{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/boehlke/programs/anaconda3/envs/ava-test4/lib/python3.8/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np \n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "import glob\n",
    "import pickle as pkl\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "import scipy\n",
    "import torch\n",
    "import io\n",
    "from scipy import signal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting results\n",
    "With this notebook I looked at the results in more detail, how many images were selcted from each class for rehearsal, how many images per class were selected by the memory filling strategies or how class error and class frequency based methods compare at each evaluation step. Unfortunately I did not have the time to organise it properly but maybe something usefull can be found here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CPU_Unpickler(pkl.Unpickler):\n",
    "    def find_class(self, module, name):\n",
    "        if module == 'torch.storage' and name == '_load_from_bytes':\n",
    "            return lambda b: torch.load(io.BytesIO(b), map_location='cpu')\n",
    "        else:\n",
    "            return super().find_class(module, name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def flattened(list_w_sublists):\n",
    "    flattened = []\n",
    "    for item in list_w_sublists:\n",
    "        if  isinstance(item, list):\n",
    "            for val in item:\n",
    "                flattened.append(val)\n",
    "        else:\n",
    "            flattened.append(item)\n",
    "    return flattened"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_items([(1, 'Bird'), (7, 'Domestic Cattle'), (5, 'Eurasian Lynx'), (6, 'Eurasian Red Squirrel'), (8, 'European Hare'), (9, 'Pine Marten'), (10, 'Red Deer'), (2, 'Red Fox'), (4, 'Roe Deer'), (0, 'Undefined Mouse'), (3, 'Wild Boar-Domestic Pig-Hybrid')])\n"
     ]
    }
   ],
   "source": [
    "label_dict_path = \"/home/boehlke/AMMOD/cam_trap_classification/data/csv_files/BIRDS_11_Species.pkl\"\n",
    "\n",
    "with open(label_dict_path, 'rb') as p:\n",
    "    BW_label_dict = pkl.load(p)\n",
    "print(BW_label_dict.items())\n",
    "class_names_ordered = ['Mouse', 'Bird','Fox' , 'Boar', 'Roe Deer', 'Lynx','Squirrel','Cattle',  'Hare', 'Marten', 'Red Deer']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Mouse',\n",
       " 'Bird',\n",
       " 'Fox',\n",
       " 'Boar',\n",
       " 'Roe Deer',\n",
       " 'Lynx',\n",
       " 'Squirrel',\n",
       " 'Cattle',\n",
       " 'Hare',\n",
       " 'Marten',\n",
       " 'Red Deer']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "['Mouse', 'Bird','Fox' , 'Boar', 'Roe Deer', 'Lynx','Squirrel','Cattle',  'Hare', 'Marten', 'Red Deer']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_train_number():\n",
    "    results_root ='/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/'\n",
    "    strategy = 'rehersal_ce_dataratio8.0_ep3_ne*/'\n",
    "    test_doct = '*exp26*_test_confusion_data_dict.pkl'\n",
    "    dr = random.choice(['2.0','8.0'])\n",
    "    ep = random.choice(['3', '5'])\n",
    "    strategy = 'rehersal_ce_dataratio'+dr+'_ep'+ep+'_ne5/'\n",
    "    cv = random.choice(['0', '1', '2', '3', '4'])\n",
    "    cv_split = 'cv'+cv+'_expsize128_crop/'\n",
    "    strategies = ['ce', 'cf', 'rr']\n",
    "    results_dict = {}\n",
    "    for s in strategies:\n",
    "        strategy = 'rehersal_'+s+'_dataratio'+dr+'_ep'+ep+'_ne*/'\n",
    "        res_pkl = glob.glob(results_root+cv_split+strategy+'*exp26*_test_confusion_data_dict.pkl')\n",
    "        if len(res_pkl)>1:\n",
    "            exp_eval_list =[]\n",
    "            for i in res_pkl:\n",
    "                exp_eval = int(i.split('_after_exp')[-1].split('_')[0])\n",
    "                exp_eval_list.append(exp_eval)\n",
    "            last_eval_exp = np.argmax(np.array(exp_eval_list))\n",
    "            res_pkl = [res_pkl[last_eval_exp]]\n",
    "        with open(res_pkl[0], 'rb') as handle:\n",
    "            result_data = pkl.load(handle)\n",
    "        results_dict[s] = result_data['total_train_cls_dict']\n",
    "    results_dict['u'] = result_data['unique_train_cls_dict']\n",
    "    u = []\n",
    "    for i , nr in results_dict['u'].items():\n",
    "        u.append(nr.item())\n",
    "    u = np.array(u)\n",
    "    cf = []\n",
    "    for i , nr in results_dict['cf'].items():\n",
    "        cf.append(nr.item())\n",
    "    cf = np.array(cf)\n",
    "\n",
    "    ce = []\n",
    "    for i , nr in results_dict['ce'].items():\n",
    "        ce.append(nr.item())\n",
    "    ce = np.array(ce)\n",
    "    rr = []\n",
    "    for i , nr in results_dict['rr'].items():\n",
    "        rr.append(nr.item())\n",
    "    rr = np.array(rr)\n",
    "    title = cv_split+': _dataratio'+dr+'_ep'+ep\n",
    "    return ce, cf, rr, u, title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_rehersal_cbrs_memory_by_class(results_path, strategy, cv):\n",
    "    cv_split = '/cv'+cv+'_expsize128_crop/'\n",
    "    #strategy = 'rehersal_'+'stdrs_memsize_'+'0.25_'+strategy+'_dataratio'+dr+'_ep'+ep+'_ne*/'\n",
    "    #strategy =  'rehersal_'+strategy+'_dataratio'+dr+'_ep'+ep+'_ne*/'\n",
    "    res_pkl = glob.glob(results_path+cv_split+strategy+'*exp26*_test_confusion_data_dict.pkl')\n",
    "    print(results_path+cv_split+strategy+'*exp*_test_confusion_data_dict.pkl')\n",
    "    print(res_pkl)\n",
    "    if len(res_pkl)>1:\n",
    "        exp_eval_list =[]\n",
    "        for i in res_pkl:\n",
    "            exp_eval = int(i.split('_after_exp')[-1].split('_')[0])\n",
    "            exp_eval_list.append(exp_eval)\n",
    "        last_eval_exp = np.argmax(np.array(exp_eval_list))\n",
    "        print(res_pkl.shape)\n",
    "        res_pkl = res_pkl[last_eval_exp]\n",
    "        print(last_eval_exp)\n",
    "        print(res_pkl[0])\n",
    "    with open(res_pkl[0], 'rb') as handle:\n",
    "        #result_data = torch.load(res_pkl[0],map_location ='cpu')\n",
    "        result_data =  CPU_Unpickler(handle).load()\n",
    "    result_dict = {}\n",
    "    result_dict[strategy] = result_data['total_train_cls_dict']\n",
    "    result_dict['u'] = result_data['unique_train_cls_dict']\n",
    "    result_dict['rehersal_idxs_picked'] = result_data['rehearsal_indicies_picked']\n",
    "    result_dict['memory_targets'] = result_data['memory_dataset_targets']\n",
    "    result_dict['memory_paths'] =result_data['memory_dataset_paths']\n",
    "    result_dict['cumulative_paths'] = np.array(result_data['cumulative_dataset_paths'])\n",
    "    \n",
    "    train_stream_path = '/home/boehlke/AMMOD/continual_learning/data/BayerWald-G_squares2/' + cv_split[:-1] + '_train_stream.pkl'\n",
    "    with open(train_stream_path, 'rb') as handle: \n",
    "        train_stream = pkl.load(handle)\n",
    "    train_stream_array = np.array(flattened(train_stream))\n",
    "    if 'cbrs' in strategy or 'stdrs' in strategy:\n",
    "        \n",
    "        \n",
    "        result_dict['memory_distribution'] = {}\n",
    "        for exp in range(0, len(train_stream)):\n",
    "            u, c = np.unique(result_dict['memory_targets'][exp], return_counts=True)\n",
    "            if c.shape[0] ==11:\n",
    "                result_dict['memory_distribution'][exp] = c\n",
    "            else: \n",
    "                c_non_full = np.zeros((11,))\n",
    "                for j, cls in enumerate(u):\n",
    "                    c_non_full[cls] = c[j]\n",
    "                result_dict['memory_distribution'][exp]   = c_non_full\n",
    "        \n",
    "       \n",
    "    else:\n",
    "        result_dict['rehearsal_set'] = {}\n",
    "        result_dict['exp_set'] = {}\n",
    "        for exp in result_dict['rehersal_idxs_picked'].keys():\n",
    "\n",
    "            if result_dict['rehersal_idxs_picked'][exp] is not None:\n",
    "\n",
    "                selected_set = result_dict['cumulative_paths'][np.array(result_dict['rehersal_idxs_picked'][exp])]\n",
    "                mask = np.isin(train_stream_array[:,0], selected_set)\n",
    "                train_data_subset = train_stream_array[mask,:]\n",
    "                rehersal_label = train_data_subset[:,1].astype(int)\n",
    "                u, c = np.unique(rehersal_label, return_counts=True)\n",
    "\n",
    "                if c.shape[0] ==11:\n",
    "                    result_dict['rehearsal_set'][exp] = c\n",
    "                else: \n",
    "                    c_non_full = np.zeros((11,))\n",
    "                    for j, cls in enumerate(u):\n",
    "                        c_non_full[cls] = c[j]\n",
    "                    result_dict['rehearsal_set'][exp]   = c_non_full\n",
    "\n",
    "        for i in range(int(result_dict['cumulative_paths'].shape[0]/128)):\n",
    "            exp_set = result_dict['cumulative_paths'][i*128:(i+1)*128]\n",
    "            mask = np.isin(train_stream_array[:,0], exp_set)\n",
    "            train_data_subset = train_stream_array[mask,:]\n",
    "            exp_label = train_data_subset[:,1].astype(int)\n",
    "            u, c = np.unique(exp_label, return_counts=True)\n",
    "            if c.shape[0] ==11:\n",
    "                result_dict['exp_set'][i] = c\n",
    "            else: \n",
    "                c_non_full = np.zeros((11,))\n",
    "                for j, cls in enumerate(u):\n",
    "                    c_non_full[cls] = c[j]\n",
    "                result_dict['exp_set'][i]   = c_non_full\n",
    "    return result_dict\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/boehlke_data/stdrs_results/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/rehersal_stdrs_memsize_0.25_shuffled_ce_dataratio8.0_ep5_ne*/*exp*_test_confusion_data_dict.pkl\n",
      "['/home/boehlke_data/stdrs_results/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/rehersal_stdrs_memsize_0.25_shuffled_ce_dataratio8.0_ep5_ne5/2022-08-09_11-10-13-23_after_exp266_test_confusion_data_dict.pkl']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-106-2d88545bfbad>:10: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
