Domain-Adaptation/utils/test-save-load.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda', index=0)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "import torch\n",
    "import os\n",
    "\n",
    "from pytorch_adapt.adapters import DANN, MCD, VADA, CDAN, RTN, ADDA, Aligner, SymNets\n",
    "from pytorch_adapt.containers import Models, Optimizers, LRSchedulers\n",
    "from pytorch_adapt.models import Discriminator,  office31C, office31G\n",
    "from pytorch_adapt.containers import Misc\n",
    "from pytorch_adapt.layers import RandomizedDotProduct\n",
    "from pytorch_adapt.layers import MultipleModels, CORALLoss, MMDLoss\n",
    "from pytorch_adapt.utils import common_functions\n",
    "from pytorch_adapt.containers import LRSchedulers\n",
    "\n",
    "from classifier_adapter import ClassifierAdapter\n",
    "\n",
    "from utils import HP, DAModels\n",
    "\n",
    "import copy\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "import os\n",
    "import gc\n",
    "from datetime import datetime\n",
    "\n",
    "from pytorch_adapt.datasets import DataloaderCreator, get_office31\n",
    "from pytorch_adapt.frameworks.ignite import CheckpointFnCreator, Ignite\n",
    "from pytorch_adapt.validators import AccuracyValidator, IMValidator, ScoreHistory, DiversityValidator, EntropyValidator, MultipleValidators\n",
    "\n",
    "from models import get_model\n",
    "from utils import DAModels\n",
    "\n",
    "from vis_hook import VizHook\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Namespace(batch_size=64, data_root='./datasets/pytorch-adapt/', download=False, gamma=0.99, hp_tune=False, initial_trial=0, lr=0.0001, max_epochs=1, model_names=['DANN'], num_workers=1, patience=2, results_root='./results/', root='./', source=None, target=None, trials_count=1, vishook_frequency=5)\n"
     ]
    }
   ],
   "source": [
    "import argparse\n",
    "parser = argparse.ArgumentParser()\n",
    "parser.add_argument('--max_epochs', default=1, type=int)\n",
    "parser.add_argument('--patience', default=2, type=int)\n",
    "parser.add_argument('--batch_size', default=64, type=int)\n",
    "parser.add_argument('--num_workers', default=1, type=int)\n",
    "parser.add_argument('--trials_count', default=1, type=int)\n",
    "parser.add_argument('--initial_trial', default=0, type=int)\n",
    "parser.add_argument('--download', default=False, type=bool)\n",
    "parser.add_argument('--root', default=\"./\")\n",
    "parser.add_argument('--data_root', default=\"./datasets/pytorch-adapt/\")\n",
    "parser.add_argument('--results_root', default=\"./results/\")\n",
    "parser.add_argument('--model_names', default=[\"DANN\"], nargs='+')\n",
    "parser.add_argument('--lr', default=0.0001, type=float)\n",
    "parser.add_argument('--gamma', default=0.99, type=float)\n",
    "parser.add_argument('--hp_tune', default=False, type=bool)\n",
    "parser.add_argument('--source', default=None)\n",
    "parser.add_argument('--target', default=None) \n",
    "parser.add_argument('--vishook_frequency', default=5, type=int)\n",
    "    \n",
    "\n",
    "args = parser.parse_args(\"\")\n",
    "print(args)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "source_domain = 'amazon'\n",
    "target_domain = 'webcam'\n",
    "datasets = get_office31([source_domain], [],\n",
    "                            folder=args.data_root,\n",
    "                            return_target_with_labels=True,\n",
    "                            download=args.download)\n",
    "\n",
    "dc = DataloaderCreator(batch_size=args.batch_size,\n",
    "                    num_workers=args.num_workers,\n",
    "                    )\n",
    "\n",
    "weights_root = os.path.join(args.data_root, \"weights\")\n",
    "\n",
    "G = office31G(pretrained=True, model_dir=weights_root).to(device)\n",
    "C = office31C(domain=source_domain, pretrained=True,\n",
    "                model_dir=weights_root).to(device)\n",
    "\n",
    "\n",
    "optimizers = Optimizers((torch.optim.Adam, {\"lr\": 1e-4}))\n",
    "lr_schedulers = LRSchedulers((torch.optim.lr_scheduler.ExponentialLR, {\"gamma\": 0.99}))    \n",
    "\n",
    "models = Models({\"G\": G, \"C\": C})\n",
