saba.hashemi
/
Domain-Adaptation
Watch 1
Star 0
Fork 0
Code Issues 0 Pull Requests 0 Releases 0 Wiki Activity
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
2 Commits
1 Branch
Branch: master
Branches Tags
${ item.name }
Create branch ${ searchTerm }
from 'master'
${ noResults }
Domain-Adaptation/utils/experiment-saba-pytorch.ipynb

experiment-saba-pytorch.ipynb 861KB
Raw Permalink Blame History

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185186187188189190191192193194195196197198199200201202203204205206207208209210211212213214215216217218219220221222223224225226227228229230231232233234235236237238239240241242243244245246247248249250251252253254255256257258259260261262263264265266267268269270271272273274275276277278279280281282283284285286287288289290291292293294295296297298299300301302303304305306307308309310311312313314315316317318319320321322323324325326327328329330331332333334335336337338339340341342343344345346347348349350351352353354355356357358359360361362363364365366367368369370371372373374375376377378379380381382383384385386387388389390391392393394395396397398399400401402403404405406407408409410411412413414415416417418419420421422423424425426427428429430431432433434435436437438439440441442443444445446447448449450451452453454455456457458459460461462463464465466467468469470471472473474475476477478479480481482483484485486487488489490491492493494495496497498499500501502503504505506507508509510511512513514515516517518519520521522523524525526527528529530531532533534535536537538539540541542543544545546547548549550551552553554555556557558559560561562563564565566567568569570571572573574575576577578579580581582583584585586587588589590591592593594595596597598599600601602603604605606607608609610611612613614615616617618619620621622623624625626627628629630631632633634635636637638639640641642643644645646647648649650651652653654655656657658659660661662663664665666667668669670671672673674675676677678679680681682683684685686687688689690691692693694695696697698699700701702703704705706707708709710711712713714715716717718719720721722723724725726727728729730731732733734735736737738739740741742743744745746747748749750751752753754755756757758759760761762763764765766767768769770771772773774775776777778779780781782783784785786787788789790791792793794795796797798799800801802803804805806807808809810811812813814815816817818819820821822823824825826827828829830831832833834835836837838839840841842843844845846847848849850851852853854855856857858859860861862863864865866867868869870871872873874875876877878879880881882883884885886887888889890891892893894895896897898899900901902903904905906907908909910911912913914915916917918919920921922923924925926927928929930931932933934935936937938939940941942943944945946947948949950951952953954955956957958959960961962963964965966967968969970971972973974975976977978979980981982983984985986987988989990991992993994995996997998999100010011002100310041005100610071008100910101011101210131014101510161017101810191020102110221023102410251026102710281029103010311032103310341035103610371038103910401041104210431044104510461047104810491050105110521053105410551056105710581059106010611062106310641065106610671068106910701071107210731074107510761077107810791080108110821083108410851086108710881089109010911092109310941095109610971098109911001101110211031104110511061107110811091110111111121113111411151116111711181119112011211122112311241125112611271128112911301131113211331134113511361137113811391140114111421143114411451146114711481149115011511152115311541155115611571158115911601161116211631164116511661167116811691170117111721173117411751176117711781179118011811182118311841185118611871188118911901191119211931194119511961197119811991200120112021203120412051206120712081209121012111212121312141215121612171218121912201221122212231224122512261227122812291230123112321233123412351236123712381239124012411242124312441245124612471248124912501251125212531254125512561257125812591260126112621263126412651266126712681269127012711272127312741275127612771278127912801281128212831284128512861287128812891290129112921293129412951296129712981299130013011302130313041305130613071308130913101311131213131314131513161317131813191320132113221323132413251326132713281329133013311332133313341335133613371338133913401341134213431344134513461347134813491350135113521353135413551356135713581359136013611362136313641365136613671368136913701371137213731374137513761377137813791380138113821383138413851386138713881389139013911392139313941395139613971398139914001401140214031404140514061407140814091410141114121413141414151416 
  1. {

  2.  "cells": [

  3.   {

  4.    "cell_type": "code",

  5.    "execution_count": 2,

  6.    "metadata": {},

  7.    "outputs": [],

  8.    "source": [

  9.     "import torch\n",

  10.     "from tqdm import tqdm\n",

  11.     "import os\n",

  12.     "from pytorch_adapt.containers import Models, Optimizers\n",

  13.     "from pytorch_adapt.datasets import DataloaderCreator, get_mnist_mnistm, get_office31\n",

  14.     "from pytorch_adapt.datasets.office31 import Office31\n",

  15.     "from pytorch_adapt.hooks import DANNHook\n",

  16.     "from pytorch_adapt.adapters import DANN\n",

  17.     "\n",

  18.     "from pytorch_adapt.models import Discriminator, mnistC, mnistG, office31C, office31G\n",

  19.     "from pytorch_adapt.utils.common_functions import batch_to_device\n",

  20.     "from pytorch_adapt.validators import IMValidator, AccuracyValidator\n",

  21.     "\n",

  22.     "from pprint import pprint\n"

  23.    ]

  24.   },

  25.   {

  26.    "cell_type": "code",

  27.    "execution_count": 18,

  28.    "metadata": {},

  29.    "outputs": [],

  30.    "source": [

  31.     "root=\"datasets/pytorch-adapt/\"\n",

  32.     "batch_size=32\n",

  33.     "num_workers=2\n",

  34.     "\n",

  35.     "datasets = get_office31([\"amazon\"], [\"webcam\"], folder=root, return_target_with_labels=True)\n",

  36.     "dc = DataloaderCreator(batch_size=batch_size, \n",

  37.     "                        num_workers=num_workers, \n",

  38.     "                        train_names=[\"train\"],\n",

  39.     "                        val_names=[\"src_train\", \"target_train\", \"src_val\", \"target_val\", \"target_train_with_labels\", \"target_val_with_labels\"])\n",

  40.     "dataloaders = dc(**datasets)"

  41.    ]

  42.   },

  43.   {

  44.    "cell_type": "code",

  45.    "execution_count": 3,

  46.    "metadata": {},

  47.    "outputs": [

  48.     {

  49.      "name": "stdout",

  50.      "output_type": "stream",

  51.      "text": [

  52.       "dict_keys(['src_train', 'src_val', 'target_train', 'target_val', 'target_train_with_labels', 'target_val_with_labels', 'train'])\n"

  53.      ]

  54.     }

  55.    ],

  56.    "source": [

  57.     "print(dataloaders.keys())\n",

  58.     "# 'train' dataset type is `CombinedSourceAndTargetDataset` and contains both source and unlabeled target data\n",

  59.     "# 'src_train' and 'src_val' datasets types are `SourceDataset`\n",

  60.     "# Other datasets are typed `TargetDataset`"

  61.    ]

  62.   },

  63.   {

  64.    "cell_type": "code",

  65.    "execution_count": 4,

  66.    "metadata": {},

  67.    "outputs": [

  68.     {

  69.      "name": "stdout",

  70.      "output_type": "stream",

  71.      "text": [

  72.       "src_train 71 2253\n",

  73.       "dict_keys(['src_imgs', 'src_domain', 'src_labels', 'src_sample_idx'])\n",

  74.       "src_val 18 564\n",

  75.       "dict_keys(['src_imgs', 'src_domain', 'src_labels', 'src_sample_idx'])\n",

  76.       "target_train 20 636\n",

  77.       "dict_keys(['target_imgs', 'target_domain', 'target_sample_idx'])\n",

  78.       "target_val 5 159\n",

  79.       "dict_keys(['target_imgs', 'target_domain', 'target_sample_idx'])\n",

  80.       "target_train_with_labels 20 636\n",

  81.       "dict_keys(['target_imgs', 'target_domain', 'target_sample_idx', 'target_labels'])\n",

  82.       "target_val_with_labels 5 159\n",

  83.       "dict_keys(['target_imgs', 'target_domain', 'target_sample_idx', 'target_labels'])\n",

  84.       "train 19 636\n",

  85.       "dict_keys(['src_imgs', 'src_domain', 'src_labels', 'src_sample_idx', 'target_imgs', 'target_domain', 'target_sample_idx'])\n"

  86.      ]

  87.     }

  88.    ],

  89.    "source": [

  90.     "for k in dataloaders.keys():\n",

  91.     "    print(k, len(dataloaders[k]), len(dataloaders[k].dataset))\n",

  92.     "    for a in dataloaders[k]:\n",

  93.     "        print(a.keys())\n",

  94.     "        break"

  95.    ]

  96.   },

  97.   {

  98.    "cell_type": "code",

  99.    "execution_count": 3,

  100.    "metadata": {},

  101.    "outputs": [],

  102.    "source": [

  103.     "\n",

  104.     "device = torch.device(\"cuda\")\n",

  105.     "weights_root = os.path.join(root, \"weights\")\n",

  106.     "trained_domain = \"amazon\"\n",

  107.     "\n",

  108.     "G = office31G(pretrained=True, model_dir=weights_root).to(device)\n",

  109.     "C = office31C(domain=trained_domain, pretrained=True, model_dir=weights_root).to(device)\n",

  110.     "D = Discriminator(in_size=2048, h=256).to(device)\n",

  111.     "models = Models({\"G\": G, \"C\": C, \"D\": D})\n",

  112.     "optimizers = Optimizers((torch.optim.Adam, {\"lr\": 0.001}))\n",

  113.     "optimizers.create_with(models)\n",

  114.     "optimizers = list(optimizers.values())\n",

  115.     "\n",

  116.     "hook = DANNHook(optimizers)\n",

  117.     "\n",

  118.     "from pytorch_adapt.validators import AccuracyValidator, BaseValidator\n",

  119.     "from pytorch_adapt.layers import BNMLoss\n",

  120.     "\n",

  121.     "class CustomTargetValidator(BaseValidator):\n",

  122.     "    def compute_score(self, target_train):\n",

  123.     "        return BNMLoss()(target_train[\"preds\"])\n",

  124.     "\n",

  125.     "src_train_validator = AccuracyValidator(key_map={\"src_train\": \"src_val\"})\n",

  126.     "src_val_validator = AccuracyValidator(key_map={\"src_val\": \"src_val\"})\n",

  127.     "target_validator = CustomTargetValidator()\n",

  128.     "target_train_oracle_validator = AccuracyValidator(key_map={\"target_train\": \"src_val\"})\n",

  129.     "target_val_oracle_validator = AccuracyValidator(key_map={\"target_val\": \"src_val\"})\n",

  130.     "targen_im_validator = IMValidator()\n"

  131.    ]

  132.   },

  133.   {

  134.    "cell_type": "code",

  135.    "execution_count": 4,

  136.    "metadata": {},

  137.    "outputs": [

  138.     {

  139.      "name": "stderr",

  140.      "output_type": "stream",

  141.      "text": [

  142.       "100%|██████████| 19/19 [00:49<00:00,  2.63s/it]\n"

  143.      ]

  144.     },

  145.     {

  146.      "name": "stdout",

  147.      "output_type": "stream",

  148.      "text": [

  149.       "{'total_loss': {'src_c_loss': 3.168257236480713,\n",

  150.       "                'src_domain_loss': 0.7317013740539551,\n",

  151.       "                'target_domain_loss': 0.6301205158233643,\n",

  152.       "                'total': 1.5100263357162476}}\n"

  153.      ]

  154.     },

  155.     {

  156.      "name": "stderr",

  157.      "output_type": "stream",

  158.      "text": [

  159.       "100%|██████████| 18/18 [00:08<00:00,  2.03it/s]\n",

  160.       "100%|██████████| 5/5 [00:02<00:00,  1.69it/s]\n",

  161.       "100%|██████████| 20/20 [00:09<00:00,  2.16it/s]\n"

  162.      ]

  163.     },

  164.     {

  165.      "name": "stdout",

  166.      "output_type": "stream",

  167.      "text": [

  168.       "Target Evaluation:\n",

  169.       "src_val accuracy \t= 0.12411347776651382\n",

  170.       "target_train accuracy \t= 0.08805031329393387\n",

  171.       "target_train score (IM) \t= 0.4806191921234131\n"

  172.      ]

  173.     },

  174.     {

  175.      "name": "stderr",

  176.      "output_type": "stream",

  177.      "text": [

  178.       "100%|██████████| 19/19 [00:51<00:00,  2.69s/it]\n"

  179.      ]

  180.     },

  181.     {

  182.      "name": "stdout",

  183.      "output_type": "stream",

  184.      "text": [

  185.       "{'total_loss': {'src_c_loss': 2.804849624633789,\n",

  186.       "                'src_domain_loss': 0.6947869062423706,\n",

  187.       "                'target_domain_loss': 0.6891149282455444,\n",

  188.       "                'total': 1.3962504863739014}}\n"

  189.      ]

  190.     },

  191.     {

  192.      "name": "stderr",

  193.      "output_type": "stream",

  194.      "text": [

  195.       "100%|██████████| 18/18 [00:08<00:00,  2.02it/s]\n",

  196.       "100%|██████████| 5/5 [00:02<00:00,  1.68it/s]\n",

  197.       "100%|██████████| 20/20 [00:09<00:00,  2.15it/s]\n"

  198.      ]

  199.     },

  200.     {

  201.      "name": "stdout",

  202.      "output_type": "stream",

  203.      "text": [

  204.       "Target Evaluation:\n",

  205.       "src_val accuracy \t= 0.13652482628822327\n",

  206.       "target_train accuracy \t= 0.16352201998233795\n",

  207.       "target_train score (IM) \t= 0.6105575561523438\n"

  208.      ]

  209.     },

  210.     {

  211.      "name": "stderr",

  212.      "output_type": "stream",

  213.      "text": [

  214.       "100%|██████████| 19/19 [00:51<00:00,  2.71s/it]\n"

  215.      ]

  216.     },

  217.     {

  218.      "name": "stdout",

  219.      "output_type": "stream",

  220.      "text": [

  221.       "{'total_loss': {'src_c_loss': 3.0151782035827637,\n",

  222.       "                'src_domain_loss': 0.7150613069534302,\n",

  223.       "                'target_domain_loss': 0.8466196060180664,\n",

  224.       "                'total': 1.5256197452545166}}\n"

  225.      ]

  226.     },

  227.     {

  228.      "name": "stderr",

  229.      "output_type": "stream",

  230.      "text": [

  231.       "100%|██████████| 18/18 [00:08<00:00,  2.03it/s]\n",

  232.       "100%|██████████| 5/5 [00:02<00:00,  1.68it/s]\n",

  233.       "100%|██████████| 20/20 [00:09<00:00,  2.14it/s]\n"

  234.      ]

  235.     },

  236.     {

  237.      "name": "stdout",

  238.      "output_type": "stream",

  239.      "text": [

  240.       "Target Evaluation:\n",

  241.       "src_val accuracy \t= 0.14007091522216797\n",

  242.       "target_train accuracy \t= 0.1949685513973236\n",

  243.       "target_train score (IM) \t= 1.0187971591949463\n"

  244.      ]

  245.     },

  246.     {

  247.      "name": "stderr",

  248.      "output_type": "stream",

  249.      "text": [

  250.       "100%|██████████| 19/19 [00:52<00:00,  2.75s/it]\n"

  251.      ]

  252.     },

  253.     {

  254.      "name": "stdout",

  255.      "output_type": "stream",

  256.      "text": [

  257.       "{'total_loss': {'src_c_loss': 2.8740577697753906,\n",

  258.       "                'src_domain_loss': 0.6584925651550293,\n",

  259.       "                'target_domain_loss': 0.7099870443344116,\n",

  260.       "                'total': 1.4141792058944702}}\n"

  261.      ]

  262.     },

  263.     {

  264.      "name": "stderr",

  265.      "output_type": "stream",

  266.      "text": [

  267.       "100%|██████████| 18/18 [00:08<00:00,  2.17it/s]\n",

  268.       "100%|██████████| 5/5 [00:02<00:00,  1.73it/s]\n",

  269.       "100%|██████████| 20/20 [00:09<00:00,  2.15it/s]\n"

  270.      ]

  271.     },

  272.     {

  273.      "name": "stdout",

  274.      "output_type": "stream",

  275.      "text": [

  276.       "Target Evaluation:\n",

  277.       "src_val accuracy \t= 0.13297872245311737\n",

  278.       "target_train accuracy \t= 0.23270440101623535\n",

  279.       "target_train score (IM) \t= 0.8761472702026367\n"

  280.      ]

  281.     },

  282.     {

  283.      "name": "stderr",

  284.      "output_type": "stream",

  285.      "text": [

  286.       " 89%|████████▉ | 17/19 [00:48<00:05,  2.82s/it]\n"

  287.      ]

  288.     },

  289.     {

  290.      "ename": "KeyboardInterrupt",

  291.      "evalue": "",

  292.      "output_type": "error",

  293.      "traceback": [

  294.       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",

  295.       "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",

  296.       "Cell \u001b[0;32mIn[4], line 29\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[39mfor\u001b[39;00m data \u001b[39min\u001b[39;00m tqdm(dataloaders[\u001b[39m\"\u001b[39m\u001b[39mtrain\u001b[39m\u001b[39m\"\u001b[39m]):\n\u001b[1;32m     28\u001b[0m     data \u001b[39m=\u001b[39m batch_to_device(data, device)\n\u001b[0;32m---> 29\u001b[0m     _, loss \u001b[39m=\u001b[39m hook({\u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mmodels, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mdata})\n\u001b[1;32m     30\u001b[0m pprint(loss)\n\u001b[1;32m     32\u001b[0m \u001b[39m# eval loop\u001b[39;00m\n",

  297.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/pytorch_adapt/hooks/base.py:52\u001b[0m, in \u001b[0;36mBaseHook.__call__\u001b[0;34m(self, inputs, losses)\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m     51\u001b[0m     inputs \u001b[39m=\u001b[39m c_f\u001b[39m.\u001b[39mmap_keys(inputs, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mkey_map)\n\u001b[0;32m---> 52\u001b[0m     x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcall(inputs, losses)\n\u001b[1;32m     53\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(x, (\u001b[39mbool\u001b[39m, np\u001b[39m.\u001b[39mbool_)):\n\u001b[1;32m     54\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlogger\u001b[39m.\u001b[39mreset()\n",

  298.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/pytorch_adapt/hooks/base.py:194\u001b[0m, in \u001b[0;36mBaseWrapperHook.call\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    192\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mcall\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m    193\u001b[0m     \u001b[39m\"\"\"\"\"\"\u001b[39;00m\n\u001b[0;32m--> 194\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mhook(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",

  299.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/pytorch_adapt/hooks/base.py:52\u001b[0m, in \u001b[0;36mBaseHook.__call__\u001b[0;34m(self, inputs, losses)\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m     51\u001b[0m     inputs \u001b[39m=\u001b[39m c_f\u001b[39m.\u001b[39mmap_keys(inputs, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mkey_map)\n\u001b[0;32m---> 52\u001b[0m     x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcall(inputs, losses)\n\u001b[1;32m     53\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(x, (\u001b[39mbool\u001b[39m, np\u001b[39m.\u001b[39mbool_)):\n\u001b[1;32m     54\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlogger\u001b[39m.\u001b[39mreset()\n",

  300.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/pytorch_adapt/hooks/utils.py:109\u001b[0m, in \u001b[0;36mChainHook.call\u001b[0;34m(self, inputs, losses)\u001b[0m\n\u001b[1;32m    107\u001b[0m all_losses \u001b[39m=\u001b[39m {\u001b[39m*\u001b[39m\u001b[39m*\u001b[39mall_losses, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mprev_losses}\n\u001b[1;32m    108\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconditions[i](all_inputs, all_losses):\n\u001b[0;32m--> 109\u001b[0m     x \u001b[39m=\u001b[39m h(all_inputs, all_losses)\n\u001b[1;32m    110\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    111\u001b[0m     x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39malts[i](all_inputs, all_losses)\n",

  301.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/pytorch_adapt/hooks/base.py:52\u001b[0m, in \u001b[0;36mBaseHook.__call__\u001b[0;34m(self, inputs, losses)\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m     51\u001b[0m     inputs \u001b[39m=\u001b[39m c_f\u001b[39m.\u001b[39mmap_keys(inputs, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mkey_map)\n\u001b[0;32m---> 52\u001b[0m     x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcall(inputs, losses)\n\u001b[1;32m     53\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(x, (\u001b[39mbool\u001b[39m, np\u001b[39m.\u001b[39mbool_)):\n\u001b[1;32m     54\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlogger\u001b[39m.\u001b[39mreset()\n",

  302.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/pytorch_adapt/hooks/optimizer.py:51\u001b[0m, in \u001b[0;36mOptimizerHook.call\u001b[0;34m(self, inputs, losses)\u001b[0m\n\u001b[1;32m     49\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mcall\u001b[39m(\u001b[39mself\u001b[39m, inputs, losses):\n\u001b[1;32m     50\u001b[0m     \u001b[39m\"\"\"\"\"\"\u001b[39;00m\n\u001b[0;32m---> 51\u001b[0m     outputs, losses \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mhook(inputs, losses)\n\u001b[1;32m     52\u001b[0m     combined \u001b[39m=\u001b[39m c_f\u001b[39m.\u001b[39massert_dicts_are_disjoint(inputs, outputs)\n\u001b[1;32m     53\u001b[0m     new_outputs, losses \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mreducer(combined, losses)\n",

  303.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/pytorch_adapt/hooks/base.py:52\u001b[0m, in \u001b[0;36mBaseHook.__call__\u001b[0;34m(self, inputs, losses)\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m     51\u001b[0m     inputs \u001b[39m=\u001b[39m c_f\u001b[39m.\u001b[39mmap_keys(inputs, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mkey_map)\n\u001b[0;32m---> 52\u001b[0m     x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcall(inputs, losses)\n\u001b[1;32m     53\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(x, (\u001b[39mbool\u001b[39m, np\u001b[39m.\u001b[39mbool_)):\n\u001b[1;32m     54\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlogger\u001b[39m.\u001b[39mreset()\n",

  304.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/pytorch_adapt/hooks/utils.py:109\u001b[0m, in \u001b[0;36mChainHook.call\u001b[0;34m(self, inputs, losses)\u001b[0m\n\u001b[1;32m    107\u001b[0m all_losses \u001b[39m=\u001b[39m {\u001b[39m*\u001b[39m\u001b[39m*\u001b[39mall_losses, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mprev_losses}\n\u001b[1;32m    108\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconditions[i](all_inputs, all_losses):\n\u001b[0;32m--> 109\u001b[0m     x \u001b[39m=\u001b[39m h(all_inputs, all_losses)\n\u001b[1;32m    110\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    111\u001b[0m     x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39malts[i](all_inputs, all_losses)\n",

  305.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/pytorch_adapt/hooks/base.py:52\u001b[0m, in \u001b[0;36mBaseHook.__call__\u001b[0;34m(self, inputs, losses)\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m     51\u001b[0m     inputs \u001b[39m=\u001b[39m c_f\u001b[39m.\u001b[39mmap_keys(inputs, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mkey_map)\n\u001b[0;32m---> 52\u001b[0m     x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcall(inputs, losses)\n\u001b[1;32m     53\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(x, (\u001b[39mbool\u001b[39m, np\u001b[39m.\u001b[39mbool_)):\n\u001b[1;32m     54\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlogger\u001b[39m.\u001b[39mreset()\n",

  306.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/pytorch_adapt/hooks/utils.py:318\u001b[0m, in \u001b[0;36mAssertHook.call\u001b[0;34m(self, inputs, losses)\u001b[0m\n\u001b[1;32m    316\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mcall\u001b[39m(\u001b[39mself\u001b[39m, inputs, losses):\n\u001b[1;32m    317\u001b[0m     \u001b[39m\"\"\"\"\"\"\u001b[39;00m\n\u001b[0;32m--> 318\u001b[0m     outputs, losses \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mhook(inputs, losses)\n\u001b[1;32m    319\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39massert_fn(outputs)\n\u001b[1;32m    320\u001b[0m     \u001b[39mreturn\u001b[39;00m outputs, losses\n",

  307.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/pytorch_adapt/hooks/base.py:52\u001b[0m, in \u001b[0;36mBaseHook.__call__\u001b[0;34m(self, inputs, losses)\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m     51\u001b[0m     inputs \u001b[39m=\u001b[39m c_f\u001b[39m.\u001b[39mmap_keys(inputs, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mkey_map)\n\u001b[0;32m---> 52\u001b[0m     x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcall(inputs, losses)\n\u001b[1;32m     53\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(x, (\u001b[39mbool\u001b[39m, np\u001b[39m.\u001b[39mbool_)):\n\u001b[1;32m     54\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlogger\u001b[39m.\u001b[39mreset()\n",

  308.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/pytorch_adapt/hooks/utils.py:220\u001b[0m, in \u001b[0;36mOnlyNewOutputsHook.call\u001b[0;34m(self, inputs, losses)\u001b[0m\n\u001b[1;32m    218\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mcall\u001b[39m(\u001b[39mself\u001b[39m, inputs, losses):\n\u001b[1;32m    219\u001b[0m     \u001b[39m\"\"\"\"\"\"\u001b[39;00m\n\u001b[0;32m--> 220\u001b[0m     outputs, losses \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mhook(inputs, losses)\n\u001b[1;32m    221\u001b[0m     outputs \u001b[39m=\u001b[39m {k: outputs[k] \u001b[39mfor\u001b[39;00m k \u001b[39min\u001b[39;00m (outputs\u001b[39m.\u001b[39mkeys() \u001b[39m-\u001b[39m inputs\u001b[39m.\u001b[39mkeys())}\n\u001b[1;32m    222\u001b[0m     c_f\u001b[39m.\u001b[39massert_dicts_are_disjoint(inputs, outputs)\n",

  309.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/pytorch_adapt/hooks/base.py:52\u001b[0m, in \u001b[0;36mBaseHook.__call__\u001b[0;34m(self, inputs, losses)\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m     51\u001b[0m     inputs \u001b[39m=\u001b[39m c_f\u001b[39m.\u001b[39mmap_keys(inputs, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mkey_map)\n\u001b[0;32m---> 52\u001b[0m     x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcall(inputs, losses)\n\u001b[1;32m     53\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(x, (\u001b[39mbool\u001b[39m, np\u001b[39m.\u001b[39mbool_)):\n\u001b[1;32m     54\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlogger\u001b[39m.\u001b[39mreset()\n",

  310.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/pytorch_adapt/hooks/utils.py:109\u001b[0m, in \u001b[0;36mChainHook.call\u001b[0;34m(self, inputs, losses)\u001b[0m\n\u001b[1;32m    107\u001b[0m all_losses \u001b[39m=\u001b[39m {\u001b[39m*\u001b[39m\u001b[39m*\u001b[39mall_losses, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mprev_losses}\n\u001b[1;32m    108\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconditions[i](all_inputs, all_losses):\n\u001b[0;32m--> 109\u001b[0m     x \u001b[39m=\u001b[39m h(all_inputs, all_losses)\n\u001b[1;32m    110\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    111\u001b[0m     x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39malts[i](all_inputs, all_losses)\n",