      "  fig, axs = plt.subplots(1,1)\n"
     ]
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   "source": [
    "cbrs_res_path = '/home/boehlke_data/stdrs_results/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128'\n",
    "ep ='5'\n",
    "dr = '8.0'\n",
    "rm = 'ce'\n",
    "shuffle = 'shuffled_'\n",
    "cv = '3'\n",
    "strategy = 'rehersal_'+'stdrs_memsize_'+'0.25_'+shuffle+rm+'_dataratio'+dr+'_ep'+ep+'_ne*/'\n",
    "result_dict = get_rehersal_cbrs_memory_by_class(results_path=cbrs_res_path, strategy=strategy, cv=cv)\n",
    "for i in range(0,260, 3):\n",
    "    fig, axs = plt.subplots(1,1)\n",
    "    fig.set_size_inches(22.5, 10.5)\n",
    "    classes = torch.unique(result_dict['memory_targets'][i], return_counts = True)[0]\n",
    "    unique = torch.unique(result_dict['memory_targets'][i], return_counts = True)[1]\n",
    "    \n",
    "    bar2 = axs.bar(classes, unique, width=width, align='center', color='#BB4430', label='exp')\n",
    "    #bar1 = axs.bar(np.arange(11), result_dict['rehearsal_set'][exp], width=width, align='center', bottom=result_dict['exp_set'][exp], color='#7C72A0', label='mem')\n",
    "\n",
    "    axs.bar_label(bar2, padding=3, fontsize=22)\n",
    "\n",
    "    axs.set_xticks(np.arange(0,11))\n",
    "    axs.set_xticklabels(fontsize=15,rotation= 'vertical',labels=['Mouse', 'Bird','Fox' , 'Boar', 'Roe Deer', 'Lynx','Squirrel','Cattle',  'Hare', 'Marten', 'Red Deer'])\n",
    "    axs.legend(fontsize=19)\n",
    "    axs.set_title(folder.split('_expsize')[0]+'\\n exp: '+str(exp), fontsize=22)\n",
    "    fig.savefig(videos_path+'stdrs/'+'exp_'+str(i)+'.png',facecolor='white')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/boehlke_data/fe_results/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv2_expsize128_crop/rehersal_fe_dataratio8.0_ep5_ne*/*exp*_test_confusion_data_dict.pkl\n",
      "['/home/boehlke_data/fe_results/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv2_expsize128_crop/rehersal_fe_dataratio8.0_ep5_ne100/2022-06-30_21-53-13-46_after_exp266_test_confusion_data_dict.pkl']\n"
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   "source": [
    "fe_res_path = '/home/boehlke_data/fe_results/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128'\n",
    " #strategy =  'rehersal_'+strategy+'_dataratio'+dr+'_ep'+ep+'_ne*/'\n",
    "cv='2' \n",
    "ep='5'\n",
    "dr='8.0'    \n",
    "shuffle=''\n",
    "rm ='fe'\n",
    "strategy =  'rehersal_'+shuffle+rm+'_dataratio'+dr+'_ep'+ep+'_ne*/'\n",
    "result_dict = get_rehersal_cbrs_memory_by_class(results_path=fe_res_path, strategy=strategy,cv=cv)\n",
    "for exp in range(9,260):\n",
    "    fig, axs = plt.subplots(1,1)\n",
    "    fig.set_size_inches(22.5, 10.5)\n",
    "    bar2 = axs.bar(np.arange(11), result_dict['exp_set'][exp], width=width, align='center', color='#BB4430', label='exp')\n",
    "    bar1 = axs.bar(np.arange(11), result_dict['rehearsal_set'][exp], width=width, align='center', bottom=result_dict['exp_set'][exp], color='#7C72A0', label='mem')\n",
    "\n",
    "    axs.bar_label(bar2, padding=3, fontsize=22)\n",
    "\n",
    "    axs.set_xticks(np.arange(0,11))\n",
    "    axs.set_xticklabels(fontsize=15,rotation= 'vertical',labels=['Mouse', 'Bird','Fox' , 'Boar', 'Roe Deer', 'Lynx','Squirrel','Cattle',  'Hare', 'Marten', 'Red Deer'])\n",
    "    axs.legend(fontsize=19)\n",
    "    axs.set_title(folder.split('_expsize')[0]+'\\n exp: '+str(exp), fontsize=22)\n",
    "    fig.savefig(videos_path+'fe_shares/'+'exp_'+str(exp)+'.png',facecolor='white')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_class_weights_during_training(dr= None, ep= None, cv= None, err=1, inv=1, rec=1, shuffle=''):\n",
    "    results_root_orig ='/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/'\n",
    "    results_root_temp ='/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/'\n",
    "    results_root_wce = '/home/boehlke/AMMOD/continual_learning/wce_results/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/'\n",
    "    #dr = random.choice(['2.0','8.0'])\n",
    "    #ep = random.choice(['3', '5'])\n",
    "\n",
    "    if dr is None:\n",
    "        dr = '8.0'\n",
    "    if ep is None:\n",
    "        ep= '3'\n",
    "    if cv is None:\n",
    "        cv = random.choice(['0','1', '2', '3'])\n",
    "    strategy = 'rehersal_shuffled_ce_dataratio'+str(dr)+'_ep'+str(ep)+'_ne5/'\n",
    "    cv_split = 'cv'+str(cv)+'_expsize128_crop/'\n",
    "    \n",
    "    gauss_12_sig3 = signal.gaussian(24, 3, sym=True)[-12:]\n",
    "    gauss_6_sig3 = signal.gaussian(12, 3, sym=True)[-6:]\n",
    "    gauss_6_sig15 = signal.gaussian(12, 1.5, sym=True)[-6:]\n",
    "    res_pkl = glob.glob(results_root_orig+cv_split+strategy+'*exp26*_validation_confusion_data_dict.pkl')\n",
    "\n",
    "    if len(res_pkl)>0:\n",
    "        exp_eval_list =[]\n",
    "        for i in res_pkl:\n",
    "            exp_eval = int(i.split('_after_exp')[-1].split('_')[0])\n",
    "            exp_eval_list.append(exp_eval)\n",
    "        last_eval_exp = np.argmax(np.array(exp_eval_list))\n",
    "        last_eval_exp = int(res_pkl[last_eval_exp].split('exp')[-1].split('_')[0])\n",
    "            \n",
    "            \n",
    "    if str(dr) == '8.0' and str(ep) =='3':\n",
    "        \n",
    "        strategies = ['ce', 'wce_navg6_sigma3.0', 'wce_navg12_sigma3.0', 'ce_temp0.1', 'ce_temp0.2', 'ce_temp0.3']\n",
    "    else:\n",
    "         strategies = ['ce', 'wce_navg6_sigma3.0', 'wce_navg12_sigma3.0']\n",
    "    \n",
    "    results_dict = {}\n",
    "    results_dict['alt_probs'] = {}\n",
    "    train_stream_path = '/home/boehlke/AMMOD/continual_learning/data/BayerWald-G_squares2/' + cv_split[:-1] + '_train_stream.pkl'\n",
    "\n",
    "    with open(train_stream_path, 'rb') as handle: \n",
    "        train_stream = pkl.load(handle)\n",
    "    train_stream_array = np.array(flattened(train_stream))\n",
    "\n",
    "    results_dict['cf'] ={}\n",
    "    results_dict['cls_count'] = {}\n",
    "    cls_count = np.zeros((11,))\n",
    "    results_dict['cf'][0] = np.zeros((11,))\n",
    "    \n",
    "    for i_exp, exp in enumerate(train_stream):\n",
    "        #print(i_exp)\n",
    "        exp_label = np.array(exp)[:,1]\n",
    "        \n",
    "        labels, counts = np.unique(exp_label, return_counts=True) \n",
    "        \n",
    "        for i, label in enumerate(labels):\n",
    "            \n",
    "            cls_count[int(label)] += counts[i]\n",
    "      \n",
    "        if i_exp%5 ==0 or i_exp == last_eval_exp:\n",
    "            cls_inv_freq = np.zeros((11,))\n",
    "            for i, count in enumerate(cls_count):\n",
    "                if count != 0:\n",
    "                    cls_inv_freq[i] = 1/count\n",
    "            cls_inv_freq = cls_inv_freq/np.sum(cls_inv_freq)\n",
    "            results_dict['cf'][i_exp] = cls_inv_freq.copy()\n",
    "\n",
    "        results_dict['cls_count'][i_exp] = cls_count.copy() \n",
    "\n",
    "    for s in strategies:\n",
    "        results_dict[s] = {}\n",
    "        strategy = 'rehersal_'+shuffle+s+'_dataratio'+str(dr)+'_ep'+str(ep)+'_ne*/'\n",
    "        if 'temp' not in s:\n",
    "            results_root = results_root_orig\n",
    "            if 'wce' in s:\n",
    "                results_root = results_root_wce\n",
    "        else:\n",
    "            results_root = results_root_temp\n",
    "        res_pkl = glob.glob(results_root+cv_split+strategy+'*exp26*_validation_confusion_data_dict.pkl')\n",
    "\n",
    "\n",
    "        for exp in np.concatenate((np.arange(5,len(train_stream)+1,5), np.array([last_eval_exp]))):\n",
    "\n",
    "            res_pkl = glob.glob(results_root+cv_split+strategy+'*exp'+str(exp)+'_validation_confusion_data_dict.pkl')\n",
    "\n",
    "            with open(res_pkl[0], 'rb') as handle:\n",
    "                result_data = CPU_Unpickler(handle).load()\n",
    "\n",
    "            accs = result_data['cls_acc_dict']\n",
    "\n",
    "            cls_acc = np.zeros((len(result_data['cls_acc_dict']),))\n",
    "            cls_err = np.zeros((len(result_data['cls_acc_dict']),))\n",
    "\n",
    "            for key, val in accs.items():\n",
    "                acc =val.result()[0]\n",
    "                if acc !=0 and results_dict['cls_count'][exp][key]>0:\n",
    "                    cls_err[key] = 1-acc\n",
    "                    cls_acc[key] = acc\n",
    "\n",
    "                elif acc ==0 and results_dict['cls_count'][exp][key]>0:\n",
    "                    cls_err[key] =1\n",
    "                    cls_acc[key] =0\n",
    "                else:   \n",
    "                    cls_err[key] = 0\n",
    "                    cls_acc[key] = 0\n",
    "            results_dict[s][exp] ={}\n",
    "            results_dict[s][exp]['cls_err'] = (cls_err/np.sum(cls_err)).copy()\n",
    "            results_dict[s][exp]['cls_acc'] = cls_acc.copy()\n",
    "            if exp == last_eval_exp:\n",
    "                rehearsal_idxs = result_data['rehearsal_indicies_picked']\n",
    "                for e in range(1,last_eval_exp+1):\n",
    "                    cls_in_exp = np.zeros((len(result_data['cls_acc_dict']),))\n",
    "                    cls_in_reh = np.zeros((len(result_data['cls_acc_dict']),))\n",
    "                    r_u = None\n",
    "\n",
    "                    e_data = np.array(train_stream[e])\n",
    "                    e_data_label = e_data[:,1]\n",
    "                    u, c = np.unique(e_data_label, return_counts=True)\n",
    "         \n",
    "                    rehearsal_data = rehearsal_idxs[e]\n",
    "                    if rehearsal_data is not None:\n",
    "                        rehearsal_data = np.array(rehearsal_data)\n",
    "                        rehearsal_label = train_stream_array[:,1][rehearsal_data].astype(int)\n",
    "                        r_u, r_c = np.unique(rehearsal_label, return_counts = True)\n",