    "adapter= ClassifierAdapter(models=models, optimizers=optimizers, lr_schedulers=lr_schedulers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f28aaf5a334d4f91a9beb21e714c43a5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "[1/35]   3%|2         |it [00:00<?]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7131d8b9099c4d0a95155595919c55f5",
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      "text/plain": [
       "[1/9]  11%|#1        |it [00:00<?]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best_score=None, best_epoch=None\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'Namespace' object has no attribute 'dataroot'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[39], line 31\u001b[0m\n\u001b[1;32m     28\u001b[0m plt\u001b[39m.\u001b[39msavefig(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m{\u001b[39;00moutput_dir\u001b[39m}\u001b[39;00m\u001b[39m/val_accuracy.png\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m     29\u001b[0m plt\u001b[39m.\u001b[39mclose(\u001b[39m'\u001b[39m\u001b[39mall\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m---> 31\u001b[0m datasets \u001b[39m=\u001b[39m get_office31([source_domain], [target_domain], folder\u001b[39m=\u001b[39margs\u001b[39m.\u001b[39;49mdataroot, return_target_with_labels\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n\u001b[1;32m     32\u001b[0m dc \u001b[39m=\u001b[39m DataloaderCreator(batch_size\u001b[39m=\u001b[39margs\u001b[39m.\u001b[39mbatch_size, num_workers\u001b[39m=\u001b[39margs\u001b[39m.\u001b[39mnum_workers, all_val\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n\u001b[1;32m     34\u001b[0m validator \u001b[39m=\u001b[39m AccuracyValidator(key_map\u001b[39m=\u001b[39m{\u001b[39m\"\u001b[39m\u001b[39msrc_val\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39m\"\u001b[39m\u001b[39msrc_val\u001b[39m\u001b[39m\"\u001b[39m})\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'Namespace' object has no attribute 'dataroot'"
     ]
    }
   ],
   "source": [
    "\n",
    "output_dir = \"tmp\"\n",
    "checkpoint_fn = CheckpointFnCreator(dirname=f\"{output_dir}/saved_models\", require_empty=False)\n",
    "\n",
    "sourceAccuracyValidator = AccuracyValidator()\n",
    "val_hooks = [ScoreHistory(sourceAccuracyValidator)]\n",
    "\n",
    "trainer = Ignite(\n",
    "    adapter, val_hooks=val_hooks, checkpoint_fn=checkpoint_fn, device=device\n",
    ")\n",
    "print(trainer.device)\n",
    "\n",
    "early_stopper_kwargs = {\"patience\": args.patience}\n",
    "\n",
    "start_time = datetime.now()\n",
    "\n",
    "best_score, best_epoch = trainer.run(\n",
    "    datasets, dataloader_creator=dc, max_epochs=args.max_epochs, early_stopper_kwargs=early_stopper_kwargs\n",
    ")\n",
    "\n",
    "end_time = datetime.now()\n",
    "training_time = end_time - start_time\n",
    "\n",
    "print(f\"best_score={best_score}, best_epoch={best_epoch}\")\n",
    "\n",
    "plt.plot(val_hooks[0].score_history, label='source')\n",
    "plt.title(\"val accuracy\")\n",
    "plt.legend()\n",
    "plt.savefig(f\"{output_dir}/val_accuracy.png\")\n",
    "plt.close('all')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "173d54ab994d4abda6e4f0897ad96c49",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "[1/9]  11%|#1        |it [00:00<?]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Source acc: 0.868794322013855\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "13b4a1ccc3b34456b68c73357d14bc21",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "[1/3]  33%|###3      |it [00:00<?]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target acc: 0.74842768907547\n",
      "---------\n"
     ]
    }
   ],
   "source": [
    "\n",
    "datasets = get_office31([source_domain], [target_domain], folder=args.data_root, return_target_with_labels=True)\n",
    "dc = DataloaderCreator(batch_size=args.batch_size, num_workers=args.num_workers, all_val=True)\n",
    "\n",
    "validator = AccuracyValidator(key_map={\"src_val\": \"src_val\"})\n",
    "src_score = trainer.evaluate_best_model(datasets, validator, dc)\n",
    "print(\"Source acc:\", src_score)\n",
    "\n",
    "validator = AccuracyValidator(key_map={\"target_val_with_labels\": \"src_val\"})\n",