  311.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/pytorch_adapt/hooks/base.py:52\u001b[0m, in \u001b[0;36mBaseHook.__call__\u001b[0;34m(self, inputs, losses)\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m     51\u001b[0m     inputs \u001b[39m=\u001b[39m c_f\u001b[39m.\u001b[39mmap_keys(inputs, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mkey_map)\n\u001b[0;32m---> 52\u001b[0m     x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcall(inputs, losses)\n\u001b[1;32m     53\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(x, (\u001b[39mbool\u001b[39m, np\u001b[39m.\u001b[39mbool_)):\n\u001b[1;32m     54\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlogger\u001b[39m.\u001b[39mreset()\n",

  312.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/pytorch_adapt/hooks/utils.py:249\u001b[0m, in \u001b[0;36mApplyFnHook.call\u001b[0;34m(self, inputs, losses)\u001b[0m\n\u001b[1;32m    247\u001b[0m \u001b[39m\"\"\"\"\"\"\u001b[39;00m\n\u001b[1;32m    248\u001b[0m x \u001b[39m=\u001b[39m c_f\u001b[39m.\u001b[39mextract(inputs, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mapply_to)\n\u001b[0;32m--> 249\u001b[0m outputs \u001b[39m=\u001b[39m {k: \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfn(v) \u001b[39mfor\u001b[39;00m k, v \u001b[39min\u001b[39;00m \u001b[39mzip\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mapply_to, x)}\n\u001b[1;32m    250\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mis_loss:\n\u001b[1;32m    251\u001b[0m     \u001b[39mreturn\u001b[39;00m outputs, {}\n",

  313.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/pytorch_adapt/hooks/utils.py:249\u001b[0m, in \u001b[0;36m<dictcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    247\u001b[0m \u001b[39m\"\"\"\"\"\"\u001b[39;00m\n\u001b[1;32m    248\u001b[0m x \u001b[39m=\u001b[39m c_f\u001b[39m.\u001b[39mextract(inputs, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mapply_to)\n\u001b[0;32m--> 249\u001b[0m outputs \u001b[39m=\u001b[39m {k: \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfn(v) \u001b[39mfor\u001b[39;00m k, v \u001b[39min\u001b[39;00m \u001b[39mzip\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mapply_to, x)}\n\u001b[1;32m    250\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mis_loss:\n\u001b[1;32m    251\u001b[0m     \u001b[39mreturn\u001b[39;00m outputs, {}\n",

  314.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/torch/nn/modules/module.py:889\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    887\u001b[0m     result \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_slow_forward(\u001b[39m*\u001b[39m\u001b[39minput\u001b[39m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[1;32m    888\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 889\u001b[0m     result \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mforward(\u001b[39m*\u001b[39;49m\u001b[39minput\u001b[39;49m, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m    890\u001b[0m \u001b[39mfor\u001b[39;00m hook \u001b[39min\u001b[39;00m itertools\u001b[39m.\u001b[39mchain(\n\u001b[1;32m    891\u001b[0m         _global_forward_hooks\u001b[39m.\u001b[39mvalues(),\n\u001b[1;32m    892\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks\u001b[39m.\u001b[39mvalues()):\n\u001b[1;32m    893\u001b[0m     hook_result \u001b[39m=\u001b[39m hook(\u001b[39mself\u001b[39m, \u001b[39minput\u001b[39m, result)\n",

  315.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/pytorch_adapt/layers/gradient_reversal.py:31\u001b[0m, in \u001b[0;36mGradientReversal.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     29\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39mself\u001b[39m, x):\n\u001b[1;32m     30\u001b[0m     \u001b[39m\"\"\"\"\"\"\u001b[39;00m\n\u001b[0;32m---> 31\u001b[0m     \u001b[39mreturn\u001b[39;00m _GradientReversal\u001b[39m.\u001b[39mapply(x, pml_cf\u001b[39m.\u001b[39;49mto_device(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mweight, x))\n",

  316.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/pytorch_metric_learning/utils/common_functions.py:492\u001b[0m, in \u001b[0;36mto_device\u001b[0;34m(x, tensor, device, dtype)\u001b[0m\n\u001b[1;32m    490\u001b[0m dv \u001b[39m=\u001b[39m device \u001b[39mif\u001b[39;00m device \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39melse\u001b[39;00m tensor\u001b[39m.\u001b[39mdevice\n\u001b[1;32m    491\u001b[0m \u001b[39mif\u001b[39;00m x\u001b[39m.\u001b[39mdevice \u001b[39m!=\u001b[39m dv:\n\u001b[0;32m--> 492\u001b[0m     x \u001b[39m=\u001b[39m x\u001b[39m.\u001b[39;49mto(dv)\n\u001b[1;32m    493\u001b[0m \u001b[39mif\u001b[39;00m dtype \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    494\u001b[0m     x \u001b[39m=\u001b[39m to_dtype(x, dtype\u001b[39m=\u001b[39mdtype)\n",

  317.       "\u001b[0;31mKeyboardInterrupt\u001b[0m: "

  318.      ]

  319.     },

  320.     {

  321.      "ename": "",

  322.      "evalue": "",

  323.      "output_type": "error",

  324.      "traceback": [

  325.       "\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."

  326.      ]

  327.     }

  328.    ],

  329.    "source": [

  330.     "torch.cuda.empty_cache()\n",

  331.     "\n",

  332.     "\n",

  333.     "from tqdm import tqdm\n",

  334.     "from pytorch_adapt.utils.common_functions import batch_to_device\n",

  335.     "from collections import defaultdict\n",

  336.     "\n",

  337.     "\n",

  338.     "def get_preds_and_labels(split, full_key):\n",

  339.     "    output = defaultdict(list)\n",

  340.     "    with torch.no_grad():\n",

  341.     "        for data in tqdm(dataloaders[full_key]):\n",

  342.     "            data = batch_to_device(data, device)\n",

  343.     "            logits = C(G(data[f\"{split}_imgs\"]))\n",

  344.     "            output[\"logits\"].append(logits)\n",

  345.     "            curr_preds = torch.softmax(logits, dim=1)\n",

  346.     "            output[\"preds\"].append(curr_preds)\n",

  347.     "            if full_key in [\"src_train\", \"src_val\", \"target_train_with_labels\", \"target_val_with_labels\"]:\n",

  348.     "                output[\"labels\"].append(data[f\"{split}_labels\"])\n",

  349.     "    for k,v in output.items():\n",

  350.     "        output[k] = torch.cat(v, dim=0)\n",

  351.     "    return output\n",

  352.     "    \n",

  353.     "for epoch in range(100):\n",

  354.     "    # train loop\n",

  355.     "    models.train()\n",

  356.     "    for data in tqdm(dataloaders[\"train\"]):\n",

  357.     "        data = batch_to_device(data, device)\n",

  358.     "        _, loss = hook({**models, **data})\n",

  359.     "    pprint(loss)\n",

  360.     "\n",

  361.     "    # eval loop\n",

  362.     "    scores = []\n",

  363.     "    # src_outputs = get_preds_and_labels(\"src\", \"src_train\")\n",

  364.     "    # score = src_train_validator(src_train=src_outputs)\n",

  365.     "    # scores.append(f\"src_train accuracy \\t= {score}\")\n",

  366.     "\n",

  367.     "    src_outputs = get_preds_and_labels(\"src\", \"src_val\")\n",

  368.     "    score = src_val_validator(src_val=src_outputs)\n",

  369.     "    scores.append(f\"src_val accuracy \\t= {score}\")\n",

  370.     "\n",

  371.     "    # target_outputs = get_preds_and_labels(\"target\", \"target_train_with_labels\")\n",

  372.     "    # score = target_train_oracle_validator(target_train=target_outputs)\n",

  373.     "    # scores.append(f\"target_train accuracy \\t= {score}\")\n",

  374.     "\n",

  375.     "    target_outputs = get_preds_and_labels(\"target\", \"target_val_with_labels\")\n",

  376.     "    score = target_val_oracle_validator(target_val=target_outputs)\n",

  377.     "    scores.append(f\"target_train accuracy \\t= {score}\")\n",

  378.     "\n",

  379.     "    target_outputs = get_preds_and_labels(\"target\", \"target_train\")\n",

  380.     "    score = targen_im_validator(target_train={\"logits\": target_outputs['logits']})\n",

  381.     "    scores.append(f\"target_train score (IM)\\t= {score}\")\n",

  382.     "\n",

  383.     "    # score = target_validator(target_train=target_outputs)\n",

  384.     "    # scores.append(f\"target_train score (BNM) \\t= {score}\")\n",

  385.     "\n",

  386.     "    print(f\"Target Evaluation:\")\n",

  387.     "    print(*scores, sep=\"\\n\")"

  388.    ]

  389.   },

  390.   {

  391.    "cell_type": "code",

  392.    "execution_count": null,

  393.    "metadata": {},

  394.    "outputs": [],

  395.    "source": [

  396.     "torch.cuda.empty_cache()"

  397.    ]

  398.   },

  399.   {

  400.    "attachments": {},

  401.    "cell_type": "markdown",

  402.    "metadata": {},

  403.    "source": [

  404.     "----"

  405.    ]

  406.   },

  407.   {

  408.    "attachments": {},

  409.    "cell_type": "markdown",

  410.    "metadata": {},

  411.    "source": [

  412.     "## Dataset Visualization"

  413.    ]

  414.   },

  415.   {

  416.    "cell_type": "code",

  417.    "execution_count": null,

  418.    "metadata": {},

  419.    "outputs": [

  420.     {

  421.      "name": "stdout",

  422.      "output_type": "stream",

  423.      "text": [

  424.       "['amazon']\n"

  425.      ]

  426.     },

  427.     {

  428.      "data": {

  429.       "image/png": "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",

  430.       "text/plain": [

  431.        "<Figure size 800x400 with 1 Axes>"

  432.       ]

  433.      },

  434.      "metadata": {},

  435.      "output_type": "display_data"

  436.     },

  437.     {

  438.      "name": "stdout",

  439.      "output_type": "stream",

  440.      "text": [

  441.       "['dslr']\n"

  442.      ]

  443.     },

  444.     {

  445.      "data": {

  446.       "image/png": "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",

  447.       "text/plain": [

  448.        "<Figure size 800x400 with 1 Axes>"

  449.       ]

  450.      },

  451.      "metadata": {},

  452.      "output_type": "display_data"

  453.     },

  454.     {

  455.      "name": "stdout",

  456.      "output_type": "stream",

  457.      "text": [

  458.       "['webcam']\n"

  459.      ]

  460.     },

  461.     {

  462.      "data": {

  463.       "image/png": "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",

  464.       "text/plain": [

  465.        "<Figure size 800x400 with 1 Axes>"

  466.       ]

  467.      },

  468.      "metadata": {},

  469.      "output_type": "display_data"

  470.     }

  471.    ],

  472.    "source": [

  473.     "import matplotlib.pyplot as plt\n",

  474.     "import numpy as np\n",

  475.     "import torchvision\n",

  476.     "\n",

  477.     "mean = [0.485, 0.456, 0.406]\n",

  478.     "std = [0.229, 0.224, 0.225]\n",

  479.     "\n",

  480.     "inv_normalize = torchvision.transforms.Normalize(\n",

  481.     "    mean=[-m / s for m, s in zip(mean, std)], std=[1 / s for s in std]\n",

  482.     ")\n",

  483.     "\n",

  484.     "def imshow(img, figsize=(8, 4)):\n",

  485.     "    img = inv_normalize(img)\n",

  486.     "    npimg = img.numpy()\n",

  487.     "    plt.figure(figsize=figsize)\n",

  488.     "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",

  489.     "    plt.show()\n",

  490.     "\n",

  491.     "def imshow_many(datasets, src, target):\n",

  492.     "    d = datasets[\"train\"]\n",

  493.     "    for name in [\"src_imgs\", \"target_imgs\"]:\n",

  494.     "        domains = src if name == \"src_imgs\" else target\n",

  495.     "        if len(domains) == 0:\n",

  496.     "            continue\n",

  497.     "        print(domains)\n",

  498.     "        imgs = [d[i][name] for i in np.random.choice(len(d), size=16, replace=False)]\n",

  499.     "        imshow(torchvision.utils.make_grid(imgs))\n",

  500.     "\n",

  501.     "for src, target in [([\"amazon\"], [\"dslr\"]), ([\"webcam\"], [])]:\n",

  502.     "    datasets = get_office31(src, target,folder=root)\n",

  503.     "    imshow_many(datasets, src, target)"

  504.    ]

  505.   },

  506.   {

  507.    "attachments": {},

  508.    "cell_type": "markdown",

  509.    "metadata": {},

  510.    "source": [

  511.     "---"

  512.    ]

  513.   },

  514.   {

  515.    "attachments": {},

  516.    "cell_type": "markdown",

  517.    "metadata": {},

  518.    "source": [

  519.     "## Ignite"

  520.    ]

  521.   },

  522.   {

  523.    "cell_type": "code",

  524.    "execution_count": 4,

  525.    "metadata": {},

  526.    "outputs": [],

  527.    "source": [

  528.     "import logging\n",

  529.     "\n",

  530.     "import matplotlib.pyplot as plt\n",

  531.     "import pandas as pd\n",

  532.     "import seaborn as sns\n",

  533.     "import torch\n",

  534.     "import umap\n",

  535.     "from tqdm import tqdm\n",

  536.     "import os\n",

  537.     "\n",

  538.     "from pytorch_adapt.adapters import DANN, MCD\n",

  539.     "from pytorch_adapt.containers import Models, Optimizers, LRSchedulers\n",

  540.     "from pytorch_adapt.datasets import DataloaderCreator, get_mnist_mnistm, get_office31\n",

  541.     "from pytorch_adapt.frameworks.ignite import CheckpointFnCreator, Ignite\n",

  542.     "from pytorch_adapt.models import Discriminator, mnistC, mnistG, office31C, office31G\n",

  543.     "from pytorch_adapt.validators import AccuracyValidator, IMValidator, ScoreHistory\n",

  544.     "\n",

  545.     "from pprint import pprint\n",

  546.     "\n",

  547.     "\n",

  548.     "logging.basicConfig()\n",

  549.     "logging.getLogger(\"pytorch-adapt\").setLevel(logging.INFO)"

  550.    ]

  551.   },

  552.   {

  553.    "cell_type": "code",

  554.    "execution_count": 5,

  555.    "metadata": {},

  556.    "outputs": [],

  557.    "source": [

  558.     "class VizHook:\n",

  559.     "    def __init__(self):\n",

  560.     "        self.required_data = [\"src_val\", \"target_val\", \"target_val_with_labels\"]\n",

  561.     "\n",

  562.     "    def __call__(self, epoch, src_val, target_val, target_val_with_labels, **kwargs):\n",

  563.     "\n",

  564.     "        accuracy_validator = AccuracyValidator()\n",

  565.     "        accuracy = accuracy_validator.compute_score(src_val=src_val)\n",

  566.     "        print(\"src_val accuracy:\", accuracy)\n",

  567.     "        accuracy_validator = AccuracyValidator()\n",

  568.     "        accuracy = accuracy_validator.compute_score(src_val=target_val_with_labels)\n",

  569.     "        print(\"target_val accuracy:\", accuracy)\n",

  570.     "\n",

  571.     "        if epoch % 1 != 0:\n",

  572.     "            return\n",

  573.     "\n",

  574.     "        features = [src_val[\"features\"], target_val[\"features\"]]\n",

  575.     "        domain = [src_val[\"domain\"], target_val[\"domain\"]]\n",

  576.     "        features = torch.cat(features, dim=0).cpu().numpy()\n",

  577.     "        domain = torch.cat(domain, dim=0).cpu().numpy()\n",

  578.     "        emb = umap.UMAP().fit_transform(features)\n",

  579.     "\n",

  580.     "        df = pd.DataFrame(emb).assign(domain=domain)\n",

  581.     "        df[\"domain\"] = df[\"domain\"].replace({0: \"Source\", 1: \"Target\"})\n",

  582.     "        sns.set_theme(style=\"white\", rc={\"figure.figsize\": (6, 4)})\n",

  583.     "        sns.scatterplot(data=df, x=0, y=1, hue=\"domain\", s=15)\n",

  584.     "        plt.savefig(f\"results/vishook/dann/val_{epoch}.png\") \n",

  585.     "        plt.show()\n",

  586.     "        plt.close('all')"

  587.    ]

  588.   },

  589.   {

  590.    "cell_type": "code",

  591.    "execution_count": 6,

  592.    "metadata": {},

  593.    "outputs": [],

  594.    "source": [

  595.     "root=\"datasets/pytorch-adapt/\"\n",

  596.     "batch_size=32\n",

  597.     "num_workers=2\n",

  598.     "\n",

  599.     "datasets = get_office31([\"amazon\"], [\"webcam\"], folder=root, return_target_with_labels=True)\n",

  600.     "dc = DataloaderCreator(batch_size=batch_size, \n",

  601.     "                        num_workers=num_workers, \n",

  602.     "                        train_names=[\"train\"],\n",

  603.     "                        val_names=[\"src_train\", \"target_train\", \"src_val\", \"target_val\", \"target_train_with_labels\", \"target_val_with_labels\"])\n",

  604.     "dataloaders = dc(**datasets)"

  605.    ]

  606.   },

  607.   {

  608.    "cell_type": "code",

  609.    "execution_count": 4,

  610.    "metadata": {},

  611.    "outputs": [

  612.     {

  613.      "name": "stderr",

  614.      "output_type": "stream",

  615.      "text": [

  616.       "2021-05-21 13:49:42,518 ignite.distributed.auto.auto_model INFO: Apply torch DataParallel on model\n",

  617.       "2021-05-21 13:49:42,519 ignite.distributed.auto.auto_model INFO: Apply torch DataParallel on model\n",

  618.       "2021-05-21 13:49:42,520 ignite.distributed.auto.auto_model INFO: Apply torch DataParallel on model\n"

  619.      ]

  620.     }

  621.    ],

  622.    "source": [

  623.     "device = torch.device(\"cuda\")\n",

  624.     "weights_root = os.path.join(root, \"weights\")\n",

  625.     "trained_domain = \"amazon\"\n",

  626.     "\n",

  627.     "G = office31G(pretrained=True, model_dir=weights_root).to(device)\n",

  628.     "C = office31C(domain=trained_domain, pretrained=True, model_dir=weights_root).to(device)\n",

  629.     "D = Discriminator(in_size=2048, h=1024).to(device)\n",

  630.     "\n",

  631.     "models = Models({\"G\": G, \"C\": C, \"D\": D})\n",

  632.     "\n",

  633.     "optimizers = Optimizers((torch.optim.Adam, {\"lr\": 0.0005}))\n",

  634.     "lr_schedulers = LRSchedulers((torch.optim.lr_scheduler.ExponentialLR, {\"gamma\": 0.99}))\n",

  635.     "\n",

  636.     "adapter = DANN(models=models, optimizers=optimizers, lr_schedulers=lr_schedulers)\n",

  637.     "checkpoint_fn = CheckpointFnCreator(dirname=\"saved_models\", require_empty=False)\n",

  638.     "validator = ScoreHistory(IMValidator())\n",

  639.     "tarAccuracyValidator = AccuracyValidator(key_map={\"target_val_with_labels\":\"src_val\"})\n",

  640.     "val_hooks = [ScoreHistory(AccuracyValidator()), ScoreHistory(tarAccuracyValidator), VizHook()]\n",

  641.     "trainer = Ignite(\n",

  642.     "    adapter, validator=validator, val_hooks=val_hooks, checkpoint_fn=checkpoint_fn\n",

  643.     ")"

  644.    ]

  645.   },

  646.   {

  647.    "cell_type": "code",

  648.    "execution_count": 7,

  649.    "metadata": {},

  650.    "outputs": [

  651.     {

  652.      "name": "stderr",

  653.      "output_type": "stream",

  654.      "text": [

  655.       "2021-05-21 14:16:07,148 ignite.distributed.auto.auto_model INFO: Apply torch DataParallel on model\n",

  656.       "2021-05-21 14:16:07,152 ignite.distributed.auto.auto_model INFO: Apply torch DataParallel on model\n"

  657.      ]

  658.     }

  659.    ],

  660.    "source": [

  661.     "from pytorch_adapt.layers import MultipleModels\n",

  662.     "from pytorch_adapt.utils import common_functions \n",

  663.     "import copy\n",

  664.     "\n",

  665.     "device = torch.device(\"cuda\")\n",

  666.     "weights_root = os.path.join(root, \"weights\")\n",

  667.     "trained_domain = \"amazon\"\n",

  668.     "\n",

  669.     "G = office31G(pretrained=True, model_dir=weights_root).to(device)\n",

  670.     "C0 = office31C(domain=trained_domain, pretrained=True, model_dir=weights_root).to(device)\n",

  671.     "C1 = common_functions.reinit(copy.deepcopy(C0))\n",

  672.     "C = MultipleModels(C0, C1)\n",

  673.     "\n",

  674.     "models = Models({\"G\": G, \"C\": C})\n",

  675.     "\n",

  676.     "optimizers = Optimizers((torch.optim.Adam, {\"lr\": 0.0005}))\n",

  677.     "lr_schedulers = LRSchedulers((torch.optim.lr_scheduler.ExponentialLR, {\"gamma\": 0.99}))\n",

  678.     "\n",

  679.     "adapter= MCD(models=models, optimizers=optimizers, lr_schedulers=lr_schedulers)\n",

  680.     "# adapter = DANN(models=models, optimizers=optimizers, lr_schedulers=lr_schedulers)\n",

  681.     "checkpoint_fn = CheckpointFnCreator(dirname=\"saved_models\", require_empty=False)\n",

  682.     "validator = ScoreHistory(IMValidator())\n",

  683.     "tarAccuracyValidator = AccuracyValidator(key_map={\"target_val_with_labels\":\"src_val\"})\n",

  684.     "val_hooks = [ScoreHistory(AccuracyValidator()), ScoreHistory(tarAccuracyValidator), VizHook()]\n",

  685.     "trainer = Ignite(\n",

  686.     "    adapter, validator=validator, val_hooks=val_hooks, checkpoint_fn=checkpoint_fn\n",

  687.     ")"

  688.    ]

  689.   },

  690.   {

  691.    "cell_type": "code",

  692.    "execution_count": 8,

  693.    "metadata": {},

  694.    "outputs": [

  695.     {

  696.      "name": "stderr",

  697.      "output_type": "stream",

  698.      "text": [

  699.       "WARNING:pytorch-adapt:val_hook has no state_dict or load_state_dict so it will not be saved or loaded\n"

  700.      ]

  701.     },

  702.     {

  703.      "ename": "RuntimeError",

  704.      "evalue": "CUDA error: out of memory",

  705.      "output_type": "error",

  706.      "traceback": [

  707.       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",

  708.       "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",

  709.       "Cell \u001b[0;32mIn[8], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m logging\u001b[39m.\u001b[39mgetLogger(\u001b[39m\"\u001b[39m\u001b[39mpytorch-adapt\u001b[39m\u001b[39m\"\u001b[39m)\u001b[39m.\u001b[39msetLevel(logging\u001b[39m.\u001b[39mWARNING)\n\u001b[0;32m----> 3\u001b[0m best_score, best_epoch \u001b[39m=\u001b[39m trainer\u001b[39m.\u001b[39;49mrun(\n\u001b[1;32m      4\u001b[0m     datasets, dataloader_creator\u001b[39m=\u001b[39;49mdc, max_epochs\u001b[39m=\u001b[39;49m\u001b[39m5\u001b[39;49m, check_initial_score\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m\n\u001b[1;32m      5\u001b[0m )\n\u001b[1;32m      6\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mbest_score=\u001b[39m\u001b[39m{\u001b[39;00mbest_score\u001b[39m}\u001b[39;00m\u001b[39m, best_epoch=\u001b[39m\u001b[39m{\u001b[39;00mbest_epoch\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n",

  710.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/pytorch_adapt/frameworks/ignite/ignite.py:233\u001b[0m, in \u001b[0;36mIgnite.run\u001b[0;34m(self, datasets, dataloader_creator, dataloaders, val_interval, early_stopper_kwargs, resume, check_initial_score, **trainer_kwargs)\u001b[0m\n\u001b[1;32m    230\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mload_checkpoint(resume)\n\u001b[1;32m    232\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m i_g\u001b[39m.\u001b[39mis_done(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtrainer, max_epochs):\n\u001b[0;32m--> 233\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtrainer\u001b[39m.\u001b[39;49mrun(dataloaders[\u001b[39m\"\u001b[39;49m\u001b[39mtrain\u001b[39;49m\u001b[39m\"\u001b[39;49m], \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mtrainer_kwargs)\n\u001b[1;32m    235\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mremove_temp_events()\n\u001b[1;32m    237\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mvalidator:\n",

  711.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/ignite/engine/engine.py:704\u001b[0m, in \u001b[0;36mEngine.run\u001b[0;34m(self, data, max_epochs, epoch_length, seed)\u001b[0m\n\u001b[1;32m    701\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mepoch_length should be provided if data is None\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m    703\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstate\u001b[39m.\u001b[39mdataloader \u001b[39m=\u001b[39m data\n\u001b[0;32m--> 704\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_internal_run()\n",

  712.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/ignite/engine/engine.py:783\u001b[0m, in \u001b[0;36mEngine._internal_run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    781\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_dataloader_iter \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m    782\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlogger\u001b[39m.\u001b[39merror(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mEngine run is terminating due to exception: \u001b[39m\u001b[39m{\u001b[39;00me\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n\u001b[0;32m--> 783\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_handle_exception(e)\n\u001b[1;32m    785\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_dataloader_iter \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m    786\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstate\n",

  713.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/ignite/engine/engine.py:466\u001b[0m, in \u001b[0;36mEngine._handle_exception\u001b[0;34m(self, e)\u001b[0m\n\u001b[1;32m    464\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_fire_event(Events\u001b[39m.\u001b[39mEXCEPTION_RAISED, e)\n\u001b[1;32m    465\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 466\u001b[0m     \u001b[39mraise\u001b[39;00m e\n",

  714.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/ignite/engine/engine.py:745\u001b[0m, in \u001b[0;36mEngine._internal_run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    743\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m    744\u001b[0m     start_time \u001b[39m=\u001b[39m time\u001b[39m.\u001b[39mtime()\n\u001b[0;32m--> 745\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_fire_event(Events\u001b[39m.\u001b[39;49mSTARTED)\n\u001b[1;32m    746\u001b[0m     \u001b[39mwhile\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstate\u001b[39m.\u001b[39mepoch \u001b[39m<\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstate\u001b[39m.\u001b[39mmax_epochs \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mshould_terminate:  \u001b[39m# type: ignore[operator]\u001b[39;00m\n\u001b[1;32m    747\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstate\u001b[39m.\u001b[39mepoch \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n",

  715.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/ignite/engine/engine.py:421\u001b[0m, in \u001b[0;36mEngine._fire_event\u001b[0;34m(self, event_name, *event_args, **event_kwargs)\u001b[0m\n\u001b[1;32m    419\u001b[0m kwargs\u001b[39m.\u001b[39mupdate(event_kwargs)\n\u001b[1;32m    420\u001b[0m first, others \u001b[39m=\u001b[39m ((args[\u001b[39m0\u001b[39m],), args[\u001b[39m1\u001b[39m:]) \u001b[39mif\u001b[39;00m (args \u001b[39mand\u001b[39;00m args[\u001b[39m0\u001b[39m] \u001b[39m==\u001b[39m \u001b[39mself\u001b[39m) \u001b[39melse\u001b[39;00m ((), args)\n\u001b[0;32m--> 421\u001b[0m func(\u001b[39m*\u001b[39;49mfirst, \u001b[39m*\u001b[39;49m(event_args \u001b[39m+\u001b[39;49m others), \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",