    "                       \n",
    "                    else: \n",
    "                        \n",
    "                        rehearsal_label = train_stream_array[:128*(e+1),1].astype(int)\n",
    "                        r_u, r_c = np.unique(rehearsal_label, return_counts = True)\n",
    "                        \n",
    "                    if e not in results_dict[s].keys():\n",
    "                        results_dict[s][e] ={}\n",
    "                    for i, label in enumerate(u.astype(int)):\n",
    "                        cls_in_exp[label] = c[i]\n",
    "                    cls_in_exp = cls_in_exp/np.sum(cls_in_exp)\n",
    "\n",
    "                    results_dict[s][e]['exp_cls_count'] = cls_in_exp.copy()\n",
    "                    \n",
    "                    if r_u is not None:\n",
    "                        for i, label in enumerate(r_u):\n",
    "                            cls_in_reh[label] = r_c[i]\n",
    "                        cls_in_reh = cls_in_reh/np.sum(cls_in_reh)\n",
    "                        results_dict[s][e]['reh_cls_count'] = cls_in_reh.copy()\n",
    "                    if e%5 ==0 or e== last_eval_exp:\n",
    "                        last_6_accs = np.zeros((6, 11))\n",
    "                        last_12_accs = np.zeros((12, 11))\n",
    "                        for n_i in range(6):\n",
    "                            if e-5*n_i in results_dict[s].keys():\n",
    "                                last_6_accs[n_i,:] = results_dict[s][e-5*n_i]['cls_err']\n",
    "                        for n_i in range(12):\n",
    "                             if e-5*n_i in results_dict[s].keys():\n",
    "                                last_12_accs[n_i,:] = results_dict[s][e-5*n_i]['cls_err']\n",
    "                        last_6_sig15 = np.mean((gauss_6_sig15*last_6_accs.T).T, axis=0)\n",
    "                        last_6_sig3 =np.mean( (gauss_6_sig3*last_6_accs.T).T, axis=0)\n",
    "                        last_12_sig3 = np.mean((gauss_12_sig3*last_12_accs.T).T, axis=0)\n",
    "                        last_6_sig15 = last_6_sig15/np.sum(last_6_sig15)\n",
    "                        last_6_sig3 = last_6_sig3/np.sum(last_6_sig3)\n",
    "                        last_12_sig3 =  last_12_sig3/np.sum(last_12_sig3)\n",
    "                        recent_data = results_dict[s][e]['reh_cls_count']+results_dict[s][e]['exp_cls_count']\n",
    "                        recent_data = np.nan_to_num(recent_data)\n",
    "                        recent_data = recent_data/np.sum(recent_data)\n",
    "\n",
    "                        recent_data_to_total_stream = recent_data\n",
    "                        recent_data_to_total_stream = recent_data/(results_dict['cls_count'][e]/np.sum(results_dict['cls_count'][e]))\n",
    "                        \n",
    "                        recent_data_to_total_stream = np.nan_to_num(recent_data_to_total_stream)\n",
    "                        recent_data_to_total_stream = recent_data_to_total_stream/np.sum(recent_data_to_total_stream)\n",
    "                        recent_data = recent_data_to_total_stream                \n",
    "                        alternative_probs = (rec*recent_data+err*results_dict[s][e]['cls_err']+inv*results_dict['cf'][e])\n",
    "                        alternative_probs = alternative_probs/np.sum(alternative_probs)\n",
    "                        results_dict[s][e]['cefa'] = alternative_probs.copy()\n",
    "                        results_dict[s][e]['last_6_sig15'] = last_6_sig15.copy()\n",
    "                        results_dict[s][e]['last_6_sig3'] = last_6_sig3.copy()\n",
    "                        results_dict[s][e]['last_12_sig3'] = last_12_sig3.copy()\n",
    "            res_pkl = glob.glob(results_root+cv_split+strategy+'*exp'+str(last_eval_exp)+'_test_confusion_data_dict.pkl')\n",
    "\n",
    "            with open(res_pkl[0], 'rb') as handle:\n",
    "                    result_data = CPU_Unpickler(handle).load()\n",
    "            accs = result_data['cls_acc_dict']\n",
    "            cls_acc = np.zeros((len(result_data['cls_acc_dict']),))\n",
    "\n",
    "            for key, val in accs.items():\n",
    "                cls_acc[key] = val.result()[0]\n",
    "            results_dict[s]['test_cls_accs'] = cls_acc\n",
    "            \n",
    "\n",
    "        \n",
    "\n",
    "    strategy = 'rehersal_cf_dataratio'+str(dr)+'_ep'+str(ep)+'_ne*/'\n",
    "    res_pkl = glob.glob(results_root_orig+cv_split+strategy+'*exp'+str(last_eval_exp)+'_test_confusion_data_dict.pkl')\n",
    "    with open(res_pkl[0], 'rb') as handle:\n",
    "                    result_data = CPU_Unpickler(handle).load()\n",
    "    accs = result_data['cls_acc_dict']\n",
    "    cls_acc = np.zeros((len(result_data['cls_acc_dict']),))\n",
    "\n",
    "    for key, val in accs.items():\n",
    "        cls_acc[key] = val.result()[0]\n",
    "    results_dict['cf']['test_cls_accs'] = cls_acc\n",
    "    return results_dict\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-30-7781e0cb29d3>:163: RuntimeWarning: invalid value encountered in true_divide\n",
      "  recent_data_to_total_stream = recent_data/(results_dict['cls_count'][e]/np.sum(results_dict['cls_count'][e]))\n",
      "<ipython-input-36-0850d8ce025a>:25: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
      "  fig, axs = plt.subplots(1,1)\n"
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   ],
   "source": [
    "videos_path = '/home/boehlke/AMMOD/continual_learning/explaining_results/cls_share_investigations/'\n",
    "method = 'ce' #ce_temp0.2\n",
    "width = 0.1\n",
    "set_off2 = width*2+width/2\n",
    "set_off1 =width+width/2\n",
    "set_off0 = width/2\n",
    "err=1\n",
    "inv=1\n",
    "cv =1\n",
    "rec=0\n",
    "for cv in [0]:\n",
    "    for ep in ['3']:\n",
    "        for dr in ['8.0']:\n",
    "            results_dict = get_class_weights_during_training(dr=dr, ep=ep, cv=cv, err=err, rec=rec, inv=inv)\n",
    "            exps = results_dict['cf'].keys()\n",
    "            max_exp = np.max(list(exps)[:-1])\n",
    "\n",
    "            title = 'err'+str(err)+'_rec'+str(rec)+'_inv'+str(inv)\n",
    "            folder = 'cv'+str(cv)+'dr_'+str(dr)+'_ep'+str(ep)+'_expsize128_crop/'\n",
    "            \n",
    "            if not os.path.isdir(videos_path+title+'_'+folder):\n",
    "                os.mkdir('/home/boehlke/AMMOD/continual_learning/explaining_results/cls_share_investigations/'+title+'_'+folder)\n",
    "            for exp in range(5,max_exp,5):\n",
    "                \n",
    "                fig, axs = plt.subplots(1,1)\n",
    "                fig.set_size_inches(22.5, 10.5)\n",
    "\n",
    "   \n",
    "                bar1 = axs.bar(np.arange(11)-set_off2, results_dict['cls_count'][exp]/np.sum(results_dict['cls_count'][exp]),width=width, align='center', color='#B4869F')\n",
    "                bar2 = axs.bar(np.arange(11)-set_off1, results_dict['cf'][exp]/np.sum(results_dict['cf'][exp]), width=width, align='center', color='#985F6F')\n",
    "                #recent_data_1 = (results_dict[method][exp-1]['reh_cls_count']+results_dict[method][exp-1]['reh_cls_count'])/np.sum(results_dict[method][exp-1]['reh_cls_count']+results_dict[method][exp-1]['reh_cls_count'])\n",
    "                #bar4 = axs.bar(np.arange(11)-set_off0, recent_data_1, width=width, align='center', color='#F3CE57')\n",
    "                #recent_data = (results_dict[method][exp]['reh_cls_count']+results_dict[method][exp]['reh_cls_count'])/np.sum(results_dict[method][exp]['reh_cls_count']+results_dict[method][exp]['reh_cls_count'])\n",
    "                #bar5 = axs.bar(np.arange(11)+set_off0, recent_data, width=width, align='center', color='#EFBD1A')\n",
    "                bar3 = axs.bar(np.arange(11)-set_off0,  results_dict[method][exp]['cls_acc'], width=width, align='center', color='#81C3D7')\n",
    "                bar4 = axs.bar(np.arange(11)+set_off0, results_dict[method][exp]['cls_err']/np.sum(results_dict[method][exp]['cls_err']), width=width, align='center', color='#3A7CA5')\n",
    "                bar5 = axs.bar(np.arange(11)+set_off1, results_dict['wce_navg12_sigma3.0'][exp]['cls_acc'], width=width, align='center', color='#F3AC50')\n",
    "\n",
    "                #bar5 = axs.bar(np.arange(11)+set_off1, results_dict[method][exp]['last_12_sig3'], width=width, align='center', color='#F3CE57')\n",
    "                bar6 = axs.bar(np.arange(11)+set_off2, results_dict[method][exp]['last_12_sig3'], width=width, align='center', color='#CB5F0C')\n",
    "                #bar7 = axs.bar(np.arange(11)+set_off2+width/2, results_dict[method][exp]['cefa'], width=width, align='center', fill=False, edgecolor='#F24236')\n",
    "                if exp+5==max_exp:\n",
    "                    bar8 = axs.bar(np.arange(11)-set_off2,  results_dict['cf']['test_cls_accs'], width=width, align='center', fill=False, edgecolor='#007D16')\n",
    "                    bar9 = axs.bar(np.arange(11)-set_off1,  results_dict[method]['test_cls_accs'], width=width, align='center', fill=False, edgecolor='#007D16')\n",
    "                    bar10 = axs.bar(np.arange(11)+set_off1,  results_dict['wce_navg12_sigma3.0']['test_cls_accs'], width=width, align='center', fill=False, edgecolor='#007D16')\n",
    "                axs.set_ylim([0, 1])\n",
    "                axs.legend((bar1[0], bar2[0], bar3[0], bar4[0], bar5[0], bar6[0]), ('class share', 'class freq','class acc (ce)', 'cls err (ce)', 'class acc (wce)', 'class err (wce)') , fontsize=16, loc='upper center')\n",
    "                \n",
    "                axs.set_title(title+'\\n'+ folder.split('_expsize')[0]+'\\n exp: '+str(exp), fontsize=22)\n",
    "                fig.savefig(videos_path+folder+'exp_'+str(exp)+'.png',facecolor='white')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_ce_temp0.3_dataratio8.0_ep3_ne*/*exp250_validation_confusion_data_dict.pkl\n",
      "res_pkl: \n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_ce_temp0.3_dataratio8.0_ep3_ne*/*exp255_validation_confusion_data_dict.pkl\n",