    "target_score = trainer.evaluate_best_model(datasets, validator, dc)\n",
    "print(\"Target acc:\", target_score)\n",
    "print(\"---------\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "C2 = copy.deepcopy(C)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "source_domain = 'amazon'\n",
    "target_domain = 'webcam'\n",
    "G = office31G(pretrained=False).to(device)\n",
    "C = office31C(pretrained=False).to(device)\n",
    "\n",
    "\n",
    "optimizers = Optimizers((torch.optim.Adam, {\"lr\": 1e-4}))\n",
    "lr_schedulers = LRSchedulers((torch.optim.lr_scheduler.ExponentialLR, {\"gamma\": 0.99}))    \n",
    "\n",
    "models = Models({\"G\": G, \"C\": C})\n",
    "adapter= ClassifierAdapter(models=models, optimizers=optimizers, lr_schedulers=lr_schedulers)\n",
    "\n",
    "\n",
    "output_dir = \"tmp\"\n",
    "checkpoint_fn = CheckpointFnCreator(dirname=f\"{output_dir}/saved_models\", require_empty=False)\n",
    "\n",
    "sourceAccuracyValidator = AccuracyValidator()\n",
    "val_hooks = [ScoreHistory(sourceAccuracyValidator)]\n",
    "\n",
    "new_trainer = Ignite(\n",
    "    adapter, val_hooks=val_hooks, checkpoint_fn=checkpoint_fn, device=device\n",
    ")\n",
    "print(trainer.device)\n",
    "\n",
    "from pytorch_adapt.frameworks.ignite import (\n",
    "    CheckpointFnCreator,\n",
    "    IgniteValHookWrapper,\n",
    "    checkpoint_utils,\n",
    ")\n",
    "\n",
    "objs = [\n",
    "        {\n",
    "            \"engine\": new_trainer.trainer,\n",
    "            \"validator\": new_trainer.validator,\n",
    "            \"val_hook0\": val_hooks[0],\n",
    "            **checkpoint_utils.adapter_to_dict(new_trainer.adapter),\n",
    "        }\n",
    "    ]\n",
    "    \n",
    "# best_score, best_epoch = trainer.run(\n",
    "#     datasets, dataloader_creator=dc, max_epochs=args.max_epochs, early_stopper_kwargs=early_stopper_kwargs\n",
    "# )\n",
    "\n",
    "for to_load in objs:\n",
    "    checkpoint_fn.load_best_checkpoint(to_load)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "32f01ff7ea254739909e4567a133b00a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "[1/9]  11%|#1        |it [00:00<?]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Source acc: 0.868794322013855\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cef345c05e5e46eb9fc0e1cc40b02435",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "[1/3]  33%|###3      |it [00:00<?]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target acc: 0.74842768907547\n",
      "---------\n"
     ]
    }
   ],
   "source": [
    "\n",
    "datasets = get_office31([source_domain], [target_domain], folder=args.data_root, return_target_with_labels=True)\n",
    "dc = DataloaderCreator(batch_size=args.batch_size, num_workers=args.num_workers, all_val=True)\n",
    "\n",
    "validator = AccuracyValidator(key_map={\"src_val\": \"src_val\"})\n",
    "src_score = new_trainer.evaluate_best_model(datasets, validator, dc)\n",
    "print(\"Source acc:\", src_score)\n",
    "\n",
    "validator = AccuracyValidator(key_map={\"target_val_with_labels\": \"src_val\"})\n",
    "target_score = new_trainer.evaluate_best_model(datasets, validator, dc)\n",
    "print(\"Target acc:\", target_score)\n",
    "print(\"---------\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "datasets = get_office31([source_domain], [target_domain],\n",
    "                            folder=args.data_root,\n",
    "                            return_target_with_labels=True,\n",
    "                            download=args.download)\n",
    "    \n",
    "dc = DataloaderCreator(batch_size=args.batch_size,\n",
    "                    num_workers=args.num_workers,\n",
    "                    train_names=[\"train\"],\n",
    "                    val_names=[\"src_train\", \"target_train\", \"src_val\", \"target_val\",\n",
    "                                    \"target_train_with_labels\", \"target_val_with_labels\"])\n",
    "\n",
    "G = new_trainer.adapter.models[\"G\"]\n",
    "C = new_trainer.adapter.models[\"C\"]\n",
    "D = Discriminator(in_size=2048, h=1024).to(device)\n",
    "\n",