  716.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/pytorch_adapt/frameworks/ignite/utils.py:30\u001b[0m, in \u001b[0;36mget_validation_runner.<locals>.run_validation\u001b[0;34m(engine)\u001b[0m\n\u001b[1;32m     28\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mrun_validation\u001b[39m(engine):\n\u001b[1;32m     29\u001b[0m     epoch \u001b[39m=\u001b[39m engine\u001b[39m.\u001b[39mstate\u001b[39m.\u001b[39mepoch\n\u001b[0;32m---> 30\u001b[0m     collected_data \u001b[39m=\u001b[39m collect_from_dataloaders(collector, dataloaders, required_data)\n\u001b[1;32m     31\u001b[0m     score \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m     32\u001b[0m     \u001b[39mif\u001b[39;00m validator:\n",

  717.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/pytorch_adapt/frameworks/ignite/utils.py:53\u001b[0m, in \u001b[0;36mcollect_from_dataloaders\u001b[0;34m(collector, dataloaders, required_data)\u001b[0m\n\u001b[1;32m     51\u001b[0m curr_dataset \u001b[39m=\u001b[39m curr_dataloader\u001b[39m.\u001b[39mdataset\n\u001b[1;32m     52\u001b[0m iterable \u001b[39m=\u001b[39m curr_dataloader\u001b[39m.\u001b[39m\u001b[39m__iter__\u001b[39m()\n\u001b[0;32m---> 53\u001b[0m curr_collected \u001b[39m=\u001b[39m accumulate_collector_output(collector, iterable, k)\n\u001b[1;32m     54\u001b[0m c_f\u001b[39m.\u001b[39mval_collected_data_checks(curr_collected, curr_dataset)\n\u001b[1;32m     55\u001b[0m collected_data[k] \u001b[39m=\u001b[39m curr_collected\n",

  718.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/pytorch_adapt/frameworks/ignite/utils.py:98\u001b[0m, in \u001b[0;36maccumulate_collector_output\u001b[0;34m(collector, iterable, output_name)\u001b[0m\n\u001b[1;32m     96\u001b[0m accumulator \u001b[39m=\u001b[39m DictionaryAccumulator()\n\u001b[1;32m     97\u001b[0m accumulator\u001b[39m.\u001b[39mattach(collector, output_name)\n\u001b[0;32m---> 98\u001b[0m collector\u001b[39m.\u001b[39;49mrun(iterable)\n\u001b[1;32m     99\u001b[0m accumulator\u001b[39m.\u001b[39mdetach(collector)\n\u001b[1;32m    100\u001b[0m output \u001b[39m=\u001b[39m collector\u001b[39m.\u001b[39mstate\u001b[39m.\u001b[39mmetrics[output_name]\n",

  719.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/ignite/engine/engine.py:704\u001b[0m, in \u001b[0;36mEngine.run\u001b[0;34m(self, data, max_epochs, epoch_length, seed)\u001b[0m\n\u001b[1;32m    701\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mepoch_length should be provided if data is None\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m    703\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstate\u001b[39m.\u001b[39mdataloader \u001b[39m=\u001b[39m data\n\u001b[0;32m--> 704\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_internal_run()\n",

  720.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/ignite/engine/engine.py:783\u001b[0m, in \u001b[0;36mEngine._internal_run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    781\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_dataloader_iter \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m    782\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlogger\u001b[39m.\u001b[39merror(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mEngine run is terminating due to exception: \u001b[39m\u001b[39m{\u001b[39;00me\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n\u001b[0;32m--> 783\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_handle_exception(e)\n\u001b[1;32m    785\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_dataloader_iter \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m    786\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstate\n",

  721.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/ignite/engine/engine.py:466\u001b[0m, in \u001b[0;36mEngine._handle_exception\u001b[0;34m(self, e)\u001b[0m\n\u001b[1;32m    464\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_fire_event(Events\u001b[39m.\u001b[39mEXCEPTION_RAISED, e)\n\u001b[1;32m    465\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 466\u001b[0m     \u001b[39mraise\u001b[39;00m e\n",

  722.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/ignite/engine/engine.py:753\u001b[0m, in \u001b[0;36mEngine._internal_run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    750\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_dataloader_iter \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    751\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_setup_engine()\n\u001b[0;32m--> 753\u001b[0m time_taken \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_run_once_on_dataset()\n\u001b[1;32m    754\u001b[0m \u001b[39m# time is available for handlers but must be update after fire\u001b[39;00m\n\u001b[1;32m    755\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstate\u001b[39m.\u001b[39mtimes[Events\u001b[39m.\u001b[39mEPOCH_COMPLETED\u001b[39m.\u001b[39mname] \u001b[39m=\u001b[39m time_taken\n",

  723.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/ignite/engine/engine.py:854\u001b[0m, in \u001b[0;36mEngine._run_once_on_dataset\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    852\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m    853\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlogger\u001b[39m.\u001b[39merror(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mCurrent run is terminating due to exception: \u001b[39m\u001b[39m{\u001b[39;00me\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n\u001b[0;32m--> 854\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_handle_exception(e)\n\u001b[1;32m    856\u001b[0m \u001b[39mreturn\u001b[39;00m time\u001b[39m.\u001b[39mtime() \u001b[39m-\u001b[39m start_time\n",

  724.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/ignite/engine/engine.py:466\u001b[0m, in \u001b[0;36mEngine._handle_exception\u001b[0;34m(self, e)\u001b[0m\n\u001b[1;32m    464\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_fire_event(Events\u001b[39m.\u001b[39mEXCEPTION_RAISED, e)\n\u001b[1;32m    465\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 466\u001b[0m     \u001b[39mraise\u001b[39;00m e\n",

  725.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/ignite/engine/engine.py:840\u001b[0m, in \u001b[0;36mEngine._run_once_on_dataset\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    838\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstate\u001b[39m.\u001b[39miteration \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n\u001b[1;32m    839\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_fire_event(Events\u001b[39m.\u001b[39mITERATION_STARTED)\n\u001b[0;32m--> 840\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstate\u001b[39m.\u001b[39moutput \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_function(\u001b[39mself\u001b[39;49m, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mstate\u001b[39m.\u001b[39;49mbatch)\n\u001b[1;32m    841\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_fire_event(Events\u001b[39m.\u001b[39mITERATION_COMPLETED)\n\u001b[1;32m    843\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mshould_terminate \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mshould_terminate_single_epoch:\n",

  726.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/pytorch_adapt/frameworks/ignite/ignite.py:332\u001b[0m, in \u001b[0;36mIgnite.get_collector_step.<locals>.collector_step\u001b[0;34m(engine, batch)\u001b[0m\n\u001b[1;32m    330\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mcollector_step\u001b[39m(engine, batch):\n\u001b[1;32m    331\u001b[0m     batch \u001b[39m=\u001b[39m c_f\u001b[39m.\u001b[39mbatch_to_device(batch, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdevice)\n\u001b[0;32m--> 332\u001b[0m     \u001b[39mreturn\u001b[39;00m f_utils\u001b[39m.\u001b[39;49mcollector_step(inference, batch, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mval_output_dict_fn)\n",

  727.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/pytorch_adapt/frameworks/utils.py:33\u001b[0m, in \u001b[0;36mcollector_step\u001b[0;34m(inference, batch, output_dict_fn)\u001b[0m\n\u001b[1;32m     31\u001b[0m data \u001b[39m=\u001b[39m extract_data(batch)\n\u001b[1;32m     32\u001b[0m \u001b[39mwith\u001b[39;00m torch\u001b[39m.\u001b[39mno_grad():\n\u001b[0;32m---> 33\u001b[0m     f_dict \u001b[39m=\u001b[39m inference(data[\u001b[39m\"\u001b[39;49m\u001b[39mimgs\u001b[39;49m\u001b[39m\"\u001b[39;49m], domain\u001b[39m=\u001b[39;49mdata[\u001b[39m\"\u001b[39;49m\u001b[39mdomain\u001b[39;49m\u001b[39m\"\u001b[39;49m])\n\u001b[1;32m     34\u001b[0m data\u001b[39m.\u001b[39mpop(\u001b[39m\"\u001b[39m\u001b[39mimgs\u001b[39m\u001b[39m\"\u001b[39m)  \u001b[39m# we don't want to collect imgs\u001b[39;00m\n\u001b[1;32m     35\u001b[0m f_dict \u001b[39m=\u001b[39m output_dict_fn(f_dict)\n",

  728.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/pytorch_adapt/adapters/base_adapter.py:120\u001b[0m, in \u001b[0;36mBaseAdapter.inference\u001b[0;34m(self, x, domain)\u001b[0m\n\u001b[1;32m    109\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39minference\u001b[39m(\n\u001b[1;32m    110\u001b[0m     \u001b[39mself\u001b[39m, x: torch\u001b[39m.\u001b[39mTensor, domain: \u001b[39mint\u001b[39m \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m    111\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Tuple[torch\u001b[39m.\u001b[39mTensor, torch\u001b[39m.\u001b[39mTensor]:\n\u001b[1;32m    112\u001b[0m     \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m    113\u001b[0m \u001b[39m    Arguments:\u001b[39;00m\n\u001b[1;32m    114\u001b[0m \u001b[39m        x: The input to the model\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    118\u001b[0m \u001b[39m        Features and logits\u001b[39;00m\n\u001b[1;32m    119\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 120\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49minference_fn(\n\u001b[1;32m    121\u001b[0m         x\u001b[39m=\u001b[39;49mx,\n\u001b[1;32m    122\u001b[0m         domain\u001b[39m=\u001b[39;49mdomain,\n\u001b[1;32m    123\u001b[0m         models\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mmodels,\n\u001b[1;32m    124\u001b[0m         misc\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mmisc,\n\u001b[1;32m    125\u001b[0m     )\n",

  729.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/pytorch_adapt/inference/inference.py:145\u001b[0m, in \u001b[0;36mmcd_fn\u001b[0;34m(x, models, get_all, **kwargs)\u001b[0m\n\u001b[1;32m    140\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mmcd_fn\u001b[39m(x, models, get_all\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m    141\u001b[0m     \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m    142\u001b[0m \u001b[39m    Returns:\u001b[39;00m\n\u001b[1;32m    143\u001b[0m \u001b[39m        Features and logits, where ```logits = sum(C(features))```.\u001b[39;00m\n\u001b[1;32m    144\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 145\u001b[0m     features \u001b[39m=\u001b[39m models[\u001b[39m\"\u001b[39;49m\u001b[39mG\u001b[39;49m\u001b[39m\"\u001b[39;49m](x)\n\u001b[1;32m    146\u001b[0m     logits_list \u001b[39m=\u001b[39m models[\u001b[39m\"\u001b[39m\u001b[39mC\u001b[39m\u001b[39m\"\u001b[39m](features)\n\u001b[1;32m    147\u001b[0m     logits \u001b[39m=\u001b[39m \u001b[39msum\u001b[39m(logits_list)\n",

  730.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/torch/nn/modules/module.py:889\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    887\u001b[0m     result \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_slow_forward(\u001b[39m*\u001b[39m\u001b[39minput\u001b[39m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[1;32m    888\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 889\u001b[0m     result \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mforward(\u001b[39m*\u001b[39;49m\u001b[39minput\u001b[39;49m, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m    890\u001b[0m \u001b[39mfor\u001b[39;00m hook \u001b[39min\u001b[39;00m itertools\u001b[39m.\u001b[39mchain(\n\u001b[1;32m    891\u001b[0m         _global_forward_hooks\u001b[39m.\u001b[39mvalues(),\n\u001b[1;32m    892\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks\u001b[39m.\u001b[39mvalues()):\n\u001b[1;32m    893\u001b[0m     hook_result \u001b[39m=\u001b[39m hook(\u001b[39mself\u001b[39m, \u001b[39minput\u001b[39m, result)\n",

  731.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/torch/nn/parallel/data_parallel.py:157\u001b[0m, in \u001b[0;36mDataParallel.forward\u001b[0;34m(self, *inputs, **kwargs)\u001b[0m\n\u001b[1;32m    152\u001b[0m     \u001b[39mif\u001b[39;00m t\u001b[39m.\u001b[39mdevice \u001b[39m!=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msrc_device_obj:\n\u001b[1;32m    153\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mRuntimeError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mmodule must have its parameters and buffers \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    154\u001b[0m                            \u001b[39m\"\u001b[39m\u001b[39mon device \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m (device_ids[0]) but found one of \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    155\u001b[0m                            \u001b[39m\"\u001b[39m\u001b[39mthem on device: \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39msrc_device_obj, t\u001b[39m.\u001b[39mdevice))\n\u001b[0;32m--> 157\u001b[0m inputs, kwargs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mscatter(inputs, kwargs, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdevice_ids)\n\u001b[1;32m    158\u001b[0m \u001b[39m# for forward function without any inputs, empty list and dict will be created\u001b[39;00m\n\u001b[1;32m    159\u001b[0m \u001b[39m# so the module can be executed on one device which is the first one in device_ids\u001b[39;00m\n\u001b[1;32m    160\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m inputs \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m kwargs:\n",

  732.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/torch/nn/parallel/data_parallel.py:174\u001b[0m, in \u001b[0;36mDataParallel.scatter\u001b[0;34m(self, inputs, kwargs, device_ids)\u001b[0m\n\u001b[1;32m    173\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mscatter\u001b[39m(\u001b[39mself\u001b[39m, inputs, kwargs, device_ids):\n\u001b[0;32m--> 174\u001b[0m     \u001b[39mreturn\u001b[39;00m scatter_kwargs(inputs, kwargs, device_ids, dim\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdim)\n",

  733.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/torch/nn/parallel/scatter_gather.py:44\u001b[0m, in \u001b[0;36mscatter_kwargs\u001b[0;34m(inputs, kwargs, target_gpus, dim)\u001b[0m\n\u001b[1;32m     42\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mscatter_kwargs\u001b[39m(inputs, kwargs, target_gpus, dim\u001b[39m=\u001b[39m\u001b[39m0\u001b[39m):\n\u001b[1;32m     43\u001b[0m     \u001b[39mr\u001b[39m\u001b[39m\"\"\"Scatter with support for kwargs dictionary\"\"\"\u001b[39;00m\n\u001b[0;32m---> 44\u001b[0m     inputs \u001b[39m=\u001b[39m scatter(inputs, target_gpus, dim) \u001b[39mif\u001b[39;00m inputs \u001b[39melse\u001b[39;00m []\n\u001b[1;32m     45\u001b[0m     kwargs \u001b[39m=\u001b[39m scatter(kwargs, target_gpus, dim) \u001b[39mif\u001b[39;00m kwargs \u001b[39melse\u001b[39;00m []\n\u001b[1;32m     46\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(inputs) \u001b[39m<\u001b[39m \u001b[39mlen\u001b[39m(kwargs):\n",

  734.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/torch/nn/parallel/scatter_gather.py:36\u001b[0m, in \u001b[0;36mscatter\u001b[0;34m(inputs, target_gpus, dim)\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[39m# After scatter_map is called, a scatter_map cell will exist. This cell\u001b[39;00m\n\u001b[1;32m     31\u001b[0m \u001b[39m# has a reference to the actual function scatter_map, which has references\u001b[39;00m\n\u001b[1;32m     32\u001b[0m \u001b[39m# to a closure that has a reference to the scatter_map cell (because the\u001b[39;00m\n\u001b[1;32m     33\u001b[0m \u001b[39m# fn is recursive). To avoid this reference cycle, we set the function to\u001b[39;00m\n\u001b[1;32m     34\u001b[0m \u001b[39m# None, clearing the cell\u001b[39;00m\n\u001b[1;32m     35\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> 36\u001b[0m     res \u001b[39m=\u001b[39m scatter_map(inputs)\n\u001b[1;32m     37\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[1;32m     38\u001b[0m     scatter_map \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n",

  735.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/torch/nn/parallel/scatter_gather.py:23\u001b[0m, in \u001b[0;36mscatter.<locals>.scatter_map\u001b[0;34m(obj)\u001b[0m\n\u001b[1;32m     21\u001b[0m     \u001b[39mreturn\u001b[39;00m [\u001b[39mtype\u001b[39m(obj)(\u001b[39m*\u001b[39margs) \u001b[39mfor\u001b[39;00m args \u001b[39min\u001b[39;00m \u001b[39mzip\u001b[39m(\u001b[39m*\u001b[39m\u001b[39mmap\u001b[39m(scatter_map, obj))]\n\u001b[1;32m     22\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(obj, \u001b[39mtuple\u001b[39m) \u001b[39mand\u001b[39;00m \u001b[39mlen\u001b[39m(obj) \u001b[39m>\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[0;32m---> 23\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mlist\u001b[39m(\u001b[39mzip\u001b[39;49m(\u001b[39m*\u001b[39;49m\u001b[39mmap\u001b[39;49m(scatter_map, obj)))\n\u001b[1;32m     24\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(obj, \u001b[39mlist\u001b[39m) \u001b[39mand\u001b[39;00m \u001b[39mlen\u001b[39m(obj) \u001b[39m>\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[1;32m     25\u001b[0m     \u001b[39mreturn\u001b[39;00m [\u001b[39mlist\u001b[39m(i) \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mzip\u001b[39m(\u001b[39m*\u001b[39m\u001b[39mmap\u001b[39m(scatter_map, obj))]\n",

  736.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/torch/nn/parallel/scatter_gather.py:19\u001b[0m, in \u001b[0;36mscatter.<locals>.scatter_map\u001b[0;34m(obj)\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mscatter_map\u001b[39m(obj):\n\u001b[1;32m     18\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(obj, torch\u001b[39m.\u001b[39mTensor):\n\u001b[0;32m---> 19\u001b[0m         \u001b[39mreturn\u001b[39;00m Scatter\u001b[39m.\u001b[39;49mapply(target_gpus, \u001b[39mNone\u001b[39;49;00m, dim, obj)\n\u001b[1;32m     20\u001b[0m     \u001b[39mif\u001b[39;00m is_namedtuple(obj):\n\u001b[1;32m     21\u001b[0m         \u001b[39mreturn\u001b[39;00m [\u001b[39mtype\u001b[39m(obj)(\u001b[39m*\u001b[39margs) \u001b[39mfor\u001b[39;00m args \u001b[39min\u001b[39;00m \u001b[39mzip\u001b[39m(\u001b[39m*\u001b[39m\u001b[39mmap\u001b[39m(scatter_map, obj))]\n",

  737.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/torch/nn/parallel/_functions.py:93\u001b[0m, in \u001b[0;36mScatter.forward\u001b[0;34m(ctx, target_gpus, chunk_sizes, dim, input)\u001b[0m\n\u001b[1;32m     90\u001b[0m \u001b[39mif\u001b[39;00m torch\u001b[39m.\u001b[39mcuda\u001b[39m.\u001b[39mis_available() \u001b[39mand\u001b[39;00m ctx\u001b[39m.\u001b[39minput_device \u001b[39m==\u001b[39m \u001b[39m-\u001b[39m\u001b[39m1\u001b[39m:\n\u001b[1;32m     91\u001b[0m     \u001b[39m# Perform CPU to GPU copies in a background stream\u001b[39;00m\n\u001b[1;32m     92\u001b[0m     streams \u001b[39m=\u001b[39m [_get_stream(device) \u001b[39mfor\u001b[39;00m device \u001b[39min\u001b[39;00m target_gpus]\n\u001b[0;32m---> 93\u001b[0m outputs \u001b[39m=\u001b[39m comm\u001b[39m.\u001b[39;49mscatter(\u001b[39minput\u001b[39;49m, target_gpus, chunk_sizes, ctx\u001b[39m.\u001b[39;49mdim, streams)\n\u001b[1;32m     94\u001b[0m \u001b[39m# Synchronize with the copy stream\u001b[39;00m\n\u001b[1;32m     95\u001b[0m \u001b[39mif\u001b[39;00m streams \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",

  738.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/torch/nn/parallel/comm.py:189\u001b[0m, in \u001b[0;36mscatter\u001b[0;34m(tensor, devices, chunk_sizes, dim, streams, out)\u001b[0m\n\u001b[1;32m    187\u001b[0m \u001b[39mif\u001b[39;00m out \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    188\u001b[0m     devices \u001b[39m=\u001b[39m [_get_device_index(d) \u001b[39mfor\u001b[39;00m d \u001b[39min\u001b[39;00m devices]\n\u001b[0;32m--> 189\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mtuple\u001b[39m(torch\u001b[39m.\u001b[39;49m_C\u001b[39m.\u001b[39;49m_scatter(tensor, devices, chunk_sizes, dim, streams))\n\u001b[1;32m    190\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    191\u001b[0m     \u001b[39mif\u001b[39;00m devices \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",

  739.       "\u001b[0;31mRuntimeError\u001b[0m: CUDA error: out of memory"

  740.      ]

  741.     }

  742.    ],

  743.    "source": [

  744.     "logging.getLogger(\"pytorch-adapt\").setLevel(logging.WARNING)\n",

  745.     "\n",

  746.     "early_stopper_kwargs = {\"patience\":2}\n",

  747.     "\n",

  748.     "\n",

  749.     "best_score, best_epoch = trainer.run(\n",

  750.     "    datasets, dataloader_creator=dc, max_epochs=5, check_initial_score=True, early_stopper_kwargs=early_stopper_kwargs\n",

  751.     ")\n",

  752.     "print(f\"best_score={best_score}, best_epoch={best_epoch}\")"

  753.    ]

  754.   },

  755.   {

  756.    "cell_type": "code",

  757.    "execution_count": 10,

  758.    "metadata": {},

  759.    "outputs": [

  760.     {

  761.      "data": {

  762.       "image/png": "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",

  763.       "text/plain": [

  764.        "<Figure size 600x400 with 1 Axes>"

  765.       ]

  766.      },

  767.      "metadata": {},

  768.      "output_type": "display_data"

  769.     },

  770.     {

  771.      "data": {

  772.       "image/png": "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",

  773.       "text/plain": [

  774.        "<Figure size 600x400 with 1 Axes>"

  775.       ]

  776.      },

  777.      "metadata": {},

  778.      "output_type": "display_data"

  779.     },

  780.     {

  781.      "ename": "",

  782.      "evalue": "",

  783.      "output_type": "error",

  784.      "traceback": [

  785.       "\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."

  786.      ]

  787.     }

  788.    ],

  789.    "source": [

  790.     "plt.plot(validator.score_history[1:])\n",

  791.     "plt.title(\"score_history\")\n",

  792.     "plt.show()\n",

  793.     "\n",

  794.     "plt.plot(val_hooks[0].score_history[1:], label='source')\n",

  795.     "plt.plot(val_hooks[1].score_history[1:], label='target')\n",

  796.     "plt.legend()\n",

  797.     "plt.title(\"src, target val accuracy\")\n",

  798.     "plt.show()"

  799.    ]

  800.   },

  801.   {

  802.    "cell_type": "code",

  803.    "execution_count": 6,

  804.    "metadata": {},

  805.    "outputs": [

  806.     {

  807.      "name": "stdout",

  808.      "output_type": "stream",

  809.      "text": [

  810.       "validator score history [2.85106766 1.07307923 1.06300926 1.43146741 1.88468337 1.96975768\n",

  811.       " 2.14979064 2.62666798 2.61236888 2.4836719  2.59688711 2.05844843\n",

  812.       " 2.02083945 2.18156433 2.30989116 2.43664771 2.50495136 2.57028472\n",

  813.       " 2.55985087 2.59315497 2.65624613]\n",

  814.       "src_val accuracy history [0.90425533 0.20921986 0.40602836 0.52482271 0.49822694 0.5673759\n",

  815.       " 0.57624114 0.59042555 0.59751773 0.57978725 0.63297874 0.57446808\n",

  816.       " 0.65602839 0.66312057 0.66489363 0.66666669 0.65957445 0.65957445\n",

  817.       " 0.67730498 0.67375886 0.66666669]\n"

  818.      ]

  819.     }

  820.    ],

  821.    "source": []

  822.   },

  823.   {

  824.    "cell_type": "code",

  825.    "execution_count": 7,

  826.    "metadata": {},

  827.    "outputs": [

  828.     {

  829.      "name": "stderr",

  830.      "output_type": "stream",

  831.      "text": [

  832.       "INFO:pytorch-adapt:***EVALUATING BEST MODEL***\n",

  833.       "INFO:pytorch-adapt:Collecting target_val_with_labels\n",

  834.       "INFO:pytorch-adapt:Setting models to eval() mode\n"

  835.      ]

  836.     },

  837.     {

  838.      "data": {

  839.       "application/vnd.jupyter.widget-view+json": {

  840.        "model_id": "4980a70229194e1eb8fee18c5af651c1",

  841.        "version_major": 2,

  842.        "version_minor": 0

  843.       },

  844.       "text/plain": [

  845.        "[1/5]  20%|##        |it [00:00<?]"

  846.       ]

  847.      },

  848.      "metadata": {},

  849.      "output_type": "display_data"

  850.     },

  851.     {

  852.      "name": "stdout",

  853.      "output_type": "stream",

  854.      "text": [

  855.       "0.48427674174308777\n"

  856.      ]

  857.     }

  858.    ],

  859.    "source": [

  860.     "validator = AccuracyValidator(key_map={\"target_val_with_labels\": \"src_val\"})\n",

  861.     "score = trainer.evaluate_best_model(datasets, validator, dc)\n",

  862.     "print(score)"

  863.    ]

  864.   },

  865.   {

  866.    "cell_type": "code",

  867.    "execution_count": 8,

  868.    "metadata": {},

  869.    "outputs": [

  870.     {

  871.      "name": "stderr",

  872.      "output_type": "stream",

  873.      "text": [

  874.       "INFO:pytorch-adapt:***EVALUATING BEST MODEL***\n",

  875.       "INFO:pytorch-adapt:Collecting src_val\n",

  876.       "INFO:pytorch-adapt:Setting models to eval() mode\n"

  877.      ]

  878.     },

  879.     {

  880.      "data": {

  881.       "application/vnd.jupyter.widget-view+json": {

  882.        "model_id": "c372abf93f1e4808b286822e23a49261",

  883.        "version_major": 2,

  884.        "version_minor": 0

  885.       },

  886.       "text/plain": [

  887.        "[1/18]   6%|5         |it [00:00<?]"