      "res_pkl: \n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_ce_temp0.3_dataratio8.0_ep3_ne*/*exp260_validation_confusion_data_dict.pkl\n",
      "res_pkl: \n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_ce_temp0.3_dataratio8.0_ep3_ne*/*exp260_validation_confusion_data_dict.pkl\n",
      "260\n",
      "dict_keys([0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, 200, 205, 210, 215, 220, 225, 230, 235, 240, 245, 250, 255, 260, 'test_cls_accs'])\n"
     ]
    },
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     "text": [
      "<ipython-input-112-e152c4289169>:22: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
      "  fig, axs = plt.subplots(1,1)\n"
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   ],
   "source": [
    "videos_path = '/home/boehlke/AMMOD/continual_learning/explaining_results/cls_share_investigations/'\n",
    "\n",
    "width = 0.12\n",
    "set_off2 = width*2+width/2\n",
    "set_off1 =width+width/2\n",
    "set_off0 = width/2\n",
    "cv = 0\n",
    "ep = '3'\n",
    "dr = '8.0'\n",
    "temp  = '0.3'\n",
    "\n",
    "results_dict = get_class_weights_during_training(dr=dr, ep=ep, cv=cv)\n",
    "exps = results_dict['cf'].keys()\n",
    "max_exp = np.max(list(exps)[:-1])\n",
    "print(max_exp)\n",
    "print(exps)\n",
    "\n",
    "folder = 'cv'+str(cv)+'dr_'+str(dr)+'_ep'+str(ep)+'_temp'+str(temp)+'_expsize128_crop/'\n",
    "if not os.path.isdir(videos_path+folder):\n",
    "    os.mkdir('/home/boehlke/AMMOD/continual_learning/explaining_results/cls_share_investigations/'+folder)\n",
    "for exp in range(10,max_exp,5):\n",
    "    fig, axs = plt.subplots(1,1)\n",
    "    fig.set_size_inches(22.5, 10.5)\n",
    "\n",
    "    bar1 = axs.bar(np.arange(11)-set_off2, results_dict['cls_count'][exp]/np.sum(results_dict['cls_count'][exp]),width=width, align='center', color='#B4869F')\n",
    "    bar2 = axs.bar(np.arange(11)-set_off1, results_dict['cf'][exp], width=width, align='center', color='#985F6F')\n",
    "    bar3 = axs.bar(np.arange(11)-set_off0,  results_dict['ce'][exp]['cls_acc'], width=width, align='center', color='#81C3D7')\n",
    "    bar4 = axs.bar(np.arange(11)+set_off0, results_dict['ce'][exp]['cls_err'], width=width, align='center', color='#3A7CA5')\n",
    "    bar5 = axs.bar(np.arange(11)+set_off1, results_dict['ce_temp'+str(temp)][exp]['cls_acc'], width=width, align='center', color='#8CB369')\n",
    "    bar6 = axs.bar(np.arange(11)+set_off2,  results_dict['ce_temp'+str(temp)][exp]['cls_err'], width=width, align='center', color='#368156')\n",
    "    if exp+5==max_exp:\n",
    "        bar7 = axs.bar(np.arange(11)-set_off2,  results_dict['cf']['test_cls_accs'], width=width, align='center', fill=False, edgecolor='red')\n",
    "        bar8 = axs.bar(np.arange(11)-set_off0,  results_dict['ce']['test_cls_accs'], width=width, align='center', fill=False, edgecolor='red')\n",
    "        bar9 = axs.bar(np.arange(11)+set_off1,  results_dict['ce_temp'+str(temp)]['test_cls_accs'], width=width, align='center', fill=False, edgecolor='red')\n",
    "    axs.set_ylim([0, 1])\n",
    "    axs.set_xticks(np.arange(0,11))\n",
    "    axs.set_xticklabels(fontsize=15,rotation= 'vertical',labels=['Mouse', 'Bird','Fox' , 'Boar', 'Roe Deer', 'Lynx','Squirrel','Cattle',  'Hare', 'Marten', 'Red Deer'])\n",
    "    axs.legend((bar1[0], bar2[0], bar3[0], bar4[0], bar5[0], bar6[0]), ('cls share', 'inv freq','cls acc', 'cls err', 'cls acc t'+str(temp),  'cls err t'+str(temp)) , fontsize=16, loc='upper center')\n",
    "    axs.set_title(folder.split('_expsize')[0]+'\\n exp: '+str(exp), fontsize=22)\n",
    "    fig.savefig(videos_path+folder+'exp_'+str(exp)+'.png',facecolor='white')\n",
    "\n",
    "\n",
    "                    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1017,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.07034108 0.08667879 0.11811614 0.07000477 0.08032543 0.13704013\n",
      " 0.07212855 0.07590528 0.09959058 0.11366719 0.07620206]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f667ba3de50>"
      ]
     },
     "execution_count": 1017,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "[encoded data, 18372 characters]\n",
      "text/plain": [
       "<Figure size 1620x756 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tempA = 0.05\n",
    "tempB= 0.6\n",
    "tempC = 1.0\n",
    "temp_a= softmax_temp(results_dict['ce'][240]['cls_err'], temp = tempA)\n",
    "temp_b= softmax_temp(results_dict['ce'][240]['cls_err'], temp = tempB)\n",
    "temp_c = softmax_temp(results_dict['ce'][240]['cls_err'], temp=tempC)\n",
    "fig, axs = plt.subplots(1,1)\n",
    "print(temp03)\n",
    "fig.set_size_inches(22.5, 10.5)\n",
    "bar0 = axs.bar(np.arange(11)-set_off1, results_dict['ce'][240]['cls_err'],width=width, align='center', color='#B4869F')\n",
    "bar1 = axs.bar(np.arange(11)-set_off0, temp_a, width=width, align='center', color='#81C3D7')\n",
    "bar2 = axs.bar(np.arange(11)+set_off0, temp_b, width=width, align='center', color='#8CB369')\n",
    "bar3 = axs.bar(np.arange(11)+set_off1, temp_c, width=width, align='center', color='#EEAD34')\n",
    "axs.legend((bar0[0], bar1[0], bar2[0], bar3[0]), ('cls err', 'temp'+str(tempA),'temp'+str(tempB), 'temp'+str(tempC)) , fontsize=16, loc='upper center')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1000,
   "metadata": {},
   "outputs": [],
   "source": [
    "def softmax_temp(data, temp=0.3):\n",
    "    \n",
    "    data_exp = np.exp(np.array(data)/temp)\n",
    "    return data_exp/np.sum(data_exp)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_accuracies(strategy, root_res_path, dataset, stream_root_path):\n",
    "    strategy_path = root_res_path / dataset/ strategy\n",
    "    print(strategy_path)\n",
    "    strategy_path = glob.glob(str(strategy_path)+'*')\n",
    "    if strategy_path == []:\n",
    "        return None\n",
    "    strategy_path = Path(strategy_path[0])\n",
    "    csv_data_path = strategy_path / 'gen_csvlogs_*'\n",
    "\n",
    "    train_stream_path = stream_root_path + dataset + '_train_stream.pkl'\n",
    "    with open(train_stream_path, 'rb') as handle: \n",
    "        train_stream = pkl.load(handle)\n",
    "    nr_evaluations = len(train_stream) -len(train_stream)%5-1\n",
    "\n",
    "    if 'old' in str(csv_data_path):\n",
    "        return None\n",
    "    eval_stream_path = glob.glob(str(csv_data_path)+'/_test_stream.csv')\n",
    "    if eval_stream_path == []:\n",
    "        return None\n",
    "    else:\n",
    "\n",
    "        eval_stream = pd.read_csv(eval_stream_path[0], low_memory=False)\n",
    "        \n",
    "        if eval_stream['train_exp'].shape[0]>0:\n",
    "            \n",
    "            last_eval = eval_stream['train_exp'].iloc[-1 ]\n",
    "            if last_eval >= nr_evaluations:\n",
    "\n",
    "                top1_acc = eval_stream['Top_1Acc_Stream'].iloc[-1]\n",
    "                seq_acc = eval_stream['SeqTop_1Acc_Stream'].iloc[-1]\n",
    "                cls_avg_acc = eval_stream['ClassTop_1AvgAcc_Stream'].iloc[-1]\n",
    "                seq_cls_avg_acc = eval_stream['SeqClassTop_1AvgAcc_Stream'].iloc[-1]\n",
    "                seq_max_acc = np.NaN\n",
    "                seq_max_cls_avg_acc = np.NaN\n",
    "                if 'SeqMAXAcc_Stream' in eval_stream.keys():\n",
    "                    seq_max_acc = eval_stream['SeqMAXAcc_Stream'].iloc[-1]\n",
    "                    seq_max_cls_avg_acc = eval_stream['SeqClassAvgMAXAcc_Stream'].iloc[-1]\n",
    "                return top1_acc, seq_acc, cls_avg_acc, seq_cls_avg_acc, seq_max_acc, seq_max_cls_avg_acc\n",
    "            else:\n",
    "                print('last eval >nr evaluatiosn')\n",
    "                return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "naive_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/naive_ep5_ne\n",
      "naive_ep9_ne\n",
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      "naive_ep3_ne\n",
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      "naive_ep5_ne\n",
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      "naive_ep9_ne\n",
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      "cumulative_ep3_ne\n",
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      "cumulative_ep9_ne\n",
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      "cumulative_ep5_ne\n",
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      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.25_cf_temp0.2_dataratio2.0_ep3_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.25_cf_temp0.2_dataratio2.0_ep3_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cf_temp0.3_dataratio2.0_ep3_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cf_temp0.3_dataratio2.0_ep3_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cf_temp0.3_dataratio2.0_ep3_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cf_temp0.3_dataratio2.0_ep3_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.1_cf_temp0.3_dataratio2.0_ep3_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.1_cf_temp0.3_dataratio2.0_ep3_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.1_cf_temp0.3_dataratio2.0_ep3_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.1_cf_temp0.3_dataratio2.0_ep3_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.25_cf_temp0.3_dataratio2.0_ep3_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.25_cf_temp0.3_dataratio2.0_ep3_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.25_cf_temp0.3_dataratio2.0_ep3_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.25_cf_temp0.3_dataratio2.0_ep3_ne\n",
      "temp:  \n",
      "_cf_\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cf_dataratio2.0_ep5_ne\n",
      "temp:  \n",
      "_cf_\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cf_dataratio2.0_ep5_ne\n",
      "temp:  \n",
      "_cf_\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cf_dataratio2.0_ep5_ne\n",
      "temp:  \n",