    "optimizers = Optimizers((torch.optim.Adam, {\"lr\": 0.001}))\n",
    "lr_schedulers = LRSchedulers((torch.optim.lr_scheduler.ExponentialLR, {\"gamma\": 0.99}))\n",
    "# lr_schedulers = LRSchedulers((torch.optim.lr_scheduler.MultiStepLR, {\"milestones\": [2, 5, 10, 20, 40], \"gamma\": hp.gamma}))\n",
    "\n",
    "models = Models({\"G\": G, \"C\": C, \"D\": D})\n",
    "adapter = DANN(models=models, optimizers=optimizers, lr_schedulers=lr_schedulers)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bf490d18567444149070191e100f8c45",
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       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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    },
    {
     "data": {
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       "model_id": "bd1158f548e746cdab88d608b22ab65c",
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "196fa120037b48fdb4e9a879e7e7c79b",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6795edb658a84309b1a03bcea6a24643",
       "version_major": 2,
       "version_minor": 0
      },
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       "[1/9]  11%|#1        |it [00:00<?]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "output_dir = \"tmp\"\n",
    "checkpoint_fn = CheckpointFnCreator(dirname=f\"{output_dir}/saved_models\", require_empty=False)\n",
    "\n",
    "sourceAccuracyValidator = AccuracyValidator()\n",
    "targetAccuracyValidator = AccuracyValidator(key_map={\"target_val_with_labels\": \"src_val\"})\n",
    "val_hooks = [ScoreHistory(sourceAccuracyValidator), ScoreHistory(targetAccuracyValidator)]\n",
    "\n",
    "trainer = Ignite(\n",
    "    adapter, val_hooks=val_hooks, device=device\n",
    ")\n",
    "print(trainer.device)\n",
    "\n",
    "best_score, best_epoch = trainer.run(\n",
    "    datasets, dataloader_creator=dc, max_epochs=args.max_epochs, early_stopper_kwargs=early_stopper_kwargs, check_initial_score=True\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ScoreHistory(\n",
       "  validator=AccuracyValidator(required_data=['src_val'])\n",
       "  latest_score=0.30319148302078247\n",
       "  best_score=0.868794322013855\n",
       "  best_epoch=0\n",
       ")"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_hooks[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ScoreHistory(\n",
       "  validator=AccuracyValidator(required_data=['target_val_with_labels'])\n",
       "  latest_score=0.2515723407268524\n",
       "  best_score=0.74842768907547\n",
       "  best_epoch=0\n",
       ")"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_hooks[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "del trainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21169"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import gc\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "args.vishook_frequency = 133"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Namespace(batch_size=64, data_root='./datasets/pytorch-adapt/', download=False, gamma=0.99, hp_tune=False, initial_trial=0, lr=0.0001, max_epochs=1, model_names=['DANN'], num_workers=1, patience=2, results_root='./results/', root='./', source=None, target=None, trials_count=1, vishook_frequency=133)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\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."
     ]
    }
   ],
   "source": [
    "args"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = \"/media/10TB71/shashemi/Domain-Adaptation/results/DAModels.CDAN/2000/a2d/saved_models\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "source_domain = 'amazon'\n",
    "target_domain = 'dslr'\n",
    "G = office31G(pretrained=False).to(device)\n",
    "C = office31C(pretrained=False).to(device)\n",
    "\n",
    "\n",
    "optimizers = Optimizers((torch.optim.Adam, {\"lr\": 1e-4}))\n",
    "lr_schedulers = LRSchedulers((torch.optim.lr_scheduler.ExponentialLR, {\"gamma\": 0.99}))    \n",
    "\n",
    "models = Models({\"G\": G, \"C\": C})\n",
    "adapter= ClassifierAdapter(models=models, optimizers=optimizers, lr_schedulers=lr_schedulers)\n",
    "\n",
    "\n",
    "output_dir = \"tmp\"\n",
    "checkpoint_fn = CheckpointFnCreator(dirname=f\"{output_dir}/saved_models\", require_empty=False)\n",