  888.       ]

  889.      },

  890.      "metadata": {},

  891.      "output_type": "display_data"

  892.     },

  893.     {

  894.      "name": "stdout",

  895.      "output_type": "stream",

  896.      "text": [

  897.       "0.6666666865348816\n"

  898.      ]

  899.     }

  900.    ],

  901.    "source": [

  902.     "validator = AccuracyValidator(key_map={\"src_val\": \"src_val\"})\n",

  903.     "score = trainer.evaluate_best_model(datasets, validator, dc)\n",

  904.     "print(score)"

  905.    ]

  906.   },

  907.   {

  908.    "cell_type": "code",

  909.    "execution_count": null,

  910.    "metadata": {},

  911.    "outputs": [],

  912.    "source": []

  913.   },

  914.   {

  915.    "attachments": {},

  916.    "cell_type": "markdown",

  917.    "metadata": {},

  918.    "source": [

  919.     "### LOAD"

  920.    ]

  921.   },

  922.   {

  923.    "cell_type": "code",

  924.    "execution_count": 6,

  925.    "metadata": {},

  926.    "outputs": [],

  927.    "source": [

  928.     "path = \"/media/10TB71/shashemi/Domain-Adaptation/results/DAModels.CORAL/0/w2d/saved_models/checkpointer_49.pt\"\n",

  929.     "base_path = \"/media/10TB71/shashemi/Domain-Adaptation/results/DAModels.CORAL/0/w2d/saved_models/\"\n",

  930.     "checkpoint = torch.load(path)"

  931.    ]

  932.   },

  933.   {

  934.    "cell_type": "code",

  935.    "execution_count": 22,

  936.    "metadata": {},

  937.    "outputs": [],

  938.    "source": [

  939.     "base_path = \"/media/10TB71/shashemi/Domain-Adaptation/results/DAModels.DANN/0/w2a/saved_models/\""

  940.    ]

  941.   },

  942.   {

  943.    "cell_type": "code",

  944.    "execution_count": 23,

  945.    "metadata": {},

  946.    "outputs": [

  947.     {

  948.      "name": "stderr",

  949.      "output_type": "stream",

  950.      "text": [

  951.       "2021-05-25 17:32:04,308 ignite.distributed.auto.auto_model INFO: Apply torch DataParallel on model\n",

  952.       "2021-05-25 17:32:04,309 ignite.distributed.auto.auto_model INFO: Apply torch DataParallel on model\n",

  953.       "2021-05-25 17:32:04,311 ignite.distributed.auto.auto_model INFO: Apply torch DataParallel on model\n"

  954.      ]

  955.     }

  956.    ],

  957.    "source": [

  958.     "\n",

  959.     "import matplotlib.pyplot as plt\n",

  960.     "import torch\n",

  961.     "import os\n",

  962.     "import gc\n",

  963.     "from datetime import datetime\n",

  964.     "\n",

  965.     "from pytorch_adapt.datasets import DataloaderCreator, get_office31\n",

  966.     "from pytorch_adapt.frameworks.ignite import CheckpointFnCreator, Ignite\n",

  967.     "from pytorch_adapt.validators import AccuracyValidator, IMValidator, ScoreHistory, DiversityValidator, EntropyValidator, MultipleValidators\n",

  968.     "\n",

  969.     "from models import get_model\n",

  970.     "from utils import DAModels\n",

  971.     "\n",

  972.     "from pytorch_adapt.frameworks.ignite import (\n",

  973.     "    CheckpointFnCreator,\n",

  974.     "    IgniteValHookWrapper,\n",

  975.     "    checkpoint_utils,\n",

  976.     ")\n",

  977.     "\n",

  978.     "checkpoint_fn = CheckpointFnCreator(dirname=base_path, require_empty=False, n_saved=None)\n",

  979.     "    \n",

  980.     "    \n",

  981.     "sourceAccuracyValidator = AccuracyValidator()\n",

  982.     "validators = {\n",

  983.     "    \"entropy\": EntropyValidator(),\n",

  984.     "    \"diversity\": DiversityValidator(),\n",

  985.     "    # \"accuracy\": sourceAccuracyValidator,\n",

  986.     "}\n",

  987.     "validator = ScoreHistory(MultipleValidators(validators))\n",

  988.     "\n",

  989.     "\n",

  990.     "targetAccuracyValidator = AccuracyValidator(key_map={\"target_val_with_labels\": \"src_val\"})\n",

  991.     "\n",

  992.     "\n",

  993.     "val_hooks = [ScoreHistory(sourceAccuracyValidator), \n",

  994.     "            ScoreHistory(targetAccuracyValidator)]\n",

  995.     "\n",

  996.     "source_domain =\"webcam\"\n",

  997.     "target_domain = \"amazon\"\n",

  998.     "\n",

  999.     "G = office31G(pretrained=True, model_dir=weights_root).to(device)\n",

  1000.     "C = office31C(domain=source_domain, pretrained=True,\n",

  1001.     "                model_dir=weights_root).to(device)\n",

  1002.     "D = Discriminator(in_size=2048, h=1024).to(device)\n",

  1003.     "\n",

  1004.     "optimizers = Optimizers((torch.optim.Adam, {\"lr\": 1e-4}))\n",

  1005.     "lr_schedulers = LRSchedulers((torch.optim.lr_scheduler.ExponentialLR, {\"gamma\": 0.99}))\n",

  1006.     "\n",

  1007.     "\n",

  1008.     "models = Models({\"G\": G, \"C\": C, \"D\": D})\n",

  1009.     "adapter = DANN(models=models, optimizers=optimizers, lr_schedulers=lr_schedulers)\n",

  1010.     "\n",

  1011.     "\n",

  1012.     "trainer = Ignite(\n",

  1013.     "    adapter, validator=validator, val_hooks=val_hooks, checkpoint_fn=checkpoint_fn\n",

  1014.     ")\n"

  1015.    ]

  1016.   },

  1017.   {

  1018.    "cell_type": "code",

  1019.    "execution_count": 37,

  1020.    "metadata": {},

  1021.    "outputs": [

  1022.     {

  1023.      "ename": "RuntimeError",

  1024.      "evalue": "Error(s) in loading state_dict for DataParallel:\n\tMissing key(s) in state_dict: \"module.conv1.weight\", \"module.bn1.weight\", \"module.bn1.bias\", \"module.bn1.running_mean\", \"module.bn1.running_var\", \"module.layer1.0.conv1.weight\", \"module.layer1.0.bn1.weight\", \"module.layer1.0.bn1.bias\", \"module.layer1.0.bn1.running_mean\", \"module.layer1.0.bn1.running_var\", \"module.layer1.0.conv2.weight\", \"module.layer1.0.bn2.weight\", \"module.layer1.0.bn2.bias\", \"module.layer1.0.bn2.running_mean\", \"module.layer1.0.bn2.running_var\", \"module.layer1.0.conv3.weight\", \"module.layer1.0.bn3.weight\", \"module.layer1.0.bn3.bias\", \"module.layer1.0.bn3.running_mean\", \"module.layer1.0.bn3.running_var\", \"module.layer1.0.downsample.0.weight\", \"module.layer1.0.downsample.1.weight\", \"module.layer1.0.downsample.1.bias\", \"module.layer1.0.downsample.1.running_mean\", \"module.layer1.0.downsample.1.running_var\", \"module.layer1.1.conv1.weight\", \"module.layer1.1.bn1.weight\", \"module.layer1.1.bn1.bias\", \"module.layer1.1.bn1.running_mean\", \"module.layer1.1.bn1.running_var\", \"module.layer1.1.conv2.weight\", \"module.layer1.1.bn2.weight\", \"module.layer1.1.bn2.bias\", \"module.layer1.1.bn2.running_mean\", \"module.layer1.1.bn2.running_var\", \"module.layer1.1.conv3.weight\", \"module.layer1.1.bn3.weight\", \"module.layer1.1.bn3.bias\", \"module.layer1.1.bn3.running_mean\", \"module.layer1.1.bn3.running_var\", \"module.layer1.2.conv1.weight\", \"module.layer1.2.bn1.weight\", \"module.layer1.2.bn1.bias\", \"module.layer1.2.bn1.running_mean\", \"module.layer1.2.bn1.running_var\", \"module.layer1.2.conv2.weight\", \"module.layer1.2.bn2.weight\", \"module.layer1.2.bn2.bias\", \"module.layer1.2.bn2.running_mean\", \"module.layer1.2.bn2.running_var\", \"module.layer1.2.conv3.weight\", \"module.layer1.2.bn3.weight\", \"module.layer1.2.bn3.bias\", \"module.layer1.2.bn3.running_mean\", \"module.layer1.2.bn3.running_var\", \"module.layer2.0.conv1.weight\", \"module.layer2.0.bn1.weight\", \"module.layer2.0.bn1.bias\", \"module.layer2.0.bn1.running_mean\", \"module.layer2.0.bn1.running_var\", \"module.layer2.0.conv2.weight\", \"module.layer2.0.bn2.weight\", \"module.layer2.0.bn2.bias\", \"module.layer2.0.bn2.running_mean\", \"module.layer2.0.bn2.running_var\", \"module.layer2.0.conv3.weight\", \"module.layer2.0.bn3.weight\", \"module.layer2.0.bn3.bias\", \"module.layer2.0.bn3.running_mean\", \"module.layer2.0.bn3.running_var\", \"module.layer2.0.downsample.0.weight\", \"module.layer2.0.downsample.1.weight\", \"module.layer2.0.downsample.1.bias\", \"module.layer2.0.downsample.1.running_mean\", \"module.layer2.0.downsample.1.running_var\", \"module.layer2.1.conv1.weight\", \"module.layer2.1.bn1.weight\", \"module.layer2.1.bn1.bias\", \"module.layer2.1.bn1.running_mean\", \"module.layer2.1.bn1.running_var\", \"module.layer2.1.conv2.weight\", \"module.layer2.1.bn2.weight\", \"module.layer2.1.bn2.bias\", \"module.layer2.1.bn2.running_mean\", \"module.layer2.1.bn2.running_var\", \"module.layer2.1.conv3.weight\", \"module.layer2.1.bn3.weight\", \"module.layer2.1.bn3.bias\", \"module.layer2.1.bn3.running_mean\", \"module.layer2.1.bn3.running_var\", \"module.layer2.2.conv1.weight\", \"module.layer2.2.bn1.weight\", \"module.layer2.2.bn1.bias\", \"module.layer2.2.bn1.running_mean\", \"module.layer2.2.bn1.running_var\", \"module.layer2.2.conv2.weight\", \"module.layer2.2.bn2.weight\", \"module.layer2.2.bn2.bias\", \"module.layer2.2.bn2.running_mean\", \"module.layer2.2.bn2.running_var\", \"module.layer2.2.conv3.weight\", \"module.layer2.2.bn3.weight\", \"module.layer2.2.bn3.bias\", \"module.layer2.2.bn3.running_mean\", \"module.layer2.2.bn3.running_var\", \"module.layer2.3.conv1.weight\", \"module.layer2.3.bn1.weight\", \"module.layer2.3.bn1.bias\", \"module.layer2.3.bn1.running_mean\", \"module.layer2.3.bn1.running_var\", \"module.layer2.3.conv2.weight\", \"module.layer2.3.bn2.weight\", \"module.layer2.3.bn2.bias\", \"module.layer2.3.bn2.running_mean\", \"module.layer2.3.bn2.running_var\", \"module.layer2.3.conv3.weight\", \"module.layer2.3.bn3.weight\", \"module.layer2.3.bn3.bias\", \"module.layer2.3.bn3.running_mean\", \"module.layer2.3.bn3.running_var\", \"module.layer3.0.conv1.weight\", \"module.layer3.0.bn1.weight\", \"module.layer3.0.bn1.bias\", \"module.layer3.0.bn1.running_mean\", \"module.layer3.0.bn1.running_var\", \"module.layer3.0.conv2.weight\", \"module.layer3.0.bn2.weight\", \"module.layer3.0.bn2.bias\", \"module.layer3.0.bn2.running_mean\", \"module.layer3.0.bn2.running_var\", \"module.layer3.0.conv3.weight\", \"module.layer3.0.bn3.weight\", \"module.layer3.0.bn3.bias\", \"module.layer3.0.bn3.running_mean\", \"module.layer3.0.bn3.running_var\", \"module.layer3.0.downsample.0.weight\", \"module.layer3.0.downsample.1.weight\", \"module.layer3.0.downsample.1.bias\", \"module.layer3.0.downsample.1.running_mean\", \"module.layer3.0.downsample.1.running_var\", \"module.layer3.1.conv1.weight\", \"module.layer3.1.bn1.weight\", \"module.layer3.1.bn1.bias\", \"module.layer3.1.bn1.running_mean\", \"module.layer3.1.bn1.running_var\", \"module.layer3.1.conv2.weight\", \"module.layer3.1.bn2.weight\", \"module.layer3.1.bn2.bias\", \"module.layer3.1.bn2.running_mean\", \"module.layer3.1.bn2.running_var\", \"module.layer3.1.conv3.weight\", \"module.layer3.1.bn3.weight\", \"module.layer3.1.bn3.bias\", \"module.layer3.1.bn3.running_mean\", \"module.layer3.1.bn3.running_var\", \"module.layer3.2.conv1.weight\", \"module.layer3.2.bn1.weight\", \"module.layer3.2.bn1.bias\", \"module.layer3.2.bn1.running_mean\", \"module.layer3.2.bn1.running_var\", \"module.layer3.2.conv2.weight\", \"module.layer3.2.bn2.weight\", \"module.layer3.2.bn2.bias\", \"module.layer3.2.bn2.running_mean\", \"module.layer3.2.bn2.running_var\", \"module.layer3.2.conv3.weight\", \"module.layer3.2.bn3.weight\", \"module.layer3.2.bn3.bias\", \"module.layer3.2.bn3.running_mean\", \"module.layer3.2.bn3.running_var\", \"module.layer3.3.conv1.weight\", \"module.layer3.3.bn1.weight\", \"module.layer3.3.bn1.bias\", \"module.layer3.3.bn1.running_mean\", \"module.layer3.3.bn1.running_var\", \"module.layer3.3.conv2.weight\", \"module.layer3.3.bn2.weight\", \"module.layer3.3.bn2.bias\", \"module.layer3.3.bn2.running_mean\", \"module.layer3.3.bn2.running_var\", \"module.layer3.3.conv3.weight\", \"module.layer3.3.bn3.weight\", \"module.layer3.3.bn3.bias\", \"module.layer3.3.bn3.running_mean\", \"module.layer3.3.bn3.running_var\", \"module.layer3.4.conv1.weight\", \"module.layer3.4.bn1.weight\", \"module.layer3.4.bn1.bias\", \"module.layer3.4.bn1.running_mean\", \"module.layer3.4.bn1.running_var\", \"module.layer3.4.conv2.weight\", \"module.layer3.4.bn2.weight\", \"module.layer3.4.bn2.bias\", \"module.layer3.4.bn2.running_mean\", \"module.layer3.4.bn2.running_var\", \"module.layer3.4.conv3.weight\", \"module.layer3.4.bn3.weight\", \"module.layer3.4.bn3.bias\", \"module.layer3.4.bn3.running_mean\", \"module.layer3.4.bn3.running_var\", \"module.layer3.5.conv1.weight\", \"module.layer3.5.bn1.weight\", \"module.layer3.5.bn1.bias\", \"module.layer3.5.bn1.running_mean\", \"module.layer3.5.bn1.running_var\", \"module.layer3.5.conv2.weight\", \"module.layer3.5.bn2.weight\", \"module.layer3.5.bn2.bias\", \"module.layer3.5.bn2.running_mean\", \"module.layer3.5.bn2.running_var\", \"module.layer3.5.conv3.weight\", \"module.layer3.5.bn3.weight\", \"module.layer3.5.bn3.bias\", \"module.layer3.5.bn3.running_mean\", \"module.layer3.5.bn3.running_var\", \"module.layer4.0.conv1.weight\", \"module.layer4.0.bn1.weight\", \"module.layer4.0.bn1.bias\", \"module.layer4.0.bn1.running_mean\", \"module.layer4.0.bn1.running_var\", \"module.layer4.0.conv2.weight\", \"module.layer4.0.bn2.weight\", \"module.layer4.0.bn2.bias\", \"module.layer4.0.bn2.running_mean\", \"module.layer4.0.bn2.running_var\", \"module.layer4.0.conv3.weight\", \"module.layer4.0.bn3.weight\", \"module.layer4.0.bn3.bias\", \"module.layer4.0.bn3.running_mean\", \"module.layer4.0.bn3.running_var\", \"module.layer4.0.downsample.0.weight\", \"module.layer4.0.downsample.1.weight\", \"module.layer4.0.downsample.1.bias\", \"module.layer4.0.downsample.1.running_mean\", \"module.layer4.0.downsample.1.running_var\", \"module.layer4.1.conv1.weight\", \"module.layer4.1.bn1.weight\", \"module.layer4.1.bn1.bias\", \"module.layer4.1.bn1.running_mean\", \"module.layer4.1.bn1.running_var\", \"module.layer4.1.conv2.weight\", \"module.layer4.1.bn2.weight\", \"module.layer4.1.bn2.bias\", \"module.layer4.1.bn2.running_mean\", \"module.layer4.1.bn2.running_var\", \"module.layer4.1.conv3.weight\", \"module.layer4.1.bn3.weight\", \"module.layer4.1.bn3.bias\", \"module.layer4.1.bn3.running_mean\", \"module.layer4.1.bn3.running_var\", \"module.layer4.2.conv1.weight\", \"module.layer4.2.bn1.weight\", \"module.layer4.2.bn1.bias\", \"module.layer4.2.bn1.running_mean\", \"module.layer4.2.bn1.running_var\", \"module.layer4.2.conv2.weight\", \"module.layer4.2.bn2.weight\", \"module.layer4.2.bn2.bias\", \"module.layer4.2.bn2.running_mean\", \"module.layer4.2.bn2.running_var\", \"module.layer4.2.conv3.weight\", \"module.layer4.2.bn3.weight\", \"module.layer4.2.bn3.bias\", \"module.layer4.2.bn3.running_mean\", \"module.layer4.2.bn3.running_var\". \n\tUnexpected key(s) in state_dict: \"conv1.weight\", \"bn1.weight\", \"bn1.bias\", \"bn1.running_mean\", \"bn1.running_var\", \"bn1.num_batches_tracked\", \"layer1.0.conv1.weight\", \"layer1.0.bn1.weight\", \"layer1.0.bn1.bias\", \"layer1.0.bn1.running_mean\", \"layer1.0.bn1.running_var\", \"layer1.0.bn1.num_batches_tracked\", \"layer1.0.conv2.weight\", \"layer1.0.bn2.weight\", \"layer1.0.bn2.bias\", \"layer1.0.bn2.running_mean\", \"layer1.0.bn2.running_var\", \"layer1.0.bn2.num_batches_tracked\", \"layer1.0.conv3.weight\", \"layer1.0.bn3.weight\", \"layer1.0.bn3.bias\", \"layer1.0.bn3.running_mean\", \"layer1.0.bn3.running_var\", \"layer1.0.bn3.num_batches_tracked\", \"layer1.0.downsample.0.weight\", \"layer1.0.downsample.1.weight\", \"layer1.0.downsample.1.bias\", \"layer1.0.downsample.1.running_mean\", \"layer1.0.downsample.1.running_var\", \"layer1.0.downsample.1.num_batches_tracked\", \"layer1.1.conv1.weight\", \"layer1.1.bn1.weight\", \"layer1.1.bn1.bias\", \"layer1.1.bn1.running_mean\", \"layer1.1.bn1.running_var\", \"layer1.1.bn1.num_batches_tracked\", \"layer1.1.conv2.weight\", \"layer1.1.bn2.weight\", \"layer1.1.bn2.bias\", \"layer1.1.bn2.running_mean\", \"layer1.1.bn2.running_var\", \"layer1.1.bn2.num_batches_tracked\", \"layer1.1.conv3.weight\", \"layer1.1.bn3.weight\", \"layer1.1.bn3.bias\", \"layer1.1.bn3.running_mean\", \"layer1.1.bn3.running_var\", \"layer1.1.bn3.num_batches_tracked\", \"layer1.2.conv1.weight\", \"layer1.2.bn1.weight\", \"layer1.2.bn1.bias\", \"layer1.2.bn1.running_mean\", \"layer1.2.bn1.running_var\", \"layer1.2.bn1.num_batches_tracked\", \"layer1.2.conv2.weight\", \"layer1.2.bn2.weight\", \"layer1.2.bn2.bias\", \"layer1.2.bn2.running_mean\", \"layer1.2.bn2.running_var\", \"layer1.2.bn2.num_batches_tracked\", \"layer1.2.conv3.weight\", \"layer1.2.bn3.weight\", \"layer1.2.bn3.bias\", \"layer1.2.bn3.running_mean\", \"layer1.2.bn3.running_var\", \"layer1.2.bn3.num_batches_tracked\", \"layer2.0.conv1.weight\", \"layer2.0.bn1.weight\", \"layer2.0.bn1.bias\", \"layer2.0.bn1.running_mean\", \"layer2.0.bn1.running_var\", \"layer2.0.bn1.num_batches_tracked\", \"layer2.0.conv2.weight\", \"layer2.0.bn2.weight\", \"layer2.0.bn2.bias\", \"layer2.0.bn2.running_mean\", \"layer2.0.bn2.running_var\", \"layer2.0.bn2.num_batches_tracked\", \"layer2.0.conv3.weight\", \"layer2.0.bn3.weight\", \"layer2.0.bn3.bias\", \"layer2.0.bn3.running_mean\", \"layer2.0.bn3.running_var\", \"layer2.0.bn3.num_batches_tracked\", \"layer2.0.downsample.0.weight\", \"layer2.0.downsample.1.weight\", \"layer2.0.downsample.1.bias\", \"layer2.0.downsample.1.running_mean\", \"layer2.0.downsample.1.running_var\", \"layer2.0.downsample.1.num_batches_tracked\", \"layer2.1.conv1.weight\", \"layer2.1.bn1.weight\", \"layer2.1.bn1.bias\", \"layer2.1.bn1.running_mean\", \"layer2.1.bn1.running_var\", \"layer2.1.bn1.num_batches_tracked\", \"layer2.1.conv2.weight\", \"layer2.1.bn2.weight\", \"layer2.1.bn2.bias\", \"layer2.1.bn2.running_mean\", \"layer2.1.bn2.running_var\", \"layer2.1.bn2.num_batches_tracked\", \"layer2.1.conv3.weight\", \"layer2.1.bn3.weight\", \"layer2.1.bn3.bias\", \"layer2.1.bn3.running_mean\", \"layer2.1.bn3.running_var\", \"layer2.1.bn3.num_batches_tracked\", \"layer2.2.conv1.weight\", \"layer2.2.bn1.weight\", \"layer2.2.bn1.bias\", \"layer2.2.bn1.running_mean\", \"layer2.2.bn1.running_var\", \"layer2.2.bn1.num_batches_tracked\", \"layer2.2.conv2.weight\", \"layer2.2.bn2.weight\", \"layer2.2.bn2.bias\", \"layer2.2.bn2.running_mean\", \"layer2.2.bn2.running_var\", \"layer2.2.bn2.num_batches_tracked\", \"layer2.2.conv3.weight\", \"layer2.2.bn3.weight\", \"layer2.2.bn3.bias\", \"layer2.2.bn3.running_mean\", \"layer2.2.bn3.running_var\", \"layer2.2.bn3.num_batches_tracked\", \"layer2.3.conv1.weight\", \"layer2.3.bn1.weight\", \"layer2.3.bn1.bias\", \"layer2.3.bn1.running_mean\", \"layer2.3.bn1.running_var\", \"layer2.3.bn1.num_batches_tracked\", \"layer2.3.conv2.weight\", \"layer2.3.bn2.weight\", \"layer2.3.bn2.bias\", \"layer2.3.bn2.running_mean\", \"layer2.3.bn2.running_var\", \"layer2.3.bn2.num_batches_tracked\", \"layer2.3.conv3.weight\", \"layer2.3.bn3.weight\", \"layer2.3.bn3.bias\", \"layer2.3.bn3.running_mean\", \"layer2.3.bn3.running_var\", \"layer2.3.bn3.num_batches_tracked\", \"layer3.0.conv1.weight\", \"layer3.0.bn1.weight\", \"layer3.0.bn1.bias\", \"layer3.0.bn1.running_mean\", \"layer3.0.bn1.running_var\", \"layer3.0.bn1.num_batches_tracked\", \"layer3.0.conv2.weight\", \"layer3.0.bn2.weight\", \"layer3.0.bn2.bias\", \"layer3.0.bn2.running_mean\", \"layer3.0.bn2.running_var\", \"layer3.0.bn2.num_batches_tracked\", \"layer3.0.conv3.weight\", \"layer3.0.bn3.weight\", \"layer3.0.bn3.bias\", \"layer3.0.bn3.running_mean\", \"layer3.0.bn3.running_var\", \"layer3.0.bn3.num_batches_tracked\", \"layer3.0.downsample.0.weight\", \"layer3.0.downsample.1.weight\", \"layer3.0.downsample.1.bias\", \"layer3.0.downsample.1.running_mean\", \"layer3.0.downsample.1.running_var\", \"layer3.0.downsample.1.num_batches_tracked\", \"layer3.1.conv1.weight\", \"layer3.1.bn1.weight\", \"layer3.1.bn1.bias\", \"layer3.1.bn1.running_mean\", \"layer3.1.bn1.running_var\", \"layer3.1.bn1.num_batches_tracked\", \"layer3.1.conv2.weight\", \"layer3.1.bn2.weight\", \"layer3.1.bn2.bias\", \"layer3.1.bn2.running_mean\", \"layer3.1.bn2.running_var\", \"layer3.1.bn2.num_batches_tracked\", \"layer3.1.conv3.weight\", \"layer3.1.bn3.weight\", \"layer3.1.bn3.bias\", \"layer3.1.bn3.running_mean\", \"layer3.1.bn3.running_var\", \"layer3.1.bn3.num_batches_tracked\", \"layer3.2.conv1.weight\", \"layer3.2.bn1.weight\", \"layer3.2.bn1.bias\", \"layer3.2.bn1.running_mean\", \"layer3.2.bn1.running_var\", \"layer3.2.bn1.num_batches_tracked\", \"layer3.2.conv2.weight\", \"layer3.2.bn2.weight\", \"layer3.2.bn2.bias\", \"layer3.2.bn2.running_mean\", \"layer3.2.bn2.running_var\", \"layer3.2.bn2.num_batches_tracked\", \"layer3.2.conv3.weight\", \"layer3.2.bn3.weight\", \"layer3.2.bn3.bias\", \"layer3.2.bn3.running_mean\", \"layer3.2.bn3.running_var\", \"layer3.2.bn3.num_batches_tracked\", \"layer3.3.conv1.weight\", \"layer3.3.bn1.weight\", \"layer3.3.bn1.bias\", \"layer3.3.bn1.running_mean\", \"layer3.3.bn1.running_var\", \"layer3.3.bn1.num_batches_tracked\", \"layer3.3.conv2.weight\", \"layer3.3.bn2.weight\", \"layer3.3.bn2.bias\", \"layer3.3.bn2.running_mean\", \"layer3.3.bn2.running_var\", \"layer3.3.bn2.num_batches_tracked\", \"layer3.3.conv3.weight\", \"layer3.3.bn3.weight\", \"layer3.3.bn3.bias\", \"layer3.3.bn3.running_mean\", \"layer3.3.bn3.running_var\", \"layer3.3.bn3.num_batches_tracked\", \"layer3.4.conv1.weight\", \"layer3.4.bn1.weight\", \"layer3.4.bn1.bias\", \"layer3.4.bn1.running_mean\", \"layer3.4.bn1.running_var\", \"layer3.4.bn1.num_batches_tracked\", \"layer3.4.conv2.weight\", \"layer3.4.bn2.weight\", \"layer3.4.bn2.bias\", \"layer3.4.bn2.running_mean\", \"layer3.4.bn2.running_var\", \"layer3.4.bn2.num_batches_tracked\", \"layer3.4.conv3.weight\", \"layer3.4.bn3.weight\", \"layer3.4.bn3.bias\", \"layer3.4.bn3.running_mean\", \"layer3.4.bn3.running_var\", \"layer3.4.bn3.num_batches_tracked\", \"layer3.5.conv1.weight\", \"layer3.5.bn1.weight\", \"layer3.5.bn1.bias\", \"layer3.5.bn1.running_mean\", \"layer3.5.bn1.running_var\", \"layer3.5.bn1.num_batches_tracked\", \"layer3.5.conv2.weight\", \"layer3.5.bn2.weight\", \"layer3.5.bn2.bias\", \"layer3.5.bn2.running_mean\", \"layer3.5.bn2.running_var\", \"layer3.5.bn2.num_batches_tracked\", \"layer3.5.conv3.weight\", \"layer3.5.bn3.weight\", \"layer3.5.bn3.bias\", \"layer3.5.bn3.running_mean\", \"layer3.5.bn3.running_var\", \"layer3.5.bn3.num_batches_tracked\", \"layer4.0.conv1.weight\", \"layer4.0.bn1.weight\", \"layer4.0.bn1.bias\", \"layer4.0.bn1.running_mean\", \"layer4.0.bn1.running_var\", \"layer4.0.bn1.num_batches_tracked\", \"layer4.0.conv2.weight\", \"layer4.0.bn2.weight\", \"layer4.0.bn2.bias\", \"layer4.0.bn2.running_mean\", \"layer4.0.bn2.running_var\", \"layer4.0.bn2.num_batches_tracked\", \"layer4.0.conv3.weight\", \"layer4.0.bn3.weight\", \"layer4.0.bn3.bias\", \"layer4.0.bn3.running_mean\", \"layer4.0.bn3.running_var\", \"layer4.0.bn3.num_batches_tracked\", \"layer4.0.downsample.0.weight\", \"layer4.0.downsample.1.weight\", \"layer4.0.downsample.1.bias\", \"layer4.0.downsample.1.running_mean\", \"layer4.0.downsample.1.running_var\", \"layer4.0.downsample.1.num_batches_tracked\", \"layer4.1.conv1.weight\", \"layer4.1.bn1.weight\", \"layer4.1.bn1.bias\", \"layer4.1.bn1.running_mean\", \"layer4.1.bn1.running_var\", \"layer4.1.bn1.num_batches_tracked\", \"layer4.1.conv2.weight\", \"layer4.1.bn2.weight\", \"layer4.1.bn2.bias\", \"layer4.1.bn2.running_mean\", \"layer4.1.bn2.running_var\", \"layer4.1.bn2.num_batches_tracked\", \"layer4.1.conv3.weight\", \"layer4.1.bn3.weight\", \"layer4.1.bn3.bias\", \"layer4.1.bn3.running_mean\", \"layer4.1.bn3.running_var\", \"layer4.1.bn3.num_batches_tracked\", \"layer4.2.conv1.weight\", \"layer4.2.bn1.weight\", \"layer4.2.bn1.bias\", \"layer4.2.bn1.running_mean\", \"layer4.2.bn1.running_var\", \"layer4.2.bn1.num_batches_tracked\", \"layer4.2.conv2.weight\", \"layer4.2.bn2.weight\", \"layer4.2.bn2.bias\", \"layer4.2.bn2.running_mean\", \"layer4.2.bn2.running_var\", \"layer4.2.bn2.num_batches_tracked\", \"layer4.2.conv3.weight\", \"layer4.2.bn3.weight\", \"layer4.2.bn3.bias\", \"layer4.2.bn3.running_mean\", \"layer4.2.bn3.running_var\", \"layer4.2.bn3.num_batches_tracked\". ",