      "_cf_\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cf_dataratio2.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.1_cf_dataratio2.0_ep5_ne\n",
      "last eval >nr evaluatiosn\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.1_cf_dataratio2.0_ep5_ne\n",
      "last eval >nr evaluatiosn\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.1_cf_dataratio2.0_ep5_ne\n",
      "last eval >nr evaluatiosn\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.1_cf_dataratio2.0_ep5_ne\n",
      "last eval >nr evaluatiosn\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.25_cf_dataratio2.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.25_cf_dataratio2.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.25_cf_dataratio2.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.25_cf_dataratio2.0_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cf_temp0.1_dataratio2.0_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cf_temp0.1_dataratio2.0_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cf_temp0.1_dataratio2.0_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cf_temp0.1_dataratio2.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.1_cf_temp0.1_dataratio2.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.1_cf_temp0.1_dataratio2.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.1_cf_temp0.1_dataratio2.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.1_cf_temp0.1_dataratio2.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.25_cf_temp0.1_dataratio2.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.25_cf_temp0.1_dataratio2.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.25_cf_temp0.1_dataratio2.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.25_cf_temp0.1_dataratio2.0_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cf_temp0.2_dataratio2.0_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cf_temp0.2_dataratio2.0_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cf_temp0.2_dataratio2.0_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cf_temp0.2_dataratio2.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.1_cf_temp0.2_dataratio2.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.1_cf_temp0.2_dataratio2.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.1_cf_temp0.2_dataratio2.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.1_cf_temp0.2_dataratio2.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.25_cf_temp0.2_dataratio2.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.25_cf_temp0.2_dataratio2.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.25_cf_temp0.2_dataratio2.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.25_cf_temp0.2_dataratio2.0_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cf_temp0.3_dataratio2.0_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cf_temp0.3_dataratio2.0_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cf_temp0.3_dataratio2.0_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cf_temp0.3_dataratio2.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.1_cf_temp0.3_dataratio2.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.1_cf_temp0.3_dataratio2.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.1_cf_temp0.3_dataratio2.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.1_cf_temp0.3_dataratio2.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.25_cf_temp0.3_dataratio2.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.25_cf_temp0.3_dataratio2.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.25_cf_temp0.3_dataratio2.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.25_cf_temp0.3_dataratio2.0_ep5_ne\n",
      "temp:  \n",
      "_cf_\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cf_dataratio8.0_ep3_ne\n",
      "temp:  \n",
      "_cf_\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cf_dataratio8.0_ep3_ne\n",
      "temp:  \n",
      "_cf_\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cf_dataratio8.0_ep3_ne\n",
      "temp:  \n",
      "_cf_\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cf_dataratio8.0_ep3_ne\n"
     ]
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     "text": [
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.1_cf_dataratio8.0_ep3_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.1_cf_dataratio8.0_ep3_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.1_cf_dataratio8.0_ep3_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.1_cf_dataratio8.0_ep3_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.25_cf_dataratio8.0_ep3_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.25_cf_dataratio8.0_ep3_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.25_cf_dataratio8.0_ep3_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.25_cf_dataratio8.0_ep3_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cf_temp0.1_dataratio8.0_ep3_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cf_temp0.1_dataratio8.0_ep3_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cf_temp0.1_dataratio8.0_ep3_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cf_temp0.1_dataratio8.0_ep3_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.1_cf_temp0.1_dataratio8.0_ep3_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.1_cf_temp0.1_dataratio8.0_ep3_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.1_cf_temp0.1_dataratio8.0_ep3_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.1_cf_temp0.1_dataratio8.0_ep3_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.25_cf_temp0.1_dataratio8.0_ep3_ne\n",
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      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_temp0.1_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_temp0.1_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_temp0.1_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_temp0.1_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_temp0.1_dataratio8.0_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_shuffled_cefa_temp0.2_dataratio8.0_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_shuffled_cefa_temp0.2_dataratio8.0_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_shuffled_cefa_temp0.2_dataratio8.0_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_shuffled_cefa_temp0.2_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_temp0.2_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_temp0.2_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_temp0.2_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_temp0.2_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_temp0.2_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_temp0.2_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_temp0.2_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_temp0.2_dataratio8.0_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_shuffled_cefa_temp0.3_dataratio8.0_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_shuffled_cefa_temp0.3_dataratio8.0_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_shuffled_cefa_temp0.3_dataratio8.0_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_shuffled_cefa_temp0.3_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_temp0.3_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_temp0.3_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_temp0.3_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_temp0.3_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_temp0.3_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_temp0.3_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_temp0.3_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_temp0.3_dataratio8.0_ep5_ne\n",
      "joint_ep3_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/joint_ep3_ne\n",
      "last eval >nr evaluatiosn\n",
      "joint_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/joint_ep5_ne\n",
      "joint_ep9_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/joint_ep9_ne\n",
      "last eval >nr evaluatiosn\n",
      "joint_ep3_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/joint_ep3_ne\n",
      "last eval >nr evaluatiosn\n",
      "joint_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/joint_ep5_ne\n",
      "joint_ep9_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv0_expsize128_crop/joint_ep9_ne\n",
      "last eval >nr evaluatiosn\n",
      "naive_ep3_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/naive_ep3_ne\n"
     ]
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      "naive_ep5_ne\n",
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      "naive_ep9_ne\n",
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      "naive_ep3_ne\n",
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      "naive_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/naive_ep5_ne\n",
      "naive_ep9_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/naive_ep9_ne\n",
      "cumulative_ep3_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/cumulative_ep3_ne\n",
      "cumulative_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/cumulative_ep5_ne\n",
      "cumulative_ep9_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/cumulative_ep9_ne\n",
      "cumulative_ep3_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/cumulative_ep3_ne\n",
      "cumulative_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/cumulative_ep5_ne\n",
      "cumulative_ep9_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/cumulative_ep9_ne\n",
      "temp:  \n",
      "_rr_\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_rr_dataratio2.0_ep3_ne\n",
      "temp:  \n",
      "_rr_\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_rr_dataratio2.0_ep3_ne\n",
      "temp:  \n",
      "_rr_\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_rr_dataratio2.0_ep3_ne\n",
      "temp:  \n",
      "_rr_\n",