    "\n",
    "sourceAccuracyValidator = AccuracyValidator()\n",
    "val_hooks = [ScoreHistory(sourceAccuracyValidator)]\n",
    "\n",
    "new_trainer = Ignite(\n",
    "    adapter, val_hooks=val_hooks, checkpoint_fn=checkpoint_fn, device=device\n",
    ")\n",
    "\n",
    "from pytorch_adapt.frameworks.ignite import (\n",
    "    CheckpointFnCreator,\n",
    "    IgniteValHookWrapper,\n",
    "    checkpoint_utils,\n",
    ")\n",
    "\n",
    "objs = [\n",
    "        {\n",
    "            \"engine\": new_trainer.trainer,\n",
    "            \"validator\": new_trainer.validator,\n",
    "            \"val_hook0\": val_hooks[0],\n",
    "            **checkpoint_utils.adapter_to_dict(new_trainer.adapter),\n",
    "        }\n",
    "    ]\n",
    "    \n",
    "# best_score, best_epoch = trainer.run(\n",
    "#     datasets, dataloader_creator=dc, max_epochs=args.max_epochs, early_stopper_kwargs=early_stopper_kwargs\n",
    "# )\n",
    "\n",
    "for to_load in objs:\n",
    "    checkpoint_fn.load_best_checkpoint(to_load)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "926966dd640e4979ade6a45cf0fcdd49",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "[1/9]  11%|#1        |it [00:00<?]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Source acc: 0.868794322013855\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "64cd5cfb052c4f52af9af1a63a4c0087",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "[1/2]  50%|#####     |it [00:00<?]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target acc: 0.7200000286102295\n",
      "---------\n"
     ]
    }
   ],
   "source": [
    "\n",
    "datasets = get_office31([source_domain], [target_domain], folder=args.data_root, return_target_with_labels=True)\n",
    "dc = DataloaderCreator(batch_size=args.batch_size, num_workers=args.num_workers, all_val=True)\n",
    "\n",
    "validator = AccuracyValidator(key_map={\"src_val\": \"src_val\"})\n",
    "src_score = new_trainer.evaluate_best_model(datasets, validator, dc)\n",
    "print(\"Source acc:\", src_score)\n",
    "\n",
    "validator = AccuracyValidator(key_map={\"target_val_with_labels\": \"src_val\"})\n",
    "target_score = new_trainer.evaluate_best_model(datasets, validator, dc)\n",
    "print(\"Target acc:\", target_score)\n",
    "print(\"---------\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "source_domain = 'amazon'\n",
    "target_domain = 'dslr'\n",
    "G = new_trainer.adapter.models[\"G\"]\n",
    "C = new_trainer.adapter.models[\"C\"]\n",
    "\n",
    "G.fc = C.net[:6]\n",
    "C.net = C.net[6:]\n",
    "\n",
    "\n",
    "optimizers = Optimizers((torch.optim.Adam, {\"lr\": 1e-4}))\n",
    "lr_schedulers = LRSchedulers((torch.optim.lr_scheduler.ExponentialLR, {\"gamma\": 0.99}))    \n",
    "\n",
    "models = Models({\"G\": G, \"C\": C})\n",
    "adapter= ClassifierAdapter(models=models, optimizers=optimizers, lr_schedulers=lr_schedulers)\n",
    "\n",
    "\n",
    "output_dir = \"tmp\"\n",
    "checkpoint_fn = CheckpointFnCreator(dirname=f\"{output_dir}/saved_models\", require_empty=False)\n",
    "\n",
    "sourceAccuracyValidator = AccuracyValidator()\n",
    "val_hooks = [ScoreHistory(sourceAccuracyValidator)]\n",
    "\n",
    "more_new_trainer = Ignite(\n",
    "    adapter, val_hooks=val_hooks, checkpoint_fn=checkpoint_fn, device=device\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pytorch_adapt.hooks import FeaturesAndLogitsHook\n",
    "\n",
    "h1 = FeaturesAndLogitsHook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "in FeaturesAndLogitsHook: __call__\nin FeaturesHook: __call__\nFeaturesHook: Getting src\nFeaturesHook: Getting output: ['src_imgs_features']\nFeaturesHook: Using model G with inputs: src_imgs\nG",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[19], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m h1(datasets)\n",
      "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",
      "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",
      "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",