  1025.      "output_type": "error",

  1026.      "traceback": [

  1027.       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",

  1028.       "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",

  1029.       "Cell \u001b[0;32mIn[37], line 23\u001b[0m\n\u001b[1;32m     12\u001b[0m     objs \u001b[39m=\u001b[39m [\n\u001b[1;32m     13\u001b[0m         {\n\u001b[1;32m     14\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39mengine\u001b[39m\u001b[39m\"\u001b[39m: trainer\u001b[39m.\u001b[39mtrainer,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     19\u001b[0m         }\n\u001b[1;32m     20\u001b[0m     ]\n\u001b[1;32m     22\u001b[0m \u001b[39mfor\u001b[39;00m to_load \u001b[39min\u001b[39;00m objs:\n\u001b[0;32m---> 23\u001b[0m     checkpoint_fn\u001b[39m.\u001b[39;49mload_best_checkpoint(to_load)\n",

  1030.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/pytorch_adapt/frameworks/ignite/checkpoint_utils.py:97\u001b[0m, in \u001b[0;36mCheckpointFnCreator.load_best_checkpoint\u001b[0;34m(self, to_load)\u001b[0m\n\u001b[1;32m     95\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mload_best_checkpoint\u001b[39m(\u001b[39mself\u001b[39m, to_load):\n\u001b[1;32m     96\u001b[0m     last_checkpoint \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mget_best_checkpoint()\n\u001b[0;32m---> 97\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mload_objects(to_load, last_checkpoint)\n",

  1031.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/pytorch_adapt/frameworks/ignite/checkpoint_utils.py:93\u001b[0m, in \u001b[0;36mCheckpointFnCreator.load_objects\u001b[0;34m(self, to_load, checkpoint, global_step)\u001b[0m\n\u001b[1;32m     89\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mobjs\u001b[39m.\u001b[39mreload_objects(\n\u001b[1;32m     90\u001b[0m         to_load, name\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mcheckpoint\u001b[39m\u001b[39m\"\u001b[39m, global_step\u001b[39m=\u001b[39mglobal_step\n\u001b[1;32m     91\u001b[0m     )\n\u001b[1;32m     92\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m---> 93\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mobjs\u001b[39m.\u001b[39;49mload_objects(to_load, \u001b[39mstr\u001b[39;49m(checkpoint))\n",

  1032.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/ignite/handlers/checkpoint.py:618\u001b[0m, in \u001b[0;36mCheckpoint.load_objects\u001b[0;34m(to_load, checkpoint, **kwargs)\u001b[0m\n\u001b[1;32m    616\u001b[0m \u001b[39mif\u001b[39;00m k \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m checkpoint_obj:\n\u001b[1;32m    617\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mObject labeled by \u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00mk\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m from `to_load` is not found in the checkpoint\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m--> 618\u001b[0m _load_object(obj, checkpoint_obj[k])\n",

  1033.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/ignite/handlers/checkpoint.py:605\u001b[0m, in \u001b[0;36mCheckpoint.load_objects.<locals>._load_object\u001b[0;34m(obj, chkpt_obj)\u001b[0m\n\u001b[1;32m    603\u001b[0m     obj\u001b[39m.\u001b[39mload_state_dict(chkpt_obj, strict\u001b[39m=\u001b[39mis_state_dict_strict)\n\u001b[1;32m    604\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 605\u001b[0m     obj\u001b[39m.\u001b[39;49mload_state_dict(chkpt_obj)\n",

  1034.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/pytorch_adapt/containers/base_container.py:158\u001b[0m, in \u001b[0;36mBaseContainer.load_state_dict\u001b[0;34m(self, state_dict)\u001b[0m\n\u001b[1;32m    156\u001b[0m c_f\u001b[39m.\u001b[39massert_state_dict_keys(state_dict, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mkeys())\n\u001b[1;32m    157\u001b[0m \u001b[39mfor\u001b[39;00m k, v \u001b[39min\u001b[39;00m state_dict\u001b[39m.\u001b[39mitems():\n\u001b[0;32m--> 158\u001b[0m     \u001b[39mself\u001b[39;49m[k]\u001b[39m.\u001b[39;49mload_state_dict(v)\n",

  1035.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/torch/nn/modules/module.py:1223\u001b[0m, in \u001b[0;36mModule.load_state_dict\u001b[0;34m(self, state_dict, strict)\u001b[0m\n\u001b[1;32m   1218\u001b[0m         error_msgs\u001b[39m.\u001b[39minsert(\n\u001b[1;32m   1219\u001b[0m             \u001b[39m0\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mMissing key(s) in state_dict: \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m. \u001b[39m\u001b[39m'\u001b[39m\u001b[39m.\u001b[39mformat(\n\u001b[1;32m   1220\u001b[0m                 \u001b[39m'\u001b[39m\u001b[39m, \u001b[39m\u001b[39m'\u001b[39m\u001b[39m.\u001b[39mjoin(\u001b[39m'\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m\"\u001b[39m\u001b[39m'\u001b[39m\u001b[39m.\u001b[39mformat(k) \u001b[39mfor\u001b[39;00m k \u001b[39min\u001b[39;00m missing_keys)))\n\u001b[1;32m   1222\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(error_msgs) \u001b[39m>\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[0;32m-> 1223\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mRuntimeError\u001b[39;00m(\u001b[39m'\u001b[39m\u001b[39mError(s) in loading state_dict for \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m:\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m\\t\u001b[39;00m\u001b[39m{}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m.\u001b[39mformat(\n\u001b[1;32m   1224\u001b[0m                        \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m\u001b[39m__class__\u001b[39m\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m\\t\u001b[39;00m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mjoin(error_msgs)))\n\u001b[1;32m   1225\u001b[0m \u001b[39mreturn\u001b[39;00m _IncompatibleKeys(missing_keys, unexpected_keys)\n",

  1036.       "\u001b[0;31mRuntimeError\u001b[0m: Error(s) in loading state_dict for DataParallel:\n\tMissing key(s) in state_dict: \"module.conv1.weight\", \"module.bn1.weight\", \"module.bn1.bias\", \"module.bn1.running_mean\", \"module.bn1.running_var\", \"module.layer1.0.conv1.weight\", \"module.layer1.0.bn1.weight\", \"module.layer1.0.bn1.bias\", \"module.layer1.0.bn1.running_mean\", \"module.layer1.0.bn1.running_var\", \"module.layer1.0.conv2.weight\", \"module.layer1.0.bn2.weight\", \"module.layer1.0.bn2.bias\", \"module.layer1.0.bn2.running_mean\", \"module.layer1.0.bn2.running_var\", \"module.layer1.0.conv3.weight\", \"module.layer1.0.bn3.weight\", \"module.layer1.0.bn3.bias\", \"module.layer1.0.bn3.running_mean\", \"module.layer1.0.bn3.running_var\", \"module.layer1.0.downsample.0.weight\", \"module.layer1.0.downsample.1.weight\", \"module.layer1.0.downsample.1.bias\", \"module.layer1.0.downsample.1.running_mean\", \"module.layer1.0.downsample.1.running_var\", \"module.layer1.1.conv1.weight\", \"module.layer1.1.bn1.weight\", \"module.layer1.1.bn1.bias\", \"module.layer1.1.bn1.running_mean\", \"module.layer1.1.bn1.running_var\", \"module.layer1.1.conv2.weight\", \"module.layer1.1.bn2.weight\", \"module.layer1.1.bn2.bias\", \"module.layer1.1.bn2.running_mean\", \"module.layer1.1.bn2.running_var\", \"module.layer1.1.conv3.weight\", \"module.layer1.1.bn3.weight\", \"module.layer1.1.bn3.bias\", \"module.layer1.1.bn3.running_mean\", \"module.layer1.1.bn3.running_var\", \"module.layer1.2.conv1.weight\", \"module.layer1.2.bn1.weight\", \"module.layer1.2.bn1.bias\", \"module.layer1.2.bn1.running_mean\", \"module.layer1.2.bn1.running_var\", \"module.layer1.2.conv2.weight\", \"module.layer1.2.bn2.weight\", \"module.layer1.2.bn2.bias\", \"module.layer1.2.bn2.running_mean\", \"module.layer1.2.bn2.running_var\", \"module.layer1.2.conv3.weight\", \"module.layer1.2.bn3.weight\", \"module.layer1.2.bn3.bias\", \"module.layer1.2.bn3.running_mean\", \"module.layer1.2.bn3.running_var\", \"module.layer2.0.conv1.weight\", \"module.layer2.0.bn1.weight\", \"module.layer2.0.bn1.bias\", \"module.layer2.0.bn1.running_mean\", \"module.layer2.0.bn1.running_var\", \"module.layer2.0.conv2.weight\", \"module.layer2.0.bn2.weight\", \"module.layer2.0.bn2.bias\", \"module.layer2.0.bn2.running_mean\", \"module.layer2.0.bn2.running_var\", \"module.layer2.0.conv3.weight\", \"module.layer2.0.bn3.weight\", \"module.layer2.0.bn3.bias\", \"module.layer2.0.bn3.running_mean\", \"module.layer2.0.bn3.running_var\", \"module.layer2.0.downsample.0.weight\", \"module.layer2.0.downsample.1.weight\", \"module.layer2.0.downsample.1.bias\", \"module.layer2.0.downsample.1.running_mean\", \"module.layer2.0.downsample.1.running_var\", \"module.layer2.1.conv1.weight\", \"module.layer2.1.bn1.weight\", \"module.layer2.1.bn1.bias\", \"module.layer2.1.bn1.running_mean\", \"module.layer2.1.bn1.running_var\", \"module.layer2.1.conv2.weight\", \"module.layer2.1.bn2.weight\", \"module.layer2.1.bn2.bias\", \"module.layer2.1.bn2.running_mean\", \"module.layer2.1.bn2.running_var\", \"module.layer2.1.conv3.weight\", \"module.layer2.1.bn3.weight\", \"module.layer2.1.bn3.bias\", \"module.layer2.1.bn3.running_mean\", \"module.layer2.1.bn3.running_var\", \"module.layer2.2.conv1.weight\", \"module.layer2.2.bn1.weight\", \"module.layer2.2.bn1.bias\", \"module.layer2.2.bn1.running_mean\", \"module.layer2.2.bn1.running_var\", \"module.layer2.2.conv2.weight\", \"module.layer2.2.bn2.weight\", \"module.layer2.2.bn2.bias\", \"module.layer2.2.bn2.running_mean\", \"module.layer2.2.bn2.running_var\", \"module.layer2.2.conv3.weight\", \"module.layer2.2.bn3.weight\", \"module.layer2.2.bn3.bias\", \"module.layer2.2.bn3.running_mean\", \"module.layer2.2.bn3.running_var\", \"module.layer2.3.conv1.weight\", \"module.layer2.3.bn1.weight\", \"module.layer2.3.bn1.bias\", \"module.layer2.3.bn1.running_mean\", \"module.layer2.3.bn1.running_var\", \"module.layer2.3.conv2.weight\", \"module.layer2.3.bn2.weight\", \"module.layer2.3.bn2.bias\", \"module.layer2.3.bn2.running_mean\", \"module.layer2.3.bn2.running_var\", \"module.layer2.3.conv3.weight\", \"module.layer2.3.bn3.weight\", \"module.layer2.3.bn3.bias\", \"module.layer2.3.bn3.running_mean\", \"module.layer2.3.bn3.running_var\", \"module.layer3.0.conv1.weight\", \"module.layer3.0.bn1.weight\", \"module.layer3.0.bn1.bias\", \"module.layer3.0.bn1.running_mean\", \"module.layer3.0.bn1.running_var\", \"module.layer3.0.conv2.weight\", \"module.layer3.0.bn2.weight\", \"module.layer3.0.bn2.bias\", \"module.layer3.0.bn2.running_mean\", \"module.layer3.0.bn2.running_var\", \"module.layer3.0.conv3.weight\", \"module.layer3.0.bn3.weight\", \"module.layer3.0.bn3.bias\", \"module.layer3.0.bn3.running_mean\", \"module.layer3.0.bn3.running_var\", \"module.layer3.0.downsample.0.weight\", \"module.layer3.0.downsample.1.weight\", \"module.layer3.0.downsample.1.bias\", \"module.layer3.0.downsample.1.running_mean\", \"module.layer3.0.downsample.1.running_var\", \"module.layer3.1.conv1.weight\", \"module.layer3.1.bn1.weight\", \"module.layer3.1.bn1.bias\", \"module.layer3.1.bn1.running_mean\", \"module.layer3.1.bn1.running_var\", \"module.layer3.1.conv2.weight\", \"module.layer3.1.bn2.weight\", \"module.layer3.1.bn2.bias\", \"module.layer3.1.bn2.running_mean\", \"module.layer3.1.bn2.running_var\", \"module.layer3.1.conv3.weight\", \"module.layer3.1.bn3.weight\", \"module.layer3.1.bn3.bias\", \"module.layer3.1.bn3.running_mean\", \"module.layer3.1.bn3.running_var\", \"module.layer3.2.conv1.weight\", \"module.layer3.2.bn1.weight\", \"module.layer3.2.bn1.bias\", \"module.layer3.2.bn1.running_mean\", \"module.layer3.2.bn1.running_var\", \"module.layer3.2.conv2.weight\", \"module.layer3.2.bn2.weight\", \"module.layer3.2.bn2.bias\", \"module.layer3.2.bn2.running_mean\", \"module.layer3.2.bn2.running_var\", \"module.layer3.2.conv3.weight\", \"module.layer3.2.bn3.weight\", \"module.layer3.2.bn3.bias\", \"module.layer3.2.bn3.running_mean\", \"module.layer3.2.bn3.running_var\", \"module.layer3.3.conv1.weight\", \"module.layer3.3.bn1.weight\", \"module.layer3.3.bn1.bias\", \"module.layer3.3.bn1.running_mean\", \"module.layer3.3.bn1.running_var\", \"module.layer3.3.conv2.weight\", \"module.layer3.3.bn2.weight\", \"module.layer3.3.bn2.bias\", \"module.layer3.3.bn2.running_mean\", \"module.layer3.3.bn2.running_var\", \"module.layer3.3.conv3.weight\", \"module.layer3.3.bn3.weight\", \"module.layer3.3.bn3.bias\", \"module.layer3.3.bn3.running_mean\", \"module.layer3.3.bn3.running_var\", \"module.layer3.4.conv1.weight\", \"module.layer3.4.bn1.weight\", \"module.layer3.4.bn1.bias\", \"module.layer3.4.bn1.running_mean\", \"module.layer3.4.bn1.running_var\", \"module.layer3.4.conv2.weight\", \"module.layer3.4.bn2.weight\", \"module.layer3.4.bn2.bias\", \"module.layer3.4.bn2.running_mean\", \"module.layer3.4.bn2.running_var\", \"module.layer3.4.conv3.weight\", \"module.layer3.4.bn3.weight\", \"module.layer3.4.bn3.bias\", \"module.layer3.4.bn3.running_mean\", \"module.layer3.4.bn3.running_var\", \"module.layer3.5.conv1.weight\", \"module.layer3.5.bn1.weight\", \"module.layer3.5.bn1.bias\", \"module.layer3.5.bn1.running_mean\", \"module.layer3.5.bn1.running_var\", \"module.layer3.5.conv2.weight\", \"module.layer3.5.bn2.weight\", \"module.layer3.5.bn2.bias\", \"module.layer3.5.bn2.running_mean\", \"module.layer3.5.bn2.running_var\", \"module.layer3.5.conv3.weight\", \"module.layer3.5.bn3.weight\", \"module.layer3.5.bn3.bias\", \"module.layer3.5.bn3.running_mean\", \"module.layer3.5.bn3.running_var\", \"module.layer4.0.conv1.weight\", \"module.layer4.0.bn1.weight\", \"module.layer4.0.bn1.bias\", \"module.layer4.0.bn1.running_mean\", \"module.layer4.0.bn1.running_var\", \"module.layer4.0.conv2.weight\", \"module.layer4.0.bn2.weight\", \"module.layer4.0.bn2.bias\", \"module.layer4.0.bn2.running_mean\", \"module.layer4.0.bn2.running_var\", \"module.layer4.0.conv3.weight\", \"module.layer4.0.bn3.weight\", \"module.layer4.0.bn3.bias\", \"module.layer4.0.bn3.running_mean\", \"module.layer4.0.bn3.running_var\", \"module.layer4.0.downsample.0.weight\", \"module.layer4.0.downsample.1.weight\", \"module.layer4.0.downsample.1.bias\", \"module.layer4.0.downsample.1.running_mean\", \"module.layer4.0.downsample.1.running_var\", \"module.layer4.1.conv1.weight\", \"module.layer4.1.bn1.weight\", \"module.layer4.1.bn1.bias\", \"module.layer4.1.bn1.running_mean\", \"module.layer4.1.bn1.running_var\", \"module.layer4.1.conv2.weight\", \"module.layer4.1.bn2.weight\", \"module.layer4.1.bn2.bias\", \"module.layer4.1.bn2.running_mean\", \"module.layer4.1.bn2.running_var\", \"module.layer4.1.conv3.weight\", \"module.layer4.1.bn3.weight\", \"module.layer4.1.bn3.bias\", \"module.layer4.1.bn3.running_mean\", \"module.layer4.1.bn3.running_var\", \"module.layer4.2.conv1.weight\", \"module.layer4.2.bn1.weight\", \"module.layer4.2.bn1.bias\", \"module.layer4.2.bn1.running_mean\", \"module.layer4.2.bn1.running_var\", \"module.layer4.2.conv2.weight\", \"module.layer4.2.bn2.weight\", \"module.layer4.2.bn2.bias\", \"module.layer4.2.bn2.running_mean\", \"module.layer4.2.bn2.running_var\", \"module.layer4.2.conv3.weight\", \"module.layer4.2.bn3.weight\", \"module.layer4.2.bn3.bias\", \"module.layer4.2.bn3.running_mean\", \"module.layer4.2.bn3.running_var\". \n\tUnexpected key(s) in state_dict: \"conv1.weight\", \"bn1.weight\", \"bn1.bias\", \"bn1.running_mean\", \"bn1.running_var\", \"bn1.num_batches_tracked\", \"layer1.0.conv1.weight\", \"layer1.0.bn1.weight\", \"layer1.0.bn1.bias\", \"layer1.0.bn1.running_mean\", \"layer1.0.bn1.running_var\", \"layer1.0.bn1.num_batches_tracked\", \"layer1.0.conv2.weight\", \"layer1.0.bn2.weight\", \"layer1.0.bn2.bias\", \"layer1.0.bn2.running_mean\", \"layer1.0.bn2.running_var\", \"layer1.0.bn2.num_batches_tracked\", \"layer1.0.conv3.weight\", \"layer1.0.bn3.weight\", \"layer1.0.bn3.bias\", \"layer1.0.bn3.running_mean\", \"layer1.0.bn3.running_var\", \"layer1.0.bn3.num_batches_tracked\", \"layer1.0.downsample.0.weight\", \"layer1.0.downsample.1.weight\", \"layer1.0.downsample.1.bias\", \"layer1.0.downsample.1.running_mean\", \"layer1.0.downsample.1.running_var\", \"layer1.0.downsample.1.num_batches_tracked\", \"layer1.1.conv1.weight\", \"layer1.1.bn1.weight\", \"layer1.1.bn1.bias\", \"layer1.1.bn1.running_mean\", \"layer1.1.bn1.running_var\", \"layer1.1.bn1.num_batches_tracked\", \"layer1.1.conv2.weight\", \"layer1.1.bn2.weight\", \"layer1.1.bn2.bias\", \"layer1.1.bn2.running_mean\", \"layer1.1.bn2.running_var\", \"layer1.1.bn2.num_batches_tracked\", \"layer1.1.conv3.weight\", \"layer1.1.bn3.weight\", \"layer1.1.bn3.bias\", \"layer1.1.bn3.running_mean\", \"layer1.1.bn3.running_var\", \"layer1.1.bn3.num_batches_tracked\", \"layer1.2.conv1.weight\", \"layer1.2.bn1.weight\", \"layer1.2.bn1.bias\", \"layer1.2.bn1.running_mean\", \"layer1.2.bn1.running_var\", \"layer1.2.bn1.num_batches_tracked\", \"layer1.2.conv2.weight\", \"layer1.2.bn2.weight\", \"layer1.2.bn2.bias\", \"layer1.2.bn2.running_mean\", \"layer1.2.bn2.running_var\", \"layer1.2.bn2.num_batches_tracked\", \"layer1.2.conv3.weight\", \"layer1.2.bn3.weight\", \"layer1.2.bn3.bias\", \"layer1.2.bn3.running_mean\", \"layer1.2.bn3.running_var\", \"layer1.2.bn3.num_batches_tracked\", \"layer2.0.conv1.weight\", \"layer2.0.bn1.weight\", \"layer2.0.bn1.bias\", \"layer2.0.bn1.running_mean\", \"layer2.0.bn1.running_var\", \"layer2.0.bn1.num_batches_tracked\", \"layer2.0.conv2.weight\", \"layer2.0.bn2.weight\", \"layer2.0.bn2.bias\", \"layer2.0.bn2.running_mean\", \"layer2.0.bn2.running_var\", \"layer2.0.bn2.num_batches_tracked\", \"layer2.0.conv3.weight\", \"layer2.0.bn3.weight\", \"layer2.0.bn3.bias\", \"layer2.0.bn3.running_mean\", \"layer2.0.bn3.running_var\", \"layer2.0.bn3.num_batches_tracked\", \"layer2.0.downsample.0.weight\", \"layer2.0.downsample.1.weight\", \"layer2.0.downsample.1.bias\", \"layer2.0.downsample.1.running_mean\", \"layer2.0.downsample.1.running_var\", \"layer2.0.downsample.1.num_batches_tracked\", \"layer2.1.conv1.weight\", \"layer2.1.bn1.weight\", \"layer2.1.bn1.bias\", \"layer2.1.bn1.running_mean\", \"layer2.1.bn1.running_var\", \"layer2.1.bn1.num_batches_tracked\", \"layer2.1.conv2.weight\", \"layer2.1.bn2.weight\", \"layer2.1.bn2.bias\", \"layer2.1.bn2.running_mean\", \"layer2.1.bn2.running_var\", \"layer2.1.bn2.num_batches_tracked\", \"layer2.1.conv3.weight\", \"layer2.1.bn3.weight\", \"layer2.1.bn3.bias\", \"layer2.1.bn3.running_mean\", \"layer2.1.bn3.running_var\", \"layer2.1.bn3.num_batches_tracked\", \"layer2.2.conv1.weight\", \"layer2.2.bn1.weight\", \"layer2.2.bn1.bias\", \"layer2.2.bn1.running_mean\", \"layer2.2.bn1.running_var\", \"layer2.2.bn1.num_batches_tracked\", \"layer2.2.conv2.weight\", \"layer2.2.bn2.weight\", \"layer2.2.bn2.bias\", \"layer2.2.bn2.running_mean\", \"layer2.2.bn2.running_var\", \"layer2.2.bn2.num_batches_tracked\", \"layer2.2.conv3.weight\", \"layer2.2.bn3.weight\", \"layer2.2.bn3.bias\", \"layer2.2.bn3.running_mean\", \"layer2.2.bn3.running_var\", \"layer2.2.bn3.num_batches_tracked\", \"layer2.3.conv1.weight\", \"layer2.3.bn1.weight\", \"layer2.3.bn1.bias\", \"layer2.3.bn1.running_mean\", \"layer2.3.bn1.running_var\", \"layer2.3.bn1.num_batches_tracked\", \"layer2.3.conv2.weight\", \"layer2.3.bn2.weight\", \"layer2.3.bn2.bias\", \"layer2.3.bn2.running_mean\", \"layer2.3.bn2.running_var\", \"layer2.3.bn2.num_batches_tracked\", \"layer2.3.conv3.weight\", \"layer2.3.bn3.weight\", \"layer2.3.bn3.bias\", \"layer2.3.bn3.running_mean\", \"layer2.3.bn3.running_var\", \"layer2.3.bn3.num_batches_tracked\", \"layer3.0.conv1.weight\", \"layer3.0.bn1.weight\", \"layer3.0.bn1.bias\", \"layer3.0.bn1.running_mean\", \"layer3.0.bn1.running_var\", \"layer3.0.bn1.num_batches_tracked\", \"layer3.0.conv2.weight\", \"layer3.0.bn2.weight\", \"layer3.0.bn2.bias\", \"layer3.0.bn2.running_mean\", \"layer3.0.bn2.running_var\", \"layer3.0.bn2.num_batches_tracked\", \"layer3.0.conv3.weight\", \"layer3.0.bn3.weight\", \"layer3.0.bn3.bias\", \"layer3.0.bn3.running_mean\", \"layer3.0.bn3.running_var\", \"layer3.0.bn3.num_batches_tracked\", \"layer3.0.downsample.0.weight\", \"layer3.0.downsample.1.weight\", \"layer3.0.downsample.1.bias\", \"layer3.0.downsample.1.running_mean\", \"layer3.0.downsample.1.running_var\", \"layer3.0.downsample.1.num_batches_tracked\", \"layer3.1.conv1.weight\", \"layer3.1.bn1.weight\", \"layer3.1.bn1.bias\", \"layer3.1.bn1.running_mean\", \"layer3.1.bn1.running_var\", \"layer3.1.bn1.num_batches_tracked\", \"layer3.1.conv2.weight\", \"layer3.1.bn2.weight\", \"layer3.1.bn2.bias\", \"layer3.1.bn2.running_mean\", \"layer3.1.bn2.running_var\", \"layer3.1.bn2.num_batches_tracked\", \"layer3.1.conv3.weight\", \"layer3.1.bn3.weight\", \"layer3.1.bn3.bias\", \"layer3.1.bn3.running_mean\", \"layer3.1.bn3.running_var\", \"layer3.1.bn3.num_batches_tracked\", \"layer3.2.conv1.weight\", \"layer3.2.bn1.weight\", \"layer3.2.bn1.bias\", \"layer3.2.bn1.running_mean\", \"layer3.2.bn1.running_var\", \"layer3.2.bn1.num_batches_tracked\", \"layer3.2.conv2.weight\", \"layer3.2.bn2.weight\", \"layer3.2.bn2.bias\", \"layer3.2.bn2.running_mean\", \"layer3.2.bn2.running_var\", \"layer3.2.bn2.num_batches_tracked\", \"layer3.2.conv3.weight\", \"layer3.2.bn3.weight\", \"layer3.2.bn3.bias\", \"layer3.2.bn3.running_mean\", \"layer3.2.bn3.running_var\", \"layer3.2.bn3.num_batches_tracked\", \"layer3.3.conv1.weight\", \"layer3.3.bn1.weight\", \"layer3.3.bn1.bias\", \"layer3.3.bn1.running_mean\", \"layer3.3.bn1.running_var\", \"layer3.3.bn1.num_batches_tracked\", \"layer3.3.conv2.weight\", \"layer3.3.bn2.weight\", \"layer3.3.bn2.bias\", \"layer3.3.bn2.running_mean\", \"layer3.3.bn2.running_var\", \"layer3.3.bn2.num_batches_tracked\", \"layer3.3.conv3.weight\", \"layer3.3.bn3.weight\", \"layer3.3.bn3.bias\", \"layer3.3.bn3.running_mean\", \"layer3.3.bn3.running_var\", \"layer3.3.bn3.num_batches_tracked\", \"layer3.4.conv1.weight\", \"layer3.4.bn1.weight\", \"layer3.4.bn1.bias\", \"layer3.4.bn1.running_mean\", \"layer3.4.bn1.running_var\", \"layer3.4.bn1.num_batches_tracked\", \"layer3.4.conv2.weight\", \"layer3.4.bn2.weight\", \"layer3.4.bn2.bias\", \"layer3.4.bn2.running_mean\", \"layer3.4.bn2.running_var\", \"layer3.4.bn2.num_batches_tracked\", \"layer3.4.conv3.weight\", \"layer3.4.bn3.weight\", \"layer3.4.bn3.bias\", \"layer3.4.bn3.running_mean\", \"layer3.4.bn3.running_var\", \"layer3.4.bn3.num_batches_tracked\", \"layer3.5.conv1.weight\", \"layer3.5.bn1.weight\", \"layer3.5.bn1.bias\", \"layer3.5.bn1.running_mean\", \"layer3.5.bn1.running_var\", \"layer3.5.bn1.num_batches_tracked\", \"layer3.5.conv2.weight\", \"layer3.5.bn2.weight\", \"layer3.5.bn2.bias\", \"layer3.5.bn2.running_mean\", \"layer3.5.bn2.running_var\", \"layer3.5.bn2.num_batches_tracked\", \"layer3.5.conv3.weight\", \"layer3.5.bn3.weight\", \"layer3.5.bn3.bias\", \"layer3.5.bn3.running_mean\", \"layer3.5.bn3.running_var\", \"layer3.5.bn3.num_batches_tracked\", \"layer4.0.conv1.weight\", \"layer4.0.bn1.weight\", \"layer4.0.bn1.bias\", \"layer4.0.bn1.running_mean\", \"layer4.0.bn1.running_var\", \"layer4.0.bn1.num_batches_tracked\", \"layer4.0.conv2.weight\", \"layer4.0.bn2.weight\", \"layer4.0.bn2.bias\", \"layer4.0.bn2.running_mean\", \"layer4.0.bn2.running_var\", \"layer4.0.bn2.num_batches_tracked\", \"layer4.0.conv3.weight\", \"layer4.0.bn3.weight\", \"layer4.0.bn3.bias\", \"layer4.0.bn3.running_mean\", \"layer4.0.bn3.running_var\", \"layer4.0.bn3.num_batches_tracked\", \"layer4.0.downsample.0.weight\", \"layer4.0.downsample.1.weight\", \"layer4.0.downsample.1.bias\", \"layer4.0.downsample.1.running_mean\", \"layer4.0.downsample.1.running_var\", \"layer4.0.downsample.1.num_batches_tracked\", \"layer4.1.conv1.weight\", \"layer4.1.bn1.weight\", \"layer4.1.bn1.bias\", \"layer4.1.bn1.running_mean\", \"layer4.1.bn1.running_var\", \"layer4.1.bn1.num_batches_tracked\", \"layer4.1.conv2.weight\", \"layer4.1.bn2.weight\", \"layer4.1.bn2.bias\", \"layer4.1.bn2.running_mean\", \"layer4.1.bn2.running_var\", \"layer4.1.bn2.num_batches_tracked\", \"layer4.1.conv3.weight\", \"layer4.1.bn3.weight\", \"layer4.1.bn3.bias\", \"layer4.1.bn3.running_mean\", \"layer4.1.bn3.running_var\", \"layer4.1.bn3.num_batches_tracked\", \"layer4.2.conv1.weight\", \"layer4.2.bn1.weight\", \"layer4.2.bn1.bias\", \"layer4.2.bn1.running_mean\", \"layer4.2.bn1.running_var\", \"layer4.2.bn1.num_batches_tracked\", \"layer4.2.conv2.weight\", \"layer4.2.bn2.weight\", \"layer4.2.bn2.bias\", \"layer4.2.bn2.running_mean\", \"layer4.2.bn2.running_var\", \"layer4.2.bn2.num_batches_tracked\", \"layer4.2.conv3.weight\", \"layer4.2.bn3.weight\", \"layer4.2.bn3.bias\", \"layer4.2.bn3.running_mean\", \"layer4.2.bn3.running_var\", \"layer4.2.bn3.num_batches_tracked\". "