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      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_shuffled_cefa_temp0.2_dataratio8.0_ep3_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_shuffled_cefa_temp0.2_dataratio8.0_ep3_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_temp0.2_dataratio8.0_ep3_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_temp0.2_dataratio8.0_ep3_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_temp0.2_dataratio8.0_ep3_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_temp0.2_dataratio8.0_ep3_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_temp0.2_dataratio8.0_ep3_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_temp0.2_dataratio8.0_ep3_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_temp0.2_dataratio8.0_ep3_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_temp0.2_dataratio8.0_ep3_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_shuffled_cefa_temp0.3_dataratio8.0_ep3_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_shuffled_cefa_temp0.3_dataratio8.0_ep3_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_shuffled_cefa_temp0.3_dataratio8.0_ep3_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_shuffled_cefa_temp0.3_dataratio8.0_ep3_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_temp0.3_dataratio8.0_ep3_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_temp0.3_dataratio8.0_ep3_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_temp0.3_dataratio8.0_ep3_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_temp0.3_dataratio8.0_ep3_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_temp0.3_dataratio8.0_ep3_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_temp0.3_dataratio8.0_ep3_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_temp0.3_dataratio8.0_ep3_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_temp0.3_dataratio8.0_ep3_ne\n",
      "temp:  \n",
      "_cefa_\n",
      "/home/boehlke/AMMOD/continual_learning/cefa_results/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_shuffled_cefa_dataratio8.0_ep5_ne\n",
      "temp:  \n",
      "_cefa_\n",
      "/home/boehlke/AMMOD/continual_learning/cefa_results/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_shuffled_cefa_dataratio8.0_ep5_ne\n",
      "temp:  \n",
      "_cefa_\n",
      "/home/boehlke/AMMOD/continual_learning/cefa_results/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_shuffled_cefa_dataratio8.0_ep5_ne\n",
      "temp:  \n",
      "_cefa_\n",
      "/home/boehlke/AMMOD/continual_learning/cefa_results/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_shuffled_cefa_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_dataratio8.0_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_shuffled_cefa_temp0.1_dataratio8.0_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_shuffled_cefa_temp0.1_dataratio8.0_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_shuffled_cefa_temp0.1_dataratio8.0_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_shuffled_cefa_temp0.1_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_temp0.1_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_temp0.1_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_temp0.1_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_temp0.1_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_temp0.1_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_temp0.1_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_temp0.1_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_temp0.1_dataratio8.0_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_shuffled_cefa_temp0.2_dataratio8.0_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_shuffled_cefa_temp0.2_dataratio8.0_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_shuffled_cefa_temp0.2_dataratio8.0_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_shuffled_cefa_temp0.2_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_temp0.2_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_temp0.2_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_temp0.2_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_temp0.2_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_temp0.2_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_temp0.2_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_temp0.2_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_temp0.2_dataratio8.0_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_shuffled_cefa_temp0.3_dataratio8.0_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_shuffled_cefa_temp0.3_dataratio8.0_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_shuffled_cefa_temp0.3_dataratio8.0_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_shuffled_cefa_temp0.3_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_temp0.3_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_temp0.3_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_temp0.3_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_temp0.3_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_temp0.3_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_temp0.3_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_temp0.3_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_temp0.3_dataratio8.0_ep5_ne\n",
      "joint_ep3_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/joint_ep3_ne\n",
      "last eval >nr evaluatiosn\n",
      "joint_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/joint_ep5_ne\n",
      "joint_ep9_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/joint_ep9_ne\n",
      "last eval >nr evaluatiosn\n",
      "joint_ep3_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/joint_ep3_ne\n",
      "last eval >nr evaluatiosn\n",
      "joint_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/joint_ep5_ne\n",
      "joint_ep9_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv1_expsize128_crop/joint_ep9_ne\n",
      "last eval >nr evaluatiosn\n",
      "naive_ep3_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv2_expsize128_crop/naive_ep3_ne\n",
      "naive_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv2_expsize128_crop/naive_ep5_ne\n",
      "last eval >nr evaluatiosn\n",
      "naive_ep9_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv2_expsize128_crop/naive_ep9_ne\n",
      "naive_ep3_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv2_expsize128_crop/naive_ep3_ne\n",
      "naive_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv2_expsize128_crop/naive_ep5_ne\n",
      "last eval >nr evaluatiosn\n",
      "naive_ep9_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv2_expsize128_crop/naive_ep9_ne\n",
      "cumulative_ep3_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv2_expsize128_crop/cumulative_ep3_ne\n",
      "cumulative_ep5_ne\n",
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      "cumulative_ep9_ne\n",
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      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv2_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_temp0.2_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv2_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_temp0.2_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv2_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_temp0.2_dataratio8.0_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv2_expsize128_crop/rehersal_shuffled_cefa_temp0.3_dataratio8.0_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv2_expsize128_crop/rehersal_shuffled_cefa_temp0.3_dataratio8.0_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv2_expsize128_crop/rehersal_shuffled_cefa_temp0.3_dataratio8.0_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv2_expsize128_crop/rehersal_shuffled_cefa_temp0.3_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv2_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_temp0.3_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv2_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_temp0.3_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv2_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_temp0.3_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv2_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_temp0.3_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv2_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_temp0.3_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv2_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_temp0.3_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv2_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_temp0.3_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv2_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_temp0.3_dataratio8.0_ep5_ne\n",
      "joint_ep3_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv2_expsize128_crop/joint_ep3_ne\n",
      "last eval >nr evaluatiosn\n",
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      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv2_expsize128_crop/joint_ep5_ne\n",
      "joint_ep9_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv2_expsize128_crop/joint_ep9_ne\n",
      "last eval >nr evaluatiosn\n",
      "joint_ep3_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv2_expsize128_crop/joint_ep3_ne\n",
      "last eval >nr evaluatiosn\n",
      "joint_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv2_expsize128_crop/joint_ep5_ne\n"
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      "joint_ep9_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv2_expsize128_crop/joint_ep9_ne\n",
      "last eval >nr evaluatiosn\n",
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      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/naive_ep3_ne\n",
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      "naive_ep3_ne\n",
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      "naive_ep5_ne\n",
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      "naive_ep9_ne\n",
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      "cumulative_ep3_ne\n",
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      "cumulative_ep5_ne\n",
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      "cumulative_ep9_ne\n",
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      "cumulative_ep3_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/cumulative_ep3_ne\n",
      "cumulative_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/cumulative_ep5_ne\n",
      "cumulative_ep9_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/cumulative_ep9_ne\n",
