC               2.0481 +- 0.2777    q0: 0.5649    q10: 1.7109    q20: 1.8569    q30: 1.9499    q40: 2.0216    q50: 2.0813    q60: 2.1374    q70: 2.2010    q80: 2.2718    q90: 2.3476    q100: 2.6884\n",
      "one_class_1        1.8936 +- 0.3857    q0: 0.4436    q10: 1.4038    q20: 1.6226    q30: 1.7768    q40: 1.8682    q50: 1.9483    q60: 2.0252    q70: 2.1209    q80: 2.2012    q90: 2.3215    q100: 2.8144\n",
      "[one_class_1 CSI 0.6224] [one_class_1 best 0.6224] \n",
      "[one_class_mean CSI 0.6224] [one_class_mean best 0.6224] \n",
      "0.6224\t0.6224\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.08\n",
    "# blur_sigma  : 4\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --blur_sigma 4 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist0.5/blur/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_resize_factor0.08_color_dist0.5_blur_sigma4.0_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "c59e2e1d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0024\t0.0049\t0.0029\t0.0029\n",
      "weight_shi:\t0.3727\t0.6016\t-2.1896\t-1.0076\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.7071838381996982\n",
      "CNMC               2.1791 +- 0.2772    q0: 0.2329    q10: 1.8709    q20: 2.0154    q30: 2.0976    q40: 2.1641    q50: 2.2225    q60: 2.2692    q70: 2.3190    q80: 2.3739    q90: 2.4494    q100: 2.9055\n",
      "one_class_1        1.9359 +- 0.4103    q0: -0.1517    q10: 1.4452    q20: 1.7034    q30: 1.8312    q40: 1.9334    q50: 2.0115    q60: 2.0642    q70: 2.1519    q80: 2.2408    q90: 2.3584    q100: 2.8261\n",
      "[one_class_1 CSI 0.7072] [one_class_1 best 0.7072] \n",
      "[one_class_mean CSI 0.7072] [one_class_mean best 0.7072] \n",
      "0.7072\t0.7072\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.08\n",
    "# blur_sigma  : 3\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --blur_sigma 3 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist0.5/blur/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_resize_factor0.08_color_dist0.5_blur_sigma3.0_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "5827615d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0019\t0.0027\t0.0022\t0.0026\n",
      "weight_shi:\t0.1899\t-0.4837\t-0.3535\t-0.3448\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.7415028509504856\n",
      "CNMC               2.1337 +- 0.1823    q0: 0.9904    q10: 1.9206    q20: 2.0374    q30: 2.0868    q40: 2.1243    q50: 2.1600    q60: 2.1945    q70: 2.2226    q80: 2.2642    q90: 2.3222    q100: 2.5460\n",
      "one_class_1        1.9400 +- 0.2874    q0: 0.6323    q10: 1.5817    q20: 1.7870    q30: 1.8866    q40: 1.9416    q50: 1.9843    q60: 2.0207    q70: 2.0829    q80: 2.1566    q90: 2.2468    q100: 2.5500\n",
      "[one_class_1 CSI 0.7415] [one_class_1 best 0.7415] \n",
      "[one_class_mean CSI 0.7415] [one_class_mean best 0.7415] \n",
      "0.7415\t0.7415\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.08\n",
    "# blur_sigma  : 2\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --blur_sigma 2 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist0.5/blur/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_2.0_resize_factor_0.08_color_dist0.5_one_class_0_7415/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "65baeab1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0022\t0.0033\t0.0025\t0.0029\n",
      "weight_shi:\t0.4059\t-6.1160\t-2.6702\t-1.5404\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.7402292913641013\n",
      "CNMC               2.5607 +- 0.6482    q0: -0.8214    q10: 1.7859    q20: 2.1154    q30: 2.3402    q40: 2.5031    q50: 2.6399    q60: 2.7654    q70: 2.9084    q80: 3.0413    q90: 3.2796    q100: 4.2054\n",
      "one_class_1        1.8328 +- 0.9715    q0: -2.1220    q10: 0.6263    q20: 1.0844    q30: 1.4277    q40: 1.6691    q50: 1.8643    q60: 2.1102    q70: 2.3723    q80: 2.6504    q90: 3.0382    q100: 4.2076\n",
      "[one_class_1 CSI 0.7402] [one_class_1 best 0.7402] \n",
      "[one_class_mean CSI 0.7402] [one_class_mean best 0.7402] \n",
      "0.7402\t0.7402\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.08\n",
    "# blur_sigma  : 1.5\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --blur_sigma 1.5 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist0.5/blur/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_resize_factor0.08_color_dist0.5_blur_sigma1.5_one_class_0/last.model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "a9c1c45f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-compute global statistics...\n",
      "axis size: 3527 3527 3527 3527\n",
      "weight_sim:\t0.0077\t0.0092\t0.0076\t0.0081\n",
      "weight_shi:\t-0.2259\t0.4304\t0.6270\t0.7623\n",
      "Pre-compute features...\n",
      "Compute OOD scores... (score: CSI)\n",
      "One_class_real_mean: 0.36727255694305183\n",
      "CNMC               1.9613 +- 0.2117    q0: 1.4830    q10: 1.7342    q20: 1.7989    q30: 1.8500    q40: 1.8941    q50: 1.9317    q60: 1.9759    q70: 2.0278    q80: 2.0943    q90: 2.2144    q100: 3.2689\n",
      "one_class_1        2.0967 +- 0.3131    q0: 1.5468    q10: 1.7976    q20: 1.8621    q30: 1.9190    q40: 1.9608    q50: 2.0266    q60: 2.0831    q70: 2.1520    q80: 2.2654    q90: 2.5291    q100: 3.5407\n",
      "[one_class_1 CSI 0.3673] [one_class_1 best 0.3673] \n",
      "[one_class_mean CSI 0.3673] [one_class_mean best 0.3673] \n",
      "0.3673\t0.3673\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset     : CNMC\n",
    "# res         : 450px\n",
    "# id_class    : all\n",
    "# epoch       : 100\n",
    "# shift_tr    : blur\n",
    "# crop        : 0.08\n",
    "# blur_sigma  : 1.0\n",
    "# color_dist  : 0.5\n",
    "!CUDA_VISIBLE_DEVICES=0 python3 \"eval.py\" --color_distort 0.5 --resize_factor 0.08 --blur_sigma 1.0 --mode ood_pre --dataset CNMC --model resnet18_imagenet --ood_score CSI --shift_trans_type blur --print_score --save_score --ood_samples 10 --resize_fix --one_class_idx 0 --load_path \"logs/id_all/color_dist0.5/blur/CNMC_resnet18_imagenet_unsup_simclr_CSI_450px_shift_blur_resize_factor0.08_color_dist0.5_blur_sigma1.0_one_class_0/last.model\""
   ]
  }
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