  1037.      ]

  1038.     }

  1039.    ],

  1040.    "source": [

  1041.     "\n",

  1042.     "load_partial = False\n",

  1043.     "\n",

  1044.     "if load_partial:\n",

  1045.     "    objs = [\n",

  1046.     "        {\n",

  1047.     "            \"engine\": trainer.trainer,\n",

  1048.     "            **checkpoint_utils.adapter_to_dict(trainer.adapter),\n",

  1049.     "        },\n",

  1050.     "        {\"validator\": validator},\n",

  1051.     "    ]\n",

  1052.     "else:\n",

  1053.     "    objs = [\n",

  1054.     "        {\n",

  1055.     "            \"engine\": trainer.trainer,\n",

  1056.     "            \"validator\": trainer.validator,\n",

  1057.     "            \"models\": trainer.adapter.models\n",

  1058.     "            # \"adapter\": trainer.adapter,\n",

  1059.     "            # **checkpoint_utils.adapter_to_dict(trainer.adapter),\n",

  1060.     "        }\n",

  1061.     "    ]\n",

  1062.     "\n",

  1063.     "for to_load in objs:\n",

  1064.     "    checkpoint_fn.load_best_checkpoint(to_load)\n",

  1065.     "\n",

  1066.     "# trainer.adapter.models\n"

  1067.    ]

  1068.   },

  1069.   {

  1070.    "cell_type": "code",

  1071.    "execution_count": 21,

  1072.    "metadata": {},

  1073.    "outputs": [

  1074.     {

  1075.      "data": {

  1076.       "text/plain": [

  1077.        "{'models': G: DataParallel(\n",

  1078.        "   (module): ResNet(\n",

  1079.        "     (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",

  1080.        "     (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1081.        "     (act1): ReLU(inplace=True)\n",

  1082.        "     (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",

  1083.        "     (layer1): Sequential(\n",

  1084.        "       (0): Bottleneck(\n",

  1085.        "         (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",

  1086.        "         (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1087.        "         (act1): ReLU(inplace=True)\n",

  1088.        "         (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",

  1089.        "         (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1090.        "         (act2): ReLU(inplace=True)\n",

  1091.        "         (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",

  1092.        "         (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1093.        "         (act3): ReLU(inplace=True)\n",

  1094.        "         (downsample): Sequential(\n",

  1095.        "           (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",

  1096.        "           (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1097.        "         )\n",

  1098.        "       )\n",

  1099.        "       (1): Bottleneck(\n",

  1100.        "         (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",

  1101.        "         (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1102.        "         (act1): ReLU(inplace=True)\n",

  1103.        "         (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",

  1104.        "         (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1105.        "         (act2): ReLU(inplace=True)\n",

  1106.        "         (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",

  1107.        "         (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1108.        "         (act3): ReLU(inplace=True)\n",

  1109.        "       )\n",

  1110.        "       (2): Bottleneck(\n",

  1111.        "         (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",

  1112.        "         (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1113.        "         (act1): ReLU(inplace=True)\n",

  1114.        "         (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",

  1115.        "         (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1116.        "         (act2): ReLU(inplace=True)\n",

  1117.        "         (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",

  1118.        "         (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1119.        "         (act3): ReLU(inplace=True)\n",

  1120.        "       )\n",

  1121.        "     )\n",

  1122.        "     (layer2): Sequential(\n",

  1123.        "       (0): Bottleneck(\n",

  1124.        "         (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",

  1125.        "         (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1126.        "         (act1): ReLU(inplace=True)\n",

  1127.        "         (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",

  1128.        "         (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1129.        "         (act2): ReLU(inplace=True)\n",

  1130.        "         (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",

  1131.        "         (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1132.        "         (act3): ReLU(inplace=True)\n",

  1133.        "         (downsample): Sequential(\n",

  1134.        "           (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",

  1135.        "           (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1136.        "         )\n",

  1137.        "       )\n",

  1138.        "       (1): Bottleneck(\n",

  1139.        "         (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",

  1140.        "         (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1141.        "         (act1): ReLU(inplace=True)\n",

  1142.        "         (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",

  1143.        "         (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1144.        "         (act2): ReLU(inplace=True)\n",

  1145.        "         (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",

  1146.        "         (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1147.        "         (act3): ReLU(inplace=True)\n",

  1148.        "       )\n",

  1149.        "       (2): Bottleneck(\n",

  1150.        "         (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",

  1151.        "         (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1152.        "         (act1): ReLU(inplace=True)\n",

  1153.        "         (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",

  1154.        "         (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1155.        "         (act2): ReLU(inplace=True)\n",

  1156.        "         (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",

  1157.        "         (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1158.        "         (act3): ReLU(inplace=True)\n",

  1159.        "       )\n",

  1160.        "       (3): Bottleneck(\n",

  1161.        "         (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",

  1162.        "         (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1163.        "         (act1): ReLU(inplace=True)\n",

  1164.        "         (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",

  1165.        "         (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1166.        "         (act2): ReLU(inplace=True)\n",

  1167.        "         (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",

  1168.        "         (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1169.        "         (act3): ReLU(inplace=True)\n",

  1170.        "       )\n",

  1171.        "     )\n",

  1172.        "     (layer3): Sequential(\n",

  1173.        "       (0): Bottleneck(\n",

  1174.        "         (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",

  1175.        "         (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1176.        "         (act1): ReLU(inplace=True)\n",

  1177.        "         (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",

  1178.        "         (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1179.        "         (act2): ReLU(inplace=True)\n",

  1180.        "         (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",

  1181.        "         (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1182.        "         (act3): ReLU(inplace=True)\n",

  1183.        "         (downsample): Sequential(\n",

  1184.        "           (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",

  1185.        "           (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1186.        "         )\n",

  1187.        "       )\n",

  1188.        "       (1): Bottleneck(\n",

  1189.        "         (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",

  1190.        "         (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1191.        "         (act1): ReLU(inplace=True)\n",

  1192.        "         (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",

  1193.        "         (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1194.        "         (act2): ReLU(inplace=True)\n",

  1195.        "         (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",

  1196.        "         (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1197.        "         (act3): ReLU(inplace=True)\n",

  1198.        "       )\n",

  1199.        "       (2): Bottleneck(\n",

  1200.        "         (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",

  1201.        "         (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1202.        "         (act1): ReLU(inplace=True)\n",

  1203.        "         (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",

  1204.        "         (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1205.        "         (act2): ReLU(inplace=True)\n",

  1206.        "         (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",

  1207.        "         (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1208.        "         (act3): ReLU(inplace=True)\n",

  1209.        "       )\n",

  1210.        "       (3): Bottleneck(\n",

  1211.        "         (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",

  1212.        "         (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1213.        "         (act1): ReLU(inplace=True)\n",

  1214.        "         (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",

  1215.        "         (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1216.        "         (act2): ReLU(inplace=True)\n",

  1217.        "         (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",

  1218.        "         (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1219.        "         (act3): ReLU(inplace=True)\n",

  1220.        "       )\n",

  1221.        "       (4): Bottleneck(\n",

  1222.        "         (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",

  1223.        "         (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1224.        "         (act1): ReLU(inplace=True)\n",

  1225.        "         (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",

  1226.        "         (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1227.        "         (act2): ReLU(inplace=True)\n",

  1228.        "         (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",

  1229.        "         (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1230.        "         (act3): ReLU(inplace=True)\n",

  1231.        "       )\n",

  1232.        "       (5): Bottleneck(\n",

  1233.        "         (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",

  1234.        "         (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1235.        "         (act1): ReLU(inplace=True)\n",

  1236.        "         (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",

  1237.        "         (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1238.        "         (act2): ReLU(inplace=True)\n",

  1239.        "         (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",

  1240.        "         (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1241.        "         (act3): ReLU(inplace=True)\n",

  1242.        "       )\n",

  1243.        "     )\n",

  1244.        "     (layer4): Sequential(\n",

  1245.        "       (0): Bottleneck(\n",

  1246.        "         (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",

  1247.        "         (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1248.        "         (act1): ReLU(inplace=True)\n",

  1249.        "         (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",

  1250.        "         (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1251.        "         (act2): ReLU(inplace=True)\n",

  1252.        "         (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",

  1253.        "         (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1254.        "         (act3): ReLU(inplace=True)\n",

  1255.        "         (downsample): Sequential(\n",

  1256.        "           (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",

  1257.        "           (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1258.        "         )\n",

  1259.        "       )\n",

  1260.        "       (1): Bottleneck(\n",

  1261.        "         (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",

  1262.        "         (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1263.        "         (act1): ReLU(inplace=True)\n",

  1264.        "         (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",

  1265.        "         (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1266.        "         (act2): ReLU(inplace=True)\n",

  1267.        "         (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",

  1268.        "         (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1269.        "         (act3): ReLU(inplace=True)\n",

  1270.        "       )\n",

  1271.        "       (2): Bottleneck(\n",

  1272.        "         (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",

  1273.        "         (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1274.        "         (act1): ReLU(inplace=True)\n",

  1275.        "         (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",

  1276.        "         (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1277.        "         (act2): ReLU(inplace=True)\n",

  1278.        "         (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",

  1279.        "         (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",

  1280.        "         (act3): ReLU(inplace=True)\n",

  1281.        "       )\n",

  1282.        "     )\n",

  1283.        "     (global_pool): SelectAdaptivePool2d (pool_type=avg, flatten=True)\n",

  1284.        "     (fc): Identity()\n",

  1285.        "   )\n",

  1286.        " )\n",

  1287.        " C: DataParallel(\n",

  1288.        "   (module): Classifier(\n",

  1289.        "     (net): Sequential(\n",

  1290.        "       (0): Linear(in_features=2048, out_features=256, bias=True)\n",

  1291.        "       (1): ReLU()\n",

  1292.        "       (2): Dropout(p=0.5, inplace=False)\n",

  1293.        "       (3): Linear(in_features=256, out_features=128, bias=True)\n",

  1294.        "       (4): ReLU()\n",

  1295.        "       (5): Dropout(p=0.5, inplace=False)\n",

  1296.        "       (6): Linear(in_features=128, out_features=31, bias=True)\n",

  1297.        "     )\n",

  1298.        "   )\n",

  1299.        " ),\n",

  1300.        " 'optimizers': G: Adam (\n",

  1301.        " Parameter Group 0\n",

  1302.        "     amsgrad: False\n",

  1303.        "     betas: (0.9, 0.999)\n",

  1304.        "     eps: 1e-08\n",

  1305.        "     initial_lr: 0.0001\n",

  1306.        "     lr: 0.0001\n",

  1307.        "     weight_decay: 0\n",

  1308.        " )\n",

  1309.        " C: Adam (\n",

  1310.        " Parameter Group 0\n",

  1311.        "     amsgrad: False\n",

  1312.        "     betas: (0.9, 0.999)\n",

  1313.        "     eps: 1e-08\n",

  1314.        "     initial_lr: 0.0001\n",

  1315.        "     lr: 0.0001\n",

  1316.        "     weight_decay: 0\n",

  1317.        " ),\n",

  1318.        " 'lr_schedulers': G: <torch.optim.lr_scheduler.ExponentialLR object at 0x7f5960bcdd90>\n",

  1319.        " C: <torch.optim.lr_scheduler.ExponentialLR object at 0x7f5960bcdee0>,\n",

  1320.        " 'misc': }"

  1321.       ]

  1322.      },

  1323.      "execution_count": 21,

  1324.      "metadata": {},

  1325.      "output_type": "execute_result"

  1326.     }

  1327.    ],

  1328.    "source": [

  1329.     "checkpoint_utils.adapter_to_dict(trainer.adapter)"

  1330.    ]