      "temp:  \n",
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      "temp:  \n",
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      "temp:  \n",
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      "temp:  \n",
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      "temp:  \n",
      "_cefa_\n",
      "/home/boehlke/AMMOD/continual_learning/cefa_results/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/rehersal_shuffled_cefa_dataratio8.0_ep5_ne\n",
      "temp:  \n",
      "_cefa_\n",
      "/home/boehlke/AMMOD/continual_learning/cefa_results/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/rehersal_shuffled_cefa_dataratio8.0_ep5_ne\n",
      "temp:  \n",
      "_cefa_\n",
      "/home/boehlke/AMMOD/continual_learning/cefa_results/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/rehersal_shuffled_cefa_dataratio8.0_ep5_ne\n",
      "temp:  \n",
      "_cefa_\n",
      "/home/boehlke/AMMOD/continual_learning/cefa_results/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/rehersal_shuffled_cefa_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_dataratio8.0_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/rehersal_shuffled_cefa_temp0.1_dataratio8.0_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/rehersal_shuffled_cefa_temp0.1_dataratio8.0_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/rehersal_shuffled_cefa_temp0.1_dataratio8.0_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/rehersal_shuffled_cefa_temp0.1_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_temp0.1_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_temp0.1_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_temp0.1_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_temp0.1_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_temp0.1_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_temp0.1_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_temp0.1_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_temp0.1_dataratio8.0_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/rehersal_shuffled_cefa_temp0.2_dataratio8.0_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/rehersal_shuffled_cefa_temp0.2_dataratio8.0_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/rehersal_shuffled_cefa_temp0.2_dataratio8.0_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/rehersal_shuffled_cefa_temp0.2_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_temp0.2_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_temp0.2_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_temp0.2_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_temp0.2_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_temp0.2_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_temp0.2_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_temp0.2_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_temp0.2_dataratio8.0_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/rehersal_shuffled_cefa_temp0.3_dataratio8.0_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/rehersal_shuffled_cefa_temp0.3_dataratio8.0_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/rehersal_shuffled_cefa_temp0.3_dataratio8.0_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/rehersal_shuffled_cefa_temp0.3_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_temp0.3_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_temp0.3_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_temp0.3_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/rehersal_cbrs_memsize_0.1_shuffled_cefa_temp0.3_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_temp0.3_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_temp0.3_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_temp0.3_dataratio8.0_ep5_ne\n",
      "/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/rehersal_cbrs_memsize_0.25_shuffled_cefa_temp0.3_dataratio8.0_ep5_ne\n",
      "joint_ep3_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/joint_ep3_ne\n",
      "last eval >nr evaluatiosn\n",
      "joint_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/joint_ep5_ne\n",
      "joint_ep9_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/joint_ep9_ne\n",
      "last eval >nr evaluatiosn\n",
      "joint_ep3_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/joint_ep3_ne\n",
      "last eval >nr evaluatiosn\n",
      "joint_ep5_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/joint_ep5_ne\n",
      "joint_ep9_ne\n",
      "/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128/cv3_expsize128_crop/joint_ep9_ne\n",
      "last eval >nr evaluatiosn\n"
     ]
    }
   ],
   "source": [
    "root_res_path_std = Path('/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128')\n",
    "root_res_path_temp = Path('/home/boehlke/AMMOD/continual_learning/basic_strategies_results_bayerwald_temp/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128')\n",
    "wce_res_path = Path('/home/boehlke/AMMOD/continual_learning/wce_results/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128')\n",
    "fe_res_path = Path('/home/boehlke/AMMOD/continual_learning/fe_results/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128')\n",
    "cefa_res_path = Path('/home/boehlke/AMMOD/continual_learning/cefa_results/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128')\n",
    "stream_root_path = '/home/boehlke/AMMOD/continual_learning/data/BayerWald-G_squares2/'\n",
    "cbrs_root_path = Path('/home/boehlke_data/BW_results_cbrs/resnet18_smoothloss_adamW_eps0.0001wd0.075_bs128')\n",
    "\n",
    "results_dict = {'128':{}, '384':{}}\n",
    "progression_dict ={}\n",
    "for cv_split in  ['0' ,'1', '2', '3']:\n",
    "    for exp_size in ['128']:\n",
    "        dataset =  'cv'+str(cv_split)+'_expsize'+str(exp_size)+'_crop'\n",
    "        for strategy in ['naive', 'cumulative', 'rehersal', 'joint']:\n",
    "            for shuffeled in ['', '_shuffled']:\n",
    "                if 'rehersal' in strategy:\n",
    "                    for selection in ['_rr_', '_ce_','_cf_', '_mir_', '_fe_', '_wce_', '_cefa_']:\n",
    "                            for dr in ['2.0', '8.0']:\n",
    "                                for epoch in ['3', '5']:\n",
    "                                    for temp in ['', '0.1', '0.2', '0.3']:\n",
    "                                        for mems in ['', '0.1', '0.25']:\n",
    "                                            for navg in ['6', '12']:\n",
    "                                                for sigma in ['1.5', '3.0']:\n",
    "                                                    if mems != '':\n",
    "                                                        root_res_path = cbrs_root_path \n",
    "                                                        if temp != '':\n",
    "                                                            strategy_name = 'rehersal_cbrs_memsize_'+mems+shuffeled+selection+'temp'+temp+'_dataratio'+dr+ '_ep'+epoch+'_ne'\n",
    "                                                        else:\n",
    "                                                            strategy_name = 'rehersal_cbrs_memsize_'+mems+shuffeled+selection+'dataratio'+dr+ '_ep'+epoch+'_ne'\n",
    "                                                    elif temp=='':\n",
    "                                                        print('temp: ', temp)\n",
    "                                                        print(selection)\n",
    "                                                        if selection in ['_fe_', '_wce_', '_cefa_']:\n",
    "                                                            if selection =='_fe_':\n",
    "                                                                if shuffeled == '':\n",
    "                                                                    continue\n",
    "                                                                root_res_path = fe_res_path\n",
    "                                                                strategy_name = 'rehersal'+shuffeled+selection+'dataratio'+dr+ '_ep'+epoch+'_ne'\n",
    "                                                            elif selection == '_wce_':\n",
    "                                                                root_res_path = wce_res_path\n",
    "                                                                strategy_name = 'rehersal'+shuffeled+selection+'navg'+navg+'_sigma'+sigma+'_dataratio'+dr+ '_ep'+epoch+'_ne'\n",
    "                                                            else: \n",
    "                                                                root_res_path = cefa_res_path\n",
    "                                                                strategy_name = 'rehersal'+shuffeled+selection+'dataratio'+dr+ '_ep'+epoch+'_ne'\n",
    "                                                        else:\n",
    "                                                            root_res_path = root_res_path_std\n",
    "                                                            strategy_name = 'rehersal'+shuffeled+selection+'dataratio'+dr+ '_ep'+epoch+'_ne'\n",
    "                                                    else: \n",
    "                                                        root_res_path = root_res_path_temp\n",
    "                                                        strategy_name = 'rehersal'+shuffeled+selection+'temp'+temp+'_dataratio'+dr+ '_ep'+epoch+'_ne'\n",
    "\n",
    "                                                    results  = get_accuracies(strategy_name, root_res_path, dataset, stream_root_path)\n",
    "\n",
    "                                                    if results is not None:\n",
    "                                                        top1_acc, seq_acc, cls_avg_acc, seq_cls_avg_acc , seq_max_acc, seq_max_cls_avg_acc = results\n",
    "                                                        if strategy_name in results_dict[exp_size].keys():\n",
    "                                                            results_dict[exp_size][strategy_name]['top1_acc'].append(top1_acc)\n",