      "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/pytorch_adapt/hooks/features.py:80\u001b[0m, in \u001b[0;36mBaseFeaturesHook.call\u001b[0;34m(self, inputs, losses)\u001b[0m\n\u001b[1;32m     78\u001b[0m     func \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmode_detached \u001b[39mif\u001b[39;00m detach \u001b[39melse\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmode_with_grad\n\u001b[1;32m     79\u001b[0m     in_keys \u001b[39m=\u001b[39m c_f\u001b[39m.\u001b[39mfilter(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39min_keys, \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m^\u001b[39m\u001b[39m{\u001b[39;00mdomain\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n\u001b[0;32m---> 80\u001b[0m     func(inputs, outputs, domain, in_keys)\n\u001b[1;32m     82\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcheck_outputs_requires_grad(outputs)\n\u001b[1;32m     83\u001b[0m \u001b[39mreturn\u001b[39;00m outputs, {}\n",
      "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/pytorch_adapt/hooks/features.py:106\u001b[0m, in \u001b[0;36mBaseFeaturesHook.mode_with_grad\u001b[0;34m(self, inputs, outputs, domain, in_keys)\u001b[0m\n\u001b[1;32m    104\u001b[0m output_keys \u001b[39m=\u001b[39m c_f\u001b[39m.\u001b[39mfilter(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_out_keys(), \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m^\u001b[39m\u001b[39m{\u001b[39;00mdomain\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n\u001b[1;32m    105\u001b[0m output_vals \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mget_kwargs(inputs, output_keys)\n\u001b[0;32m--> 106\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49madd_if_new(\n\u001b[1;32m    107\u001b[0m     outputs, output_keys, output_vals, inputs, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mmodel_name, in_keys, domain\n\u001b[1;32m    108\u001b[0m )\n\u001b[1;32m    109\u001b[0m \u001b[39mreturn\u001b[39;00m output_keys, output_vals\n",
      "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/pytorch_adapt/hooks/features.py:133\u001b[0m, in \u001b[0;36mBaseFeaturesHook.add_if_new\u001b[0;34m(self, outputs, full_key, output_vals, inputs, model_name, in_keys, domain)\u001b[0m\n\u001b[1;32m    130\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39madd_if_new\u001b[39m(\n\u001b[1;32m    131\u001b[0m     \u001b[39mself\u001b[39m, outputs, full_key, output_vals, inputs, model_name, in_keys, domain\n\u001b[1;32m    132\u001b[0m ):\n\u001b[0;32m--> 133\u001b[0m     c_f\u001b[39m.\u001b[39;49madd_if_new(\n\u001b[1;32m    134\u001b[0m         outputs,\n\u001b[1;32m    135\u001b[0m         full_key,\n\u001b[1;32m    136\u001b[0m         output_vals,\n\u001b[1;32m    137\u001b[0m         inputs,\n\u001b[1;32m    138\u001b[0m         model_name,\n\u001b[1;32m    139\u001b[0m         in_keys,\n\u001b[1;32m    140\u001b[0m         logger\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mlogger,\n\u001b[1;32m    141\u001b[0m     )\n",
      "File \u001b[0;32m/media/10TB71/shashemi/miniconda3/envs/cdtrans/lib/python3.8/site-packages/pytorch_adapt/utils/common_functions.py:96\u001b[0m, in \u001b[0;36madd_if_new\u001b[0;34m(d, key, x, kwargs, model_name, in_keys, other_args, logger)\u001b[0m\n\u001b[1;32m     94\u001b[0m condition \u001b[39m=\u001b[39m is_none\n\u001b[1;32m     95\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39many\u001b[39m(condition(y) \u001b[39mfor\u001b[39;00m y \u001b[39min\u001b[39;00m x):\n\u001b[0;32m---> 96\u001b[0m     model \u001b[39m=\u001b[39m kwargs[model_name]\n\u001b[1;32m     97\u001b[0m     input_vals \u001b[39m=\u001b[39m [kwargs[k] \u001b[39mfor\u001b[39;00m k \u001b[39min\u001b[39;00m in_keys] \u001b[39m+\u001b[39m \u001b[39mlist\u001b[39m(other_args\u001b[39m.\u001b[39mvalues())\n\u001b[1;32m     98\u001b[0m     new_x \u001b[39m=\u001b[39m try_use_model(model, model_name, input_vals)\n",
      "\u001b[0;31mKeyError\u001b[0m: in FeaturesAndLogitsHook: __call__\nin FeaturesHook: __call__\nFeaturesHook: Getting src\nFeaturesHook: Getting output: ['src_imgs_features']\nFeaturesHook: Using model G with inputs: src_imgs\nG"
     ]
    }
   ],
   "source": [
    "h1(datasets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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