  1331.   },

  1332.   {

  1333.    "cell_type": "code",

  1334.    "execution_count": 19,

  1335.    "metadata": {},

  1336.    "outputs": [

  1337.     {

  1338.      "ename": "RuntimeError",

  1339.      "evalue": "Error(s) in loading state_dict for DataParallel:\n\tMissing key(s) in state_dict: \"module.conv1.weight\", \"module.bn1.weight\", \"module.bn1.bias\", \"module.bn1.running_mean\", \"module.bn1.running_var\", \"module.layer1.0.conv1.weight\", \"module.layer1.0.bn1.weight\", \"module.layer1.0.bn1.bias\", \"module.layer1.0.bn1.running_mean\", \"module.layer1.0.bn1.running_var\", \"module.layer1.0.conv2.weight\", \"module.layer1.0.bn2.weight\", \"module.layer1.0.bn2.bias\", \"module.layer1.0.bn2.running_mean\", \"module.layer1.0.bn2.running_var\", \"module.layer1.0.conv3.weight\", \"module.layer1.0.bn3.weight\", \"module.layer1.0.bn3.bias\", \"module.layer1.0.bn3.running_mean\", \"module.layer1.0.bn3.running_var\", \"module.layer1.0.downsample.0.weight\", \"module.layer1.0.downsample.1.weight\", \"module.layer1.0.downsample.1.bias\", \"module.layer1.0.downsample.1.running_mean\", \"module.layer1.0.downsample.1.running_var\", \"module.layer1.1.conv1.weight\", \"module.layer1.1.bn1.weight\", \"module.layer1.1.bn1.bias\", \"module.layer1.1.bn1.running_mean\", \"module.layer1.1.bn1.running_var\", \"module.layer1.1.conv2.weight\", \"module.layer1.1.bn2.weight\", \"module.layer1.1.bn2.bias\", \"module.layer1.1.bn2.running_mean\", \"module.layer1.1.bn2.running_var\", \"module.layer1.1.conv3.weight\", \"module.layer1.1.bn3.weight\", \"module.layer1.1.bn3.bias\", \"module.layer1.1.bn3.running_mean\", \"module.layer1.1.bn3.running_var\", \"module.layer1.2.conv1.weight\", \"module.layer1.2.bn1.weight\", \"module.layer1.2.bn1.bias\", \"module.layer1.2.bn1.running_mean\", \"module.layer1.2.bn1.running_var\", \"module.layer1.2.conv2.weight\", \"module.layer1.2.bn2.weight\", \"module.layer1.2.bn2.bias\", \"module.layer1.2.bn2.running_mean\", \"module.layer1.2.bn2.running_var\", \"module.layer1.2.conv3.weight\", \"module.layer1.2.bn3.weight\", \"module.layer1.2.bn3.bias\", \"module.layer1.2.bn3.running_mean\", \"module.layer1.2.bn3.running_var\", \"module.layer2.0.conv1.weight\", \"module.layer2.0.bn1.weight\", \"module.layer2.0.bn1.bias\", \"module.layer2.0.bn1.running_mean\", \"module.layer2.0.bn1.running_var\", \"module.layer2.0.conv2.weight\", \"module.layer2.0.bn2.weight\", \"module.layer2.0.bn2.bias\", \"module.layer2.0.bn2.running_mean\", \"module.layer2.0.bn2.running_var\", \"module.layer2.0.conv3.weight\", \"module.layer2.0.bn3.weight\", \"module.layer2.0.bn3.bias\", \"module.layer2.0.bn3.running_mean\", \"module.layer2.0.bn3.running_var\", \"module.layer2.0.downsample.0.weight\", \"module.layer2.0.downsample.1.weight\", \"module.layer2.0.downsample.1.bias\", \"module.layer2.0.downsample.1.running_mean\", \"module.layer2.0.downsample.1.running_var\", \"module.layer2.1.conv1.weight\", \"module.layer2.1.bn1.weight\", \"module.layer2.1.bn1.bias\", \"module.layer2.1.bn1.running_mean\", \"module.layer2.1.bn1.running_var\", \"module.layer2.1.conv2.weight\", \"module.layer2.1.bn2.weight\", \"module.layer2.1.bn2.bias\", \"module.layer2.1.bn2.running_mean\", \"module.layer2.1.bn2.running_var\", \"module.layer2.1.conv3.weight\", \"module.layer2.1.bn3.weight\", \"module.layer2.1.bn3.bias\", \"module.layer2.1.bn3.running_mean\", \"module.layer2.1.bn3.running_var\", \"module.layer2.2.conv1.weight\", \"module.layer2.2.bn1.weight\", \"module.layer2.2.bn1.bias\", \"module.layer2.2.bn1.running_mean\", \"module.layer2.2.bn1.running_var\", \"module.layer2.2.conv2.weight\", \"module.layer2.2.bn2.weight\", \"module.layer2.2.bn2.bias\", \"module.layer2.2.bn2.running_mean\", \"module.layer2.2.bn2.running_var\", \"module.layer2.2.conv3.weight\", \"module.layer2.2.bn3.weight\", \"module.layer2.2.bn3.bias\", \"module.layer2.2.bn3.running_mean\", \"module.layer2.2.bn3.running_var\", \"module.layer2.3.conv1.weight\", \"module.layer2.3.bn1.weight\", \"module.layer2.3.bn1.bias\", \"module.layer2.3.bn1.running_mean\", \"module.layer2.3.bn1.running_var\", \"module.layer2.3.conv2.weight\", \"module.layer2.3.bn2.weight\", \"module.layer2.3.bn2.bias\", \"module.layer2.3.bn2.running_mean\", \"module.layer2.3.bn2.running_var\", \"module.layer2.3.conv3.weight\", \"module.layer2.3.bn3.weight\", \"module.layer2.3.bn3.bias\", \"module.layer2.3.bn3.running_mean\", \"module.layer2.3.bn3.running_var\", \"module.layer3.0.conv1.weight\", \"module.layer3.0.bn1.weight\", \"module.layer3.0.bn1.bias\", \"module.layer3.0.bn1.running_mean\", \"module.layer3.0.bn1.running_var\", \"module.layer3.0.conv2.weight\", \"module.layer3.0.bn2.weight\", \"module.layer3.0.bn2.bias\", \"module.layer3.0.bn2.running_mean\", \"module.layer3.0.bn2.running_var\", \"module.layer3.0.conv3.weight\", \"module.layer3.0.bn3.weight\", \"module.layer3.0.bn3.bias\", \"module.layer3.0.bn3.running_mean\", \"module.layer3.0.bn3.running_var\", \"module.layer3.0.downsample.0.weight\", \"module.layer3.0.downsample.1.weight\", \"module.layer3.0.downsample.1.bias\", \"module.layer3.0.downsample.1.running_mean\", \"module.layer3.0.downsample.1.running_var\", \"module.layer3.1.conv1.weight\", \"module.layer3.1.bn1.weight\", \"module.layer3.1.bn1.bias\", \"module.layer3.1.bn1.running_mean\", \"module.layer3.1.bn1.running_var\", \"module.layer3.1.conv2.weight\", \"module.layer3.1.bn2.weight\", \"module.layer3.1.bn2.bias\", \"module.layer3.1.bn2.running_mean\", \"module.layer3.1.bn2.running_var\", \"module.layer3.1.conv3.weight\", \"module.layer3.1.bn3.weight\", \"module.layer3.1.bn3.bias\", \"module.layer3.1.bn3.running_mean\", \"module.layer3.1.bn3.running_var\", \"module.layer3.2.conv1.weight\", \"module.layer3.2.bn1.weight\", \"module.layer3.2.bn1.bias\", \"module.layer3.2.bn1.running_mean\", \"module.layer3.2.bn1.running_var\", \"module.layer3.2.conv2.weight\", \"module.layer3.2.bn2.weight\", \"module.layer3.2.bn2.bias\", \"module.layer3.2.bn2.running_mean\", \"module.layer3.2.bn2.running_var\", \"module.layer3.2.conv3.weight\", \"module.layer3.2.bn3.weight\", \"module.layer3.2.bn3.bias\", \"module.layer3.2.bn3.running_mean\", \"module.layer3.2.bn3.running_var\", \"module.layer3.3.conv1.weight\", \"module.layer3.3.bn1.weight\", \"module.layer3.3.bn1.bias\", \"module.layer3.3.bn1.running_mean\", \"module.layer3.3.bn1.running_var\", \"module.layer3.3.conv2.weight\", \"module.layer3.3.bn2.weight\", \"module.layer3.3.bn2.bias\", \"module.layer3.3.bn2.running_mean\", \"module.layer3.3.bn2.running_var\", \"module.layer3.3.conv3.weight\", \"module.layer3.3.bn3.weight\", \"module.layer3.3.bn3.bias\", \"module.layer3.3.bn3.running_mean\", \"module.layer3.3.bn3.running_var\", \"module.layer3.4.conv1.weight\", \"module.layer3.4.bn1.weight\", \"module.layer3.4.bn1.bias\", \"module.layer3.4.bn1.running_mean\", \"module.layer3.4.bn1.running_var\", \"module.layer3.4.conv2.weight\", \"module.layer3.4.bn2.weight\", \"module.layer3.4.bn2.bias\", \"module.layer3.4.bn2.running_mean\", \"module.layer3.4.bn2.running_var\", \"module.layer3.4.conv3.weight\", \"module.layer3.4.bn3.weight\", \"module.layer3.4.bn3.bias\", \"module.layer3.4.bn3.running_mean\", \"module.layer3.4.bn3.running_var\", \"module.layer3.5.conv1.weight\", \"module.layer3.5.bn1.weight\", \"module.layer3.5.bn1.bias\", \"module.layer3.5.bn1.running_mean\", \"module.layer3.5.bn1.running_var\", \"module.layer3.5.conv2.weight\", \"module.layer3.5.bn2.weight\", \"module.layer3.5.bn2.bias\", \"module.layer3.5.bn2.running_mean\", \"module.layer3.5.bn2.running_var\", \"module.layer3.5.conv3.weight\", \"module.layer3.5.bn3.weight\", \"module.layer3.5.bn3.bias\", \"module.layer3.5.bn3.running_mean\", \"module.layer3.5.bn3.running_var\", \"module.layer4.0.conv1.weight\", \"module.layer4.0.bn1.weight\", \"module.layer4.0.bn1.bias\", \"module.layer4.0.bn1.running_mean\", \"module.layer4.0.bn1.running_var\", \"module.layer4.0.conv2.weight\", \"module.layer4.0.bn2.weight\", \"module.layer4.0.bn2.bias\", \"module.layer4.0.bn2.running_mean\", \"module.layer4.0.bn2.running_var\", \"module.layer4.0.conv3.weight\", \"module.layer4.0.bn3.weight\", \"module.layer4.0.bn3.bias\", \"module.layer4.0.bn3.running_mean\", \"module.layer4.0.bn3.running_var\", \"module.layer4.0.downsample.0.weight\", \"module.layer4.0.downsample.1.weight\", \"module.layer4.0.downsample.1.bias\", \"module.layer4.0.downsample.1.running_mean\", \"module.layer4.0.downsample.1.running_var\", \"module.layer4.1.conv1.weight\", \"module.layer4.1.bn1.weight\", \"module.layer4.1.bn1.bias\", \"module.layer4.1.bn1.running_mean\", \"module.layer4.1.bn1.running_var\", \"module.layer4.1.conv2.weight\", \"module.layer4.1.bn2.weight\", \"module.layer4.1.bn2.bias\", \"module.layer4.1.bn2.running_mean\", \"module.layer4.1.bn2.running_var\", \"module.layer4.1.conv3.weight\", \"module.layer4.1.bn3.weight\", \"module.layer4.1.bn3.bias\", \"module.layer4.1.bn3.running_mean\", \"module.layer4.1.bn3.running_var\", \"module.layer4.2.conv1.weight\", \"module.layer4.2.bn1.weight\", \"module.layer4.2.bn1.bias\", \"module.layer4.2.bn1.running_mean\", \"module.layer4.2.bn1.running_var\", \"module.layer4.2.conv2.weight\", \"module.layer4.2.bn2.weight\", \"module.layer4.2.bn2.bias\", \"module.layer4.2.bn2.running_mean\", \"module.layer4.2.bn2.running_var\", \"module.layer4.2.conv3.weight\", \"module.layer4.2.bn3.weight\", \"module.layer4.2.bn3.bias\", \"module.layer4.2.bn3.running_mean\", \"module.layer4.2.bn3.running_var\". \n\tUnexpected key(s) in state_dict: \"conv1.weight\", \"bn1.weight\", \"bn1.bias\", \"bn1.running_mean\", \"bn1.running_var\", \"bn1.num_batches_tracked\", \"layer1.0.conv1.weight\", \"layer1.0.bn1.weight\", \"layer1.0.bn1.bias\", \"layer1.0.bn1.running_mean\", \"layer1.0.bn1.running_var\", \"layer1.0.bn1.num_batches_tracked\", \"layer1.0.conv2.weight\", \"layer1.0.bn2.weight\", \"layer1.0.bn2.bias\", \"layer1.0.bn2.running_mean\", \"layer1.0.bn2.running_var\", \"layer1.0.bn2.num_batches_tracked\", \"layer1.0.conv3.weight\", \"layer1.0.bn3.weight\", \"layer1.0.bn3.bias\", \"layer1.0.bn3.running_mean\", \"layer1.0.bn3.running_var\", \"layer1.0.bn3.num_batches_tracked\", \"layer1.0.downsample.0.weight\", \"layer1.0.downsample.1.weight\", \"layer1.0.downsample.1.bias\", \"layer1.0.downsample.1.running_mean\", \"layer1.0.downsample.1.running_var\", \"layer1.0.downsample.1.num_batches_tracked\", \"layer1.1.conv1.weight\", \"layer1.1.bn1.weight\", \"layer1.1.bn1.bias\", \"layer1.1.bn1.running_mean\", \"layer1.1.bn1.running_var\", \"layer1.1.bn1.num_batches_tracked\", \"layer1.1.conv2.weight\", \"layer1.1.bn2.weight\", \"layer1.1.bn2.bias\", \"layer1.1.bn2.running_mean\", \"layer1.1.bn2.running_var\", \"layer1.1.bn2.num_batches_tracked\", \"layer1.1.conv3.weight\", \"layer1.1.bn3.weight\", \"layer1.1.bn3.bias\", \"layer1.1.bn3.running_mean\", \"layer1.1.bn3.running_var\", \"layer1.1.bn3.num_batches_tracked\", \"layer1.2.conv1.weight\", \"layer1.2.bn1.weight\", \"layer1.2.bn1.bias\", \"layer1.2.bn1.running_mean\", \"layer1.2.bn1.running_var\", \"layer1.2.bn1.num_batches_tracked\", \"layer1.2.conv2.weight\", \"layer1.2.bn2.weight\", \"layer1.2.bn2.bias\", \"layer1.2.bn2.running_mean\", \"layer1.2.bn2.running_var\", \"layer1.2.bn2.num_batches_tracked\", \"layer1.2.conv3.weight\", \"layer1.2.bn3.weight\", \"layer1.2.bn3.bias\", \"layer1.2.bn3.running_mean\", \"layer1.2.bn3.running_var\", \"layer1.2.bn3.num_batches_tracked\", \"layer2.0.conv1.weight\", \"layer2.0.bn1.weight\", \"layer2.0.bn1.bias\", \"layer2.0.bn1.running_mean\", \"layer2.0.bn1.running_var\", \"layer2.0.bn1.num_batches_tracked\", \"layer2.0.conv2.weight\", \"layer2.0.bn2.weight\", \"layer2.0.bn2.bias\", \"layer2.0.bn2.running_mean\", \"layer2.0.bn2.running_var\", \"layer2.0.bn2.num_batches_tracked\", \"layer2.0.conv3.weight\", \"layer2.0.bn3.weight\", \"layer2.0.bn3.bias\", \"layer2.0.bn3.running_mean\", \"layer2.0.bn3.running_var\", \"layer2.0.bn3.num_batches_tracked\", \"layer2.0.downsample.0.weight\", \"layer2.0.downsample.1.weight\", \"layer2.0.downsample.1.bias\", \"layer2.0.downsample.1.running_mean\", \"layer2.0.downsample.1.running_var\", \"layer2.0.downsample.1.num_batches_tracked\", \"layer2.1.conv1.weight\", \"layer2.1.bn1.weight\", \"layer2.1.bn1.bias\", \"layer2.1.bn1.running_mean\", \"layer2.1.bn1.running_var\", \"layer2.1.bn1.num_batches_tracked\", \"layer2.1.conv2.weight\", \"layer2.1.bn2.weight\", \"layer2.1.bn2.bias\", \"layer2.1.bn2.running_mean\", \"layer2.1.bn2.running_var\", \"layer2.1.bn2.num_batches_tracked\", \"layer2.1.conv3.weight\", \"layer2.1.bn3.weight\", \"layer2.1.bn3.bias\", \"layer2.1.bn3.running_mean\", \"layer2.1.bn3.running_var\", \"layer2.1.bn3.num_batches_tracked\", \"layer2.2.conv1.weight\", \"layer2.2.bn1.weight\", \"layer2.2.bn1.bias\", \"layer2.2.bn1.running_mean\", \"layer2.2.bn1.running_var\", \"layer2.2.bn1.num_batches_tracked\", \"layer2.2.conv2.weight\", \"layer2.2.bn2.weight\", \"layer2.2.bn2.bias\", \"layer2.2.bn2.running_mean\", \"layer2.2.bn2.running_var\", \"layer2.2.bn2.num_batches_tracked\", \"layer2.2.conv3.weight\", \"layer2.2.bn3.weight\", \"layer2.2.bn3.bias\", \"layer2.2.bn3.running_mean\", \"layer2.2.bn3.running_var\", \"layer2.2.bn3.num_batches_tracked\", \"layer2.3.conv1.weight\", \"layer2.3.bn1.weight\", \"layer2.3.bn1.bias\", \"layer2.3.bn1.running_mean\", \"layer2.3.bn1.running_var\", \"layer2.3.bn1.num_batches_tracked\", \"layer2.3.conv2.weight\", \"layer2.3.bn2.weight\", \"layer2.3.bn2.bias\", \"layer2.3.bn2.running_mean\", \"layer2.3.bn2.running_var\", \"layer2.3.bn2.num_batches_tracked\", \"layer2.3.conv3.weight\", \"layer2.3.bn3.weight\", \"layer2.3.bn3.bias\", \"layer2.3.bn3.running_mean\", \"layer2.3.bn3.running_var\", \"layer2.3.bn3.num_batches_tracked\", \"layer3.0.conv1.weight\", \"layer3.0.bn1.weight\", \"layer3.0.bn1.bias\", \"layer3.0.bn1.running_mean\", \"layer3.0.bn1.running_var\", \"layer3.0.bn1.num_batches_tracked\", \"layer3.0.conv2.weight\", \"layer3.0.bn2.weight\", \"layer3.0.bn2.bias\", \"layer3.0.bn2.running_mean\", \"layer3.0.bn2.running_var\", \"layer3.0.bn2.num_batches_tracked\", \"layer3.0.conv3.weight\", \"layer3.0.bn3.weight\", \"layer3.0.bn3.bias\", \"layer3.0.bn3.running_mean\", \"layer3.0.bn3.running_var\", \"layer3.0.bn3.num_batches_tracked\", \"layer3.0.downsample.0.weight\", \"layer3.0.downsample.1.weight\", \"layer3.0.downsample.1.bias\", \"layer3.0.downsample.1.running_mean\", \"layer3.0.downsample.1.running_var\", \"layer3.0.downsample.1.num_batches_tracked\", \"layer3.1.conv1.weight\", \"layer3.1.bn1.weight\", \"layer3.1.bn1.bias\", \"layer3.1.bn1.running_mean\", \"layer3.1.bn1.running_var\", \"layer3.1.bn1.num_batches_tracked\", \"layer3.1.conv2.weight\", \"layer3.1.bn2.weight\", \"layer3.1.bn2.bias\", \"layer3.1.bn2.running_mean\", \"layer3.1.bn2.running_var\", \"layer3.1.bn2.num_batches_tracked\", \"layer3.1.conv3.weight\", \"layer3.1.bn3.weight\", \"layer3.1.bn3.bias\", \"layer3.1.bn3.running_mean\", \"layer3.1.bn3.running_var\", \"layer3.1.bn3.num_batches_tracked\", \"layer3.2.conv1.weight\", \"layer3.2.bn1.weight\", \"layer3.2.bn1.bias\", \"layer3.2.bn1.running_mean\", \"layer3.2.bn1.running_var\", \"layer3.2.bn1.num_batches_tracked\", \"layer3.2.conv2.weight\", \"layer3.2.bn2.weight\", \"layer3.2.bn2.bias\", \"layer3.2.bn2.running_mean\", \"layer3.2.bn2.running_var\", \"layer3.2.bn2.num_batches_tracked\", \"layer3.2.conv3.weight\", \"layer3.2.bn3.weight\", \"layer3.2.bn3.bias\", \"layer3.2.bn3.running_mean\", \"layer3.2.bn3.running_var\", \"layer3.2.bn3.num_batches_tracked\", \"layer3.3.conv1.weight\", \"layer3.3.bn1.weight\", \"layer3.3.bn1.bias\", \"layer3.3.bn1.running_mean\", \"layer3.3.bn1.running_var\", \"layer3.3.bn1.num_batches_tracked\", \"layer3.3.conv2.weight\", \"layer3.3.bn2.weight\", \"layer3.3.bn2.bias\", \"layer3.3.bn2.running_mean\", \"layer3.3.bn2.running_var\", \"layer3.3.bn2.num_batches_tracked\", \"layer3.3.conv3.weight\", \"layer3.3.bn3.weight\", \"layer3.3.bn3.bias\", \"layer3.3.bn3.running_mean\", \"layer3.3.bn3.running_var\", \"layer3.3.bn3.num_batches_tracked\", \"layer3.4.conv1.weight\", \"layer3.4.bn1.weight\", \"layer3.4.bn1.bias\", \"layer3.4.bn1.running_mean\", \"layer3.4.bn1.running_var\", \"layer3.4.bn1.num_batches_tracked\", \"layer3.4.conv2.weight\", \"layer3.4.bn2.weight\", \"layer3.4.bn2.bias\", \"layer3.4.bn2.running_mean\", \"layer3.4.bn2.running_var\", \"layer3.4.bn2.num_batches_tracked\", \"layer3.4.conv3.weight\", \"layer3.4.bn3.weight\", \"layer3.4.bn3.bias\", \"layer3.4.bn3.running_mean\", \"layer3.4.bn3.running_var\", \"layer3.4.bn3.num_batches_tracked\", \"layer3.5.conv1.weight\", \"layer3.5.bn1.weight\", \"layer3.5.bn1.bias\", \"layer3.5.bn1.running_mean\", \"layer3.5.bn1.running_var\", \"layer3.5.bn1.num_batches_tracked\", \"layer3.5.conv2.weight\", \"layer3.5.bn2.weight\", \"layer3.5.bn2.bias\", \"layer3.5.bn2.running_mean\", \"layer3.5.bn2.running_var\", \"layer3.5.bn2.num_batches_tracked\", \"layer3.5.conv3.weight\", \"layer3.5.bn3.weight\", \"layer3.5.bn3.bias\", \"layer3.5.bn3.running_mean\", \"layer3.5.bn3.running_var\", \"layer3.5.bn3.num_batches_tracked\", \"layer4.0.conv1.weight\", \"layer4.0.bn1.weight\", \"layer4.0.bn1.bias\", \"layer4.0.bn1.running_mean\", \"layer4.0.bn1.running_var\", \"layer4.0.bn1.num_batches_tracked\", \"layer4.0.conv2.weight\", \"layer4.0.bn2.weight\", \"layer4.0.bn2.bias\", \"layer4.0.bn2.running_mean\", \"layer4.0.bn2.running_var\", \"layer4.0.bn2.num_batches_tracked\", \"layer4.0.conv3.weight\", \"layer4.0.bn3.weight\", \"layer4.0.bn3.bias\", \"layer4.0.bn3.running_mean\", \"layer4.0.bn3.running_var\", \"layer4.0.bn3.num_batches_tracked\", \"layer4.0.downsample.0.weight\", \"layer4.0.downsample.1.weight\", \"layer4.0.downsample.1.bias\", \"layer4.0.downsample.1.running_mean\", \"layer4.0.downsample.1.running_var\", \"layer4.0.downsample.1.num_batches_tracked\", \"layer4.1.conv1.weight\", \"layer4.1.bn1.weight\", \"layer4.1.bn1.bias\", \"layer4.1.bn1.running_mean\", \"layer4.1.bn1.running_var\", \"layer4.1.bn1.num_batches_tracked\", \"layer4.1.conv2.weight\", \"layer4.1.bn2.weight\", \"layer4.1.bn2.bias\", \"layer4.1.bn2.running_mean\", \"layer4.1.bn2.running_var\", \"layer4.1.bn2.num_batches_tracked\", \"layer4.1.conv3.weight\", \"layer4.1.bn3.weight\", \"layer4.1.bn3.bias\", \"layer4.1.bn3.running_mean\", \"layer4.1.bn3.running_var\", \"layer4.1.bn3.num_batches_tracked\", \"layer4.2.conv1.weight\", \"layer4.2.bn1.weight\", \"layer4.2.bn1.bias\", \"layer4.2.bn1.running_mean\", \"layer4.2.bn1.running_var\", \"layer4.2.bn1.num_batches_tracked\", \"layer4.2.conv2.weight\", \"layer4.2.bn2.weight\", \"layer4.2.bn2.bias\", \"layer4.2.bn2.running_mean\", \"layer4.2.bn2.running_var\", \"layer4.2.bn2.num_batches_tracked\", \"layer4.2.conv3.weight\", \"layer4.2.bn3.weight\", \"layer4.2.bn3.bias\", \"layer4.2.bn3.running_mean\", \"layer4.2.bn3.running_var\", \"layer4.2.bn3.num_batches_tracked\". ",

  1340.      "output_type": "error",

  1341.      "traceback": [

  1342.       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",

  1343.       "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",

  1344.       "Cell \u001b[0;32mIn[19], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m validator \u001b[39m=\u001b[39m AccuracyValidator(key_map\u001b[39m=\u001b[39m{\u001b[39m\"\u001b[39m\u001b[39mtarget_val_with_labels\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39m\"\u001b[39m\u001b[39msrc_val\u001b[39m\u001b[39m\"\u001b[39m})\n\u001b[0;32m----> 2\u001b[0m score \u001b[39m=\u001b[39m trainer\u001b[39m.\u001b[39;49mevaluate_best_model(datasets, validator, dc)\n\u001b[1;32m      3\u001b[0m \u001b[39mprint\u001b[39m(score)\n",

  1345.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/pytorch_adapt/frameworks/ignite/ignite.py:323\u001b[0m, in \u001b[0;36mIgnite.evaluate_best_model\u001b[0;34m(self, datasets, validator, dataloader_creator)\u001b[0m\n\u001b[1;32m    321\u001b[0m dataloader_creator \u001b[39m=\u001b[39m c_f\u001b[39m.\u001b[39mdefault(dataloader_creator, DataloaderCreator, {})\n\u001b[1;32m    322\u001b[0m dataloaders \u001b[39m=\u001b[39m dataloader_creator(\u001b[39m*\u001b[39m\u001b[39m*\u001b[39mdatasets)\n\u001b[0;32m--> 323\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcheckpoint_fn\u001b[39m.\u001b[39;49mload_best_checkpoint({\u001b[39m\"\u001b[39;49m\u001b[39mmodels\u001b[39;49m\u001b[39m\"\u001b[39;49m: \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49madapter\u001b[39m.\u001b[39;49mmodels})\n\u001b[1;32m    324\u001b[0m collected_data \u001b[39m=\u001b[39m i_g\u001b[39m.\u001b[39mcollect_from_dataloaders(\n\u001b[1;32m    325\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcollector, dataloaders, validator\u001b[39m.\u001b[39mrequired_data\n\u001b[1;32m    326\u001b[0m )\n\u001b[1;32m    327\u001b[0m \u001b[39mreturn\u001b[39;00m val_utils\u001b[39m.\u001b[39mcall_val_hook(validator, collected_data)\n",

  1346.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/pytorch_adapt/frameworks/ignite/checkpoint_utils.py:97\u001b[0m, in \u001b[0;36mCheckpointFnCreator.load_best_checkpoint\u001b[0;34m(self, to_load)\u001b[0m\n\u001b[1;32m     95\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mload_best_checkpoint\u001b[39m(\u001b[39mself\u001b[39m, to_load):\n\u001b[1;32m     96\u001b[0m     last_checkpoint \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mget_best_checkpoint()\n\u001b[0;32m---> 97\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mload_objects(to_load, last_checkpoint)\n",

  1347.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/pytorch_adapt/frameworks/ignite/checkpoint_utils.py:93\u001b[0m, in \u001b[0;36mCheckpointFnCreator.load_objects\u001b[0;34m(self, to_load, checkpoint, global_step)\u001b[0m\n\u001b[1;32m     89\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mobjs\u001b[39m.\u001b[39mreload_objects(\n\u001b[1;32m     90\u001b[0m         to_load, name\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mcheckpoint\u001b[39m\u001b[39m\"\u001b[39m, global_step\u001b[39m=\u001b[39mglobal_step\n\u001b[1;32m     91\u001b[0m     )\n\u001b[1;32m     92\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m---> 93\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mobjs\u001b[39m.\u001b[39;49mload_objects(to_load, \u001b[39mstr\u001b[39;49m(checkpoint))\n",

  1348.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/ignite/handlers/checkpoint.py:618\u001b[0m, in \u001b[0;36mCheckpoint.load_objects\u001b[0;34m(to_load, checkpoint, **kwargs)\u001b[0m\n\u001b[1;32m    616\u001b[0m \u001b[39mif\u001b[39;00m k \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m checkpoint_obj:\n\u001b[1;32m    617\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mObject labeled by \u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00mk\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m from `to_load` is not found in the checkpoint\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m--> 618\u001b[0m _load_object(obj, checkpoint_obj[k])\n",

  1349.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/ignite/handlers/checkpoint.py:605\u001b[0m, in \u001b[0;36mCheckpoint.load_objects.<locals>._load_object\u001b[0;34m(obj, chkpt_obj)\u001b[0m\n\u001b[1;32m    603\u001b[0m     obj\u001b[39m.\u001b[39mload_state_dict(chkpt_obj, strict\u001b[39m=\u001b[39mis_state_dict_strict)\n\u001b[1;32m    604\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 605\u001b[0m     obj\u001b[39m.\u001b[39;49mload_state_dict(chkpt_obj)\n",

  1350.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/pytorch_adapt/containers/base_container.py:158\u001b[0m, in \u001b[0;36mBaseContainer.load_state_dict\u001b[0;34m(self, state_dict)\u001b[0m\n\u001b[1;32m    156\u001b[0m c_f\u001b[39m.\u001b[39massert_state_dict_keys(state_dict, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mkeys())\n\u001b[1;32m    157\u001b[0m \u001b[39mfor\u001b[39;00m k, v \u001b[39min\u001b[39;00m state_dict\u001b[39m.\u001b[39mitems():\n\u001b[0;32m--> 158\u001b[0m     \u001b[39mself\u001b[39;49m[k]\u001b[39m.\u001b[39;49mload_state_dict(v)\n",

  1351.       "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/torch/nn/modules/module.py:1223\u001b[0m, in \u001b[0;36mModule.load_state_dict\u001b[0;34m(self, state_dict, strict)\u001b[0m\n\u001b[1;32m   1218\u001b[0m         error_msgs\u001b[39m.\u001b[39minsert(\n\u001b[1;32m   1219\u001b[0m             \u001b[39m0\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mMissing key(s) in state_dict: \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m. \u001b[39m\u001b[39m'\u001b[39m\u001b[39m.\u001b[39mformat(\n\u001b[1;32m   1220\u001b[0m                 \u001b[39m'\u001b[39m\u001b[39m, \u001b[39m\u001b[39m'\u001b[39m\u001b[39m.\u001b[39mjoin(\u001b[39m'\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m\"\u001b[39m\u001b[39m'\u001b[39m\u001b[39m.\u001b[39mformat(k) \u001b[39mfor\u001b[39;00m k \u001b[39min\u001b[39;00m missing_keys)))\n\u001b[1;32m   1222\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(error_msgs) \u001b[39m>\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[0;32m-> 1223\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mRuntimeError\u001b[39;00m(\u001b[39m'\u001b[39m\u001b[39mError(s) in loading state_dict for \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m:\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m\\t\u001b[39;00m\u001b[39m{}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m.\u001b[39mformat(\n\u001b[1;32m   1224\u001b[0m                        \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m\u001b[39m__class__\u001b[39m\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m\\t\u001b[39;00m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mjoin(error_msgs)))\n\u001b[1;32m   1225\u001b[0m \u001b[39mreturn\u001b[39;00m _IncompatibleKeys(missing_keys, unexpected_keys)\n",