    "                                                            results_dict[exp_size][strategy_name]['seq_acc'].append(seq_acc)\n",
    "                                                            results_dict[exp_size][strategy_name]['cls_avg_acc'].append(cls_avg_acc)\n",
    "                                                            results_dict[exp_size][strategy_name]['seq_cls_avg_acc'].append(seq_cls_avg_acc)\n",
    "                                                            results_dict[exp_size][strategy_name]['seq_max_acc'].append(seq_max_acc)\n",
    "                                                            results_dict[exp_size][strategy_name]['seq_max_cls_avg_acc'].append(seq_max_cls_avg_acc)\n",
    "                                                        else:\n",
    "                                                            results_dict[exp_size][strategy_name] = {'top1_acc':[top1_acc],\n",
    "                                                                                                'seq_acc':[seq_acc],\n",
    "                                                                                                'cls_avg_acc': [cls_avg_acc],\n",
    "                                                                                                'seq_cls_avg_acc':[seq_cls_avg_acc],\n",
    "                                                                                                'seq_max_acc': [seq_max_acc] ,    \n",
    "                                                                                                'seq_max_cls_avg_acc':[seq_max_cls_avg_acc]}\n",
    "                else:\n",
    "                    for epoch in ['3', '5', '9']:\n",
    "                        \n",
    "                        strategy_name = strategy+ '_ep'+epoch+'_ne' \n",
    "                        print(strategy_name)\n",
    "                        root_res_path = root_res_path_std\n",
    "                        results  = get_accuracies(strategy_name, root_res_path, dataset, stream_root_path)\n",
    "                        if results is not None:\n",
    "                            top1_acc, seq_acc, cls_avg_acc, seq_cls_avg_acc , seq_max_acc, seq_max_cls_avg_acc = results\n",
    "                            if strategy_name in results_dict[exp_size].keys():\n",
    "                                results_dict[exp_size][strategy_name]['top1_acc'].append(top1_acc)\n",
    "                                results_dict[exp_size][strategy_name]['seq_acc'].append(seq_acc)\n",
    "                                results_dict[exp_size][strategy_name]['cls_avg_acc'].append(cls_avg_acc)\n",
    "                                results_dict[exp_size][strategy_name]['seq_cls_avg_acc'].append(seq_cls_avg_acc)\n",
    "                                results_dict[exp_size][strategy_name]['seq_max_acc'].append(seq_max_acc)\n",
    "                                results_dict[exp_size][strategy_name]['seq_max_cls_avg_acc'].append(seq_max_cls_avg_acc)\n",
    "                            else:\n",
    "                                results_dict[exp_size][strategy_name] = {'top1_acc':[top1_acc],\n",
    "                                                                    'seq_acc':[seq_acc],\n",
    "                                                                    'cls_avg_acc': [cls_avg_acc],\n",
    "                                                                    'seq_cls_avg_acc':[seq_cls_avg_acc],\n",
    "                                                                    'seq_max_acc': [seq_max_acc],     \n",
    "                                                                    'seq_max_cls_avg_acc':[seq_max_cls_avg_acc]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['naive_ep3_ne', 'naive_ep5_ne', 'rehersal_rr_dataratio2.0_ep3_ne', 'rehersal_rr_dataratio2.0_ep5_ne', 'rehersal_cbrs_memsize_0.1_rr_dataratio2.0_ep5_ne', 'rehersal_cbrs_memsize_0.25_rr_dataratio2.0_ep5_ne', 'rehersal_rr_dataratio8.0_ep3_ne', 'rehersal_rr_dataratio8.0_ep5_ne', 'rehersal_cbrs_memsize_0.1_rr_dataratio8.0_ep5_ne', 'rehersal_cbrs_memsize_0.25_rr_dataratio8.0_ep5_ne', 'rehersal_ce_dataratio2.0_ep3_ne', 'rehersal_ce_dataratio2.0_ep5_ne', 'rehersal_cbrs_memsize_0.1_ce_dataratio2.0_ep5_ne', 'rehersal_cbrs_memsize_0.25_ce_dataratio2.0_ep5_ne', 'rehersal_ce_dataratio8.0_ep3_ne', 'rehersal_ce_temp0.1_dataratio8.0_ep3_ne', 'rehersal_ce_temp0.2_dataratio8.0_ep3_ne', 'rehersal_ce_temp0.3_dataratio8.0_ep3_ne', 'rehersal_ce_dataratio8.0_ep5_ne', 'rehersal_cbrs_memsize_0.1_ce_dataratio8.0_ep5_ne', 'rehersal_cbrs_memsize_0.25_ce_dataratio8.0_ep5_ne', 'rehersal_cf_dataratio2.0_ep3_ne', 'rehersal_cf_dataratio2.0_ep5_ne', 'rehersal_cbrs_memsize_0.25_cf_dataratio2.0_ep5_ne', 'rehersal_cf_dataratio8.0_ep3_ne', 'rehersal_cf_dataratio8.0_ep5_ne', 'rehersal_cbrs_memsize_0.1_cf_dataratio8.0_ep5_ne', 'rehersal_cbrs_memsize_0.25_cf_dataratio8.0_ep5_ne', 'rehersal_mir_dataratio2.0_ep3_ne', 'rehersal_mir_dataratio2.0_ep5_ne', 'rehersal_mir_dataratio8.0_ep3_ne', 'rehersal_mir_dataratio8.0_ep5_ne', 'rehersal_wce_navg6_sigma3.0_dataratio8.0_ep3_ne', 'rehersal_wce_navg12_sigma3.0_dataratio8.0_ep3_ne', 'rehersal_cefa_dataratio8.0_ep3_ne', 'rehersal_cefa_dataratio8.0_ep5_ne', 'rehersal_shuffled_rr_dataratio2.0_ep3_ne', 'rehersal_shuffled_rr_dataratio2.0_ep5_ne', 'rehersal_cbrs_memsize_0.1_shuffled_rr_dataratio2.0_ep5_ne', 'rehersal_cbrs_memsize_0.25_shuffled_rr_dataratio2.0_ep5_ne', 'rehersal_shuffled_rr_dataratio8.0_ep3_ne', 'rehersal_shuffled_rr_dataratio8.0_ep5_ne', 'rehersal_cbrs_memsize_0.1_shuffled_rr_dataratio8.0_ep5_ne', 'rehersal_cbrs_memsize_0.25_shuffled_rr_dataratio8.0_ep5_ne', 'rehersal_shuffled_ce_dataratio2.0_ep3_ne', 'rehersal_shuffled_ce_dataratio2.0_ep5_ne', 'rehersal_cbrs_memsize_0.1_shuffled_ce_dataratio2.0_ep5_ne', 'rehersal_cbrs_memsize_0.25_shuffled_ce_dataratio2.0_ep5_ne', 'rehersal_shuffled_ce_dataratio8.0_ep3_ne', 'rehersal_shuffled_ce_temp0.1_dataratio8.0_ep3_ne', 'rehersal_shuffled_ce_temp0.2_dataratio8.0_ep3_ne', 'rehersal_shuffled_ce_temp0.3_dataratio8.0_ep3_ne', 'rehersal_shuffled_ce_dataratio8.0_ep5_ne', 'rehersal_cbrs_memsize_0.1_shuffled_ce_dataratio8.0_ep5_ne', 'rehersal_cbrs_memsize_0.25_shuffled_ce_dataratio8.0_ep5_ne', 'rehersal_shuffled_cf_dataratio2.0_ep3_ne', 'rehersal_shuffled_cf_dataratio2.0_ep5_ne', 'rehersal_cbrs_memsize_0.1_shuffled_cf_dataratio2.0_ep5_ne', 'rehersal_cbrs_memsize_0.25_shuffled_cf_dataratio2.0_ep5_ne', 'rehersal_shuffled_cf_dataratio8.0_ep3_ne', 'rehersal_shuffled_cf_dataratio8.0_ep5_ne', 'rehersal_cbrs_memsize_0.1_shuffled_cf_dataratio8.0_ep5_ne', 'rehersal_cbrs_memsize_0.25_shuffled_cf_dataratio8.0_ep5_ne', 'rehersal_shuffled_mir_dataratio2.0_ep3_ne', 'rehersal_shuffled_mir_dataratio2.0_ep5_ne', 'rehersal_shuffled_mir_dataratio8.0_ep3_ne', 'rehersal_shuffled_mir_dataratio8.0_ep5_ne', 'rehersal_shuffled_fe_dataratio8.0_ep3_ne', 'rehersal_shuffled_fe_dataratio8.0_ep5_ne', 'rehersal_shuffled_wce_navg6_sigma1.5_dataratio8.0_ep3_ne', 'rehersal_shuffled_wce_navg6_sigma3.0_dataratio8.0_ep3_ne', 'rehersal_shuffled_wce_navg12_sigma3.0_dataratio8.0_ep3_ne', 'rehersal_shuffled_cefa_dataratio8.0_ep3_ne', 'rehersal_shuffled_cefa_dataratio8.0_ep5_ne', 'rehersal_cbrs_memsize_0.1_cf_dataratio2.0_ep5_ne', 'rehersal_ce_temp0.1_dataratio8.0_ep5_ne', 'rehersal_shuffled_ce_temp0.1_dataratio8.0_ep5_ne'])"
      ]
     },
     "execution_count": 354,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_dict['128'].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_res_array(experiments_type , header, metrics, eps =['3', '5']):\n",
    "    if header is None: \n",
    "        header = ['metric', 'epoch', 'ce2.0', 'ce8.0','cf2.0', 'cf8.0','rr2.0', 'rr8.0','mir2.0', 'mir8.0' ]\n",
    "    if metrics is None: \n",
    "        metrics = ['top1_acc', 'seq_acc', 'cls_avg_acc', 'seq_cls_avg_acc']\n",
    "    results_array = np.array(header).reshape((1, len(header)))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "experiments_type = 'rehersal_shuffled_'\n",
    "experiments_type1 = 'rehersal_'\n",
    "results_array = np.array(['metric', 'epoch', 'Shuffce8.0', 'ce8.0','Shuffcf8.0','cf8.0', 'Shuffrr8.0','rr8.0', 'Shuffmir8.0', 'mir8.0' ]).reshape((1,10))\n",
    "for metric in ['top1_acc', 'seq_acc', 'cls_avg_acc', 'seq_cls_avg_acc']:\n",
    "\n",
    "    for ep in ['5']:\n",
    "        new_row = np.array([metric, ep]).reshape((1,2))\n",
    "        ce2 = results_dict['128'][experiments_type1+'ce_dataratio8.0_ep'+str(ep)+'_ne'][metric]\n",
    "        ce8 = results_dict['128'][experiments_type+'ce_dataratio8.0_ep'+str(ep)+'_ne'][metric]\n",
    "        cf2 = results_dict['128'][experiments_type1+'cf_dataratio8.0_ep'+str(ep)+'_ne'][metric]\n",
    "        cf8 = results_dict['128'][experiments_type+'cf_dataratio8.0_ep'+str(ep)+'_ne'][metric]\n",
    "        rr2 = results_dict['128'][experiments_type1+'rr_dataratio8.0_ep'+str(ep)+'_ne'][metric]\n",
    "        rr8 = results_dict['128'][experiments_type+'rr_dataratio8.0_ep'+str(ep)+'_ne'][metric]\n",
    "        mir2 = results_dict['128'][experiments_type1+'mir_dataratio8.0_ep'+str(ep)+'_ne'][metric]\n",
    "        mir8 = results_dict['128'][experiments_type+'mir_dataratio8.0_ep'+str(ep)+'_ne'][metric]\n",
    "        ce2_mean = np.mean(ce2)\n",
    "        for val in [ce2, ce8, cf2, cf8, rr2, rr8, mir2, mir8]:\n",
    "        #for val in [ ce8, cf8, rr8, mir8]:\n",
    "            mean = np.round(np.mean(np.array(val)*100), 2)\n",
    "            std = np.round(np.std(np.array(val)*100),2)\n",
    "            res =np.array([ str(mean)+'±'+str(std)]).reshape((1,1))\n",
    "\n",
    "            new_row = np.concatenate([new_row, res], axis=1)\n",
    "      \n",
    "        results_array= np.concatenate([results_array, new_row], axis=0)\n",
    "results_df = pd.DataFrame(results_array[1:, :],columns=['metric', 'epoch', 'Shuffce8.0', 'ce8.0','Shuffcf8.0','cf8.0', 'Shuffrr8.0','rr8.0', 'Shuffmir8.0', 'mir8.0' ])\n",
    "\n",
    "results_df.to_csv('results_array_both_Caltech_val.csv')"
   ]
  }
 ],
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