  1352.       "\u001b[0;31mRuntimeError\u001b[0m: Error(s) in loading state_dict for DataParallel:\n\tMissing key(s) in state_dict: \"module.conv1.weight\", \"module.bn1.weight\", \"module.bn1.bias\", \"module.bn1.running_mean\", \"module.bn1.running_var\", \"module.layer1.0.conv1.weight\", \"module.layer1.0.bn1.weight\", \"module.layer1.0.bn1.bias\", \"module.layer1.0.bn1.running_mean\", \"module.layer1.0.bn1.running_var\", \"module.layer1.0.conv2.weight\", \"module.layer1.0.bn2.weight\", \"module.layer1.0.bn2.bias\", \"module.layer1.0.bn2.running_mean\", \"module.layer1.0.bn2.running_var\", \"module.layer1.0.conv3.weight\", \"module.layer1.0.bn3.weight\", \"module.layer1.0.bn3.bias\", \"module.layer1.0.bn3.running_mean\", \"module.layer1.0.bn3.running_var\", \"module.layer1.0.downsample.0.weight\", \"module.layer1.0.downsample.1.weight\", \"module.layer1.0.downsample.1.bias\", \"module.layer1.0.downsample.1.running_mean\", \"module.layer1.0.downsample.1.running_var\", \"module.layer1.1.conv1.weight\", \"module.layer1.1.bn1.weight\", \"module.layer1.1.bn1.bias\", \"module.layer1.1.bn1.running_mean\", \"module.layer1.1.bn1.running_var\", \"module.layer1.1.conv2.weight\", \"module.layer1.1.bn2.weight\", \"module.layer1.1.bn2.bias\", \"module.layer1.1.bn2.running_mean\", \"module.layer1.1.bn2.running_var\", \"module.layer1.1.conv3.weight\", \"module.layer1.1.bn3.weight\", \"module.layer1.1.bn3.bias\", \"module.layer1.1.bn3.running_mean\", \"module.layer1.1.bn3.running_var\", \"module.layer1.2.conv1.weight\", \"module.layer1.2.bn1.weight\", \"module.layer1.2.bn1.bias\", \"module.layer1.2.bn1.running_mean\", \"module.layer1.2.bn1.running_var\", \"module.layer1.2.conv2.weight\", \"module.layer1.2.bn2.weight\", \"module.layer1.2.bn2.bias\", \"module.layer1.2.bn2.running_mean\", \"module.layer1.2.bn2.running_var\", \"module.layer1.2.conv3.weight\", \"module.layer1.2.bn3.weight\", \"module.layer1.2.bn3.bias\", \"module.layer1.2.bn3.running_mean\", \"module.layer1.2.bn3.running_var\", \"module.layer2.0.conv1.weight\", \"module.layer2.0.bn1.weight\", \"module.layer2.0.bn1.bias\", \"module.layer2.0.bn1.running_mean\", \"module.layer2.0.bn1.running_var\", \"module.layer2.0.conv2.weight\", \"module.layer2.0.bn2.weight\", \"module.layer2.0.bn2.bias\", \"module.layer2.0.bn2.running_mean\", \"module.layer2.0.bn2.running_var\", \"module.layer2.0.conv3.weight\", \"module.layer2.0.bn3.weight\", \"module.layer2.0.bn3.bias\", \"module.layer2.0.bn3.running_mean\", \"module.layer2.0.bn3.running_var\", \"module.layer2.0.downsample.0.weight\", \"module.layer2.0.downsample.1.weight\", \"module.layer2.0.downsample.1.bias\", \"module.layer2.0.downsample.1.running_mean\", \"module.layer2.0.downsample.1.running_var\", \"module.layer2.1.conv1.weight\", \"module.layer2.1.bn1.weight\", \"module.layer2.1.bn1.bias\", \"module.layer2.1.bn1.running_mean\", \"module.layer2.1.bn1.running_var\", \"module.layer2.1.conv2.weight\", \"module.layer2.1.bn2.weight\", \"module.layer2.1.bn2.bias\", \"module.layer2.1.bn2.running_mean\", \"module.layer2.1.bn2.running_var\", \"module.layer2.1.conv3.weight\", \"module.layer2.1.bn3.weight\", \"module.layer2.1.bn3.bias\", \"module.layer2.1.bn3.running_mean\", \"module.layer2.1.bn3.running_var\", \"module.layer2.2.conv1.weight\", \"module.layer2.2.bn1.weight\", \"module.layer2.2.bn1.bias\", \"module.layer2.2.bn1.running_mean\", \"module.layer2.2.bn1.running_var\", \"module.layer2.2.conv2.weight\", \"module.layer2.2.bn2.weight\", \"module.layer2.2.bn2.bias\", \"module.layer2.2.bn2.running_mean\", \"module.layer2.2.bn2.running_var\", \"module.layer2.2.conv3.weight\", \"module.layer2.2.bn3.weight\", \"module.layer2.2.bn3.bias\", \"module.layer2.2.bn3.running_mean\", \"module.layer2.2.bn3.running_var\", \"module.layer2.3.conv1.weight\", \"module.layer2.3.bn1.weight\", \"module.layer2.3.bn1.bias\", \"module.layer2.3.bn1.running_mean\", \"module.layer2.3.bn1.running_var\", \"module.layer2.3.conv2.weight\", \"module.layer2.3.bn2.weight\", \"module.layer2.3.bn2.bias\", \"module.layer2.3.bn2.running_mean\", \"module.layer2.3.bn2.running_var\", \"module.layer2.3.conv3.weight\", \"module.layer2.3.bn3.weight\", \"module.layer2.3.bn3.bias\", \"module.layer2.3.bn3.running_mean\", \"module.layer2.3.bn3.running_var\", \"module.layer3.0.conv1.weight\", \"module.layer3.0.bn1.weight\", \"module.layer3.0.bn1.bias\", \"module.layer3.0.bn1.running_mean\", \"module.layer3.0.bn1.running_var\", \"module.layer3.0.conv2.weight\", \"module.layer3.0.bn2.weight\", \"module.layer3.0.bn2.bias\", \"module.layer3.0.bn2.running_mean\", \"module.layer3.0.bn2.running_var\", \"module.layer3.0.conv3.weight\", \"module.layer3.0.bn3.weight\", \"module.layer3.0.bn3.bias\", \"module.layer3.0.bn3.running_mean\", \"module.layer3.0.bn3.running_var\", \"module.layer3.0.downsample.0.weight\", \"module.layer3.0.downsample.1.weight\", \"module.layer3.0.downsample.1.bias\", \"module.layer3.0.downsample.1.running_mean\", \"module.layer3.0.downsample.1.running_var\", \"module.layer3.1.conv1.weight\", \"module.layer3.1.bn1.weight\", \"module.layer3.1.bn1.bias\", \"module.layer3.1.bn1.running_mean\", \"module.layer3.1.bn1.running_var\", \"module.layer3.1.conv2.weight\", \"module.layer3.1.bn2.weight\", \"module.layer3.1.bn2.bias\", \"module.layer3.1.bn2.running_mean\", \"module.layer3.1.bn2.running_var\", \"module.layer3.1.conv3.weight\", \"module.layer3.1.bn3.weight\", \"module.layer3.1.bn3.bias\", \"module.layer3.1.bn3.running_mean\", \"module.layer3.1.bn3.running_var\", \"module.layer3.2.conv1.weight\", \"module.layer3.2.bn1.weight\", \"module.layer3.2.bn1.bias\", \"module.layer3.2.bn1.running_mean\", \"module.layer3.2.bn1.running_var\", \"module.layer3.2.conv2.weight\", \"module.layer3.2.bn2.weight\", \"module.layer3.2.bn2.bias\", \"module.layer3.2.bn2.running_mean\", \"module.layer3.2.bn2.running_var\", \"module.layer3.2.conv3.weight\", \"module.layer3.2.bn3.weight\", \"module.layer3.2.bn3.bias\", \"module.layer3.2.bn3.running_mean\", \"module.layer3.2.bn3.running_var\", \"module.layer3.3.conv1.weight\", \"module.layer3.3.bn1.weight\", \"module.layer3.3.bn1.bias\", \"module.layer3.3.bn1.running_mean\", \"module.layer3.3.bn1.running_var\", \"module.layer3.3.conv2.weight\", \"module.layer3.3.bn2.weight\", \"module.layer3.3.bn2.bias\", \"module.layer3.3.bn2.running_mean\", \"module.layer3.3.bn2.running_var\", \"module.layer3.3.conv3.weight\", \"module.layer3.3.bn3.weight\", \"module.layer3.3.bn3.bias\", \"module.layer3.3.bn3.running_mean\", \"module.layer3.3.bn3.running_var\", \"module.layer3.4.conv1.weight\", \"module.layer3.4.bn1.weight\", \"module.layer3.4.bn1.bias\", \"module.layer3.4.bn1.running_mean\", \"module.layer3.4.bn1.running_var\", \"module.layer3.4.conv2.weight\", \"module.layer3.4.bn2.weight\", \"module.layer3.4.bn2.bias\", \"module.layer3.4.bn2.running_mean\", \"module.layer3.4.bn2.running_var\", \"module.layer3.4.conv3.weight\", \"module.layer3.4.bn3.weight\", \"module.layer3.4.bn3.bias\", \"module.layer3.4.bn3.running_mean\", \"module.layer3.4.bn3.running_var\", \"module.layer3.5.conv1.weight\", \"module.layer3.5.bn1.weight\", \"module.layer3.5.bn1.bias\", \"module.layer3.5.bn1.running_mean\", \"module.layer3.5.bn1.running_var\", \"module.layer3.5.conv2.weight\", \"module.layer3.5.bn2.weight\", \"module.layer3.5.bn2.bias\", \"module.layer3.5.bn2.running_mean\", \"module.layer3.5.bn2.running_var\", \"module.layer3.5.conv3.weight\", \"module.layer3.5.bn3.weight\", \"module.layer3.5.bn3.bias\", \"module.layer3.5.bn3.running_mean\", \"module.layer3.5.bn3.running_var\", \"module.layer4.0.conv1.weight\", \"module.layer4.0.bn1.weight\", \"module.layer4.0.bn1.bias\", \"module.layer4.0.bn1.running_mean\", \"module.layer4.0.bn1.running_var\", \"module.layer4.0.conv2.weight\", \"module.layer4.0.bn2.weight\", \"module.layer4.0.bn2.bias\", \"module.layer4.0.bn2.running_mean\", \"module.layer4.0.bn2.running_var\", \"module.layer4.0.conv3.weight\", \"module.layer4.0.bn3.weight\", \"module.layer4.0.bn3.bias\", \"module.layer4.0.bn3.running_mean\", \"module.layer4.0.bn3.running_var\", \"module.layer4.0.downsample.0.weight\", \"module.layer4.0.downsample.1.weight\", \"module.layer4.0.downsample.1.bias\", \"module.layer4.0.downsample.1.running_mean\", \"module.layer4.0.downsample.1.running_var\", \"module.layer4.1.conv1.weight\", \"module.layer4.1.bn1.weight\", \"module.layer4.1.bn1.bias\", \"module.layer4.1.bn1.running_mean\", \"module.layer4.1.bn1.running_var\", \"module.layer4.1.conv2.weight\", \"module.layer4.1.bn2.weight\", \"module.layer4.1.bn2.bias\", \"module.layer4.1.bn2.running_mean\", \"module.layer4.1.bn2.running_var\", \"module.layer4.1.conv3.weight\", \"module.layer4.1.bn3.weight\", \"module.layer4.1.bn3.bias\", \"module.layer4.1.bn3.running_mean\", \"module.layer4.1.bn3.running_var\", \"module.layer4.2.conv1.weight\", \"module.layer4.2.bn1.weight\", \"module.layer4.2.bn1.bias\", \"module.layer4.2.bn1.running_mean\", \"module.layer4.2.bn1.running_var\", \"module.layer4.2.conv2.weight\", \"module.layer4.2.bn2.weight\", \"module.layer4.2.bn2.bias\", \"module.layer4.2.bn2.running_mean\", \"module.layer4.2.bn2.running_var\", \"module.layer4.2.conv3.weight\", \"module.layer4.2.bn3.weight\", \"module.layer4.2.bn3.bias\", \"module.layer4.2.bn3.running_mean\", \"module.layer4.2.bn3.running_var\". \n\tUnexpected key(s) in state_dict: \"conv1.weight\", \"bn1.weight\", \"bn1.bias\", \"bn1.running_mean\", \"bn1.running_var\", \"bn1.num_batches_tracked\", \"layer1.0.conv1.weight\", \"layer1.0.bn1.weight\", \"layer1.0.bn1.bias\", \"layer1.0.bn1.running_mean\", \"layer1.0.bn1.running_var\", \"layer1.0.bn1.num_batches_tracked\", \"layer1.0.conv2.weight\", \"layer1.0.bn2.weight\", \"layer1.0.bn2.bias\", \"layer1.0.bn2.running_mean\", \"layer1.0.bn2.running_var\", \"layer1.0.bn2.num_batches_tracked\", \"layer1.0.conv3.weight\", \"layer1.0.bn3.weight\", \"layer1.0.bn3.bias\", \"layer1.0.bn3.running_mean\", \"layer1.0.bn3.running_var\", \"layer1.0.bn3.num_batches_tracked\", \"layer1.0.downsample.0.weight\", \"layer1.0.downsample.1.weight\", \"layer1.0.downsample.1.bias\", \"layer1.0.downsample.1.running_mean\", \"layer1.0.downsample.1.running_var\", \"layer1.0.downsample.1.num_batches_tracked\", \"layer1.1.conv1.weight\", \"layer1.1.bn1.weight\", \"layer1.1.bn1.bias\", \"layer1.1.bn1.running_mean\", \"layer1.1.bn1.running_var\", \"layer1.1.bn1.num_batches_tracked\", \"layer1.1.conv2.weight\", \"layer1.1.bn2.weight\", \"layer1.1.bn2.bias\", \"layer1.1.bn2.running_mean\", \"layer1.1.bn2.running_var\", \"layer1.1.bn2.num_batches_tracked\", \"layer1.1.conv3.weight\", \"layer1.1.bn3.weight\", \"layer1.1.bn3.bias\", \"layer1.1.bn3.running_mean\", \"layer1.1.bn3.running_var\", \"layer1.1.bn3.num_batches_tracked\", \"layer1.2.conv1.weight\", \"layer1.2.bn1.weight\", \"layer1.2.bn1.bias\", \"layer1.2.bn1.running_mean\", \"layer1.2.bn1.running_var\", \"layer1.2.bn1.num_batches_tracked\", \"layer1.2.conv2.weight\", \"layer1.2.bn2.weight\", \"layer1.2.bn2.bias\", \"layer1.2.bn2.running_mean\", \"layer1.2.bn2.running_var\", \"layer1.2.bn2.num_batches_tracked\", \"layer1.2.conv3.weight\", \"layer1.2.bn3.weight\", \"layer1.2.bn3.bias\", \"layer1.2.bn3.running_mean\", \"layer1.2.bn3.running_var\", \"layer1.2.bn3.num_batches_tracked\", \"layer2.0.conv1.weight\", \"layer2.0.bn1.weight\", \"layer2.0.bn1.bias\", \"layer2.0.bn1.running_mean\", \"layer2.0.bn1.running_var\", \"layer2.0.bn1.num_batches_tracked\", \"layer2.0.conv2.weight\", \"layer2.0.bn2.weight\", \"layer2.0.bn2.bias\", \"layer2.0.bn2.running_mean\", \"layer2.0.bn2.running_var\", \"layer2.0.bn2.num_batches_tracked\", \"layer2.0.conv3.weight\", \"layer2.0.bn3.weight\", \"layer2.0.bn3.bias\", \"layer2.0.bn3.running_mean\", \"layer2.0.bn3.running_var\", \"layer2.0.bn3.num_batches_tracked\", \"layer2.0.downsample.0.weight\", \"layer2.0.downsample.1.weight\", \"layer2.0.downsample.1.bias\", \"layer2.0.downsample.1.running_mean\", \"layer2.0.downsample.1.running_var\", \"layer2.0.downsample.1.num_batches_tracked\", \"layer2.1.conv1.weight\", \"layer2.1.bn1.weight\", \"layer2.1.bn1.bias\", \"layer2.1.bn1.running_mean\", \"layer2.1.bn1.running_var\", \"layer2.1.bn1.num_batches_tracked\", \"layer2.1.conv2.weight\", \"layer2.1.bn2.weight\", \"layer2.1.bn2.bias\", \"layer2.1.bn2.running_mean\", \"layer2.1.bn2.running_var\", \"layer2.1.bn2.num_batches_tracked\", \"layer2.1.conv3.weight\", \"layer2.1.bn3.weight\", \"layer2.1.bn3.bias\", \"layer2.1.bn3.running_mean\", \"layer2.1.bn3.running_var\", \"layer2.1.bn3.num_batches_tracked\", \"layer2.2.conv1.weight\", \"layer2.2.bn1.weight\", \"layer2.2.bn1.bias\", \"layer2.2.bn1.running_mean\", \"layer2.2.bn1.running_var\", \"layer2.2.bn1.num_batches_tracked\", \"layer2.2.conv2.weight\", \"layer2.2.bn2.weight\", \"layer2.2.bn2.bias\", \"layer2.2.bn2.running_mean\", \"layer2.2.bn2.running_var\", \"layer2.2.bn2.num_batches_tracked\", \"layer2.2.conv3.weight\", \"layer2.2.bn3.weight\", \"layer2.2.bn3.bias\", \"layer2.2.bn3.running_mean\", \"layer2.2.bn3.running_var\", \"layer2.2.bn3.num_batches_tracked\", \"layer2.3.conv1.weight\", \"layer2.3.bn1.weight\", \"layer2.3.bn1.bias\", \"layer2.3.bn1.running_mean\", \"layer2.3.bn1.running_var\", \"layer2.3.bn1.num_batches_tracked\", \"layer2.3.conv2.weight\", \"layer2.3.bn2.weight\", \"layer2.3.bn2.bias\", \"layer2.3.bn2.running_mean\", \"layer2.3.bn2.running_var\", \"layer2.3.bn2.num_batches_tracked\", \"layer2.3.conv3.weight\", \"layer2.3.bn3.weight\", \"layer2.3.bn3.bias\", \"layer2.3.bn3.running_mean\", \"layer2.3.bn3.running_var\", \"layer2.3.bn3.num_batches_tracked\", \"layer3.0.conv1.weight\", \"layer3.0.bn1.weight\", \"layer3.0.bn1.bias\", \"layer3.0.bn1.running_mean\", \"layer3.0.bn1.running_var\", \"layer3.0.bn1.num_batches_tracked\", \"layer3.0.conv2.weight\", \"layer3.0.bn2.weight\", \"layer3.0.bn2.bias\", \"layer3.0.bn2.running_mean\", \"layer3.0.bn2.running_var\", \"layer3.0.bn2.num_batches_tracked\", \"layer3.0.conv3.weight\", \"layer3.0.bn3.weight\", \"layer3.0.bn3.bias\", \"layer3.0.bn3.running_mean\", \"layer3.0.bn3.running_var\", \"layer3.0.bn3.num_batches_tracked\", \"layer3.0.downsample.0.weight\", \"layer3.0.downsample.1.weight\", \"layer3.0.downsample.1.bias\", \"layer3.0.downsample.1.running_mean\", \"layer3.0.downsample.1.running_var\", \"layer3.0.downsample.1.num_batches_tracked\", \"layer3.1.conv1.weight\", \"layer3.1.bn1.weight\", \"layer3.1.bn1.bias\", \"layer3.1.bn1.running_mean\", \"layer3.1.bn1.running_var\", \"layer3.1.bn1.num_batches_tracked\", \"layer3.1.conv2.weight\", \"layer3.1.bn2.weight\", \"layer3.1.bn2.bias\", \"layer3.1.bn2.running_mean\", \"layer3.1.bn2.running_var\", \"layer3.1.bn2.num_batches_tracked\", \"layer3.1.conv3.weight\", \"layer3.1.bn3.weight\", \"layer3.1.bn3.bias\", \"layer3.1.bn3.running_mean\", \"layer3.1.bn3.running_var\", \"layer3.1.bn3.num_batches_tracked\", \"layer3.2.conv1.weight\", \"layer3.2.bn1.weight\", \"layer3.2.bn1.bias\", \"layer3.2.bn1.running_mean\", \"layer3.2.bn1.running_var\", \"layer3.2.bn1.num_batches_tracked\", \"layer3.2.conv2.weight\", \"layer3.2.bn2.weight\", \"layer3.2.bn2.bias\", \"layer3.2.bn2.running_mean\", \"layer3.2.bn2.running_var\", \"layer3.2.bn2.num_batches_tracked\", \"layer3.2.conv3.weight\", \"layer3.2.bn3.weight\", \"layer3.2.bn3.bias\", \"layer3.2.bn3.running_mean\", \"layer3.2.bn3.running_var\", \"layer3.2.bn3.num_batches_tracked\", \"layer3.3.conv1.weight\", \"layer3.3.bn1.weight\", \"layer3.3.bn1.bias\", \"layer3.3.bn1.running_mean\", \"layer3.3.bn1.running_var\", \"layer3.3.bn1.num_batches_tracked\", \"layer3.3.conv2.weight\", \"layer3.3.bn2.weight\", \"layer3.3.bn2.bias\", \"layer3.3.bn2.running_mean\", \"layer3.3.bn2.running_var\", \"layer3.3.bn2.num_batches_tracked\", \"layer3.3.conv3.weight\", \"layer3.3.bn3.weight\", \"layer3.3.bn3.bias\", \"layer3.3.bn3.running_mean\", \"layer3.3.bn3.running_var\", \"layer3.3.bn3.num_batches_tracked\", \"layer3.4.conv1.weight\", \"layer3.4.bn1.weight\", \"layer3.4.bn1.bias\", \"layer3.4.bn1.running_mean\", \"layer3.4.bn1.running_var\", \"layer3.4.bn1.num_batches_tracked\", \"layer3.4.conv2.weight\", \"layer3.4.bn2.weight\", \"layer3.4.bn2.bias\", \"layer3.4.bn2.running_mean\", \"layer3.4.bn2.running_var\", \"layer3.4.bn2.num_batches_tracked\", \"layer3.4.conv3.weight\", \"layer3.4.bn3.weight\", \"layer3.4.bn3.bias\", \"layer3.4.bn3.running_mean\", \"layer3.4.bn3.running_var\", \"layer3.4.bn3.num_batches_tracked\", \"layer3.5.conv1.weight\", \"layer3.5.bn1.weight\", \"layer3.5.bn1.bias\", \"layer3.5.bn1.running_mean\", \"layer3.5.bn1.running_var\", \"layer3.5.bn1.num_batches_tracked\", \"layer3.5.conv2.weight\", \"layer3.5.bn2.weight\", \"layer3.5.bn2.bias\", \"layer3.5.bn2.running_mean\", \"layer3.5.bn2.running_var\", \"layer3.5.bn2.num_batches_tracked\", \"layer3.5.conv3.weight\", \"layer3.5.bn3.weight\", \"layer3.5.bn3.bias\", \"layer3.5.bn3.running_mean\", \"layer3.5.bn3.running_var\", \"layer3.5.bn3.num_batches_tracked\", \"layer4.0.conv1.weight\", \"layer4.0.bn1.weight\", \"layer4.0.bn1.bias\", \"layer4.0.bn1.running_mean\", \"layer4.0.bn1.running_var\", \"layer4.0.bn1.num_batches_tracked\", \"layer4.0.conv2.weight\", \"layer4.0.bn2.weight\", \"layer4.0.bn2.bias\", \"layer4.0.bn2.running_mean\", \"layer4.0.bn2.running_var\", \"layer4.0.bn2.num_batches_tracked\", \"layer4.0.conv3.weight\", \"layer4.0.bn3.weight\", \"layer4.0.bn3.bias\", \"layer4.0.bn3.running_mean\", \"layer4.0.bn3.running_var\", \"layer4.0.bn3.num_batches_tracked\", \"layer4.0.downsample.0.weight\", \"layer4.0.downsample.1.weight\", \"layer4.0.downsample.1.bias\", \"layer4.0.downsample.1.running_mean\", \"layer4.0.downsample.1.running_var\", \"layer4.0.downsample.1.num_batches_tracked\", \"layer4.1.conv1.weight\", \"layer4.1.bn1.weight\", \"layer4.1.bn1.bias\", \"layer4.1.bn1.running_mean\", \"layer4.1.bn1.running_var\", \"layer4.1.bn1.num_batches_tracked\", \"layer4.1.conv2.weight\", \"layer4.1.bn2.weight\", \"layer4.1.bn2.bias\", \"layer4.1.bn2.running_mean\", \"layer4.1.bn2.running_var\", \"layer4.1.bn2.num_batches_tracked\", \"layer4.1.conv3.weight\", \"layer4.1.bn3.weight\", \"layer4.1.bn3.bias\", \"layer4.1.bn3.running_mean\", \"layer4.1.bn3.running_var\", \"layer4.1.bn3.num_batches_tracked\", \"layer4.2.conv1.weight\", \"layer4.2.bn1.weight\", \"layer4.2.bn1.bias\", \"layer4.2.bn1.running_mean\", \"layer4.2.bn1.running_var\", \"layer4.2.bn1.num_batches_tracked\", \"layer4.2.conv2.weight\", \"layer4.2.bn2.weight\", \"layer4.2.bn2.bias\", \"layer4.2.bn2.running_mean\", \"layer4.2.bn2.running_var\", \"layer4.2.bn2.num_batches_tracked\", \"layer4.2.conv3.weight\", \"layer4.2.bn3.weight\", \"layer4.2.bn3.bias\", \"layer4.2.bn3.running_mean\", \"layer4.2.bn3.running_var\", \"layer4.2.bn3.num_batches_tracked\". "

  1353.      ]

  1354.     }

  1355.    ],

  1356.    "source": [

  1357.     "validator = AccuracyValidator(key_map={\"target_val_with_labels\": \"src_val\"})\n",

  1358.     "score = trainer.evaluate_best_model(datasets, validator, dc)\n",

  1359.     "print(score)"

  1360.    ]

  1361.   },

  1362.   {

  1363.    "cell_type": "code",

  1364.    "execution_count": 32,

  1365.    "metadata": {},

  1366.    "outputs": [

  1367.     {

  1368.      "ename": "TypeError",

  1369.      "evalue": "__call__() missing 2 required positional arguments: 'engine' and 'to_save'",

  1370.      "output_type": "error",

  1371.      "traceback": [

  1372.       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",

  1373.       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",

  1374.       "Cell \u001b[0;32mIn[32], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m checkpoint_fn\u001b[39m.\u001b[39;49mobjs()\n",

  1375.       "\u001b[0;31mTypeError\u001b[0m: __call__() missing 2 required positional arguments: 'engine' and 'to_save'"

  1376.      ]

  1377.     }

  1378.    ],

  1379.    "source": []

  1380.   },

  1381.   {

  1382.    "cell_type": "code",

  1383.    "execution_count": null,

  1384.    "metadata": {},

  1385.    "outputs": [],

  1386.    "source": []

  1387.   }

  1388.  ],

  1389.  "metadata": {

  1390.   "kernelspec": {

  1391.    "display_name": "cdtrans",

  1392.    "language": "python",

  1393.    "name": "python3"

  1394.   },

  1395.   "language_info": {

  1396.    "codemirror_mode": {

  1397.     "name": "ipython",

  1398.     "version": 3

  1399.    },

  1400.    "file_extension": ".py",

  1401.    "mimetype": "text/x-python",

  1402.    "name": "python",

  1403.    "nbconvert_exporter": "python",

  1404.    "pygments_lexer": "ipython3",

  1405.    "version": "3.8.15"

  1406.   },

  1407.   "orig_nbformat": 4,

  1408.   "vscode": {

  1409.    "interpreter": {

  1410.     "hash": "959b82c3a41427bdf7d14d4ba7335271e0c50cfcddd70501934b27dcc36968ad"

  1411.    }

  1412.   }

  1413.  },

  1414.  "nbformat": 4,

  1415.  "nbformat_minor": 2

  1416. }